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Fulltext Demographic and psychosocial correlates of measurement error and reactivity bias in a four-day image-based mobile food record among adults with overweight and obesity

Whitton C, Healy JD, Dhaliwal SS, Shoneye C, Harray AJ, Mullan BA, et al.

Br J Nutr, 2022 May 19;129(4):1-39.
PMID: 35587722 DOI: 10.1017/S0007114522001532

Improving dietary reporting among people living with obesity is challenging as many factors influence reporting accuracy. Reactive reporting may occur in response to dietary recording but little is known about how image-based methods influence this process. Using a 4-day image-based mobile food record (mFRTM), this study aimed to identify demographic and psychosocial correlates of measurement error and reactivity bias, among adults with BMI 25-40kg/m2. Participants (n=155, aged 18-65y) completed psychosocial questionnaires, and kept a 4-day mFRTM. Energy expenditure (EE) was estimated using ≥4 days of hip-worn accelerometer data, and energy intake (EI) was measured using mFRTM. Energy intake: energy expenditure ratios were calculated, and participants in the highest tertile were considered to have Plausible Intakes. Negative changes in EI according to regression slopes indicated Reactive Reporting. Mean EI was 72% (SD=21) of estimated EE. Among participants with Plausible Intakes, mean EI was 96% (SD=13) of estimated EE. Higher BMI (OR 0.81, 95%CI 0.72-0.92) and greater need for social approval (OR 0.31, 95% CI 0.10-0.96), were associated with lower likelihood of Plausible Intakes. Estimated EI decreased by 3% per day of recording (IQR -14%,6%) among all participants. The EI of Reactive Reporters (n=52) decreased by 17%/day (IQR -23%,-13%). A history of weight loss (>10kg) (OR 3.4, 95% CI 1.5-7.8), and higher percentage of daily energy from protein (OR 1.1, 95%CI 1.0-1.2) were associated with greater odds of Reactive Reporting. Identification of reactivity to measurement, as well as Plausible Intakes, is recommended in community-dwelling studies to highlight and address sources of bias.
Fulltext Accuracy of energy and nutrient intake estimation versus observed intake using 4 technology-assisted dietary assessment methods: a randomized crossover feeding study

Whitton C, Collins CE, Mullan BA, Rollo ME, Dhaliwal SS, Norman R, et al.

Am J Clin Nutr, 2024 Jul;120(1):196-210.
PMID: 38710447 DOI: 10.1016/j.ajcnut.2024.04.030

BACKGROUND: Technology-assisted 24-h dietary recalls (24HRs) have been widely adopted in population nutrition surveillance. Evaluations of 24HRs inform improvements, but direct comparisons of 24HR methods for accuracy in reference to a measure of true intake are rarely undertaken in a single study population.
OBJECTIVES: To compare the accuracy of energy and nutrient intake estimation of 4 technology-assisted dietary assessment methods relative to true intake across breakfast, lunch, and dinner.
METHODS: In a controlled feeding study with a crossover design, 152 participants [55% women; mean age 32 y, standard deviation (SD) 11; mean body mass index 26 kg/m2, SD 5] were randomized to 1 of 3 separate feeding days to consume breakfast, lunch, and dinner, with unobtrusive weighing of foods and beverages consumed. Participants undertook a 24HR the following day [Automated Self-Administered Dietary Assessment Tool-Australia (ASA24); Intake24-Australia; mobile Food Record-Trained Analyst (mFR-TA); or Image-Assisted Interviewer-Administered 24-hour recall (IA-24HR)]. When assigned to IA-24HR, participants referred to images captured of their meals using the mobile Food Record (mFR) app. True and estimated energy and nutrient intakes were compared, and differences among methods were assessed using linear mixed models.
RESULTS: The mean difference between true and estimated energy intake as a percentage of true intake was 5.4% (95% CI: 0.6, 10.2%) using ASA24, 1.7% (95% CI: -2.9, 6.3%) using Intake24, 1.3% (95% CI: -1.1, 3.8%) using mFR-TA, and 15.0% (95% CI: 11.6, 18.3%) using IA-24HR. The variances of estimated and true energy intakes were statistically significantly different for all methods (P < 0.01) except Intake24 (P = 0.1). Differential accuracy in nutrient estimation was present among the methods.
CONCLUSIONS: Under controlled conditions, Intake24, ASA24, and mFR-TA estimated average energy and nutrient intakes with reasonable validity, but intake distributions were estimated accurately by Intake24 only (energy and protein). This study may inform considerations regarding instruments of choice in future population surveillance. This trial was registered at Australian New Zealand Clinical Trials Registry as ACTRN12621000209897.

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