Eating context and ultraprocessed food consumption among UK adolescents.

We aimed to evaluate the association between eating context patterns and ultraprocessed food consumption at two main meal occasions in a representative sample of UK adolescents. Data were acquired from 4-d food records of adolescents aged 11-18 years, who participated in the 2014-2016 UK National Diet and Nutrition Survey (n 542). The eating context was assessed considering the location of the meal (lunch and dinner) occasion, the individuals present, whether the television was on and if the food was consumed at a table. Ultraprocessed foods were identified using the NOVA classification. Exploratory factor analysis was used to identify eating context patterns for lunch and dinner. Linear regression models adjusted for the covariates were utilised to test the association between eating context patterns and the proportion of total daily energy intake derived from ultraprocessed foods. Their contribution was about 67 % to energy intake. Three patterns were retained for lunch ('At school with friends', 'TV during family meal' and 'Out-of-home (no school)'), and three patterns were retained for dinner ('Watching TV alone in the bedroom', 'TV during family meal' and 'Out-of-home with friends'). At lunch, there was no significant association between any of the three patterns and ultraprocessed food consumption. At dinner, the patterns 'Watching TV alone in the bedroom' (coefficient: 4·95; 95 % CI 1·87, 8·03) and 'Out-of-home with friends' (coefficient: 3·13; 95 % CI 0·21, 6·14) were associated with higher consumption of ultraprocessed food. Our findings suggest a potential relationship between the immediate eating context and ultraprocessed food consumption by UK adolescents.

Healthy eating strategies: Individually different or context-dependent?

Individuals can apply different healthy eating strategies to help them make healthy eating choices. Previous research showed that individuals differ in their preferred strategy, but also that a mix of strategies is often applied by a single person across contexts. The current research investigated the extent to which differences within an individual across contexts (i.e., meal moments, social environment and physical environment) predicted openness to healthy eating strategies in addition to personal predictors that differ between individuals (i.e., intrinsic motivation, self-efficacy, physical opportunity and social opportunity). A representative sample of the Dutch adult population was recruited (N = 892). The within-individual (contextual) predictors were measured nine times just before a meal moment over a period of three weeks, by means of a smartphone application. The between-individual (personal) predictors were administered with a baseline questionnaire. Exploratory factor analysis distinguished three healthy eating strategies: Increasing healthy foods, Limiting unhealthy foods and consuming Light products. A random intercept model, in which within-individual predictors and between-individual predictors were entered successively, showed that context matters for openness to all three strategies, but is most important for increasing healthy foods and least important for light products. Individuals are most open to increase healthy foods at dinner as compared to breakfast, whereas the opposite is true for limiting unhealthy foods and consuming light products. Eating at home is beneficial for openness to all three strategies and eating with others positively influences openness to increase healthy foods but has no effect on the other strategies. Insights gained from this research increase our understanding of an individual's openness to apply healthy eating strategies.

High-risk environments for eating foods surplus to requirements: a multilevel analysis of adolescents' non-core food intake in the National Diet and Nutrition Survey (NDNS).

OBJECTIVE: Interventions to reduce adolescents' non-core food intake (i.e. foods high in fat and sugar) could target specific people or specific environments, but the relative importance of environmental contexts v. individual characteristics is unknown. DESIGN: Cross-sectional. SETTING: Data from 4d food diaries in the UK National Diet and Nutrition Survey (NDNS) 2008-2012 were analysed. NDNS food items were classified as 'non-core' based on fat and sugar cut-off points per 100g of food. Linear multilevel models investigated associations between 'where' (home, school, etc.) and 'with whom' (parents, friends, etc.) eating contexts and non-core food energy (kcal) per eating occasion (EO), adjusting for variables at the EO (e.g. time of day) and adolescent level (e.g. gender).ParticipantsAdolescents (n 884) aged 11-18 years. RESULTS: Only 11 % of variation in non-core energy intake was attributed to differences between adolescents. In adjusted models, non-core food intake was 151 % higher (ratio; 95 % CI) in EO at 'Eateries' (2·51; 2·14, 2·95) and 88 % higher at 'School' (1·88; 1·65, 2·13) compared with 'Home'. EO with 'Friends' (1·16; CI 1·03, 1·31) and 'Family & friends' (1·21; 1·07, 1·37) contained 16-21 % more non-core food compared with eating 'Alone'. At the individual level, total energy intake and BMI, but not social class, gender or age, were weakly associated with more non-core energy intake. CONCLUSIONS: Regardless of individual characteristics, adolescents' non-core food consumption was higher outside the home, especially at eateries. Targeting specific eating contexts, not individuals, may contribute to more effective public health interventions.

When do healthiness and liking drive children's food choices? The influence of social context and weight status.

Children identify liking and healthiness of foods as factors influencing their food choices. However, the food decision making process is also influenced by both personal characteristics and food contexts. The present study explored the influence of liking and perceived healthiness of foods in normal- and overweight children's food choices intentions in a pleasure-oriented social eating context and a health-oriented social eating context. Children aged from 6 to 11 years old (n = 63; 34 children who were of normal weight and 29 who were overweight) were asked to select 5 foods among 10, based on food pictures, to make up a snack that would be suitable for their birthday party or a nutrition class. In addition, they rated their liking and healthiness perception of the foods. No significant difference in food choices was found between children who were of normal weight and children who were overweight. Both groups of children chose more healthy food items in a health-oriented social context (i.e., a fictive nutrition class) than in a pleasure-oriented social context (i.e., a fictive birthday party). Moreover, only liking significantly predicted food choices in the pleasure-oriented social context whereas both healthiness and liking significantly predicted food choices in the health-oriented social context. Overall these results advance our understanding on how children make food decisions and inform strategies that may help children to adopt a healthy diet. Because liking predicted children's food choices in both eating contexts, emphasizing the "good" taste of healthy foods and providing children with healthy foods they like could be efficient strategies to promote healthy eating habits in children.

Adolescents' non-core food intake: a description of what, where and with whom adolescents consume non-core foods.

OBJECTIVE: Little is known about adolescents' non-core food intake in the UK and the eating context in which they consume non-core foods. The present study aimed to describe types of non-core foods consumed by British adolescents in total and across different eating contexts. DESIGN: A descriptive analysis, using cross-sectional data from food diaries. Non-core foods were classified based on cut-off points of fat and sugar from the Australian Guide to Healthy Eating. Eating context was defined as 'where' and 'with whom' adolescents consumed each food. Percentages of non-core energy were calculated for each food group in total and across eating contexts. A combined ranking was then created to account for each food's contribution to non-core energy intake and its popularity of consumption (percentage of consumers). SETTING: The UK National Diet and Nutrition Survey 2008-2011. SUBJECTS: Adolescents across the UK aged 11-18 years (n 666). RESULTS: Non-core food comprised 39·5 % of total energy intake and was mostly 'Regular soft drinks', 'Crisps & savoury snacks', 'Chips & potato products', 'Chocolate' and 'Biscuits'. Adolescents ate 57·0 % and 51·3 % of non-core food at 'Eateries' or with 'Friends', compared with 33·2 % and 32·1 % at 'Home' or with 'Parents'. Persistent foods consumed across eating contexts were 'Regular soft drinks' and 'Chips & potato products'. CONCLUSIONS: Regular soft drinks contribute the most energy and are the most popular non-core food consumed by adolescents regardless of context, and represent a good target for interventions to reduce non-core food consumption.

Validity and Feasibility of the Monitoring and Modeling Family Eating Dynamics System to Automatically Detect In-field Family Eating Behavior: Observational Study.

BACKGROUND: The field of dietary assessment has a long history, marked by both controversies and advances. Emerging technologies may be a potential solution to address the limitations of self-report dietary assessment methods. The Monitoring and Modeling Family Eating Dynamics (M2FED) study uses wrist-worn smartwatches to automatically detect real-time eating activity in the field. The ecological momentary assessment (EMA) methodology was also used to confirm whether eating occurred (ie, ground truth) and to measure other contextual information, including positive and negative affect, hunger, satiety, mindful eating, and social context. OBJECTIVE: This study aims to report on participant compliance (feasibility) to the 2 distinct EMA protocols of the M2FED study (hourly time-triggered and eating event-triggered assessments) and on the performance (validity) of the smartwatch algorithm in automatically detecting eating events in a family-based study. METHODS: In all, 20 families (58 participants) participated in the 2-week, observational, M2FED study. All participants wore a smartwatch on their dominant hand and responded to time-triggered and eating event-triggered mobile questionnaires via EMA while at home. Compliance to EMA was calculated overall, for hourly time-triggered mobile questionnaires, and for eating event-triggered mobile questionnaires. The predictors of compliance were determined using a logistic regression model. The number of true and false positive eating events was calculated, as well as the precision of the smartwatch algorithm. The Mann-Whitney U test, Kruskal-Wallis test, and Spearman rank correlation were used to determine whether there were differences in the detection of eating events by participant age, gender, family role, and height. RESULTS: The overall compliance rate across the 20 deployments was 89.26% (3723/4171) for all EMAs, 89.7% (3328/3710) for time-triggered EMAs, and 85.7% (395/461) for eating event-triggered EMAs. Time of day (afternoon odds ratio [OR] 0.60, 95% CI 0.42-0.85; evening OR 0.53, 95% CI 0.38-0.74) and whether other family members had also answered an EMA (OR 2.07, 95% CI 1.66-2.58) were significant predictors of compliance to time-triggered EMAs. Weekend status (OR 2.40, 95% CI 1.25-4.91) and deployment day (OR 0.92, 95% CI 0.86-0.97) were significant predictors of compliance to eating event-triggered EMAs. Participants confirmed that 76.5% (302/395) of the detected events were true eating events (ie, true positives), and the precision was 0.77. The proportion of correctly detected eating events did not significantly differ by participant age, gender, family role, or height (P>.05). CONCLUSIONS: This study demonstrates that EMA is a feasible tool to collect ground-truth eating activity and thus evaluate the performance of wearable sensors in the field. The combination of a wrist-worn smartwatch to automatically detect eating and a mobile device to capture ground-truth eating activity offers key advantages for the user and makes mobile health technologies more accessible to nonengineering behavioral researchers.

A narrative review of the impact of digital immersive technology on affective and sensory responses during product testing in digital eating contexts.

The environments and/or contexts typically used to determine consumer affective and sensory responses have been questioned for their ecological validity. However, conducting consumer testing in real-life scenarios is costly, logistically complex, and hard to standardise between participants due to a lack of control over external cues and product preparation. Immersive environments, representative of product consumption contexts, may provide more ecologically valid data. Recently, digital immersion technologies have been proposed to contextualise consumer studies whilst maintaining experimental control. This narrative review summarised published consumer studies including digital immersion in addition to traditional sensory booths and/or a real-life immersive contexts in their study design, to measure the impact of these contexts on liking, emotional response and intensity of sensory attributes. The findings suggest that emotional response ratings are more comparable to real-life, and that consumer engagement and reliability increases, when testing is conducted using digital immersive techniques compared to traditional sensory booths. Therefore, digital immersive techniques look promising to improve ecological validity of consumer testing, but further development and research is required.

A review of evidence supporting current strategies, challenges, and opportunities to reduce portion sizes.

Although there is considerable evidence for the portion-size effect and its potential impact on health, much of this has not been successfully applied to help consumers reduce portion sizes. The objective of this review is to provide an update on the strength of evidence supporting strategies with potential to reduce portion sizes across individuals and eating contexts. Three levels of action are considered: food-level strategies (targeting commercial snack and meal portion sizes, packaging, food labels, tableware, and food sensory properties), individual-level strategies (targeting eating rate and bite size, portion norms, plate-cleaning tendencies, and cognitive processes), and population approaches (targeting the physical, social, and economic environment and health policy). Food- and individual-level strategies are associated with small to moderate effects; however, in isolation, none seem to have sufficient impact on food intake to reverse the portion-size effect and its consequences. Wider changes to the portion-size environment will be necessary to support individual- and food-level strategies leading to portion control.

A Real-Time Eating Detection System for Capturing Eating Moments and Triggering Ecological Momentary Assessments to Obtain Further Context: System Development and Validation Study.

BACKGROUND: Eating behavior has a high impact on the well-being of an individual. Such behavior involves not only when an individual is eating, but also various contextual factors such as with whom and where an individual is eating and what kind of food the individual is eating. Despite the relevance of such factors, most automated eating detection systems are not designed to capture contextual factors. OBJECTIVE: The aims of this study were to (1) design and build a smartwatch-based eating detection system that can detect meal episodes based on dominant hand movements, (2) design ecological momentary assessment (EMA) questions to capture meal contexts upon detection of a meal by the eating detection system, and (3) validate the meal detection system that triggers EMA questions upon passive detection of meal episodes. METHODS: The meal detection system was deployed among 28 college students at a US institution over a period of 3 weeks. The participants reported various contextual data through EMAs triggered when the eating detection system correctly detected a meal episode. The EMA questions were designed after conducting a survey study with 162 students from the same campus. Responses from EMAs were used to define exclusion criteria. RESULTS: Among the total consumed meals, 89.8% (264/294) of breakfast, 99.0% (406/410) of lunch, and 98.0% (589/601) of dinner episodes were detected by our novel meal detection system. The eating detection system showed a high accuracy by capturing 96.48% (1259/1305) of the meals consumed by the participants. The meal detection classifier showed a precision of 80%, recall of 96%, and F1 of 87.3%. We found that over 99% (1248/1259) of the detected meals were consumed with distractions. Such eating behavior is considered "unhealthy" and can lead to overeating and uncontrolled weight gain. A high proportion of meals was consumed alone (680/1259, 54.01%). Our participants self-reported 62.98% (793/1259) of their meals as healthy. Together, these results have implications for designing technologies to encourage healthy eating behavior. CONCLUSIONS: The presented eating detection system is the first of its kind to leverage EMAs to capture the eating context, which has strong implications for well-being research. We reflected on the contextual data gathered by our system and discussed how these insights can be used to design individual-specific interventions.

Where Do Adolescents Eat Less-Healthy Foods? Correspondence Analysis and Logistic Regression Results from the UK National Diet and Nutrition Survey.

This study investigates the relationship between the consumption of foods and eating locations (home, school/work and others) in British adolescents, using data from the UK National Diet and Nutrition Survey Rolling Program (2008-2012 and 2013-2016). A cross-sectional analysis of 62,523 food diary entries from this nationally representative sample was carried out for foods contributing up to 80% total energy to the daily adolescent's diet. Correspondence analysis (CA) was used to generate food-location relationship hypotheses followed by logistic regression (LR) to quantify the evidence in terms of odds ratios and formally test those hypotheses. The less-healthy foods that emerged from CA were chips, soft drinks, chocolate and meat pies. Adjusted odds ratios (99% CI) for consuming specific foods at a location "other" than home (H) or school/work (S) in the 2008-2012 survey sample were: for soft drinks, 2.8 (2.1 to 3.8) vs. H and 2.0 (1.4 to 2.8) vs. S; for chips, 2.8 (2.2 to 3.7) vs. H and 3.4 (2.1 to 5.5) vs. S; for chocolates, 2.6 (1.9 to 3.5) vs. H and 1.9 (1.2 to 2.9) vs. S; and for meat pies, 2.7 (1.5 to 5.1) vs. H and 1.3 (0.5 to 3.1) vs. S. These trends were confirmed in the 2013-2016 survey sample. Interactions between location and BMI were not significant in either sample. In conclusion, public health policies to discourage less-healthy food choices in locations away from home and school/work are warranted for adolescents, irrespective of their BMI.