Approaches to analysis of dietary data: relationship between planned analyses and choice of methodology

Beaton, George H.

doi:10.1093/ajcn/59.1.253s

Cited by 230 publications

(175 citation statements)

References 32 publications

Supporting

Mentioning

141

Contrasting

Unclassified

Order By: Relevance

“…It could be argued that the clinical relevance of our results are scarce because of the small, albeit significant regression coefficients for some variables, However, it has been recognised that it is impossible to estimate the dietary intake of individuals without error and that the high random error associated with estimates of nutrient intake biases the correlation coefficients and slopes of regression lines toward zero [45]. Thus, the effect on HbA 1c of dietary intake is likely to be larger than estimated here.…”

Section: Discussionmentioning

confidence: 62%

The influence of dietary intake and meal pattern on blood glucose control in children and adolescents using intensive insulin treatment

et al. 2007

View full text Add to dashboard Cite

Aims/hypothesis We studied dietary factors and their association with blood glucose control in type 1 diabetic children and adolescents using intensive insulin treatment. Materials and methods A total of 550 children and adolescents with type 1 diabetes mellitus (age 2-19 years) recorded their diet for 4 days in pre-coded food diaries. Of the study group, 34% used insulin pumps, 43% used four or more injections and 16% three injections per day. HbA 1c was related to targets of optimal blood glucose control defined by the International Society for Pediatric and Adolescent Diabetes (ISPAD). Results Adolescents with optimal glucose control (HbA 1c ≤7.5%) had a lower intake of added sugar (7.7 vs 9.1% of energy intake, p=0.004), a higher intake of fibre (19.3 vs 17.0 g/day, p=0.01) and a higher intake of fruits and vegetables (257 vs 227 g/day, p=0.04) than those with suboptimal metabolic control (HbA 1c >7.5%). Multiple regression analysis in adolescents showed that fibre and meal pattern were significantly associated with blood glucose control (effect fibre intake=−0.02, p=0.04, effect having breakfast regularly=−0.89, p=0.009). In children meal pattern was associated with blood glucose control (effect having dinner regularly=−0.66, p=0.02, effect having supper regularly=−0.78, p=0.03). Conclusions/interpretation In diabetic adolescents both intake of fibre and having a regular meal pattern are associated with blood glucose control. Lower intake of added sugar and sugar-sweetened soft drinks and higher intake of fruits and vegetables are observed among those with optimal compared with those with suboptimal blood glucose control. Dietary guidance should be intensified during adolescence to improve dietary intake and blood glucose control.

show abstract

Section: Discussionmentioning

confidence: 62%

The influence of dietary intake and meal pattern on blood glucose control in children and adolescents using intensive insulin treatment

et al. 2007

View full text Add to dashboard Cite

show abstract

“…Systematic error results in a consistent over-or under-estimation in activity levels across the sample, while random error occurs when over-and under-estimations are made for an individual's estimation of their true mean; however across the sample these errors occur randomly. 1 Error may result from measurement validity issues, such as over-or underreporting of activity, or from large intra-individual variability in daily activity patterns, making it difficult to measure the true mean. [2][3][4][5] Therefore, activity measured on a single day is likely to be a poor estimate of activity over a week, a month or a year.…”

Section: Introductionmentioning

confidence: 99%

“…When predicting or comparing means from independent samples, random intra-individual variability across a sample will not bias estimated group means. 1,6 The main impact of intra-individual variation in these situations is to increase total variance, therefore decreasing statistical power, making it more difficult to detect a true difference between groups. 1 When regression or correlational analyses are performed (e.g.…”

Section: Introductionmentioning

confidence: 99%

Intra-individual variation in children's physical activity patterns: Implications for measurement

Ridley

Olds

Hands

et al. 2009

Journal of Science and Medicine in Sport

View full text Add to dashboard Cite

Children's physical activity (PA) patterns change from day to day. This intra-individual variability affects precision when measuring key physical activity and sedentary behaviour variables. This paper discusses strategies used to reduce the random error associated with intraindividual variability and demonstrates the implications for assessing PA when varying number of days are sampled. Self-reported data collected on two hundred and ninety eight 13-14-year-olds were used to compare estimates of PA and sedentary behaviour derived from between 1 and 7 days of recall data. Large intra-individual coefficients of variation were calculated for physical activity level (14.5%), moderate-to-vigorous physical activity (83.4%), screen time (60.8%) and sleep (12.2%). While the magnitude of error associated with estimating means decreased as more days were sampled, the paper notes that depending on the nature of the research question being asked, sampling fewer days may yield sufficiently precise estimates. Therefore, researchers should conduct power analyses based on estimated inter-and intra-individual variability and sample size to determine how many days to sample when assessing children's PA patterns.

show abstract

“…It is widely acknowledged that dietary intake cannot be measured without a degree of error (Beaton, 1994). Nonetheless, the identification of certain sources of error and consequent strategies aimed at minimizing and limiting their impact has the potential to improve statistical power and to minimize bias in dietary intake studies.…”

Section: Introductionmentioning

confidence: 99%

Minimizing random error in dietary intakes assessed by 24-h recall, in overweight and obese adults

et al. 2007

View full text Add to dashboard Cite

Objective: To determine the minimum number of days of dietary intake interviews required to reduce the effects of random error (day-to-day variability in dietary intake) when using the multiple-pass, multiple-day, 24-h recall method. Design: Cross-sectional study. Setting: University research department. Subjects: A total of 50 healthy non-smoking overweight and obese (body mass index ¼ 26-40 kg/m 2 ) adult men and women aged 39-45 years completed the study. Participants were randomly selected from volunteers for a larger unrelated study. Interventions: Each participant completed 10, multiple-pass, 24-h recall interviews on randomly chosen days over 4 weeks. The minimum number of record days was determined for each macronutrient (carbohydrate, fat, protein) and energy, for each gender, to obtain a 'true' (unobservable) representative intake from reported (observed) dietary intakes. Results: The greatest number of days required to obtain a 'true' representative intake was 8 days. Carbohydrate intakes required the greatest number of days of dietary record among males (7 days), whereas protein required the greatest number of days among females (8 days) in this cohort. Sunday was the day of the week that showed greatest variability in macronutrient intakes. Protein (Po0.05) and fat (Po0.001) intakes were significantly more variable than carbohydrate on Sundays compared with weekdays, for both men and women. Conclusion: A logistically achievable 8 days of dietary intake interviews was sufficient to minimize the effect of random error when using the multiple-pass, 24-h recall dietary intake method. Sunday should be included among the dietary interview days to ensure a 'true' representation of macronutrient intakes. This method can be confidently applied to small cohort studies in which dietary intakes from different groups are to be compared or to investigations of associations between nutrient intakes and disease.

show abstract

Approaches to analysis of dietary data: relationship between planned analyses and choice of methodology

Cited by 230 publications

References 32 publications

The influence of dietary intake and meal pattern on blood glucose control in children and adolescents using intensive insulin treatment

The influence of dietary intake and meal pattern on blood glucose control in children and adolescents using intensive insulin treatment

Intra-individual variation in children's physical activity patterns: Implications for measurement

Minimizing random error in dietary intakes assessed by 24-h recall, in overweight and obese adults

Contact Info

Product

Resources

About