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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.

Open access

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.

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Erratum. COVID-19 Highlights the Potential for a More Dynamic Approach to Physical Activity Surveillance

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Volume 5 (2022): Issue 2 (Jun 2022)

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ActiGraph Cutpoints Impact Physical Activity and Sedentary Behavior Outcomes in Young Children

Becky Breau, Hannah J. Coyle-Asbil, Jess Haines, David W.L. Ma, Lori Ann Vallis, and on behalf of the Guelph Family Health Study

Purpose: Examine the effect of cutpoint selection on physical activity (PA) metrics calculated from young children’s accelerometer data and on the proportion of children meeting PA guidelines. Methods: A total of 262 children (3.6 ± 1.4 years, 126 males) wore ActiGraph wGT3X-BT accelerometers on their right hip for 7 days, 24 hr/day. Ten cutpoint sets were applied to the sample categorized by age, based on populations of the original cutpoint calibration studies using ActiLife software. Resulting sedentary behavior, light PA, moderate to vigorous PA, and total PA were compared using repeated-measures analysis of variance. Proportion of children meeting age-appropriate PA guidelines based on each cutpoint set was assessed using Cochran’s q tests. Results: Children wore the accelerometer for an average of 7.6 ± 1.2 days for an average of 11.9 ± 1.2 hr/day. Significant differences in time spent in each intensity were found across all cutpoints except for sedentary, and total PA for three comparisons (Trost vs. Butte Vertical Axis [VA], Pate vs. Puyau, and Costa VA vs. Evenson) and moderate to vigorous PA for four comparisons (Trost vs. Pate, Trost vs. Pate and Pfeiffer, Pate vs. Pate and Pfeiffer, and van Cauwenberghe vs. Evenson). When examined within age-appropriate groups, all sets of cutpoints resulted in significant differences across all intensities and in the number of children meeting PA guidelines. Conclusion: Choice of cutpoints applied to data from young children significantly affects times calculated for different movement intensities, which in turn impacts the proportion of children meeting guidelines. Thus, comparisons of movement intensities should not be made across studies using different sets of cutpoints.