Background: Excessive screen time ( $$\ge$$≥ 2 h per day) is associated with childhood overweight and obesity, physical inactivity, increased sedentary time, unfavorable dietary behaviors, and disrupted sleep. Previous reviews suggest intervening on screen time is associated with reductions in screen time and improvements in other obesogenic behaviors. However, it is unclear what study characteristics and behavior change techniques are potential mechanisms underlying the effectiveness of behavioral interventions. The purpose of this meta-analysis was to identify the behavior change techniques and study characteristics associated with effectiveness in behavioral interventions to reduce children’s (0–18 years) screen time. Methods: A literature search of four databases (Ebscohost, Web of Science, EMBASE, and PubMed) was executed between January and February 2020 and updated during July 2021. Behavioral interventions targeting reductions in children’s (0–18 years) screen time were included. Information on study characteristics (e.g., sample size, duration) and behavior change techniques (e.g., information, goal-setting) were extracted. Data on randomization, allocation concealment, and blinding was extracted and used to assess risk of bias. Meta-regressions were used to explore whether intervention effectiveness was associated with the presence of behavior change techniques and study characteristics. Results: The search identified 15,529 articles, of which 10,714 were screened for relevancy and 680 were retained for full-text screening. Of these, 204 studies provided quantitative data in the meta-analysis. The overall summary of random effects showed a small, beneficial impact of screen time interventions compared to controls (SDM = 0.116, 95CI 0.08 to 0.15). Inclusion of the Goals, Feedback, and Planning behavioral techniques were associated with a positive impact on intervention effectiveness (SDM = 0.145, 95CI 0.11 to 0.18). Interventions with smaller sample sizes (n < 95) delivered over short durations (< 52 weeks) were associated with larger effects compared to studies with larger sample sizes delivered over longer durations. In the presence of the Goals, Feedback, and Planning behavioral techniques, intervention effectiveness diminished as sample size increased. Conclusions: Both intervention content and context are important to consider when designing interventions to reduce children’s screen time. As interventions are scaled, determining the active ingredients to optimize interventions along the translational continuum will be crucial to maximize reductions in children’s screen time.

Summary: Background: COVID‐19 school closures pose a threat to children's wellbeing, but no COVID‐19‐related studies have assessed children's behaviours over multiple years .Objective: To examine children's obesogenic behaviours during spring and summer of the COVID‐19 pandemic compared to previous data collected from the same children during the same calendar period in the 2 years prior.Methods: Physical activity and sleep data were collected via Fitbit Charge‐2 in 231 children (7–12 years) over 6 weeks during spring and summer over 3 years. Parents reported their child's screen time and dietary intake via a survey on 2–3 random days/week.Results: Children's behaviours worsened at a greater rate following the pandemic onset compared to pre‐pandemic trends. During pandemic spring, sedentary behaviour increased (+79 min; 95% CI = 60.6, 97.1) and MVPA decreased (−10 min, 95% CI = −18.2, −1.1) compared to change in previous springs (2018–2019). Sleep timing shifted later (+124 min; 95% CI = 112.9, 135.5). Screen time (+97 min, 95% CI = 79.0, 115.4) and dietary intake increased (healthy: +0.3 foods, 95% CI = 0.2, 0.5; unhealthy: +1.2 foods, 95% CI = 1.0, 1.5). Similar patterns were observed during summer.Conclusions: Compared to pre‐pandemic measures, children's PA, sedentary behaviour, sleep, screen time, and diet were adversely altered during the COVID‐19 pandemic. This may ultimately exacerbate childhood obesity.

Heart rate (HR), when combined with accelerometry, can dramatically improve estimates of energy expenditure and sleep. Advancements in technology, via the development and introduction of small, low-cost photoplethysmography devices embedded within wrist-worn consumer wearables, have made the collection of heart rate (HR) under free-living conditions more feasible. This systematic review and meta-analysis compared the validity of wrist-worn HR estimates to a criterion measure of HR (electrocardiography ECG or chest strap). Searches of PubMed/Medline, Web of Science, EBSCOhost, PsycINFO, and EMBASE resulted in a total of 44 articles representing 738 effect sizes across 15 different brands. Multi-level random effects meta-analyses resulted in a small mean difference (beats per min, bpm) of −0.40 bpm (95 confidence interval (CI) −1.64 to 0.83) during sleep, −0.01 bpm (−0.02 to 0.00) during rest, −0.51 bpm (−1.60 to 0.58) during treadmill activities (walking to running), while the mean difference was larger during resistance training (−7.26 bpm, −10.46 to −4.07) and cycling (−4.55 bpm, −7.24 to −1.87). Mean difference increased by 3 bpm (2.5 to 3.5) per 10 bpm increase of HR for resistance training. Wrist-worn devices that measure HR demonstrate acceptable validity compared to a criterion measure of HR for most common activities.

Chronic kidney disease is a progressive, incurable condition that involves a gradual loss of kidney function. While there are no non-invasive biomarkers available to determine whether individuals are susceptible to developing chronic kidney disease, small RNAs within urinary exosomes have recently emerged as a potential candidate to use for assessing renal function. Ultracentrifugation is the gold standard for urinary exosome isolation. However, extravesicular small RNA contamination can occur when isolating exosomes from biological fluids using ultracentrifugation, which may lead to misidentifying the presence of certain small RNA species in human urinary exosomes. Therefore, we characterized human urinary exosomal preparations isolated by ultracentrifugation alone, or via ultracentrifugation followed by size exclusion chromatography (SEC) column-purification. Using nanoparticle tracking analysis, we identified SEC fractions containing robust amounts of exosome-sized particles, that we further characterized using immunoblotting. When compared to exosomal preparations isolated by ultracentrifugation only, SEC fractionated exosomal preparations showed higher levels of the exosome-positive marker CD81. Moreover, while the exosome-negative marker calnexin was undetectable in SEC fractionated exosomal preparations, we did observe calnexin detection in the exosomal preparations isolated by ultracentrifugation alone, which implies contamination in these preparations. Lastly, we imaged SEC fractionated exosomal preparations using transmission electron microscopy to confirm these preparations contained human urinary exosomes. Our results indicate that combining ultracentrifugation and SEC column-purification exosome isolation strategies is a powerful approach for collecting contaminant-free human urinary exosomes and should be considered when exosomes devoid of contamination are needed for downstream applications.

S: Background: Pilot/feasibility or studies with small sample sizes may be associated with inflated effects. This study explores the vibration of effect sizes (VoE) in meta-analyses when considering different inclusion criteria based upon sample size or pilot/feasibility status. Methods: Searches were to identify systematic reviews that conducted meta-analyses of behavioral interventions on topics related to the prevention/treatment of childhood obesity from January 2016 to October 2019. The computed summary effect sizes (ES) were extracted from each meta-analysis. Individual studies included in the meta-analyses were classified into one of the following four categories: self-identified pilot/feasibility studies or based upon sample size but not a pilot/feasibility study (N ≤ 100, N > 100, and N > 370 the upper 75th of sample size). The VoE was defined as the absolute difference (ABS) between the re-estimations of summary ES restricted to study classifications compared to the originally reported summary ES. Concordance (kappa) of statistical significance of summary ES between the four categories of studies was assessed. Fixed and random effects models and meta-regressions were estimated. Three case studies are presented to illustrate the impact of including pilot/feasibility and N ≤ 100 studies on the estimated summary ES. Results: A total of 1602 effect sizes, representing 145 reported summary ES, were extracted from 48 meta-analyses containing 603 unique studies (avg. 22 studies per meta-analysis, range 2–108) and included 227,217 participants. Pilot/feasibility and N ≤ 100 studies comprised 22% (0–58%) and 21% (0–83%) of studies included in the meta-analyses. Meta-regression indicated the ABS between the re-estimated and original summary ES where summary ES ranged from 0.20 to 0.46 depending on the proportion of studies comprising the original ES were either mostly small (e.g., N ≤ 100) or mostly large (N > 370). Concordance was low when removing both pilot/feasibility and N ≤ 100 studies (kappa = 0.53) and restricting analyses only to the largest studies (N > 370, kappa = 0.35), with 20% and 26% of the originally reported statistically significant ES rendered non-significant. Reanalysis of the three case study meta-analyses resulted in the re-estimated ES rendered either non-significant or half of the originally reported ES. Conclusions: When meta-analyses of behavioral interventions include a substantial proportion of both pilot/feasibility and N ≤ 100 studies, summary ES can be affected markedly and should be interpreted with caution.