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  1. Müller AM, Chen B, Wang NX, Whitton C, Direito A, Petrunoff N, et al.
    Obes Rev, 2020 04;21(4):e12976.
    PMID: 31919972 DOI: 10.1111/obr.12976
    The objective of this study is to systematically review the evidence on correlates of sedentary behaviour (SB) among Asian adults. We searched for studies that examined individual, environmental, and political/cultural correlates of total and domain-specific SB (transport, occupation, leisure, and screen time) in Asian adults published from 2000 onwards in nine scientific databases. Two reviewers independently screened identified references. Following quality assessment of included studies, we performed narrative synthesis that considered differences based on SB measurements, regions, and population characteristics (PROSPERO: CRD42018095268). We identified 13 249 papers of which we included 49, from four regions and 12 countries. Researchers conducted cross-sectional analyses and most relied on SB self-report for SB measurement. Of the 118 correlates studied, the following associations were consistent: higher age, living in an urban area (East Asia), and lower mental health with higher total SB; higher education with higher total and occupational SB; higher income with higher leisure-time SB; higher transit density with higher total SB in older East Asians; and being an unmarried women with higher SB in the Middle East. We encourage more research in non-high-income countries across regions, further exploration of important but neglected correlates using longitudinal designs and qualitative research, and the use of objective instruments to collect SB data.
  2. Whitton C, Ho JCY, Tay Z, Rebello SA, Lu Y, Ong CN, et al.
    Nutrients, 2017 Sep 25;9(10).
    PMID: 28946670 DOI: 10.3390/nu9101059
    The assessment of diets in multi-ethnic cosmopolitan settings is challenging. A semi-quantitative 163-item food frequency questionnaire (FFQ) was developed for the adult Singapore population, and this study aimed to assess its reproducibility and relative validity against 24-h dietary recalls (24 h DR) and biomarkers. The FFQ was administered twice within a six-month interval in 161 adults (59 Chinese, 46 Malay, and 56 Indian). Fasting plasma, overnight urine, and 24 h DR were collected after one month and five months. Intra-class correlation coefficients between the two FFQ were above 0.70 for most foods and nutrients. The median correlation coefficient between energy-adjusted deattenuated FFQ and 24 h DR nutrient intakes was 0.40 for FFQ1 and 0.39 for FFQ2, highest for calcium and iron, and lowest for energy and carbohydrates. Significant associations were observed between urinary isoflavones and soy protein intake (r = 0.46), serum carotenoids and fruit and vegetable intake (r = 0.34), plasma eicosapentaenoic acid and docosahexaenoic acid (EPA + DHA) and fish/seafood intake (r = 0.36), and plasma odd chain saturated fatty acids (SFA) and dairy fat intake (r = 0.25). Associations between plasma EPA + DHA and fish/seafood intake were consistent across ethnic groups (r = 0.28-0.49), while differences were observed for other associations. FFQ assessment of dietary intakes in modern cosmopolitan populations remains feasible for the purpose of ranking individuals' dietary exposures in epidemiological studies.
  3. Whitton C, Rebello SA, Lee J, Tai ES, van Dam RM
    J Nutr, 2018 Apr 01;148(4):616-623.
    PMID: 29659965 DOI: 10.1093/jn/nxy016
    BACKGROUND: Healthful dietary patterns are associated with cardiovascular disease risk factors in Western populations. However, a consistent healthful dietary pattern across major Asian ethnic groups has yet to be identified.

    OBJECTIVE: We aimed to identify a posteriori dietary patterns for Chinese, Malay, and Indian ethnic groups in an urban Asian setting, compare these with a priori dietary patterns, and ascertain associations with cardiovascular disease risk factors including hypertension, obesity, and abnormal blood lipid concentrations.

    METHODS: We used cross-sectional data from 8433 Singapore residents (aged 21-94 y) from the Multi-Ethnic Cohort study of Chinese, Malay, and Indian ethnicity. Food consumption was assessed using a validated 169-item food-frequency questionnaire. With the use of 28 food groups, dietary patterns were derived by principal component analysis, and their association with cardiovascular disease risk factors was assessed using multiple linear regression. Associations between derived patterns and a priori patterns (aHEI-2010-Alternative Healthy Eating Index-2010, aMED-alternate Mediterranean Diet, and DASH-Dietary Approaches to Stop Hypertension) were assessed, and the magnitude of associations with risk factors compared.

    RESULTS: We identified a "healthy" dietary pattern, similar across ethnic groups, and characterized by high intakes of whole grains, fruit, dairy, vegetables, and unsaturated cooking oil and low intakes of Western fast foods, sugar-sweetened beverages, poultry, processed meat, and flavored rice. This "healthy" pattern was inversely associated with body mass index (BMI; in kg/m2) (-0.26 per 1 SD of the pattern score; 95% CI: -0.36, -0.16), waist circumference (-0.57 cm; 95% CI: -0.82, -0.32), total cholesterol (-0.070 mmol/L; 95% CI: -0.091, -0.048), LDL cholesterol (-0.054 mmol/L; 95% CI: -0.074, -0.035), and fasting triglycerides (-0.22 mmol/L; 95% CI: -0.04, -0.004) and directly associated with HDL cholesterol (0.013 mmol/L; 95% CI: 0.006, 0.021). Generally, "healthy" pattern associations were at least as strong as a priori pattern associations with cardiovascular disease risk factors.

    CONCLUSION: A healthful dietary pattern that correlated well with a priori patterns and was associated with lower BMI, serum LDL cholesterol, total cholesterol, and fasting triglyceride concentrations was identified across 3 major Asian ethnic groups.

  4. Whitton C, Ramos-García C, Kirkpatrick SI, Healy JD, Dhaliwal SS, Boushey CJ, et al.
    Adv Nutr, 2022 Dec 22;13(6):2620-2665.
    PMID: 36041186 DOI: 10.1093/advances/nmac085
    Error in self-reported food and beverage intake affects the accuracy of dietary intake data. Systematically synthesizing available data on contributors to error within and between food groups has not been conducted but may help inform error mitigation strategies. In this review we aimed to systematically identify, quantify, and compare contributors to error in estimated intake of foods and beverages, based on short-term self-report dietary assessment instruments, such as 24-h dietary recalls and dietary records. Seven research databases were searched for studies including self-reported dietary assessment and a comparator measure of observed intake (e.g., direct observation or controlled feeding studies) in healthy adults up until December 2021. Two reviewers independently screened and extracted data from included studies, recording quantitative data on omissions, intrusions, misclassifications, and/or portion misestimations. Risk of bias was assessed using the QualSyst tool. A narrative synthesis focused on patterns of error within and between food groups. Of 2328 articles identified, 29 met inclusion criteria and were included, corresponding to 2964 participants across 15 countries. Most frequently reported contributors to error were omissions and portion size misestimations of food/beverage items. Although few consistent patterns were seen in omission of consumed items, beverages were omitted less frequently (0-32% of the time), whereas vegetables (2-85%) and condiments (1-80%) were omitted more frequently than other items. Both under- and overestimation of portion size was seen for most single food/beverage items within study samples and most food groups. Studies considered and reported error in different ways, impeding the interpretation of how error contributors interact to impact overall misestimation. We recommend that future studies report 1) all error contributors for each food/beverage item evaluated (i.e., omission, intrusion, misclassification, and portion misestimation), and 2) measures of variation of the error. The protocol of this review was registered in PROSPERO as CRD42020202752 (https://www.crd.york.ac.uk/prospero/).
  5. 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.
  6. Whitton C, Healy JD, Collins CE, Mullan B, Rollo ME, Dhaliwal SS, et al.
    JMIR Res Protoc, 2021 Dec 16;10(12):e32891.
    PMID: 34924357 DOI: 10.2196/32891
    BACKGROUND: The assessment of dietary intake underpins population nutrition surveillance and nutritional epidemiology and is essential to inform effective public health policies and programs. Technological advances in dietary assessment that use images and automated methods have the potential to improve accuracy, respondent burden, and cost; however, they need to be evaluated to inform large-scale use.

    OBJECTIVE: The aim of this study is to compare the accuracy, acceptability, and cost-effectiveness of 3 technology-assisted 24-hour dietary recall (24HR) methods relative to observed intake across 3 meals.

    METHODS: Using a controlled feeding study design, 24HR data collected using 3 methods will be obtained for comparison with observed intake. A total of 150 healthy adults, aged 18 to 70 years, will be recruited and will complete web-based demographic and psychosocial questionnaires and cognitive tests. Participants will attend a university study center on 3 separate days to consume breakfast, lunch, and dinner, with unobtrusive documentation of the foods and beverages consumed and their amounts. Following each feeding day, participants will complete a 24HR process using 1 of 3 methods: the Automated Self-Administered Dietary Assessment Tool, Intake24, or the Image-Assisted mobile Food Record 24-Hour Recall. The sequence of the 3 methods will be randomized, with each participant exposed to each method approximately 1 week apart. Acceptability and the preferred 24HR method will be assessed using a questionnaire. Estimates of energy, nutrient, and food group intake and portion sizes from each 24HR method will be compared with the observed intake for each day. Linear mixed models will be used, with 24HR method and method order as fixed effects, to assess differences in the 24HR methods. Reporting bias will be assessed by examining the ratios of reported 24HR intake to observed intake. Food and beverage omission and intrusion rates will be calculated, and differences by 24HR method will be assessed using chi-square tests. Psychosocial, demographic, and cognitive factors associated with energy misestimation will be evaluated using chi-square tests and multivariable logistic regression. The financial costs, time costs, and cost-effectiveness of each 24HR method will be assessed and compared using repeated measures analysis of variance tests.

    RESULTS: Participant recruitment commenced in March 2021 and is planned to be completed by the end of 2021.

    CONCLUSIONS: This protocol outlines the methodology of a study that will evaluate the accuracy, acceptability, and cost-effectiveness of 3 technology-enabled dietary assessment methods. This will inform the selection of dietary assessment methods in future studies on nutrition surveillance and epidemiology.

    TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12621000209897; https://tinyurl.com/2p9fpf2s.

    INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32891.

  7. 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.

  8. Vandevijvere S, Barquera S, Caceres G, Corvalan C, Karupaiah T, Kroker-Lobos MF, et al.
    Obes Rev, 2019 11;20 Suppl 2:57-66.
    PMID: 30609260 DOI: 10.1111/obr.12819
    The Healthy Food Environment Policy Index (Food-EPI) aims to assess the extent of implementation of recommended food environment policies by governments compared with international best practices and prioritize actions to fill implementation gaps. The Food-EPI was applied in 11 countries across six regions (2015-2018). National public health nutrition panels (n = 11-101 experts) rated the extent of implementation of 47 policy and infrastructure support good practice indicators by their government(s) against best practices, using an evidence document verified by government officials. Experts identified and prioritized actions to address implementation gaps. The proportion of indicators at "very low if any," "low," "medium," and "high" implementation, overall Food-EPI scores, and priority action areas were compared across countries. Inter-rater reliability was good (GwetAC2 = 0.6-0.8). Chile had the highest proportion of policies (13%) rated at "high" implementation, while Guatemala had the highest proportion of policies (83%) rated at "very low if any" implementation. The overall Food-EPI score was "medium" for Australia, England, Chile, and Singapore, while "very low if any" for Guatemala. Policy areas most frequently prioritized included taxes on unhealthy foods, restricting unhealthy food promotion and front-of-pack labelling. The Food-EPI was found to be a robust tool and process to benchmark governments' progress to create healthy food environments.
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