Independent and joint associations of aerobic and muscle-strengthening exercise with mental health in adolescents: A cross-sectional analysis before and during COVID-19 using the 2015-2021 National Youth Risk Behavior Survey.

PURPOSE: COVID-19 led to social isolation that may have compromised adolescent mental health. This study examined the independent and joint associations of aerobic physical activity (PA) and muscle-strengthening exercise (MSE) with mental health problems in adolescents. METHODS: Participants were US adolescents who completed the 2015-2021 National Youth Risk Behavior Survey (N = 61,298; 45.7% female). Data were collected between 2015 and 2021 and analyzed in 2023. Outcomes were binary response items asking about feeling sad/hopeless, having difficulty concentrating, remembering, or making decisions, and having a suicidal ideation. Preventive exposure variables were items asking about frequencies of aerobic PA and MSE with responses dichotomized to align with recommendations. Independent and joint associations were examined using robust Poisson regression with covariates selected using double selection lasso. Structural equation models examined the associations treating PA and MSE as continuous predictors and poor mental health as a latent dependent variable. RESULTS: Meeting either recommendation alone associated with a 4-10% lower prevalence of mental health problems (APR = 0.90-0.96, p < 0.05), and meeting both recommendations associated with a 15%-20% lower prevalence of mental health problems (APR = 0.80-0.85, p < 0.001). Although categorical joint associations were stronger in males (p < 0.05), multiplicative interactions were observed in females using continuous variables for PA and MSE (ß = -0.09, p < 0.001). CONCLUSION: Meeting aerobic PA and MSE recommendations associated with lower prevalence of mental health problems. Participation in MSE below recommended levels may be beneficial for females when combined with aerobic PA. Future research should examine these associations by acquiring contextual information and device-based assessments.

Dose-response associations of Monitor-Independent Movement Summary with health-related fitness in youth.

The purpose of this study was to determine the dose-response associations between Monitor-Independent Movement Summary (MIMS) units and health-related fitness in youth. The sample comprised US children and adolescents who participated in the 2012 National Youth Fitness Survey (NNYFS; N = 1158, 48.9% female). Health-related fitness domains were assessed using tests of cardiorespiratory endurance (timed maximal and graded treadmill tests), muscular strength (modified pull-up and grip tests), and muscular endurance (plank test). Movement data were collected using wrist-worn ActiGraph accelerometers with raw data processed using MIMS and the calculated metrics of average MIMS/day, Peak 60-min MIMS, and Peak 30-min MIMS. Weighted regression models examined linear associations between MIMS metrics and fitness test scores. Nonlinear associations were examined using weighted spline models with knots placed at the 10th, 50th, and 90th percentiles. Models were adjusted for covariates and fit was examined using the coefficient of determination (R2 ). The strongest adjusted linear relationships included a positive association between MIMS/day (per every 1000 units) and maximal endurance times (b = 5.5 s, p < 0.001) and between Peak 60-min MIMS (per every 10 units) and estimated aerobic capacity (b = 1.7 mL/kg/min, p < 0.001), modified pull-ups (b = 0.7 reps, p < 0.001), and plank test scores (b = 5.0 s, p < 0.001). Linear spline models had slightly higher R2 values (R2 range = 16.9%-74.8%) compared to linear models (R2 range = 15.0%-74.5%). The relationship between MIMS metrics and fitness test scores was best modeled as piecewise linear functions. Although all MIMS metrics associated with cardiorespiratory endurance, Peak 60-min MIMS showed stronger associations with tests of muscular strength and endurance.

A randomized trial to promote physical activity in adult pre-hypertensive and hypertensive patients.

The purpose of the pilot study was to determine the effectiveness of wearable activity trackers alone or in combination with behaviour change strategies for promoting physical activity (PA) among individuals with pre-hypertension or hypertension. A sample of 44 adults (68% female and mean age 55) were randomized to receive either a Fitbit Charge HR 3 alone (FB) or the Fitbit in combination with behaviour change strategies (i.e., goal setting, behaviour goal review, adaptive feedback) delivered by a trained health coach (FB+) for 12 weeks. Moderate and vigorous PA (MVPA), steps, and sedentary time (ST) were assessed using ActiGraph wGT3X-BT. The FB+ group significantly increased PA [+1854 (2518) steps/day, p < .01] and MVPA [+26 (34) mins/day, p < .05], and decreased their ST [-63 (73) mins/day, p < .01]. The FB group significantly increased  MVPA [+11 (16) mins/day, p < .05], and decreased their ST [-87 (117) mins/day, p < .01]. Participants in FB+ had a significantly greater increase in MVPA/day compared to FB only with a between-group effect size of 0.6 (p < .05). Using Fitbit for self-monitoring is effective in increasing PA and reducing ST among pre-hypertensive and hypertensive participants. Additional behaviour change support amplified the intervention effectiveness for promoting MVPA.

Validation of the Apple Watch for Estimating Moderate-to-Vigorous Physical Activity and Activity Energy Expenditure in School-Aged Children.

The Apple Watch is one of the most popular wearable devices designed to monitor physical activity (PA). However, it is currently unknown whether the Apple Watch accurately estimates children's free-living PA. Therefore, this study assessed the concurrent validity of the Apple Watch 3 in estimating moderate-to-vigorous physical activity (MVPA) time and active energy expenditure (AEE) for school-aged children under a simulated and a free-living condition. Twenty elementary school students (Girls: 45%, age: 9.7 ± 2.0 years) wore an Apple Watch 3 device on their wrist and performed prescribed free-living activities in a lab setting. A subgroup of participants (N = 5) wore the Apple Watch for seven consecutive days in order to assess the validity in free-living condition. The K5 indirect calorimetry (K5) and GT3X+ were used as the criterion measure under simulated free-living and free-living conditions, respectively. Mean absolute percent errors (MAPE) and Bland-Altman (BA) plots were conducted to assess the validity of the Apple Watch 3 compared to those from the criterion measures. Equivalence testing determined the statistical equivalence between the Apple Watch and K5 for MVPA time and AEE. The Apple Watch provided comparable estimates for MVPA time (mean bias: 0.3 min, p = 0.91, MAPE: 1%) and for AEE (mean bias: 3.8 kcal min, p = 0.75, MAPE: 4%) during the simulated free-living condition. The BA plots indicated no systematic bias for the agreement in MVPA and AEE estimates between the K5 and Apple Watch 3. However, the Apple Watch had a relatively large variability in estimating AEE in children. The Apple Watch was statistically equivalent to the K5 within ±17.7% and ±20.8% for MVPA time and AEE estimates, respectively. Our findings suggest that the Apple Watch 3 has the potential to be used as a PA assessment tool to estimate MVPA in school-aged children.

The Validity of MotionSense HRV in Estimating Sedentary Behavior and Physical Activity under Free-Living and Simulated Activity Settings.

MotionSense HRV is a wrist-worn accelerometery-based sensor that is paired with a smartphone and is thus capable of measuring the intensity, duration, and frequency of physical activity (PA). However, little information is available on the validity of the MotionSense HRV. Therefore, the purpose of this study was to assess the concurrent validity of the MotionSense HRV in estimating sedentary behavior (SED) and PA. A total of 20 healthy adults (age: 32.5 ± 15.1 years) wore the MotionSense HRV and ActiGraph GT9X accelerometer (GT9X) on their non-dominant wrist for seven consecutive days during free-living conditions. Raw acceleration data from the devices were summarized into average time (min/day) spent in SED and moderate-to-vigorous PA (MVPA). Additionally, using the Cosemed K5 indirect calorimetry system (K5) as a criterion measure, the validity of the MotionSense HRV was examined in simulated free-living conditions. Pearson correlations, mean absolute percent errors (MAPE), Bland-Altman (BA) plots, and equivalence tests were used to examine the validity of the MotionSense HRV against criterion measures. The correlations between the MotionSense HRV and GT9X were high and the MAPE were low for both the SED (r = 0.99, MAPE = 2.4%) and MVPA (r = 0.97, MAPE = 9.1%) estimates under free-living conditions. BA plots illustrated that there was no systematic bias between the MotionSense HRV and criterion measures. The estimates of SED and MVPA from the MotionSense HRV were significantly equivalent to those from the GT9X; the equivalence zones were set at 16.5% for SED and 29% for MVPA. The estimates of SED and PA from the MotionSense HRV were less comparable when compared with those from the K5. The MotionSense HRV yielded comparable estimates for SED and PA when compared with the GT9X accelerometer under free-living conditions. We confirmed the promising application of the MotionSense HRV for monitoring PA patterns for practical and research purposes.

Concurrent validity of the Fitbit for assessing sedentary behavior and moderate-to-vigorous physical activity.

BACKGROUND: Recent advances in sensor technologies have promoted the use of consumer-based accelerometers such as Fitbit Flex in epidemiological and clinical research; however, the validity of the Fitbit Flex in measuring sedentary behavior (SED) and physical activity (PA) has not been fully determined against previously validated research-grade accelerometers such as ActiGraph GT3X+. Therefore, the purpose of this study was to examine the concurrent validity of the Fitbit Flex against ActiGraph GT3X+ in a free-living condition. METHODS: A total of 65 participants (age: M = 42, SD = 14 years, female: 72%) each wore a Fitbit Flex and GT3X+ for seven consecutive days. After excluding sleep and non-wear time, time spent (min/day) in SED and moderate-to-vigorous PA (MVPA) were estimated using various cut-points for GT3X+ and brand-specific algorithms for Fitbit, respectively. Repeated measures one-way ANOVA and mean absolute percent errors (MAPE) served to examine differences and measurement errors in SED and MVPA estimates between Fitbit Flex and GT3X+, respectively. Pearson and Spearman correlations and Bland-Altman (BA) plots were used to evaluate the association and potential systematic bias between Fitbit Flex and GT3X+. PROC MIXED procedure in SAS was used to examine the equivalence (i.e., the 90% confidence interval with ±10% equivalence zone) between the devices. RESULTS: Fitbit Flex produced similar SED and low MAPE (mean difference [MD] = 37 min/day, P = .21, MAPE = 6.8%), but significantly higher MVPA and relatively large MAPE (MD = 59-77 min/day, P < .0001, MAPE = 56.6-74.3%) compared with the estimates from GT3X+ using three different cut-points. The correlations between Fitbit Flex and GT3X+ were consistently higher for SED (r = 0.90, ρ = 0.86, P < .01), but weaker for MVPA (r = 0.65-0.76, ρ = 0.69-0.79, P < .01). BA plots revealed that there is no apparent bias in estimating SED. CONCLUSION: In comparison with the GT3X+ accelerometer, the Fitbit Flex provided comparatively accurate estimates of SED, but the Fitbit Flex overestimated MVPA under free-living conditions. Future investigations using the Fitbit Flex should be aware of present findings.

The Use of a Fitbit Device for Assessing Physical Activity and Sedentary Behavior in Preschoolers.

OBJECTIVE: To determine the accuracy of the Fitbit Flex (FF) activity monitor for assessing preschoolers' physical activity (PA) and sedentary behavior (SED) in free-living conditions. STUDY DESIGN: This is a cross-sectional study of 27 preschoolers (41% girls; age, 4.9 years) who each wore the FF and ActiGraph GT3X+ (AG) simultaneously for 24 hours. Raw data from each device were temporally matched and summarized into estimates of SED, moderate-to-vigorous PA, and total PA after removing sleep and nonwear time periods. Equivalence testing was performed to compare the 90% CIs of the estimates from the FF with the respective equivalence zone (±15% of the mean estimates) from the AG. RESULTS: The FF yielded equivalent estimates of SED (mean difference, 42-71 minutes/day) and total PA (mean difference, 42-70 minutes/day), but lower moderate-to-vigorous PA (mean difference, 29-67 minutes/day) than the AG, on average. The FF showed strong correlations and relatively low mean absolute percent errors (MAPEs) in relation to the AG for SED (r = 0.87; MAPE = 9.1%) and total PA (r = 0.71; MAPE = 14.3%), but a moderate correlation and high MAPE for moderate-to-vigorous PA (r = 0.59; MAPE = 70.1%). CONCLUSIONS: The FF activity monitor accurately estimated the amount of time spent in SED and overall PA in preschool-aged children, but with an underestimation of moderate-to-vigorous PA. Our findings support the use of the FF as a feasible and a relatively accurate PA monitoring tool in clinical and research settings.

Objectively measured sedentary behavior in preschool children: comparison between Montessori and traditional preschools.

BACKGROUND: This study aimed to compare the levels of objectively-measured sedentary behavior in children attending Montessori preschools with those attending traditional preschools. METHODS: The participants in this study were preschool children aged 4 years old who were enrolled in Montessori and traditional preschools. The preschool children wore ActiGraph accelerometers. Accelerometers were initialized using 15-second intervals and sedentary behavior was defined as <200 counts/15-second. The accelerometry data were summarized into the average minutes per hour spent in sedentary behavior during the in-school, the after-school, and the total-day period. Mixed linear regression models were used to determine differences in the average time spent in sedentary behavior between children attending traditional and Montessori preschools, after adjusting for selected potential correlates of preschoolers' sedentary behavior. RESULTS: Children attending Montessori preschools spent less time in sedentary behavior than those attending traditional preschools during the in-school (44.4. min/hr vs. 47.1 min/hr, P = 0.03), after-school (42.8. min/hr vs. 44.7 min/hr, P = 0.04), and total-day (43.7 min/hr vs. 45.5 min/hr, P = 0. 009) periods. School type (Montessori or traditional), preschool setting (private or public), socio-demographic factors (age, gender, and socioeconomic status) were found to be significant predictors of preschoolers' sedentary behavior. CONCLUSIONS: Levels of objectively-measured sedentary behavior were significantly lower among children attending Montessori preschools compared to children attending traditional preschools. Future research should examine the specific characteristics of Montessori preschools that predict the lower levels of sedentary behavior among children attending these preschools compared to children attending traditional preschools.

Estimating relative intensity using individualized accelerometer cutpoints: the importance of fitness level.

BACKGROUND: Accelerometer cutpoints based on absolute intensity may under or overestimate levels of physical activity due to the lack of consideration for an individual's current fitness level. The purpose of this study was to illustrate the interindividual variability in accelerometer activity counts measured at relative intensities (40 and 60% heart rate reserve (HRR)) and demonstrate the differences between relative activity counts between low, moderate and high fitness groups. METHODS: Seventy-three subjects (38 men, 35 women) with a wide range of cardiorespiratory fitness (maximal oxygen consumption (VO2max): 27.9 to 58.5 ml · kg⁻¹ · min⁻¹), performed a submaximal exercise test with measures of heart rate (HR) and accelerometer activity counts. Linear regression equations were developed for each subject to determine accelerometer activity counts for moderate and vigorous intensity physical activity corresponding to 40% and 60% of HRR. Interindividual variability of activity counts between subjects at both 40% and 60% of HRR was demonstrated by plotting values using a box and whisker plot. To examine the difference between absolute and relative activity cutpoints, subjects were categorized into 3 fitness groups based on metabolic equivalents (MET) (<10 MET, 10-13 MET, >13 MET). RESULTS: At 40 and 60% of HRR, activity counts ranged from 1455-7520, and 3459-10066 counts · min-1, respectively. Activity counts at 40% HRR (3385 ± 850, 4048 ± 1090, and 5037 ± 1019 counts · min⁻¹) and 60% HRR (5159 ± 765, 5995 ± 1131 and 7367 ± 1374 counts · min-1) significantly increased across fitness groups (<10 MET, 10-13 MET, and >13 MET, respectively). CONCLUSION: This study revealed interindividual variability in activity counts at relative moderate (40% HRR) and vigorous (60% HRR) intensities, while fitness level was shown to have a significant influence on relative activity counts measured at these intensities. Individualizing activity count cutpoints may be more representative of an individual's PA level relative to their fitness capacity, compared to absolute activity count cutpoints.

Associations between screen-based sedentary behavior and cardiovascular disease risk factors in Korean youth.

The purposes of this study were to: 1) describe the patterns of screen-based sedentary behaviors, and 2) examine the association between screen-based sedentary behavior and cardiovascular disease (CVD) risk factors in representative Korean children and adolescents, aged 12 to 18 yr, in the Korean National Health and Nutrition Examination Survey. Screen-based sedentary behavior was measured using self-report questionnaires that included items for time spent watching TV and playing PC/video games. Physical activity was measured using items for frequency and duration of moderate-to-vigorous physical activity (MVPA). CVD risk factors such as body mass index (BMI), waist circumference, LDL cholesterol, HDL cholesterol, total cholesterol, triglycerides, glucose, systolic blood pressure, and diastolic blood pressure were measured. Boys spent more time playing PC/video games, and girls spent more time watching TV. After adjusting for age, gender, annual household income, and MVPA, an additional hour of watching TV was significantly associated with the risk of overweight (OR 1.17 [95% CI 1.03-1.33]), high abdominal adiposity (OR 1.27 [1.06-1.51]), and low HDL cholesterol (OR 1.27 [1.10-1.47]). An additional hour spent playing PC/video games also increased the risk of high abdominal adiposity (OR 1.20 [1.03-1.40]). Prospective observations and interventions are needed to determine causal relationships between screen-based sedentary behavior and CVD risk profiles in Korean youth.

The Effects of Playground Interventions on Accelerometer-Assessed Physical Activity in Pediatric Populations: A Meta-Analysis.

Playgrounds are designed to be a safe, enjoyable, and effective means to promote physical activity in children and adolescents. The purpose of this study was to conduct a meta-analysis to determine the effectiveness of playground interventions for improving accelerometer-assessed ambulatory moderate-to-vigorous physical activity (MVPA) and to identify common aspects of playground interventions that may be beneficial to promote behavior change. An internet database search was performed. The final analyzed sample of studies was obtained from several criteria, including being a playground-based intervention targeting children or adolescents, having a control or comparison group, having an accelerometer-assessed MVPA outcome target variable, and reporting of the mean difference scores' variability. A random-effects model meta-analysis was employed to obtain pooled effect sizes. Ten studies (n = 10) were analyzed from the internet search. The weighted pooled effect (Hedges' g) across all studies was Hedges' g = 0.13, 95% CI: 0.02-0.24, p = 0.023. There was moderate study heterogeneity (I2 = 55.3%) but no evidence for publication bias (p = 0.230). These results suggest that school-based playground interventions have a small effect on increasing accelerometer-assessed MVPA within the pediatric population. The playground should still be an environmental target during school or community-based interventions aimed at providing opportunities to promote MVPA.

Bidirectional relationships of physical activity and gross motor skills before and after summer break: Application of a cross-lagged panel model.

BACKGROUND: Gross motor skills are postulated to have a bidirectional relationship with physical activity (PA); however, no study has tested this relationship before and after a summer break. The purpose of this study was to examine the bidirectional relationships between school PA and gross motor skills in children before and after a summer break. METHODS: Participants were a sample of 440 children recruited from 3 low-income schools (age = 8.9 ± 1.2 years, mean ± SD). PA was assessed as average school-day step counts using Yamax DigiWalker pedometers (Yamasa Tokei Keiki, Tokyo, Japan) worn for 5 consecutive school days. Gross motor skills were assessed using the Test for Gross Motor Development, 3rd edition. Data were collected at 2 timepoints: at the end of spring semester (T1) and at the beginning of the subsequent fall semester (T2). An age- and body mass index-adjusted cross-lagged model was employed to relate T1 school step counts with T2 gross motor skills and T1 gross motor skills with T2 school step counts. RESULTS: T1 gross motor skills significantly predicted T2 school step counts (ß = 0.24, 95 % confidence interval (95%CI): 0.08-0.40, p = 0.003); however, T1 school step counts did not predict T2 gross motor skills (ß = 0.04, 95%CI: -0.06 to 0.14, p = 0.445). The model explained 35.4% and 15.9% of the variances of T2 gross motor skills and T2 school step counts, respectively. Additional analyses indicated that these relationships were driven primarily by ball skills. CONCLUSION: The relationship between gross motor skills and school PA was not bidirectional; however, higher gross motor skills, specifically ball skills, predicted higher school PA after a 3-month summer break.

Animal-Based Dietary Protein Intake Is Not A Risk Factor For Metabolic Syndrome Among Young Or Middle-Aged Females.

Background: Metabolic syndrome (MetS) increases risk for morbidity and premature mortality. Blood pressure, waist circumference, and fasting triglycerides (TG), blood glucose (BG), and high-density lipoprotein cholesterol (HDL) are factors for determining MetS. The Simple Method for Quantifying Metabolic Syndrome (siMS) score and risk score estimate risk of MetS. The purpose for this study was to exam the relationship of animal-based (ABP) and plant-based protein (PLP) with MetS as estimated by siMS score and risk score. Physical activty is another important consideration in MetS as it can reduce blood pressure, waist circumference and blood glucose, and affect blood lipid and lipoprotein concentrations. Methods: A cross-sectional study examined whether physical activity (PA) level and dietary protein source (i.e., animal- or plant-based) among young (18-24 years) and middle-aged (45-60 years) females were associated with siMS score and siMS risk score. Average time spent in sedentary, light, and moderate-to-vigorous PA (MVPA; min/wk), steps (steps/day), energy intake (kcal/day), percent dietary protein to total energy intake, ABP and PLP dietary intake, and ABP:PLP ratio (g/day) were included in the analysis. Volunteers were recruited from North Dakota and Minnesota from 2017 to 2019. Results: Eighty-one female participants (mean ± SD; young, n = 38, 20.4 ± 1.7 years, middle-aged, 52.5 ± 4.8 years) were included in the independent t-tests used to examine group differences in age, body mass index, HDL, BG, TG, systolic blood pressure, waist circumference, energy intake, energy intake percentage of total carbohydrates, fat, protein, ABP, and PLP, ABP:PLP, siMS score, and siMS risk score. Stepwise linear regressions were used to evaluate whether PA level and dietary protein source were predictors of siMS score and siMS risk score among young and middle-aged adult females. There was an inverse relationship between PLP intake and siMS score. The model explained 6.9% of the variance in siMS risk score (F1, 80 = 5.93). Plant-based protein intake was inversely related to siMS risk score while light PA was positively associated with siMS risk score. The model explained 16% of the variance in siMS risk score (F1, 80 = 7.53). Animal-based dietary protein intake did not impact siMS score (p = 0.180) and siMS risk score (p = 0.283). Conclusions: Plant-based protein intake was associated with a lower risk of MetS via siMS scores, while ABP was not associated. Given the nature of the cross-sectional design of this study, no causal relationship can be determined, but longitudinal studies or randomized control trials to confirm the results from this study are needed in the future.

Evenness of Dietary Protein Intake Is Positively Associated with Lean Mass and Strength in Healthy Women.

Background: Evenness of protein intake is associated with increased lean mass, but its relationship with muscle strength and performance is uncertain. Objectives: We determined the association of evenness of protein intake with lean mass, muscle strength and endurance, and functional ability. Design: This was a cross-sectional study. Setting: Data were collected at a research university in the upper midwestern United States. Participants: One hundred ninety-two healthy women, aged 18 to 79 years, mean ± SEM 41.9 ± 1.3, completed the study. Measurements: Dietary intake was assessed using 3-day food diaries verified with food frequency questionnaires. To assess evenness of protein intake, the day was divided into 3 periods: waking to 11:30, 11:31 to 16:30, and after 16:30. Lean mass was measured with dual energy X-ray absorptiometry. Lower-body muscle strength and endurance were determined using isokinetic dynamometry. Upper-body muscle strength was maximal handgrip strength. Functional ability was assessed using 6-m gait speed and 30-second chair stand tests. Accelerometry measured physical activity. Results: Intakes of 25 g or more of protein at 1 or more of the 3 periods was positively associated with lean mass (ß ± S.E.; 1.067 ± 0.273 kg, P < .001) and upper-body (3.274 ± 0.737 kg, P < .001) and lower-body strength (22.858 ± 7.918 Nm, P = .004) when controlling for age, body mass index, physical activity, and energy and protein intakes. Consuming at least 0.24 g/kg/period for those under 60 years and 0.4 g/kg/period for those 60 years and older was related to lean mass (0.754 ± 0.244 kg, P = .002), upper-body strength (2.451 ± 0.658 kg, P < .001), and lower-body endurance (184.852 ± 77.185 J, P = .018), controlling for the same variables. Conclusions: Evenness of protein intake is related to lean mass, muscle strength, and muscular endurance in women. Spreading protein intake throughout the day maximizes the anabolic response to dietary protein, benefiting muscle mass and performance.

Moderate to vigorous physical activity, leucine, and protein intake contributions to muscle health in middle age.

Objective: Identify contributors to differences in the muscle size and strength of sedentary and active young and middle-aged adults. Methods: This cross-sectional study included 98 participants aged 20-65 years. Participants were categorized based on age and self-reported physical activity (PA) habits. Participants completed a strength assessment of knee extensors (KEPT), knee flexors (KFPT), plantar flexors (PFPT), and dorsiflexors (DFPT), a 3-day dietary intake log, 7-day accelerometry, and a magnetic resonance imaging (MRI) scan for muscle cross-sectional area analysis of the right quadriceps (CSAq). Results: There were significant age and activity-related group effects for relative protein intake (p<0.001), relative energy intake (p=0.04), KEPT (p=0.01), CSAq (p=0.002), PFPT (p=0.004) and DFPT (p=0.003). Moderate, moderate-to-vigorous, and vigorous PA were positively associated with CSAq (R2=0.69- 0.71; p<0.05), KEPT (R2=0.61-0.63; p<0.05), and PFPT (R2=0.31-0.36; p<0.05). Relative protein intake and daily leucine intake were significantly and positively associated with CSAq (R2=0.70 and 0.67 respectively; p<0.05), KEPT (R2=0.62 and 0.65 respectively; p<0.05), and PFPT (R2=0.29 and 0.28 respectively; p<0.05). Conclusion: Muscle size and strength were lower in middle age relative to younger age, but increased PA, protein intake, and leucine intake was associated with the preservation of muscle size and strength in larger muscle groups of the lower body.

Sedentary behaviour in youth.

The purpose of this review is to describe the amount of time children spend in sedentary behaviour and to determine if there are specific factors that associate with sedentary behaviour in children. The following search terms were used to identify relevant articles: sedentary behaviour, inactivity, television, computer, video games, small screen, sitting, prevalence, patterns, correlates, factors and determinants. The databases used to conduct the search included PubMed, PsycINFO, ERIC (Education Resources Information Center) and Academic Search Premier. The studies reviewed were limited to those that sampled children (2-18 years), were written in English and used a measure of sedentary behaviour as the dependent variable. Several studies reported the time spent watching television or the proportion of children at or above a threshold for television viewing (eg, ≥3 h/day). Among the accelerometer studies included, the National Health and Nutrition Examination Survey is the largest and reported ∼6.1, 7.5 and 8.0 h/day mean sedentary time in children 6-11, 12-15 and 16-19 years old, respectively. Taken together, the existing literature across the world indicates a slightly higher level of sedentary behaviour in older children. Higher levels of sedentary behaviour were also reported in non-white children, children from lower socioeconomic status background and children from households with more access to televisions/computers. Lower levels of sedentary behaviour were reported in children whose parents have rules/limitations on screen time.

Inter-Device Agreement between Fitbit Flex 1 and 2 for Assessing Sedentary Behavior and Physical Activity.

This study examined the inter-model agreement between the Fitbit Flex (FF) and FF2 in estimating sedentary behavior (SED) and physical activity (PA) during a free-living condition. 33 healthy adults wore the FF and FF2 on non-dominant wrist for 14 consecutive days. After excluding sleep and non-wear time, data from the FF and FF2 was converted to the time spent (min/day) in SED and PA using a proprietary algorithm. Pearson's correlation was used to evaluate the association between the estimates from FF and FF2. Mean absolute percent errors (MAPE) were used to examine differences and measurement agreement in SED and PA estimates between FF and FF2. Bland-Altman (BA) plots were used to examine systematic bias between two devices. Equivalence testing was conducted to examine the equivalence between the FF and FF2. The FF2 had strong correlations with the FF in estimating SED and PA times. Compared to the FF, the FF2 yielded similar SED and PA estimates along with relatively low measurement discords and did not have significant systematic biases for SED and Moderate-to-vigorous PA estimates. Our findings suggest that researchers may choose FF2 as a measurement of SED and PA when FF is not available in the market during the longitudinal PA research.

Segmented School Physical Activity and Weight Status in Children: Application of Compositional Data Analysis.

The purpose of this study was to apply compositional data analysis (CoDA) for the analysis of segmented school step counts and associate the school step count composition to body mass index (BMI) z-scores in a sample of children. Participants were 855 (51.8% female) children recruited from the fourth and fifth grades from four schools following a 7-h school schedule. Using piezoelectric pedometers, step count data were collected during physical education, recess, lunch, and during academic class time. A multi-level mixed effects model associated the step count composition with BMI z-scores. Compositional isotemporal substitution determined changes in BMI z-scores per reallocation of steps between pairs of school segments. A higher percentage of steps accrued during physical education (b = -0.34, 95%CI: -0.65--0.03, p = 0.036) and recess (b = -0.47, 95%CI: -0.83--0.11, p = 0.012), relative to other segments, was associated with lower BMI z-scores. Specifically, a 5% to 15% reallocation of steps accrued during lunchtime to either physical education or recess was associated with lower BMI z-scores, ranging from -0.07 to -0.25 standard deviation units. Focusing school-based promotion of physical activity during physical education and recess may have greater relative importance if targeted outcomes are weight-related.

Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting.

OBJECTIVES: The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting. METHODS: Forty-eight participants (31 females, 17 males; ages 18-59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement. RESULTS: The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%. CONCLUSION: All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.

School-based physical activity interventions in rural and urban/suburban communities: A systematic review and meta-analysis.

Disparities in physical activity and health outcomes exist between urban and rural youth. School settings can be utilized to promote physical activity in youth regardless of urban-rural status. This systematic review and meta-analysis aimed to assess and compare the effect of rural and urban/suburban school-based physical activity programs on total physical activity in youth. A search of five databases was conducted. A total of 33 studies remained after the exclusion process, 28 of which took place in urban/suburban schools and five of which took place in rural schools. The DerSimonian and Laird random effects model was employed with the estimates of heterogeneity taken from the inverse-variance fixed-effect model. For rural studies, the Hartung-Knapp-Sidak-Jonkman method was used to obtain error estimates. Results from the total sample indicated a significant but small pooled increase in daily physical activity (Hedge's g = 0.12, 95% confidence interval [CI]: 0.06-0.18), which held for interventions conducted in urban/suburban schools (Hedge's g = 0.12, 95% CI: 0.06-0.19). For rural school-based interventions, there was no significant pooled effect (Hedge's g = 0.06, 95% CI: -0.50 to 0.61). This meta-analysis provides evidence that school-based interventions can be marginally effective for increasing daily physical activity in children and adolescents; however, no effect was observed for interventions implemented in rural settings.