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.

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).

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.

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).

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.

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.

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 ).

The Influence of Friends and Psychosocial Factors on Physical Activity and Screen Time in Normal and Overweight Adolescents: A Mixed-Methods Analysis.

BACKGROUND: Understanding factors that influence physical activity (PA) and sedentary behavior is crucial to develop interventions to improve adolescents' health-related behaviors. PURPOSE: To compare the influence of friends and psychosocial factors on moderate-to-vigorous physical activity (MVPA) and screen time (ST) between normal weight (NW) and overweight (OW) adolescents. METHODS: In all, 21 OW and 21 NW adolescents wore accelerometers and completed questionnaires assessing MVPA, ST, and psychosocial variables. The MVPA and ST were assessed in nominated friends. Adolescents participated in focus groups assessing influence on activity behaviors. RESULTS: There were no differences in MVPA; however, NW adolescents reported less ST than OW adolescents (8.9 vs 13.1 h/wk, P = .04). For OW adolescents, friends' ST ( P = .002) and psychosocial factors ( P = .05) were associated with ST, while only PA self-efficacy was associated with MVPA. For NW adolescents, only friends' MVPA ( P = .04) was associated with self-reported PA. Exploratory analyses revealed differences among weight status and gender. Focus group discussions revealed that friends influenced both OW and NW adolescents' MVPA; however, this appeared to be more apparent for NW males, while psychosocial factors played a role in both OW and NW females. The OW adolescents reported that friends were more of an influence on their ST levels, while NW adolescents indicated that their ST was not affected by their friends' behaviors. CONCLUSIONS: Interventions to increase MVPA and/or decrease ST may need to be tailored for NW and OW adolescents.

School-based secondary prevention of overweight and obesity among 8- to 12-year old children: Design and sample characteristics of the SNAPSHOT trial.

Rising levels of severe obesity among children, worsening disparities by race and ethnicity and reluctance of primary care clinicians' to provide obesity management to children are compelling reasons to consider alternatives to primary care management of childhood obesity. The Students Nurses and Parents Seeking Healthy Options Together (SNAPSHOT) trial will test the efficacy of an elementary school-based, school nurse-led, healthy weight management program to reduce excess weight gain among children, 8- to 12-years old with a body mass index (BMI) ≥75th percentile, by increasing healthy dietary practices and physical activity and decreasing sedentary behaviors. SNAPSHOT has enrolled and randomized 132 child/parent dyads to either the: (1) 9-month SNAPSHOT intervention that includes four home visits, 14 kid groups held during out-of-school time and five parent groups or (2) a newsletter program consisting of monthly mailings and family-focused healthy lifestyle information. Outcomes are assessed at baseline, 12-months (post intervention) and 24-months (follow-up) post randomization. The primary outcome is child age- and gender-adjusted BMI z-score. Secondary outcomes include child dietary intake assessed with 24-h dietary recall interviews and accelerometer-measured activity levels. The SNAPSHOT intervention is a model of secondary obesity prevention for children that addresses the urgent need for theory-informed, evidence-based and safe weight management programs, delivered by skilled health professionals in accessible settings. This report describes development of the SNAPSHOT trial, including recruitment and randomization procedures, assessments, intervention and implementation plans, and baseline characteristics of the study sample.

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.

Impacts of active school design on school-time sedentary behavior and physical activity: A pilot natural experiment.

BACKGROUND: Children spend a significant portion of their days in sedentary behavior (SB) and on average fail to engage in adequate physical activity (PA). The school built environment may influence SB and PA, but research is limited. This natural experiment evaluated whether an elementary school designed to promote movement impacted students' school-time SB and PA. METHODS: Accelerometers measured SB and PA at pre and post time-points in an intervention group who moved to the new school (n = 21) and in a comparison group experiencing no school environmental change (n = 20). Difference-in-difference (DD) analysis examined SB and PA outcomes in these groups. Measures were also collected post-intervention from an independent, grade-matched group of students in the new school (n = 21). RESULTS: As expected, maturational increases in SB were observed. However, DD analysis estimated that the intervention attenuated increase in SB by 81.2 ± 11.4 minutes/day (p<0.001), controlling for time in moderate to vigorous physical activity (MVPA). The intervention was also estimated to increase daily number of breaks from SB by 23.4 ± 2.6 (p < .001) and to increase light physical activity (LPA) by 67.7 ± 10.7 minutes/day (p<0.001). However, the intervention decreased MVPA by 10.3 ± 2.3 minutes/day (p<0.001). Results of grade-matched independent samples analysis were similar, with students in the new vs. old school spending 90.5 ± 16.1 fewer minutes/day in SB, taking 21.1 ± 2.7 more breaks from SB (p<0.001), and spending 64.5 ± 14.8 more minutes in LPA (p<0.001), controlling for time in MVPA. Students in the new school spent 13.1 ± 2.7 fewer minutes in MVPA (p<0.001) than their counterparts in the old school. CONCLUSIONS: This pilot study found that active school design had beneficial effects on SB and LPA, but not on MVPA. Mixed results point to a need for active classroom design strategies to mitigate SB, and quick access from classrooms to areas permissive of high-intensity activities to promote MVPA. Integrating active design with programs/policies to promote PA may yield greatest impact on PA of all intensities.

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.

Psychosocial and Friend Influences on Objective Sedentary Behavior and Screen Time: A Mixed Methods Analysis.

BACKGROUND: Sedentary behavior (SB) increases throughout adolescence, and is associated with adverse health outcomes. PURPOSE: Examine psychosocial and friend influences on SB and screen time in adolescents using a mixed-methods design. METHODS: 108 middle and high school students wore accelerometers to measure objective SB, completed screen time and psychosocial questionnaires, and nominated friends to complete activity questionnaires. Focus groups centered around influences on SB behavior. Regression analyses and NVivo software analyzed quantitative and qualitative data. RESULTS: Screen time was associated with greater screen time enjoyment, lower self-efficacy, and friends' screen time (r2 = .21, P < .0001). Friends influenced whether adolescents engaged in screen time behaviors, with active friends encouraging less screen time. CONCLUSION: Active friends influenced adolescents to engage in less SB. Interventions should place an emphasis on encouraging less screen time, and providing opportunities for adolescents and their friends to engage in activities that promote physical activity rather than SB.

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.

Social and Psychological Factors Associated With Adolescent Physical Activity.

OBJECTIVE: The purpose of this study was to examine, using structural equation modeling, the associations between nominated friend physical activity (PA), friend social support with individual PA-related psychological factors, and adolescent PA. METHODS: Data were obtained from EAT 2010 (Eating and Activity Among Teens), a large cross-sectional study conducted in 20 middle and high schools. The sample consisted of 1951 adolescents (mean age: 14.25 ± 1.96, 54% female, 68% ethnic minorities). PA, parent and friend social support (perceived social support for PA from parents and friends), and psychological measures (PA enjoyment, PA self-efficacy, and PA barriers) were assessed by self-report questionnaires. The SEM analysis consisted of 1 observed variable: friend PA, and 2 latent constructs: psychological factors, perceived social support. RESULTS: The model was a good fit, indicating that there were significant direct effects of both friend PA (P < .01) and psychological factors (P < .0001) on adolescent PA. In addition, psychological factors mediated the association between friend PA and adolescent PA. CONCLUSION: The results of this model suggest that psychological factors and friend PA are associated with adolescent PA, and that psychological factors may play an important role. Future studies should further examine the association of both friend PA and psychological variables with adolescent PA.

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.

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.

Effect of a School Choice Policy Change on Active Commuting to Elementary School.

PURPOSE: The purposes of this study were to assess the effect of restricting school choice on changes in travel distance to school and transportation mode for elementary school students. DESIGN: Study design was pre-post (spring 2010-fall 2010) quasi-experimental. SETTING: Study setting was all public elementary schools in Minneapolis, Minnesota. SUBJECTS: Subjects comprised approximately 20,500 students across 39 schools. INTERVENTION: Study assessed a school choice policy change that restricted school choice to a school closer to the family's home. MEASURES: School district transportation data were used to determine distance to school. Direct observations of student travel modes (two morning and two afternoon commutes at each time point) were used to assess transportation mode. ANALYSIS: Chi-square and independent-sample t-tests were calculated to describe the schools. Repeated measures general linear models were used to assess changes in travel distance to school and observed commuting behavior. RESULTS: Distance to school significantly decreased (1.83 ± .48 miles to 1.74 ± .46 miles; p = .002). We failed to observe any significant changes in morning (+.7%) or afternoon (-.7%) active commuting (both p = .08) or the number of automobiles in the morning (-7 autos per school; p = .06) or afternoon (+3 autos per school; p = .14). CONCLUSION: The more restrictive school choice policy decreased distance to school but had no significant effect on active commuting. Policy interventions designed to increase active commuting to school may require additional time to gain traction and programmatic support to induce changes in behavior.