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.