Novel Online or Mobile Methods to Assess Eating Patterns

Pendergast, Felicity J.; Leech, Rebecca M.; McNaughton, Sarah A.

doi:10.1007/s13668-017-0211-0

Cited by 19 publications

(11 citation statements)

References 70 publications

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“…This study used data obtained from the Measuring Eating in everyday Life Study (MEALS) which was designed to examine correlates of young adults eating patterns including patterning, format and context of eating occasions [36,39]. For this cross-sectional study, participants aged 18-30 years and living in Victoria, Australia were recruited between April 2015 and April 2016 using social media (Facebook, twitter) and advertising at a range of sites and locations relevant to this age group (e.g.…”

Section: Participants and Proceduresmentioning

confidence: 99%

“…A key reason for the paucity of research in this area is the widespread use of food frequency questionnaires where participants report their frequency of consumption of foods, but do not provide data on eating occasions. New technologies available via Smartphone "apps" and use of the principles of ecological momentary assessment techniques (EMA), [37,38] allow real-time data collection in the settings in which the food is consumed [39] and allow the capture of data relating to situational factors [36,39]. Understanding the drivers of SSB consumption in everyday eating situations will help inform the development of practical intervention strategies to modify behaviour and improve health outcomes.…”

Section: Introductionmentioning

confidence: 99%

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

McNaughton

Pendergast

Worsley

et al. 2020

Int J Behav Nutr Phys Act

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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.

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Section: Participants and Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

McNaughton

Pendergast

Worsley

et al. 2020

Int J Behav Nutr Phys Act

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“…Furthermore, differences observed in the sub-scores associated with the stage of the training cycle suggest the importance of measuring diet quality longitudinally across a competitive season (i.e., pre-season vs. in season). As the electronic ADI evaluates dietary intake based on the previous seven days, fluctuations in dietary intake due to the periodisation of training over the micro- (i.e., 1 week), meso- (i.e., 2 to 6 weeks), and macro-cycle (i.e., competitive season) [ 3 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 ] support the importance of longitudinal measurement.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the provision of nutrition servicing in high-performance sporting programs is often limited by funding and the allocation of resources [ 1 , 2 , 7 ]. Technology-based dietary assessment methods have the potential to reduce the time associated with dietary assessment [ 8 ], enhance participant compliance [ 6 , 9 ], and allow real-time data collection [ 10 ], thereby offering a practical and efficient approach for sports dietitians who typically support numerous athletes in high-performance sporting programs.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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“…The patterning of food intake at eating occasions, or eating patterns, are an integral, yet poorly understood, intermediary step to achieving a healthy dietary pattern and preventing obesity and its complications [ 7 ]. Eating patterns incorporate aspects of timing, frequency, regularity, as well as, the food and nutrient composition of eating occasions [ 8 ]. There is growing recognition and research to suggest that aspects of eating patterns play an important role in weight management and health [ 7 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

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

Leech

Boushey

McNaughton

2021

Int J Behav Nutr Phys Act

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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.

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Novel Online or Mobile Methods to Assess Eating Patterns

Cited by 19 publications

References 70 publications

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

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

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

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

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