Eating Occasions Research Articles

Energy and Macronutrient Intakes With Eating Occasions Consumed by European Children From Ages 3 to 8 Years: The EU Childhood Obesity Project Study

Abstract Objectives We characterized the distribution of energy and macronutrient intakes across eating occasions (EO) in a longitudinal study in children from 3 to 8 yrs in 5 European countries. Methods In the Childhood Obesity Project Study, 3-day weighed food records were collected at 3, 4, 5, 6 and 8 yrs of age for children from Belgium, Germany, Italy, Poland and Spain. Food intakes were assigned to EO based on country-specific times of the day and EO size. A consistent pattern with 3 meals (breakfast, lunch, supper) and 2 snacks (morning, afternoon) was identified. We analyzed longitudinal changes in diurnal energy and nutrient intake with age and estimated mixed effects models with child-specific intercept and slope for age. Age was analysed as quadratic term. Results The analyzed food intakes of 740 children showed the proportion of energy consumed within the day as follows: lunch (30.3%±8.3, M ± SD), supper (25.3%±8.0), breakfast (18.6%±6.6) and snacks (afternoon (16.3%±7.2); morning (10.1%±6.7)). Italian children had the lowest intake during morning (5.4%± 3.8) and the highest proportion of skipping morning snack (49%). The portion of fat and protein (as % total energy intake per EO) was high at lunch (fat 36.3%±9.1; protein 18.1%±5.2) and supper (fat 39.1%±10.9; protein 17.7%±5.6) and for carbohydrates at snacks (61.6%±11.6) and breakfast (53.7%±11.7), respectively. In Spain, fat and protein intakes were above study average at all EO. Energy intakes at snacks varied significantly with age (morning βage = -36.22kcal, βage2 = 3.29kcal; afternoon βage = 28.37 kcal, βage2 = −3.21kcal; P >0.001). There were no diurnal energy and nutrient differences between sexes. Conclusions The studied children consumed the major portions of dietary energy, protein and fat at lunch and supper, while most carbohydrates were provided with snacks and breakfast. With increasing age, energy intakes varied with morning and afternoon snacks. Further studies should explore whether diurnal variation of dietary intakes is associated with health outcomes. Funding Sources Partial financial support was received from the Commission of the European Community, the EU Seventh Framework Programme, project EarlyNutrition, the EU H2020 project PHC-2014-DynaHealth and the European Research Council Advanced Grant META-GROWTH.

Temporal Dietary Patterns Are Associated with Obesity in US Adults

The integration of time with dietary patterns throughout a day, or temporal dietary patterns (TDPs), have been linked with dietary quality but relations to health are unknown. The association between TDPs and selected health status indicators and obesity, type 2 diabetes (T2D), and metabolic syndrome (MetS) was determined. The first-day 24-h dietary recall from 1627 nonpregnant US adult participants aged 20-65 y from the NHANES 2003-2006 was used to determine timing, amount of energy intake, and sequence of eating occasions (EOs). Modified dynamic time warping (MDTW) and kernel k-means algorithm clustered participants into 4 groups representing distinct TDPs. Multivariate regression models determined associations between TDPs and health status, controlling for potential confounders, and adjusting for the survey design and multiple comparisons (P<0.05/6). A cluster representing a TDP with evenly spaced, energy balanced EOs reaching ≤1200kcal between 06:00 to 10:00, 12:00 to 15:00, and 18:00 to 22:00, had statistically significant and clinically meaningful lower mean BMI (P<0.0001), waist circumference (WC) (P<0.0001), and 75% lower odds of obesity compared with 3 other clusters representing patterns with much higher peaks of energy: 1000-2400kcal between 15:00 and 18:00 (OR: 5.3; 95% CI: 2.8, 10.1), 800-2400kcal between 11:00 and 15:00 (OR: 4.4; 95% CI: 2.5, 7.9), and 1000-2600kcal between 18:00 and 23:00 (OR: 6.7; 95% CI: 3.9, 11.6). Individuals with a TDP characterized by evenly spaced, energy balanced EOs had significantly lower mean BMI, WC, and odds of obesity compared with the other patterns with higher energy intake peaks at different times throughout the day, providing evidence that incorporating time with other aspects of a dietary pattern may be important to health status.

Predictors of vegetable consumption in children and adolescents: Analyses of the UK National Diet and Nutrition Survey (2008-2017).

Children's vegetable consumption is generally below national recommendations in the UK. This study examined predictors of vegetable intake by children aged 1.5-18 years using counts and portion sizes derived from four-day UK National Diet and Nutrition Survey food diaries. Data from 6,548 children were examined using linear and logit multilevel models. Specifically, we examined whether demographic variables predicted vegetable consumption, whether environmental context influenced portion sizes of vegetables consumed and which food groups predicted the presence (or absence) of vegetables at an eating occasion (EO). A larger average daily intake of vegetables (g) was predicted by age, ethnicity, equivalized income, variety of vegetables eaten and average energy intake per-day (R2 = 0.549). At a single EO, vegetables were consumed in larger portion sizes at home, with family members and at evening mealtimes (Conditional R2 = 0.308). Within EOs, certain configurations of food groups such as carbohydrates and protein predicted higher odds of vegetables being present (OR: 12.85, 95% CI: 9.42-17.54); whereas foods high in fats, sugars and salt predicted a lower likelihood of vegetable presence (OR: 0.03, 95% CI: 0.02-0.04). Vegetables were rarely eaten alone without other food groups. These findings demonstrate that only one portion of vegetables was eaten per-day (median) and this was consumed at a single EO, therefore falling below recommendations. Future research should investigate ways to encourage vegetable intake at times when vegetables are not regularly eaten, such as for breakfast and as snacks, whilst considering which other, potentially competing, foods are presented alongside vegetables.

Predictors of vegetable consumption in children and adolescents: analyses of the UK National Diet and Nutrition Survey (2008-2017).

Children's vegetable consumption is generally below national recommendations in the UK. This study examined predictors of vegetable intake by children aged 1·5-18 years using counts and portion sizes derived from 4-d UK National Diet and Nutrition Survey food diaries. Data from 6548 children were examined using linear and logit multilevel models. Specifically, we examined whether demographic variables predicted vegetable consumption, whether environmental context influenced portion sizes of vegetables consumed and which food groups predicted the presence (or absence) of vegetables at an eating occasion (EO). Larger average daily intake of vegetables (g) was predicted by age, ethnicity, equivalised income, variety of vegetables eaten and average energy intake per d (R2 0·549). At a single EO, vegetables were consumed in larger portion sizes at home, with family members and at evening mealtimes (Conditional R2 0·308). Within EO, certain configurations of food groups such as carbohydrates and protein predicted higher odds of vegetables being present (OR 12·85, 95 % CI 9·42, 17·54), whereas foods high in fats, sugars and salt predicted a lower likelihood of vegetable presence (OR 0·03, 95 % CI 0·02, 0·04). Vegetables were rarely eaten alone without other food groups. These findings demonstrate that only one portion of vegetables was eaten per d (median) and this was consumed at a single EO, therefore falling below recommendations. Future research should investigate ways to encourage vegetable intake at times when vegetables are not regularly eaten, such as for breakfast and as snacks, whilst considering which other, potentially competing, foods are presented alongside vegetables.

An Investigation Of Dietary Patterns And Macronutrient Intakes Among Resistance-trained Men

PURPOSE: This study aimed to assess the adequacy of dietary patterns and macronutrient intakes in support of the adaptations to resistance training within a weekly microcycle of resistance-trained (RT) men using traditional dietary assessment methods. METHODS: Thirty-seven RT men (age (y) Mdn (IQR) 24.9 (20.7-29.7), body mass (kg) M (SD) 81.3 (11.8)) were recruited to participate in this study. Dietary data were collected by self-reported 7-day weighed intake record and analysed on both a daily and per eating occasion (EO) basis using nutrition software. Adequacy was assessed against recommendations for this population (ACSM, 2016; IOC, 2018). Data are reported as M (SD), Mdn (25-75 percentile) and p-value (p). RESULTS: Average daily energy intake for training day (TD) and rest day (RD) was 36 (7) and 34 (8) kcal·kg-1·d-1, respectively. Daily protein (PRO) intake (g·kg-1·d-1) was significantly greater than recommended minimum (1.6 g·kg-1·d-1) on TD [2.1 (0.5), P < .001) but was not different on RD [1.8 (0.6), P = .058]. Carbohydrate (CHO) intake (g·kg-1·d-1) was significantly lower than 5 g·kg-1·d-1 on both TD [3.5 (1.1), P < .001], and RD [3.3 (1.0), P < .001)]. Daily frequency of EO was significantly higher than the recommended 3-4 EO (P < .001) for TD [5 (5-6)] and RD [5 (4-5)]. When analysed per EO (g·kg-1·EO-1), average PRO intake was significantly greater per main meal (MM) on TD [0.5 (0.4-0.6), P < .001] and RD [0.5 (0.4-0.6), P < .001], but not significantly different per snack (SN) [0.2 (0.1-0.3)] for TD (P = .254) and RD (P = .111) vs recommended 0.25 g·kg1·EO-1. CHO intake (g·kg-1·EO-1) per MM was 0.9 (0.7-1.1) for TD and 1.0 (0.7-1.1) for RD. CHO (g·kg-1·EO-1) consumed per SN was 0.4 (0.3-0.5) for both TD and RD. Daily number of MM was 3 (3-3) for TD and RD, and of SN were 3 (2-4) for TD, 2 (1-3) for RD. CONCLUSION: RT men met dietary recommendations to optimise adaptation to resistance training. However, the traditional dietary assessment methods do not address the importance of quantity, quality (source), timing, distribution and frequency of nutrients relative to a specific training session, termed peri-training nutrition (PTN), to optimise training adaptation. Future work must work towards analysis methods in support of periodised, personalised nutrition, relative to a specific training stimulus. FHI Grant TC20130001.

2005-P: A Smartphone Intervention to Promote Time-Restricted Eating Reduces Body Weight and Blood Pressure in Adults with Overweight and Obesity

Prolonged eating into late evening hours is associated with overweight (OW)/obesity (OB) and increases metabolic risk. Reducing the daily eating window, or time restricted eating (TRE), results in weight loss (WL) and improved metabolism. Our goal was to assess: 1) The eating patterns of adults with OW or OB and 2) The effect of 10h/d TRE, in persons who habitually eat for ≥14h/d, on WL and systolic (SBP) and diastolic (DBP) blood pressure. Methods: In this open label study, eligible men and women with BMI ≥25kg/m2 were instructed to enter all caloric eating occasions (EO) via time-stamped photos using the validated MyCircadianClock smartphone application (app) for 2 weeks (run-in). No instructions were provided on food choice or calorie count. Upon completion of this run-in period, eligible participants entered a 3-mo personalized 10h/d TRE intervention administered remotely via the app. Adherence was defined as logging 2 or more EO/d, occurring at least 5h apart, and achieving a 4h reduction in eating window during TRE. Results: Participants in the run-in period (n=30, 20% men) were 51.9±11.9y old, had a BMI of 31.1±7.3 kg/m2, waist circumference of 100.1±13.2 cm, SBP of 117.3±15.0 mmHg and DPB of 76.0± 10.5 mmHg. Logging into the app occurred on 10.9±3.2 days with a total of 61±30 [10-121] EO. The duration of the daily eating window varied greatly amongst participants (range: 8h02min - 23h26min) and ∼50% of them ate ≥14h/d. During the 3-mo TRE (n=12), login adherence was observed on 63% of days, with a mean of 3.4±1.7 EO/d; the mean eating duration decreased by 5.79±2.74 h/d (p=0.001). Weight decreased by 3.0± 2.7 kg (p=0.025). SBP and DBP also decreased by 21±11 mmHg (p=0.013) and 13±20 mm Hg (p=0.04), respectively. Conclusion: TRE administered via a smartphone app is an effective and easy-to-implement intervention to reduce body weight and blood pressure in adults with OW and OB. These data need to be confirmed in randomized trials. Disclosure M.A. Prasad: None. E. Manoogian: Stock/Shareholder; Spouse/Partner; Abbott Laboratories. F.M. Zuraikat: None. N. Nair: None. M. St-Onge: None. S. Panda: Other Relationship; Self; Penguin Random House.

Temporal Eating Patterns Among U.S. Infants and Toddlers, NHANES 2011–2016

Abstract Objectives As characterization of temporal eating patterns of U.S. infants and toddlers is limited, we aimed to explore eating frequency and interval, and to reveal relevant challenges related to investigation among this age group. Methods Using a single 24-hour recall from NHANES 2011–2016, we estimated eating frequency in categories (1–4, 5–7, 8–10, and ≥11 times per day) (%, Standard Error (SE)) and eating interval (mean, SE) in hours, by age group (0–5 months, 6–11 months, and 12–23 months) among infants and toddlers younger than 2 years old (n = 1704). Eating interval was defined as the last reported consumption time minus the first reported consumption time. SAS was used to incorporate weights and the complex survey design. We also explored describing the data by parent report of eating occasion (breakfast, lunch, dinner, supper, snack, etc.). Results Among children 0–23 months, 8% (SE 0.8), 54% (SE 1.3), 32% (SE 1.1), and 6% (SE 0.6) reported eating 1–4, 5–7, 8–10, and ≥11 times per day, respectively. The mean eating interval length decreased with increasing age category: 17.6 hours (SE 0.19), 15.6 hours (0.21), and 13.1 hours (SE 0.20), among infants and toddlers 0–5, 6–11, and 12–23 months (P &amp;lt; 0.001), respectively. Attempts to evaluate eating frequency by parent-reported eating occasion revealed misalignment of clock time with reported eating occasion. For example, a parent may report multiple eating occasions as “breakfast” in a given day between the hours of 6:00 AM and 10:00 PM, interspersed among other eating occasions reported as lunch, snack, or dinner. Conclusions There is a dearth of national data on the eating habits of infants and toddlers younger than 2 years, and even less is known about the temporal eating patterns for this group. We found that more than half of infants and toddlers reported 5–7 eating occasions on a given day and that toddlers 12–23 months had shorter eating intervals compared to infants 0–5 and 6–12 months. We also found some discordance with how eating occasions were reported among infants and toddlers, as the naming does not align with more typical classification that is used among older children and adults. Therefore, caution is advised when characterizing temporal eating patterns for this group. Funding Sources N/A

MON-606 Changes in Eating Frequency but Not in Food Quality During Time Restricted Eating: Analysis from the See Food Study

Time-restricted eating (TRE) is a form of intermittent fasting that has gained interest in nutrition research and interventions as one of the dietary patterns to promote weight loss and other metabolic benefits. TRE is an eating pattern in which all nutrient intakes occur within a few hours (< 12 hours) every day, with no overt attempt to alter nutrient quality or quantity. The “See Food” study was a randomized, unblinded, controlled, behavioral interventional pilot clinical trial conducted from 2017-2018, that aimed to study the effect of a TRE intervention on metabolic health of overweight and obese participants, using the help of novel digital mobile technology. The objective of the current study is to analyze changes in diet quality among participants of the “See Food” study. Specifically, food intake patterns (frequency and type of meals, snacks and beverages) were compared between subjects in the 8-hour TRE intervention group, versus the unrestricted intake control group. Inclusion criteria included age 18-65, BMI >25 kg/m2, had stable sleep and work schedule and owned a smartphone. Participants with diabetes, cardiovascular disease, uncontrolled pulmonary disease, pregnancy and nursing were excluded. A total of 20 participants with overweight or obesity (9 in control group and 11 in TRE group) were enrolled. Participants were instructed to use the “myCircadianClock” smartphone application to document their time of eating, type of meal and food images at baseline and during the intervention period of 14 weeks. The TRE group was instructed to consume calories within 8 hours each day. We compared the data between 14 days at baseline and 14 days at the end of the intervention. An eating occasion (EO) as defined as an occasion when a food or beverage (other than water) was consumed, and was separate from another EO by at least 15 minutes. Compared to baseline, both the TRE (3.8±0.4 vs 5.3±0.4, p<0.0001) and control group (4.9±0.4 vs 5.6±0.5, p=0.007) had less eating frequency. The TRE group had less eating frequency compared to control group (-1.5±0.2 vs -0.6±0.2, p=0.01) at the end of the study. Meal quality was classified by meal or snack type using a food-based classification system, and included 6 Eos ranging from a complete meal, to a low-quality snack. Beverages were classified separately by type. There were no differences in meal quality between the TRE and control group. The TRE group had less frequency of high-quality snack (-0.5±0.1 vs 0.05±0.1, p=0.008), mixed quality snack (-0.03±0.06 vs -0.3±0.07, p=0.01) and caffeinated beverage (-0.6±0.09 vs -0.008±0.1) compared to the control group. Conclusions: There was a reduction in eating frequency but no change in food quality when following an 8-hour TRE. Estimation of calories intake was limited in this study. A strength of the study is the use of a novel mobile app to track timing of meals. This would be feasible to implement in a real life setting.

Capturing Complexity: A Latent Variable Mixture Modelling Approach to Examining Foods Consumed at Eating Occasions and Associations with adiposity (OR06-01-19)

ObjectivesUnderstanding how foods are consumed together in eating occasions (EOs) may be useful when developing messages and strategies for nutrition promotion. However, examining the many combinations of food consumed at EOs requires novel analytic approaches to capture this complexity. We applied a latent variable mixture modelling approach to understand how foods are consumed in relation to each other at breakfast EOs in adults, and examined associations with adiposity. MethodsDietary intake at breakfast (n = 8361 EO) was assessed via 24-h recall during the 2011–12 Australian National Nutrition and Physical Activity Survey (n = 3545 men, n = 4127 women, ⩾19 y). Two-part latent variable mixture modelling, appropriate for use with semi-continuous data, was used to determine distinct breakfast food profiles based on 35 food groups. F and adjusted-chi2 tests assessed differences in socio-demographic factors and regression models, adjusted for covariates, were used to estimate differences in adiposity measures (BMI, waist circumference [WC]) between the latent breakfast profiles. ResultsFive distinct breakfast food 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). BMI and WC were lowest among men and women with a “Wholegrain cereals and milks” profile, and were significantly lower when compared to the “Mixed cereals and milks” pattern (women only, P < 0.01) and the “Breads and spreads” profiles (men only, P < 0.01). ConclusionsWe identified five breakfast food profiles in adults that varied by socio-demographic factors and adiposity. Latent variable mixture modelling is a useful approach to capture the complexity of food combinations at EO. Funding SourcesRM Leech is financially supported by an Alfred Deakin Postdoctoral Research Fellowship and SA McNaughton is supported by a National Health and Medical Research Council Career Development Fellowship (ID1104636). All authors had no financial relationships with any organisations that might have an interest in the presented work in the previous three years; no other relationships or activities that could appear to have influenced the presented work.

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).

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. Cross-sectional. 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. 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 &amp; 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. 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.

Association between eating frequency and eating behaviours related to appetite from 4 to 7 years of age: Findings from the population-based birth cohort generation XXI

The examination of specific characteristics of eating habits in childhood and its influence on eating behaviours is needed to better understand the establishment of appetite-related eating behaviours. This study aimed to assess the association between eating frequency (EF) (main meals vs. snacks) at 4 and 7y and problematic eating behaviours and appetite-related eating behaviours at 7y. The analysis included 1359 children from the birth cohort Generation XXI who provided 3-day food diaries, at both ages, and complete information on the Child Eating Behaviour Questionnaire, at 7y. Problematic eating behaviours were assessed using parental perception of child's poor eating (including not eating enough, eating too slow or overeating) at both ages. A time-of-day approach was used to distinguish main meals from snacks. A cross-lagged analysis was performed to check the direction of the association between EF and parental concerns regarding problematic eating behaviours. Associations between EF and appetite-related eating behaviours were evaluated through linear regression models. Between 4 and 7y a moderate tracking of EF was described as well as a strong tracking of parental concerns. We found a bidirectional relationship between EF and parental concerns. Prospectively, a higher frequency of snacks at 4y was independently associated with higher scores in ‘Desire to Drink’ at 7y (β = 0.051, 95%CI:0.009; 0.093). At 7y, a higher EF had a positive association with ‘Satiety Responsiveness’ but an inverse association with ‘Enjoyment of Food’. A higher number of main meals had a positive association with ‘Enjoyment of Food’ and ‘Food Responsiveness’. In conclusion, children who had more eating occasions seem to have more food-avoidance behaviours, traits that might protect these children from gaining excessive weight.

Eating patterns of Australian adults: associations with blood pressure and hypertension prevalence

PurposeEating patterns have been linked to obesity, an established risk factor for hypertension; however, their contribution to hypertension is poorly understood. This study aimed to examine associations of frequency of meals, snacks and all eating occasions (EO), and temporal eating patterns, with blood pressure (BP) and hypertension.MethodsDietary data collected via two 24-h recalls during the 2011–2012 Australian National Nutrition and Physical Activity Survey (n = 4482 adults, ≥ 19 years) were analysed. Frequencies of EO, meals, and snacks were calculated. Temporal eating patterns were determined using latent class analysis. Multivariate regression models assessed associations of eating patterns with systolic BP (SBP), diastolic BP (DBP), and hypertension prevalence.ResultsAmong men, a higher snack frequency was inversely associated with DBP [β = − 0.59, 95% confidence interval (CI) (− 1.12, − 0.07)] and hypertension [odds ratio (OR) 0.86, 95% CI (0.75, 0.98)] after adjustment for covariates and BMI. However, these associations disappeared after additional adjustment for total energy intake and overall diet quality. Among women, a temporal eating pattern characterized by a later “lunch” meal was associated with SBP [β = 2.45, 95% CI (0.05, 4.84)], DBP [β = 1.69, 95% CI (0.25, 3.13)], and hypertension [OR = 1.49, 95% CI (1.00, 2.22)], when compared to a “conventional” eating pattern.ConclusionsIn this study, an inverse association found between snack frequency and BP among men disappeared after adjustment for dietary factors and a “later lunch” pattern was associated with higher BP in women. Future research is needed to understand the relationship and potential mechanistic pathways between eating patterns and BP.

Eating patterns and food intake of persons with type 1 diabetes within the T1D exchange

AimsTo identify dietary intake and eating patterns of people with type 1 diabetes from childhood to later adulthood in relation to HbA1c. MethodsTrained interviewers conducted 24-hour recalls via phone utilizing a multiple pass approach and administered two nutrition questionnaires; 463 participants (or parents of participants) within the T1D Exchange clinic registry were included. Participants were 5 to 81 years with 80–101 participants in five age groups; 56% were female, and 92% were white, with a median diabetes duration of 11.1 years and a median HbA1c of 7.4% [57 mmol/mol]). ResultsThose with type 1 diabetes consumed less calories from carbohydrates and more from fats and protein than those in the general population, based on the National Health and Nutrition Examination Survey data. Carbohydrate intake was not correlated with HbA1c levels. Increased fiber intake, more eating occasions, higher Healthy Eating Index scores, and higher nutrition knowledge scores were each associated with lower HbA1c levels. ConclusionsFood intake, eating patterns and nutrition knowledge are associated with glycemic control across a registry-based cohort of adults and children with type 1 diabetes. Additionally, these data can inform the design of future studies to advance our understanding of nutritional influences on type 1 diabetes self-care and control.

Skipping breakfast among Australian children and adolescents; findings from the 2011–12 National Nutrition and Physical Activity Survey

ObjectiveSkipping breakfast has been linked with poor diet quality, higher BMI and adverse cardiometabolic outcomes. This study aimed to determine the prevalence and correlates of skipping breakfast among Australian children and adolescents. MethodsA total of 1,592 2–17‐year‐olds completed two 24‐hour recalls, collected via face‐to‐face and telephone interview, in the 2011–12 National Nutrition and Physical Activity Survey. Breakfast was an eating occasion of ≥210kJ named as ‘breakfast’ by the participant. Child, household and adult correlates of skipping breakfast were reported. Odds ratios were calculated using ordinal regression. Linear regression was used to examine differences in dietary intake. Survey weights were applied to give nationally representative estimates. ResultsMost (86.8% of boys, 81.4% of girls) ate breakfast on both days, 11.8% of boys and 14.8% girls skipped on one day and 1.4% boys and 3.8% girls skipped on both days. Characteristics associated with skipping breakfast were being female, being older, being underweight or overweight/obese, poorer diet, lower physical activity, inadequate sleep, lower household income, greater socioeconomic disadvantage, and being from a single‐parent home. ConclusionSkipping breakfast was common among Australian adolescents but few consistently skipped. Implications for public healthInterventions to increase breakfast should target adolescents, particularly girls, and low SEP households.

Patterns of added sugars intake by eating occasion among a nationally representative sample of Australians

To examine the eating occasion (EO) where most added sugars (AS) were consumed using a nationally representative dataset. Plausible dietary data from the Australian Health Survey respondents (n = 8202), collected by a multiple-pass 24-h recall, were analyzed. EO was self-reported during the recall. AS content of the foods reported was estimated using a previously published method. Proportion of daily AS consumed (%ASdaily) and the main food sources, at each EO, were calculated. Differences between children/adolescents and adults were tested by one-way ANOVA. Further stratification by age group and sex was performed. The majority of the %ASdaily came from non-main meal occasions (NMMOs; 48.3 %, 95 % CI 47.5–49.0 %), followed by breakfast/brunch (20.6 %, 95 % CI 20.1–21.1 %). Children and adolescents consumed more %ASdaily during NMMOs compared with adults (52 vs. 47 %; p < 0.001), while girls/women consumed more %ASdaily during NMMO compared with boys (54 vs. 49 %; p = 0.002) and men (50 vs. 45 %; p < 0.001). Sugar-sweetened beverages were the top contributors to AS at lunch, dinner and NMMOs, while sugar and sweet spreads were the top contributor at breakfast/brunch. Other top contributors at NMMOs included “other foods,” ice cream and cakes and biscuits, pastries and batter-based products. Australians consumed nearly half of %ASdaily during NMMOs, most of which came from high-sugar energy-dense nutrient-poor foods. While the common perception that most AS come from snacks holds true, our results suggest that main meals are also important intervention targets.

The role of energy intake and energy misreporting in the associations between eating patterns and adiposity.

Research examining associations between eating occasion (EO) frequency and adiposity is inconclusive; studies examining the impact of energy misreporting are rare. This study examined associations between eating patterns and adiposity, with adjustment for energy misreporting, in a nationally representative sample of Australian adults. Dietary intake was assessed via two 24-h recalls collected during the 2011-12 National Nutrition and Physical Activity Survey (n=4050 adults, aged ⩾19 years). Frequencies of all EOs, meals and snacks were calculated. Height, weight and waist circumference (WC) were measured. Energy misreporting was assessed as the ratio of energy intake to predicted energy expenditure (EI:EE). Energy misreporters were identified by EI:EE ratios, <0.68 or >1.32. Multivariate regression models assessed associations between eating patterns and body mass index (BMI), WC, overweight/obesity (BMI ⩾25 kg m-2) and central overweight/obesity (WC ⩾94 cm in men and ⩾80 cm in women). After adjustment for covariates and EI:EE, frequency of all EOs, meals (women only) and snacks was positively associated with WC and BMI (all P<0.01). Snack, but not meal frequency, was also associated with overweight/obesity (men: OR=1.22, 95% CI 1.07-1.39; women: OR=1.26, 95% CI 1.10-1.43) and central overweight/obesity (men: OR=1.17, 95% CI 1.04-1.32; women: OR=1.21, 95% CI 1.06-1.37). Multivariate analysis that excluded energy misreporters and adjusted for EI yielded either null or inverse associations (P<0.05). These findings suggest that the associations between eating patterns and adiposity are complicated by the role of EI and energy misreporting. Longitudinal research that considers the impact of EI and energy misreporting is needed to better understand the relationship between eating patterns and obesity.

Sugar‐Sweetened Beverage Consumption in Young Adults: Understanding Eating Occasion Contextual Factors

Background/ObjectivesYoung adulthood represents an influential transitional period, marked by poor dietary habits and excess weight gain. Sugar‐sweetened beverages (SSBs) 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 correlates of SSB consumption including eating occasion contextual factors, using real‐time assessment of young adults' eating occasions (EOs).MethodsData were collected during the Measuring Meals in Everyday Life (MEALS) study (n=557, adults 18–30 years). Participants reported all foods and beverages consumed over 3–4 non‐consecutive days using a food diary recorded via a Smartphone app. Contextual characteristics such as eating location, purchase location, presence of others and activities while eating were documented in the app at each EO. Information on sociodemographic factors and health behaviours were collected via an online questionnaire. An “eating occasion” was defined as an occasion when any food or beverage was ingested, while a “sugar‐sweetened beverage occasion” was defined as any EO where a SSB was consumed (with or without other items). SSB consumers were categorised as low or high SSB consumers based on the median frequency of consumption. Associations for individual level and EO level characteristics with level of SSB consumption were analysed using multivariate logistic regression, accounting for the clustered nature of the data.ResultsOverall, 30% of participants consumed SSBs. Most SSBs were consumed: either alone (43%) or with family/partner (42%); at home (50%), and while undertaking screen‐based activities (46%). Most SSBs consumed were purchased from the supermarket/grocery store (58%). Compared to non‐consumers, SSB consumers had a significantly higher BMI (mean [SD]: 23.1 [5.7] v 21.8 [3.9], P&lt;0.001). Being a current/past smoker, being overweight/obese and participating in screen‐based activities were associated with being a high SSB consumer (P&lt;0.01). After adjustment for covariates, smoking status (OR [95% CI]=5.69 [1.98,16.31]; P&lt;0.01) and weight status (2.31 [1.06,5.01]; P&lt;0.05) remained significantly associated with high SSB consumption.ConclusionsAlmost a third of young adults in this study consumed SSBs and most SSBs were consumed alone or with family/partner, at home and while undertaking screen‐based activities. However, individual factors, rather than EO contextual factors, were more strongly associated with being a high SSB consumer. Future research is needed to explore how SSB occasions differ from other beverage consumption occasions in relation to their accompanying foods and contextual characteristics.Support or Funding InformationAustralian Research Council, Victorian Health Promotion Foundation, National Health and Medical Research Council

Temporal eating patterns: associations with nutrient intakes, diet quality, and measures of adiposity

Background: Some evidence suggests that higher energy intake (EI) later in the day is associated with poor diet quality and obesity. However, EI at one eating occasion (EO) is also dependent on EI at surrounding EOs. Studies that examine the distribution of EOs across the day are rare.Objective: The aim of this study was to examine associations between temporal eating patterns, nutrient intakes, diet quality, and measures of adiposity in a representative sample of Australian adults.Design: Dietary data from two 24-h recalls collected during the cross-sectional 2011-2012 Australian National Nutrition and Physical Activity Survey were analyzed (n = 4544 adults, aged ≥19 y). Temporal eating patterns, based on the distribution of EOs across the day, were determined by using latent class analysis. Diet quality estimated adherence to healthy eating recommendations and was assessed by using the 2013 Dietary Guidelines Index (DGI). Multivariate regression models assessed associations between temporal eating patterns, nutrient intakes, diet quality, and adiposity (body mass index, waist circumference, weight status, and central weight status). Models were adjusted for potential confounders and energy misreporting.Results: Three patterns, labeled "conventional," "later lunch," and "grazing," were identified. Compared with a "conventional" or "later lunch" pattern, men and women with a "grazing" pattern had lower DGI scores and higher intakes of discretionary (noncore) foods (P < 0.05). Among women, the "grazing" pattern was associated with overweight or obesity (OR: 1.57; 95% CI: 1.15, 2.13) and central overweight or obesity (OR: 1.73; 95% CI: 1.19, 2.50). These associations were attenuated after the exclusion of energy misreporters and adjustment for total EI.Conclusions: This study found that a "grazing" temporal eating pattern was modestly but significantly associated with poorer diet quality and adiposity among women, after adjustment for covariates and energy misreporting. Future research should consider the impact of energy misreporting on the relation between temporal eating patterns and adiposity. This secondary analysis was registered at anzctr.org.au as ACTRN12617001029381.

Evaluation of a smartphone food diary application using objectively measured energy expenditure

BackgroundDietary assessment methods are limited in their ability to adequately measure food and beverage consumption. Smartphone applications may provide a novel method of dietary assessment to capture real-time food intake and the contextual factors surrounding eating occasions. The aim of this study is to evaluate the capability of a Smartphone meal diary app (“FoodNow”) to measure food intake using a validated objective method for assessing energy expenditure among young adults.MethodsParticipants (18–30 years) used FoodNow over four non-consecutive days recording all eating occasions through a combination of written text, and/or optional images and voice recordings. A series of contextual questions were also completed. Participants wore the validated SenseWear Armband (BodyMedia Inc, USA) during the same period to measure free-living energy expenditure. Intra-class correlation coefficients (ICC) estimated the reliability of FoodNow to measure estimated energy intake compared to measured energy expenditure.ResultsNinety participants (71 female, 19 male; mean age = 24.9 ± 4.1 years) were recruited to use the FoodNow app to record their eating occasions. Thirteen were excluded as they did not meet minimum requirements for number of reporting days (n = 3) or SenseWear Armband wear time (5 days of 11 h), while 21 participants were excluded after being identified as mis-reporters (Huang method). Among the remaining sample (n = 56), reliability between estimated energy intake and measured energy expenditure was high (ICC, 95% CI: 0.75, 0.61–0.84).ConclusionsFoodNow is a suitable method for capturing estimated energy intake data from young adults. Despite wide levels of agreement at the individual level (−3709 kJ to 2056 kJ), at the group level, FoodNow appears to have potential as a dietary assessment tool. This new dietary assessment method will offer an alternative and novel method of dietary assessment which is capable of collecting both estimated energy intake and contextual factors surrounding eating occasions. Information collected may be used to inform future public health messages or research interventions.

Chronotype differences in timing of energy and macronutrient intakes: A population-based study in adults.

To examine the association between chronotype and timing of energy and macronutrient intakes in adults. The study sample included 1,854 participants from the National FINRISK 2007 and FINDIET 2007 studies, aged 25 to 74 years. Diet was assessed with 48-hour dietary recalls. Chronotype was assessed with a shortened version of Horne and Östberg's Morningness-eveningness Questionnaire. Associations between chronotype and intakes of energy and macronutrients in the morning (by 10 am) and in the evening (after 8 pm) were analyzed with linear regression and ANOVA followed by Bonferroni post hoc test. In the morning, evening types had lower energy and macronutrient intakes (except for sucrose of which they had a higher intake) than morning types (P < 0.05), while in the evening, evening types had higher intakes of energy, sucrose, fat, and saturated fatty acids than morning types (P < 0.05). On the weekend, chronotype differences in evening intakes of energy, sucrose, and fat intake were more pronounced, and evening types had more eating occasions and more irregular meal times than morning types. Postponed energy and macronutrient intake timing of evening types with unfavorable dietary patterns may put them at higher risk of obesity and metabolic disturbances in the future.