Search

Purpose: We investigated convergent validity of commonly used ActiGraph scoring methods with various activPAL scoring methods in youth and adults. Methods: Youth and adults wore an ActiGraph and activPAL simultaneously for 1–3 days. We compared moderate to vigorous physical activity (MVPA) estimates from the ActiGraph Evenson 15-s (youth) and Freedson 60-s (adult) cut-point scoring methods and four activPAL scoring methods based on metabolic equivalents (METs), step counts, vertical axis counts, and vector magnitude counts. All activPAL methods were applied to 15-s epochs for youth and 60-s epochs for adults, and the METs method was also applied to 1-s epochs. Epoch-level agreement was examined with classification tests (sensitivity, positive predictive value, and F1) using the ActiGraph methods as the referent. Day-level agreement was examined using tests of mean error, mean absolute error, and Spearman correlations. Results: Relative to ActiGraph methods, which indicated a mean MVPA of 41 min/day for youth and 24 min/day for adults, the activPAL METs method applied to 15-s epochs in youth and 60-s epochs in adults yielded the most comparable estimates of MVPA. Daily MVPA estimated from all other activPAL scoring methods generally had poor agreement with ActiGraph methods in youth and adults. Conclusion: When using the same epoch lengths between monitors, MVPA estimation via the activPAL METs scoring method appears to have good comparability to ActiGraph cut points at the group-level and moderate comparability at the individual-level in youth and adults. When using this scoring method, the activPAL appears to be appropriate for measuring daily minutes of MVPA in youth and adults.

Physical behaviors (e.g., sleep, sedentary behavior, and physical activity) often occur in sustained bouts that are punctuated with brief interruptions. To detect and classify these interrupted bouts, researchers commonly use wearable devices and specialized algorithms. Most algorithms examine the data in chronological order, initiating and terminating bouts whenever specific criteria are met. Consequently, the bouts may encapsulate or overlap with later periods that also meet the activation and termination criteria (i.e., alternative bout solutions). In some cases, it is desirable to compare these alternative bout solutions before making a final classification. Thus, comparison-focused algorithms are needed, which can be used in isolation or in concert with their chronology-focused counterparts. In this technical note, we present a comparison-focused algorithm called CRIB (Clustered Recognition of Interrupted Bouts). It uses agglomerative hierarchical clustering to facilitate the comparison of different bout solutions, with the final classification being made in favor of the smallest number of bouts that comply with user-specified criteria (i.e., limits on the number, individual duration, and cumulative duration of interruptions). For demonstration, we use CRIB to assess bouts of moderate to vigorous physical activity in accelerometer data from the National Health and Nutrition Examination Survey, and we include a comparison against results from two established chronology-focused algorithms. Our discussion explores strengths and limitations of CRIB, as well as potential considerations and applications for using it in future studies. An online vignette (https://github.com/paulhibbing/PBpatterns/blob/main/vignettes/CRIB.pdf) is available to assist users with implementing CRIB in R.

Introduction This study examines the associations of neighborhood environments with BMI, HbA1c, and diabetes across 6 years in Hispanic/Latino adults. Methods Participants from the Hispanic Community Health Study/Study of Latinos San Diego site (n=3,851, mean age=39.4 years, 53.3% women, 94.0% Mexican heritage) underwent assessment of metabolic risk factors and diabetes status (categorized as normoglycemia, prediabetes, and diabetes) at baseline (2008–2011) and approximately 6 years later (2014–2017). In the Study of Latinos Community and Surrounding Areas Study ancillary study (2015–2020), participant baseline addresses were geocoded, and neighborhoods were defined using 800-meter circular buffers. Neighborhood variables representing socioeconomic deprivation, residential stability, social disorder, walkability, and greenness were created using Census and other public databases. Analyses were conducted in 2020–2021. Results Complex survey regression analyses revealed that greater neighborhood socioeconomic deprivation was associated with higher BMI (β=0.14, p

Background A better understanding of the extent to which psychosocial and environmental correlates of physical activity are specific to locations would inform intervention optimization. Purpose To investigate cross-sectional associations of location-general and location-specific variables with physical activity and sedentary time in three common locations adolescents spend time. Methods Adolescents (N = 472,Mage = 14.1,SD = 1.5) wore an accelerometer and global positioning systems (GPS) tracker and self-reported on psychosocial (e.g., self-efficacy) and environmental (e.g., equipment) factors relevant to physical activity and sedentary time. We categorized each survey item based on whether it was specific to a location to generate psychosocial and environmental indices that were location-general or specific to either school, non-school, or home location. Physical activity (MVPA) and sedentary time were based on time/location match to home, school, or all “other” locations. Mixed-effects models investigated the relation of each index with location-specific activity. Results The location-general and non-school physical activity psychosocial indices were related to greater MVPA at school and “other” locations. The school physical activity environment index was related to greater MVPA and less sedentary time at school. The home activity environment index was related to greater MVPA at home. The non-school sedentary psychosocial index was related to less sedentary time at home. Interactions among indices revealed adolescents with low support on one index benefited (i.e., exhibited more optimal behavior) from high support on another index (e.g., higher scores on the location-general PA psychosocial index moderated lower scores on the home PA environment index). Concurrent high support on two indices did not provide additional benefit. Conclusions No psychosocial or environment indices, including location-general indices, were related to activity in all locations. Most of the location-specific indices were associated with activity in the matching location(s). These findings provide preliminary evidence that psychosocial and environmental correlates of activity are location specific. Future studies should further develop location-specific measures and evaluate these constructs and whether interventions may be optimized by targeting location-specific psychosocial and environmental variables across multiple locations.

Availability of accelerometer data has made it possible to objectively and continuously monitor sedentary behavior. Various summaries of the extensive accelerometer data have been used to understand the relationship between sedentary behavior and health. However, the widely used summary measures on sedentary bouts, average bout length or its derivatives, fail to reveal patterns of accumulated sedentary behavior over time. Studies have suggested that prolonged uninterrupted sedentary behavior can be an important metric that is related to health states. Yet existing measures to capture the prolonged sedentary patterns either rely on parametric assumptions on the underlying distribution of sedentary bout length or have to categorize sedentary bout length into somewhat arbitrary categories. Gini index was also used; however, it only measures the variability in bout lengths but not the actual length. To overcome these limitations, we proposed a non-parametric weighted survival function to characterize uninterrupted sedentary behavior over time in a continuous fashion and used the area under the survival curve as a new summary measure to quantify sedentary behavior. We showed that this measure is a weighted average of bout length and contains the information on both the mean and variability of bout lengths. We demonstrated in the simulation studies that the proposed measure could better identify prolonged uninterrupted sedentary behavior and predict health outcomes. We applied this new measure and existing sedentary measures to data from the Hispanic Community Health Study/Study of Latinos to examine the association between sedentary behavior and overweight/obesity.

Introduction: Interventions targeting multiple levels of influence and settings may have a greater impact on children’s total daily physical activity than those targeting a single setting. This study evaluated the addition of family supports to a classroom-based physical activity intervention. Methods: 6 schools were randomized to a remotely delivered classroom-based physical activity only (CBPA) or classroom-based physical activity plus family (CBPA+) arm. The latter added behavior change tools (e.g., self-regulation) delivered via text messages and newsletters to caregiver/child dyads. The intervention lasted for 7 weeks with a 1-week baseline and 1-week post-intervention assessment (9 weeks total). Children who enrolled in the evaluation study received a Garmin activity monitor, which was used to tailor the text messages and evaluate changes in moderate to vigorous physical activity (MVPA). Caregivers completed surveys evaluating intervention acceptability. Results: The 53 child participants (CBPA n=35; CBPA+ n=18) were 9.7±0.7 years old, 64% were female, 59% were Black, and 23% were Hispanic/Latinx. Almost 90% of caregivers reported high satisfaction with the added family-based intervention content. Adherence to wearing the Garmin was higher in the CBPA+ arm. MVPA was low at baseline and during the first 3 weeks of the intervention (CBPA 7.5±3.1 minutes/day; CBPA+ 7.9±2.7 minutes/day) and increased by ~45 min/day by weeks 6-8 (CBPA 56.8±34.2 minutes/day; CBPA+ 49.2±18.7minutes/day). Changes in MVPA were similar between arms. Conclusion: The high acceptability and intervention engagement in the CBPA+ arm show promise for integrating mHealth tools to provide additional family support in multilevel multiapproach interventions targeting children’s physical activity.

Supplemental digital content is available in the text., Introduction Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method. Methods CHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification). Results For minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%–83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP’s positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min). Conclusion CHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.

Background: Total sedentary time and prolonged sedentary patterns can negatively impact health. This study investigated rates of various sedentary pattern variables in Hispanic/Latino youth. Methods: Participants were 956 youths (50.9% female) in the Hispanic Community Health Study/Study of Latinos Youth, a population-based cohort study of Hispanic/Latino 8- to 16-year-olds from 4 geographic regions in the United States (2012–2014). Total sedentary time and 10 sedentary pattern variables were measured through 1 week of accelerometer wear. Differences were examined by sociodemographic characteristics, geographic location, weekdays versus weekends, and season. Results: On average, youth were sedentary during 67.3% of their accelerometer wear time, spent 24.2% engaged in 10- to 29-minute sedentary bouts, and 7.2% in ≥60-minute bouts. 8- to 12-year-olds had more favorable sedentary patterns (less time in extended bouts and more breaks) than 13- to 16-year-olds across all sedentary variables. Sedentary patterns also differed by Hispanic/Latino background, with few differences across sex, household income, season, and place of birth, and none between weekdays versus weekends. Conclusions: Variables representing prolonged sedentary time were high among Hispanic/Latino youth. Adolescents in this group appear to be at especially high risk for unhealthy sedentary patterns. Population-based efforts are needed to prevent youth from engaging in increasingly prolonged sedentary patterns.

Purpose To test the validity of the Ecological Video Identification of Physical Activity (EVIP) computer vision algorithms for automated video-based ecological assessment of physical activity in settings such as parks and schoolyards. Methods Twenty-seven hours of video were collected from stationary overhead video cameras across 22 visits in nine sites capturing organized activities. Each person in the setting wore an accelerometer, and each second was classified as moderate-to-vigorous physical activity or sedentary/light activity. Data with 57,987 s were used to train and test computer vision algorithms for estimating the total number of people in the video and number of people active (in moderate-to-vigorous physical activity) each second. In the testing data set (38,658 s), video-based System for Observing Play and Recreation in Communities (SOPARC) observations were conducted every 5 min (130 observations). Concordance correlation coefficients (CCC) and mean absolute errors (MAE) assessed agreement between (1) EVIP and ground truth (people counts+accelerometry) and (2) SOPARC observation and ground truth. Site and scene-level correlates of error were investigated. Results Agreement between EVIP and ground truth was high for number of people in the scene (CCC = 0.88; MAE = 2.70) and moderate for number of people active (CCC = 0.55; MAE = 2.57). The EVIP error was uncorrelated with camera placement, presence of obstructions or shadows, and setting type. For both number in scene and number active, EVIP outperformed SOPARC observations in estimating ground truth values (CCC were larger by 0.11–0.12 and MAE smaller by 41%–48%). Conclusions Computer vision algorithms are promising for automated assessment of setting-based physical activity. Such tools would require less manpower than human observation, produce more and potentially more accurate data, and allow for ongoing monitoring and feedback to inform interventions.

We examined associations of micro-scale environment attributes (e.g., sidewalks, street crossings) with three physical activity (PA) measures among Hispanic/Latino adults (n = 1776) living in San Diego County, CA. Systematic observation was used to quantify micro-scale environment attributes near each participant's home. Total PA was assessed with accelerometers, and PA for transportation and recreation were assessed by validated self-report. Although several statistically significant interactions between individual and neighborhood characteristics were identified, there was little evidence micro-scale attributes were related to PA. An important limitation was restricted environmental variability for this sample which lived in a small area of a single county.

Neighborhood-level socioeconomic deprivation can increase risk for higher blood pressure or hypertension, while greater neighborhood safety and walkability may protect against hypertension. Large-scale prospective research, particularly among Hispanics/Latinos, is lacking. We examined cross-sectional and prospective associations between neighborhood environments and blood pressure and hypertension among 3851 Hispanic/Latinos enrolled in the Hispanic Community Health Study/Study of Latinos San Diego, CA cohort. Addresses from Visit 1 (2008-2011) were geocoded and neighborhood characteristics were determined as part of the SOL CASAS ancillary study. Home addresses were geocoded and home areas created using 800m circular radial buffers. Neighborhood indices socioeconomic deprivation, residential stability, and social disorder were created using Census and other publicly available data. Walkability was computed as density of intersections, retail spaces, and residences. Greenness was measured via satellite imagery using the Normalized Difference Vegetation Index. Visit 1 and Visit 2 (2014-2017) clinical outcomes included systolic (SBP) and diastolic (DBP) blood pressure, as well as prevalent and 6-year incident hypertension, defined as SBP/DBP ≥140/90mmHg or antihypertensive medication use. Complex survey regression models adjusted for covariates revealed cross-sectional associations between greater walkability and lower SBP (B=-0.05; 95% CI: -0.09, -0.003). In prospective analyses, greater neighborhood social disorder was related to increasing SBP (B=0.05; 95% CI: 0.01, 0.09) and DBP (B=0.07; 95% CI: 0.02, 0.12) over time. Greater socioeconomic deprivation (OR=1.47; 95% CI: 1.06, 2.04) and greater social disorder (OR=1.25; 95% CI: 1.02, 1.54) were associated with higher odds of incident hypertension. All other associations were not significant. Beyond individual-level characteristics, greater neighborhood social disorder and socioeconomic deprivation were related to adverse changes in blood pressure over 6 years among Hispanics/Latinos. Neighborhood social environment may help identify, or be an area for future intervention for, cardiovascular risk among Hispanics/Latinos.

Background Hip-worn accelerometer cut-points have poor validity for assessing children’s sedentary time, which may partly explain the equivocal health associations shown in prior research. Improved processing/classification methods for these monitors would enrich the evidence base and inform the development of more effective public health guidelines. The present study aimed to develop and evaluate a novel computational method (CHAP-child) for classifying sedentary time from hip-worn accelerometer data. Methods Participants were 278, 8–11-year-olds recruited from nine primary schools in Melbourne, Australia with differing socioeconomic status. Participants concurrently wore a thigh-worn activPAL (ground truth) and hip-worn ActiGraph (test measure) during up to 4 seasonal assessment periods, each lasting up to 8 days. activPAL data were used to train and evaluate the CHAP-child deep learning model to classify each 10-s epoch of raw ActiGraph acceleration data as sitting or non-sitting, creating comparable information from the two monitors. CHAP-child was evaluated alongside the current practice 100 counts per minute (cpm) method for hip-worn ActiGraph monitors. Performance was tested for each 10-s epoch and for participant-season level sedentary time and bout variables (e.g., mean bout duration). Results Across participant-seasons, CHAP-child correctly classified each epoch as sitting or non-sitting relative to activPAL, with mean balanced accuracy of 87.6% (SD = 5.3%). Sit-to-stand transitions were correctly classified with mean sensitivity of 76.3% (SD = 8.3). For most participant-season level variables, CHAP-child estimates were within ± 11% (mean absolute percent error [MAPE]) of activPAL, and correlations between CHAP-child and activPAL were generally very large (> 0.80). For the current practice 100 cpm method, most MAPEs were greater than ± 30% and most correlations were small or moderate (≤ 0.60) relative to activPAL. Conclusions There was strong support for the concurrent validity of the CHAP-child classification method, which allows researchers to derive activPAL-equivalent measures of sedentary time, sit-to-stand transitions, and sedentary bout patterns from hip-worn triaxial ActiGraph data. Applying CHAP-child to existing datasets may provide greater insights into the potential impacts and influences of sedentary time in children.

Background No cut-points have been developed for youth with Down syndrome; there is concern that altered gait patterns, decreased energy expenditure and exercise capacity of individuals with Down syndrome may produce inaccurate physical activity data if accelerometer data are analyzed using cut-points from populations with typical development and other IDD diagnoses. Aim To compare physical activity and sedentary time across existing accelerometer cut-point methods in adolescents with Down syndrome. Methods In this cross-sectional analysis, participants diagnosed with Down syndrome (n = 37; 15.5 ± 1.9 years; 57 % female) wore an accelerometer on their non-dominant hip for seven-days. Data were analyzed and compared across four physical activity intensity cut-points: Evenson, Freedson 4-MET, McGarty, and Romanizi. Outcomes & results Differences in time spent in each intensity across cut-point methods were evident for sedentary (448−615 min/day), light (72−303 min/day) and moderate-to-vigorous (12−77 min/day) activities. Between 0.0–67.6 % of the sample met the physical activity guidelines, depending on the cut-point method selected. Conclusions & implications This study presents the wide variation of accumulated physical activity minutes when different cut-points are applied to individuals with Down syndrome. There is a critical need to establish Down syndrome-specific measures of physical activity assessment rather than applying methods developed for their peers with typical development. What this paper adds This paper highlights concerns over the application of objective measurements of physical activity in youth with Down syndrome from measurement methods derived from populations with typical development. This is the first manuscript to examine this issue in a sample comprised solely of youth with Down syndrome. Results demonstrate the large variation in time spent in each activity intensity that arise due to the application of different cut-point methods.

The health effects of sedentary behavior (SB) may be moderated by SB accumulation patterns (e.g., in prolonged bouts). These patterns are challenging to assess thoroughly and interpretably. PURPOSE To introduce SB profiles (i.e., groups of similar SB accumulation patterns) as a new approach to pattern-focused SB research. METHODS Data came from the National Health and Nutrition Examination Survey (2003-2006). SB bouts were determined from hip-worn accelerometer data, and k-medoid clustering was used to define three SB profiles (Interrupted, Intermediate, and Prolonged). These were used as the exposure in logistic regression models to examine odds ratios (OR) of having high (>20%) predicted-10-year risk of cardiovascular disease (CVD). For comparison, the models were also fitted using standard metrics (i.e., tertiles of mean SB bout duration and daily total SB time). RESULTS Those with Interrupted profile (n = 1740) were typically sedentary ~32% of the time, in bouts ≤15 min. Those with Intermediate profile (n = 2453) were typically sedentary ~47% of the time, in bouts ≤20 min. Those with Prolonged profile (n = 1302) were typically sedentary ~61% of the time, frequently in bouts >20 min. The fully adjusted model showed that high CVD risk was more common for the Intermediate (OR = 1.87) and Prolonged (OR = 4.65) profiles than the Interrupted profile (all p < 0.001). For standard metrics, OR ranged from 1.46 (middle tertile of daily total SB time) to 4.11 (upper tertile of mean bout duration) with all p < 0.002. CONCLUSION SB profiles represent a promising new avenue for understanding and combating adverse SB trends. The value of the approach was highlighted by the strong associations with CVD risk.

Little is known about how sedentary behavior (SB) metrics derived from hip- and thigh-worn accelerometers agree for older adults. Thigh-worn activPAL (AP) micro monitors were concurrently worn with hip-worn ActiGraph (AG) GT3X+ accelerometers (with SB measured using the 100 counts per minute [cpm] cut point; AG100cpm) by 953 older adults (age 77 ± 6.6, 54% women) for 4–7 days. Device agreement for sedentary time and five SB pattern metrics was assessed using mean error and correlations. Logistic regression tested associations with four health outcomes using standardized (i.e., z scores) and unstandardized SB metrics. Mean errors (AP − AG100cpm) and 95% limits of agreement were: sedentary time −54.7 [−223.4, 113.9] min/day; time in 30+ min bouts 77.6 [−74.8, 230.1] min/day; mean bout duration 5.9 [0.5, 11.4] min; usual bout duration 15.2 [0.4, 30] min; breaks in sedentary time −35.4 [−63.1, −7.6] breaks/day; and alpha −.5 [−.6, −.4]. Respective Pearson correlations were: .66, .78, .73, .79, .51, and .40. Concordance correlations were: .57, .67, .40, .50, .14, and .02. The statistical significance and direction of associations were identical for AG100cpm and AP metrics in 46 of 48 tests, though significant differences in the magnitude of odds ratios were observed among 13 of 24 tests for unstandardized and five of 24 for standardized SB metrics. Caution is needed when interpreting SB metrics and associations with health from AG100cpm due to the tendency for it to overestimate breaks in sedentary time relative to AP. However, high correlations between AP and AG100cpm measures and similar standardized associations with health outcomes suggest that studies using AG100cpm are useful, though not ideal, for studying SB in older adults.

Objective: To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Methods: Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit’s SmartTrack, and Garmin’s Move IQ. Trip- and day-level agreement were tested. Results: The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. Conclusions: The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS’ pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research. Objective: To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Methods: Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit’s SmartTrack, and Garmin’s Move IQ. Trip- and day-level agreement were tested. Results: The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. Conclusions: The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS’ pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research.

Purpose: To investigate potential time drift between devices when using Global Positioning Systems (GPS) and accelerometers in field-based research. Methods: Six Qstarz BT-Q1000XT GPS trackers, activPAL3 accelerometers, and ActiGraph GT3X+ and GT3X accelerometers were tested over 1–3 waves, each lasting 9–14 days. Once per day an event marker was created on each pair of devices concurrently. The difference in seconds between the time stamps for each event marker were calculated between each pair of GPS and activPAL devices and GPS and ActiGraph devices. Mixed-effects linear regression tested time drift across days and waves and between two rooms/locations (in an inner room vs. on a windowsill in an outer room). Results: The GPS trackers remained within one second of the computer clock across days and waves and between rooms. The activPAL devices drifted an average of 8.38 seconds behind the GPS devices over 14 days (p p p Conclusions: Time drift between the GPS and accelerometer models tested was minimal and is unlikely to be problematic when addressing many common research questions. However, studies that require high levels of precision when matching short (e.g., 1-second) time intervals may benefit from consideration of time drift and potential adjustments.

Background: This study investigated whether change in maternal BMI was associated with change in child's moderate-to-vigorous physical activity (MVPA) and prolonged sedentary time during the course of family-based behavioral interventions (FBBIs) for pediatric weight management. Methods: Children (n = 120) ages 5–12 [mean age = 9.04 ± 1.7) years with a baseline BMI ≥85th percentile (mean BMIz = 1.8 ± 0.5) and families were enrolled in one of three similar FBBIs for pediatric weight management and followed over 6–12 months. Activity data were collected through accelerometers. Mixed effects regression models assessed the relationship of maternal change in BMI to child change in (1) minutes/d of MVPA and (2) proportion of time spent in sedentary bouts lasting ≥10 minutes (termed prolonged sedentary time), and whether the effect of maternal BMI change was moderated by child age, sex, and race/ethnicity. Results: A decrease in maternal BMI was associated with both an increase in child MVPA, B = −2.77, t = −2.03, p = 0.048, and a decrease in proportion/d of prolonged sedentary time, B = 0.02, t = 2.40, p = 0.020, from baseline to follow-up. Child age moderated the association between maternal BMI change and change in child prolonged sedentary time (p = 0.095), whereby the association was limited to 5- to 10-year-olds and became stronger as age decreased. Conclusions: Improvement in maternal BMI showed important positive associations with child MVPA and prolonged sedentary time over the course of FBBIs for pediatric weight management. Targeting parent weight loss could improve child outcomes in FBBIs, particularly in younger children.

Improving sedentary measurement is critical to understanding sedentary-health associations in youth. This study assessed agreement between the thigh-worn activPAL and commonly used hip-worn ActiGraph accelerometer methods for assessing sedentary patterns in children. Both devices were worn by 8-12-year-olds (N = 195) for 4.6 +/- 1.9 days. Two ActiGraph cut-points were applied to two epoch durations: 20%. ActiGraph 300c/60s had a better agreement than the other cut-points, but all ICCs were

Background: Machine learning has been used for classification of physical behavior bouts from hip-worn accelerometers; however, this research has been limited due to the challenges of directly observing and coding human behavior “in the wild.” Deep learning algorithms, such as convolutional neural networks (CNNs), may offer better representation of data than other machine learning algorithms without the need for engineered features and may be better suited to dealing with free-living data. The purpose of this study was to develop a modeling pipeline for evaluation of a CNN model on a free-living data set and compare CNN inputs and results with the commonly used machine learning random forest and logistic regression algorithms. Method: Twenty-eight free-living women wore an ActiGraph GT3X+ accelerometer on their right hip for 7 days. A concurrently worn thigh-mounted activPAL device captured ground truth activity labels. The authors evaluated logistic regression, random forest, and CNN models for classifying sitting, standing, and stepping bouts. The authors also assessed the benefit of performing feature engineering for this task. Results: The CNN classifier performed best (average balanced accuracy for bout classification of sitting, standing, and stepping was 84%) compared with the other methods (56% for logistic regression and 76% for random forest), even without performing any feature engineering. Conclusion: Using the recent advancements in deep neural networks, the authors showed that a CNN model can outperform other methods even without feature engineering. This has important implications for both the model’s ability to deal with the complexity of free-living data and its potential transferability to new populations.

Background Remote drop-off programs allow children living “unwalkable” distances from school to walk partway by being dropped off by personal vehicle or bus closer to the school, supporting physical activity and health. However, little evidence exists to guide implementation of such programs. Methods Semi-structured interviews were conducted with key informants from 7 remote drop-off programs to capture descriptive information and qualitative content (e.g., barriers, facilitators, outcomes). Qualitative content was analyzed using inductive thematic analysis and identified themes were organized within implementation science frameworks. Results Programs were from low and high socioeconomic areas (free/reduced price lunch range = 4%–92%) and initiated by various champions (school staff = 29%, parents = 29%, external = 42%). 29% of programs incorporated the yellow school bus, 43% involved >100 students, and 71% involved route distances ≥0.5 miles. Twenty themes were identified across 5 implementation science domains (Intervention Characteristics, Inner Setting, Outer Setting, Implementation Process, and Outcomes). Positive outcomes included physical activity, socialization, and improved focus for students; decreased traffic; and positive perceptions of the program by students, parents, and school staff/administrators. Barriers included traffic, weather, and student engagement. Facilitators included having a champion and support from school leaders and the community, conducting process improvements, and incentivizing participation. Conclusions Remote drop-offs are feasible for supporting active school commuting but underutilized. Promising strategies for supporting uptake and implementation of such programs include communicating benefits, developing champions, engaging school and community leaders, and improving the neighborhood built environment.

Background Active travel to school contributes to multiple physical and psychosocial benefits for youth, yet population rates of active travel to school are alarmingly low in the USA and many other countries. Though walking school bus interventions are effective for increasing rates of active travel to school and children’s overall physical activity, uptake of such interventions has been low. The objective of this study was to conduct a mixed methods implementation evaluation to identify contextual factors that serve as barriers and facilitators among existing walking school bus programs. Methods Semi-structured interviews guided by the Consolidated Framework for Implementation Research (CFIR) were conducted with leaders of low-sustainability (n = 9) and high-sustainability (n = 11) programs across the USA. A combination of quantitative (CFIR-based) coding and inductive thematic analysis was used. The CFIR-based ratings were compared between the low- and high-sustainability programs and themes, subthemes, and exemplary quotes were provided to summarize the thematic analysis. Results In both the low- and high-sustainability programs, three of the 15 constructs assessed were commonly rated as positive (i.e., favorable for supporting implementation): student/family needs and resources, implementation climate, and planning. Three constructs were more often rated as positive in the high-sustainability programs: organizational incentives and rewards, engaging students and parents, and reflecting and evaluating. Three constructs were more often rated as positive in the low-sustainability programs: student/family needs and resources - built environment, available resources, and access to knowledge and information. Four themes emerged from the thematic analysis: planning considerations, ongoing coordination considerations, resources and supports, and benefits. Conclusions Engagement of students, parents, and community members were among the factors that emerged across the quantitative and qualitative analyses as most critical for supporting walking school bus program implementation. The information provided by program leaders can help in the selection of implementation strategies that overcome known barriers for increasing the long-term success of community-based physical activity interventions such as the walking school bus.

Purpose:This study examined dietary indicators, sedentary time, and physical activity as potential mediators of the association between TV time and BMIz in youth.Design:Cross-sectional study in 2 independent samples of youth.Setting:Data collection occurred by mail and telephone for adolescents and either at home or in medical settings for children.Sample:928 youth ages 12-16 and 756 youth ages 6-12 and a parent.Measures:TV time, snacking/eating while watching TV, and a 3-day dietary recall were assessed via child/parent report. Physical activity and sedentary time were assessed by accelerometer wear.Analysis:Direct and indirect associations (through 8 diet and activity variables) of TV time with BMIz were tested in boys and girls in each sample.Results:TV time had a positive association with BMIz in 6-12 year old boys and girls. Direct associations emerged between TV time and the diet/activity variables, and between diet/activity variables and BMIz. Snacking/eating while watching TV had a significant positive association with BMIz in younger boys and mediated the association between TV time and BMIz ( β = .06, p = .019; 25% attenuation).Conclusions:Snacking/eating while watching TV may be a possible reason TV time is consistently associated with obesity in youth. Targeting reductions in TV time and associated snacking could improve health impacts.

This project characterized the system of childhood obesity-related programs and functions based on a socioecological framework within the Kansas City region to determine strengths, weaknesses, and leverage points for informing collective impact. A mixed-method approach was employed to identify and collect data ∼260 childhood obesity-related programs provided by 89 organizations. Findings indicated no major gaps in population or location served although few programs specifically focused on service to minority groups or neighborhoods. The region has many programs working within the system, yet the distribution of programs does not occur as expected throughout the dimensions of a standard socioecological model or community health system. In addition, several organizations perform certain leadership functions such as coordination, resource allocation, or monitoring, but none perform all, indicating the lack of a traditional "backbone" organization. These findings demonstrate how a region's childhood obesity prevention and treatment programs can be evaluated using a socioecological framework.

Socioeconomic factors appear to impact mental health conditions such as depression, but little is known about the relative and combined role of neighborhood and personal socioeconomic deprivation among Hispanics/Latinos. This study examined cross-sectional associations of neighborhood and personal socioeconomic deprivation with depression symptoms in a US Hispanic/Latino population from the San Diego Field Center of the Hispanic Community Health Study/Study of Latinos (n=3,851). Depression symptoms were assessed with the ten-item Centers for Epidemiological Studies in Depression Scale. Neighborhood socioeconomic deprivation was a composite of eleven variables (e.g., neighborhood income, education, employment, household crowding). Greater personal socioeconomic deprivation based on education, income, and employment was generally associated with higher depression symptoms, including after adjusting for neighborhood socioeconomic deprivation. Greater neighborhood socioeconomic deprivation was associated with higher depression symptoms in females but not males, but the association in females became non-significant when adjusting for personal socioeconomic deprivation. Neighborhood socioeconomic deprivation did not significantly interact with personal socioeconomic deprivation in relation to depression symptoms. The present findings support the association of personal socioeconomic status with mental health (indicated by depression symptoms) among Hispanic/Latino populations, whereas neighborhood socioeconomic deprivation did not relate to depression beyond the impact of personal indicators.

Background: The authors assessed agreement between participant diaries and two automated algorithms applied to activPAL (PAL Technologies Ltd, Glasgow, United Kingdom) data for classifying awake wear time in three age groups. Methods: Study 1 involved 20 youth and 23 adults who, by protocol, removed the activPAL occasionally to create nonwear periods. Study 2 involved 744 older adults who wore the activPAL continuously. Both studies involved multiple assessment days. In-bed, out-of-bed, and nonwear times were recorded in the participant diaries. The CREA (in PAL processing suite) and ProcessingPAL (secondary application) algorithms estimated out-of-bed wear time. Second- and day-level agreement between the algorithms and diary was investigated, as were associations of sedentary variables with self-rated health. Results: The overall accuracy for classifying out-of-bed wear time as compared with the diary was 89.7% (Study 1) to 95% (Study 2) for CREA and 89.4% (Study 1) to 93% (Study 2) for ProcessingPAL. Over 90% of the nonwear time occurring in nonwear periods >165 min was detected by both algorithms, while Conclusion: The automated awake wear-time classification algorithms performed similarly to the diary information on days without short (≤2.5–2.75 hr) nonwear periods. Because both diary and algorithm data can have inaccuracies, best practices likely involve integrating diary and algorithm output.

Purpose and Objectives Walking school bus programs increase children’s physical activity through active travel to school; however, research to inform large-scale implementation of such programs is limited. We investigated contextual factors, implementation outcomes, and student outcomes in existing walking school bus programs in the United States and internationally. Intervention Approach Walking school bus programs involve a group of children walking to school together with an adult leader. On the trip to school, these adults provide social support, address potential traffic and interpersonal safety, and serve as role models to the children while children increase their physical activity levels. Evaluation Methods We conducted surveys with existing walking school bus programs identified through internet searches, referrals, and relevant email listservs. Leaders from 184 programs that operated at least 1 trip per week completed the survey. We used regression analyses to compare differences in contextual factors by area income and location, associations between contextual factors and implementation outcomes, and associations between implementation outcomes and student outcomes. Results Walking school bus programs in low-income communities had more route leaders and engaged in more active travel to school-related activities of being sustained than those in higher income. Programs that had no external funding, multiple route leaders, and coordination by a school or district staff member (as opposed to a parent) had greater student participation than other programs. Providing more trips than other programs per week was associated with reduced tardiness, reduced bullying, and improved neighborhood walkability. The greatest barriers to implementation were recruiting and maintaining students and identifying and maintaining route leaders. Implications for Public Health Walking school bus programs can be implemented successfully in many contexts using various models. The involvement of several people in leadership roles is critical for sustainability. Evidence-based implementation strategies that overcome barriers can improve reach, implementation, and sustainability of walking school bus programs and can increase children’s physical activity.

The purpose of this study was to determine the feasibility of using Garmin HRM Swim and Forerunner 935XT/735XT devices to measure physical activity (PA) in children with autism spectrum disorder (ASD) while swimming and explore the effects of swimming on body composition and child function. Fifteen children with ASD participated in eight swim lessons. Pre- and posttest data included heart-rate, body composition, and caregiver surveys. Results indicated that the HRM and Forerunner devices were user-friendly, well tolerated by children, and provided useful data to measure heart rate and PA levels for all participants. Most participants met the level for moderate to vigorous PA suggested for typically developing children. Researchers explored results for healthy changes in body mass index (BMI) and parent’s perceptions on child health and function. This study provides exploratory evidence that swimming may improve BMI and behavior in children with ASD with recommendations for future research. Subscribe to TRJ

Introduction: Physical activity has been associated with a lower risk of diabetes but less is known about the association of steps per day (steps/d) and step intensity with incident diabetes. Hypothesis: We hypothesized an inverse association between both number of steps/d and time spent at a stepping rate corresponding to purposeful steps ( > 40 steps/min) with the risk of incident diabetes. Methods: We included 7247 participants free of diabetes at baseline in the prospective HCHS/SOL cohort of Latinos sampled from four communities in the United States. Steps/d and minutes per day (min/d) at a stepping rate > 40 steps/min were assessed at baseline with the Actical accelerometer (2008-2011), and incident diabetes was ascertained at a follow-up visit (2014-2017). The association of steps with incidence rates (per 1,000 person-years) of diabetes and incidence rate ratios (IRR) was estimated using Poisson regression, accounting for the sampling design and adjusted for age, ethnicity, gender, education, smoking status, alcohol intake, and accelerometer wear time. Results: The average age at baseline was 38 years (range 18 - 74 years) and 52% were female. Over a median of 5.9 years, 850 participants (9%) developed diabetes. The median step count was 7360 steps/d and the median minutes per day at a stepping rate of > 40 steps/min was 55 minutes. For each 1,000 step/d increment, the risk of diabetes was 5% lower (Table, IRR= 0.95(95% confidence interval (CI) 0.88, 1.02)). Compared to adults who engaged in 2,000 steps/d, those who engaged in 5,000 and 10,000 steps/d had a 13% and 26% lower risk of diabetes. The risk of diabetes was 5% lower (IRR=0.95 (95% CI 0.89, 1.00)) per 10 minute increase in time spent at a stepping rate > 40 steps/min. Compared to adults who spent 10 min/d at > 40 step/min, those who spent 30, 60, and 120 min/d had a 10%, 20%, and 32% lower risk of diabetes. Conclusion: The number of steps per day and time spent at a stepping rate that corresponds to purposeful steps are inversely associated with the risk of developing diabetes among adults.

Objectives: To document challenges to and benefits from research involving the use of images by capturing examples of such research to assess physical activity– or nutrition-related behaviors and/or environments.Methods: Researchers (i.e., key informants) using image capture in their research were identified through knowledge and networks of the authors of this paper and through literature search. Twenty-nine key informants completed a survey covering the type of research, source of images, and challenges and benefits experienced, developed specifically for this study.Results: Most respondents used still images in their research, with only 26.7% using video. Image sources were categorized as participant generated (n = 13; e.g., participants using smartphones for dietary assessment), researcher generated (n = 10; e.g., wearable cameras with automatic image capture), or curated from third parties (n = 7; e.g., Google Street View). Two of the major challenges that emerged included the need for automated processing of large datasets (58.8%) and participant recruitment/compliance (41.2%). Benefit-related themes included greater perspectives on obesity with increased data coverage (34.6%) and improved accuracy of behavior and environment assessment (34.6%).Conclusions: Technological advances will support the increased use of images in the assessment of physical activity, nutrition behaviors, and environments. To advance this area of research, more effective collaborations are needed between health and computer scientists. In particular development of automated data extraction methods for diverse aspects of behavior, environment, and food characteristics are needed. Additionally, progress in standards for addressing ethical issues related to image capture for research purposes is critical.