Novel Online or Mobile Methods to Assess Eating Patterns

Analytic Methods for Understanding the Temporal Patterning of Dietary and 24-H Movement Behaviors: A Scoping Review

Dietary and movement behaviors [physical activity (PA), sedentary behavior (SED), and sleep] occur throughout a 24-h day and involve multiple contexts. Understanding the temporal patterning of these 24-h behaviors and their contextual determinants is key to determining their combined effect on health. A scoping review was conducted to identify novel analytic methods for determining temporal behavior patterns and their contextual correlates. We searched Embase, ProQuest, and EBSCOhost databases in July 2022 to identify studies published between 1997 and 2022 on temporal patterns and their contextual correlates (e.g., locational, social, environmental, personal). We included 14 studies after title and abstract (n = 33,292) and full-text (n = 135) screening, of which 11 were published after 2018. Most studies (n = 4 in adults; n = 5 in children and adolescents), examined waking behavior patterns (i.e., both PA and SED) of which 3 also included sleep and 6 included contextual correlates. PA and diet were examined together in only 1 study of adults. Contextual correlates of dietary, PA, and sleep temporal behavior patterns were also examined. Machine learning with various clustering algorithms and model-based clustering techniques were most used to determine 24-h temporal behavior patterns. Although the included studies used a diverse range of methods, behavioral variables, and assessment periods, results showed that temporal patterns characterized by high SED and low PA were linked to poorer health outcomes, than those with low SED and high PA. This review identified temporal behavior patterns, and their contextual correlates, which were associated with adiposity and cardiometabolic disease risk, suggesting these methods hold promise for the discovery of holistic lifestyle exposures important to health. Standardized reporting of methods and patterns and multidisciplinary collaboration among nutrition, PA, and sleep researchers; statisticians; and computer scientists were identified as key pathways to advance future research on temporal behavior patterns in relation to health.

Construct validity and test-retest reliability of a chrononutrition questionnaire for shift work and non-shift work populations

The irregular eating patterns of both shift workers and evening chronotypes adversely affect cardiometabolic health. A tool that conveniently captures temporal patterns of eating alongside an indicator of circadian rhythm such as chronotype will enable researchers to explore relationships with diverse health outcome measures. We aimed to investigate the test-retest reliability and convergent validity of a Chrononutrition Questionnaire (CNQ) that captures temporal patterns of eating and chronotype in the general population (non-shift workers, university students, retirees, unemployed individuals) and shift work population. Participants attended two face-to-face/virtual sessions and completed the CNQ and food/sleep/work diaries. Outcomes included subjective chronotype, wake/sleep/mid-sleep time, sleep duration, meal/snack regularity, meal/snack/total frequency, times of first/last/largest eating occasions (EO), main meal (MM) 1/2/3, and duration of eating window (DEW). 116 participants enrolled (44.5 ± 16.5 years, BMI: 27.3 ± 5.8 kg/m2, 73% female, 52% general population); 105 completed the study. Reliability was acceptable for chronotype, sleep, and all temporal eating patterns except on night shifts. Convergent validity was good for chronotype and sleep except for certain shift/shift-free days. Generally, meal/snack regularity and frequency, and times of first/last EO showed good validity for the general population but not shift workers. Validity was good for DEW (except work-free days and afternoon shifts) and times of MM 1/2/3 (except afternoon and night shifts), while time of largest EO had poor validity. The CNQ has good test-retest reliability and acceptable convergent validity for the general and shift work population, although it will benefit from further validation, especially regarding regularity, frequency, and times of first and last eating occasions across more days amongst a larger sample size of shift workers. Use of the CNQ by researchers will expand our current understanding of chrononutrition as relationships between timing of food intake and the multitude of health outcomes are examined.

Analysis of Dietary Patterns Associated with Kidney Stone Disease Based on Data-Driven Approaches: A Case-Control Study in Shanghai

The main objective of this study was to analyze dietary patterns using data-driven approaches and to explore preventive or risk dietary factors for kidney stone disease (KSD). A case-control matching study was conducted in adults (n = 6396) from a suburb of Shanghai. A food frequency questionnaire was used to assess the consumption of various types of food, and B-ultrasound was used to identify kidney stones. Principal component analysis and regression were used to generate dietary patterns and further explore the relationship between dietary patterns and KSD. LASSO regression and post-selection inference were used to identify food groups most associated with KSD. Among males, the "balanced but no-sugary-beverages pattern" (OR = 0.78, p < 0.05) and the "nuts and pickles pattern" (OR = 0.84, p < 0.05) were protective dietary patterns. Among females, "high vegetables and low-sugary-beverages pattern" (OR = 0.83, p < 0.05) and "high-crustaceans and low-vegetables pattern" (OR = 0.79, p < 0.05) were protective dietary patterns, while the "comprehensive pattern with a preference for meat" (OR = 1.06, p < 0.05) and "sugary beverages pattern" (OR = 1.16, p < 0.05) were risk dietary patterns. We further inferred that sugary beverages (p < 0.05) were risk factors and pickles (p < 0.05) and crustaceans (p < 0.05) were protective factors.

Nutritional Epidemiology and Dietary Assessment for Patients With Kidney Disease: A Primer

Nutritional epidemiology seeks to understand nutritional determinants of disease in human populations using experimental and observational study designs. Though randomized controlled trials provide the strongest evidence of causality, the expense and difficulty of sustaining adherence to dietary interventions are substantial barriers to investigating dietary determinants of kidney disease. Therefore, nutritional epidemiology commonly employs observational study designs, particularly prospective cohort studies, to investigate long-term associations between dietary exposures and kidney disease. Due to the covarying nature and synergistic effects of dietary components, holistic characterizations of dietary exposures that simultaneously consider patterns of foods and nutrients regularly consumed are generally more relevant to disease etiology than single nutrients or foods. Dietary intakes have traditionally been self-reported and are subject to bias. Statistical methods including energy adjustment and regression calibration can reduce random and systematic measurement errors associated with self-reported diet. Novel approaches that assess diet more objectively are gaining popularity but have not yet fully replaced self-report and require refinement and validation in populations with chronic kidney disease. More accurate and frequent diet assessment in existing and future studies will yield evidence to better personalize dietary recommendations for the prevention and treatment of kidney disease.

Validation of Dietary Intake Estimated by Web-Based Dietary Assessment Methods and Usability Using Dietary Records or 24-h Dietary Recalls: A Scoping Review

The goal was to summarize studies comparing the accuracy of web-based dietary assessments with those of conventional face-to-face or paper-based assessments using 24-h dietary recall or dietary record methods in the general population. Using two databases, mean differences and correlation coefficients (CCs) for intakes of energy, macronutrients, sodium, vegetables, and fruits were extracted from each study independently by the authors. We also collected information regarding usability from articles reporting this. From 17 articles included in this review, the mean dietary intake differences in the web-based dietary assessment compared to conventional methods, were -11.5-16.1% for energy, -12.1-14.9% for protein, -16.7-17.6% for fat, -10.8-8.0% for carbohydrates, -11.2-9.6% for sodium, -27.4-3.9% for vegetables, and -5.1-47.6% for fruits. The CC was 0.17-0.88 for energy, protein, fat, carbohydrates, and sodium, and 0.23-0.85 for vegetables and fruits. In three out of four studies reporting usability, more than half of the participants preferred the web-based dietary assessment. In conclusion, % difference and CC of dietary intake were acceptable in both web-based dietary records and 24-h dietary recalls. The findings from this review highlight the possibility of wide-spread application of the web-based dietary assessment in the future.

Energy-dense dietary patterns high in free sugars and saturated fat and associations with obesity in young adults

PURPOSE

To derive dietary patterns based on dietary energy density (DED), free sugars, SFA, and fiber and investigate association with odds of overweight/obesity in young adults.

METHODS

Cross-sectional data from 625 young Australian adults (18-30 years) were used. Dietary patterns were derived using reduced rank regression based on dietary data from a smartphone food diary using DED, free sugars, SFA, and fiber density as response variables. Multivariable logistic regression was used to investigate associations between dietary patterns and odds of self-reported overweight/obesity (BMI ≥ 25 kg/m2).

RESULTS

Two dietary patterns were identified (DP1 and DP2). DP-1 was positively correlated with DED, free sugars, and SFA, and inversely correlated with fiber density. It was characterized by higher sugar-sweetened beverages intake and lower vegetable intake, and associated with higher odds of overweight/obesity (OR: 1.22; 95% CI 1.05, 1.42). DP-2 was positively correlated with fiber density and free sugars, and inversely correlated with DED and SFA. It was characterized by higher sugar-sweetened beverages intake and lower non-lean red meat intake, and was not significantly associated with overweight/obesity.

CONCLUSION

An energy-dense dietary pattern high in free sugars and SFA and low in fiber was associated with higher odds of obesity in young adults. These findings support dietary interventions that target reductions in energy-dense foods and sugar-sweetened beverages.

Low Comparability of Nutrition-Related Mobile Apps against the Polish Reference Method-A Validity Study

Nutrition-related mobile applications (apps) are commonly used to provide information about the user’s dietary intake, however, limited research has been carried out to assess to what extent their results agree with those from the reference method (RM). The main aim of this study was to evaluate the agreement of popular nutrition-related apps with the Polish RM (Dieta 6.0). The dietary data from two days of dietary records previously obtained from adults (60 males and 60 females) were compared with values calculated in five selected apps (FatSecret, YAZIO, Fitatu, MyFitnessPal, and Dine4Fit). The selection of apps was performed between January and February 2021 and based on developed criteria (e.g., availability in the Polish language, access to the food composition database, and the number of downloads). The data was entered by experienced clinical dietitians and checked by one more researcher. The mean age of study participants was 41.7 ± 14.8. We observed that all the apps tended to overestimate the energy intake, however, when considering the macronutrient intake, over- and underestimation were observed. According to our assumed criterion (±5% as perfect agreement, ±10% as sufficient agreement), none of the apps can be recommended as a replacement for the reference method both for scientific as well as clinical use. According to the Bland-Altman analysis, the smallest bias was observed in Dine4Fit in relation to energy, protein, and fat intake (respectively: −23 kcal; −0.7 g, 3 g), however, a wide range between the upper and lower limits of agreement were reported. According to the carbohydrate intake, the lowest bias was observed when FatSecret and Fitatu were used. These results indicate that the leading nutrition-related apps present a critical issue in the assessment of energy and macronutrient intake. Therefore, the implementation of validation studies for quality assessment is crucial to develop apps with satisfying quality.

Meal Pattern Analysis in Nutritional Science: Recent Methods and Findings

There is a scarcity of dietary intake research focusing on the intake of whole meals rather than on the nutrients and foods of which those meals are composed. This growing area of research has recently begun to utilize advanced statistical techniques to manage the large number of variables and permutations associated with these complex meal patterns. The aim of this narrative review was to evaluate those techniques and the meal patterns they detect. The 10 observational studies identified used techniques such as principal components analysis, clustering, latent class analysis, and decision trees. They examined meal patterns under 3 categories: temporal patterns (relating to the timing and distribution of meals), content patterns (relating to combinations of foods within a meal and combinations of those meals over a day), and context patterns (relating to external elements of the meal, such as location, activities while eating, and the presence or absence of others). The most common temporal meal patterns were the 3 meals/d pattern, the skipped breakfast pattern, and a grazing pattern consisting of smaller but more frequent meals. The 3 meals/d pattern was associated with increased diet quality compared with the other 2 patterns. Studies identified between 7 and 12 content patterns with limited similarities between studies and no clear associations between the patterns and diet quality or health. One study simultaneously examined temporal and context meal patterns, finding limited associations with diet quality. No study simultaneously examined other combinations of meal patterns. Future research that further develops the statistical techniques required for meal pattern analysis is necessary to clarify the relations between meal patterns and diet quality and health.

What do Australian adults eat for breakfast? A latent variable mixture modelling approach for understanding combinations of foods at eating occasions

BACKGROUND

The patterning of food intake at eating occasions is a poorly understood, albeit important, step towards achieving a healthy dietary pattern. However, to capture the many permutations of food combinations at eating occasions, novel analytic approaches are required. We applied a latent variable mixture modelling (LVMM) approach to understand how foods are consumed in relation to each other at breakfast.

METHODS

Dietary intake at breakfast (n = 8145 occasions) was assessed via 24-h recall during the 2011-12 Australian National Nutrition and Physical Activity Survey (n = 3545 men and n = 4127 women, ⩾19 y). LVMM was used to determine breakfast food profiles based on 35 food group variables, reflecting compliance with Australian Dietary Guidelines. F and adjusted-chi2 tests assessed differences in timing of consumption and participant characteristics between the breakfast profiles. Regression models, adjusted for covariates, were used to examine associations between breakfast food profiles and objective adiposity measures (BMI and waist circumference).

RESULTS

Five distinct profiles were found. Three were similar for men and women. These were labelled: "Wholegrain cereals and milks" (men: 16%, women: 17%), "Protein-foods" (men and women: 11%) and "Mixed cereals and milks" (men: 33%, women: 37%). Two "Breads and spreads" profiles were also found that were differentiated by their accompanying beverages (men) or type of grain (women). Profiles were found to vary by timing of consumption, participant characteristics and adiposity indicators. For example, the "Protein-foods" profile occurred more frequently on weekends and after 9 am. Men with a "Bread and spreads (plus tea/coffee)" profile were older (P < 0.001) and had lower income and education levels (P < 0.05), when compared to the other profiles. Women with a "Protein-foods" profile were younger (P < 0.001) and less likely to be married (P < 0.01). Both men and women with a "Wholegrain cereals and milks" profile had the most favourable adiposity estimates (P < 0.05).

CONCLUSIONS

We identified five breakfast food profiles in adults that varied by timing of consumption, participant characteristics and adiposity indicators. LVMM was a useful approach for capturing the complexity of food combinations at breakfast. Future research could collect contextual information about eating occasions to understand the complex factors that influence food choices.

Individual, social-environmental and physical-environmental correlates of diet quality in young adults aged 18-30 years

Many young adults eat a poor-quality diet. However, understanding of the social-ecological correlates of diet quality in young adults is limited. The aim of the present study was to examine the correlates of diet quality in a cohort of young Australian adults. Data from the cross-sectional Measuring EAting in everyday Life Study were used. Young adults (n = 625; 18-30 years; 73% female) were included if they provided dietary data over three or four non-consecutive days using 'FoodNow', a real-time food diary smartphone application. Diet quality was estimated using the 2013 Dietary Guidelines Index (DGI). Thirty correlates from three levels of the social-ecological framework were collected using an online questionnaire: individual (e.g., self-efficacy), social-environmental (e.g., social support) and physical-environmental (e.g., living situation). Linear regression analyses were used to examine associations between correlates and DGI. Six individual-level correlates were associated with DGI: perceived time scarcity (b = -0.664, CI: 1.160, -0.168), food insecurity (b = -0.962, CI: 1.746, -0.178), self-efficacy (b = 0.230, CI: 0.137, 0.323), being born in Australia (b = -3.165, CI: 5.521, -0.808), being employed in non-trade roles (b = -4.578, CI: 8.903, -0.252) and preparing a meals with vegetables daily (b = 4.576, CI: 1.652, 7.500). No social-environmental or physical-environmental correlates were associated with DGI. Overall, this study showed that young adults had a higher diet quality if they had higher self-efficacy, perceived themselves to be less time scarce and less food insecure, were born in Australia, were employed in non-trade roles and prepared a meal with vegetables daily. Healthy eating policies and interventions in young adults may benefit from targeting individual-level correlates.

Diet Quality of Elite Australian Athletes Evaluated Using the Athlete Diet Index

While athletes’ nutrient intakes have been widely reported, few studies have assessed the diet quality of athletes. This is the first study to evaluate the diet quality of athletes using the purpose-built Athlete Diet Index (ADI). A convenience sample of 165 elite athletes from Australian sporting institutions completed the ADI online, with subsequent automated results provided to their respective accredited sports dietitians (ASDs). At the completion of athlete participation, ASDs (n = 12) responded to a range of survey items using a Likert scale (i.e., 1 = strongly agree to 5 = strongly disagree) to determine the suitability of the ADI in practice. Differences in ADI scores for demographics and sport-specific variables were investigated using independent t-tests, analysis of variance (ANOVA) and Bonferroni multiple comparisons. Spearman’s rank correlation was used to assess the association between total scores and demographics. The mean total ADI score was 91.4 ± 12.2 (range 53–117, out of a possible 125). While there was no difference in total scores based on demographics or sport-specific variables; team sport athletes scored higher than individual sport athletes (92.7 vs. 88.5, p < 0.05). Athletes training fewer hours (i.e., 0–11 h/week) scored higher on Dietary Habits sub-scores compared with athletes training more hours (≥12 h/week; p < 0.05), suggesting that athletes who train longer may be at risk of a compromised dietary pattern or less than optimal nutrition practices that support training. Most (75%) ASDs surveyed strongly agreed with the perceived utility of the ADI for screening athletes and identifying areas for nutrition support, confirming its suitability for use in practice.

Accuracy of applications to monitor food intake: Evaluation by comparison with 3-d food diary

OBJECTIVE

The availability of nutrition applications (apps) has increased in recent years. The aim of this study was to assess the accuracy of nutrient intake calculations from some of the leading apps.

METHODS

We identified five apps according to some selection criteria: >4-star ratings, >1 million downloads, including a food composition database, and in Italian language. Apps were used for 2 wk each. Using a 3-d food diary, the nutritional values obtained from each app were compared to a reference method including the Food Composition Database for Epidemiologic Studies in Italy. Energy intake differences were calculated for single nutrient and 3-d food diary between single app and reference method after food-item matching. Bland-Altman plots were used to assess agreement of the methods.

RESULTS

Apps identified were FatSecret, Lifesum, MyFitnessPal, Yazio, and Melarossa. Apps tended to underestimate total energy intake compared with the reference method, from a minimum of -2 kcal for Lifesum, to a maximum of -5.4 kcal for Yazio (average per item). Apps tended to underestimate lipids, and to a lesser extent carbohydrate and fiber intake, except for Yazio and Lifesum, which overestimated the intake of protein. These discrepancies appear to be due to the use of no country-specific food composition databases and to user customization of the food list.

CONCLUSIONS

The present findings suggest that the leading nutrition apps present critical issues in assessing the intake of energy and nutrients. Implementation of a framework for quality assessment is necessary to drive the design and development of higher-quality apps. Further research on efficacy and use of apps to monitor food intake is also warranted and some recommendations are provided.

A systematic review of lifestyle patterns and their association with adiposity in children aged 5-12 years

Diet, physical activity, sedentary behaviour and sleep are typically examined independently with childhood adiposity; however, their combined influence remains uncertain. This review aims to systematically summarize evidence on the clustering of these behaviours through lifestyle patterns and evaluate associations with adiposity in children aged 5-12 years. Search strategies were run in six databases. Twenty-eight papers met the inclusion criteria, six of which included all four behaviours. A range of lifestyle patterns were identified (healthy, unhealthy and mixed). Mixed patterns were most frequently reported. Unhealthy patterns comprising low physical activity and high sedentary behaviour were also frequently observed. Mixed patterns comprising healthy diets, low physical activity and high sedentary behaviour were more commonly seen in girls, whereas boys were more physically active, similarly sedentary and had unhealthier diets. Children from lower socio-economic backgrounds tended to more frequently display unhealthy patterns. Unhealthy lifestyle patterns were more often associated with adiposity risk than healthy and mixed patterns. With few studies including all four behaviours, it is difficult to establish a clear picture of their interplay and associations with adiposity. Nonetheless, reliance on lifestyle patterns is likely more beneficial than individual behaviours in targeting adiposity and improving understanding of how these behaviours influence health.

Eating occasion situational factors and sugar-sweetened beverage consumption in young adults

Background

Young adulthood represents an influential transitional period marked by poor dietary habits and excess weight gain. Sugar-sweetened beverages (SSB) are a major source of excess caloric intake among young adults, yet little is known about the correlates of SSB consumption. This study examines the individual and situational correlates of SSB consumption, using real-time assessment of Australian young adults’ eating occasions.

Methods

Dietary, sociodemographic and health behaviour data were collected during the Measuring EAting in Everyday Life (MEALS) study (n = 675 adults, 18–30 y). Participants reported all foods and beverages consumed over 3–4 non-consecutive days using a real-time Smartphone food diary application (“FoodNow”). For every eating occasion, food and beverage intake was recorded along with situational characteristics (eating location, purchase location, presence of others and activities while eating). A beverage occasion was defined as any eating occasion where a beverage was consumed and a SSB occasion was defined as any eating occasion where a SSB was consumed. Multilevel logistic regression was used to examine individual and situational characteristics with SSB intake at beverage occasions (i.e. factors associated with choosing a SSB over other non-alcoholic beverages) and to examine factors associated with consuming a SSB at any occasion where food and/or beverages were consumed.

Results

Thirty-five percent of participants consumed SSBs during the recording period (n = 237). Of the 2185 beverage eating occasions reported by SSB consumers, 481 (20%) contained a SSB. SSB were rarely consumed on their own (i.e. other foods were present). Having a lower than tertiary education (odds ratio [95% confidence interval]: 1.53 [1.16, 2.01]; p < 0.01); eating in a café/restaurant, compared to at home (3.02 [1.58, 5.78]; p < 0.001), and purchasing beverages from a convenience outlet, compared to a supermarket/grocery store (4.58 [2.85, 7.38]; p < 0.001) were associated with SSB intake at beverage eating occasions. Similar associations were also found when all food and/or beverage eating occasions were examined.

Conclusion

In this study, SSB were often consumed with other foods and intake was associated with individual and situational factors. Further studies are needed to confirm these findings and explore how SSB are consumed in relation to their accompanying foods.