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Follow-up phone calls increase nutrient intake estimated by three-day food diaries in 13-year-old participants of the Raine study.


Aim: Our primary objective was to determine the effect of follow-up phone calls on estimated nutrient intakes obtained by three-day food diaries from 13-year-old adolescents.

Methods: Food diaries were recorded using household measures and entered into a dietary analysis software program, before and after follow up by telephone. A sample of 340 participants aged 13 years born into the Western Australian Pregnancy Cohort (Raine) Study, a population-based longitudinal cohort followed from 16 to 20 weeks' gestation to 13 years of age (current follow up). After face-to-face instruction, participants completed three-day food diaries at home and returned them by post. Follow-up telephone calls were made to each participant to improve data collection response and to verify missing details in the food diaries. Nutrient intakes before and after telephone follow up were compared using Student's t-tests in SPSS. Results were also compared with those of the Child and Adolescent Physical Activity and Nutrition survey.

Results: Follow-up phone calls significantly increased the estimated intake of total kilojoules, water, total carbohydrates, sugars and magnesium (P < 0.05).

Conclusion: These results indicate the importance of follow-up phone calls to obtain missing details in three-day food diaries completed by adolescents.

Key words: adolescent nutrition, diet record, food diary, nutrition assessment.


There is growing interest in lifestyle habits of children and adolescents because of the association of these habits with adult health outcomes, such as obesity, cardiovascular disease, some cancers (1) and psychosocial health outcomes. (2,3) Accurate assessment of dietary intake in childhood and adolescence (4,5) is required to monitor the observance of dietary recommendations and to better understand dietary factors that may be predictive of adult health outcomes. (6) Food frequency questionnaires (FFQs), food diaries (FDs), weighed food records (WFRs) and 24-hour recalls are commonly used for dietary assessment in adults, although in children and adolescents, measurement of dietary intake is especially difficult and prone to error. (6,7) FFQs ask usual frequency of consumption of a list of foods within a defined period. Portion size may be quantified or not. FDs involve participants using household measurements to record food intake over a defined period, whereas WFRs require participants to weigh and record food served and any waste. Twenty-four-hour recalls require a trained interviewer to elicit detailed information regarding the participant's food intake over the previous 24 hours. (8)

For younger children, time may be difficult to conceptualise, and children and adolescents are often unsure of how their foods are prepared, and they have a limited capacity to appraise portion sizes. (5,6) Dietary assessment in children may be confounded by reliance on parents/primary caregivers to report their child's intake. (9) Parents may not be able to accurately report dietary intake of their children due to increasing independence with age and the number of meals consumed away from home. (6) In addition, child and parental recollection may be influenced by memory failure, motivation to accurately report food intake, and level of nutrition knowledge. (10)

Measurement of dietary intake is difficult, and no single method has proved wholly successful. WFRs are considered by some to be the gold standard of dietary intake assessment. (11) However, WFRs still have limitations; they are associated with a high participant burden and financial cost, limitations of the database used for analysis, operator interpretation of food descriptions, and operator error in data entry. In the present study, operator interpretation was of limited variability as a single operator was utilised. Studies have shown a lack of agreement between FD and FFQ methods of assessing usual dietary intake. (12) In children, FFQs may overestimate total energy intake by as much as 50%, compared with the doubly labelled water (DLW) method. (13) On the other hand, FDs may underestimate total energy intake by 19% in non-obese adolescents and 41% in obese adolescents compared with total energy expenditure estimated by DLW. (6,14)

When FDs are administered, the ideal scenario is for the participant to complete the instrument and for a trained interviewer to probe and clarify details face-to-face. (15) However, this is not always practical in large, multidimensional studies with many assessments and high respondent burden. The purpose of the present study was to test the hypothesis that follow-up phone calls would increase completeness of data collection and, hence, contribute a better estimation of the nutrient intake of 13-year-olds.



The Western Australian Pregnancy Cohort (Raine) Study recruited 2979 mothers between the 16th and 20th weeks of gestation from May 1989 to November 1991. The initial cohort comprised 2860 live births. The study methods are reported elsewhere. (16,17) In brief, data were collected at intervals throughout childhood (overall response rate of 76%). The 13-year follow up commenced in 2003, as close to the participants' 13th birthday as possible, and will be completed in 2006 (the children were born over a period of 2.5 years). Data collection was extensive and included: anthropometry, physical activity, cardiovascular functioning and dietary intake (three-day FD, and Commonwealth Science and Industry Research Organisation FFQ). The current analysis is based on a subset of 340 participants who completed three-day FDs (response rate of approximately 65%) in the 13-year follow up. Only in exceptional circumstances that would place an unreasonable burden on the child or carer, were participants not asked to complete a FD. Exceptional circumstances included the presence of severe learning difficulties or a suspected eating disorder in the child, sibling or carer, as well as lack of cooperation in other components of the survey

Dietary assessment methodology

Food diaries were administered at the end of an intensive period of data collection. Participants were provided with written and verbal instructions by trained research assistants on how to record their dietary intake for three days, and were provided with metric measuring cups and spoons. This method was defined by Nelson et al. (18) as an 'estimated diet record'. Reply-paid envelopes were provided for return of questionnaires, as further contact with the participants was not scheduled.

Food diaries were completed by the adolescents themselves, with parental support if requested by the child. Despite thorough instructions, pilot testing found that the detail in many diaries was scarce. A dietitian was subsequently employed to check each FD and to seek clarification by telephoning each participant. Follow-up phone calls were made as soon as possible. Care was taken to ensure that open-ended questions were used to elicit missing information. For example, 'Did you add anything to your cereal?', and 'What type of milk would you have used on your cereal?', as opposed to 'Did you use low-fat milk on your cereal?'

All FDs were entered into FoodWorks (19) (Xyris Software, Highgate Hill, Queensland, Australia) before and after follow up by the dietitian (KGD). Food composition data that were not available in the FoodWorks database were extracted from food manufacturers' data available from a commercial Australian nutrition website. (20) 'New foods' were created in FoodWorks based on similar foods already existing in the database. Data on total kilojoules, protein, total fat, saturated fat, carbohydrate, sugars and sodium available from the commercial website (20) were used to override existing information on the similar foods in the FoodWorks database. The vitamin, mineral and fibre profiles of the similar foods were retained because they were not available from the commercial website.

Food diaries may have been completed on any day of the week, even on school holidays, as the days required for completion of the FD were not specified because of the high participant burden. Attached to the FD was a checklist asking whether each of the three days of the FD was typical of the participants' usual intake. If a day was atypical, participants were asked to give a reason for this. Responses were useful for guiding follow-up probing for completeness and included: being away from home, doing sport/activity/working, attending a party/special occasion/consuming fast food, being on school holidays, not being hungry and dieting.

Completeness and accuracy of dietary assessment could not be assessed on an individual basis. However, as a gross estimation, mean intakes of nutrients were compared with population estimates for children of similar age and body size, derived from the 2003 Western Australian Child and Adolescent Physical Activity and Nutrition (CAPAN) survey (21) (Table 3). The CAPAN survey collected data from children in school years 3, 5, 7, 8, 10 and 11 during the second semester of the 2003 Western Australian school year. The sample was stratified to be representative of the Western Australian population. The study consisted of five components: (i) a physical activity questionnaire, (ii) a seven-day pedometer diary, (iii) a 24-hour dietary record, (iv) a FFQ, and (v) anthropometry. (21) Dietary assessment in the CAPAN survey included a 24-hour FD, followed by a face-to-face interview with a trained research assistant, and FoodWorks (19) (Xyris Software) was used for nutrient analysis. For the purpose of the present study, we have only used the Year 8 (13-year-old) data for our comparison.

Statistical methods

Statistical analyses were performed using SPSS software (SPSSV11) (SPSS Inc., Chicago, IL, USA). To compare before and after estimates of nutrient intakes, standard errors of the mean (SEM) and the mean differences were calculated for both male and female individuals. Student's t-tests were used to compare nutrient data recorded before and after follow up. Statistical significance was defined as P < 0.05. SEM were calculated to determine the differences in mean nutrient intake between the present study and reported results of the CAPAN survey (where SEM intervals did not overlap, it was concluded that results were significantly different).


Ninety-two per cent (n = 1169) of participants completing 13-year assessments agreed to complete the FD. If not returned after within a reasonable time frame, a phone call was made to remind participants of outstanding FDs and other questionnaires. Sixty-two per cent (n = 724) of participants agreeing to complete FDs attempted to complete and returned their FDs. Seven per cent (n = 53) of returned FDs were partially completed, with entire days or multiple meals missing that were not able to be recalled by the participant during follow up. These records were excluded from the study Of the 671 complete returned food diaries, a sample of 340 (51%) were entered into FoodWorks before and after follow up. The sample consisted of the first 340 FDs entered into FoodWorks. An average of approximately 2.2 phone calls was required to make contact with each participant. Mean body weight, height and body mass index (BMI) were also not significantly different between the studies (Table 1).

Values for all nutrients increased post follow up, indicating an overall significant underestimation by FD method prior to telephone follow up. On average, reported energy intake was 597 kJ higher after telephone follow up (P = 0.006) for male and female students combined. Significant differences before and after follow up were seen for total kilojoules, carbohydrates, sugar, water and magnesium (P < 0.05) (Table 2). P-values for total fat and subfractions of fat were nearing significance. No significant differences were seen for protein, cholesterol, fibre, thiamine, riboflavin, niacin, vitamin C, folate, vitamin A, retinol, [beta]-carotene, potassium, calcium, iron and zinc. Discretionary fat and sugar, as well as fluids, were often missing from FDs.

There was less than 5% variation in estimated mean intake of energy between the present study after follow up and 13-year-old male and female individuals from the CAPAN survey (Table 3). Differences in mean intakes of macronutrients were less than 10% of Raine study estimates, and differences in mean vitamin and mineral intakes were less than 20%. Where differences occurred, intakes in the Raine study, compared with the CAPAN survey, were generally lower for girls and higher for boys.


In a prospective cohort study of 13-year-olds, we administered FDs as part of a comprehensive follow up. We implemented follow-up phone calls to increase the accuracy of data collected by FDs, and found that estimated nutrient intakes increased significantly as a result. These results highlight the importance of personal checking and follow up of FDs administered to adolescents. The results also support the general principal previously recommended for adult studies (22) that personal interviews are advisable following the administration of FDs.

Other research has shown that energy intake is generally underreported by 20% when compared with estimated energy expenditure, (6,23) with underreporting being at its highest among older adolescents. (24,25) When energy intake is underestimated, it is likely that intakes of other nutrients correlated with energy intake (macronutrients, most minerals and the B-vitamins) are also underestimated. (26) The results of the present study have shown that the estimated energy intake after follow up was significantly higher than that estimated prior to follow up (approximately 6% higher). It was expected that other nutrients would also be significantly higher after follow up, as macronutrients are highly correlated with energy intake. From our data, total kilojoules were significantly (P < 0.01) correlated with total carbohydrates (R = 0.93), total fat (R = 0.91) and protein (R = 0.65).

Livingstone et al. reviewed 10 validation studies of energy intake that demonstrated underreporting of energy intake, and found that underreporting varies with age, weight status/body image, and method of dietary assessment used. (6) Low-energy reporting is positively associated with a high BMI. (26) This finding has also been supported in children aged 4-11 years, among whom it was shown that underreporting of total energy intake was more common among heavier children with a high body fat content and relative weight. (23)

The strengths of the present study include a large sample size (n = 340) and the FD being completed by the adolescents themselves. Previous studies have shown limitations of parents accurately reporting their child's food intake when they are away from home (i.e. at school). (10) The primary advantage of FDs is that they are collected prospectively, so they do not rely on memory. A limitation of the present study was that the follow-up phone calls filled in missing information retrospectively, hence introducing memory-related limitations. Memory errors may be due to forgetting, confusion of intake at the time of the FD with current intake, or difficulty in recalling portion sizes. (6) Another limitation was that actual intake could not be observed; therefore, improvement in estimation of intake achieved by follow up could not be validated. However, because mean intakes of energy and nutrients of the present study group were not substantially or consistently different from population estimates derived from the CAPAN survey, follow-up checking by telephone is likely to achieve comparable results to face-to-face interviews.

Follow-up interviewing showed that many adolescents did not report discretionary fat intake (i.e. butter and margarine as spread on bread or used in cooking). The same was for sugar, with many participants not reporting discretionary sugar added to cereal or hot drinks. The clarification of the use of discretionary fat and sugar may, in part, explain the significant difference in the estimated intake of carbohydrate, sugars and total kilojoules after follow up. In addition, our analyses show that magnesium intake, which was also significantly different after follow up, was positively correlated with sugar (0.57) (P < 0.01).

Fluid intake was poorly reported, which may explain the significant increase in estimated water intake. Fluids also include milk, soft drink and juice; an increase in such fluids may also contribute to the observed increased carbohydrate (sugar), calcium, potassium, phosphate and magnesium intake. Water intake was significantly correlated with magnesium (0.73) and calcium intake (0.63) (P < 0.01).

In conclusion, the results of the present study provide evidence that the role of the interviewer is essential to ensure completeness of three-day FDs implemented in adolescents. Where face-to-face contact is not practical, telephone follow up is a satisfactory alternative. To date, studies of nutrient intake in children and adolescents have demonstrated the presence of misreporting, and more work is needed to refine methods of dietary assessment in child and adolescent populations. Dietary assessment methods that are simple and time-efficient may improve response rates and reporting accuracy. With computer literacy increasing among children and adolescents, interactive packages may be useful to automate data entry, to encourage participants to review inconsistent data, to assist with assessment of portion sizes, and to ensure that an appropriate level of detail is recorded. (27)


Sincere thanks are due to all study families, without whose participation this research could not have been conducted. Many thanks are also extended to the Western Australian Pregnancy Cohort study investigators and research staff. The authors acknowledge the funding from the Raine Medical Research Foundation, University of Western Australia, in supporting the establishment of this birth cohort. Thanks are extended to Curtin Research Grant Schemes and Telstra Research Foundation for funding this component of the Raine Study 13-year follow up.


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Kathryn D.I. CANDILO, (1) Wendy ODDY, (1,2) Margaret MILLER, (3) Nick SLOAN, (1) Garth KENDALL (4) and Nicholas D.E. KLERK (1)

(1) Telethon Institute for Child Health Research, (2) School of Public Health, (4) School of Nursing and Midwifery, Curtin University of Technology, and (3) Health Department of Western Australia, Edith Cowan University, Perth, Western Australia, Australia

K.D.I. Candilo, BappSci, APD, Research Officer

W. Oddy, PhD, NHMRC Population Health Research Fellow

M. Miller, BappSci, APD, Acting Principal Policy Officer

N. Sloan, BAppSci Grad Dip, Project Coordinator

G. Kendall, PhD, Associate Professor

N.D.E. Klerk, PhD, Professor

Correspondence: W. Oddy, PO Box 855 West, Perth, WA 6872, Australia. Email:
Table 1 Sample characteristics of 13-year-old participants in the Raine
study (n = 340) compared with the CAPANS (n = 227)

 Raine study CAPANS
 Female Male Female Male
 (n = 160) (n = 180) (n = 92) (n = 135)

Mean height (m) 1.62 1.66 1.61 1.60
Mean weight (kg) 56.89 56.86 55.90 52.80
Mean BMI (kg/[m.sup.2]) 21.63 20.51 21.56 20.42

BMI = body mass index; CAPAN = Child and Adolescent Physical Activity
and Nutrition survey.

Table 2 Comparison of estimated nutrient intake before and after follow
up in Raine study participants at 13 years (n = 340)

 Female (n = 160)
 Before After Mean difference
Nutrient Mean (SEM) Mean (SEM) (after-before)

Energy (kJ) 7790.71 (173.48) 8297.4 (181.27) 506.7
Protein (g) 71.7 (1.85) 74.5 (1.93) 2.8
Total fat (g) 70.3 (1.84) 74.6 (1.97) 4.3
SFAs (g) 30.7 (0.86) 32.7 (0.93) 2.0
PUFAs (g) 9.3 (0.31) 9.9 (0.33) 0.6
MUFAs (g) 25.9 (0.71) 27.5 (0.77) 1.6
Cholesterol (mg) 226.6 (9.66) 237.2 (10.10) 10.6
Total carbohydrate 228.7 (5.70) 246.3 (5.86) 17.6
Sugars (g) 102.9 (3.89) 115.1 (3.69) 12.2
Starch (g) 125.1 (3.43) 130.4 (3.46) 5.3
Fibre (g) 19.2 (0.57) 19.7 (0.58) 0.5
Water (g) Vitamins 1546.4 (54.10) 1864.1 (53.99) 317.7
Thiamine (mg) 1.5 (0.06) 1.5 (0.06) 0.7
Riboflavin (mg) 1.7 (0.06) 1.8 (0.07) 1.0
Niacin (mg) 17.4 (0.52) 18.1 (0.53) 0.68
Niacin Equ. (mg) 31.8 (0.85) 33.0 (0.86) 1.3
Vitamin C (mg) 113.0 (6.48) 122.2 (6.20) 9.2
Folate ([micro]g) 237.8 (7.31) 247.6 (7.42) 9.8
Vitamin A ([micro]g) 727.3 (28.45) 790.2 (29.13) 62.9
Retinol ([micro]g) 341.0 (13.30) 377.8 (14.26) 36.8
[beta]-carotene Equ. 2283.0 (143.09) 2438.4 (142.30) 155.4
Potassium (mg) 2495.7 (63.90) 2630.3 (65.26) 134.6
Magnesium (mg) 261.9 (6.48) 284.3 (6.82) 22.4
Calcium (mg) 820.8 (30.72) 894.0 (27.03) 73.2
Phosphorous (mg) 1233.4 (29.68) 1302.5 (31.96) 69.1
Iron (mg) 10.5 (0.30) 10.7 (0.30) 0.3
Zinc (mg) 9.4 (0.26) 9.7 (0.26) 0.3

 Female (n = 160) Male (n = 180)
 P-value Before
Nutrient (Sig. (2-tailed)) Mean (SEM)

Energy (kJ) 0.044 (a) 10125.9 (205.54)
Protein (g) 0.293 100.5 (3.37)
Total fat (g) 0.113 91.1 (2.21)
SFAs (g) 0.113 41.5 (1.19)
PUFAs (g) 0.171 11.6 (0.34)
MUFAs (g) 0.131 33.8 (0.86)
Cholesterol (mg) 0.450 308.1 (10.00)
Total carbohydrate 0.032 (a) 293.8 (6.50)
Sugars (g) 0.015 (a) 133.6 (4.43)
Starch (g) 0.276 161.3 (3.62)
Fibre (g) 0.523 23.7 (0.62)
Water (g) Vitamins 0.000 (a) 1832.7 (59.44)
Thiamine (mg) 0.437 2.1 (0.07)
Riboflavin (mg) 0.297 2.5 (0.09)
Niacin (mg) 0.361 22.9 (0.56)
Niacin Equ. (mg) 0.303 42.7 (1.03)
Vitamin C (mg) 0.308 132.9 (7.79)
Folate ([micro]g) 0.350 319.4 (10.75)
Vitamin A ([micro]g) 0.124 992.3 (55.54)
Retinol ([micro]g) 0.060 529.9 (40.96)
[beta]-carotene Equ. 0.442 2745.8 (201.71)
Potassium (mg) 0.141 3232.9 (80.45)
Magnesium (mg) 0.018 (a) 336.2 (8.27)
Calcium (mg) 0.075 1127.8 (41.18)
Phosphorous (mg) 0.114 1687.2 (40.11)
Iron (mg) 0.534 14.9 (0.41)
Zinc (mg) 0.470 13.1 (0.35)

 Male (n = 180)
 Mean difference P-value (Sig.
Nutrient After Mean (SEM) (after-before) (2-tailed))

Energy (kJ) 10804.0 (215.18) 679.1 0.023 (a)
Protein (g) 104.2 (3.45) 3.7 0.443
Total fat (g) 96.9 (2.31) 5.8 0.070
SFAs (g) 44.2 (1.24) 2.7 0.118
PUFAs (g) 12.5 (0.37) 0.9 0.087
MUFAs (g) 35.9 (0.89) 2.1 0.083
Cholesterol (mg) 320.7 (10.35) 12.6 0.384
Total carbohydrate 317.3 (6.88) 23.5 0.014 (a)
Sugars (g) 149.2 (4.72) 15.6 0.017 (a)
Starch (g) 169.3 (3.91) 8.0 0.135
Fibre (g) 24.6 (0.64) 0.9 0.329
Water (g) Vitamins 2203.1 (59.11) 370.4 0.000 (a)
Thiamine (mg) 2.2 (0.07) 0.1 0.352
Riboflavin (mg) 2.7 (0.10) 0.2 0.275
Niacin (mg) 23.8 (0.57) 0.9 0.267
Niacin Equ. (mg) 44.4 (1.06) 1.7 0.251
Vitamin C (mg) 139.5 (8.05) 6.7 0.551
Folate ([micro]g) 331.6 (10.94) 12.2 0.427
Vitamin A ([micro]g) 1041.0 (54.50) 48.6 0.532
Retinol ([micro]g) 568.9 (41.42) 39.0 0.504
[beta]-carotene Equ. 2804.1 (186.81) 58.3 0.832
Potassium (mg) 3421.23 (84.91) 188.4 0.108
Magnesium (mg) 362.8 (8.57) 26.6 0.026 (a)
Calcium (mg) 1223.8 (40.34) 96.0 0.097
Phosphorous (mg) 1783.4 (42.59) 96.2 0.101
Iron (mg) 15.3 (0.42) 0.4 0.492
Zinc (mg) 13.5 (0.32) 0.4 0.421

(a) Significance at the level of P < 0.05 using Student's t-tests.
Equ. = equivalent; MUFA = monounsaturated fatty acid; PUFA =
polyunsaturated fatty acid; SEM = standard error of the mean; SFA =
stearic fatty acid; sig. = significance.

Table 3 Comparison of estimated nutrient intake between the Raine study
and CAPANS participants at 13 years

 Raine study CAPANS
 (n = 160) (n = 87)
Nutrient Mean (SEM) Mean (SEM)

Energy (kJ) 8297.4 (181.27) 8680.0 (354.44)
Protein (g) 74.5 (1.93) 79.4 (3.42)
Total fat (g) 74.6 (1.97) 76.4 (4.25)
SFAs (g) 32.7 (0.93) 34.2 (2.15)
PUFAs (g) 9.9 (0.33) 10.6 (0.84)
MUFAs (g) 27.5 (0.77) 27.3 (1.65)
Cholesterol (mg) 237.2 (10.10) 249.0 (17.40)
Total carbohydrate (g) 246.3 (5.86) 261.1 (11.15)
Sugars (g) 115.1 (3.69) 121.3 (5.97)
Starch (g) 130.4 (3.46) 136.5 (7.49)
Fibre (g) 19.7 (0.58) 19.4 (0.88)
Thiamine (mg) 1.5 (0.06) 1.7 (0.12)
Riboflavin (mg) 1.8 (0.07) (a) 2.1 (0.14)
Niacin (mg) 18.1 (0.53) (a) 20.1 (1.05)
Niacin Equ. (mg) 33.0 (0.86) (a) 35.9 (1.61)
Vitamin C (mg) 122.2 (6.20) (a) 144.1 (14.34)
Folate ([micro]g) 247.6 (7.42) (a) 271.6 (13.08)
Vitamin A ([micro]g) 790.2 (29.13) 806.0 (58.51)
Retinol ([micro]g) 377.8 (14.26) 400.0 (31.70)
[beta]-carotene Equ. ([micro]g) 2438.4 (142.30) 2368.4 (281.53)
Potassium (mg) 2630.3 (65.26) 2661.4 (118.05)
Magnesium (mg) 284.3 (6.82) (a) 262.4 (11.09)
Calcium (mg) 894.0 (27.03) 871.8 (59.24)
Phosphorous (mg) 1302.5 (31.96) (a) 1420.2 (66.32)
Iron (mg) 10.7 (0.30) (a) 11.7 (0.63)
Zinc (mg) 9.7 (0.26) 10.3 (0.47)

 Raine study CAPANS
 (n = 180) (n = 112)
Nutrient Mean (SEM) Mean (SEM)

Energy (kJ) 10804.0 (215.18) 10339.0 (356.51)
Protein (g) 104.2 (3.45) (a) 96.4 (4.15)
Total fat (g) 96.9 (2.31) (a) 90.2 (3.96)
SFAs (g) 44.2 (1.24) (a) 39.6 (1.97)
PUFAs (g) 12.5 (0.37) 11.6 (0.66)
MUFAs (g) 35.9 (0.89) (a) 32.0 (1.44)
Cholesterol (mg) 320.7 (10.35) (a) 286.0 (15.31)
Total carbohydrate (g) 317.3 (6.88) 310.2 (11.18)
Sugars (g) 149.2 (4.72) (a) 137.4 (6.54)
Starch (g) 169.3 (3.91) 164.8 (7.10)
Fibre (g) 24.6 (0.64) (a) 22.1 (0.86)
Thiamine (mg) 2.2 (0.07) 2.1 (0.15)
Riboflavin (mg) 2.7 (0.10) 2.8 (0.19)
Niacin (mg) 23.8 (0.57) 23.2 (1.22)
Niacin Equ. (mg) 44.4 (1.06) 42.3 (1.89)
Vitamin C (mg) 139.5 (8.05) (a) 113.4 (10.71)
Folate ([micro]g) 331.6 (10.94) 322.7 (14.45)
Vitamin A ([micro]g) 1041.0 (54.50) 1023.8 (79.15)
Retinol ([micro]g) 568.9 (41.42) 536.9 (42.02)
[beta]-carotene Equ. ([micro]g) 2804.1 (186.81) 2907.1 (379.46)
Potassium (mg) 3421.3 (84.91) (a) 3046.8 (119.38)
Magnesium (mg) 362.8 (8.57) (a) 306.6 (10.67)
Calcium (mg) 1223.8 (40.34) 1121.5 (70.04)
Phosphorous (mg) 1783.4 (42.59) 1704.3 (67.59)
Iron (mg) 15.3 (0.42) (a) 13.9 (0.51)
Zinc (mg) 13.5 (0.32) (a) 12.5 (0.61)

(a) Overlapping of SEM intervals.
CAPANS = Child and Adolescent Physical Activity and Nutrition Survey;
Equ. = equivalent; MUFA = monounsaturated fatty acid; PUFA =
polyunsaturated fatty acid; SEM = standard error of the mean; SFA =
stearic fatty acid.
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Article Details
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Author:Candilo, Kathryn D.I.; Oddy, Wendy; Miller, Margaret; Sloan, Nick; Kendall, Garth; Klerk, Nicholas D
Publication:Nutrition & Dietetics: The Journal of the Dietitians Association of Australia
Article Type:Clinical report
Geographic Code:8AUST
Date:Sep 1, 2007
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