Mobile Apps for Dietary and Food Timing Assessment: Evaluation for Use in Clinical Research.

BACKGROUND: Over the last decade, health mobile apps have become an increasingly popular tool used by clinicians and researchers to track food consumption and exercise. However, many consumer apps lack the technological features for facilitating the capture of critical food timing details. OBJECTIVE: This study aimed to introduce users to 11 apps from US app stores that recorded both dietary intake and food timing to establish which one would be the most appropriate for clinical research. METHODS: To determine a viable app that recorded both dietary intake and food timing for use in a food timing-related clinical study, we evaluated the time stamp data, usability, privacy policies, the accuracy of nutrient estimates, and general features of 11 mobile apps for dietary assessment that were available on US app stores. The following apps were selected using a keyword search of related terms and reviewed: text entry apps-Cronometer, DiaryNutrition, DietDiary, FoodDiary, Macros, and MyPlate; image entry apps-FoodView and MealLogger; and text plus image entry apps-Bitesnap, myCircadianClock, and MyFitnessPal. RESULTS: Our primary goal was to identify apps that recorded food time stamps, which 8 (73%) of the 11 reviewed apps did. Of the 11 apps, only 4 (36%) allowed users to edit the time stamps. Next, we sought to evaluate the usability of the apps using the System Usability Scale across 2 days, and 82% (9/11) of the apps received favorable scores for usability. To enable use in research and clinical settings, the privacy policies of each app were systematically reviewed using common criteria, with 1 (9%) Health Insurance Portability and Accountability Act-compliant app (Cronometer). Furthermore, protected health information was collected by 9 (82%) of the 11 apps. Finally, to assess the accuracy of the nutrient estimates generated by these apps, we selected 4 sample food items and a 3-day dietary record to input into each app. The caloric and macronutrient estimates of the apps were compared with the nutrient estimates provided by a registered dietitian using the Nutrition Data System for Research database. In terms of the 3-day food record, the apps were found to consistently underestimate daily calories and macronutrients compared with the Nutrition Data System for Research output. CONCLUSIONS: Overall, we found that the Bitesnap app provided flexible dietary and food timing functionality capable of being used in research and clinical settings, whereas most other apps lacked in the necessary food timing functionality or user privacy.

Association of Employees' Meal Skipping Patterns with Workplace Food Purchases, Dietary Quality, and Cardiometabolic Risk: A Secondary Analysis from the ChooseWell 365 Trial.

BACKGROUND: Employed adults may skip meals due to time or financial constraints, challenging work schedules, or limited workplace food choices. Little is known about the relationship between employees' meal skipping patterns and workplace dietary choices and health. OBJECTIVE: To examine whether hospital employees' meal skipping patterns were associated with workplace food purchases, dietary quality, and cardiometabolic risk factors (ie, obesity, hypertension, and prediabetes/diabetes). DESIGN: This is a secondary cross-sectional analysis of baseline data from the ChooseWell 365 randomized controlled trial. Employees reported meal-skipping frequency in a baseline survey. The healthfulness of workplace food purchases was determined with a validated Healthy Purchasing Score (HPS) (range = 0 to 100 where higher scores = healthier purchases) calculated using sales data for participants' purchases in the 3 months before study enrollment. Dietary quality was measured with the 2015 Healthy Eating Index (range = 0 to 100 where higher score = healthier diet) from two 24-hour recalls. Cardiometabolic risk factors were ascertained from clinic measurements. PARTICIPANTS/SETTING: Participants were 602 hospital employees who regularly visited workplace cafeterias and enrolled in ChooseWell 365, a workplace health promotion study in Boston, MA, during 2016-2018. MAIN OUTCOME MEASURES: Primary outcomes were HPS, 2015 Healthy Eating Index, and cardiometabolic risk factors. STATISTICAL ANALYSES: Regression analyses examined differences in HPS, 2015 Healthy Eating Index, and cardiometabolic variables by meal skipping frequency, adjusting for demographic characteristics. RESULTS: Participants' mean (standard deviation) age was 43.6 (12.2) years and 478 (79%) were women. Overall, 45.8% skipped breakfast, 36.2% skipped lunch, and 24.9% skipped dinner ≥ 1 day/week. Employees who skipped breakfast ≥ 3 days/week (n = 102) had lower HPS (65.1 vs 70.4; P < 0.01) and 2015 Healthy Eating Index score (55.9 vs 62.8; P < 0.001) compared with those who never skipped. Skipping lunch ≥ 3 days/week and dinner ≥ 1 day/week were associated with significantly lower HPS compared with never skipping. Employees who worked nonstandard shifts skipped more meals than those who worked standard shifts. Meal skipping was not associated with obesity or other cardiometabolic variables. CONCLUSIONS: Skipping meals was associated with less healthy food purchases at work, and skipping breakfast was associated with lower dietary quality. Future research to understand employees' reasons for skipping meals may inform how employers could support healthier dietary intake at work.

Sleep characteristics across the lifespan in 1.1 million people from the Netherlands, United Kingdom and United States: a systematic review and meta-analysis.

We aimed to obtain reliable reference charts for sleep duration, estimate the prevalence of sleep complaints across the lifespan and identify risk indicators of poor sleep. Studies were identified through systematic literature search in Embase, Medline and Web of Science (9 August 2019) and through personal contacts. Eligible studies had to be published between 2000 and 2017 with data on sleep assessed with questionnaires including ≥100 participants from the general population. We assembled individual participant data from 200,358 people (aged 1-100 years, 55% female) from 36 studies from the Netherlands, 471,759 people (40-69 years, 55.5% female) from the United Kingdom and 409,617 people (≥18 years, 55.8% female) from the United States. One in four people slept less than age-specific recommendations, but only 5.8% slept outside of the 'acceptable' sleep duration. Among teenagers, 51.5% reported total sleep times (TST) of less than the recommended 8-10 h and 18% report daytime sleepiness. In adults (≥18 years), poor sleep quality (13.3%) and insomnia symptoms (9.6-19.4%) were more prevalent than short sleep duration (6.5% with TST < 6 h). Insomnia symptoms were most frequent in people spending ≥9 h in bed, whereas poor sleep quality was more frequent in those spending <6 h in bed. TST was similar across countries, but insomnia symptoms were 1.5-2.9 times higher in the United States. Women (≥41 years) reported sleeping shorter times or slightly less efficiently than men, whereas with actigraphy they were estimated to sleep longer and more efficiently than man. This study provides age- and sex-specific population reference charts for sleep duration and efficiency which can help guide personalized advice on sleep length and preventive practices.

Quantifying Diet Intake and Its Association with Cardiometabolic Risk in the UK Airwave Health Monitoring Study: A Data-Driven Approach.

We used data-driven approaches to identify independent diet exposures among 45 candidate variables, for which we then probed cross-sectional associations with cardiometabolic risk (CMR). We derived average daily caloric intake and macronutrient composition, daily meal frequencies, and irregularity of energy and macronutrient intake from 7-day food diaries in the Airwave Health Monitoring Study participants (N = 8090). We used K-means and hierarchical clustering to identify non-redundant diet exposures with representative exposures for each cluster chosen by silhouette value. We then used multi-variable adjusted logistic regression to estimate prevalence ratios (PR) and 95% confidence intervals (95%CI) for CMR (≥3 criteria: dyslipidemia, hypertension, central adiposity, inflammation and impaired glucose control) across diet exposure quartiles. We identified four clusters: i) fat intake, ii) carbohydrate intake, iii) protein intake and intake regularity, and iv) meal frequencies and energy intake. Of these clusters, higher carbohydrate intake was associated with lower likelihood of CMR (PR = 0.89, 95%CI = 0.81-0.98; ptrend = 0.02), as was higher fiber intake (PR = 0.76, 95%CI = 0.68-0.85; ptrend < 0.001). Higher meal frequency was also associated with lower likelihood of CMR (PR = 0.76, 95%CI = 0.68-0.85; ptrend < 0.001). Our results highlight a novel, data-driven approach to select non-redundant, minimally collinear, primary exposures across a host of potentially relevant exposures (including diet composition, temporal distribution, and regularity), as often encountered in nutritional epidemiology.

Assessment of MTNR1B Type 2 Diabetes Genetic Risk Modification by Shift Work and Morningness-Eveningness Preference in the UK Biobank.

Night shift work, behavioral rhythms, and the common MTNR1B risk single nucleotide polymorphism (SNP), rs10830963, associate with type 2 diabetes; however, whether they exert joint effects to exacerbate type 2 diabetes risk is unknown. Among employed participants of European ancestry in the UK Biobank (N = 189,488), we aimed to test the cross-sectional independent associations and joint interaction effects of these risk factors on odds of type 2 diabetes (n = 5,042 cases) and HbA1c levels (n = 175,156). Current shift work, definite morning or evening preference, and MTNR1B rs10830963 risk allele associated with type 2 diabetes and HbA1c levels. The effect of rs10830963 was not modified by shift work schedules. While marginal evidence of interaction between self-reported morningness-eveningness preference and rs10830963 on risk of type 2 diabetes was seen, this interaction did not persist when analysis was expanded to include all participants regardless of employment status and when accelerometer-derived sleep midpoint was used as an objective measure of morningness-eveningness preference. Our findings suggest that MTNR1B risk allele carriers who carry out shift work or have more extreme morningness-eveningness preference may not have enhanced risk of type 2 diabetes.