Sleep Measurement Using Wrist-Worn Accelerometer Data Compared with Polysomnography.

This study determined if using alternative sleep onset (SO) definitions impacted accelerometer-derived sleep estimates compared with polysomnography (PSG). Nineteen participants (48%F) completed a 48 h visit in a home simulation laboratory. Sleep characteristics were calculated from the second night by PSG and a wrist-worn ActiGraph GT3X+ (AG). Criterion sleep measures included PSG-derived Total Sleep Time (TST), Sleep Onset Latency (SOL), Wake After Sleep Onset (WASO), Sleep Efficiency (SE), and Efficiency Once Asleep (SE_ASLEEP). Analogous variables were derived from temporally aligned AG data using the Cole-Kripke algorithm. For PSG, SO was defined as the first score of 'sleep'. For AG, SO was defined three ways: 1-, 5-, and 10-consecutive minutes of 'sleep'. Agreement statistics and linear mixed effects regression models were used to analyze 'Device' and 'Sleep Onset Rule' main effects and interactions. Sleep-wake agreement and sensitivity for all AG methods were high (89.0-89.5% and 97.2%, respectively); specificity was low (23.6-25.1%). There were no significant interactions or main effects of 'Sleep Onset Rule' for any variable. The AG underestimated SOL (19.7 min) and WASO (6.5 min), and overestimated TST (26.2 min), SE (6.5%), and SE_ASLEEP (1.9%). Future research should focus on developing sleep-wake detection algorithms and incorporating biometric signals (e.g., heart rate).

Walking cadence (steps/min) and intensity in 21-40 year olds: CADENCE-adults.

BACKGROUND: Previous studies have reported that walking cadence (steps/min) is associated with absolutely-defined intensity (metabolic equivalents; METs), such that cadence-based thresholds could serve as reasonable proxy values for ambulatory intensities. PURPOSE: To establish definitive heuristic (i.e., evidence-based, practical, rounded) thresholds linking cadence with absolutely-defined moderate (3 METs) and vigorous (6 METs) intensity. METHODS: In this laboratory-based cross-sectional study, 76 healthy adults (10 men and 10 women representing each 5-year age-group category between 21 and 40 years, BMI = 24.8 ± 3.4 kg/m2) performed a series of 5-min treadmill bouts separated by 2-min rests. Bouts began at 0.5 mph and increased in 0.5 mph increments until participants: 1) chose to run, 2) achieved 75% of their predicted maximum heart rate, or 3) reported a Borg rating of perceived exertion > 13. Cadence was hand-tallied, and intensity (METs) was measured using a portable indirect calorimeter. Optimal cadence thresholds for moderate and vigorous ambulatory intensities were identified using a segmented regression model with random coefficients, as well as Receiver Operating Characteristic (ROC) models. Positive predictive values (PPV) of candidate heuristic thresholds were assessed to determine final heuristic values. RESULTS: Optimal cadence thresholds for 3 METs and 6 METs were 102 and 129 steps/min, respectively, using the regression model, and 96 and 120 steps/min, respectively, using ROC models. Heuristic values were set at 100 steps/min (PPV of 91.4%), and 130 steps/min (PPV of 70.7%), respectively. CONCLUSIONS: Cadence thresholds of 100 and 130 steps/min can serve as reasonable heuristic thresholds representative of absolutely-defined moderate and vigorous ambulatory intensity, respectively, in 21-40 year olds. These values represent useful proxy values for recommending and modulating the intensity of ambulatory behavior and/or as measurement thresholds for processing accelerometer data. TRIAL REGISTRATION: Clinicaltrials.gov ( NCT02650258 ).

Sedentary behavior and physical activity of young adult university students.

Movement and non-movement behaviors include sleep, sedentary behavior (SB) and physical activity (PA). While young adults are generally perceived as healthy, the level and relationship of SB and PA in college-age students has not been greatly explored. The purpose of this study was to objectively measure the levels of SB and PA in 18-20 year-old university students, record their self-reported extracurricular activities, and explore the relationship of all these with body mass index (BMI) and waist circumference (WC). Male (n = 48) and female (n = 46) students participated in this cross-sectional study. Hierarchical multiple regression analyses were used to examine time spent in SB, moderate to vigorous physical activity (MVPA), number of self-reported sedentary extracurricular activities, and their relation to the dependent variables of BMI and WC. In correlation analyses, SB (p < .001) and MVPA (p = .017) both were negatively associated with BMI, and "other" race or ethnicity (African American, Hispanic, mixed; p = .013) and number of self-reported sedentary extracurricular activities (p = .006) were positively associated with BMI. In the WC regression model, SB (p = .018) was negatively associated and number of self-reported sedentary extracurricular activities (p = .006) was positively associated with WC. University students may be both highly active and highly sedentary. Future researchers should consider targeting interventions to reduce SB in addition to improving PA.

Youth Oriented Activity Trackers: Comprehensive Laboratory- and Field-Based Validation.

BACKGROUND: Commercial activity trackers are growing in popularity among adults and some are beginning to be marketed to children. There is, however, a paucity of independent research examining the validity of these devices to detect physical activity of different intensity levels. OBJECTIVES: The purpose of this study was to determine the validity of the output from 3 commercial youth-oriented activity trackers in 3 phases: (1) orbital shaker, (2) structured indoor activities, and (3) 4 days of free-living activity. METHODS: Four units of each activity tracker (Movband [MB], Sqord [SQ], and Zamzee [ZZ]) were tested in an orbital shaker for 5-minutes at three frequencies (1.3, 1.9, and 2.5 Hz). Participants for Phase 2 (N=14) and Phase 3 (N=16) were 6-12 year old children (50% male). For Phase 2, participants completed 9 structured activities while wearing each tracker, the ActiGraph GT3X+ (AG) research accelerometer, and a portable indirect calorimetry system to assess energy expenditure (EE). For Phase 3, participants wore all 4 devices for 4 consecutive days. Correlation coefficients, linear models, and non-parametric statistics evaluated the criterion and construct validity of the activity tracker output. RESULTS: Output from all devices was significantly associated with oscillation frequency (r=.92-.99). During Phase 2, MB and ZZ only differentiated sedentary from light intensity (P<.01), whereas the SQ significantly differentiated among all intensity categories (all comparisons P<.01), similar to AG and EE. During Phase 3, AG counts were significantly associated with activity tracker output (r=.76, .86, and .59 for the MB, SQ, and ZZ, respectively). CONCLUSIONS: Across study phases, the SQ demonstrated stronger validity than the MB and ZZ. The validity of youth-oriented activity trackers may directly impact their effectiveness as behavior modification tools, demonstrating a need for more research on such devices.

Cross-Validation of Two Accelerometers for Assessment of Physical Activity and Sedentary Time in Preschool Children.

PURPOSE: The purpose of this study was to cross-validate previously developed Actiwatch (AW; Ekblom et al. 2012) and AcitGraph (AG; Sirard et al. 2005; AG-P, Pate et al. 2006) cut-point equations to categorize free-living physical activity (PA) of preschoolers using direct observation (DO) as the criterion measure. A secondary aim was to compare output from the AW and the AG from previously developed equations. METHODS: Participants' (n = 33; age = 4.4 ± 0.8 yrs; females, n=12) PA was directly observed for three 10-min periods during the preschool-day while wearing the AW (nondominant wrist) and AG (waist). Device specific cut-points were used to reduce the AW-E (Ekblom et al. 2012) and AG (AG-S, Sirard et al. 2005; AG-P, Pate et al. 2006) data into intensity categories. Spearman correlations (rsp) and agreement statistics were used to assess associations between the DO intensity categories and device data. Mixed model regression was used to identify differences in times spent in activity intensity categories. RESULTS: There was a significant correlation between AW and AG output across all data (rsp = 0.41, p < .0001) and both were associated with the DO intensity categories (AW: rsp = 0.47, AG: rsp = 0.47; p < .001). At the individual level, all devices demonstrated relatively low sensitivity but higher specificity. At the group level, AW-E and AG-P provided similar estimates of time spent in moderate-to-vigorous PA (MVPA, AW-E: 4.7 ± 4.1, AG-P: 4.4 ± 3.3), compared with DO (5.1 ± 3.5). CONCLUSION: The AW-E and AG-P estimated times spent in MVPA were similar to DO, but the weak agreement statistics indicate that neither device cut-point equations provided accurate estimates at the individual level.

The influence of friends and psychosocial factors on physical activity and screen time behavior in adolescents: a mixed-methods analysis.

(1) Determine the association between adolescent moderate-to-vigorous physical activity (MVPA) and screen time with their nominated friends' behaviors and (2) explore potential mechanisms of friends' social influences on MVPA and screen time. Participants consisted of 152 adolescents (mean age: 14.5 years, 53 % female, 50 % high school, 80 % Caucasian). MVPA was measured with an Actigraph GT3X+ accelerometer. Demographic and psychosocial variables were assessed via questionnaires. Participants nominated up to 5 friends who completed MVPA and screen time questionnaires. A subset of adolescents (n = 108) participated in focus groups that examined potential mechanism of friends' influence on MVPA and screen time. Multiple regression analysis examined the association of demographic, psychological, and nominated friend variables with participants' MVPA and screen time. NVivo 10.0 was used to analyze qualitative data. Greater levels of friends' MVPA was associated with greater levels of MVPA in both males (p < .0001) and females (p < .0001). Greater levels of friends' screen time was associated with greater levels of screen time in males (p = .04) while psychosocial variables, such as increased screen time enjoyment, were associated with increased screen time in females (p = .01). School level was not associated with either MVPA or screen time. Focus group data indicated that friends positively influenced participants' MVPA through engaging in activity with participants, verbal encouragement, and modeling of MVPA. All participants preferred to be active with friends rather than alone, however, females preferred activity with a close friend while males preferred to be active with a group. Enjoyment of MVPA was the most cited reason for engaging in MVPA with friends. The majority of participants reported friends not having an influence on screen time. Adolescents with active friends are more likely to be physically active and spend less time engaging in screen-based behaviors. Interventions to increase MVPA in youth could be designed to include the recruitment of friends to increase enjoyment of MVPA.

Comparison of the Effects of Stable and Dynamic Furniture on Physical Activity and Learning in Children.

We compared the effects of traditional (stable) and non-traditional (dynamic) school furniture on children's physical activity (PA), energy expenditure (EE), information retention, and math skills. Participants were 12 students (8.3 years, 58 % boys) in grades 1-5. Participants wore an Actigraph GT3X+ accelerometer (to assess PA), and an Oxycon Mobile indirect calorimetry device (to assess EE) for 40 min (20 min for each session). Each session consisted of a nutrition lecture, multiple choice questions related to the lecture, and grade-appropriate math problems. We used paired t tests to examine differences between the stable and dynamic furniture conditions. Average activity counts were significantly greater in the dynamic than the stable furniture condition (40.82 vs. 9.81, p < 0.05). We found no significant differences between conditions for average oxygen uptake (p = 0.34), percentage of nutrition questions (p = 0.5), or math problems (p = 0.93) answered correctly. Movement was significantly greater in the dynamic than the stable furniture condition, and did not impede information acquisition or concentration. Future studies should compare the long-term effects of traditional and dynamic furniture on health and academic outcomes in schools and other settings.

Validation of Accelerometer Thresholds and Inclinometry for Measurement of Sedentary Behavior in Young Adult University Students.

Sedentary behavior (SB) is a major contributor to obesity and significant morbidity and mortality in adolescence and adulthood, yet measurement of SB is still evolving. The purpose of this study was to assess the degree of construct validity of the inclinometer function and single-axis and vector magnitude accelerometry metrics of the ActiGraph GT3X+ in objectively measuring SB and physical activity in 28 young adult university students who performed nine semi-structured activities, each for five minutes: lying, sitting, reading, seated video gaming, video watching, seated conversation, standing, stationary biking, and treadmill walking. Inclinometry and four output metrics from the ActiGraph were analyzed in comparison to direct observation by a researcher recorded each minute. For overall accuracy in measuring both SB and physical activity, all four accelerometer metrics (94.7-97.8%) outperformed the inclinometer function (70.9%). Vector magnitude accelerometry with a threshold of 150 counts per minute as the cut point for sedentary behavior was superior to other methods. While accelerometry was more accurate overall at detecting the behaviors tested, inclinometry had some advantages over accelerometry methods at detecting walking, biking, and standing. The findings support use of accelerometry as a valid objective measure of body movement, while use of inclinometry as a sole measure is not recommended. Additional research would be beneficial to improve the calibration of the inclinometer and explore ways of combining this with accelerometer data for objectively measuring SB and physical activity.

Disentangling associations between pubertal development, healthy activity behaviors, and sex in adolescent social networks.

With the onset of puberty, youth begin to choose their social environments and develop health-promoting habits, making it a vital period to study social and biological factors contextually. An important question is how pubertal development and behaviors such as physical activity and sleep may be differentially linked with youths' friendships. Cross-sectional statistical network models that account for interpersonal dependence were used to estimate associations between three measures of pubertal development and youth friendships at two large US schools drawn from the National Longitudinal Study of Adolescent to Adult Health. Whole-network models suggest that friendships are more likely between youth with similar levels of pubertal development, physical activity, and sleep. Sex-stratified models suggest that girls' friendships are more likely given a similar age at menarche. Attention to similar pubertal timing within friendship groups may offer inclusive opportunities for tailored developmental puberty education in ways that reduce stigma and improve health behaviors.

The relationship of area-level sociodemographic characteristics, household composition and individual-level socioeconomic status on walking behavior among adults.

Understanding the contextual factors associated with why adults walk is important for those interested in increasing walking as a mode of transportation and leisure. This paper investigates the relationships between neighborhood-level sociodemographic context, individual level sociodemographic characteristics and walking for leisure and transport. Data from two community-based studies of adults (n=550) were used to determine the association between the area-sociodemographic environment (ASDE), calculated from U.S. Census variables, and individual-level SES as potential correlates of walking behavior. Descriptive statistics, mean comparisons and Pearson's correlations coefficients were used to assess bivariate relationships. Generalized estimating equations were used to model the relationship between ASDE, as quartiles, and walking behavior. Adjusted models suggest adults engage in more minutes of walking for transportation and less walking for leisure in the most disadvantaged compared to the least disadvantaged neighborhoods but adding individual level demographics and SES eliminated the significant results. However, when models were stratified for free or reduced cost lunch, of those with children who qualified for free or reduced lunch, those who lived in the wealthiest neighborhoods engaged in 10.7 minutes less of total walking per day compared to those living in the most challenged neighborhoods (p<0.001). Strategies to increase walking for transportation or leisure need to take account of individual level socioeconomic factors in addition to area-level measures.

Association of Time-of-Day Physical Activity With Incident Cardiovascular Disease: The UK Biobank Study.

INTRODUCTION: Early morning is characterized by an increased risk of cardiovascular events, a sudden rise in blood pressure, impaired endothelial function, and exacerbated hemodynamic changes during physical activity. The study aims to examine whether the time of day of physical activity is associated with incident cardiovascular disease (CVD). METHODS: We prospectively analyzed 83,053 participants in the UK Biobank with objectively measured physical activity and initially free of CVD. Based on the diurnal patterns of physical activity, participants were categorized into 4 groups: early morning (n = 15,908), late morning (n = 22,371), midday (n = 24,764), and evening (n = 20,010). Incident CVD was defined as the first diagnosis of coronary heart disease or stroke. RESULTS: During 197.4 million person-years of follow-up, we identified 3454 CVD cases. After adjusting for the overall acceleration average, the hazard ratios and 95% confidence intervals were 0.95 (0.86-1.07) for late morning, 1.15 (1.03-1.27) for midday, and 1.03 (0.92-1.15) for evening, as compared with the early morning group. In the joint analyses, higher levels of physical activity were associated with a lower risk of incident CVD in a similar manner across the early morning, late morning, and evening groups. However, the beneficial association was attenuated in the midday group. CONCLUSION: In conclusion, early morning, late morning, and evening are all favorable times of day to engage in physical activity for the primary prevention of CVD, while midday physical activity is associated with an increased risk of CVD compared with early morning physical activity after controlling for the levels of physical activity.

Association of the time of day of peak physical activity with cardiovascular mortality: Findings from the UK Biobank study.

Cardiovascular physiology and pathophysiology display pronounced circadian rhythms. The study is designed to examine whether the time of day of physical activity is associated with cardiovascular mortality. We analyzed 94,489 UK Biobank adults with objectively measured physical activity, including 53,328 morning-type participants and 30,962 evening-type participants based on self-reported chronotypes. The time of day of peak physical activity was categorized using a machine learning algorithm: early morning (n = 18,477), late morning (n = 25,700), midday (reference) (n = 27,803), and night (n = 22,509). Hazard ratios of cardiovascular mortality were examined using the Cox proportional hazards model. During a median follow-up of 6.9 years (interquartile range, 6.3-7.4 years), we identified 629 cardiovascular deaths. The hazard of cardiovascular mortality was elevated in the early morning group (hazard ratio = 1.56, 95% Confidence Interval [1.23-1.98]) and night group (1.49, [1.18-1.88]) but not in the late morning group (1.21, [0.98-1.47]) compared to the referent midday group. In the chronotype-stratified analysis, the increased cardiovascular mortality in the morning group was only observed in the evening-type participants, while the increased cardiovascular mortality in the night group was only observed in the morning-type participants. In conclusion, optimizing the timing of peak physical activity according to cardiovascular circadian rhythms and individual chronotypes could be a potential therapeutic target that brings additional health benefits.

Multilevel predictors of adolescent physical activity: a longitudinal analysis.

BACKGROUND: To examine how factors from a social ecologic model predict physical activity (PA) among adolescents using a longitudinal analysis. METHODS: Participants in this longitudinal study were adolescents (ages 10-16 at baseline) and one parent enrolled in the Transdisciplinary Research on Energetics and Cancer-Identifying Determinants of Eating and Activity (TREC-IDEA) and the Etiology of Childhood Obesity (ECHO). Both studies were designed to assess a socio-ecologic model of adolescent obesity risk. PA was collected using ActiGraph activity monitors at two time points 24 months apart. Other measures included objective height and weight, adolescent and parent questionnaires on multilevel psychological, behavioral and social determinants of PA, and a home PA equipment inventory. Analysis was conducted using SAS, including descriptive characteristics, bivariate and stepped multivariate mixed models, using baseline adjustment. Models were stratified by gender. RESULTS: There were 578 adolescents with complete data. Results suggest few statistically significant longitudinal associations with physical activity measured as minutes of MVPA or total counts from accelerometers. For boys, greater self-efficacy (B = 0.75, p = 0.01) and baseline MVPA (B = 0.55, p < 0.01) remained significantly associated with MVPA at follow-up. A similar pattern was observed for total counts. For girls, baseline MVPA (B = 0.58, p = 0.01) and barriers (B = -0.32, p = 0.05) significantly predicted MVPA at follow-up in the full model. The full multilevel model explained 30% of the variance in PA among boys and 24% among girls. CONCLUSIONS: PA change in adolescents is a complex issue that is not easily understood. Our findings suggest early PA habits are the most important predictor of PA levels in adolescence. Intervention may be necessary prior to middle school to maintain PA through adolescence.

Adolescents' attitudes toward sports, exercise, and fitness predict physical activity 5 and 10 years later.

OBJECTIVE: To determine whether adolescent attitudes towards sports, exercise, and fitness predict moderate-to-vigorous physical activity 5 and 10 years later. METHOD: A diverse group of 1902 adolescents participating in Project Eating and Activity in Teens, reported weekly moderate-to-vigorous physical activity and attitudes toward sports, exercise, and fitness in Eating and Activity in Teens-I (1998-99), Eating and Activity in Teens-II (2003-04), and Eating and Activity in Teens-III (2008-09). RESULTS: Mean moderate-to-vigorous physical activity was 6.4, 5.1, and 4.0 hours/week at baseline, 5-year, and 10-year follow-up, respectively. Attitudes toward sports, exercise, and fitness together predicted moderate-to-vigorous physical activity at 5 and 10 years. Among the predictors of 5- and 10-year moderate-to-vigorous physical activity, attitude's effect size, though modest, was comparable to the effect sizes for sports participation and body mass index. Adolescents with more-favorable attitudes toward sports, exercise, and fitness engaged in approximately 30%-40% more weekly moderate-to-vigorous physical activity at follow-up (2.1 hour/week at 5 years and 1.2 hour/week at 10 years) than those with less-favorable attitudes. CONCLUSION: Adolescents' exercise-related attitudes predict subsequent moderate-to-vigorous physical activity independent of baseline behavior suggesting that youth moderate-to-vigorous physical activity promotion efforts may provide long-term benefits by helping youth develop favorable exercise attitudes.

School-Based Secondary Obesity Prevention for Eight- to Twelve-Year-Olds: Results from the Students, Nurses, and Parents Seeking Healthy Options Together Randomized Trial.

Objective: The primary aim of this randomized controlled trial, conducted in Minneapolis/St. Paul, Minnesota (2014-2019), was to evaluate the effects of a school-based, school nurse-delivered, secondary obesity prevention intervention to reduce excess weight gain among preadolescent children with obesity or at risk of developing obesity. Methods: Parent/child dyads (n = 132) were randomized to the 9-month Students, Nurses, and Parents Seeking Healthy Options Together (SNAPSHOT) intervention (32.5 contact hours) or newsletter-only control group. Eligible children were 8 to 12 years old, proficient in English, and with a BMI ≥75th percentile, calculated using height/weight reported by a parent, school nurse, or clinician. The primary outcome was child BMI for sex/age z-score (BMIz) at postintervention (12 months) and follow-up (24 months). Results: Among children, 63% were non-White, 51% were male, and 51% with obesity, including 21% with severe obesity. Among families, 59% received economic assistance and 30% reported food insecurity. The mean number of intervention contact hours received was 20 (range: 0-32.5). Among dyads (n = 54) receiving the intervention, parents were very satisfied/satisfied with SNAPSHOT and SNAPSHOT staff, 96% and 100%, respectively, and very likely/likely (97%) to recommend SNAPSHOT to others. Most (70%) children liked the kid group sessions "a lot." In an intent-to-treat analysis, there were no significant between-group differences in child BMIz at 12 [0.04; 95% confidence interval (CI) -0.07 to 0.16] or 24 months (0.06; 95% CI -0.08 to 0.20), with participant retention of 92% and 93%, respectively. Conclusions: The SNAPSHOT intervention was well received, but did not improve BMI in a mostly diverse, low-income preadolescent population. Approaches to intervention delivery that are feasible, maximize accessibility, and optimize clinician and school nurse collaboration warrant consideration. Clinical trial registration identifier NCT02029976.

Adolescent physical activity and screen time: associations with the physical home environment.

BACKGROUND: Previous research on the environment and physical activity has mostly focused on macro-scale environments, such as the neighborhood environment. There has been a paucity of research on the role of micro-scale and proximal environments, such as that of the home which may be particularly relevant for younger adolescents who have more limited independence and mobility. The purpose of this study was to describe associations between the home environment and adolescent physical activity, sedentary time, and screen time. METHODS: A total of 613 parent-adolescent dyads were included in these analyses from two ongoing cohort studies. Parents completed a Physical Activity and Media Inventory (PAMI) of their home environment. Adolescent participants (49% male, 14.5 ± 1.8 years) self-reported their participation in screen time behaviors and wore an ActiGraph accelerometer for one week to assess active and sedentary time. RESULTS: After adjusting for possible confounders, physical activity equipment density in the home was positively associated with accelerometer-measured physical activity (p < 0.01) among both males and females. Most of the PAMI-derived measures of screen media equipment in the home were positively associated with adolescent female's screen time behavior (p ≤ 0.03). In addition, the ratio of activity to media equipment was positively associated with physical activity (p = 0.04) in both males and females and negatively associated with screen time behavior for females (p < 0.01). CONCLUSIONS: The home environment was associated with physical activity and screen time behavior in adolescents and differential environmental effects for males and females were observed. Additional research is warranted to more comprehensively assess the home environment and to identify obesogenic typologies of families so that early identification of at-risk families can lead to more informed, targeted intervention efforts.

The relative influence of demographic, individual, social, and environmental factors on physical activity among boys and girls.

BACKGROUND: This study aimed to evaluate the associations of selected demographic, individual, social, and environmental factors with moderate-to-vigorous physical activity (MVPA) in a sample of children and adolescents. METHODS: MVPA was assessed among youth (n = 294) 10-17-years-old using the ActiGraph accelerometer. Youth completed measures of demographic and individual variables related to physical activity (PA), perceived social support by parents and peers, and perceived neighborhood characteristics. Parents completed the long-form of the International Physical Activity Questionnaire. The Physical Activity and Media Inventory was used to measure the home environment and Geographical Information Systems software was used to measure the physical neighborhood environment. Bivariate correlations and hierarchical multiple regression were conducted stratified by gender. RESULTS: Boys participated in significantly more MVPA than girls. In hierarchical analyses, peer support, home PA equipment, and temperature were significantly associated with MVPA among boys whereas distance to the school they attended was associated with MVPA among girls. The final models accounted for 25% and 15% of the variance in MVPA among boys and girls, respectively. CONCLUSIONS: Important differences exist among the individual, social, and environmental factors related to MVPA between boys and girls. Boys' levels of activity appear to be influenced by factors closely linked to unstructured and social types of activities whereas girls' activities relate to internal and external barriers as well as their proximity to their schools. The prospective contribution of these important individual, social, and environmental factors to changes in MVPA among children and adolescents remains to be determined.

Sleep is Inversely Associated with Sedentary Time among Youth with Obesity.

Objective: Pathways underlying the sleep-obesity relationship in youth are poorly understood. In this study, we examined associations of sleep with sedentary time and moderate-to-vigorous physical activity (MVPA) among youth, stratified by weight category (obesity versus no obesity). A sub-aim examined whether controlling for screen time changed the sleep-sedentary time association. Methods: Methods entailed secondary analysis of baseline data collected June-August 2014-2017 during a school-based healthy weight management trial in Minneapolis/St. Paul, Minnesota. Participants (N = 114) were 8-to-12 years old with BMI ≥ 75th percentile, most of whom were members of racial/ethnic minority groups (57%) or from households receiving economic assistance (55%). Mean nightly sleep duration and daily screen time were measured by survey, MVPA and sedentary time by accelerometer, and height and weight by research staff. Multivariate linear regression examined associations of sleep with sedentary time and MVPA. Results: Sleep was inversely associated with hours of sedentary time (ß = -1.34 [-2.11, -0.58] p = .001) and percent of time spent sedentary (ß = -2.92 [-4.83, -1.01], p = .004), for youth with obesity only. The association was unchanged by screen time. Sleep was not significantly associated with MVPA in total sample or stratified models. Conclusions: Associations among sleep, activity levels, and obesity may differ based upon movement type (sedentary time vs MVPA) and weight category (obesity vs no obesity).

Self-Efficacy, Not Peer or Parent Support, Is Associated With More Physical Activity and Less Sedentary Time Among 8- to 12-Year-Old Youth With Elevated Body Mass Index.

BACKGROUND: Youth experience a decrease in physical activity (PA) and an increase in sedentary time during adolescence. Better understanding of factors associated with activity levels during preadolescence may inform interventions to minimize decline. This study compared the association of self-efficacy for PA, parent support for PA, and peer support for PA with moderate-to-vigorous PA (MVPA) and sedentary time among 8- to 12-year-old children with body mass index ≥75th percentile. METHODS: This study analyzed baseline data from a school-based healthy weight management intervention trial, conducted in metropolitan Minnesota. Self-efficacy for PA, parent support for PA, and peer support for PA were measured by child survey using reliable tools. MVPA and sedentary time were measured using accelerometer. RESULTS: Participants included 114 children; mean age was 9.4 (0.9) years, 51% were females, 55% received public assistance, and 57% were racial/ethnic minorities. Self-efficacy for PA was positively associated with moderate to vigorous PA for girls (ß = 1.83, P < .01) and inversely with sedentary time for the total sample (ß = -7.00, P = .03). Parent support for PA was positively associated with sedentary time for girls (ß = 9.89, P = .04) and the total sample (ß = 7.83, P = .04). CONCLUSIONS: Interventions for preadolescents with elevated body mass index may improve activity levels by increasing self-efficacy for PA.

Estimating Sedentary Time from a Hip- and Wrist-Worn Accelerometer.

PURPOSE: This study aimed to determine the validity of existing methods to estimate sedentary behavior (SB) under free-living conditions using ActiGraph GT3X+ accelerometers (AG). METHODS: Forty-eight young (18-25 yr) adults wore an AG on the right hip and nondominant wrist and were video recorded during four 1-h sessions in free-living settings (home, community, school, and exercise). Direct observation videos were coded for postural orientation, activity type (e.g., walking), and METs derived from the Compendium of Physical Activities, which served as the criterion measure of SB (sitting or lying posture, <1.5 METs). Thirteen methods using cut points from vertical counts per minute (CPM), counts per 15-s (CP15s), and vector magnitude (VM) counts (e.g., CPM1853VM), raw acceleration and arm angle (sedentary sphere), Euclidean norm minus one (ENMO) corrected for gravity (mg) thresholds, uni- or triaxial sojourn hybrid machine learning models (Soj1x and Soj3x), random forest (RF), and decision tree (TR) models were used to estimate SB minutes from AG data. Method bias, mean absolute percent error, and their 95% confidence intervals were estimated using repeated-measures linear mixed models. RESULTS: On average, participants spent 34.1 min per session in SB. CPM100, CPM150, Soj1x, and Soj3x were the only methods to accurately estimate SB from the hip. Sedentary sphere and ENMO44.8 overestimated SB by 3.9 and 6.1 min, respectively, whereas the remaining wrist methods underestimated SB (range, 9.5-2.5 min). In general, mean absolute percent error was lower using hip methods compared with wrist methods. CONCLUSION: Accurate group-level estimates of SB from a hip-worn AG can be achieved using either simpler count-based approaches (CPM100 and CPM150) or machine learning models (Soj1x and Soj3x). Wrist methods did not provide accurate or precise estimates of SB. The development of large open-source free-living calibration data sets may lead to improvements in SB estimates.