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User Guide 2007Australian National Children's Nutrition and

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1. Interviewer comments on why the stride test was not completed strideno The first stride measurement in cm of the respondent strites1 The second stride measurement in cm of the respondent strites2 The third stride measurement in cm of the respondent strites3 SHORT DIETARY QUESTIONS What is the main type of milk that you usually use milktype How many serves of vegetables do you usually eat each day One serve is equal numserve to half a cup How many serves of fruit do you usually eat each day One serve is equal to half numserva a cup Does the person who prepares your meal add salt when they are cooking saltadde Is salt added at cooking iodised ie contains iodine cookings Do you add salt to your meal at the table saltadda Is salt added at table iodised ie contains iodine 2 tablesal In the past 12 months have you always had sufficient money to buy food foodsecu If no did you go without food gowithou Has study child been breastfed everbrea Breastfeeding pattern breastfe Number of weeks breastfed brstweek Number of months breastfed brstmths Has child ever been given infant formula regularly everinfa If formula fed at what age weeks was your child first given infant formula formweek regularly If formula fed at what age months was your child first given infant formula formmths regularly Units used for recordin
2. category code activity 240051 walking light 240052 walking medium 240053 walking hard 240071 climbing stairs light 240072 climbing stairs medium 240073 climbing stairs hard 24009 1 walking carrying a load light 240092 walking carrying a load medium 240093 walking carrying a load hard 241080 walking using crutches 341241 riding a bicycle bike light 341242 riding a bicycle bike medium 341243 riding a bicycle bike hard 341251 riding a scooter light 341252 riding a scooter medium 341253 riding a scooter hard 341271 riding a skateboard light 341272 riding a skateboard medium 341273 riding a skateboard hard 341311 rollerblading in line skating light 341312 rollerblading in line skating medium 341313 rollerblading in line skating hard 341461 rollerskating light 341462 rollerskating medium 341463 rollerskating hard 112 Screen Time MARCA codes and activity names for activities included under the screen time category code activity 111030 watching TV lying quietly 121050 watching TV sitting 420050 computer work e g typing internet 722190 computer playstation games Note that the following activities are excluded from screen time code activity 114190 sending text messages SMS lying down 124170 se
3. code activity code _ activity 341753 aerobics health hustle hard 342382 netball medium 341751 _ aerobics health hustle light 341393 orienteering hard 341752 aerobics health hustle medium 341391 jorienteering light 331000 larchery 341392 orienteering medium athletics track and field hurdles 341663 steeplechase hard 342403 paddleball hard athletics track and field hurdles 341661 steeplechase light 342401 paddleball light athletics track and field hurdles 341662 steeplechase medium 342402 paddleball medium 341653 athletics track and field jumping hard 332040 pool billiards snooker 341651 athletics track and field jumping light 341893 race walking hard 341652 __jathletics track and field jumping medium 341891 race walking light 341673 _jathletics track and field throwing hard 341892 race walking medium 341671 athletics track and field throwing light 342433 racketball hard athletics track and field throwing 341672 medium 342431 racketballl light 342013 badminton hard 342432 _ racketball medium 342011 badminton light 341453 rockclimbing hard 342012 badminton medium 341451 rockclimbing light 341793 _ ballet hard 341452 rockclimbing medium 341791 ballet light 342163 rugby league hard 341792 ballet medium 342161 rugby league light 342023 baseball hard 342162 rugby
4. code activity code _ activity 342123 curling hard 342183 soccer field indoor hard 342121 _ curling light 342181 _ soccer field indoor light 342122 curling medium 342182 soccer field indoor medium 342253 European handball team hard 342563 softball or t ball hard 342251 European handball team light 342561 softball or t balll light 342252 European handball team medium 342562 softball or t ball medium 321870 __ fishing 341573 _ speed skating competitive hard 342153 football Australian hard 341571 soeed skating competitive light 342151 football Australian light 341572 _ speed skating competitive medium 342152 football Australian medium 342583 squash hard 341213 golf hard 342581 squash light 341211 golf light 342582 squash medium 341212 golf medium 341613 swimming laps hard 341223 gymnastics hard 341611 swimming laps light 341221 gymnastics light 341612 swimming laps medium 341222 _ gymnastics medium 342623 table tennis hard 342263 hockey field hard 342621 table tennis light 342261 hockey field light 342622 table tennis medium 342262 hockey field medium 341803 tap dancing hard 342273 hockey ice hard 341801 itap dancing light 342271 hockey ice light 341802 _ tap dancing medium 342272 hockey ice medium 342643 tennis court hard 32
5. Participant characteristics Table 17 shows the sample characteristics of the study children at the different response stages completed CAPI completed CATI and complete datasets for analysis Tables 18 20 show the characteristics of participants parents caregivers and households at different stages in the response process This analysis excludes households who did not participate in the CAPI Characteristics with a significant difference at 95 confidence level between the sample that completed the CAPI nN 4837 and the sample who were compliant for study components n 350 were e parent or care giver speaks a language other than English at home e parent or care giver not working e five or more people in the household e income less than 20 799 per annum 43 Table 17 Study child characteristic Compliant datasets for Completed CAPI Completed CATI analysis Study Child Characteristic 4 Z Z Study Child Male 2 439 50 2 360 50 2 249 50 Female 2 398 50 2 335 50 2 238 50 4 837 4 695 4 487 Age 2 3 years 1191 25 1140 24 1071 24 4 8 years 1264 26 1230 26 1216 27 9 13 years 1220 25 1187 25 1110 25 14 16 years 1162 24 1138 24 1090 24 4837 4695 4487 BMI category Very Underweight 34 34 32 Underweight 191 4 184 4 180 4 Normal 3486 72 3384 72 3267 73 Overweight 805 17 784 17 761 17 Obese 271 6 261 6 247 6 Refused 50 48 O Total 4837 4695 4487 Medical Conditions None 3 839 79 3 722 79 3
6. G5 LINZ24 NOT COMPLETED specify reason G CARER FORM USED 1 Yes data included 2 To be edited later upon return 3 No 86 SECTION H FOOD HABITS SURVEY Address the child if over 9 years otherwise address the parent care giver Interviewer Note Please give parent Food Habits Survey questions if not already completed during ANTHRO H1 What is the main type of milk that you usually use Parent 1 Study Child if 9 years only 1 Whole full cream 2 Low reduced fat 3 Skim 4 Evaporated or sweetened condensed 5 Soy milk None of the above 6 7 Does not drink milk or 8 Don t know 1 Whole full cream 2 Low reduced fat 3 Skim 4 Evaporated or sweetened condensed 5 Soy milk 6 None of the above 7 Does not drink milk or 8 Don t know H2 How many serves of vegetables do you usually eat each day One serve is equal to half a cup INTERVIEWER NOTE Show food prompt if necessary Less than one serve 2 One serve 3 Two serves 4 Three serves 5 Four serves 6 Five serves 7 Six or more serves or 8 Don t eat vegetables 1 Less than one serve 2 One serve 3 Two serves 4 Three serves 5 Four serves 6 Five serves 7 Six or more serves or 8 Don t
7. 2 3 4 8 9 13 14 16 2 16 NSW Metro 149 149 49 127 574 NSW Rest of State 153 162 57 157 629 QLD Metro 9 94 92 94 371 QLD Rest of State 101 113 20 104 438 SA Metro 167 174 64 156 661 SA Rest of State 66 67 69 69 271 VIC Metro 132 144 22 118 516 VIC Rest of State 107 105 13 101 426 WA Metro 71 74 70 75 290 WA Rest of State 41 44 44 44 173 NT Rest of State 24 29 25 24 102 TAS Rest of State 43 37 50 48 198 ACT Metro 46 51 45 45 187 All 1191 1264 1220 1162 4837 Sample population weight applied Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 83937 223772 213854 122847 644410 NSW Rest of State 67178 160918 182985 113988 525069 QLD Metro 40993 101254 106945 61463 310654 QLD Rest of State 47980 129768 134754 82256 394759 SA Metro 22661 58489 62354 39908 183412 SA Rest of State 8830 23894 25004 12954 70682 VIC Metro 69589 174912 175249 103726 523477 VIC Rest of State 42967 109045 123504 75550 351066 WA Metro 32719 81785 87978 56106 258588 WA Rest of State 9452 28537 29756 14953 82698 NT Rest of State 3666 8969 8797 4884 26316 TAS Rest of State 10252 27834 30202 17914 86202 ACT Metro 7500 17469 18361 11396 94726 All 447724 1146646 1199743 717946 3512059 Adjusted sample weight applied Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 116 308 295 169 888 NSW Rest of State 93 222 252 157 723 QLD Metro 56 139 147 85 428 QLD Rest of State 66 179 186 113 544 SA Metro 31 81
8. 1 Yes 2No gt Goto J5 3 Permanently not intending to work if aged 65 only gt Go to J7 1 hour or more 000 gt Write number Hours gt go to J8 Less than 1 gt hour go to J5 1 Yes full time work 2 Yes part time work 3 Yes casual work 4 No gt Go to J7 5 Don t know gt Go to J7 1 Yes gt Go to J10 if no parent 2 2 No gt Go to J10 if no parent 2 1 Within the last three months 23 up to 6 months ago 3 6 up to 12 months ago 4 1 up to 2 years ago 5 2 up to 5 years ago 6 More than 5 years ago 7 Has never worked for 2 weeks or more 91 Family Details Parent 1 Parent 2 J8 In the main job held last week what was s occupation GET FULL TITLE J9 What are the main tasks that usually perform s in that occupation GET FULL DETAILS J10 Before income tax is taken out what is your present yearly income for you and your partner combined INCLUDE PENSIONS AND ALLOWANCES BEFORE TAX SUPERANNUATION OR HEALTH INSURANCE 2400 or more per week 124 800 or more per year 1 2200 2399 per week 114 400 124 799 per year 2 2000 2199 per week 140 000 103 999 per year 3 1500 1999 per week 78 000 103 999 per year 4 1000 1499
9. D4 What was age last birthday D5 What is date of birth D How is related to parent 1 Parent 2 Partner Is there another parent of child living here or your partner 1 male 2 female 3 No person 1 legal spouse 2 de facto partner 3 other relative in aw 4 boarder housemate 5 unrelated adult Study Child Next the study child Enter child s first name 2 female 1 male D D MMY Y 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in aw 13 unrelated child Person 4 Who else lives here 1 male O12 female 3 No person 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in aw 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult 76 Family Details D7 Howis related to parent 2 partner D8 Is of Aboriginal or Torres Strait Islander origin D9 In which country was born Parent 1 1 No 2 Yes Aboriginal
10. F15a Comments Automatically recorded measure 84 F16 Final Height cm Automatically recorded measure F17 Final Mass kg Automatically recorded measure F18 Final Waist Girth cm Automatically recorded measure F18 BMI kg m2 F20 Date of measurement Day Month Year DATE STAMP AUTOMATICALLY DATE STAMP AUTOMATICALLY DATE STAMP AUTOMATICALLY F21 Time of measurement TIME STAMP AUTOMATICALLY Interviewer Alert Please write down height amp weight on a notepad needed this later for MARCA Yes No F22 ANTHRO completed F23 NOT COMPLETED specify reason If child is under 9 Thank you very much for your help today you have done very well at the measurement activities That is all I need to ask you today I now have some questions for your mum dad other 85 SECTION G Rotate between LINZ24 and MARCA Address the child over 9 years Interviewer Note If child is 9 years and older only conduct either the LINZ24 or MARCA that was not completed in SECTION E G1 MARCA Completed gt Only if 9 years or older Day One Yes ONLY No G2 ONLY DAY ONE OF MARCA COMPLETED specify reason G3 MARCA NOT COMPLETED specify reason Yes No G4 LINZ24 Completed gt If 9 years or older
11. 3 Yes T Strait slander 4 Yes both nsert SACC codes for all countries Parent 2 Partner 1 No 2 Yes Aboriginal 3 Yes T Strait Islander 4 Yes both nsert SACC codes for alll countries Study Child 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in aw 13 unrelated child 1 No 2 Yes Aboriginal 3 Yes T Strait slander 4 Yes both nsert SACC codes for all countries Person 4 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in law 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult D10 Does speak a language other than English at home nsert ASCL codes for all languages Insert ASCL codes for all anguages Insert ASCL codes for all languages Family Details D11 What is the highest year of primary or secondary school that have completed D12 What is the level of highest qualification that has ever completed Parent 1 1 School year 12 or equivalent 2 School year 11 or
12. These values are the basis for further imputation and estimation for the AUSNUT 2007 database The sum of the three fatty acid subtotals given in the database is always less than the total fat value The difference is due to the contribution of the non fatty acid components in the triglyceride unit such as the glycerol backbone possible phosphate groups and sterols The total long chain omega 3 fatty acid values were calculated by summing all omega 3 polyunsaturated fatty acids containing at least 20 carbon atoms Linoleic acid values include conjugated linoleic acid where this has been measured separately to other isomers Vitamin D NUTTAB 2006 included few analytical values for vitamin D Most of the data on this nutrient have been obtained from a limited range of unpublished analytical data label data overseas food composition databases including the USDA Danish and British food composition tables or by imputation They should be considered as indicative values only and users should be aware that reported values are likely to be revised in subsequent FSANZ publications Total vitamin D activity has been calculated as Vitamin D ug Cholecalciferol ug ergocalciferol ug 5 25 hydroxy cholecalciferol ug 5 25 hydroxy ergocalciferol ug There does not appear to be international consensus on the most appropriate factors to use when reporting total vitamin D activity based on individual vitamins Recent advice Jakobsen p
13. Total 3876721 4400 4487 Note Booster sample increases the proportion of South Australia sample in the study e g other Australian Islands t Sample achieved from final cohort of children classified as compliant see Section 13 24 7 Interviewer training and supervision Thirty seven telephone interviewers were trained for recruitment Training for the telephone recruitment staff took place in Melbourne in February 2007 There were 58 CAPI interviewers and 32 CATI interviewers 36 percent of the CAPI interviewers had a tertiary degree or higher in dietetics or nutrition The remaining interviewers had a tertiary degree in health science or other relevant disciplines All of the CATI interviewers had a health science or other relevant tertiary background Training for the CAPI and CATI interviewers was separate due to the difference in requirements for the two interview methods CAPI training took place over four and a half days in Sydney February 2007 A modified three day training program was provided for CATI interviewers which replicated the CAPI training but excluded anthropometric measurement pedometer placement and demographic questionnaire instructions CATI training was conducted in Brisbane February 2007 Training was facilitated by staff from I view research dietitians from CSIRO and exercise physiologists from UniSA Training for Multimedia Activity Recall for Children and Adolescents MARCA
14. Using the 2001 ABS population data postcodes covering areas where there were more than 50 of the population identified as Indigenous were excluded Table 2 Postcodes excluded from selection State Total ACT 2 NSW 68 NT 11 QLD 94 SA 95 TAS 26 VIC 159 WA 121 Total 576 Postcode area selection The number of postcodes selected in each region was proportional to the ABS population estimates of the distribution of 2 16 year olds in each region Postcodes had an equal chance of initial selection within each region The initial selection of 50 postcode values was expanded to include postcodes in close geographical proximity thus expanding the number of postcodes a total of 230 This minimised travel time and costs for interviewers undertaking the CAPI 17 The steps for postcode selection are summarised below Exclusion of postcodes with a Very Remote index using Accessibility Remoteness Index of Australia ARIA 2001 2 Exclusion of postcodes with less than 80 households with at least one child aged 2 16 years using 2001 ABS population data 3 Exclusion of postcodes with more than 50 of the children aged 2 16 years in the population identified as Indigenous 2001 ABS data 4 Postcodes sorted into major statistical regions capital city or rest of state for every state territory 5 Assign a random number to each postcode Excel RAND function 6 Within each region the postcodes were sorted into ascending order
15. 16 year old participants to collect objective information on the number of steps each child took and minutes of moderate to 12 vigorous physical activity each day The stride length of each child was measured and the pedometers were fitted during the CAPI This provided an objective estimate of overall activity levels Weight height waist circumference and recalled birth weight were collected for all participants during the CAPI Demographic and socioeconomic data were collected from the primary caregiver during the CAPI including state territory of residence child s country of birth primary care giver s education level household income and Indigenous status The data were checked cleaned and collated into an electronic database The Department of Health and Ageing will manage and administer these data at the completion of the survey A summary of the data collected is provided in Table 1 Table 1 Summary of data collected and target number Data Age Data collected Target Achieved group years Demographic amp socioeconomic 2 16 CAPI 4 400 4837 information Physical measurements 2 16 CAPI 4 400 4 757 Foods habits questionnaire 2 16 CAPI 4 400 4 837 2 x 24 hour dietary recall foods 2 16 CAPI amp CATI 4 400 4 655 beverages amp dietary supplements 4 x 24 hour use of time recalls 9 16 CAPI amp CATI 2 200 2 246 Objective physical activity measurements at least 6 days 5 16 Pee CARI 2 750
16. A private dwelling was defined as any household with a fixed land line telephone Interviewing was conducted across school and non school days The proportion of interviews conducted on weekdays weekends public holidays and school holidays was selected to reflect the proportions of these days across the fieldwork period see section 13 Coverage Coverage rules were designed to ensure that as far as possible eligible persons had only one chance of being selected for interview The child was deemed to be a resident of the household if they usually stayed at the selected household on average for 4 or more days per week in the case of shared care Households with more than one fixed line telephone may have had a greater chance of selection however this was identified at the screening interview It is difficult to rely fully on telephone prefixes to indicate geographic location as an increasing number of people elect to take advantage of phone number portability where they take an existing phone number with them when they move For this reason access to a full listing of numbers with an effective geographic tag such as an address postcode or Census Collector District CCD was limited RDD allows for the inclusion of silent unlisted and recently listed numbers in the sample which would not occur with a sample drawn from listed numbers i e White pages Further with the end of Desktop Marketing System s Marketing DTMS product the
17. Capital Rest of State were used to estimate the population numbers 65 Data from the survey were used to estimate the sample numbers and hence the weights for each individual child The ABS data obtained for determining the weights consisted of a table of number of households by number of children of each sex The ABS family data was used to construct a table of the number of children in each Region by Age The sex ratio for each Region was calculated from the ABS Household data and the proportions of boys and girls applied as a multiplier to each cell in the table to give estimated numbers by sex This gave a table of the estimated total number of children in each Region by Age by Sex class a total of 104 Classes The corresponding sample counts were calculated from the Sample data by tabulation The weight to be applied in computing estimated population means from the data is the ratio of the Population counts to the Sample counts for the Class to which the child belongs Results Tables of weights File WeightTable csv gives the weights for each of the 104 Classes together with the estimated population count and the sample count for each Class File Weights csv gives the weight assigned to each child in the survey identified by the Unit Record number and Respondent ID Use of weighted data The weights allow calculation of the estinated population mean levels of a variable as Sum of Sample value x weight 1 Sum of weights
18. European Society for Opinion and Marketing Research Estimated Average Requirements Food Standards Australia New Zealand International Society for the Advancement of Kinanthropometry International Standards Organisation Life In New Zealand Multimedia Activity Recall for Children and Adolescents Metabolic equivalent Moderate to Vigorous Physical Activity National Health and Medical Research Council not further specified not specified 1995 National Nutrition Survey Nutrient Reference Values Random Digit Dialing Recommended Dietary Intakes Suggested Dietary Targets Technical Error of Measurement Therapeutic Goods Association University of South Australia 2 Preface The scope of the 2007 Australian National Children s Nutrition and Physical Activity Survey was to obtain food nutrient physical activity and anthropometric data on a national sample of children aged 2 16 years The survey was jointly funded by the Commonwealth Department of Health and Ageing the Department of Agriculture Fisheries and Forestry and the Australian Food and Grocery Council A representative from each of the three funding agencies made up a Steering Group which was involved in the management of the survey The implementation of the survey was a collaborative effort between the Commonwealth Scientific and Industrial Research Organisation CSIRO Preventative Health Flagship and the University of South Australia together with management of the f
19. Or in mathematical terms N wy i l N r i l where N 4 837 is the number of children in the Survey yi is the value of the variable for the ith child in the survey and wi is the weight assigned to that child The calculation of standard errors requires calculation of an estimated variance within each Class for each variable being studied This cannot be done separately from the analysis of the variables and so values cannot be given here If the sample numbers were larger these variances could be calculated from the within class sample variances but this is not feasible here since many of the Classes have very small sample numbers Instead a model based approach to estimating the variance will be required It is not possible to prescribe in advance what model will be appropriate for each variable since this will emerge from the analysis However the general approach is likely to be similar The model will assume that the variance is constant within some larger grouping of the data such as Region by Age and the variance within each cell of that grouping estimated by the residual variance from fitting a regression model within the cell For example consider a hypothetical variable Nutritional Value which is assumed to have constant variance within each Region by Age group 66 If we believe from prior information that Family Size will have little effect on the variable but it may differ by sex then we would fit a sim
20. PersonAge lt 16 Relationship code to Parent 2 of Person 4 to Person 12 RelP2 lt wgtdata paste P 4 12 RelP2 sep Is Person 4 to 12 a child of parent 2 or in a 1 parent family ChildofP2 lt RelP2 gt 6 amp RelP2 lt 12 RelP2 0 amp PersonAge gt 2 amp PersonAge lt 16 InFamily lt ChildofPl amp ChildofP2 set ages of persons not in family to 0 so they are not counted in the family structure These are persons outside the age range or not in the same nuclear family PersonAge InFamily lt 0 function to convert ages of children to a family structure make fs lt function x x lt unlist x grps lt cut x breaks c 1 3 8 13 16 labels 1 4 return table grps Get the family structure for each study child FS lt t apply cbind SCA PersonAge 1 make fs Convert counts gt 2 to 2 FS FS gt 2 lt 2 Numerical value of FS as ternary number FS num lt apply FS 1 function x 3 3 3 x 1 x 2 x 3 x 4 FS as string FS str lt apply FS 1 paste collapse Code to reorder family structures in ascending order of family size if needed later This is not needed for the current analysis which does not use household data fs age lt NULL for i ini1 4 fs age lt cbind fs age rep rep 0 2 each 3 4 i 3 i 1 fs tot lt apply fs age 1 sum sizeorder lt order fs tot fs age 1 fs age 2 fs age 3 fs age 4 f
21. Questionnaire The demographic data items relating to each participant and their household was collected at the CAPI Responses were provided by the parent or care giver of the participant Sections 19 and 21 Household composition e Postcode of residence e State of residence e Number of adults in household aged gt 16 years e Number of children in household aged lt 16 years e Household type Family Type and Household members Parent 1 was the primary care giver who knew most about the child s food intake or activity and Parent 2 was their spouse or significant other care giver in the household Parent 1 e Gender e Age e Aboriginal and Torres Strait Islander ATS e Country of Birth e Language spoken e School Education e Higher Education Parent 2 e Gender e Relationship to Parent 1 e ATSI e Country of Birth e Language spoken e School Education e Higher Education Study Child e Gender e Child Age e Date of Birth e Relationship to Parent 1 e Relationship to Parent 2 e ATSI e Country of Birth e Language Spoken e Medical Condition Other Person e Gender e Age e Relationship to Parent 1 e Relationship to Parent 2 29 Income and Occupation Parent 1 Worked last week Unpaid work last week Away from work last week Number of hours worked on average Looking for work Start work if found a job Last worked if not currently working Job description Job tasks Australian Standard Classifica
22. Seasonality Surveillance should take into account the seasonal nature of physical activity behaviours e Consultation Key stakeholders should be consulted from the earliest stages of survey development Details of the Physical Activity workshop have been published National Public Health Partnership 2006 Survey objectives and overview The objective of this survey was to assess food and nutrient intake physical activity and measure weight height and waist circumference in a sample of children aged 2 16 years randomly selected from across Australia An initial target quota of 1 000 children 50 boys and 50 girls for each age group 2 3 years 4 8 years 9 13 years and 14 16 years was set This was supplemented in South Australia to allow more detailed estimates for that state increasing the final survey sample by 400 equally across the age groups A total of 4 487 children completed all components of the survey The sample was designed to provide e national level estimates by gender and four age groups e state and territory estimates by broad age groups It was not designed to provide a representative sample of smaller sub populations of children such as Indigenous groups children from a range of culturally and linguistically diverse backgrounds or children with special disabilities Households with children aged 2 16 years were randomly selected using random digit dialing RDD from all Australian states and territories in
23. Seeds And Seed Products 221 Seeds And Seed Products 212 Nuts And Nut Products 222 Nuts And Nut Products 22 SAVOURY SAUCES AND CONDIMENTS 23 SAVOURY SAUCES AND CONDIMENTS 221 Gravies And Savoury Sauces 231 Gravies And Savoury Sauces 222 Pickles Chutneys And Relishes 232 Pickles Chutneys And Relishes 224 Salad Dressings 233 Salad Dressings 225 Stuffings 234 Stuffings 23 VEGETABLE PRODUCTS AND DISHES 24 VEGETABLE PRODUCTS AND DISHES 96 1995 Food Code 231 232 233 234 235 236 237 238 239 24 241 242 25 251 252 253 254 2542 26 261 262 263 27 271 272 273 28 281 282 283 284 29 291 192 30 301 302 303 304 305 306 31 311 312 313 314 Food Group Name Potatoes Cabbage Cauliflower And Similar Brassica Vegetables Carrot And Similar Root Vegetables Leaf And Stalk Vegetables Peas And Beans Tomato And Tomato Products Other Fruiting Vegetables Other Vegetables And Vegetable Combinations Dishes Where Vegetable Is The Major Component LEGUME AND PULSE PRODUCTS AND DISHES Mature Legumes And Pulses Mature Legume And Pulse Products And Dishes SNACK FOODS Potato Snacks Corn Snacks Extruded Snacks Pretzels And Other Snacks Other Snacks SUGAR PRODUCTS AND DISHES Sugar Honey And Syrups Jam And Lemon Spreads Chocolate Spreads Dishes amp Products Other Than Confectionery Where Sugar Is Major Component CONFECTIONERY AND HEALTH BARS Chocolate And Chocolate Based Confection
24. and anthropometry was conducted by exercise physiologists Training for LINZ24 was conducted by research dietitians The training involved e formal lectures e familiarisation with the software package e guided exercises e interview techniques identifying gaps in responses probing open questions e experiential learning All CAPI interviewers were trained in anthropometric techniques and technical errors of measurement TEM were established Intra tester TEMs represent precision of measurement while inter tester TEMs represent accuracy by comparing measures taken by interviewers with those of an International Society for the Advancement of Kinanthropometry ISAK Level 4 criterion anthropometrist For this survey all interviewers demonstrated inter tester TEMs of lt 2 and intra tester TEMs of 1 5 All interviewers practiced interviews with children from all age groups to ensure they were competent in all aspects of the survey Volunteers were recruited for the practice and were involved in the CAPI and CATI training Interviewers were provided with a comprehensive set of manuals covering Interviewer instruction Dietary assessment Physical activity Anthropometry Reference summary sheets were provided for use during interviews During the data collection phase five CAPI and CATI interviews for every interviewer were audio recorded for assessment and feedback Interviews were reviewed by a dietitian for the dietary recall
25. children Seasonality The survey collection period February to August should be considered when interpreting the results 59 Dietary recall The 24 hour recall methodology relies on the participant s ability to recall the details of all foods beverages and supplements consumed over a 24 hour period This method is associated with under mis non reporting of foods and beverages consumed along with inaccuracies in portion size estimation and level of detail to describe the items Interviewers were trained in various techniques to minimise this source of error but it remains unavoidable Despite detailed scrutinizing of the nutrient data by trained staff there may still be some unusual intakes of individual foods Users may wish to identify and exclude outliers or mis reporting from their own analyses In recognition of the varying age groups of the participants the interviews were conducted with the primary care giver for all children below the age of nine years and with the study child for children aged 9 years and over Primary care givers were encouraged to be present for all interviews To assist with quantifying the recall during the CAPI measuring aids and a food model booklet were used During the CATI only the food model booklet was available to assist with the estimation of portion size One 24 hour recall is considered appropriate to estimate the usual mean and median intake of a group It is not suitable for assessing th
26. collect data in participants homes and therefore conveniently transport equipment A minimum of two measurements were taken for each anthropometric variable A third measure was taken where the second measure was not within 5 mm for height 0 1 kg for weight and 10 mm for waist girth The mean value was used as the final score if two measurements were taken The median value was used as the final measure if three measurements were taken Height Height was measured on all consenting participants who were able to stand upright and stand still enough while height was measured Height was measured without shoes or thick socks The stadiometer was checked before each use against a steel girth tape to ensure correct assemblage The participant stood with the heels together and the heels buttocks and upper part of the back touching the upright of the stadiometer The head was kept in the Frankfort plane while the participant held a deep breath during the measurement In a few cases particularly among the very young children the interviewer was unable to take a measure due to a restless or uncooperative participant Reasons for irregularities or missing data were recorded by the interviewer Weight Weight was measured in light indoor clothing with shoes coats and jumpers removed using Tanita HD332 portable electronic scales The scale was placed on a hard even surface not carpet The participant stood still on the centre of the scales without
27. component exercise physiologist for the physical activity component and a field supervisor for interview administration 25 Throughout the data collection phase interviewers were assisted by dedicated supervisors who were health professionals with survey experience Supervisors answered queries provided on going training reviewed interview recordings and undertook systematic field checks validations and observations of the interviewers Interviewers also had email and telephone access to the MARCA and LINZ24 trainers Dietitians from CSIRO checked every interview within a day of being lodged except interviews on Fridays Saturdays which were checked on Mondays Section 14 26 8 Data collection The households that agreed to participate in the survey were posted information including background information details of data collection the address for the survey website and the details of a 1800 hotline Interviewers then contacted recruited families to arrange a time for the CAPI Following completion of the CAPI participants were provided a randomly generated date for the CATI which was 7 21 days after the CAPI The date was rescheduled if participants could not undertake the CATI on that date The CAPI was conducted in the home of the study child The primary caregiver or a responsible adult provided consent for children aged less than 14 years Children aged 14 16 years provided their own consent along with that of a pare
28. data for 2 days of recording the summary physical activity data for each of 4 days of recording physical activity via MARCA and the pedometer data Contacts file The Contacts File record Table 23 contains one record for every eligible household Respondents who were ultimately recruited to the survey were assigned a value in the field respid The last 9 variables in this file provide detail on the existence of records in the results database files for each respondent Table 23 Contents of contacts data file defined by field description label and field name FIELD DESCRIPTION LABEL FIELD NAME IDENTIFYING ITEMS Recruitment unit record number unitrecd CONTACT INFORMATION Number of calls to recruit recrcalll Date of recruitment recrdate Total minutes for recruitment recrmins Recruitment Status recrstat CAPI Status capistat Date of scheduled CAPI Interview datecapi Number of call attempts to collect CAPI capicall CATI Status catistat Date of scheduled CATI interview LINZ dcatilnz Date of scheduled CATI interview MARCA dcatimar Number of call attempts to collect CATI caticall CATI Interview Length minutes catilgth Had respondent moved address moved If moved was respondent located located Did they receive the letter about fieldwork lafrcvd Were they aware of publicity awarepub CAPI Interview Length minutes capilgth Unique ID of t
29. equivalent 3 School year 10 or equivalent 4 School year 9 or equivalent 5 School year 8 or below 6 Never attended school 7 Still at school 1 A postgraduate diploma or higher 2 Graduate diploma Graduate certificate 3 A bachelor degree with or without honours 4 Advanced diploma diploma 5 Certificate III IV including trade ertificate 6 Other O Parent 2 Partner Study Child Person 4 1 School year 12 or equivalent 2 School year 11 or equivalent 3 School year 10 or equivalent 4 School year 9 or equivalent 5 School year 8 or below 6 Never attended school 7 Still at school 1 A postgraduate iploma or higher O 2 Graduate diploma Graduate certificate 3 A bachelor degree with or without honours 4 Advanced diploma diploma 5 Certificate III IV including trade ertificate 6 Other O 77 D13 Does lt study child gt have any medical conditions and or disabilities that have lasted or are likely to last for 6 months or more MAY SELECT MORE THAN ONE Family Details D2 What is their first name D3 Is male or female D4 What was age last birthday D5 How is related to parent 1 Person 5 Who else lives here 1 male 2 female 3 no person
30. excipient and proprietary ingredient provided on a per dose basis In a small number of cases label or web information was used to develop nutrient data Where necessary data provided by TGA were converted into the units and the modes of expression used that were in this survey Notes on nutrient data Energy The AUSNUT 2007 energy data are determined using the following equation Energy kJ Protein g 17 total sugars g 16 total fat g 37 starch g 17 dextrin g 17 maltodextrin g 17 sorbitol g 16 lactic acetic acids g 15 malic quinic citric g 10 alcohol g 29 The energy factors were the same as those used in the 1995 NNS In addition AUSNUT 2007 reports energy including contribution from fibre 8kJ g Carbohydrate For some foods data for total carbohydrates includes a contribution from glycogen sugar alcohols and oligosaccharides where the level of these carbohydrates is known For these foods the sum of the total sugars and starch will not equal the total carbohydrate value Not specified 3 Not further specific 51 Dietary fibre Total dietary fibre values have been analysed by the Association of Official Analytical Chemists AOAC enzymic gravimetric method Section 985 29 AOAC 2000 This includes soluble insoluble fibre some resistant starch and lignan Fatty acids FSANZ has updated a significant proportion of fatty acid data including omega 3 fatty acids since AUSNUT 1997
31. most recent electronic listing of residential numbers is out of date There are two situations where RDD does not provide a listing of all the eligible households in a postcode e Households where there is no fixed phone line e Households where the telephone prefix has been ported in from another area and is not a prefix allocated to the postcode they now reside in or the survey sampling database The ABS 2001 Estimated Resident Reference Population estimates and the corresponding distribution of the survey sample when based on these estimates are shown in Table 7 23 Table 7 ABS reference population estimates June 2001 and corresponding regional distribution for children aged 2 16 years SD statistical division concordance to postal areas Population Sample Required Sample Achievedt number A number o number A Sydney SD 768367 20 700 16 697 16 Rest of NSW 527228 14 400 9 395 9 Melbourne SD 638887 16 600 14 593 13 Rest of VIC 294309 8 280 6 292 7 Brisbane SD 321616 8 320 7 338 8 Rest of QLD 416547 11 420 10 424 10 Adelaide SD 207792 5 620 14 614 14 Rest of SA 88961 2 260 6 263 6 Perth SD 273450 7 240 5 260 6 Rest of WA 116547 3 160 4 152 3 Greater Hobart SD 42685 1 80 2 106 2 Rest of TAS 62772 2 80 2 78 2 Darwin SD 19070 0 40 1 55 1 Rest of NT 28725 40 4 Canberra SD 68691 2 160 4 179 4 Rest of ACT 160 0 0 0 0 0 Other territories 914 0 0 0
32. process Nutrition Survey data cccesssssccseseeceeseeceseceeesecaeesesaecsessecnevsecnaeeeeeaeseteaeens 115 Calculation of Weights 22 20 sccccsc secdsceeseeecsdeccesedes cea scteedea E EE E E E tees 117 24 Cell Counts for checking correct application of weighting factors sss 119 25 Details of tabulations in the User Guide cccccccccccccecetsceceteeeceeseeaceeaceeseeeseesseenseenseeneeaes 122 26 Glossy an eenn e a ie Street e E e lee el AS te a A Ae E dee 124 27 Units of measurement orrein faceted a E wets teeta fas eS eg aes ea 126 28 Referen eS rta Stee ES othe Sons aii os ack tobet a Senta e Seay Biche eye anion BGA Aisles 127 1 List of Abbreviations ABS ACHPER AFGC Al AMDR ARIA CAPI CATI CCD CSIRO DAFF DAA DFE DTMS DoHA ESOMAR EAR FSANZ ISAK ISO LINZ24 MARCA MET MVPA NHMRC nfs ns NNS 1995 NRV RDD RDI SDT TEM TGA UniSA Australian Bureau of Statistics Australian Council for Physical Education and Recreation Australian Food and Grocery Council Adequate Intake Acceptable Macronutrient Distribution Range Accessibility Remoteness Index of Australia Computer Assisted Personal Interview Computer Assisted Telephone Interview Census Collector District Commonwealth Scientific Industrial and Research Organisation Department of Agriculture Fisheries and Forestry Dietitians Association of Australia Dietary Folate Equivalents Desktop Marketing System Department of Health and Ageing
33. s programmable analytical engine e g minutes of screen time Researchers will often wish to access these files for detailed analysis These data are supplied in the main database set see Section 21 e Summary data Means standard deviations and percentiles for extracted data for demographic slices e g age and sex groups These are the headline data which are used in main findings reports Process for cleaning Raw MARCA profiles Raw MARCA profiles were cleaned using manual checks Cleaning involved e Checking participant IDs dates random IDs and school names for consistency with other files from the same child e Replacing any activity listed as other MARCA code 000000 with the best compendium equivalent Process for extracting data The cleaned profiles were analysed to calculate values for a number of activity sets Values for activity sets were calculated by summing the total number of minutes in the profile devoted to each activity in the activity set For example Table 21 shows the codes that constitute the activity set screen time Table 21 Screen time codes Code Activity 111030 watching TV lying quietly 121050 watching TV sitting 420050 computer work e g typing internet 722190 computer playstation games The MARCA s analytical engine searches the profile for any occurrence of each of these codes and adds together the durations for each occurrence The variables w
34. support and with the weight distributed evenly on both feet Occasionally this protocol could not be followed eg child was wearing a plaster cast and in a few cases particularly among the very young children the interviewer was unable to take a measure Reasons for irregularities or missing data were recorded by the interviewer Body Mass Index Body Mass index was calculated as weight in kilograms divided by height in metres squared Age at date of CAPI rounded to nearest half year and sex specific BMI cutoffs for normal weight overweight and obese among children and adolescents were applied to the data using Table 4 of Cole et al 2000 For underweight Grade 3 thinness corresponding to an adult BMI of 18 5 kg m2 was used as a cut off Cole et al 2007 Waist girth Waist was measured on all consenting participants who were able to stand upright and stand still enough while waist was measured Using the cross over technique the measurement tape was positioned mid way between the lower costal 10th rib border and the top of the iliac crest in the mid axillary line perpendicular to the long axis of the trunk Measurements were taken against the skin or over light clothing such as aT shirt If measured over clothing a coloured sticker was used to temporarily identify the level at which the measurement was taken The subject assumed a relaxed standing position with the arms folded across the chest The subject breathed normally and
35. the measurement was taken at the end of a normal expiration end tidal The Lufkin W606PM metal tape was used to measure waist girth 36 Occasionally this protocol could not be followed eg child was wearing heavy clothing and in a few cases the interviewer was unable to take a measure eg child refused to be measured Reasons for irregularities or missing data were recorded by the interviewer Waist to height ratio Waist to height ratio was calculated by dividing waist in centimetres by height in centimetres 37 13 Survey response Measures to maximise response The project team attempted to maximise response however some non response is unavoidable when people choose not to participate or cannot be contacted Strategies to maximise participation through all stages were Stressing the importance of selected households participating in the survey to represent others in their local area Stressing the importance of participation for planning and policy for child health Stressing the confidentiality of all information under the Privacy Legislation and ESOMAR code of conduct Provision of written information in the form of a letter about fieldwork information brochure and website Provision of a Freecall Hotline for information regarding the survey Fieldwork procedures that made every effort to contact and recruit each phone number selected Minimum of 6 call attempts at various times of the day on weekdays and weekends b
36. weight assigned to an individual child is chosen to adjust the stratum averages by the proportion of children in that stratum in the population This derivation of the appropriate weights for non proportionate sampling is described below It must be noted that it is not possible to allow for non response bias by such weighting If the likelihood of responding is related to the nutrition and activity status in some way independent of the weighting variables used here the weighting will not provide any correction Non Proportionate Sampling The sampling for the survey used randomly selected clusters of postcodes chosen to give an approximately equal number of respondents in each age group 2 3 4 8 9 13 14 16 from each of the metro and non metro areas within each State Parents were then contacted using random digit dialling Thus apart from families with no phone assumed here to be a negligible number the selection within the chosen postcode clusters was by household Thus a given child had probability proportional to 1 number of children in household of being selected so that children in small households were proportionately over represented The postcode data was converted to ABS postal area as described in Section 14 above and classified by State Territory and separately by Capital City and Rest of State within each State Territory There was no Rest of State data for ACT and no Capital City data for Tasmania or the North
37. 06 food to produce a new survey food Where the description of a NUTTAB 2006 food was similar to a survey food except for a particular characteristic the NUTTAB 2006 food was modified to account for that characteristic This approach was used for many of the low or reduced fat reduced salt fortified or intense sweetened varieties of products consumed during the survey These characteristics were modified by Imputing nutrient data from a similar NUTTAB 2006 food for example if a respondent reported consuming Milk flavoured banana reduced fat the NUTTAB 2006 food Milk flavoured banana regular fat would be used as a basis for developing a new record and the fat content would be modified based on the fat content of the NUTTAB 2006 food Milk flavoured strawberry reduced fat Other nutrient modifications may also have been necessary for example changing the cholesterol value when the fat content is reduced Using nutrient data from product labels For example if a resoondent reported consuming Juice orange no added sugar added vitamin C the NUTTAB 2006 food Juice orange unsweetened unfortified would be used as a basis for developing a new food and the vitamin C value would be modified to reflect the vitamin C value presented on the nutrition information panel of commonly consumed brands of orange juice fortified with vitamin C Recipe calculation Recipe calculations were used to generate nutrient data for mixe
38. 1293 horseback riding hard 342641 tennis court light 321291 horseback riding light 342642 tennis court medium 321292 horseback riding medium 342060 tenpin bowling 341303 _jice skating hard 342813 touch football hard 341301 ice skating light 342811 touch football light 341302 _jice skating medium 342812 touch football medium karate martial arts judo kick boxing 341322 _ medium 342703 volleyball beach hard 341323 karate martial arts judo kick boxing hard 842701 ___ volleyball beach light 341321 karate martial arts judo kick boxing light 842702 __ volleyball beach medium 341343 kayaking hard 342693 volleyball court hard 341341 kayaking light 342691 volleyball court light 341342 _ kayaking medium 342692 volleyball court medium 342363 _ lacrosse hard 342713 wallyball hard 342361 _ lacrosse light 342711 wallyball light 342362 lacrosse medium 342712 wallyball medium 342370 lawn bowls 331733 water skiing hard 331963 _ lifting weights hard 331731 water skiing light 331961 lifting weights light 331732 water skiing medium 331962 _ lifting weights medium 341743 whitewater rafting hard 342383 netball hard 341741 whitewater rafting light 342381 netball light 341742 whitewater rafting medium 111 Active Transport MARCA codes and activity names for activities included under the active transport
39. 2 0 7 amp CATI pedometer data CAPI computer assisted personal interview CATI computer assisted telephone interview Of the 2 829 pedometer records collected 252 were culled because they did not meet retention criteria see section 14 Survey arrangements This survey was jointly funded by the Commonwealth Department of Health and Ageing the Department of Agriculture Fisheries and Forestry and the Australian Food and Grocery Council A representative from each of the three funding agencies formed a Steering Group which was involved in managing the development and implementation of the survey Section 3 The Steering Group selected The University of South Australia UniSA and the Commonwealth Scientific Industrial and Research Organisation CSIRO to undertake the survey by a tender process and together they sub contracted I view Pty Ltd to manage the fieldwork This project team UniSA CSIRO and I view Section 3 further defined the content and methodology and implemented the survey UniSA provided anthropometric measurement and physical activity assessment expertise CSIRO provided dietary assessment expertise data management population weighting factors and overall project management l View managed the recruitment fieldwork and supervised the interviewers All project team members contributed to survey development and interviewer training The project team acknowledges Flinders University for contrib
40. 341552 snorkeling medium 341980 mucking around indoors walk run 331590 stretching exercises 341990 mucking around outdoors 341603 surfing body or board hard 321960 mucking around inside sitting 341601 surfing body or board light 108 code activity code activity playground equipment eg monkey bars 341903 hard 341602 surfing body or board medium playground equipment eg monkey bars 341901 light 341933 swimming playing in pool hard playground equipment eg monkey bars 341902 medium 341931 swimming playing in pool light 342773 playing catch hard 341932 swimming playing in pool medium 342771 playing catch light 331630 tai chi yoga 342772 playing catch medium 331993 totem tennis hard 321880 playing in sandpit 331991 totem tennis light 321950 playing with animals sitting 331992 totem tennis medium 341293 playing with animals walk run hard 341683 trampoline hard 341291 playing with animals walk run light 341681 trampoline light 341292 playing with animals walk run medium 341 682 trampoline medium 321920 playing with toys lego dolls action figures 342860 wrestling with mates 109 Sport MARCA codes and activity names for activities included under the organised sport and play category
41. 4358 WA Metro 16987 42529 44839 28400 132755 WA Rest of State 4664 14395 15610 8081 42751 NT Rest of State 1879 4596 4508 2503 13486 TAS Rest of State 5277 14328 15547 9221 44373 ACT Metro 4106 8913 9368 5815 28202 TOTAL 229677 58849 1 615807 367919 1801895 Adjusted sample weight applied Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 62 159 156 85 462 NSW Rest of State 44 113 124 82 365 QLD Metro 28 74 77 43 222 QLD Rest of State 35 89 94 59 276 SA Metro 16 41 45 27 130 SA Rest of State 6 17 16 10 50 VIC Metro 49 131 125 72 378 VIC Rest of State 30 70 86 54 240 WA Metro 23 59 62 39 183 WA Rest of State 6 20 21 11 59 NT Rest of State 3 6 6 3 19 TAS Rest of State 7 20 21 13 6l ACT Metro 6 12 13 8 39 TOTAL 316 811 848 507 2482 120 Females Unweighted data Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 69 74 71 63 277 NSW Rest of State 76 86 85 64 311 QLD Metro 48 40 52 51 191 QLD Rest of State 36 57 6l 50 204 SA Metro 88 86 79 76 329 SA Rest of State 27 27 42 37 133 VIC Metro 65 64 69 52 250 VIC Rest of State 53 57 59 50 219 WA Metro 31 36 35 38 140 WA Rest of State 21 20 27 22 90 NT Rest of State 11 19 16 13 59 TAS Rest of State 16 24 25 27 92 ACT Metro 2
42. 567 80 At least one 998 21 973 21 920 21 4 837 4 695 4 487 ATSI No 4 683 97 4 548 97 4 349 97 Aboriginal and or Torres Strait 150 3 143 3 134 3 Refused 4 0 4 O 4 0 4 837 4 695 4 487 Language spoken at home English only 4 504 93 4 380 93 4 189 93 Other 331 7 313 7 297 7 Refused 2 O 2 O 1 0 4 837 4 695 4 487 Country Born Australia 4 528 94 4 401 94 4 198 94 Other 309 6 294 6 289 6 4 837 4 695 4 487 Table 18 Parent caregiver characteristic Compliant datasets for Primary Parent Gare Giver Completed CAPI Completed CATI analysis Characteristic To P P Parent Care Giver Primary Male 484 10 469 10 449 10 Female 4 353 90 4 226 90 4 038 90 4 837 4 695 4 487 ATSI No 4 752 98 4 614 98 4 411 98 Aboriginal and or Torres Strait 81 2 77 2 72 2 Refused 4 O 4 O 4 0 4 837 4 695 4 487 Education Tertiary Education 2 161 45 2 113 45 2 020 45 No Tertiary Education 2 661 55 2 567 55 2 452 55 Refused 15 O 15 O 15 0 4 837 4 695 4 487 Language spoken at home English only 4 420 91 4 304 92 4 122 92 Other 416 9 390 8 364 8 Refused 1 0 1 0 O 4 837 4 695 4 487 Country Born Australia 3 820 79 3 717 79 3 547 79 Other 1 017 21 978 21 940 21 4 837 4 695 4 487 Work Status Working 3 448 71 3 359 72 3 221 72 Not Working 1 389 29 1 336 29 1 266 28 4 837 4 695 4 487 45 Table 19 Second parent caregiver characteristic Second Parent Care Giver Characteristic Parent
43. 6 202 192 3 640 40 Apr 330 326 274 268 May 462 440 311 280 Winter Jun 585 590 485 453 5 284 59 Jul 414 439 607 595 Aug 141 150 367 458 Total 2 246 2 246 2 246 2 246 8 984 100 Compliance with study components Not all participants completed all required tasks An indicator variable was derived to define the sub set of participants who completed all tasks To be classified as all tasks completed a dataset must include For children 2 4 or 5 8 years of age e Waist Height Mass 2 days diet recall and demography For children 9 16 years of age e Waist Height Mass 2 days diet recall demography and 4 days of physical activity recall including cullable records In the CAPI data file a variable was created the complian variable which identifies the subset of resoondents who completed all tasks as defined above Of the 4695 households who participated in both the CAPI and CATI 4487 had all of the above data items and are classified as complete datasets for analysis See Table 16 42 Table 16 Number of complete datasets for analysis by age group in each region 2 3 years 4 8 years 9 13 years 14 16 years Total Male Female Male Female Male Female Male Female Female Total NSW metro NSW ex metro QLD metro QLD ex metro SA metro SA ex metro VIC metro VIC ex metro WA metro WA ex metro NT ex metro TAS ex metro ACT metro Total
44. 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild Person 6 Who else lives here 1 male 3 no person 2 female Sight problems not corrected by glasses or contact lenses Hearing problems Speech problems Blackouts fits or loss of consciousness Difficulty learning or understanding things Limited use of arms or fingers Difficulty gripping things Limited use of legs or feet Nerves or emotional conditions that need treatment Any disfigurement or deformity Chronic or recurring pain Any condition that restricts physical activity or physical work e g back problems migraines Shortness of breath or difficulty breathing Any mental illness for which help or supervision is required Long term effects as a result of a head injury stroke or other brain damage Any other long term condition such as arthritis asthma heart disease Alzheimer s disease dementia etc Any other long term condition that requires treatment or medication 99 NONE Person 7 Person 8 12 Who else lives here Who else lives here 1 male 2 female 1 male 2 female 3 no person 3 no person 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 6 biological child 7 adopted child 8 step chi
45. 8 33 19 23 103 TOTAL 569 623 640 566 2398 Sample population weight applied Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 38897 108635 100537 61113 309181 NSW Rest of State 34939 78632 92655 54184 260411 QLD Metro 20532 47652 51096 30324 149603 QLD Rest of State 22860 65010 66554 39763 194188 SA Metro 10952 28749 29652 19977 89330 SA Rest of State 4396 11404 13141 5787 34729 VIC Metro 33918 79724 84250 51160 249052 VIC Rest of State 20877 58516 60830 36485 176708 WA Metro 15732 39256 43139 27706 125833 WA Rest of State 4788 14141 14146 6872 39946 NT Rest of State 1787 4373 4289 2381 12830 TAS Rest of State 4975 13506 14655 8693 41829 ACT Metro 3394 8556 8993 5581 26524 TOTAL 218047 558155 583937 350026 1710164 Adjusted sample weight applied Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 54 150 138 84 426 NSW Rest of State 48 108 128 75 359 QLD Metro 28 66 70 42 206 QLD Rest of State 31 90 92 55 267 SA Metro 15 40 41 28 123 SA Rest of State 6 16 18 8 48 VIC Metro 47 110 116 70 343 VIC Rest of State 29 81 84 50 243 WA Metro 22 54 59 38 173 WA Rest of State 7 19 19 9 55 NT Rest of State 2 6 6 3 18 TAS Rest of State if 19 20 12 58 ACT Metro 5 12 12 8 37 TOTAL 300 769 804 482 2355 121 25 Details of tabulations in the User Guide The following variables are used to generate the results tables contained in this document Table 1 Summary of data collected a
46. 86 55 253 SA Rest of State 12 33 34 18 97 VIC Metro 96 241 241 143 721 VIC Rest of State 59 150 170 104 484 WA Metro 45 113 121 77 356 WA Rest of State 13 39 41 21 114 NT Rest of State 5 12 12 7 36 TAS Rest of State 14 38 42 25 119 ACT Metro 10 24 25 16 75 All 617 1579 1652 989 4837 119 Males Unweighted data Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 80 75 78 64 297 NSW Rest of State 77 76 72 93 318 QLD Metro 43 54 40 43 80 QLD Rest of State 65 56 59 54 234 SA Metro 79 88 85 80 332 SA Rest of State 39 40 27 32 38 VIC Metro 67 80 53 66 266 VIC Rest of State 54 48 54 51 207 WA Metro 40 38 35 37 50 WA Rest of State 20 24 17 22 83 NT Rest of State 13 10 9 11 43 TAS Rest of State 27 33 25 21 06 ACT Metro 18 18 26 22 84 TOTAL 622 641 580 596 2439 Sample population weight applied Age group years 2 3 4 8 9 13 14 16 2 16 NSW Metro 45041 115137 113318 61734 335229 NSW Rest of State 32239 82286 90329 59804 264658 QLD Metro 20461 53602 55849 31139 161051 QLD Rest of State 25120 64758 68199 42494 200571 SA Metro 11710 29739 32702 19931 94082 SA Rest of State 4433 12490 11863 7167 35953 VIC Metro 35671 95188 90999 52567 274424 VIC Rest of State 22090 50529 62674 39065 17
47. 9 Compliance with study components cccecccesseeesceseceeceseccecseecaeeseecaeecaeeeaeeeeeeeeeeeseneeeeeeeeeaees 42 Participant characteristics onsas iesise ie E cencn diceudasdtersdeideesaeds 43 14 Data PROCCSSING 5 ox ities sei sesebctetssss eea aa ae EERTE ea EA Ea AE ae EERE nbecebantavedaatel 48 DSTO GTAP Ul er AE E E E AE TE NAE E A EREA 48 Dietary Ee I RAE A T E A RA 48 Nutrient analysisioeiseniensneriniienien eiii ei a E Ea EEE E aE Ti E Ei 48 Physical activity recall lt 2 lt 2sccsecicec sek cctenceictvcsecess tacdeetia ccc i AERA E E eE 53 1x00 00 0 ye ii i E A RE ERA E eE 58 Physical Measure Sonninen oiii ia E ER A OAE EEA eE 58 15 Interpretation Of FESUN e eaae aa aa iaa aa a aa aaa E aeaaea aT iaeaea 59 SULVeY and Sample Desig aniren feast Badd raana erat aa aaan eea eana aan aa aE Aani Nie IAR aea S 59 Cluster Sample SE a dees aaea a aa a a a ar aa aa aaa a iea aasia 59 Random digit dialling enaere aninion eaa aree ena aaa N aoaea aaan naa odvaseceladicenlesbecisalcsere Moses 59 SAILEI nE TAA AE A AEA EAE EE ET A 59 NATI AE EEE E E A A AE E A 8 60 Physicalactivity reCalle s 4 Ah ba kates eee Aen aes ee RAR tien a ae Se 61 Pedometry acs succinic RRA on a e WANG ae eae Ras Reeves tie Se eee 61 Physical Measures lt cesiporctinsdenens coictthgedsareuccestetstedects count telesloces maces ol tiecdsates cect steals 62 Comparison with previous SUIVECYS sceseceessceseeseeecsecseesecseesecuaeeccsaecaenecaesercn
48. 995 while the last survey to collect data on physical activity was the Australian Health and Fitness Survey in 1985 Since then many state and regional surveys have collected data on various aspects of children s food intake dietary habits and or physical activity patterns However these earlier surveys have not always collected quantitative data or used comparable data collection or sampling methodologies to the present survey preventing direct comparisons 1985 Australian Health and Fitness Survey The 1985 Australian Health and Fitness Survey surveyed 5 224 children aged 10 15 years in urban and rural schools in all Australian states and territories Department of Community Services and Health 1988 1989 Children completed a 24 hour dietary record assisted by trained physical education staff The 24 hour recording period commenced immediately after the children had received instructions at school The survey selected from a two stage list sample of firstly schools both primary and secondary and then classes within schools achieving a reported 75 3 response rate excluding initial non contacts and includes partial participants This was conducted in conjunction with the Australian Health and Fitness Survey of the Australian Council for Physical Education and Recreation ACHPER Data were collected between May and October 1985 across all weekdays Monday to Friday The survey estimates were adjusted by post stratification population wei
49. Care Giver Second Male Female No 2nd Parent ATSI No Aboriginal and or Torres Strait Refused No 2nd Parent Education Tertiary Education No Tertiary Education Refused No 2nd Parent Language spoken at home English only Other Refused No 2nd Parent Country Born Australia Other No 2nd Parent Work Status Working Not Working No 2nd Parent Completed CAPI 3 711 424 702 4 837 4 068 63 702 4 837 1 584 2 492 59 702 4 837 3 758 376 702 4 837 3 180 955 702 4 837 3833 302 702 4837 77 15 84 O 33 52 15 78 O 66 20 15 79 15 3 613 414 668 4 695 3 964 59 668 4 695 1 541 2 429 57 668 4 695 3 667 359 668 4 695 3 105 922 668 4 695 3731 296 668 4695 CATI 77 14 84 O 33 52 14 66 20 14 80 14 Compliant datasets for analysis o 3 467 77 398 9 622 14 4 487 3 805 85 56 4 O 622 14 4 487 1 483 33 2 326 52 56 622 14 4 487 3 528 79 336 8 O 622 14 4 487 2 973 66 892 20 622 14 4 487 3589 80 276 6 622 14 4487 46 Table 20 Household characteristic Household Characteristic Family Couple Lone Parent Other Number in household OONA Oa KWNDY 10 11 12 Income 2 400 wk 124 800 yr 2 200 2 399 wk 114 400 124 799 yr 2 000 2 199 wk 104 000 113 999 yr 1 500 1 999 wk 78 000 103 999 y
50. Feathered Game 183 Poultry And Feathered Game 184 Organ Meats And Offal Products And Dishes 184 Organ Meats And Offal Products And Dishes 185 Sausages Frankfurts And Saveloys 185 Sausages Frankfurts And Saveloys 186 Processed Meat 186 Processed Meat 187 Mixed Dishes Where Beef Or Veal Is The Major 187 Mixed Dishes Where Beef Veal Or Lamb Is The Major Component Component 188 Mixed Dishes Where Lamb Or Pork Bacon Ham Is 188 Mixed Dishes Where Pork Bacon Ham Is The Major The Major Component Component 189 Mixed Dishes Where Poultry Or Game Is The Major 189 Mixed Dishes Where Poultry Or Game Is The Major Component Component 191 Dairy Milk 19 MILK PRODUCTS AND DISHES 1911 Milk Fluid Fat Increased 191 Dairy Milk cow sheep and goat 192 Yoghurt 192 Yoghurt 193 Cream 193 Cream 194 Cheese 194 Cheese 195 Frozen Milk Products 195 Frozen Milk Products 196 Other Dishes Where Milk Or A Milk Product Is The 197 Other Dishes Where Milk Or A Milk Product Is The Major Major Component Component 197 Milk Substitutes 198 Flavoured Milks 198 Flavoured Milks 20 DAIRY SUBSTITUTES 201 Dairy Milk Substitutes Unflavoured 202 Dairy Milk Substitutes Flavoured 203 Cheese Substitute 204 Soy Based Ice Confection 205 Soy Based Yoghurts 20 SOUP 21 SOUP 201 Soup 211 Soup Prepared Ready to Eat 202 Dry Soup Mix 212 Dry Soup Mix 203 Canned Condensed Soup 213 Canned Condensed Soup Unprepared 21 SEED and NUT PRODUCTS AND DISHES 22 SEED and NUT PRODUCTS AND DISHES 211
51. Ingredient Batter Based Products FATS AND OILS Butters Dairy Blends Margarine and Table Spreads Vegetable Nut Oil Other Fats Unspecified Fats FISH and SEAFOOD PRODUCTS AND DISHES Fin Fish Excluding Commercially Sterile Crustacea And Molluscs Excluding Commercially Sterile Other Sea And Freshwater Foods Packed Commercially Sterile Fish And Seafood Fish And Seafood Products Homemade and Takeaway Mixed Dishes With Fish Or Seafood As The Major Component FRUIT PRODUCTS AND DISHES Pome Fruit 95 2007 1995 Revised Food Food Code Food Group Name Code Revised Food Group Name 162 Berry Fruit 162 Berry Fruit 163 Citrus Fruit 163 Citrus Fruit 164 Stone Fruit 164 Stone Fruit 165 Tropical Fruit 165 Tropical Fruit 166 Other Fruit 166 Other Fruit 167 Mixtures Of Two Or More Groups Of Fruit 167 Mixtures Of Two Or More Groups Of Fruit 168 Dried Fruit Preserved Fruit 168 Dried Fruit Preserved Fruit 169 Mixed Dishes Where Fruit Is The Major Component 169 Mixed Dishes Where Fruit Is The Major Component 17 EGG PRODUCTS AND DISHES 17 EGG PRODUCTS AND DISHES 171 Eggs 171 Eggs 172 Dishes Where Egg Is The Major Ingredient 172 Dishes Where Egg Is The Major Ingredient 173 Egg Substitutes and Dishes 173 Egg Substitutes and Dishes 18 MEAT POULTRY and GAME PRODUCTS and DISHES 18 MEAT POULTRY and GAME PRODUCTS and DISHES 181 Muscle Meat 181 Muscle Meat 182 Game And Other Carcase Meats 182 Game And Other Carcase Meats 183 Poultry And
52. LINZ was not completed linznot2 Was the carer form used CATI carerfo2 Was the pedometer worn pedworn Reason pedometer was not worn pedomnot Will resoondent wear the pedometer in future willwear Reason respondent will not wear pedometer in future pednotwe Was the pedometer log used pedometa Reason pedometer log was not used pedlogno Will the respondent send the pedometer back sendpedo 104 Food and Nutrition Records The food and nutrition record Table 26 contains one or more record for each respondent Each record contains details of the individual food beverage and dietary supplement consumed by the respondent on the day prior to each interview CAPI and CATI including the derived nutrient content Each record is identified by 3 keys in the order respid person level intervid interview number level and intlinen individual line number of each item consumed Table 26 Contents of Food and Nutrient data file defined by field description label and field name FIELD DESCRIPTION LABEL FIELD NAME IDENTIFYING ITEMS Unique ID of the respondent respid The LINZ24 identifier of the interview intervid Line number intlinen FOOD ITEMS CAPI or CATI interview capicati Actual date of dietary intake actdate Type of day of dietary intake daytype Day of the week of dietary intake d
53. Products Other Than Confectionery Where Sugar Is Major Component CONFECTIONERY AND CEREAL NUT FRUIT SEED BARS Chocolate And Chocolate Based Confectionery Cereal Fruit Nut And Seed Bars Other Confectionery ALCOHOLIC BEVERAGES Beers Wines Spirits Other Alcoholic Beverages Pre mixed drinks SPECIAL DIETARY FOODS Formula Dietary Foods Enteral formula MISCELLANEOUS Yeast Yeast Vegetable And Meat Extracts Intense Sweetening Agents Herbs Spices Seasonings And Stock Cubes Essences Chemical Raising Agents And Cooking Ingredients INFANT FORMULAE AND FOODS Infant Formulae And Human Breast Milk Infant Cereal Products Infant Foods Infant Drinks DIETARY SUPPLEMENTS Multivitamin and Mineral Single mineral Single vitamin 97 334 335 336 337 338 339 Herbal And Homeopathic Supplements Oil Supplement Protein Supplement Sports Supplement Fibre Supplement Probiotics and Prebiotics 98 21 Data items The 2007 National Children s Nutrition and Physical Activity Survey data have been provided in two separate file sets The Contacts File This contains contact information for each eligible household identified through the RDD sample selection process Eligible households were those that were identified with children of an appropriate age The Results Database Files This set of files contains the data obtained from the CAPI and CATI interviews including the food beverage and supplement intake
54. Sc Epidemiology RPHNutr Coles and Rutishauser Consultants Professor Wendy Brown BSc Hons GradDip Phys Ed MSc PhD FASMF Professor of Physical Activity and Health School of Human Movement Studies University of Queensland Professor Robert Newton BHMS Hons MHMS PhD AEP CSCSD FAAESS Foundation Professor Exercise and Sport Science Edith Cowan University Professor Martin Silink AM MB BSc Hons MD FRACP Professor of Paediatric Endocrinology University of Sydney Dr Ann Cowling PhD Ms Janis Baines BA Hons Chemistry MSc Human Nutrition Section Manager Food composition Evaluation and Modelling Section FSANZ Dr Amanda Lee Manager Nutrition and Physical Activity Health Promotion Unit Queensland Health Food composition Team prepared the food composition data base The members of the Food Composition Team were FSANZ Ms Janis Baines BA Hons Chemistry MSc Human Nutrition Section Manager Food composition Evaluation and Modelling Section FSANZ Dr Judy Cunningham BSc Food Tech PhD Food Composition Studies Ms Renee Sobolewski BAppSc Human Nutrition Mr Charles Wannop Database Support IT Contractor Millpost Technologies Pty Ltd Therapeutic Goods Administration Mr Shaun Flor ELF Technical Manager Listed Medicines and Communication Section Office of Complementary Medicines TGA 4 Background Introduction The most recent National Nutrition Survey NNS was conducted in 1995 and the las
55. User Guide 2007Australian National Children s Nutrition and Physical Activity Survey Prepared for DEPARTMENT OF HEALTH AND AGEING O L view Table of Contents Table Of Contents ss cc85 53s Ai 35 6d een Oe Saba r Es Med ON ea eat eee ath BP pea eee abs 2 T Bist of ADDrevidh Onsrccvescerti aise ES ERI eh BST Bs A 4 2 Prefa se neeaae TE AAE E A AA a i ote cules Cah SS See 5 3 Project Teamet miee iatera dae NEn Ea Aa OE asea STERE aaa ETEA EEA RENS 6 Bel BACKOVOUNG ienten e a e a ea a aaee a eae ae 8 Introduct TON 1 ERARE E E A EE T EEE 8 EXASCING IN TOTMALION WAE E E L A E E E A A 8 Survey AUO 8100 Ce i P AE R E EEA A E E E 11 Survey objectives and OVCrViCW c cccsceessesscesscessceeceseceseceaecseecseeeneeeeeceeeeeeeseceaeensecsaeesecueeneeenes 12 Survey AITAN SSMEMNES eee a a aa a a Gaskealosts Bled aaa aaa or aA Taaa aaia 13 Ethics and Reer niae rE La A E EA E E E E E A 15 5 PU Ob iis i oN ein tecaeh en Benin ana hes de couric tit d Co Marit Me coh oe daat be cea teen Gi de ehh Senta bees 16 6 Survey design irei te eiee eei EE tion EAE reas that als Sates tain daes EEEE 17 Sample Desitin ea o e E E ENE E E R E REA EE 17 Sample selection erne e E A seas ees ee ea 18 Scope and COyVETA E Onir roa e a p RE AR E E REO E AE 23 7 Interviewer training and supervision sseeeesseisessensssiseesreseesresesreseerresteressesresriseesresersesset 25 8 Data COMCCHION narine dank C OEE OA Sake TEE N E ES 27 9 Demographic Qu
56. a list of 6 digit phone number prefixes for each location based on postcodes Randomly select prefix t Randomly select 2 digit suffix t Create 8 digit phone number by appending suffix to prefix s number in database Flag acceptable phone numbers with location t Add to CATI Sample file Table 5 Kish Table gt Discard Yes Load file into CATI system in batches after exhaustion of sample Kish Table A Bl B2 D El E2 F Summary of Kish Tables Number of children aged 2 16 years 1 Select child numbered 1 1 1 1 1 1 1 1 2 ND NM ND hb HH HH 3 1 1 1 2 2 3 3 3 4 5 6 BRWWNN aAnanw kK WBNDN Rank wWwWwNnND 21 The number of children required in the sample for each of the age cohorts was not proportional to the number of children in each of the age cohorts in the population Therefore children aged 2 3 years or 14 16 years had a higher probability of selection than children aged 4 to 13 years in any one household However across the whole population a purely random selection using Kish would produce too many children aged 4 to 13 years than needed for the survey s quota As such the Kish table was biased to select a child in the following order e Child aged 14 16 years then e Child aged 2 3 years and then e C
57. aeeeceaecatenecneeatents 62 Comparison with recommendations cccceescesscessceseeesecesecececseecseeeaeeeeeeeeeeeeeneseseceaeeeseenaeeneeeaees 63 16 Estimation proced res inre Saison cal soda San Shee vattes cabs ee ah We Bes ed heen 65 Non Proportionate Sampling j cise scseecas vaste arenes eieerernasive atvneneeian adeeenwateeracdeeeranes 65 RESUltS eine noch ates even ae E tear sees hiv ame nap atte cele E econ 66 Analysis CODE eseese a EEE EAE ENEE EEEE EOAR SE N EE REE EEE 68 Effective sample Size kyai a E E wt E E E A AE 68 SEAL CAtON AEE EO E S AEE EE A OA E E E eee neon eee areteE 68 17 Data output and Dissemination cccccccccccecceceescesesseesecseseecuseesceseeseeeceeseeecieeeecnsseeenseeseeaees 69 18 IRAR ERI E E E waa Sas laa a eee Bao a Ste 70 19 GAPI SUPVEV SVIDI aen cv vencont snes edie uel EAE A toes Seen Aedes E rein Deir ot dtc cet 71 20 Food SV OUDS pea nado Sie caidas e Oh ese scons Cotdessl Ah tees Doug sts aa Ee deaa ae E Ea Eaa 95 21 D ta items rsisi aan ia a a a n a a a r 99 Contacts Tiles Paaa a a a a E A N cate tec 99 Results database files ss uns mea aa a a a a a a a 100 22 ACUVIEVS CLS oeira ire tie ceacSeeaveons od ceans iaceues Can cbunbenlesdevetncennbetnteec out eUoySous edvewsnsesneevecdsnceeeeett 108 23 R code for non proportionate sampling Weights ecce 114 Read and process ABS data files ccccccecsesssesseesseeseeeeceeeceseensecesecnseenaecaecaaecaeesaeeeseeeeeeaeeeneesss 114 Read and
58. aken end tidally The ratio of the waist circumference to height The force the body exerts in a standard gravitational field Body mass is measured with the subject in light indoor clothing 125 27 Units of measurement g grams kJ kilojoule mg milligram u microgram MET metabolic equivalent 126 28 References Abbott RA Macdonald D Mackinnin L Stubbs CO Lee AJ Harper C Davies PSW 2006 Healthy Kids Queensland Survey Summary Report Queensland Health Brisbane 2007 Athar N McLaughlin J Taylor G Mishra S 2006 The Concise New Zealand Food Composition Tables 7th edition Palmerston North New Zealand Institute for Crop and Food Research Australian Bureau of Statistics 1998a National Nutrition Survey Users Guide 1995 cat no 4801 0 ABS Canberra Australian Bureau of Statistics 19986 National Nutrition Survey Nutrient Intakes and Physical Measurements Australia 1995 cat no 4805 0 ABS Canberra Australian Bureau of Statistics 2006 National Health Survey Summary of Results Australia 2004 05 cat no 4364 0 ABS Canberra Booth M Okely AD Denney Wilson E Hardy L Yang B Dobbins T 2006 NSW Schools Physical Activity and Nutrition Survey SPANS 2004 Summary Report Sydney NSW Department of Health Cancer Council of Victoria 2006 Australian secondary schoo students use of alcohol in 2005 Report accessed on line http www health gov au internet drugstrategy publishing nsf Content monoss8 Co
59. al city statistical division or rest of state region using ABS postal area to statistical local area concordance Households private dwellings from selected postcodes were then recruited to the survey using RDD The telephone number prefix acted as a geographic indicator that corresponded to postcode Households with children aged 2 16 years eligible in scope were identified and asked if they would participate refer to section 21 contacts file for available output on in scope children One child within the household was selected as the study child for the purposes of the survey In some cases recruitment of the study child did not proceed because the age and gender quota for that location was filled Using RDD there will be more postcodes in the final sample than were sampled for recruitment because telephone number prefixes do not exactly follow postcode boundaries and some numbers may located be in adjacent suburbs or some people may have taken advantage of telephone number portability Sample Design Postcode exclusion There were 576 postcodes excluded from selection Table 2 Areas identified in the 2001 ABS Census as having very few in scope children and very remote areas were excluded from the survey sampling frame due to budgetary and time restrictions Additionally this survey was not designed to obtain information from a representative number of Indigenous groups to accurately record their intake and activity patterns
60. all information that you give me will be kept confidential and we have strict processes to ensure the security of your information will start by asking a few questions about the household how many people live here and a little bit about each person also will ask about study child s general family background such as your and your partner s work and educational background This will give us a general idea of the home environment study child grows up in Then will ask you some questions about their food intake and what they have done over the last 48 hours am also going to ask if can take some measurements of your child such as his her weight height and waist circumference will then ask the child to conduct a stride test and show you how to wear the pedometer which we ask they do over the next 7 days There will be a few questions on general food habits and finally will ask you some questions about your work and finance Once have finished asking you questions have a short form that will ask you to fill in by yourself and return with the pedometer in the envelope supplied 72 Before we start will need to obtain your formal consent for this study will read you a statement and you will need to provide your signed agreement to take part in the study So that s generally what we are going to be doing do you have any questions at this point Dol have your agreement for you to be part of this study will n
61. anual checking of some individual intakes and assessment of unusual values Quality assurance on nutrient data is detailed in the AUSNUT 2007 Users Guide Physical activity recall Each activity in the MARCA has a unique ID code and an associated child appropriate energy cost in METs or default adult value Ridley et al 2006 The MARCA s analytical engine allows data to be extracted from MARCA profiles A profile is one 24 hour recall by one child It derives the estimated number of minutes spent on an activity and the amount of energy expended in individual activities or in activity sets such as active transport organised sport play or moderate to vigorous physical activity Activity sets are defined as lists of MARCA codes Section 22 Data format Four types of files contain MARCA data e Raw profiles a profile is one 24 hour recall by one child Profiles are saved to a text file when a MARCA recall is complete Researchers will rarely want to access these files but they have been retained as part of the audit trail 53 e Cleaned profiles Profiles that have been manually checked for anomalies Researchers may choose to access these files if they want to extract data which have not been extracted as part of the process described below Contact the Commonwealth Department of Health and Ageing for access to these profiles e Extracted data Data that have been obtained when the MARCA profiles have been analysed by the MARCA
62. ard consent No gt Code as Refusal 502 Yes y Conduct CAPI 4837 Discard gode S Age by location quota full Contact Made 96 Yes iether Discard No gt Code as Refusal consent 46 39 Of the 16 598 eligible households that were contacted 10 109 agreed to participate in the study which equalled a response rate of 61 Of these 10 109 households 3 320 were subsequently not required to participate as the quota for children in their age group had already been filled Out of the 6 789 households recruited 502 then refused and 4 837 completed the CAPI The CATI was completed by 4 695 participants The final response rate for completed CAPI and CATI was 40 when calculated as a proportion of eligible households Table 11 Table 11 Overall Response Rates Sample Eligible Households 16 598 Agreed to participate 10 109 RECRUITMENT Quota full 3 320 Recruited 6 789 Refused 6 489 Recruit Response Rate 51 CAPI in home interview Quota filled between recruitment and interview date 1 450 CAPI Interview 4 837 Refused 502 CAPI Completion Rate 91 CATI follow up telephone interview Quota filled between CAPI and CATI 96 CATI Interview 4 695 Refused 46 CATI Completion Rate 99 FINAL RESPONSE Completed CAPI and CATI 4 695 Eligible less quota full4 11 732 Final Response Rate 40 Recruitment Response Rate Recruits Eligible less quota f
63. aterials that you have everything Repeat your full name and the 1800 10 80 12 phone number for the respondent to contact your supervisor if they have any queries or would like to verify the validity of the study Sign and date a PR Card and hand to respondent This card is to be left with respondents so they have your name and I View contact details and this is part of our IQCA requirements 94 12 121 122 123 124 125 126 127 128 13 13 132 133 134 135 136 14 141 142 143 144 145 15 15 152 153 154 155 156 16 161 20 Food groups Food Group Name NON ALCOHOLIC BEVERAGES Tea Coffee And Coffee Substitutes Fruit And Vegetable Juices And Drinks Soft Drinks Flavoured Mineral Waters And Electrolyte Drinks Mineral Waters And Water Water With Other Additions As A Beverage CEREALS AND CEREAL PRODUCTS Flours And Other Cereal Grains And Starches Regular Breads And Rolls Breakfast Cereals Plain Single Source Fancy Breads Flat Breads English Style Muffins And Crumpets Pasta And Pasta Products Rice And Rice Products Breakfast Cereals Mixed Source Breakfast Cereal Hot Porridge Type CEREAL BASED PRODUCTS AND DISHES Sweet Biscuits Savoury Biscuits Cakes Buns Muffins Scones Cake Type Desserts Pastries Mixed Dishes Where Cereal Is The Major Ingredien
64. automatically calculated by LINZ24 CSIRO dietitians subsequently converted the volume to mass Once the portion size was entered interviewers were prompted to ask how many of these did you have allowing entry of fractions or multiple serves consumed e g 0 5 of a banana or 3 slices of bread Food model booklet amp measuring aids To assist with estimation of portion sizes participants received a food model booklet and interviewers had a set of measuring aids The Food Model Booklet was developed based on a booklet used by the United States Department of Agriculture USDA with permission It was modified for Australian foods and adapted for children Its use in children aged 10 years was validated at the time of the pilot survey The validation showed moderate to strong correlations between weighed portion and recalled portion for almost all foods and that recall can be considered a reasonable interpretation of actual consumption at 32 the group level The booklet was used during the CAPI and remained with the participants for reference during the CATI The booklet included e Life size drawings of mugs glasses other beverage containers bowls take away food containers cans and pats for different spreads e Amorphous mounds suitable for measuring foods e g mashed potato rice or peas e Life sized photograph of potato chips e Aset of 10 concentric rings a grid and a moveable wedge to help dete
65. aynum Month of dietary intake month Seasonality of dietary intake season LINZ food code linzcode FSANZ food code fsanzcod LINZ quick list item name quiklist Item number itemno Component within item number compno Ingredient within component recipe item number ingno Location this food was consumed location Time this food was consumed timefood Amount consumed g or number of tablets if dietary supplement gramstab Water g nut Energy kilojoules kJ nut2 Energy inc Fibre kJ nut Protein g nut4 Total Fat g nut5 Sugars g nut Starch g nut7 Total Available Carbohydrates 9 nut8 Dietary Fibre g nut Calcium mg nutlO Iron mg nut 1 Thiamin mg nutl2 Vitamin C mg nutl3 Saturated Fat g nutl4 Monounsaturated Fat g nutl5 Polyunsaturated Fat g nutl LinoleicAcid g nutl7 Alpha Linolenic Acid g nutl8 LongChain Omega 3 Fatty Acid mg nutl Cholesterol mg nut20 Vitamin A Retinol Equivalent mcg nut21 Preformed Vitamin A mcg nut22 Provitamin A mcg nut23 Riboflavin mg nut24 Niacin Equivalent mg nut25 Total Folate mcg nut26 Dietary Folate Equivalent mcg nut27 105 FIELD DESCRIPTION LABEL FIELD NAME VitaminD mcg nut28 VitaminE mg nut29 Phosphorous mg nut30 Magnesium mg nut31 Zinc mg nut32 Potassium mg nut33 lodine mcg nut34 Caffeine mg nut35 Alcohol g nut3 Sodium mg nut37 Recipe number recipeno Recipe item qui
66. by their random number 7 The required number of postcodes 50 was selected to cover each region from the top of each sorted list compiled in step 6 8 4 5 postcodes of geographical proximity to the 50 locations identified in step 7 were added to meet the required number of postcodes 230 9 The postcodes identified in step 8 were allocated a code 1 50 to identify each cluster location clusterloc Table 3 shows the number of postcodes selected and the number of interviewing locations Note that 16 additional postcodes and 4 cluster locations were added to the numbers described above for the SA booster sample Table 3 Number of postcodes and locations selected for sampling within each Cluster Locations aie Capital Rest of Total Capital Rest of Total city a a a Rete oo AUREN ce Cc SA Booster o rr 4 s 3 1 4 Western Austraia f oma 3 3 2 5 ES E a a eae E E E E ESS E STO 7 Sample selection The central issue that faces any sampling selection is the sampling frame Conceptually this frame is a listing of all the members of the population to be sampled but no such complete list exists A number of possibilities for the sampling frame were considered in the planning stages These included using administrative databases such as Medicare Australia reverse telephone directory CD ROM RDD or random door to door interviewing in Census District Areas After investi
67. cklist food name quikrcpe Food group 5 level code minorgrp Major Food Group majorgrp Sub major Food Group submjgrp Fsanz food code 8 digit number codegdig FSANZ food name fsanznam Activity Records The activity record contains two files The first Table 27 contains four records for each respondent Each record contains details of individual days of recording physical activity including type of day of recording from self report physical activity levels and minutes engaged in physical activity items Each record is identified by 2 keys in the order respid person level and datetest date of activity recording The second file Table 28 contains one record per respondent and contains the pedometry information Table 27 Contents of Activity data file defined by field description label and field name FIELD DESCRIPTION LABEL FIELD NAME IDENTIFYING ITEMS Unique ID of the respondent respid The date of the activity datetest PHYSICAL ACTIVITY ITEMS The description of the type of day school day non school day marcaday Physical Activity Level in METs pal The number of minutes of moderate physical activity mpamins The number of minutes of vigorous physical activity vpamins Minutes of active transport actirans The number of minutes spent in part time work ptwkmins The number of minutes spent doing chores choremin The number of minutes sp
68. d dishes prepared at home or purchased commercially where the respondent was unable to identify the individual ingredients or their amounts The general approach involved e consulting current popular recipe books Australian food magazines and websites to identify appropriate recipes for home prepared foods e combining individual ingredients and their relative proportions e applying an appropriate nutrient retention factor to each individual ingredient if necessary e applying an appropriate weight change factor to the uncooked recipe if necessary The retention and weight change factors used in these calculations were taken from published literature e g USDA 2006 Recipes for commercial products were developed using labelled ingredients The amount of each ingredient was modified so that the final nutrient data were similar to the nutrient data presented on the product s nutrition information panel Developing a new survey food Where the methods outlined above were not appropriate new survey foods were developed by e borrowing nutrient data from international food tables and databases such as the USDA UK NZ and Danish food tables e using industry or label data 50 e imputing nutrient data from similar foods or from levels permitted in the Food Standards Code e reproducing nutrient data published in AUSNUT 1997 Where data from international food composition tables were used care was taken to ensure that the units and mo
69. d if over 9 years otherwise address the parent care giver 11 Was there anything unusual about yesterday that should be noted for the researchers analysing the food intake or activity data for this child 1 Yes record comment 2 No Skip to 13 12 Comments 13 Was there anything unusual about the day before yesterday that should be noted for the researchers analysing the activity data for this child 1 Yes record comment 2 No Skip to Section J 12 Comments 90 SECTION J DEMOGRAPHICS Address the Parent care giver Now am going to ask a few questions about your job and finances Once again your answers are totally confidential and individual responses will not be provided to government agencies We need to have a broad range of Australian families included in our study Family Details J1 Last week did you do any work at all in a job business or farm J2 Last week did you do any work without pay in a family business J3 Did you have a job business or farm that you were away from because of holidays sickness or any other reason include casual on call or agency work J4 How many hours do you usually work each week in that job that business all businesses If irregular hours average over last 4 weeks Do not include travel time J5 At any time during the last 4 weeks have you been looking for full time or part time work Mar
70. de of expression matched those used in the survey USDA 2006 Food Standards Agency 2002 Athar et al 2006 Maller et al 2005 Food descriptions with characteristics not specified Unspecified survey foods were developed where a respondent was unable to identify the exact food or cooking method of the food they consumed Nutrient data for unspecified foods were derived using two approaches 1 Ensuring the nutrient data are representative of all survey foods that had a similar description but varied with respect to the characteristic of interest For example a nutrient line for bread white not further specified drew on nutrient data for all white fresh or toasted fortified or unfortified breads weighted according to consumption patterns observed in the survey 2 Assigning an unspecified food a nutrient profile of the most frequently consumed product from the relevant category For example Chicken ns as to part cooked nfs ns as to skin could be assigned the nutrient line for Chicken breast baked without skin as this might have been the most frequently consumed type of chicken and cooking method reported during the survey The nutrient composition data developed for survey dietary supplements were derived using formulation data provided by the TGA Data provided by TGA included information on the product s name AUST L number maximum daily dose and formulation with the name and amount of each active
71. des Postcode tablelS State lt IncludedPostcodes State match tablelSpostcode IncludedPostcodes Postcode First digit in X fields is female second is male Calculate the number of Boys and Girls in each Postcode Boys lt tablel xX01 table1 X11 tablel1 X21 table1 X31 2 table1 x02 2 tablelSXx12 2 table1l X22 2 tablel X32 3 tablel X03 3 table1 X13 3 tablel X23 3 tab le1 X33 Girls lt tablel xX10 table1 X11 2 tablel X21 3 table1 X31 2 tablel SX20t table1l x12 2 table1 X22 3 tablel X32 3 tablel X30 table1l X13 2 tablel X23 3 table 1 X33 Aggregate postcode values to State x Region Boys aggr lt aggregate Boys by list Region tablel Region State tablel State sum names Boys aggr 3 lt Count Girls aggr lt aggregate Girls by list Region tablel Region State tablel State sum names Girls aggr 3 lt Count Calculate proportions of Boys and Girls for each Region Sex ratio lt cbind Boys aggr Girls aggr Count names Sex ratio 3 4 lt c Boys Girls Sex ratio Total lt Sex ratioSBoystSex ratioS Girls Sex ratio Pr Boy lt Sex ratio Boys Sex ratioSTotal Sex ratio Pr Girl lt Sex ratio Girls Sex ratio Total Table 2 contains counts of families by family structure Table2 csv is a simplified version of the ABS file 2006 Census Table 2 No of families by No of children aged 2 3 by No aged 4 8 by No aged 9 13 by No aged 14 16 Po csv in particular w
72. do just because you are wearing the pedometer Each night when the pedometer is removed would like you to open the pedometer to write the stored information on the pedometer log sheet Interviewer shows child parent the Pedometer Log Sheet First write the day and the date in the Pedometer Log Sheet here Interviewer shows study child parent care giver the Pedometer Log Sheet All you need to do then is press the MODE button until the black marker is under STEPS on the display and write the number in steps column on the Pedometer Log Sheet Interviewer shows study child parent care giver the procedure Press the MODE button again and the little black marker should be under DIST please write this in the DIST column on the Pedometer Log Sheet Interviewer shows study child parent care giver the procedure Press the MODE button again and the little black marker should be under ACT MIN please write this in the ACT column on the Pedometer Log Sheet Interviewer shows study child parent care giver the procedure You then close the pedometer and place it where it will be easily found in the morning Please remember to do this each night for the next 7 days 82 Please remove the pedometer only when you have to This will be when you go to bed each night and when the pedometer would get wet such as when you swim or have a shower or bath Each night would like you to write down on the pedometer log sheet how lo
73. e it is required to be reported by law and or there is a risk of harm to yourself or others Information that identifies you will only be disclosed to research consultants for the purposes of administering the Kids Eat Kids Play Identifying material is removed from the study data before it is made available for evaluation and research Only combined results from the study will be discussed and published Participation in this study is voluntary You may choose not to answer some of the questions and you are free to withdraw from the study at any time You should understand that you will not benefit personally from this research although the information gathered will be of use in deciding better nutrition and physical activity policies for children If you require further information or if you have any problems concerning this project or the way that it is being conducted please contact the Project Director Professor Tim Olds phone 0423 147 955 email tim olds unisa edu au You may also contact the Chair of UniSA s Human Research Ethics Committee Ms Vicki Allen 0883023118 and mention Kids Eat Kids Play or visit the website www kidseatkidsplay com au if you have any queries or if you wish to notify us of change of address details Interviewer Note Go to consent form C1 Interviewer Note Confirm that Parent has Yes No signed consent form 4 2 Terminate Interview If No terminate inter
74. e survey team using a reply paid envelope Step counts data were gathered using the following strategies and various combinations of strategies to cross check data e Step count in returned pedometer e Log sheet e Data gathered during CATI interview Data gathered in follow up phone call not CATI Pedometer log data were used as part of the strategy to determine and cross check step count and do not constitute part of the main dataset Estimating stride length Stride length was estimated during the CAPI using the 10 steps method A linear distance of approximately 10 meters was marked out with a metal tape The participant was asked to line up the toes of both feet with the zero on the tape walk normally for 10 steps and stop by bringing both feet together Two trials were conducted after an initial familiarisation trial The average distance covered in centimeters was divided by 10 to provide stride length This distance was programmed into the pedometer so that the daily distance covered was individualised The default setting in the pedometer of 76 cm is based on adult data and was inappropriate for this survey 35 12 Physical Measurements Height weight and waist girth were measured on children aged 2 16 years according to the protocols of the International Society for the Advancement of Kinanthropometry ISAK Marfell Jones et al 2006 Choice of measurement instruments was influenced by the need for interviewers to
75. e usual intake of individuals because of the considerable day to day variability in food beverage and supplement intake within individuals For this reason the present survey obtained a second 24 hour recall of intake by CATI for all participants with 99 of these completed on a non consecutive day Provided there are no systematic differences between the CAPI and CATI data the two days of intake data for each individual can be used to obtain an estimate of usual intakes Analyses were undertaken to determine the extent of any systematic bias between the two methodologies CAPI vs CATI A repeated measures analysis of variance was undertaken to determine if there was any statistical significance in nutrient intakes between the two measures Cohen s generally accepted criteria of size of the effect were used in the evaluation of the within individual variance Cohen 1988 While some of the differences in nutrient intakes collected through the two different methods were statistically significant lt 0 01 the effects were small accounting only for up to 4 of the total variance in nutrient intakes The older age groups 9 13 years and 14 16 years showed greater variation in nutrient intakes between the two interviews than the younger age groups Following the analysis described above estimates of usual nutrient intake were calculated Estimates of usual intakes should be utilised when comparing intakes to recommended Nutrient Reference Value
76. eat vegetables H3 How many serves of fruit do you usually eat each day One serve is equal to half a cup INTERVIEWER NOTE Show food prompt if necessary 1 Less than one serve 2 One serve 3 Two serves 4 Three serves 5 Four serves 6 Five serves 7 Six or more serves or 8 Don t eat fruit 1 Less than one serve 2 One serve 3 Two serves 4 Three serves 5 Four serves 6 Five serves 7 Six or more serves or 8 Don t eat fruit H4 Does the person who prepares your meal add salt when they are cooking 1 Yes usually 2 Yes sometimes 3 No 4 Don t know 1 Yes usually 2 Yes sometimes 3 No 4 Don t know H5 Is it iodised i e contains iodine 1 Yes usually 2 No 3 Don t know 1 Yes usually 2 No 3 Don t know H6 Do you add salt to your meal at the table 1 Yes usually 2 Yes sometimes 3 No 4 Don t know 1 Yes usually 2 Yes sometimes 3 No 4 Don t know H7 Is it iodised i e contains iodine 1 Yes usually 2 No 3 Don t know 1 Yes usually 2No 3 Don t know 87 H8 In the past 12 months have you always had sufficient money to buy food O 1 Yes go to H10 O2No O 3 R
77. ecially during daylight hours The definition of compliance is unclear in the guidelines Olds et al 2007 Compliance can be defined as e the child satisfies the guidelines on all days of the survey period the all days method e the child satisfies the guidelines on most days of the survey period the most days method 63 e the child satisfied the guidelines when MVPA and screen time are averaged across the survey period the average method e the level of compliance can be understood as the probability that a randomly chosen child on a randomly chosen day will satisfy the guidelines the child x day method In the main findings report all four definitions of compliance were analysed For this approach within each age group x sex x day type school vs non school slice the percentage of children who were compliant on 0 1 2 3 and all 4 recalled days can be calculated As well as the percentage of children who were compliant when MVPA and screen time were averaged across all four days The probability that a randomly chosen child on a randomly chosen day meets the guidelines can also be used to determine compliance with guidelines 64 16 Estimation procedures The survey collected data on nutrition and physical activity for 4 837 children aged 2 16 years across Australia Because stratified sampling with non proportional samples was used the results must be weighted for appropriate analysis The
78. ed to other age groups resulting in more data from these participants at the end of the survey period Natural disasters occurring in four locations Tasmania Maitland Sale Katherine which delayed interviewing for several weeks Delayed fieldwork meant that some field interviewers had to resign before the data collection was completed due to prior commitments 70 19 CAPI survey Script SECTION A START SCREEN lt 6 digit number gt lt aaal23 gt Al Enter the RESPONDENT ID number from the Family Contact Form A2 Enter the Random ID from the Family Contact Form A3 Enter the DOB of the child from the Family Contact Form A Enter the postcode Enter the State A a A7 Select Interviewer ID A8 Date of Interview A9 Day of Interview A10 Day Type lt add in a code list gt Drop down list retains last state selected NSW VIC QLD SA WA TAS NT ACT lt insert list when interviewers confirmed gt Record Automatically Record Automatically 1 Monday 2 Tuesday 3 Wednesday 4 Thursday 5 Friday 6 Saturday 7 Sunday Record Automatically 1 Weekday 2 Weekend 3 Public Holiday 4 School Holiday 71 SECTION B INTRODUCTION SCREEN Introduction child aged 2 to 4 years Address the Parent Care Giver Thanks for agreeing to take part in the Kids Eat Kids Play study First of all want to assure you that all information that yo
79. ed vitamin A retinol Micrograms ug Provitamin A beta carotene Micrograms ug Thiamin Milligrams mg Riboflavin Milligrams mg Niacin equivalents total Milligrams mg Vitamin C Milligrams mg Vitamin D Micrograms ug Vitamin E as aloha tocopherol Milligrams mg Total Folate Micrograms ug Dietary folate equivalents Micrograms ug Minerals amp electrolytes Potassium Milligrams mg Sodium Milligrams mg Calcium Milligrams mg Phosphorus Milligrams mg Magnesium Milligrams mg Iron Milligrams mg ZINC Milligrams mg lodine Micrograms ug Other Caffeine Milligrams mg 34 11 Physical Activity Physical Activity Recall Participants aged 9 16 years used the MARCA Ridley et al 2006 to self report use of time The MARCA is a computerised 24 hour recall which asks participants to recall everything they did on the previous day The MARCA shows moderate to good validity when compared to accelerometry Ridley et al 2006 It uses a segmented day format with meal times and or school bells as anchor points Within each time segment time sliders indicate the start and completion times for activities in time slices which can be as fine as 5 minutes Users choose from about 250 activities listed in a compendium under seven categories Inactivity Transport Sport and Play School Self Care Chores and Miscellaneous If the ac
80. edium 341241 riding a bicycle bike light dodge ball poison ball brandy speed 342912 ball medium 341242 riding a bicycle bike medium 342193 frisbee general hard 341253 riding a scooter hard 342191 frisbee general light 341251 riding a scooter light 342192 frisbee general medium 341252 riding a scooter medium 342203 frisbee ultimate hard 341273 riding a skateboard hard 342201 frisbee ultimate light 341271 riding a skateboard light 342202 frisbee ultimate medium 341272 riding a skateboard medium 341233 hacky sack hard 341313 rollerblading in line skating hard 341231 hacky sack light 341311 rollerblading in line skating light 341232 hacky sack medium 341312 rollerblading in line skating medium 342243 hand tennis four square hard 341463 rollerskating hard 342241 hand tennis four square light 341461 rollerskating light 342242 hand tennis four square medium 341462 rollerskating medium 342830 hide and seek 341483 running around hard 341283 hopscotch hard 341481 running around light 341281 hopscotch light 341482 running around medium 341282 hopscotch medium 341473 skipping rope jumping hard 331330 juggling 341471 skipping rope jumping light 342353 kickball hard 341472 skipping rope jumping medium 342351 kickball light 341553 snorkeling hard 342352 kickball medium 341551 snorkeling light 341970 mini golf or putt putt
81. efore classifying as a non contact Minimum of 3 call attempts to honour an appointment and where on the third attempt there is a positive indication that the family will participate additional calls were made Appointment management through flexible approach and targeted approach procedures Interviewing skills for refusal avoidance Careful monitoring of interviewer performance and overall adherence to survey procedures Use of interpreters for households that did not speak English Public awareness and publicity of the survey 38 Response rates There are several places where sample may be lost Figure 2 shows the sample loss through various stages of the study Figure 2 Sample loss flow chart RDD sample generated 347 187 Yes Yy Conduct CATI 4695 Complete Interview 4695 Discard Contact Made No Code as Non by telephone Contact 137 653 Yes Discard Discard Code as Quota j Eligible Code as Not Full t Age by location quota full Household No Eligible 3320 192 927 Yes ig there Discard consent No gt Code as Refusal 6489 Yes y Send out Letter about Fieldwork and information brochure 6789 Discard Code eae Age by location quota full by CAPI ee a i o opt ou 1450 Interviewer Yes ie there Disc
82. efused go to H10 H9 If no did you go without food O 1 Yes O 2 No O 3 Refused H10 Has child s name ever been breastfed O 1 Yes O 2 No go to H12 O 3 Don t Know go to H12 H11 If yes including times of weaning what is the total time your child was breastfed Weeks Months Currently breastfeeding o O Don t know H12 Has child s name ever been given infant formula regularly O11 Yes O12No goto H13 O 3 Don t Know go to H13 H12b If yes at what age was your child first given infant formula regularly Weeks Months O Don t know H13 At what age was child s name first given solid food regularly Weeks Months O Don t know H14 How much did child s name weigh at birth kilograms grams OR pounds ounces H14f Was a written record used to recall the birth weight O11 Yes O2No 88 H15 Which one of the following best describes child s name s usual way of eating O 1 No special way of eating O 2 Vegetarian diet O 3 Weight reduction diet O 4 Diabetic diet O 5 Fat modified diet to lower blood fat cholesterol O 6 Other Specify O 7 Refused If child is over 9 Thank you very much for your help today you have done very well at the all the activities and tasks I now have some questions for your mum dad other but I will talk to you again before I leave about the follow up survey over the telephone 89 SECTION I RECALL DAYS Address the chil
83. eks have you been looking for casual work lookingb primary carer At any time during the last 4 weeks have you been looking for work no lookingc primary carer At any time during the last 4 weeks have you been looking for work don t lookingd know primary carer If you had found a job could you have started work last week primary carer startwor 103 FIELD DESCRIPTION LABEL FIELD NAME When did you last work for two weeks or more primary carer lastwork In the main job held last week what was family member s occupation primary pljobdes carer What are the main tasks that family member usually perform s in that pljobtsk occupation primary carer Before income tax is taken out what is your present yearly income for you and annualin your partner combined Main job ASCO code primary carer plasco Person number_other carer personnm Last week did you do any work at all in a job business or farm other carer workedlb Last week did you do any work without pay in a family business other carer unpaidwa Did you have a job business or farm that you were away from because of awayfroa holidays sickness or any other reason other carer How many hours do you usually work each week other carer p2wkhrs At any time during the last 4 weeks have you been looking for full time work lookinge other carer At any time during the last 4 weeks have y
84. ent viewing TV during the day tvmins The number of minutes spent operating a computer during the day pcmins The number of minutes spent playing video or computer games vidgammi The number of minutes spent talking on the phone phonemi The number of minutes spent texting textmins Minutes of passive transport passtran The number of minutes spent in activities requiring gt 1 to lt 2 METs inactmin The number of minutes spent in activities requiring gt 2 to lt 3 METs lightact The number of minutes spent in other sedentary activity during the day 24 othseden hours The number of minutes spent lying down excluding sleep lyingawk The number of minutes spent sleeping during the day 24 hours sleepmin Time respondent woke up today waketime Time respondent went to bed today bedtime Whether the profile is a candidate for culling for low activity PAL lt 1 1 cullstat high activity PAL gt 3 0 or too few activities lt 10 106 Table 28 Contents of Pedometer data file defined by field description label and field name FIELD DESCRIPTION LABEL FIELD NAME IDENTIFYING ITEMS Unique ID of the respondent respid PEDOMETER ITEMS Average of pedometer steps per day taking into account all day types avgsteps Average of pedometer steps per day for weekdays only wkdyavgs Average of pedometer steps per day for weekend days only wkedavgs Average of pedometer dist km per day for all day
85. er person7a Person number_8th member of household personng Gender of 8th household member person8g Age last birthday of 8th household member years pers8age How is 8th household member related to primary carer person gr How is 8th household member related to partner of primary carer person8a Person number_9th member of household personnh Gender of 9th household member person9g Age last birthday of 9th household member years pers9age How is 9th household member related to primary carer person9r How is 9th household member related to partner of primary carer person9a Person number_10th member of household personni Gender of 10th household member personla Age last birthday of 10th household member years perl0age How is 10th household member related to primary carer personlb How is 10th household member related to partner of primary carer personlc Person number_1 1th member of household personnj Gender of 11th household member personld Age last birthday of 11th household member years perllage How is 11th household member related to primary carer personle How is 11th household member related to partner of primary carer person f Person number_12th member of household personnk Gender of 12th household member personlg Age last birthday of 12th household member years perl2age How is 12th household member related to primary carer personlh How is 12th household member related to partner of primary carer person li ANTHROPOMETRY The first
86. ere extracted from the MARCA cleaned data Table 22 The extracted data were collated in a single long file The rows of this file represent individual profiles The columns include the activity sets A wide file was generated where each row represented an individual child and the columns the activity sets for each recall day plus summary data across all four days Summary data Summary data were calculated for each child using the following procedure e The average value for a given activity set was obtained across all school days which could be from one to three days For example if sleep times on three recalled school days were 600 550 and 530 minutes the school days average would be 560 minutes 54 e The average value for each activity set was calculated for all non school days from one to three recalled days If one non school day was recalled with a sleep time of 660 minutes then that value was retained as the non school day average The overall average was obtained by averaging the averages of the school and non school days in this case the average of 560 and 660 minutes or 610 minutes The rationale for this procedure is that across a year children soend about one day in two at school when accounting for holidays days off teacher free days etc In some cases four days were recalled but none were school days for example due to illness or unusual timetables or none were non school days for example there was sc
87. erm condition that requires treatment or medication medicalg No medical conditions medicalr OTHER HOUSEHOLD MEMBER DETAILS Person number_4th member of household personnc Gender of 4th household member person4g Age last birthday of 4th household member years pers4age How is 4th household member related to primary carer person4r How is 4th household member related to partner of primary carer person4a Person number_Sth member of household personnd Gender of 5th household member person5g Age last birthday of 5th household member years persSage How is 5th household member related to primary carer personor How is 5th household member related to partner of primary carer personsa Person number_ 6th member of household personne 101 FIELD DESCRIPTION LABEL FIELD NAME Gender of 6th household member person g Age last birthday of 6th household member years pers 6age How is 6th household member related to primary carer person r How is 6th household member related to partner of primary carer person a Person number_7th member of household personnf Gender of 7th household member person7g Age last birthday of 7th household member years pers7age How is 7th household member related to primary carer person r How is 7th household member related to partner of primary car
88. ern Territory This gave 13 Regions which were used in deriving the weights Further in order to achieve the required equal representation of age groups in families with more than one child preference was given to children in the 2 3 and 14 16 age groups Thus children in larger families in age groups 4 8 and 9 13 were proportionally under represented in the sample In addition the gender balance in the sample was not controlled Given that it is likely that there will be differences between boys and girls in the outcome variables it is appropriate to post stratify by gender to correct for any bias resulting from chance differences in gender balance Ideally the weightings to correct for non proportionate sampling would be based on e Age e Gender e Household size e Family Structure the number of children of each age in the family e Region as defined above However this proved to give many cells with no data in the sample or very small sample numbers This would give very high weight to some individuals and result in very inaccurate results Family size and structure were unlikely to be major influences on nutritional variables and so it was decided to weight only on Age Gender and Region This leaves potential biases due to family size and structure which can be assessed with other potential biases in the non response bias study Data from the ABS based on the 2006 Census on household size by Postal Area by State by
89. ers comm To FSANZ 2008 suggests that total vitamin D values reported in AUSNUT 2007 may significantly overestimate total vitamin D activity The intakes of vitamin D estimated as part of this Survey should therefore not be relied upon as being robust Exposure to sunlight is the primary source of vitamin D for humans For most healthy individuals diet does not form a significant source of vitamin D unless supplements are taken The 1995 NNS did not include nutrient intake data on vitamin D Folate total In AUSNUT 1997 the majority of total folate data were derived from overseas food composition tables primarily the British food tables and generated using superseded methods of analysis that may underestimate naturally occurring folate Since 1995 FSANZ has undertaken a number of analytical programs involving folate and folic acid analysis These more recent data form the basis of folate data published in AUSNUT 2007 Folate values are presented as both total folates including naturally occurring folate and added folic acid as in AUSNUT 1997 and as dietary folate equivalents DFEs The following equation was used to calculate DFEs DFE ug Food Folate ug 1 67 Folic Acid ug 52 lodine lodine was not reported in the 1995 NNS or included in AUSNUT 1997 Since 2001 FSANZ has undertaken a number of analytical programs involving iodine analysis which were published in NUTTAB 2006 These data formed the basis for further imputa
90. ery Cereal Fruit Nut And Seed Bars Other Confectionery ALCOHOLIC BEVERAGES Beers Wines Spirits Other Alcoholic Beverages SPECIAL DIETARY FOODS Formula Dietary Foods Enteral formula MISCELLANEOUS Beverage Flavourings Yeast Yeast Vegetable And Meat Extracts Artificial Sweetening Agents Herbs Spices Seasonings And Stock Cubes Essences Chemical Raising Agents And Cooking Ingredients INFANT FORMULAE AND FOODS Infant Formulae And Human Breast Milk Infant Cereal Products Infant Foods Infant Drinks 2007 Revised Food Code 241 242 243 244 245 246 247 248 249 25 251 252 26 261 262 263 264 265 27 271 272 273 28 281 282 283 29 291 292 293 294 295 30 301 302 31 311 312 313 314 315 32 321 322 323 324 33 331 332 333 Revised Food Group Name Potatoes Cabbage Cauliflower And Similar Brassica Vegetables Carrot And Similar Root Vegetables Leaf And Stalk Vegetables Peas And Beans Tomato And Tomato Products Other Fruiting Vegetables Other Vegetables And Vegetable Combinations Dishes Where Vegetable Is The Major Component LEGUME AND PULSE PRODUCTS AND DISHES Mature Legumes And Pulses Mature Legume And Pulse Products And Dishes SNACK FOODS Potato Snacks Corn Snacks Extruded Or Reformed Snacks Pretzels Other Snacks SUGAR PRODUCTS AND DISHES Sugar Honey And Syrups Jam And Lemon Spreads Chocolate Spreads Sauces Dishes amp
91. espondent agegroup Compliant for diet and exercise except pedometer complian Population weighting factor sampwght Adjusted sample population weighting factor for inferential statistics adjustwt The recoded postcode of the respondent s residence pcode Postal delivery centre of recoded postcode area Urban Rural rurality Metro versus rest of state metrores The state of the respondent s residence state Region region Cluster Location clustloc The id of the interviewer intrvrid Laptop ID laptopid Date the CAPI interview took place capidate Day of the week the CAPI interview took place capiwkdy The type of day CAPI interview took place capiday Was parental consent signed parentco Reason parental consent was not signed pconsref is the child aged 14 or over childage If 14 or over was child consent signed childcon Reason child consent was not signed cconsref Was the interview recorded intervie Was the MARCA completed marcacom Reason the MARCA was not completed marcanot Reason the MARCA was completed for day one only marcaone Was the LINZ completed linzcomp Reason the LINZ was not completed linznot Was the carer form used carerfor The ID number of the pedometer used pedomete Was there anything unusual about yesterday that should be noted for the unusuald researchers analysing the food intake or activity data for this child Comments about unusual day yesterday comunuye Was there anything unusual about the day before yesterday that should be
92. estionnaire cccccccccccsscceseescesenseesceseesceseeseeseceeeecseeseesecseeseceeeecnseeeenseeseeaeeaes 29 10 EGO Gnd Nutrient Intake rotes sess Petes eRe aos So Be A EGS RR eee 31 POOd Intakes o4 eave ea oaa ahs ait aa E eE a eara Reyer deen AG erased abet RAN 31 Nutrient Intakes ma e teehee ote ine detach ess em E E ee toni E EET 33 Foods Habits QUESTIONS annm eeni aE Cosa cosuse suse VEEE E AEE E inca tees EEES 33 ii Physical Acv aare 0 casa I Nal a ete E ee A ena ae 35 Physical Activity Recalls ocics cie cessed a ie E Bete a tee Recess seostedede Mad eo EAE 35 Ped Ommetiy sc 2 ccc063 siests cede ele dh See E EE cussed Sas deh oes clade ec tuted dee ace ceo E O eee Ends 35 12 Physical Measurements cccccccceccceceeeceesceesceeseeeeeeeseeeceecesecesecaecaaecsaeeaeeeaeecseesseeeseenseensensees 36 Heighten doe dieeseustovlec E K cations seas en dcenet alas ETE ee sce doves ca to eas evades 36 WI SHE sc icsn oecce at Saltese ets E E E tua thiews E E e ise E a te ieee meh abies 36 Body Mass Index i iciesscc sco assis ok aceasta es 36 Waist eirth 02 stele Re secestaseece sae Rok E Re Ee I AL eed ce 36 Waist to heisht ratio ecenin cies eet Rs Ee ie ce O 37 13 ISUTVEV VESPONSC E eva Nn AE cl ae oe i Ret A Sala oa om ae a a hE ori 38 Measures to maximise TESPONSC ccesceeeesseessesseeeecesecesecsecesecsaecsaecaeecaeecaeceneeeaeeeeeesseeseeeteeerentees 38 Respons etats anonn eenei e ee lesa RE EE E ARR REEE E din E ATEAREN 3
93. ewritten as sums over Classes if required Note that and W are the same for all children in Class g so can be denoted by P and Wg G DWM gPg Estimated population proportion 4g With standard deviation 67 G We l B e 7 5g Sen g l where G is the number of Classes Analysis code The calculations for the weights were carried out using the R statistical package version 2 5 04 The R code used in the calculations is given in the Section 23 Effective sample size Note that the weights have been calculated such that the weighted sample size is the population size N 3512059 Therefore these weights should not be used in conjunction with inferential statistical methods or estimates of precision as the sample size will be artificially inflated and p values will overstate the significance of the hypothesis tests For use with inferential statistics a new set of weights newwght was created that rescales the original weights sampwght to the true sample size using the following formula newwght sSampwght population N x sample n sampwght 3512059 x 4837 Application of the newwght weights effectively corrects for non proportionate sampling by adjusting for gender age and region in the same manner as the original weights The effects of applying the various weighting variables to cell counts are shown in Section 24 Stratification Weighting should be used for analyses that use the entire samp
94. factor levels wt table 1 lt factor rep States each nRegions 80 4 2 levels States wt table 2 lt factor rep rep Regions each 80 4 2 nStates levels Regions wt table 3 lt rep rep 1 80 each 4 2 nStates nRegions wt table 4 lt rep rep fs tot 1 each 4 2 nStates nRegions wt table 5 lt rep rep 1 4 each 2 nStates nRegions 80 wt table 6 lt rep 1 2 nStates nRegions 80 4 cat n population counts for each row of the table ChildrenPerFamily lt fs age cbind wt table 3 1 wt table 5 number of children of this agegroup in this FS Pr Sex lt as numeric Sex ratio cbind match paste wt table State wt tableSRegion paste Sex ratioSState Sex ratioSRegion wt table Sext5 nFam lt Table2 aggr Count match paste wt tableSState wt tableSRegion wt tableSFS paste Table2 aggr State Table2 aggrSRegion Table2 aggr FS wt tableSPopn lt nFam ChildrenPerFamily Pr Sex Sample counts for each row of the table Five way array of counts with dimensions defined so that the counts are stored in the correct order temp tab lt table Sample Sex Sample AgeGrp factor FS num levels 1 80 Sample Region Samp le State Copy into wt table dim temp tab lt NULL wt tableSSample lt temp tab Calculate weights for each set of factors wt tableSWeight lt wt tableSPopn wt table Sample Wts lt wt table Weight Weights for non e
95. family structure for each study child NOTE the term child always means a person aged from 2 to 16 according to the CURF age data a child of Parent x means a child with relationship code 6 to 12 for Parent x There is some apparent inconsistency in the identification of relationships in the data For simplicity any relationship from 6 to 12 with the Parent s will be taken to indicate a child in the same family as the study child tidy up from past runs while wgtdata Sin search detach wgtdata while sink number gt 0 sink Read survey data curf lt read csv CURF_201007 csv sep header TRU Fl Qn Fl trip white TRU Read file containing short names new names lt read csv VarNames csv sep header FALSE Apply short names names curf lt new names 2 Include only those children recruited and interviewed wgtdata lt curf curfSRec Recruited amp curfSCAPSt INTERVIEW attach wgtdata Create Family Structure identifiers for each study child Identify other children in the household with the same parents Get the age of Person 4 to Person 12 PersonAge lt wgtdata paste P 4 12 A sep 115 Relationship code to Parent 1 of Person 4 to Person 12 RelPl lt wgtdata paste P 4 12 RelP1 sep Is Person 4 to 12 a child of parent 1 ChildofPl lt RelP1l gt 6 amp RelP1l lt 12 amp PersonAge gt 2 amp
96. foodcomp search 128
97. g birthweight weightat Age child was first given solid food regularly agesolid At what age weeks was child first given solid food regularly solidwks At what age months was child first given solid food regularly solidmth How much did child weigh at birth kilos pounds biwgftkil How much did child weigh at birth grams ounces biwgtgra Was a written record used to recall the birth weight writtenr Which one of the following best describes child s usual way of eating usualeat Specify other type of usual eating useatoth HOUSEHOLD DETAILS The number of adults in the household numadult The number of children in the household numchild The type of household househol Other type of household hhdtyoth The type of family familyty Other type of family famtyoth CARER EMPLOYMENT DETAILS Person number_primary carer personnl Last week did you do any work at all in a job business or farm primary carer workedla Last week did you do any work without pay in a family business primary carer unpaidwo Did you have a job business or farm that you were away from because of awayfrom holidays sickness or any other reason primary carer How many hours do you usually work each week primary carer plwkhrs At any time during the last 4 weeks have you been looking for full time work lookingf primary carer At any time during the last 4 weeks have you been looking for part time work lookinga primary carer At any time during the last 4 we
98. gation of all options RDD was considered to be within the budget and timeframe parameters and provided a representative sample of Australian children RDD has been a commonly used method of sampling for population health studies 18 Creation of the sample frame The following steps were taken to create the sample frame e Identify all possible phone number prefixes from charging zones and exchange groupings of prefixes contained in the Telstra Charging Zones classifications The version of the listing used was the latest available dated 26 May 2006 http www telstra com au customerterms bus_charging htm e Based on charging zones or exchange locations prefixes were allocated to the region which includes all or the greater part of the exchange In this way the RDD generated sample is pre allocated to a postcode Generation of sample items The sample was generated for each cluster location using prefixes flagged as belonging to the postcodes for that location For each cluster location prefixes were randomly selected with equal probability of selection A randomly generated 2 digit suffix 00 99 was then appended to the prefix to create a complete phone number The resultant number was checked to ensure it had not been generated in the previous sample The number was then appended to the bottom of the sample list which remained in order of item generation The RDD sample generation process is summarised in Figure 1 The postcode
99. ghts split by state of residence age and Sex The 1985 survey nutrient composition database was used to estimate nutrient intakes and the matching of foods consumed to appropriate nutrient data was manually performed A significant proportion of the foods consumed in 1985 did not have Australian nutrient composition data 1995 National Nutrition Survey The 1995 NNS was designed and undertaken by the Australian Bureau of Statistics ABS in collaboration with the then Commonwealth Department of Health and Family Services as a sub sample of the 1995 National Health Survey Data were collected from February 1995 to March 1996 across all Australian states and territories on all days of the week There were 13 858 participants of which 2 574 were children aged 2 15 years and another 433 were aged 16 18 years making a total of 3 007 children aged 2 18 years Participants completed a 3 phase multiple pass 24 hour dietary recall The survey data were collected via a multi stage area sample of private dwellings houses flats etc for persons aged two years and over achieving an overall response rate of 65 5 of those invited to participate Both children and adults undertook a 24 hour recall interview at home assisted by trained nutritionists and proxy interviews with parents were conducted for children aged two to four years Children aged 5 11 years were asked to participate in the 24 hour recall interview with the assistance of an adult hou
100. h Food Composition Databank revision 6 0 Food Informatics Dept Nutrition Danish Institute for Food and Veterinary Research www foodcomp dk National Public Health Partnership 2006 Monitoring and Surveillance of Physical Activity of Children and Young People Report of a National Consensus Workshop December 2005 Melbourne NPHP National Health and Medical Research Council 1991 Recommended Dietary Intakes for use in Australia Canberra NHMRC National Health and Medical Research Council 1994 The Core Food Groups The scientific basis for developing nutrition education tools Canberra NHMRC National Health and Medical Research Council 2005 Nutrient Reference Values for Australia and New Zealand Canberra NHMRC Olds T Ridley K Wake M Hesketh K Waters E Patton G Williams J 2007 How should activity guidelines for young people be operationalised International Journal of Behavioral Nutrition and Physical Activity 4 43 Parnell W Scragg R Wilson N Schaaf D Fitzgerald E 2003 NZ Food NZ Children Key results of the 2002 National Children s Nutrition Survey Wellington Ministry of Health Ridley K Olds T Hill A 2006 The Multimedia Activity Recall for Children and Adolescents MARCA development and evaluation International Journal of Behavioral Nutrition and Physical Activity 3 10 United States Department of Agriculture National Nutrient Databank for Standard Reference 2006 Release 19 www nal usda gov fnic
101. h a National Children s Nutrition and Physical Activity Survey Prior to the development of the survey a series of workshops were conducted for the purpose of achieving consensus on the best practice approach and tools for measuring and monitoring physical activity in children and young people in Australia Workshop participants included invited experts from the academic and non government sectors as well as representatives of Health Departments from most of the state and territory governments and from the Australian Government The workshops produced a series of recommendations which influenced the development and implementation of the Australian National Children s Nutrition and Physical Activity survey These recommendations included e Age range Surveillance of physical activity and sedentary behaviours should be undertaken amongst 5 18 year olds with self reporting measures alone not recommended for use with children under 10 years of age e Concurrent items for the same individuals Data collection on physical activity behaviours sedentary behaviours food and nutrient intakes and anthropometric measures should be collected simultaneously from resoondents e Objective measurement When measuring the activity levels of children younger than 10 years of age objective measures such as pedometry and accelerometry can be useful e The choice of objective measures should be developmentally appropriate for the children participa
102. he respondent respid Does a CAPI record exist capirec Does a CATI record exist catirec Does a complete MARCA record exist marcarec Does a Pedometer record exist pedorec Does a CAPI LINZ record exist capilinz Does a CATI LINZ record exist catilinz Do 2 days of dietary intake exist dietrec2 Does a complete anthropometry record exist anthrec Did respondent answer food habits questions habitrec 99 Results database files Person Records The person record file contains two files each with one record for every respondent identified by the field respid The CAPI record file Table 24 includes CAPI interview details consent information sociodemographic information household details anthropometry respondent medical conditions and short questions about usual food habits birth weight breastfeeding history special diets and food security The CATI record file Table 25 includes CATI interview details Table 24 Contents of CAPI data file defined by field description label and field name FIELD DESCRIPTION LABEL FIELD NAME IDENTIFYING ITEMS Respondent ID respid INTERVIEW AND DEMOGRAPHIC DETAILS The random id of the respondent randid The date of the birth of the respondent dob Sex of the respondent sex The age of the respondent at their last birthday in years age The age group of the r
103. height measurement in cm of the respondent height The second height measurement in cm of the respondent height2 The third and optional height measurement in cm of the respondent height3 The height in cm of the respondent This is an average of the taken height measurements The first weight measurement in Kg of the respondent weight The second weight measurement in Kg of the respondent weight2 The third and optional weight measurement in Kg of the respondent weight3 The weight in Kg of the respondent This is an average of the taken weight measurements The first waist girth measurement in cm of the respondent waigirl The second waist girth measurement in cm of the respondent waigir2 The third and optional waist girth measurement in cm of the respondent waigir3 The waist girth in cm of the respondent This is an average of the taken waigir measurements What was the waist girth measured over girthmea The calculated Body Mass Index bmi Body mass index category for respondent bmictgry Waist to height ratio whtr The date on which the measures were taken measdate Interviewer comments on the anthro measurement process for this resoondent comments Was the anthro survey completed anthroco Interviewer comments on why the anthro measurements were not completed anthnoco Was the stride test completed stridete 102 FIELD DESCRIPTION LABEL FIELD NAME
104. hen J 1988 Statistical power analysis for the behavioural sciences Hillsdale NJ Erlbaum Cole TJ Bellizzi MC Flegal KM Dietz WH 2000 Establishing a standard definition for child overweight and obesity worldwide international survey British Medical Journal 320 1240 3 Cole TJ Flegal KM Nicholls DF Jackson AA 2007 Body mass index cut offs to define thinness in children and adolescents British Medical Journal 335 7612 194 Department of Community Services and Health 1988 National dietary survey of schoolchildren 10 15 years 1985 No 1 Foods consumed AGPS Canberra Department of Community Services and Health 1989 National dietary survey of schoolchildren 10 15 years 1985 No 2 Nutrient Intakes AGPS Canberra Food Standards Agency 2002 McCance and Widdowson s The Composition of Foods 6th Summary Edition Cambridge Royal Society of Chemistry Food Standards Australia New Zealand 2007 NUTTAB 2006 Available from http www foodstandards gov au monitoringandsurveillance nuttab2006 index cfm Hands B Parker H Glasson C Brinkman S Read H 2004 Physical Activity and Nutrition Levels in Western Australian Children and Adolescents Report Perth Western Australia West Australian Government Marfell Jones M Olds T Stewart A Carter L 2006 International standards for anthropometric assessment Potchefstroom RSA North West University 127 Moller A Saxholt E Christensen AT Hartkopp HB Hess Ygil K 2005 Danis
105. hild 4 13 years However this did not rectify the observed skew towards children aged 4 13 years when recruiting participants The result was that a number of in scope households who agreed to participate were not interviewed as the quota of children in their location had been completed Table 6 Table 6 Selected child by the number of children in the household Age group of the child selected years Composition of children in the 2 3 4 8 9 13 14 16 household 2 3 years 935 0 0 0 2 8 years 550 84 0 0 2 13 years 101 10 14 0 2 3 and 9 13 years 51 0 30 0 2 3 and 9 16 years 2 0 0 19 2 16 years 17 3 10 2 3 and 14 16 years 4 0 0 6 2 8 and 14 16 years 11 1 0 4 8 years 0 1133 0 0 4 13 years 0 427 475 0 4 16 years 0 0 0 756 4 8 and 14 16 years 0 46 0 59 9 13 years 0 0 1130 0 9 16 years 0 0 85 586 14 16 years 0 24 18 200 Total 1671 1728 1753 1637 In 23 cases the parent care giver requested another child participate other than the child initially selected by the KISH table These requests were respected and details of the substitution have been retained in the data set 22 Scope and coverage Scope Urban rural and remote areas across all states and territories of Australia were included Some postcodes were excluded for a number of reasons mentioned above The scope included children aged 2 16 years who were residents of private dwellings
106. hild was sick or engaged in a sports competition In these cases the profiles were retained Pedometry Quality assurance of pedometer data Eighty three percent of participants who wore a pedometer returned a log sheet In most cases the pedometer logs were able to be validated by 3 5 days of data stored in the pedometer To be retained for analysis a participant s pedometer data met the following criteria e Minimum of 6 days of data provided e Oneach of these days there was a minimum of 1000 reported steps e Oneach of these days the pedometer was not removed for more than 4 hours 240 minutes Each day of data was sorted by number of steps distance MVPA and also a ratio of steps distance to find those that were at the extremes Outliers were then looked at ona case by case basis to see if data needed to be culled or not Of the 2829 completed pedometer records 2577 were retained for analysis Of the 2577 retained pedometer records 2547 provided complete sets of data 20 did not record data on a weekend day and therefore average of all days data could not be calculated and 10 contained missing data for distance and or MVPA Physical Measures Each measurement height weight waist girth and BMI was sorted by each age group to find those at the extremes Also ratios of height weight waist girth height and weight waist girth were sorted in the same manner Outliers were examined for plausibility Where one of the three mea
107. hool on Saturday In these cases average values for activity sets were calculated across all four days Summary data are not provided as part of the results database set but have been used for tabulations in the main findings report 55 Table 22 Items extracted from the cleaned MARCA data no item Database definition example variable name 1 participant ID respid 123456 4 date of test datetest 2 Jul 07 5 day type marcaday school day 6 PAL pal Physical Activity Level in METs 1 69 7 MPA mpamins minutes of moderate PA gt 3 to lt 6 140 METs 8 VPA vpamins minutes of vigorous PA gt 6 METs 30 9 active transport acttrans minutes of active transport MARCA 30 codes 24005x 24009x 24007x 241080 34124x 34125x 34127x 34131x 34146x 10 PT work ptwkmins minutes spent in part time work codes 40 7230x0 7330x0 7430x0 11 chores choremin minutes spent doing chores code 0 6XXXXX 12 TV tvmins minutes spent watching TV codes 400 111030 121050 13 computer pcmins minutes spent at the computer e g 25 typing internet code 420050 14 videogames vidgammi minutes spent playing video or 150 computer games code 722190 15 phone phonemi minutes spent talking on the phone 25 codes 114070 124100 134170 16 texting textmins minutes spent texting codes 20 114190 124170 134180 17 passive transport passtran minutes of passive tran
108. ial issue physical measurements were taken part way through the interview when the participant had been sitting for at least 30 minutes Measurement of weight was taken with light clothing on possibly slightly inflating the weight and BMI results Waist girth was occasionally taken over light clothing when requested by the subject and this could also increase the waist girth results The difference associated with wearing the light clothing would be small in each of these measurements Comparison with previous surveys Comparison with previous dietary surveys Dietary information recorded in this survey may differ from data obtained using a different method to assess food and nutrient intake such as a food record or a semi quantitative food frequency questionnaire a different food composition database or if different age groups were assessed The methodology used in this survey is broadly comparable to that used in the 1995 NNS Differences between the two surveys include e Sampling frame The age groups used for reporting differ between the two surveys The 1995 NNS reported intakes for 2 3 years 4 7 years 8 11 years 12 15 years and 16 18 years e Repeat 24 hour recalls were collected on all participants for this survey whereas the 1995 NNS collected repeat 24 hour recalls on only 10 of the sample and adjusted for within person variation based on this small sub sample 62 e The repeat 24 hour recall in this survey was conduc
109. ieldwork by I view Pty Ltd A Technical Reference Group was convened to provide expert advice Food Standards Australia New Zealand FSANZ in collaboration with CSIRO developed the survey specific nutrient database AUSNUT 2007 The project team acknowledges the contribution of Flinders University in the analysis of the dietary data This guide describes the objective of the survey the development and methodologies used in the survey and data processing techniques employed to assist with appropriate interpretation of the survey results 3 Project Team University of SA Professor Timothy Olds BA Hon BSpSc Dist PhD Syd PhD UNSW Dr James Dollman BS MSc DipEd PhD Mr Tim Kupke BAppSc BHIthSc Hons CSIRO Professor Lynne Cobiac moved to Flinders University at the beginning of 2007 PhD MBA Adv Post Grad Dip Nut Diet Dr Jane Bowen BSc BNut amp Diet Hons PhD Ms Jill Burnett Bsc Dip Nut amp Diet DipEd Ms Julie Syrette BSc Mr James Dempsey Blnffech Eng Mr Shane Bailie Dip IT Software Dev Dr Carlene Wilson BA Hons PhD MBA MAPS Ms Ingrid Flight BA MPH Mr Norm Good Dip IT Prof lan Saunders BA Hons DioMathStats PhD I view Pty Ltd Ms Kylie Brosnan BBus Dip MRSA Mr Daniel Pole BA Ms Mary Plumridge Acknowledgement is given to Dr Michelle Miller and Ms Alison Yaxley of Flinders University for final food and nutrient data analysis Steering Group Ms Jenny Bryant First Assistant Secre
110. ine number was been created for each recall Nutrient database FSANZ developed a nutrient database AUSNUT 2007 which contains data for 37 nutrients that are expressed per 100g edible portion food or per 100 dosage units supplements AUSNUT 2007 contains data only for those foods and supplements consumed during the survey Sources of nutrient data AUSNUT 2007 incorporates food and nutrient composition data from a range of sources These include e Food and nutrient composition data published in NUTTAB 2006 FSANZ 2007 e Unpublished food and nutrient composition data commissioned by FSANZ for the purpose of this survey e Food and nutrient composition data borrowed from international food composition tables and databases including British food tables Food Standards Agency 2002 New Zealand food tables Athar et al 2006 Danish food tables Maller et al 2005 United States Department of Agriculture USDA 2006 Australian food and supplement label data Recipes Supplement data provided by the TGA Other imputed calculated or taken from the 1995 NNS database AUSNUT 1997 Development of the nutrient database A subset of NUTTAB 2006 was used as a basis for developing the survey specific database AUSNUT 2007 The subset was developed by reproducing foods published in NUTTAB 2006 that were likely to be relevant to the survey and ensuring full coverage of all nutrients to be reported as part of the survey This involved inc
111. ioning of the head such that the line of vision is perpendicular to the body Participants positioned this way for height measurements The perpendicular distance between the transverse plane of the vertex and the inferior aspects of the feet The subject should not be wearing shoes and the head should be in the Frankfort plane No stretch is applied Foods are categorised according to major groups of foods of similar description or usage A reference nutrient database produced by FSANZ based mainly on analytical data for Australian foods Used as the basis for developing the survey specific data base generated for this survey 124 Scope Stadiometer Refers to the target population covered by a data collection The scope of this survey was children aged between 2 16 years inclusive who were residents of private dwellings in Australia Device used for measuring height 24 hour dietary recall Individual s recall of everything eaten and drunk including Waist girth Waist to height ratio Weight water and supplements over a 24 hour period In this survey it was taken from midnight to midnight The circumference of the abdomen at its narrowest point when viewed from the front between the lower costal 10th rib border and the top of the iliac crest in the mid axial line When there is no visible narrowing the circumference is measured half way between the lower costal border and the top of the iliac crest Measurements are t
112. ith the various Total columns omitted The column headers are Xwxyz where w no of 2 3yos x no of 4 8yos y no of 9 13yos z no of 14 l6yos 114 Count of number of families with family structure i table2 lt read csv Table2 csv sep Reorder to have one row for each postcode and family structure Table2 long lt reshape table2 varying list names table2 1 direction long times substr names table2 1 2 5 timevar FS idvar postcode Omit excluded postcodes Table2 long lt Table2 long Table2 longSpostcode in IncludedPostcodes Postcode Append Region information based on postcode Table2 long lt cbhind Table2 long IncludedPostcodes match Table2 longSpostcode IncludedPostcodes Postcode c 3 12 Aggregate postcode values to State x Region Table2 aggr lt aggregate Table2 long x0000 by list FS Table2 longS FS Region Table2 long Region State Table2 longSState sum names Table2 aggr 4 lt Count Replace the FS strings by th quivalent numerical values Table2 aggr FS lt rep 0 80 13 Read and process Nutrition Survey data Code to set up sample data ready for weighting calculations Since Household and Family size data from ABS seem to be inconsistent this analysis will neglect the small number of multifamily households and calculate weights purely on the basis of family structure Thus the only calculation needed here is the identification of the
113. k all that apply J6 If you had found a job could you have started work last week J7 When did you last work for two weeks or more Parent 1 1 Yes gt Go to J4 2 No 3 Permanently unable to work gt Go to J7 4 Permanently not intending to work if aged 65 only gt Go to J7 1 Yes gt Go to J4 2 No 3 Permanently not intending to work if aged 65 only gt Go to J7 1 Yes 2No gt Goto J5 3 Permanently not intending to work if aged 65 only gt Go to J7 1 hour or more 000 gt Write number Hours gt go to J8 Less than 1 gt hour go to J5 1 Yes full time work 2 Yes part time work 3 Yes casual work 4 No gt Go to J7 5 Don t know gt Go to J7 1 Yes gt Go to J10 if no parent 2 2 No gt Go to J10 if no parent 2 1 Within the last three months 2 3 up to 6 months ago 3 6 up to 12 months ago 41 up to 2 years ago 5 2 up to 5 years ago 6 More than 5 years ago 7 Has never worked for 2 weeks or more Parent 2 1 Yes gt Go to J4 2No 3 Permanently unable to work gt Go to J7 4 Permanently not intending to work if ged 65 only gt Go to J7 Q 1 Yes gt Go tol7 2No 3 Permanently not intending to work if aged 65 only gt Go to J7
114. ld 9 foster child 10 grandchild 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 78 D How is related to parent 2 partner 11 niece nephew 12 cousin 3 other relative in law 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in aw 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult 11 niece nephew 12 cousin 3 other relative in law 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in law 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adu
115. le as well as for analyses that are stratified by a grouping variable However note that the rurality variable and other variables indicative of residential location such as metrores and state provided in the data set are not the same as the region variable that was used to derive the weights Therefore the current weights may not reflect the age and gender distribution within each location for any analyses stratified by residential location variables other than the region variable and this should be noted in the interpretation of any analyses stratified by such variables 4R Development Core Team 2007 R A language and environment for statistical computing R Foundation for Statistical Computing Vienna Austria ISBN 3 900051 07 O URL http www kR project org 68 17 Data output and Dissemination In 2008 the key findings from the survey were released to stakeholders and the general public in the form of a media launch jointly presented by the Ministers for Health and Ageing and for Agriculture Fisheries and Forestry and the Chairman of the AFGC Following the official launch of the survey results interested and expert parties have the opportunity to conduct independent analysis and interpretation of the data from the survey via the results database Access to the results database is controlled by the Department of Health and Ageing However access can be made available to individuals and groups through an applica
116. league medium 342021 _ baseball light 342173 rugby union hard 342022 baseball medium 342171 rugby union light 342033 basketball hard 342172 rugby union medium 342031 basketball light 331493 __ sailboard windsurfing hard 342032 basketball medium 331491 sailboard windsurfing light 342053 _ bobsled toboggan luge hard 331492 _ sailboard windsurfing medium 342051 bobsled toboggan luge light 321943 __ sailing boating hard 342052 bobsled toboggan luge medium 321941 sailing boating light 342073 broomballl hard 321942 _ sailing boating medium 342071 broomball light 341503 shuffleboard hard 342072 _ broomball medium 341501 shuffleboard light 341083 _ calisthenics hard 341502 _ shuffleboard medium 341081 calisthenics light 341523 __ skiing cross country hard 341082 calisthenics medium 341521 skiing cross country light 341093 canoeing rowing hard 341522 _ skiing cross country medium 341091 canoeing rowing light 341533 _ skiing downhill hard 341092 canoeing rowing medium 341531 skiing downhill light 342103 cricket hard 341532 __ skiing downhill medium 342101 cricket light 341543 _ skindiving SCUBA hard 342102 _ cricket medium 341541 skindiving SCUBA light 341110 croquet 341542 skindiving SCUBA medium 110
117. leted What is the level of highest qualification that the partner of the primary carer has parent2h ever completed RESPONDENT DETAILS Person number_study child personnb Gender of study child studychi How is child related to primary carer studycha How is child related to partner of primary carer studychb Is the study child of Aboriginal or Torres Strait Islander origin studychc Country in which the study child was born studychd Does the study child speak a language other than English at home studyche RESPONDENT MEDICAL CONDITIONS Sight problems not corrected by glasses or contact lenses medicalc Hearing problems medicala Speech problems medicalb Blackouts fits or loss of Consciousness medicald Difficulty learning or understanding things medicale Limited use of arms or fingers medicalf Difficulty gripping things medicalg Limited use of legs or feet medicalh Nerves or emotional conditions that need treatment medicali Any disfigurement or deformity medicalj Chronic or recurring pain medicalk Any condition that restricts physical activity or physical work eg back problems medicall migraines Shortness of breath or difficulty breathing medicalm Any mental illness for which help or supervision is required medicaln Long term effects as a result of a head injury stroke or other brain damage medicalo Any other long term condition such as arthritis asthma heart disease Alzheimer s medicalp disease dementia etc Any other long t
118. lt 11 niece nephew 12 cousin 3 other relative in aw 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in aw 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult 11 niece nephew 12 cousin 3 other relative in law 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult 6 biological child 7 adopted child 8 step child 9 foster child 10 grandchild 11 niece nephew 12 cousin 3 other relative in law 13 unrelated child 14 sibling 15 parent 16 grandparent 17 aunt uncle 4 boarder housemate 5 unrelated adult D14 Interviewer Note Record household type Family Household with only family members present One family household Two family household Three or more family household Family Househ
119. m interventions designed to improve nutrition and increase physical activity levels The Australian National Children s Nutrition and Physical Activity Survey was conducted between February and August 2007 by the Commonwealth Scientific and Industrial Research Organisation CSIRO and the University of South Australia UniSA on behalf of the Commonwealth Department of Health and Ageing DOHA the Department of Agriculture Fisheries and Forestry DAFF and the Australian Food and Grocery Council AFCG Food beverage dietary supplement nutrient intake food habits demography anthropometry and objectively and self reported physical activity were measured in 4 487 children aged 2 16 years The information from the survey will enable food beverage supplement and nutrient intakes among children to be assessed against the Dietary Guidelines for Children and Adolescents in Australia the Australian Guide to Healthy Eating and the revised Nutrient Reference Values for Australia and New Zealand and e physical activity levels among children to be assessed against the National Physical Activity Guidelines for Children and Young People Existing information National Surveys This is the first survey to combine nutrition and physical activity in the one survey The last national survey which collected anthropometric data and quantitative information on children s food and beverage intake was the National Nutrition Survey conducted in 1
120. metropolitan rural and remote areas The number of children included from each state was proportional to the population of children in that state A response rate of 40 of eligible households was achieved The data were collected at a face to face home visit computer assisted personal interview CAPI and a subsequent telephone interview computer assisted telephone interview CATI conducted 7 21 days after the CAPI Food beverage and dietary supplement intakes were collected from all participants using a standardised computer based three pass 24 hour recall methodology during the CAPI and the CATI a total of two days dietary recall per child The food and beverage intake data were translated to daily nutrient intake data using a specifically designed Australian nutrient composition database AUSNUT 2007 Each child and or parent was asked questions relating to food habits during the CAPI such as usual consumption of fruits vegetables type of milk use of salt and earlier infant feeding practices Validated use of time software MARCA was used to collect four days of physical activity and use of time recall data for children aged 9 16 years Two days of recall were collected during the CAPI and 2 days were collected during the CATI a total of four days of activity recall per child Minutes spent in various activities and energy expenditure over 4 days was estimated from these data Pedometers were worn over 6 days in 5
121. mpty population classes and non empty sample classes Wts lt Wts wt tableSPopn gt 0 amp wt tableS Sample gt 0 Create weight table but without family structure attach wt table wt table3 lt aggregate wt table 7 8 by list Sex Sex AgeGrp AgeGrp Region Region State S tate sum na rm T detach wt table wt table3 Weight lt wt table3SPopn wt table3 Sample 117 Look up weights for each study child Sample Weight3 lt wt table3 Weight match paste Sample State Sample Region Sample AgeGrp Sample Sex paste wt table3SState wt table3SRegion wt table3SAgeGrp wt table3 Sex Weights may be missing if the postcode is in th xcluded range Set missing weights to zero Sample Weight3 is na Sample Weight3 lt 0 curf2 lt curf curfSRecruited Recruited amp curfSCAPI Status INTERVIEW Weights lt data frame curf2 Sample Weight3 write table Weights file Weights4 csv sep row names T col names T eol n 118 24 Cell Counts for checking correct application of weighting factors The following tables show the effects on cell counts for each gender age group and region when the weighting factors are applied All Children Unweighted data Age group years
122. ncluded in the reference data base which is based mainly on analytical data AUSNUT 2006 is the survey database specific to Kids Eat Kids Play Body mass index An indicator of weight status calculated from the formula weight height2 or kg m2 Estimated average requirement the average daily nutrient intake level estimated to meet the requirement of half of the healthy individuals in a particular life stage and gender groups It is used to estimate the prevalence of potentially inadequate intakes in a population group The chemical energy that is available to the body from metabolism of carbohydrates protein fat and alcohol after digestion and absorption Energy intakes are reported in kilojoules kJ One Calorie is equivalent to approximately 4 186 kJ Energy including from fermentable fibre Fat Frankfort plane Height Major food groups NUTTAB 2006 The chemical energy that is available to the body from metabolism of carbohydrates dietary fibre protein fat and alcohol after digestion and absorption Energy intakes are reported in kilojoules kJ One Calorie is equivalent to approximately 4 186 kJ Fat provides a large part of energy in the human diet carries fat soluble vitamins and is the source of essential fatty acids Three fatty acid subtotals poly mono and saturated fatty acids do not add up to total fat because total fat includes a contribution from the non fatty acid components e g glycerol Posit
123. nd target number The figures in the Achieved column were obtained from frequency count on the variables capirec anthrec habitrec dietrec2 marcarec and pedorec in the contacts data file Table 12 Response components to tasks by age group in each region Using the Contacts data file merge region variable from the CAPI Interview data file using respid as the key Tabulate the variables capirec anthrec dietrec2 marcarec and pedorec by region Table 13 comparison of days of the week for CATI and CAPI Using the CAPI Interview data file merge the catiwkdy variable from the CATI Interview data file Tabulate capiwkdy against catiwkdy Table 14 Seasonality of Dietary Recall Tabulate of interview type capicati by season and month variables for each unique dietary recall in the Food and Nutrient data file Table 15 Seasonality of Activity Recall Derive month and seasonality of recall from date of recall in Activity data file Tabulate each day of recall by season and month Table 16 Number of complete datasets for analysis by age group in each region Tabulate region by age group and sex using complian 1 in the CAPI interview data file Table 17 Study child characteristic Using the CAPI Interview data file merge catirec from the Contacts data file using respid as the key Tabulate number and percentage for each class CAPI completed CATI completed and Compliant datasets for analysis b
124. nding text messages SMS sitting 134180 sending text messages SMS standing 121130 sitting at the movies cinema theatre 732201 playing video centre e g Intencity Timezone games light 732202 playing video centre e g Intencity Timezone games medium 732203 playing video centre e g Intencity Timezone games hard 23 R code for non proportionate sampling weights Read and process ABS data files Read in data from files provided by ABS Read Postcodes with State and Region Capital vs Rest of State Note Postcodes and ABS Postal Areas are taken to be the sam postcodesl csv is from the CURF which shows the excluded postcodes allpostcodes lt read csv postcodesl csv sep names allpostcodes 2 lt Region Remove postcodes excluded from the survey IncludedPostcodes lt allpostcodes is na allpostcodesSExcluded Table 1 contains household data which is used only to determine the sex ratios for each Region Tablel csv is a simplified version of the ABS file 2006 Census Table 1 No of occupied private dwellings by no of males aged 2 16 by females aged 2 16 xls Count of Dwellings by number of males amp females aged 2 16 One row for each postcode tablel lt read csv Tablel csv sep Remove excluded postcodes tablel lt tablel tablelSpostcode in IncludedPostcodes Postcode tablel Region lt IncludedPostcodes Region match tablel Spostcode IncludedPostco
125. ng it was not worn that day except for sleeping at night Interviewer shows study child parent care giver the example on the Pedometer Log Sheet All you need to do is put the pedometer on each morning from tomorrow until insert day After you have recorded the information on the last night please put the pedometer into this envelope and post it back to us the following morning It is very important that the pedometer is in the post as soon as possible after the 7 days will leave with you these sticky reminder notes if you think it will be hard to remember to put the pedometer on each morning Interviewer discusses strategic places to stick the labels We will also send text messages during the coming week to remind you to put the pedometer on each day and write down the numbers each night If you do forget to put the pedometer on one day don t give up all together please put the pedometer on the following day and try to remember from then on until insert day just need to measure your stride length so can enter this information into the pedometer This will only take a couple of minutes need to find an area where you can walk in a line for 10 steps Negotiate a suitable location inside or outside Open out measuring tape in a straight line Please place the tips of your shoes alongside the zero on the tape Now take 10 normal steps and will measure the distance Now we need to do that one more time s
126. nt or responsible adult The CAPI gathered household demographic data 24 hour dietary recall food habits weight height and waist circumference measurements and physical activity over 48 hours depending on the age of the child Pedometers were fitted to children aged 5 years and over CATI were conducted to gather a second 24 hour food recall and a second 48 hour physical activity recall Separate telephone calls were made for the CATI food and activity recalls minimising respondent fatigue The measures and details of who provided the information are summarised in Table 8 Equipment is described in Table 9 In order to boost the number of complete records in the sample I view re contacted by telephone in August 2007 some respondents who had not completed all 4 MARCA recall days at either the CAPI or CATI This was done only in areas where the quotas had not yet been reached and for records where the MARCA was either refused the first time around or the MARCA files had been corrupted incorrectly saved Seventy six participants provided this delayed day of MARCA recall Output files generated from data obtained during the CAPI and CATI are detailed in Section 21 Interview length The average CAPI interview length varied according to the age of the child being interviewed and the number of tasks that needed to be completed The average CAPI interview length for children aged 2 4 years was 71 minutes 85 minutes for children aged 5 8 yea
127. nts The sample unit is the study child Only one child per household was invited to participate For younger children lt 9 years the primary care giver defined as the person who knows the most about the study child s diet and activity patterns provided demographic information and acted as a proxy for the study child Where there was more than one child aged 2 16 years in a household a Kish method of child selection was used to ensure adequate representation of age and gender of children within the sample The method for child selection involved Pre allocating a Kish Table to be used for the household by rotation That is the first household uses Table A the second Table B1 etc Asking the parent care giver for the name gender and age in years of each child in the household aged 2 16 years Ordering the children by age oldest to youngest Numbering the children in the sorted listing sequentially from one Assigning priority for age group if applicable highest priority assigned to children aged 14 16 years then children aged 2 3 years if present in household Looking up the person number corresponding to the total number of children aged 2 16 years in the allocated Kish table Table 5 and nominate that person as the study child 20 Figure 1 Sample Generation Flow Chart Repeat Process for each Location until required numbers achieved Compile a list of postcodes for each location Compile
128. o that can get an average Thanks that s it Interviewer calculates and enters stride length Let s leave the pedometer in a place that you will definitely find it tomorrow morning Yes No F1 Stride Test Completed F2 NOT COMPLETED specify reason Record first measure F3 Stride data 1 Record second measure F4 Stride data 2 83 Calculate Average F5 Stride data 3 Record Pedometer ID F Pedometer ID 6 digit number Record first measure F Height data 1 If smaller than 90cm or greater than 200 cm alert the interviewer to CHECK HEIGHT MEASURE But allow to override Record second measure F7 Height data 2 Record third measure F8 Height data 3 ASK IF difference between height 1 and height 2 is 5mm or greater Record first measure F9 Weight data 1 Record Second measure F10 Weight data 2 Record Second measure F11 Weight data 3 ASK IF difference between weight 1 and weight 2 is 0 1kg or greater Record first measure F12 Waist girth 1 Record Second measure F13 Waist girth 2 Record third measure F14 Waist girth data 3 ASK IF difference between waist girth 1 and waist girth 2 is 10mm or greater F15 Waist girth measured over skin clothing
129. oft drinks and confectionery Nutrient intakes were not estimated Information on how often students consumed breakfast lunch dinner ate fast food ate at a fast food outlet ate dinner in front of the television and what influenced their food choices was collected Weight status was recorded 2003 Physical Activity and Nutrition Levels in Western Australian Children and Adolescents Report A total of 2 274 children from Years 3 5 7 8 10 and 11 from 17 secondary and 19 primary schools were surveyed from a stratified sample representative of the WA population Children were asked to complete a 24 hour food record a FFQ a physical activity questionnaire and 7 day pedometer diary Hands et al 2004 Anthropometric data for weight status and waist girth were collected Additional physical activity information In addition to those surveys listed above some of the more recent major surveys are e 2004 Children and Sport Australian Sports Commission UniSA e 2006 Children s Participation in Cultural and Leisure Activities ABS A number of cohort studies have surveyed children s activity patterns e 1973 77 Busselton WA surveys e latel980s Raine Study Telethon Institute for Child Health Research 10 e 1995 Health of Young Victorians Study e 2003 Growing Up in Australia Longitudinal Study of Australian Children e 2005 LOOK Lifestyle Of Our Kids Survey development In September 2005 DOHA announced its intention to establis
130. old with non family members present One family household with non family members present Two family household with non family members present Three or more family household with non family members present Other Please specify N oO o s D15 Interviewer Note Record family type Couple family with Chi Idren under 15 years only Chi Idren under 15 years and dependent students Chi Idren under 15 years and other people Children over 15 only Chi Idren over 15 years and dependent students Chi Idren over 15 years and other people n Ala yR o n 79 One parent family Children under 15 years only Children under 15 years and dependent students Children under 15 years and other people Children over 15 only Children over 15 years and dependent students Children over 15 years and other people Other Please specify 80 SECTION E Rotate between LINZ24 and MARCA Address Parent if under 9 otherwise Address the child Interviewer Note If child is 9 years and older only rotate between LINZ24 and MARCA If child is under 9 years do the LINZ24 gt If 9 years or older E1 MARCA Completed Only if 9 years
131. or older Day One Yes ONLY No E2 ONLY DAY ONE OF MARCA COMPLETED specify reason E3 MARCA NOT COMPLETED specify reason E4 LINZ24 Completed gt If 2 8 years only Yes No E5 LINZ24 NOT COMPLETED specify reason E6 CARER FORM USED 1 Yes data included 2 To be edited later upon return 3 No 81 SECTION F Anthrop Measures and Placement of Pedometer Interviewer Note Only if child is 5 years or older If child is under 5 years skip to SECTION Anthrop Physical Measures Pedometer instructions and interviewer script Here is the pedometer that we want you to wear for 6 days straight starting tomorrow morning Once have measured your stride length will put a plastic tie around it to stop it from opening Please do not remove the tie it is there to make sure that we don t lose the information that the pedometer is collecting When you get dressed tomorrow morning would like you to fit the pedometer by placing it half way between your belly button and your hip the right way up Interviewer demonstrates position and shows what to do for different types of clothing Please clip the security strap to a belt loop belt or pocket opening this is to make sure that the pedometer is not lost if it slips off After a while you will forget that you are wearing the pedometer it is so small and light The idea is to do the things that you usually do and not to change what you
132. orporating e unpublished nutrient data from an analytical program which collected analytical data for approximately 40 foods that form the major nutrient sources for children aged 2 to 15 years e imputed borrowed estimated and calculated nutrient data The nutrient composition data developed for survey foods during the collection period were derived using a range of methods These include e Matching a single NUTTAB 2006 food to a single survey food Where the description of a food published in NUTTAB 2006 matched that of a survey food the NUTTAB 2006 nutrient data were used without amendment e Combining several NUTTAB 2006 foods to produce a single survey food Where a description of a survey food was less specific than a NUTTAB 2006 food nutrient data from several NUTTAB 2006 foods were combined to produce a representative nutrient profile for the survey food This approach was used for most of the fruits and vegetables consumed during the survey Dosage unit in this Survey refers to one tablet or capsule or to 1ml or 1 g for those supplements supplied in liquid or powder form respectively 49 where the cultivars were not reported For example the different cultivars of peeled potatoes including coliban sebago desiree and pontiac were weighted according to their approximate market share to produce a representative nutrient profile of Potato unspecified variety amp skin peeled raw e Modification of a NUTTAB 20
133. ou been looking for part time work lookingg other carer At any time during the last 4 weeks have you been looking for casual work lookingh other carer At any time during the last 4 weeks have you been looking for work no other lookingi carer At any time during the last 4 weeks have you been looking for work don t looking know other carer If you had found a job could you have started work last week other carer startwoa When did you last work for two weeks or more other carer lastwora In the main job held last week what was family member s occupation other p2jobdes carer What are the main tasks that family member usually perform s in that p2jobtsk occupation other carer Main job ASCO code other carer p2asco Table 25 Contents of CATI data file defined by field description label and field name FIELD DESCRIPTION LABEL FIELD NAME IDENTIFYING ITEMS Unique ID of the respondent respid INTERVIEW ITEMS The interviewer call number intnum The id of the interviewer intvrid2 Date of CATI interview catidate Day of the week the CATI interview took place catiwkdy The type of day CATI interview took place catiday Was the CATI interview recorded recordin Was the CATI MARCA completed marcaco2 Reason CARTI MARCA completed for day one only marcaon2 Reason CATI MARCA was not completed marcano2 Was CATI LINZ completed linzcom2 Reason CATI
134. ow get you to formally sign your consent Let s get started then Introduction child aged 9 to 13 years Address the Parent Care Giver Thanks for agreeing to take part in the Kids Eat Kids Play study First of all want to assure you that all information that you give me will be kept confidential and we have strict processes to ensure the security of your information will start by asking a few questions about the household how many people live here and a little bit about each person also will ask about study child s general family background such as your and your partner s work and educational background This will give us a general idea of the home environment study child grows up in Then will ask the child some questions about their food intake and what they have done over the last 48 hours am also going to ask if can take some measurements of your child such as his her weight height and waist circumference will then ask the child to conduct a stride test and show you how to wear the pedometer which we ask they do over the next 7 days There will be a few questions on general food habits and finally will ask you some questions about your housing and income Once have finished asking you questions have a short form that will ask you to fill in by yourself and return with the pedometer in the envelope supplied Before we start will need to obtain your formal consent for this study will read you a
135. per week 52 000 77 999 per year 5 800 999 per week 41 600 51 999 per year 6 700 799 per week 36 400 41 999 per year 7 600 699 per week 31 200 36 399 per year 8 500 599 per week 26 000 31 199 per year 2 400 499 per week 20 800 25 999 per year 10 300 399 per week 15 600 20 799 per year 11 200 299 per week 10 400 15 599 per year 12 100 199 per week 5 200 10 399 per year 13 50 99 per week 2 600 5 19 per year 14 1 49 per week 1 2 599 per year 15 Nil income 16 Negative income loss 17 Don t Know 18 Refused 19 SECTION K KEEPING IN TOUCH Address the parent care giver and child if 9 years and over If 9 16 years old We will be in touch by telephone in a fortnight on two occasions Firstly to complete a second nutrition survey and then again to complete another activity survey If under 9 we will be in touch over the next week K1 Can I please have your Home Phone Number K2 What is the best number to contact you study child on K3 What is the best time of day to call 1 Monday 2 Tuesday 3 Wednesday K4 What is the best day to call 4 Thursday 5 Friday 6 Saturday 7 Sunday NOTE INSERT ROTATION OF SURVEY DAY DEPENDANT ON YESTERDAY S DATE AND LOOK UP TABLE INSTRUCTION K5 So that we can send you a short reminder messages could I plea
136. ple regression model Nutritional Value a b Sex to the data for each Region by Age grouping The residual variance from this model for a particular Region by Age grouping would be our variance estimate for all Regions by Family Structure by Age by Sex Classes having that Region and Age It may be that the variance will not vary much between Classes so that a single variance estimate will be appropriate for all Classes When the variance estimate appropriate to each Class has been obtained the standard error of the estimate 2 is If the quantity to be estimated is a proportion of children with a particular attribute such as the proportion of children in families with family income gt 2 000 per week rather than a mean of a quantitative variable the Class standard deviation is estimated from the estimated proportions in each Class The estimated proportion for the population is where J 1 for children who have the attribute Z 0 for other children 4 W i Let g be the Class containing child i and suppose there are ng children in Class g of whom mg have the attribute so that the proportion of children in Class g with the attribute is p m n The estimated standard deviation of the indicator variable li is then 1 p Thus the standard deviation of the estimate 4 is 5 Note that the sums in equations 4 and 5 run over all children in the survey and not just over Classes They can be r
137. purpose of the survey e g estimating population means estimating prevalence of compliance with physical activity guidelines e the methods of data treatment e g normalising data via transformations There were no differences in data quality or average values for major outcome variables MVPA screen time sleep between CAPI and CATI recalls when corrected for age and day type i e school vs non school day Pedometry The data are based on complete days defined by at least 1 000 steps and the pedometer was removed for no more than 240 minutes Assuming that the sleep duration for most respondents in this survey is between 8 and 10 hours allowing 4 hours of pedometer removal still gives at least 10 to 12 hours in which data were collected This is in line with recent accelerometer studies that include measurement days on which at least 10 hours of data are recorded Several studies discard days on which the pedometer was removed for more than 60 minutes This is an issue as disregarding days when subjects participated in long periods of swimming or contact sports will lead to spurious estimates of daily physical 61 activity In the survey seasonal differences in activity choices will impact on the measurement periods with aquatic activities more likely in the summer The vast majority of reasons for pedometer removal during the waking hours as recorded on the log sheets related to unavoidable circumstances such as exposu
138. r 1 000 1 499 wk 52 000 77 999 yr 800 999 wk 42 000 51 999 yr 700 799 wk 36 400 41 999 yr 600 699 wk 31 200 36 399 yr 500 599 wk 26 000 31 199 yr 400 499 wk 20 800 25 999 yr 300 399 wk 15 600 20 799 yr 200 299 wk 10 400 15 599 yr 100 199 wk 5 200 10 399 yr 50 99 wk 2 600 5 199 yr 1 49 wk 1 2 599 yr Nil income Negative income loss Don t Know Refused Completed CAPI 4124 689 24 232 866 2 168 1 101 312 101 Al wn 725 208 313 913 1 060 388 212 187 180 162 115 67 12 11 164 105 Jo 85 14 18 45 23 OOOO NN so on N NUOO0OO0OOO NUAAAON Completed CATI 4017 655 23 219 840 2 104 1 082 298 96 wn 709 202 302 897 1 033 376 208 181 174 154 105 65 12 11 151 101 7 86 14 5 18 45 23 OoOO0Oo0o0 NOGA o K nn N KNKUOO0OO0OO0OO NKNURAA ARAON Compliant datasets for analysis o 3854 86 610 14 23 199 4 792 18 2 021 45 1 047 23 282 6 92 2 39 7 O 5 O 2 0 O 683 15 192 4 294 7 854 19 996 22 365 8 191 4 176 4 164 4 146 3 94 2 60 12 O 4 O O 8 O 11 O 138 3 98 2 47 14 Data Processing Demographics Interviewers submitted interviews to the secure web server on a daily basis view downloaded all new interviews daily for edi
139. r some questions about your housing and income Once have finished asking you both questions have a short form that will ask parent care giver to fill in by yourself and return with the pedometer in the envelope supplied Before we start will need to obtain formal consent for this study from the parent care giver and study child will now read a statement and will need you to provide your signed agreement to take part in the study So that s generally what we are going to be doing do you have any questions at this point Dol have both your agreement to be part of this study will now get you both to formally sign your consent Let s get started then 74 SECTION C CONSENT SCREEN I now have to obtain your formal consent for this study and have to read to you the following statement You and your family are being asked to take part in the Australian National Children s Nutrition and Physical Activity Survey Kids Eat Kids Play in conjunction with the CSIRO and the University of South Australia The study will measure the physical activity nutrition habits and body size and shape of a large group of children aged two to sixteen years The Kids Eat Kids Play is being conducted on behalf of the Australian Government by the CSIRO and the University of South Australia who have contracted I view to collect the data on their behalf All the information collected will be kept strictly confidential except wher
140. re to water and engagement in contact sports Relatively few were due to forgetting or refusing to wear the pedometer As pedometers are most sensitive to activities involving running and walking and are removed for aquatic activities and contact sports caution is advised when using pedometer data to assess compliance with physical activity guidelines It is recommended that engagement in sufficient physical activity also be assessed using criterion referenced step counts currently 11 000 12 000 and 13 000 15 000 per day for girls and boys respectively Having these cut off points established in accordance with weight categories normal vs overweight obese avoids the issue associated with inferring daily MVPA from pedometer data It should also be noted that Day Type weekday vs weekend in the pedometer data tables should not be interpreted as school day and weekend The weekdays in these tables include school holidays long weekends and pupil free school days Physical Measures The methodology of performing physical measurements on participants was designed to minimise errors and be consistent Normally measurements should not be taken after training or competition sauna swimming or showering since exercise warm water and heat can produce dehydration and or increased blood flow Measurements taken under those circumstances have the potential to affect body mass and girth measurements To counter this potent
141. red time of consumption and information on the place of consumption was obtained according to whether they were consumed at Home Any other residence e g friend or relative e Place of purchase e g caf or fast food outlet e Institution e g school pre school after school care e Leisure activity sport music lesson cinema park e During transport e g bus car walking e Other When ingredients were known recipes for mixed dishes were entered as either the whole recipe with the fraction of the recipe the participant consumed or the amount of each ingredient consumed by the participant Cooking methods available in the recall software were e Not cooked e Unknown method e Baked roasted e Stewed Boiled simmered poached e Steamed e Grilled BBQ e Deep fried submerged in fat e Pan Fried shallow fat e Stir fried minimal fat Microwaved The cooking method employed was incorporated into the food descriptor to facilitate appropriate conversion into nutrient intakes The options for entering portion size information were e Direct entry where the amount is known e g 20 g packet of crisps e Adrop down list of measure descriptors relating to that particular food e g one cup of cooked rice e The dimensions of the food e g slice of lasagne can be entered as 10cm x 8cm x 4cm the food model booklet was used to determine these proportions see below and the volume is
142. regional population then even where the intra cluster correlation is quite small there will be a design effect the size of which is then dependent upon the size of the cluster Cluster Sample Size The target sample size was achieved for each region There was no set quota by cluster of postcodes Some clusters were skewed with either more postcodes or postcodes with higher populations of children 2 16 years There were some postcodes where no children were selected as all numbers were exhausted with no recruitment high industrial commercial areas and there were other postcodes included that were not part of the initial selection phone number transportability but the family was still recruited Random digit dialling The majority of households in Australia have a fixed land line It is estimated that at least 96 of eligible households in an area will be covered by using RDD although over the past few years there has been an increase in households particularly high density urban with mobile only and no land line Australian Communications and Media Authority estimates that the number of basic fixed line services at 30 June 2005 was 11 46 million a 1 7 per cent decrease compared to 11 66 million services 12 months earlier RDD as a recruitment strategy is limited by the lack of information about non respondents Population weightings were applied to assist with overcoming differences in the probability of selection amongst the study
143. rmine three dimensional sizes of irregular foods e g pizza lasagna or watermelon e Photographs of beef lamb and chicken cuts chocolate milk drinks carbonated drinks juices yoghurt and muesli snack bars were included to assist with correct identification of product not portion size The following measuring aids were available for the CAPI only and included e Household spoons a metal teaspoon and tablespoon e Measuring cups labelled 1 4 3 and 1 cup e Measuring spoons labelled 4 tsp 2 tsp 1 tsp 1 tbsp e Ruler a plastic ruler with fractions e Measuring container for measuring fluids Caregiver Form A caregiver form was provided to record intake if the participant was aged less than nine years and had been in the care of others during the recall period This information was added to the dietary recall during the interview Nutrient Intake The 24 hour food beverage and supplement intakes were converted into nutrient intakes Table 10 using a nutrient composition database developed specifically for this survey by FSANZ see Section 14 for more detail Foods Habits Questions A series of food related questions provided additional information on the usual eating habits of participants All participants were asked the following questions e Usual eating habits i e lactose free vegetarian e Number of serves of fruit usually eaten each day 1 serve 1 medium piece of fruit Number of serves of vege
144. rs and 118 minutes for children aged 9 16 years The overall average telephone interview length was 37 minutes for CATI 24 hour dietary recall and 33 minutes for the CATI 48 hour use of time recall 27 Table 8 Overview of survey participation Stage Task Whom 1 Participant RDD recruitment Primary care giver recruitment Receive letter about fieldwork Primary care giver 2 CAPI Consent Primary care giver 2 13 years Primary care giver and child 14 16 years Demographics Primary care giver Dietary recalll Primary care giver for child aged 2 8 years child 9 years Food habits question Primary care giver for child aged 2 8 years child 9 years Use of time Child 9 yearst Anthropometric Child measures 3 Pedometer Child 5 years 6 days 4 CATI Dietary recall Primary care giver for child aged 2 8 years child 9 years Use of time Child 9 yearst Table 9 Overview of instrumentation Data collected Instrument 24hour dietary recall Life in New Zealand LINZ24 Otago University with modifications 24hour use of time recall Multimedia Activity Recall for Children and Adolescents MARCA UniSA Step count Pedometer New Lifestyles NL 1000 Height Stadiometer Invicta Height Measure Waist circumference Girth Lufkin W606PM metal tape Body weight Tanita HD332 scales 28 9 Demographic
145. rug Survey Secondary students n 18 486 aged between 12 and 17 years from all states except Western Australia were asked short dietary questions to measure usual fruit and vegetable intake number of serves and frequency of consumption of unhealthy non core foods per week defined as fast food meals snack foods and high energy drinks Cancer Council of Victoria 2006 State Surveys 2007 Healthy Kids Queensland Data were collected in urban and rural and remote Queensland from April to September 2006 Abbott et al 2007 A total of 3 691 school children aged 5 17 years who were undertaking the grades 1 5 or 10 at school completed a 24 hour dietary record and a food frequency questionnaire FFQ Schools n 112 were chosen using a random cluster design from all government and non government primary and secondary schools Data were weighted to take into account the sampling framework and correct for unequal probability of inclusion 2004 Schools Physical Activity and Nutrition SPANS New South Wales Overall almost 5 500 school children from 93 urban and rural primary and secondary government and non government schools ranging from kindergarten Years 2 4 6 8 and 10 i e students aged 5 to 16 years were surveyed in 2004 Booth et al 2006 A food habits and eating habits questionnaire was used on children aged 11 16 years regarding consumption of fruits vegetables bread rice and pasta meat chicken and fish milk fruit juice s
146. s age sizeorder lt fs age sizeorder fs sizeorder lt order sizeorder 1 FS sizeorder lt fs sizeorder FS num 1 Find State and Region for each study child from postcode Sample Region lt factor IncludedPostcodesS Region match wgtdata PC IncludedPostcodes Postcode Sample State lt factor IncludedPostcodesS State match wgtdataS SPC IncludedPostcodes Postcode Study Child Sex as numerical value male 1 female 2 Sample Sex lt as numeric factor StChGen levels c Male Female Study Child age and age group calculated from age Some recorded ag groups are inconsistent Sample Age lt as numeric as character wgtdata SCA Sample AgeGrp lt cut Sample Age breaks c 1 3 8 13 16 labels 1 4 116 Calculation of weights Read in Sample and ABS data and determine family structure counts for each cat flush console source data ABSAnalysis r cat flush console source CURFAnalysis txt cat flush console Set up table of population counts sample counts and weights States lt sort unique Boys aggr State nStates lt length States Regions lt unique Boys aggrSRegion nRegions lt length Regions Empty table to hold counts wt table lt data frame matrix nrow nStates nRegions 80 4 2 ncol 9 names wt table lt c State Region FS Size AgeGrp Sex Popn Sample Weight Classification
147. s and when trying to estimate the prevalence of potentially inadequate intakes in the population On any one day children may report very high or very low intakes that are not representative of their usual intake Nutrient requirements are recommended amounts to be consumed over the longer term and so relate to usual nutrient intakes therefore the effects of daily variation within individuals needs to be minimised for the purposes of comparing population intakes to the Nutrient Reference Values The C side software package Software for intake distribution estimation developed by lowa State University was utilised to obtain estimates of usual nutrient intake distributions This analysis entailed the following e preliminary data adjustment to incorporate the population weightings e transformation of data to normal distributions 60 e estimation of within and between individual variances These variances are used to determine the distributions of Usual nutrient intakes e reversion of the data to the original scale providing e population nutrient usual intake means and standard deviations medians and percentiles This effectively removes the effects of within individual variability e percentage consuming comparison less than the EAR or Al where appropriate The C side software includes the same capacity for determining usual intakes for foods Data were not collected in a way that groups foods into meals although time of cons
148. s were grouped as a cluster location for sample selection The probability of a sample being selected within each postcode was proportional to the list of phone number prefixes for that postcode within the cluster location In summary e RDDis not a precise way of targeting postcodes as postcode boundaries and telephone number prefix boundaries do not have common boundaries e The geographic location of prefixes is identified via the Telephone Exchanges they are listed against e These Telephone Exchanges often cover more than one postcode so that when a phone number is randomly generated it may in fact exist outside of the postcode required usually in an adjacent postcode e It was decided that when this did occur households obtained outside of the required postcodes would be included rather than screened out when they fell within the geographic clustered location of the selected postcodes Thus the number of postcodes increased from the 246 initially selected postcodes to 479 in the final sample See Table 4 Table 4 Number of postcodes and locations in final sample within each state territory COO OO Postcodes Chusterlocations city state city state 83 Poi s 9 4 70 a an a Faueensiond 8 Ee a Ce aa n o z an Oo 4 3 3 1 westemasraia o f i Northern Territory Australian Capital Territory Australia 39 10 O Ee a 5 isd a o are _ 5 _ ECE 19 Selection of participa
149. se have Parent Mobile Parent Email 99 do not have refused 99 do not have refused Study Child Mobile Study Child Email 99 do not have refused 99 do not have refused 93 Interviewer to check off instructions Place copies of all material to be left behind in the folder and hand it to them as their reference for the study The material includes Summary consent form Permission to contact form for carer teacher if applicable Medicare Australia data release form if agreed Change of address form Service agency listing Respondent brochure if they do not already have one Food Habits Survey Pedometer and instruction sheet Nutrition and Meal Serving Size Guide Card Reply paid Envelope Invite them to visit the respondent website www kidseatkidsplay cOm du to keep in touch with what is happening with the study Let them know that you will ring back in a two weeks time to do the follow up survey or so to see how they have gone with the self complete s and the pedometer Explain that it is possible that someone from the Office may be in touch to verify that you have conducted the interview Thank them sincerely for their time and cooperation and check if there are any outstanding queries It is very important that you take the time to talk with the respondent about any questions they have about the study Take care packing up your equipment check against the list of m
150. sehold member The 24 hour period was from midnight to midnight The information was collected using a standard interview approach and a pre determined set of probing questions ABS 1998a Approximately 10 of the survey participants provided a second 24 hour recall on a different day of the week generally within ten days of the first recall This additional data enabled an estimate of the within person variation in nutrient intake to be obtained This within person variation data was used to adjust the one day intakes from the survey to provide a more accurate approximation of the usual intake for the group ABS 1998b The 1995 NNS nutrient composition data base called AUSNUT 1997 was used The person specific weights were adjusted for regional probability of selection and non response based on a number of geo demographic characteristics 2004 05 National Health Survey This survey conducted by the ABS was designed to obtain national information on the health status of Australians their use of health services and facilities and health related aspects of their lifestyle ABS 2006 The survey included some short dietary habits questions asking about the usual fruit and vegetable consumption number of serves for participants aged 12 years and over and current breastfeeding practices for infants and children ages three and under Activity was assessed in those aged 15 years and above 2005 Australian Secondary Students Alcohol and D
151. sport codes 60 221000 221110 221120 18 inactivity inactmin minutes spent in activities requiring 21 450 to lt 2 METs 19 light activity lightact minutes spent in activities requiring 22 60 to lt 3 METs 20 othersedentary othseden minutes spent sitting lt 3 METs code 200 X2XXXXX 21 lying awake lyingawk minutes spent lying down excluding 10 sleep code x1xxxx 22 sleep sleepmin minutes of sleep code 100010 620 23 wake up time waketime 06 30 am 24 bedtime bedtime 1 15 PM 25 cull status cullstat Profile is a candidate for culling forlow OK activity PAL lt 1 1 high activity PAL gt 3 0 or too few activities lt 10 56 The summary data are in the form of a series of tables in the main findings report They are derived from the extracted data weighted to reflect the Australian population The summary data consist of descriptive means standard deviations and percentiles for the following groups of variables e physical activity e sedentary behaviour e miscellaneous activity sets and e prevalence of compliance with activity and screen time recommendations Physical activity variables The variables used to describe physical activity were e Physical Activity Level PAL The average rate of energy expenditure over the course of a day It is expressed in multiples of child specific resting metabolic rate or metabolic equivalents METs For example a PAL of 1 7 would mean tha
152. statement and you will need to provide your signed agreement to take part in the study So that s generally what we are going to be doing do you have any questions at this point Dol have your agreement for you to be part of this study will now get you to formally sign your consent Let s get started then Introduction child aged 14 to 16 years Address the Parent and Child Thanks for agreeing to take part in the Kids Eat Kids Play study First of all want to assure you both that all information that you give me will be kept confidential and we have strict processes to ensure the security of your information 73 will start by asking a few questions to parent care giver about the household how many people live here and a little bit about each person also will ask about study child s general family background such as Parent and your partner s work and educational background This will give us a general idea of the home environment child grows up in Then I will ask study child some questions about their food intake and what they have done over the last 48 hours am also going to ask if can take some measurements of study child such as his her weight height and waist circumference will then ask study child to conduct a stride test and show you how to wear the pedometer which we ask they do over the next 7 days There will be a few questions on general food habits and finally will ask parent care give
153. surements was clearly incompatible with the other two it was excluded from the calculation of the mean 58 15 Interpretation of results Survey and Sample Design There are limitations to the use of postcodes as the primary sampling unit as postcodes can cover a wide geographic area one postcode can include urban rural and remote areas However postcodes do offer a degree of clustering to enable cost effective face to face interviews to be conducted and allow a reasonable geographic distribution of the sample across Australia A potential sample design effect is the loss in statistical precision resulting from a clustered sample that does not fully cover the diversity of specific response variables evident in the entire population The extent of loss in statistical precision largely depends on whether and how much the specific response variables have underlying geographic variations The potential design effect on the precision of estimates derived from a clustered sample is essentially related to the heterogeneity of the stratum metropolitan or rural population for their state If the members of a cluster of postcodes are effectively no more like each other than they are to others within their state rural or metropolitan area population then the intra cluster correlation is zero and there is no design effect However where regional clusters result in cluster members being more like each other and less like other members of their
154. t Batter Based Products FATS AND OILS Dairy Fats Margarine Vegetable Oil Other Fats Unspecified Fats FISH and SEAFOOD PRODUCTS AND DISHES Fin Fish Excluding Canned Crustacea And Molluscs Excluding Canned Other Sea And Freshwater Foods Packed Canned And Bottled Fish And Seafood Fish And Seafood Products Mixed Dishes With Fish Or Seafood As The Major Component FRUIT PRODUCTS AND DISHES Pome Fruit 2007 Revised Food Code 11 a AOUN 12 121 122 123 124 125 126 13 131 132 133 134 135 136 14 141 142 143 144 145 146 15 151 152 153 154 155 156 16 161 Revised Food Group Name NON ALCOHOLIC BEVERAGES Tea Coffee And Coffee Substitutes Fruit And Vegetable Juices And Drinks Cordials Soft Drinks And Flavoured Mineral Waters Electrolyte Energy and Fortified Drinks Mineral Waters And Water Other Beverage Flavourings and Prepared Beverages CEREALS AND CEREAL PRODUCTS Flours And Other Cereal Grains And Starches Regular Breads And Bread Rolls Plain Unfilled Untopped Varieties English Style Muffins Flat Breads And Savoury and Sweet Breads Pasta And Pasta Products Breakfast Cereals and Bars Unfortified and Fortified Varieties Breakfast Cereal Hot Porridge Type CEREAL BASED PRODUCTS AND DISHES Sweet Biscuits Savoury Biscuits Cakes Buns Muffins Scones Cake Type Desserts Pastries Mixed Dishes Where Cereal Is The Major
155. t National Physical Activity Survey was in 1985 The intervening decades have seen substantial changes in the Australian food supply and eating habits an increase in technologies that facilitate sedentary behaviour e g video games and mobile phones and changing family life and structure e g increased participation of both primary and secondary care givers in the workforce All of these factors are likely to impact on what children eat and what they do Indeed the prevalence of overweight and obesity has rapidly increased since the mid 1980s State based surveys indicated that currently 5 of Australian children are obese and a further 20 are overweight using internationally agreed criteria National level data on children s intake and energy expenditure are needed for monitoring and understanding weight status and to assess the adequacy of children s diets and activity patterns The Dietary Guidelines for Children and Adolescents in Australia was published by the National Health and Medical Research Council NHMRC in 2003 In 2004 the Department of Health and Ageing issued the National Physical Activity Guidelines for Australians with recommendations aimed at children aged 5 18 years and included recommendations for limiting screen time television computers and video games National level dietary intake and physical activity data are needed to assess progress against these guidelines for the development of future guidelines and to infor
156. t 2007 The participation over the days of the week and the seasons are shown in Table 13 and Table 14 People generally did not want to schedule interviews on Sundays and Mother s Day and Easter Sunday were during this field period which further reduced the opportunity to survey households on Sundays Table 13 comparison of days of the week for CATI and CAPI Day of the Day of the Week of CATI CATI Not Week of CAPI Mon Tues Wed Thurs Fri Sat Sun Completed Total Mon 151 119 98 108 67 95 52 21 711 Tues 102 113 90 113 91 96 57 19 681 Wed 126 110 123 118 105 114 92 33 821 Thurs 133 109 92 112 93 106 64 18 727 Fri 126 132 87 101 84 94 5 25 700 Sat 81 109 107 129 135 122 56 19 758 Sun 57 53 50 74 76 72 50 7 439 Total 776 745 647 755 651 699 422 142 4 837 41 Table 14 Seasonality of Dietary Recall CAPI CATI Total P Summer Feb 71 2 73 1 Autumn Mar 682 508 Apr 756 674 4 516 48 May 1 092 804 Winter Jun 1 286 1 138 Jul 716 1 146 4 900 52 Aug 223 391 Total 4 826 4 663 9 489 More than half of the activity recall days were collected in winter 59 many were collected in autumn 40 and a small number at the end of summer 1 Table 15 Table 15 Seasonality of Activity Recall day 1 day 2 day 1 day 2 Total P Summer Feb 35 25 60 1 Autumn Mar 279 27
157. t a child uses on average 1 7 times the amount of energy required to sit still all day In this survey PAL was estimated from MARCA data PAL is calculated by multiplying the estimates of activity specific energy expenditure by the number of minutes reported for each activity and averaging across the 1440 minutes of each day e Moderate to vigorous physical activity MVPA The number of minutes spent performing activities which require at least 3 METs based on the MARCA Compendium Section 22 or three times resting metabolic rate e Vigorous physical activity VPA The number of minutes spent performing activities which require at least 6 METs or six times resting metabolic rate e Organised sport and play Active recreation which is structured and rule governed typically requiring specialised equipment a special play area and time It is often supervised For example games such as football and basketball activities such as horse riding Section 22 e Free play Active recreation which is essentially unstructured For example playground games riding bikes and scooters and mucking about Typically free play requires no special playing area few rules and minimal supervision Section 22 e Active transport Locomotion where the subject provides most of the energy For example walking cycling skateboarding and rollerblading Section 22 Data analysis Activity variables have been described in the main findings repor
158. t using means standard deviations and percentiles by age and sex subsets However it is important to note that some activity variables such as minutes of MVPA show very strong positive skews so median values provide more appropriate estimates of typical activity patterns Where data were analysed on per child basis i e across all four recall days for an individual child average values were determined by calculating the average for each child for all school days and then for all non school days and then taking the average of the averages The rationale for this approach is that children soend about one day in two in school over the course of a year 57 Quality assurance of physical activity data There are two main quality assurance mechanisms e Process evaluation which involves training interviewers and verifying interviewing technique Section 7 e Outcome evaluation which involves an automated assessment of profile quality The outcome evaluation involved checking each profile for Number of different activities recalled Average estimated daily energy expenditure Physical Activity Level or PAL Fewer than 10 activities recalled usually signals a poor effort at recall and PALs lt 1 1 or gt 3 0 multiples of estimated resting metabolic rate are considered suspicious Profiles meeting any of these criteria were examined on an individual basis In some cases the interviewer had noted a plausible reason e g that the c
159. tables usually eaten each day 1 serve cup cooked vegetables e Type of milk usually consumed full cream reduced fat soy e Whether salt is usually added to food after it is cooked e Whether salt is usually added to food during cooking e Use of iodised or non iodised salt The primary care giver was asked about e Food security If the household always had sufficient money to buy food e Infant feeding habits e Ifthe child had ever been breastfed and when did weaning occur e If the child had ever been given infant formula regularly e What age were solids introduced e Child s birth weight and the source of that information 33 Table 10 Nutrients and food components collected in the survey Nutrient food component Unit Proximate constituents Energy Kilojoules kJ Energy including from fermentable fibre Kilojoules kJ Moisture water Grams g Protein Grams g Fat total Grams g Saturated fatty acids total Grams g Monounsaturated fatty acids total Grams g Polyunsaturated fatty acids total Grams g alpha linolenic fatty acid Grams g linoleic acid fatty acid Grams g long chain omega 3 fatty acids Milligrams mg Cholesterol Milligrams mg Carbohydrate total Grams g Sugars total Grams g Starch Grams g Dietary fibre Grams g Alcohol Grams g Vitamins Vitamin A expressed as retinol Micrograms ug equivalents Preform
160. tary Population Health Division Department of Health and Ageing Ms Jennifer McDonald former Assistant Secretary Population Health Division Department of Health and Ageing Ms Cath Peachey Acting Assistant Secretary Population Health Division Department of Health and Ageing Mr Andrew Stuart former First Assistant Secretary Population Health Division Department of Health and Ageing Ms Margaret Lyons former First Assistant Secretary Population Health Division Department of Health and Ageing Mr Richard Souness General Manager Food Policy and Safety Branch Department of Agriculture Fisheries and Forestry Mr Dick Wells Chief Executive Officer Australian Food and Grocery Council Mr Geoffrey Annison Australian Food and Grocery Council Mr David Roberts Australian Food and Grocery Council Steering Group Project Officer Caroline Arthur Acting Director Nutrition Section Department of Health and Ageing The Technical Reference Group supplied guidance and advice to the Project Team The members of the Technical Reference Group were Professor A Stewart Truswell AO MD DSC FRCP FRACP FPHN Emiretus Professor of Human Nutrition University of Sydney Professor Katrine Baghurst BSc PhD Adjunct Professor Department of Medicine University of Adelaide Professor Jennie Brand Miller BSc Hons Food Tech PhD FAIFST FNSA Professor of Human Nutrition University of Sydney Ms Ingrid Coles Rutishauser BSc Nutrition M
161. ted with the use of CATI whereas the 1995 NNS repeat 24 hour recall also took place in the form of a personal interview e Food nutrient composition database this survey utilised the 2007 AUSNUT database and the 1995 NNS utilised the AUSNUT 1995 database Both of these food composition databases reflect the composition of foods at the time the survey was completed e The number of major food groups used to report food intake has been increased to include categories for dairy substitutes and supplements Some additional sub groups have also been created to better reflect the current food supply Comparison with recommendations Comparisons with dietary recommendations The NHMRC has recently released the Nutrient Reference Values for Australia and New Zealand NHMRC 2006 Nutrient Reference Values NRV include a range of values for comparison including the estimated average requirements EAR recommended dietary intakes RDI and adequate intakes Al for 2 3 years 4 8 years 9 13 years and 14 16 years For those aged 14 years and above the acceptable macronutrient distribution range AMDR and suggested dietary targets SDT are set for certain nutrients that may help in prevention of chronic disease The Core Food Groups NHMRC 1995 recommends quantities of cereals fruits vegetables meat and meat alternatives and dairy products which were designed to meet 70 of the RDIs for all nutrients except energy NHMRC 1991 The Core Food Gro
162. terviewer team signed a deed of confidentiality with the Commonwealth Department of Health and Ageing 15 5 Pilot A pilot took place in Whyalla South Australia and in two cluster areas of Brisbane North and South Queensland in October 2006 The objectives of the pilot were to e test the effectiveness of proposed survey methodology excluding recruitment e refine the interviewer selection and training program e test the response rate between the CAPI and CATI e test the survey instruments e trial data collection data transfer processes and analysis procedures e verify the appropriateness of equipment The interviewer training was conducted in Adelaide and recruitment of participants commenced the following week Subjects were recruited from the White pages in Whyalla and a client list in Brisbane i e the survey recruitment strategy not piloted Complete interview data i e all CAPI and CATI data were obtained for 100 children with an equal number of children from each location CAPI data collection occurred over the period of one fortnight CATI was conducted 7 21 days after CAPI The average lengths of the interviews were within the expected range The main outcomes of the pilot survey were e The need to refine the selection criteria for interviewers to include tertiary education and a intermediate or higher PC and Windows software skills e To extend the interviewer training program from 4 days to 4 5 days e Toreduce
163. the estimated number of CAPI interviews to achieve the required number of completed CATI i e fewer households needed to be recruited due to a higher than expected response rate between CAPI and CATI e To make minor modifications to the interviewer manuals e To increase the number of pedometers in circulation to allow for slower than anticipated return by participants e Toinclude a self completed daily log sheet for pedometer step counts Toimprove data edit checking audit and reconciliation of data files procedures No major equipment problems were encountered However it was originally proposed that 7 days of data stored in the pedometer would be used to determine the average number of steps taken per day by each participant During the pilot it became apparent that data stored in the pedometers was lost during transit from the participant to the survey team For the main survey a daily step count diary log was added to the survey methodology 16 6 Survey design At the time of the survey children aged 2 16 years made up just over 21 of the total Australian population and at any given time approximately one in three Australian households occupied dwellings had a child aged 2 16 years based on ABS 2001 Census data The survey was conducted using a quota sampling scheme The primary sampling units were postcodes Postcodes were converted by I view Pty Ltd to ABS postal area and then stratified by state territory and by capit
164. time and place of consumption brand and product name recipe details and portion size All elements of the recall could be edited Items could be deleted and new items added Interviewer variability was minimised by the fixed structure of the interview process and the probe questions A comprehensive food list and brand name data base enabled interviewers to immediately identify the item during the interview reducing the possibility of errors associated with the subsequent food coding Foods brand and product names and portion sizes that were not listed in LINZ24 could be entered as free text description and subsequently coded by CSIRO dietitians Permission to modify LINZ24 was given and the adaptations made by CSIRO included e Addition of an Australian brand and product names list provided by FSANZ for commonly consumed foods under major food group categories biscuits bread butter and margarine cereal dietary supplements energy drinks ice cream novelty i e stick ice cream take home i e tub juice cordial and powdered beverages milk wrapped snack bars yoghurt and dairy foods e Modification of some food names to reflect Australian terminology e Addition of foods commonly consumed in Australia e Modifications to the serving sizes where these varied between Australia and New Zealand e Modifications to the probe questions to reflect the Australian food supply and cooking methods 31 For each food ente
165. ting Interviews were reviewed and data were checked for e Logic and consistency across each demographic variable e Valid ranges e Typing errors e Completeness of survey data Clarification was obtained by telephoning interviewers or respondents where discrepancies existed General feedback was provided to all interviewers via weekly newsletters Postcodes were converted by I view Pty Ltd to ABS postal area and then to other geographical classifications such as state major statistical region and a combination of both called region in the data file using ABS postal area to statistical local area concordance Postcodes were converted to postal delivery centre using Australia Post concordance supplied by the Australian Social Science Data Archive at the Australian National University To retain confidentiality postcode values were recoded into a unique integer for each postcode value and original data was removed from the final database Dietary recall Interviewers submitted interviews to the secure web server on a daily basis CSIRO dietitians downloaded all new interviews daily for editing Interviews were reviewed and data were checked for e Unrealistic portion sizes and overall food intake e Inadequate detail e Typing errors e Completeness of recipe Clarification was obtained by telephoning CAPI interviewers and via the supervisors of CATI interviewers General feedback was provided to all interviewers via weekl
166. ting in surveys e Surveillance items for physical activity It was recommended that the survey measure the minutes of participation in organised and non organised forms of moderate and vigorous intensity physical activity and the days of the week when that participation occurred where participation occurred and the key motivators and barriers to participation e Surveillance items for sedentary behaviours The best practice approach involves measuring the minutes soent each day on small screen recreation TV DVD Computer and Internet distinguishing between these activities and when each activity took place includes day time activities week days and weekends Further the survey should also assess the number of minutes spent daily on other non screen based sedentary activities e g reading listening to music etc e Sample size The following socio demographic data always be collected age date of birth language spoken at home parental education level rurality and Indigenous status e Selection criteria The essential criteria for the selection of the physical activity and sedentary behaviour measurement and monitoring tools were stated to be validity reliability sensitivity comparability with existing state based data cost efficiency burden of response flexibility and the sustainability of data e Piloting It was recommended that the instrument as a whole be piloted in the context for which it is to be used 11 e
167. tion and estimation for the AUSNUT 2007 database Caffeine Caffeine was not reported in the 1995 NNS or included in AUSNUT 1997 Since 1995 FSANZ has undertaken an analytical program involving caffeine analysis the results of which were published in NUTTAB 2006 These data formed the basis for further imputation and estimation for the AUSNUT 2007 database Sodium FSANZ has updated a significant proportion of sodium analytical values in foods and included these in the reference database NUTTAB2006 These values are the basis for further imputation and estimation for the AUSNUT2007 database Sodium was not reported in the 1995 NNS or included in AUSNUT 1997 Food Group coding A revised dual food coding system was developed in collaboration with FSANZ CSIRO and the Technical Reference Group Section 20 It reflects the current food supply but also maintains comparability with the food groups used in the 1995 NNS Subcategories were added to separate foods on the following basis e Caffeinated or decaffeinated e Sugar sweetened or intense sweetened e Fortified product or unfortified product Additional food groups were added to reflect infant foods and formulae and dietary supplements There are a set of codes that are the same as the 1995 NNS and a second set that reflect the changes described above Quality assurance of nutrient database Quality assurance required detailed scrutiny of nutrient data by trained staff as well as m
168. tion of Occupations ASCO Code Household annual income 30 10 Food and Nutrient Intake This section describes the food and nutrient intake information collected during two 24 hour recalls conducted with each participant on non consecutive days Food Intake A license was obtained from the University of Otago to use the 24 hour dietary recall software from the Life in New Zealand LINZ24 survey Parnell et al 2003 LINZ24 allowed direct computer assisted data entry LINZ24 employs a three pass 24 hour dietary recall method to record all foods beverages and supplements consumed on the day prior to each interview from midnight to midnight The three pass methodology included the following stages e 15 pass A quick list of all foods beverages and dietary supplements consumed from midnight to midnight the day before the interview Each quick list item has a unique sequence of probe questions which follow in the 2nd pass e 2dpass The time and place of consumption for each quick list item was entered Any additions e g the spread on toast are added to the list The sequence of probe questions then allowed a detailed description of each quick list item and all additions to be entered including recipes and the ingredients portion size and brand and product name if applicable e 3d pass A recall review was used to make corrections or additions The interviewer read aloud all items
169. tion process This comprehensive database contains all of the data generated through the implementation of this survey see section 21 for details For confidentiality purposes the database contains no personal information on the respondents with resoondents names replaced with a unique identification number In 2010 the results database was audited for errors and inconsistencies following feedback from stakeholders and interested parties The updated results database and this User Guide were re released mid 2010 69 18 Issues arising Delays in data collection arose due to The original proposition that the sample would be drawn from the Medicare administrative data base but this was not achievable in the timeframe A decision was made to change to RDD to recruit participants This resulted in a 2 5 week delay to the start of field work A delay in pedometers being returned at the beginning of fieldwork Pedometers were not returned at the same rate as the pilot This resulted in insufficient pedometers available for reissue at subsequent interviews To maintain progress interviewers targeted children aged less than 5 years during this shortage pedometers were only fitted to children aged 5 years and over This resulted in a higher proportion of younger children being interviewed in the earlier phase of the fieldwork Additional pedometers were purchased to adaress this The difficulty in recruiting 14 16 year old participants compar
170. tivity required is not available in the activity compendium the participant can enter the activity as other and enter a text description Each child recalled a total of four days two days prior to the CAPI and two days prior to the CATI During both the CAPI and CATI the child recalled the two days in either order Pedometry Pedometers were used to collect objective physical activity data for most participants aged 5 16 years The pedometer used in this survey was the New Lifestyles NL 1000 which provides the number of steps a day the distance covered and the number of minutes spent in moderate to vigorous physical activity MVPA gt 3 metabolic equivalents METs The pedometer was worn for 7 consecutive days by attaching to a belt or waistband in a position corresponding to mid thigh on the right side of the body A security strap and clip was used to secure the pedometer in place and to prevent loss of the pedometer if it slipped from its position At the CAPI the participant or a parent was instructed on how to retrieve data from the pedometer and how to complete the log sheet The participant was asked to wear the pedometer from when he or she got out of bed in the morning until going to bed at night Those occasions when the pedometer was removed e g showering swimming or playing contact sports were recorded on the log sheet along with the estimated duration of removal The pedometer and log sheet were posted back to th
171. types avgdist Average of pedometer dist km per day for weekdays only wkdyavgd Average of pedometer dist km per day for weekend days only wkedavgd Average of pedometer MVPA min per day for all day types avgmvpa Average of pedometer MVPA min per day for weekdays only wkdymvpa Average of pedometer MVPA min per day for weekend days only wkedmvpa 107 22 Activity sets Free Play MARCA codes and activity names for activities included under the free play category code activity code activity 342853 chasey hard 342763 playing with young children hard 342851 chasey light 342761 playing with young children light 342852 chasey medium 342762 playing with young children medium 341840 climbing trees 341823 pogo stick hard 341133 dancing general hard 341821 pogo stick light 341131 dancing general light 341822 pogo stick medium 341132 dancing general medium 331420 quoits dodge ball poison ball brandy speed 342913 ball hard 342443 red rover hard dodge ball poison ball brandy speed 342913 ball hard 342441 red rover light dodge ball poison ball brandy speed 342911 ball light 342442 red rover medium dodge ball poison ball brandy speed 342911 ball light 341243 riding a bicycle bike hard dodge balll poison ball brandy speed 342912 ball m
172. u give me will be kept confidential and we have strict processes to ensure the security of your information will start by asking a few questions about the household how many people live here and a little bit about each person also will ask about study child s general family background such as your and your partner s work and educational background This will give us a general idea of the home environment study child grows up in Then I will ask you some questions about their food intake am also going to ask if can take some measurements of your child such as his her weight height and waist circumference There will be a few questions on general food habits and finally will ask you some questions about your work and finance Once have finished asking you questions have a short form that will ask you to fill in by yourself Before we start will need to obtain your formal consent for this study will read you a statement and you will need to provide your signed agreement to take part in the study So that s generally what we are going to be doing do you have any questions at this point Dol have your agreement for you and study child to be part of this study will now get you to formally sign your consent Let s get started then Introduction child aged 5 to 9 years Address the Parent Care Giver Thanks for agreeing to take part in the Kids Eat Kids Play study First of all I want to assure you that
173. ull 2 CAPI Completion Rate complete CAPI interview Recruits less quota full 3 CATI Completion Rate complete CATI interview CAPI Interviews less quota full 4 Eligible less quota full eligible households less quota full at recruitment Quota filled between recruitment and interview date Quota filled between CAPI and CATI Final Response Rate completed CAPI and CATI interview Eligible less quota full Table 12 shows the numbers of completed data sets for each component of the survey by age group and region Table 12 Response components to tasks in each region s TEn 2 gt Be os 8 T 5 3 8 S Region S 5 a is NSW metro 74 558 549 254 289 NSW rest of state 30 608 612 298 337 NSW total 1204 1166 1161 552 626 QLD metro 370 365 348 170 196 QLD rest of state 438 437 432 211 243 QLD total 808 802 780 381 439 SA metro 665 658 638 311 355 SA rest of state 267 264 260 130 141 SA total 932 922 898 44 496 VIC metro 517 512 503 231 265 VIC rest of state 425 421 405 201 237 VIC total 942 933 908 432 502 WA metro 290 281 273 129 152 WA rest of state 174 173 163 86 95 WA total 464 454 436 215 247 NT rest of state 102 102 97 46 48 TAS rest of state 198 194 192 92 119 ACT metro 187 184 183 87 100 Total 4837 4757 4655 2246 2577 Interviews were conducted seven days a week during the fieldwork period 22 February 2007 to 30 Augus
174. umption may be used to indicate mealtimes Physical activity recall The 24 hour recall methodology imposes the discipline of fitting all activities into a 24 hour time frame and exploits innate chronological narrative data storage and retrieval methods However all recalls are subject to the limitations of memory social desirability effects and mis estimation of time all of which vary with age sex and individual characteristics Children aged less than 9 years are not able to accurately recall what they did the day before and place events into a temporal frame Therefore the MARCA was administered only to children aged 9 years and over To assist with recall interviewers were trained to use aids such as television guides and school diaries Parents were also invited to assist with recalling events such as meal times and outside of the home commitments There is substantial random within individual day to day variability in the activity patterns of children and systematic differences such as school vs non school days For this reason activity patterns should be sampled over a number of days In this survey activity patterns were sampled on four days including wherever possible one school day and one non school day There is no gold standard for how many days should be sampled The number of days required for an accurate snapshot of typical activity varies according to e the type of activity being measured e the
175. unusuala noted for the researchers analysing the activity data for this child Comments about unusual day before yesterday comdybef CARER DETAILS Person number_primary carer personnu Gender of primary carer parentlig Age at last birthday of primary carer years parlage 100 FIELD DESCRIPTION LABEL FIELD NAME Is the primary carer of Aboriginal or Torres Strait Islander origin parentla Country in which the primary carer was born parentic Does the primary carer speak a language other than English at home parentl What is the highest year of primary or secondary school that primary carer has parentis completed What is the level of highest qualification that primary carer has ever completed parentlh Person number_partner of primary carer personna Gender of partner of primary carer parent2g Age at last birthday of partner of primary carer years par2age How is partner of primary carer related to primary carer parent2r Is the partner of the primary carer of Aboriginal or Torres Strait Ilander origin parent2a Country in which the partner of the primary carer was born parent2c Does the partner of the primary carer speak a language other than English at parent2 home What is the highest year of primary or secondary school that the partner of the parent2s primary carer has comp
176. ups were under review to account for the NRVs at the time of this publication Direct comparisons of intakes of foods with the recommended core food groups should wait until the review has been completed The Dietary Guidelines for Children and Adolescents in Australia NHMRC 2003 provides general recommendations without specifying the amounts recommended for consumption Information on the number of children consuming these foods can be examined These dietary guidelines are also being reviewed by NHMRC The Australian Guide to Healthy Eating provides consumers with information about the amounts and kinds of food that need to be eaten each day to get enough of the nutrients essential for good health and well being The Guide aims to encourage the consumption of a variety of foods from each of the five food groups every day in proportions that are consistent with the Dietary Guidelines for Australians The Guide also provides information on the number of serves required from the five food groups and offers practical examples Comparisons with physical activity recommendations The National Physical Activity Guidelines issued by DOHA in 2005 recommended levels of physical activity and sedentary behaviour for children aged 5 18 years These guidelines recommended that children get at least 60 minutes of moderate to vigorous physical activity and accumulate no more than 120 minutes of screen time television videogames and computer each day esp
177. ution towards analysis of the dietary data The Technical Reference Group appointed by the Steering Group provided advice on a variety of issues relating to conducting and interpreting nutrient intake and 13 physical activity surveys The Technical Reference Group was comprised of experts in the fields of nutrition physical activity and survey development FSANZ collaborated with CSIRO to develop the nutrient composition database AUSNUT 2007 Funding for FSANZ was provided by the Department of Health and Ageing FSANZ also liaised with the Therapeutic Goods Association TGA in relation to the nutrient composition of supplements 14 Ethics and confidentiality Ethics approval covering ethical privacy and confidentiality was obtained from the NHMRC registered Ethics Committees of CSIRO and UniSA for all of the components of the survey including the pilot study and any subsequent changes to the original protocol Relevant NHMRC guidelines for research involving children were also adhered to The sub contractor I View complied with the Australian Market and Social Research Organisations Market and Social Research Privacy Principles the European Society for Opinion and Marketing Research ESOMAR World Research Codes guidelines for interviewing children and young people and the International Chamber of Commerce ICC ESOMAR International Code of Marketing and Social Research Practice The project team l view supervisors and in
178. view Enter reason for Refusing C2 Interviewer Note Is study child 14 or over Yes No 2 Go to C4 C3 Interviewer Note Confirm that Study Child has Yes No signed consent form 1 2 Go to Section D Terminate Interview If No terminate interview Enter reason for Refusing C4 May I record this interview for training purposes Yes No and quality control procedures 5 Record Do not record 75 SECTION D ABOUT THE HOUSEHOLD Address the parent care giver Now I d like to take a few details about yourself and other members of your household Some of these may seem a bit detailed but if we sort this information out now the rest of the interview will be quicker We are interested in family members who usually live here even if they are away at present Interviewer note usually refers to family members who when not working spend at least 50 of their time residing at the household Firstly am going to ask about the people who normally live in your household D1 How many adults and children live in the household Adults Children 999 If Don t Know enter If Refused enter 999 Family Details Parent 1 Let s start with you Enter Parent 1 first name D2 What is their first name D3 Is male or female 2 female 1 male
179. y newsletters Nutrient analysis The 24 hour food beverage and supplement intakes were converted into nutrient intakes using a food and nutrient composition database developed specifically for this survey by FSANZ Each item consumed was matched to an 8 digit food code which in turn referred to a set of nutrient data The 8 digit food code was derived from 5 digit food group codes which were used to categorise foods and beverages Nutrient intake data is available for each unique food item recalled by each child for each interview Refer to Section 21 Within the LINZ24 software each unique food item was identified by a composite key consisting of four variables 1 The item number A unique number for each food described at the 14t pass see Section 10 2 The component number A unique number within the item number for each addition identified at the 2 4 pass see Section 10 3 The recipe number A unique number for each recipe for which ingredients were recalled 48 4 The ingredient number A unique number for each ingredient within each recipe For each dietary recall the values for all of these numeric variables monotonically increase in sequence but are not necessarily contiguous due to software design corrections during the 3 pass stage or corrections during post processing The purpose of the composite key is to support the LINZ24 software for ease of user interpretation a sequential interview l
180. y sex agegroup bmictgry medicalr studychc studyche and studychd Table 18 Parent caregiver characteristic Using the CAPI Interview data file merge catirec from the Contacts data file using respid as the key Tabulate number and percentage for each class CAPI completed CATI completed and Compliant datasets for analysis complian 1 by appropriate sub categories of parentlg parentla parentih parentll parentic and plasco Table 19 Second parent caregiver characteristic Using the CAPI Interview data file merge catirec from the Contacts data file using respid as the key Tabulate number and percentage for each class CAPI 122 completed CATI completed and Compliant datasets v by appropriate sub categories of parent2g parent2a parent2h parent2I parent2c and p2asco Table 20 Household characteristic Using the CAPI Interview data file merge catirec from the Contacts data file using respid as the key Tabulate number and percentage for each class CAPI completed CATI completed and Compliant datasets for analysis by appropriate sub categories of familyty number in household numadult numchild and annualin 123 26 Glossary AUSNUT BMI EAR Energy A survey specific database generated from a reference data base such as NUTTAB 2006 but providing nutrient data ona larger number of foods consumed that are relevant for a consumer intake survey Includes a subset of nutrients i

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