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Statistical Learning Methods to Identify Nonwear Periods From Accelerometer Data

Sahej Randhawa, Manoj Sharma, Madalina Fiterau, Jorge A. Banda, Farish Haydel, Kristopher Kapphahn, Donna Matheson, Hyatt Moore IV, Robyn L. Ball, Clete Kushida, Scott Delp, Dennis P. Wall, Thomas Robinson, and Manisha Desai

Background: Accelerometers are used to objectively measure movement in free-living individuals. Distinguishing nonwear from sleep and sedentary behavior is important to derive accurate measures of physical activity, sedentary behavior, and sleep. We applied statistical learning approaches to examine their promise in detecting nonwear time and compared the results with commonly used wear time (WT) algorithms. Methods: Fifteen children, aged 4–17, wore an ActiGraph wGT3X-BT monitor on their hip during overnight polysomnography. We applied Hidden Markov Models (HMM) and Gaussian Mixture Models (GMM) to classify states of nonwear and wear in triaxial acceleration data. Performance of methods was compared with WT algorithms across two conditions with differing amounts of consecutive nonwear. Clinical scoring of polysomnography served as the gold standard. Results: When the length of nonwear was less than or equal to WT algorithms’ predefined thresholds for consecutive nonwear time, GMM methods yielded improved classification error, specificity, positive predictive value, and negative predictive value over commonly used algorithms. HMM was superior to one algorithm for sensitivity and negative predictive value. When the length of nonwear was longer, results were mixed, with the commonly used algorithms performing better on some parameters but GMM with the greatest specificity. However, all approached the upper limits of performance for almost all metrics. Conclusions: GMM and HMM demonstrated robust, consistently strong performance across multiple conditions, surpassing or remaining competitive with commonly used WT algorithms which had marked inaccuracy when nonwear time periods were shorter. Of the two statistical learning algorithms, GMM was superior to HMM.

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Convergent Validity Between Epoch-Based activPAL and ActiGraph Methods for Measuring Moderate to Vigorous Physical Activity in Youth and Adults

Adrian Ortega, Bethany Forseth, Paul R. Hibbing, Chelsea Steel, and Jordan A. Carlson

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.

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Volume 6 (2023): Issue 1 (Mar 2023)

Open access

Methods to Estimate Energy Expenditure, Physical Activity, and Sedentary Time in Pregnant Women: A Validation Study Using Doubly Labeled Water

Saud Abdulaziz Alomairah, Signe de Place Knudsen, Caroline Borup Roland, Ida-Marie Hergel, Stig Molsted, Tine D. Clausen, Ellen Løkkegaard, Jane M. Bendix, Ralph Maddison, Marie Löf, Jakob Eg Larsen, Gerrit van Hall, and Bente Stallknecht

Background: Activity trackers and the Pregnancy Physical Activity Questionnaire (PPAQ) measures physical activity (PA) and sedentary time (SED). However, none of these tools have been validated against a criterion method in pregnancy. We aimed to compare a consumer activity tracker and the Danish version of PPAQ (PPAQ-DK) and to validate them using the doubly labeled water technique (DLW) as criterion method. Methods: A total of 220 healthy pregnant women participated. Total energy expenditure (TEE), PA energy expenditure (PAEE), and PA level were determined at gestational Weeks 28–29 using DLW and a Garmin Vivosport (Garmin, Olathe, KS) activity tracker. In addition, PAEE, moderate-to-vigorous intensity PA, and SED were determined using the activity tracker and PPAQ-DK during all three trimesters. Results: TEE from the activity tracker and DLW correlated (r = .63; p < .001), but the activity tracker overestimated TEE (503 kcal/day). Also, the activity tracker overestimated PAEE (303 kcal/day) and PA level compared with DLW. Likewise, PPAQ-DK overestimated PAEE (1,513 kcal/day) compared with DLW. Compared to PPAQ-DK, the activity tracker reported lower values of PAEE and moderate-to-vigorous intensity PA and higher values of SED during all three trimesters. Conclusions: When compared to DLW, we found better agreement of PAEE estimates from the activity tracker than from PPAQ-DK. TEE from the tracker and DLW correlated moderately well, but this was not the case for PAEE or PA level. The activity tracker measured lower PA and higher SED than PPAQ-DK throughout pregnancy. The consumer activity tracker performed better than the questionnaire, but both significantly overestimated PA compared to DLW.

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Validation of Smartphones and Different Low-Cost Activity Trackers for Step Counting Under Free-Living Conditions

Claire Marie Jie Lin Goh, Nan Xin Wang, Andre Matthias Müller, Rowena Yap, Sarah Edney, and Falk Müller-Riemenschneider

Background: Smartphones and wrist-worn activity trackers are increasingly popular for step counting purposes and physical activity promotion. Although trackers from popular brands have frequently been validated, the accuracy of low-cost devices under free-living conditions has not been adequately determined. Objective: To investigate the criterion validity of smartphones and low-cost wrist-worn activity trackers under free-living conditions. Methods: Participants wore a waist-worn Yamax pedometer and seven different low-cost wrist-worn activity trackers continuously over 3 days, and an activity log was completed at the end of each day. At the end of the study, the number of step counts reflected on the participants’ smartphone for each of the 3 days was also recorded. To establish criterion validity, step counts from smartphones and activity trackers were compared with the pedometers using Pearson’s correlation coefficient, mean absolute percentage error, and intraclass correlation coefficient. Results: Five of the seven activity trackers underestimated step counts and the remaining two and the smartphones overestimated step counts. Criterion validity was consistently higher for the activity trackers (r = .78–.92; mean absolute percentage error 14.5%–36.1%; intraclass correlation coefficient: .51–.91) than the smartphone (r = .37; mean absolute percentage error 55.7%; intraclass correlation coefficient: .36). Stratified analysis showed better validity of smartphones among female than for male participants. Phone wearing location also affected accuracy. Conclusions: Low-cost trackers demonstrated high accuracy in recording step counts and can be considered with confidence for research purposes or large-scale health promotion programs. The accuracy of using a smartphone for measuring step counts was substantially lower. Factors such as phone wear location and gender should also be considered when using smartphones to track step counts.

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Use of Accelerometers to Track Changes in Stepping Behavior With the Introduction of the 2020 COVID Pandemic Restrictions: A Case Study

Tiereny McGuire, Kirstie Devin, Victoria Patricks, Benjamin Griffiths, Craig Speirs, and Malcolm Granat

Introduction: The COVID-19 lockdown introduced restrictions to free-living activities. Changes to these activities can be accurately quantified using combined measurement. Using activPAL3 and self-reports to collect activity data, the study aimed to quantify changes that occurred in physical activity and sedentary behavior between prelockdown and lockdown. The study also sought to determine changes in indoor and outdoor stepping. Methods: Using activPAL3, four participants recorded physical activity data prelockdown and during lockdown restrictions (February–June 2020). Single events (sitting, standing, stepping, lying) were recorded and analyzed by the CREA algorithm using an event-based approach. The analysis focused on step count, sedentary time, and lying (in bed) time; median and interquartile range were calculated. Daily steps classified as taking place indoors and outdoors were calculated separately. Results: 33 prelockdown and 92 in-lockdown days of valid data were captured. Median daily step count across all participants reduced by 14.8% (from 5,828 prelockdown to 4,963 in-lockdown), while sedentary and lying time increased by 4% and 8%, respectively (sedentary: 9.98–10.30 hr; lying: 9.33–10.05 hr). Individual variations were observed in hours spent sedentary (001: 8.44–8.66, 002: 7.41–8.66, 003: 11.97–10.59, 004: 6.29–7.94, and lying (001: 9.69–9.49, 002: 11.46–11.66, 003: 7.63–9.34, 004: 9.7–11.12) pre- and in-lockdown. Discrepancies in self-report versus algorithm classification of indoor/outdoor stepping were observed for three participants. Conclusion: The study quantitively showed lockdown restrictions negatively impacted physical activity and sedentary behavior; two variables closely linked to health outcomes. This has important implications for public health policies to help develop targeted interventions and mandates that encourage additional physical activity and lower sedentary behavior.

Free access

Evolution of Public Health Physical Activity Applications of Accelerometers: A Personal Perspective

Richard P. Troiano

Accelerometer technology and applications have expanded and evolved rapidly over approximately the past two decades. This commentary, which reflects content presented at a keynote presentation at 8th International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM 2022), discusses aspects of this evolution from the author’s perspective. The goal is to provide historical context for newer investigators working with device-based measures of physical activity. The presentation includes discussion of the fielding of accelerometer devices in the 2003–2006 National Health and Nutrition Examination Survey, selected recommendations from relevant workshops between 2004 and 2010, and the author’s perspective on the current status of accelerometer use in population surveillance and public health. The important role of collaboration is emphasized.

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Let us Dance Around the World! Toward More Diversity, Equity, and Inclusion in Research

Mai ChinAPaw and Manou Anselma

We strongly believe that diversity, equity, and inclusion in research lead to better science, more innovations and more relevant outcomes that better serve society at large. Historically, scientific research is quite WEIRD, meaning that it is dominated by researchers and study samples from Western, Educated, Industrialized, Rich, and Democratic countries. Such WEIRD research leads to results that better serve a small, privileged group of WEIRD people, widening health inequalities. Research among a selective group with similar backgrounds and perspectives results in bias and hinders innovation. As a result, we end up missing out on the valuable holistic viewpoint that more inclusive research would gain. In this invited commentary based on the International Conference on Ambulatory Monitoring of Physical Activity and Movement (ICAMPAM) 2022 keynote presentation by Prof. ChinAPaw, we discuss the importance of diversity, equity, and inclusion in research and introduce our vision for AWESOME science—All-inclusive, Worldwide ranging, Equitable, Sincere, Open-minded, Mindful of our own implicit bias, and Essential—that is more inclusive and relevant for everyone regardless of who they are and where they live. More diversity, equity, and inclusion make our collective dance toward healthy societies more beautiful and impactful!

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Validity of the Modified SIT-Q 7d for Estimating Sedentary Break Frequency and Duration in Home-Based Office Workers During the COVID-19 Global Pandemic: A Secondary Analysis

Kirsten Dillon-Rossiter, Madison Hiemstra, Nina Bartmann, Wuyou Sui, Marc Mitchell, Scott Rollo, Paul A. Gardiner, and Harry Prapavessis

Office workers who transitioned to working from home are spending an even higher percentage of their workday sitting compared with being “in-office” and this is an emerging health concern. With many office workers continuing to work from home since the onset of the COVID-19 pandemic, it is imperative to have a validated self-report questionnaire to assess sedentary behavior, break frequency, and duration, to reduce the cost and burden of using device-based assessments. This secondary analysis study aimed to validate the modified Last 7-Day Sedentary Behavior Questionnaire (SIT-Q 7d) against an activPAL4™ device in full-time home-based “office” workers (n = 148; mean age = 44.90). Participants completed the modified SIT-Q 7d and wore an activPAL4 for a full work week. The findings showed that the modified SIT-Q 7d had low (ρ = .35–.37) and weak (ρ = .27–.28) criterion validity for accurate estimates of break frequency and break duration, respectively. The 95% limits of agreement were large for break frequency (26.85–29.01) and medium for break duration (5.81–8.47), indicating that the modified SIT-Q 7d may not be appropriate for measuring occupational sedentary behavior patterns at the individual level. Further validation is still required before confidently recommending this self-report questionnaire to be used among this population to assess breaks in sedentary time.

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The Stryd Foot Pod Is a Valid Measure of Stepping Cadence During Treadmill Walking and Running

Madeline E. Shivgulam, Jennifer L. Petterson, Liam P. Pellerine, Derek S. Kimmerly, and Myles W. O’Brien

Stepping cadence is an important determinant of activity intensity, with faster stepping associated with the most health benefits. The Stryd monitor provides real-time feedback on stepping cadence. The limited existing literature has neither validated the Stryd across slow walking to fast running speeds nor strictly followed statistical guidelines for monitor validation studies. We assessed the criterion validity of the Stryd monitor to detect stepping cadence across multiple walking and jogging/running speeds. It was hypothesized that the Stryd monitor would be an accurate measure of stepping cadence across all measured speeds. Forty-six participants (23 ± 5 years, 26 females) wore the Stryd monitor on their shoelaces during a 10-stage progressive treadmill walking (Speeds 1–5) and jogging/running (Speeds 6–10) protocol (criterion: manually counted video-recorded cadence; total stages: 438). Standardized guidelines for physical activity monitor statistical analyses were followed. A two-way repeated-measure analysis of variance revealed the Stryd monitor recorded a slightly higher cadence (<1 steps/min difference, all p < .001) at 2 miles/hr (92.1 ± 6.2 steps/min vs. 91.5 ± 6.4 steps/min, p < .001), 2.5 miles/hr (101.3 ± 6.1 steps/min vs. 100.7 ± 6.4 steps/min), and 3.5 miles/hr (117.4 ± 5.9 steps/min vs. 117.0 ± 6.0 steps/min). However, equivalence testing demonstrated high equivalence of the Stryd and manually counted cadence (equivalence zone required: ≤± 2.6%) across all speeds. The Stryd activity monitor is a valid measure of stepping cadence across walking, jogging, and running speeds. By providing real-time cadence feedback, the Stryd monitor has strong potential to help guide the general public monitor their stepping intensity to promote more habitual activity at faster cadences.