BackgroundAlthough the scientific community has acknowledged modest improvements can be made to weight status and obesogenic behaviors (i.e., physical activity, sedentary/screen time, diet, and sleep) during the school year, studies suggests improvements are erased as elementary-age children are released to summer vacation. Emerging evidence shows children return to school after summer vacation displaying accelerated weight gain compared to the weight gained occurring during the school year. Understanding how summer days differ from when children are in school is, therefore, essential.DiscussionThere is limited evidence on the etiology of accelerated weight gain during summer, with few studies comparing obesogenic behaviors on the same children during school and summer. For many children, summer days may be analogous to weekend days throughout the school year. Weekend days are often limited in consistent and formal structure, and thus differ from school days where segmented, pre-planned, restrictive, and compulsory components exist that shape obesogenic behaviors. The authors hypothesize that obesogenic behaviors are beneficially regulated when children are exposed to a structured day (i.e., school weekday) compared to what commonly occurs during summer. This is referred to as the ‘Structured Days Hypothesis’ (SDH). To illustrate how the SDH operates, this study examines empirical data that compares weekend day (less-structured) versus weekday (structured) obesogenic behaviors in U.S. elementary school-aged children. From 190 studies, 155 (~80%) demonstrate elementary-aged children’s obesogenic behaviors are more unfavorable during weekend days compared to weekdays.ConclusionIn light of the SDH, consistent evidence demonstrates the structured environment of weekdays may help to protect children by regulating obesogenic behaviors, most likely through compulsory physical activity opportunities, restricting caloric intake, reducing screen time occasions, and regulating sleep schedules. Summer is emerging as the critical period where childhood obesity prevention efforts need to be focused. The SDH can help researchers understand the drivers of obesogenic behaviors during summer and lead to innovative intervention development.

Results found comparable activity intensity classification accuracies from the ActiGraph GT3X+ wrist-worn accelerometer to previously published studies. Based on ROC and regression analyses, activity intensities can be distilled from this accelerometer using axis 1, axis 2 or VM values with similar classification accuracy.

Background: Preliminary evaluations of behavioral interventions, referred to as pilot studies, predate the conduct of many large-scale efficacy/effectiveness trial. The ability of a pilot study to inform an efficacy/effectiveness trial relies on careful considerations in the design, delivery, and interpretation of the pilot results to avoid exaggerated early discoveries that may lead to subsequent failed efficacy/effectiveness trials. "Risk of generalizability biases (RGB)" in pilot studies may reduce the probability of replicating results in a larger efficacy/effectiveness trial. We aimed to generate an operational list of potential RGBs and to evaluate their impact in pairs of published pilot studies and larger, more well-powered trial on the topic of childhood obesity. Methods: We conducted a systematic literature review to identify published pilot studies that had a published larger-scale trial of the same or similar intervention. Searches were updated and completed through December 31st, 2018. Eligible studies were behavioral interventions involving youth (≤18 yrs) on a topic related to childhood obesity (e.g., prevention/treatment, weight reduction, physical activity, diet, sleep, screen time/sedentary behavior). Extracted information included study characteristics and all outcomes. A list of 9 RGBs were defined and coded: intervention intensity bias, implementation support bias, delivery agent bias, target audience bias, duration bias, setting bias, measurement bias, directional conclusion bias, and outcome bias. Three reviewers independently coded for the presence of RGBs. Multi-level random effects meta-analyses were performed to investigate the association of the biases to study outcomes. Results: A total of 39 pilot and larger trial pairs were identified. The frequency of the biases varied: delivery agent bias (19/39 pairs), duration bias (15/39), implementation support bias (13/39), outcome bias (6/39), measurement bias (4/39), directional conclusion bias (3/39), target audience bias (3/39), intervention intensity bias (1/39), and setting bias (0/39). In meta-analyses, delivery agent, implementation support, duration, and measurement bias were associated with an attenuation of the effect size of − 0.325 (95CI − 0.556 to − 0.094), − 0.346 (− 0.640 to − 0.052), − 0.342 (− 0.498 to − 0.187), and − 0.360 (− 0.631 to − 0.089), respectively. Conclusions: Pre-emptive avoidance of RGBs during the initial testing of an intervention may diminish the voltage drop between pilot and larger efficacy/effectiveness trials and enhance the odds of successful translation.

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Objectives: The study aimed to determine the associated factors of household food security (HFS) and household dietary diversity (HDD) during the COVID−19 pandemic in Bangladesh. Design: Both online survey and face-to-face interviews were employed in this cross-sectional study. The Household Food Security Scale and Household Dietary Diversity Score were used to access HFS and HDD, respectively. The HDD scores were derived from a 24-h recall of food intake from 12 groups. Setting: Bangladesh. Participants: A total sample of 1876 households were recruited. Results: The overall mean scores of HFS and HDD were 31·86 (sd 2·52) and 6·22 (sd 5·49), respectively. Being a rural resident, having no formal education, occupation of household head other than government job and low monthly income were potential determinants of lower HFS and HDD. Approximately 45 % and 61 % of Bangladeshi households did not get the same quantity and same type of food, respectively, as they got before the pandemic. Over 10 % of respondents reported that they lost their job or had to close their businesses, and income reduction was reported by over 70 % of household income earners during the COVID-19 pandemic, which in turn was negatively associated with HFS and HDD. Conclusion: Household socio-economic variables and COVID-19 effects on occupation and income are potential predictors of lower HFS and HDD scores. HFS and HDD deserve more attention during this pandemic particularly with reference to low-earning households and the households whose earning persons’ occupation has been negatively impacted during the COVID-19 pandemic.

A majority of intervention studies to help increase older adult PA used self-report measures, even though many have little evidence of validity and reliability. We recommend that future researchers utilise valid and reliable measures of PA with well-established evidence of psychometric properties such as hip-accelerometers and the Community Health Activities Model Program for Seniors (CHAMPS) Physical Activity Questionnaire for Older Adults.

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BACKGROUND: Evidence consistently shows children in the United States gain 3 to 5 times more weight during summer vacation (∼2.5 months) compared to the 9-month school year. The purpose of this study is to examine within-child differences in 4 obesogenic behaviors (physical activity [PA], sedentary/screen-time, diet, and sleep) during school versus summer. METHODS:We used a repeated-measures within-subjects design. Children (N = 30 mean age = 8.2 years; 57% female; 37% overweight/obese; 100% African American) wore accelerometers on the nondominant wrist for 24 hr/d over 9 consecutive days during school and summer of 2016 to capture PA, sedentary time, and sleep. Parents completed a daily diary to report bed/wake times, diet, and screen-time of their child each day. Mixed-effect models compared summer and school behaviors. RESULTS:Children spent more time sedentary (69% vs 67% of wake wear time), less time in light PA (25% vs 23% of wake wear time), had higher screen-time (242 vs 123 minutes/day), slept longer (428 vs 413 minutes/night), and consumed more sugar-based foods (6 days vs 2.5 days/week) and fruit (7 days vs 4.7 days/week) during summer compared to school (p < .05). CONCLUSION:Initial evidence suggests children are displaying multiple unfavorable obesogenic behaviors during summer compared to school that may contribute to accelerated weight gain during summer.