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CHAP-Adult: A Reliable and Valid Algorithm to Classify Sitting and Measure Sitting Patterns Using Data From Hip-Worn Accelerometers in Adults Aged 35+

John Bellettiere, Supun Nakandala, Fatima Tuz-Zahra, Elisabeth A.H. Winkler, Paul R. Hibbing, Genevieve N. Healy, David W. Dunstan, Neville Owen, Mikael Anne Greenwood-Hickman, Dori E. Rosenberg, Jingjing Zou, Jordan A. Carlson, Chongzhi Di, Lindsay W. Dillon, Marta M. Jankowska, Andrea Z. LaCroix, Nicola D. Ridgers, Rong Zablocki, Arun Kumar, and Loki Natarajan

Background: Hip-worn accelerometers are commonly used, but data processed using the 100 counts per minute cut point do not accurately measure sitting patterns. We developed and validated a model to accurately classify sitting and sitting patterns using hip-worn accelerometer data from a wide age range of older adults. Methods: Deep learning models were trained with 30-Hz triaxial hip-worn accelerometer data as inputs and activPAL sitting/nonsitting events as ground truth. Data from 981 adults aged 35–99 years from cohorts in two continents were used to train the model, which we call CHAP-Adult (Convolutional Neural Network Hip Accelerometer Posture-Adult). Validation was conducted among 419 randomly selected adults not included in model training. Results: Mean errors (activPAL − CHAP-Adult) and 95% limits of agreement were: sedentary time −10.5 (−63.0, 42.0) min/day, breaks in sedentary time 1.9 (−9.2, 12.9) breaks/day, mean bout duration −0.6 (−4.0, 2.7) min, usual bout duration −1.4 (−8.3, 5.4) min, alpha .00 (−.04, .04), and time in ≥30-min bouts −15.1 (−84.3, 54.1) min/day. Respective mean (and absolute) percent errors were: −2.0% (4.0%), −4.7% (12.2%), 4.1% (11.6%), −4.4% (9.6%), 0.0% (1.4%), and 5.4% (9.6%). Pearson’s correlations were: .96, .92, .86, .92, .78, and .96. Error was generally consistent across age, gender, and body mass index groups with the largest deviations observed for those with body mass index ≥30 kg/m2. Conclusions: Overall, these strong validation results indicate CHAP-Adult represents a significant advancement in the ambulatory measurement of sitting and sitting patterns using hip-worn accelerometers. Pending external validation, it could be widely applied to data from around the world to extend understanding of the epidemiology and health consequences of sitting.

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A Comparison of Wrist- Versus Hip-Worn ActiGraph Sensors for Assessing Physical Activity in Adults: A Systematic Review

Nolan Gall, Ruopeng Sun, and Matthew Smuck

Introduction: Wrist-worn accelerometer has gained popularity recently in commercial and research use for physical activity tracking. Yet, no consensus exists for standardized wrist-worn data processing, and physical activity data derived from wrist-worn accelerometer cannot be directly compared with data derived from the historically used hip-worn accelerometer. In this work, through a systematic review, we aim to identify and analyze discrepancies between wrist-worn versus hip-worn ActiGraph accelerometers in measuring adult physical activity. Methods: A systematic review was conducted on studies involving free-living data comparison between hip- and wrist-worn ActiGraph accelerometers among adult users. We assessed the population, study protocols, data processing criteria (axis, epoch, wear-time correction, etc.), and outcome measures (step count, sedentary activity time, moderate-to-vigorous physical activity, etc.). Step count and activity count discrepancy were analyzed using meta-analysis, while meta-analysis was not attempted for others due to heterogeneous data processing criteria among the studies. Results: We screened 235 studies with 19 studies qualifying for inclusion in the systematic review. Through meta-analysis, the wrist-worn sensor recorded, on average, 3,537 steps/day more than the hip-worn sensor. Regarding sedentary activity time and moderate-to-vigorous physical activity estimation, the wrist sensor consistently overestimates moderate-to-vigorous physical activity time while underestimating sedentary activity time, with discrepancies ranging from a dozen minutes to several hours. Discussions: Our findings quantified the substantial discrepancies between wrist and hip sensors. It calls attention to the need for a cautious approach to interpreting data from different wear locations. These results may also serve as a reference for data comparisons among studies using different sensor locations.

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Volume 5 (2022): Issue 3 (Sep 2022)

Open access

A Physical Behaviour Partnership From Heaven: The Prospective Physical Activity, Sitting, and Sleep Consortium and the International Society for the Measurement of Physical Behaviour

Emmanuel Stamatakis, Bronwyn K. Clark, Matthew N. Ahmadi, Joanna M. Blodgett, Malcolm H. Granat, Alan Donnelly, Andrew J. Atkin, Li-Tang Tsai, Gregore I. Mielke, Richard M. Pulsford, Nidhi Gupta, Patrick Crawley, Matthew Stevens, Peter Johansson, Laura Brocklebank, Lauren B. Sherar, Vegar Rangul, Andreas Holtermann, Mark Hamer, and Annemarie Koster

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Calibration of an Accelerometer Activity Index Among Older Women and Its Association With Cardiometabolic Risk Factors

Guangxing Wang, Sixuan Wu, Kelly R. Evenson, Ilsuk Kang, Michael J. LaMonte, John Bellettiere, I-Min Lee, Annie Green Howard, Andrea Z. LaCroix, and Chongzhi Di

Purpose: Traditional summary metrics provided by accelerometer device manufacturers, known as counts, are proprietary and manufacturer specific, making it difficult to compare studies using different devices. Alternative summary metrics based on raw accelerometry data have been introduced in recent years. However, they were often not calibrated on ground truth measures of activity-related energy expenditure for direct translation into continuous activity intensity levels. Our purpose is to calibrate, derive, and validate thresholds among women 60 years and older based on a recently proposed transparent raw data-based accelerometer activity index (AAI) and to demonstrate its application in association with cardiometabolic risk factors. Methods: We first built calibration equations for estimating metabolic equivalents continuously using AAI and personal characteristics using internal calibration data (N = 199). We then derived AAI cutpoints to classify epochs into sedentary behavior and physical activity intensity categories. The AAI cutpoints were applied to 4,655 data units in the main study. We then utilized linear models to investigate associations of AAI sedentary behavior and physical activity intensity with cardiometabolic risk factors. Results: We found that AAI demonstrated great predictive accuracy for estimating metabolic equivalents (R 2 = .74). AAI-Based physical activity measures were associated in the expected directions with body mass index, blood glucose, and high-density lipoprotein cholesterol. Conclusion: The calibration framework for AAI and the cutpoints derived for women older than 60 years can be applied to ongoing epidemiologic studies to more accurately define sedentary behavior and physical activity intensity exposures, which could improve accuracy of estimated associations with health outcomes.

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Depressive Symptoms Are Associated With Accelerometer-Measured Physical Activity and Time in Bed Among Working-Aged Men and Women

Pauliina Husu, Kari Tokola, Henri Vähä-Ypyä, Harri Sievänen, and Tommi Vasankari

Background: Depression is a significant health problem, whereas higher physical activity (PA) associates with fewer depressive symptoms. We examined how self-reported depressive symptoms are associated with accelerometer-measured PA, standing, sedentary behavior, and time in bed (TIB) among 20- to 69-year-old men and women. Methods: The study is a part of the cross-sectional, population-based FinFit2017 study, in which depressive symptoms were assessed by modified nine-item Finnish version of the Patient Health Questionnaire, and physical behavior in terms of PA, sedentary behavior, standing, and TIB was assessed 24/7 by a triaxial accelerometer. During waking hours, the accelerometer was hip worn. Intensity of PA was analyzed by mean amplitude deviation and body posture by angle for posture estimation algorithms. During TIB, the device was wrist worn, and the analysis was based on the wrist movements. A total of 1,823 participants answered the nine-item Finnish version of the Patient Health Questionnaire and used the accelerometer 24 hr at least 4 days per week. Results: Men without depressive symptoms had on average more standing, light, and moderate to vigorous PA and steps, and less low and high movement TIB than the men with at least moderate symptoms, when age group, education, work status, marital status, and fitness were adjusted for. The asymptomatic women had more moderate to vigorous PA and steps and less high movement TIB than the women with at least moderate symptoms. Conclusions: Depressive symptoms were associated with lower levels of PA and longer TIB. It is important to identify these symptoms as early as possible to be able to initiate and target preventive actions, including PA promotion, to these symptomatic persons on time.

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Tracking of Walking and Running for Exercise: Alignment Between Ecological Momentary Assessment and Accelerometer-Based Estimates

Kelley Strohacker, Lindsay P. Toth, Lucas F. Sheridan, and Scott E. Crouter

Ecological momentary assessment (EMA) and accelerometer-based devices can be used concurrently to better understand dimensions of physical activity. This study presents procedures for analyzing data derived from both methods to examine exercise-related walking and running, as well as determine evidence for alignment between these methods. The participants (N = 29) wore an ActiGraph GT3X+ and completed four EMA surveys/day across 2 weeks to report exercise (mode and duration). GT3X+ counts per 10 s were processed using the Crouter two-regression model to identify periods of walking/running (coefficient of variation in activity counts ≤10% and >0%). Two reviewers visually inspected Crouter two-regression model data and recorded durations of walking/running in time blocks corresponding to EMA reports of exercise. The data were classified as “aligned” if the duration of walking/running between methods were within 20% of one another. Frequency analyses determined the proportion of aligned versus nonaligned exercise durations. Reviewer reliability was examined by calculating interobserver agreement (classification of aligned vs. nonaligned) and intraclass correlation coefficients (ICC; duration based on coefficient of variation). Of the 139 self-reported bouts of walking and running exercise, 25% were classified as aligned with the Crouter two-regression model coefficient of variation. Initial interobserver agreement was 91, and ICCs across data classified as aligned (ICC = .992) and nonaligned (ICC = .960) were excellent. These novel procedures offer a means of isolating exercise-related physical activity for further analysis. Due to the inability to align evidence in most cases, we discuss key considerations for optimizing EMA survey questions, choice in accelerometer-based device, and future directions for visual analysis procedures.

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Effectiveness of Fitbit Activity Prompts in Reducing Sitting Time and Increasing Physical Activity in University Employees: A Randomized Controlled Trial

Benjamin D. Boudreaux, Julie A. Schenck, Zhixuan Chu, and Michael D. Schmidt

Consumer activity devices use prompts to alter sedentary and physical activity (PA) behaviors. However, it is unclear if PA prompts are effective. Purpose: To evaluate the effectiveness of PA prompts from a consumer wearable device in reducing sitting time and increasing PA in university employees. Methods: Thirty-three university employees without a history of consumer activity device wear were randomly assigned a Fitbit Alta HR that administered PA prompts (Prompt group) or had the prompt feature deactivated (No Prompt group). Participants wore an activPAL for 5–7 days to measure baseline sitting time and PA behaviors. Participants then wore the Fitbit for 12 days during waking hours and an activPAL during the last 5–7 days of the Fitbit wear period. Changes in activPAL sitting time and PA were compared across groups. Mean Fitbit steps taken in the first 50 min and the last 10 min of each hour were calculated and compared across groups during “Inactive” hours (<250 steps in the first 50 min), where a prompt was given (Prompt group) or would have been given (No Prompt group). Results: Mean activPAL sitting time increased in the Prompt group (0.66 ± 1.70 hr/day) and remained stable in the No Prompt group (−0.04 ± 1.29 hr/day), with no statistically significant differences between groups (d = 0.33, p = .36). Moderate to vigorous PA increased modestly in both groups, but no significant differences were observed. Fitbit steps during the last 10 min of inactive hours did not differ across groups. Conclusion: Fitbit PA prompts did not alter sitting time or PA behaviors in university employees.

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The Use of Accelerometers in Young Children: A Methodological Scoping Review

Becky Breau, Hannah J. Coyle-Asbil, and Lori Ann Vallis

The purpose of this scoping review was to examine publications using accelerometers in children aged 6 months to <6 years and report on current methodologies used for data collection and analyses. We examined device make and model, device placement, sampling frequency, data collection protocol, definition of nonwear time, inclusion criteria, epoch duration, and cut points. Five online databases and three gray literature databases were searched. Studies were included if they were published in English between January 2009 and March 2021. A total of 627 articles were included for descriptive analyses. Of the reviewed articles, 75% used ActiGraph devices. The most common device placement was hip or waist. More than 80% of articles did not report a sampling frequency, and 7-day protocols during only waking hours were the most frequently reported. Fifteen-second epoch durations and the cut points developed by Pate et al. in 2006 were the most common. A total of 203 articles did not report which definition of nonwear time was used; when reported, “20 minutes of consecutive zeros” was the most frequently used. Finally, the most common inclusion criteria were “greater or equal to 10 hr/day for at least 3 days” for studies conducted in free-living environments and “greater than 50% of the school day” for studies conducted in preschool or childcare environments. Results demonstrated a major lack of reporting of methods used to analyze accelerometer data from young children. A list of recommended reporting practices was developed to encourage increased reporting of key methodological details for research in this area.

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Simulation-Based Evaluation of Methods for Handling Nonwear Time in Accelerometer Studies of Physical Activity

Kristopher I. Kapphahn, Jorge A. Banda, K. Farish Haydel, Thomas N. Robinson, and Manisha Desai

Accelerometer data are widely used in research to provide objective measurements of physical activity. Frequently, participants may remove accelerometers during their observation period resulting in missing data referred to as nonwear periods. Common approaches for handling nonwear periods include discarding data (days with insufficient hours or individuals with insufficient valid days) from analyses and single imputation (SI) methods. Purpose : This study evaluates the performance of various discard-, SI-, and multiple imputation (MI)-based approaches on the ability to accurately and precisely characterize the relationship between a summarized measure of accelerometer counts (mean counts per minute) and an outcome (body mass index). Methods : Realistic accelerometer data were simulated under various scenarios that induced nonwear. Data were analyzed using common and MI methods for handling nonwear. Bias, relative standard error, relative mean squared error, and coverage probabilities were compared across methods. Results : MI approaches were superior to commonly applied methods, with bias that ranged from −0.001 to −0.028 that was considerably lower than that of discard-based methods (ranging from −0.050 to −0.057) and SI methods (ranging from −0.061 to −0.081). We also reported substantial variation among MI strategies, with coverage probabilities ranging from .04 to .96. Conclusion : Our findings demonstrate the benefit of applying MI methods over more commonly applied discard- and SI-based approaches. Additionally, we show that how you apply MI matters, where including data from previously observed acceleration measurements in the imputation model when using MI improves model performance.