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Fulltext Healthy and Sustainable Diet Index: Development, Application and Evaluation Using Image-Based Food Records

Harray AJ, Boushey CJ, Pollard CM, Dhaliwal SS, Mukhtar SA, Delp EJ, et al.

Nutrients, 2022 Sep 16;14(18).
PMID: 36145211 DOI: 10.3390/nu14183838

There are limited methods to assess how dietary patterns adhere to a healthy and sustainable diet. The aim of this study was to develop a theoretically derived Healthy and Sustainable Diet Index (HSDI). The HSDI uses 12 components within five categories related to environmental sustainability: animal-based foods, seasonal fruits and vegetables, ultra-processed energy-dense nutrient-poor foods, packaged foods and food waste. A maximum of 90 points indicates the highest adherence. The HSDI was applied to 4-day mobile food records (mFRTM) from 247 adults (18−30 years). The mean HSDI score was 42.7 (SD 9.3). Participants who ate meat were less likely to eat vegetables (p < 0.001) and those who ate non-animal protein foods were more likely to eat more fruit (p < 0.001), vegetables (p < 0.05), and milk, yoghurt and cheese (p < 0.05). After adjusting for age, sex and body mass index, multivariable regression found the strongest predictor of the likelihood of being in the lowest total HSDI score tertile were people who only took a bit of notice [OR (95%CI) 5.276 (1.775, 15.681) p < 0.005] or did not pay much/any attention to the health aspects of their diet [OR (95%CI) 8.308 (2.572, 26.836) p < 0.0001]. HSDI provides a new reference standard to assess adherence to a healthy and sustainable diet.
Fulltext Food Insecurity, Food Assistance, and Psychological Distress among University Students: Cross-Sectional Survey Western Australia, 2020

Dana LM, Wright J, Ward R, Dantas JAR, Dhaliwal SS, Lawrence B, et al.

Nutrients, 2023 May 23;15(11).
PMID: 37299396 DOI: 10.3390/nu15112431

University students have been identified as a population sub-group vulnerable to food insecurity. This vulnerability increased in 2020 due to the COVID-19 pandemic. This study aimed to assess factors associated with food insecurity among university students and the differences between students with and without children. A cross-sectional survey of (n = 213) students attending one university in Western Australia measured food insecurity, psychological distress, and socio-demographic characteristics. Logistic regression analyses were conducted to identify factors associated with food insecurity. Forty-eight percent of students who responded to the survey had experienced food insecurity in 2020. International students who were studying in Australia were nine times more likely to experience food insecurity than domestic students (AOR = 9.13; 95% CI = 2.32-35.97). International students with children were more likely to experience food insecurity than international students without children (p < 0.001) and domestic students with (p < 0.001) or without children (p < 0.001). For each unit increase in depression level, the likelihood of experiencing food insecurity increased (AOR = 1.62; 95% CI = 1.12-2.33). Findings show a higher prevalence of food insecurity among international university students and students with children during the COVID-19 pandemic and that food insecurity was associated with higher levels of psychological distress. These findings highlight the need for targeted interventions to mitigate the risk of food insecurity among Australian university students, particularly among international students, students with children, and those experiencing psychological distress.
Fulltext A Systematic Review Examining Contributors to Misestimation of Food and Beverage Intake Based on Short-Term Self-Report Dietary Assessment Instruments Administered to Adults

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/).
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 and Cost-effectiveness of Technology-Assisted Dietary Assessment Comparing the Automated Self-administered Dietary Assessment Tool, Intake24, and an Image-Assisted Mobile Food Record 24-Hour Recall Relative to Observed Intake: Protocol for a Randomized Crossover Feeding Study

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.
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.