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Volume 6 (2023): Issue 2 (Jun 2023)

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Calibrating Physical Activity and Sedentary Behavior for Hip-Worn Accelerometry in Older Women With Two Epoch Lengths: The Women’s Health Initiative Objective Physical Activity and Cardiovascular Health Calibration Study

Kelly R. Evenson, Fang Wen, Christopher C. Moore, Michael J. LaMonte, I-Min Lee, Andrea Z. LaCroix, and Chongzhi Di

Purpose: The purpose of this study was to develop 60-s epoch accelerometer intensity cut points for vertical axis count and vector magnitude (VM) output from hip-worn triaxial accelerometers among women 60–91 years old. We also compared these cut points against cut points derived by multiplying 15-s epoch cut points by four. Methods: Two hundred apparently healthy women wore an ActiGraph GT3X+ accelerometer on their hip while performing a variety of laboratory-based activities that were sedentary (watching television and assembling a puzzle), low light (washing/drying dishes), high light (laundry and dust mopping), or moderate-to-vigorous physical activity (400-m walk) intensity. Oxygen uptake was measured using an Oxycon portable calorimeter. Sedentary behavior and physical activity intensity cut points for vertical axis and VM counts were derived for 60-s epochs from receiver operating characteristic and by multiplying the 15-s cut points by four; both were compared with oxygen uptake. Results: The median age was 74.5 years (interquartile range 70–83). The 60-s epoch cut points for vertical counts were 0 sedentary, 1–73 low light, 74–578 high light, and ≥579 moderate-to-vigorous physical activity. The 60-s epoch cut points for VM were 0–88 sedentary, 89–663 low light, 664–1,730 high light, and ≥1,731 moderate-to-vigorous physical activity. For both sets of cut points, the receiver operating characteristic approach yielded more accurate estimates than the multiplication approach. Conclusion: The derived 60-s epoch cut points for vertical counts and VM can be applied to epidemiologic studies to define sedentary behavior and physical activity intensities in older adult populations.

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Conceptualizing, Defining, and Measuring Before-School Physical Activity: A Review With Exploratory Analysis of Adolescent Data

James Woodforde, Sjaan Gomersall, Anna Timperio, Venurs Loh, Hannah Browning, Francisco Perales, Jo Salmon, and Michalis Stylianou

Physical activity (PA) among children and adolescents is often reported by time segments centered around the school day, including before school. However, there is no consistent approach to defining the before-school segment, to accurately capture PA levels and facilitate synthesis of results across studies. Therefore, this study aimed to (a) examine how studies with children and adolescents have defined the before-school segment, and (b) compare adolescents’ before-school PA using various segment definitions. We conducted a systematic search and review of literature from six databases, and subsequently analyzed accelerometer data from Australia (n = 472, mean age 14.9 years, 40% male), to compare PA across five before-school definitions. Our review found 69 studies reporting before-school PA, 59 of which used device-based measures. Definitions ranged widely, but justifications were rarely reported. Our empirical comparison of definitions resulted in a range of participants meeting wear time criteria (≥3 days at >50% of segment length) from the latest-starting definition (30 min prior to school; n = 443) to the earliest-starting definition (6:00 a.m.–school start; n = 155), implying that for many participants, accelerometer wear was low in the early hours due to sleep or noncompliance. Statistically significant differences in light and moderate-to-vigorous PA (mean minutes/school day, proportion of segment length, and proportion of wear time) were found between definitions, indicating that before-school PA could potentially be underestimated depending on definition choice. We recommend that future studies clearly report and justify segment definition, apply segment-specific wear time criteria, and collect wake time data to enable individualized segment start times and minimize risk of data misclassification.

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Using Video Cameras as a Research Tool in Public Spaces: Addressing Ethical and Information Governance Challenges Under Data Protection Legislation

Jack S. Benton, James Evans, Miranda Mourby, Mark J. Elliot, Jamie Anderson, J. Aaron Hipp, and David P. French

Systematic observation is a promising unobtrusive method of assessing human behavior in urban environments without many issues typically associated with self-report measures (e.g., recall bias, low response rates). Improvements in video camera technologies make it more feasible for researchers to conduct systematic observation, which could reduce the time, labor, and cost to facilitate high-quality observational research in urban environments at scale. However, there are important ethical and information governance challenges driven by data protection laws, which discourage many researchers from using camera-based observation methods. The European Union General Data Protection Regulation is a leading global standard for data protection. Drawing on our experiences of conducting three studies using video cameras in public spaces, we discuss how to conduct this kind of research in line with General Data Protection Regulation requirements. The paper outlines issues concerning data protection, privacy, informed consent, and confidentiality, and how we addressed them. In doing this, the paper provides support for responsible use of camera-based observation methods, which will be of value to researchers, ethics committees, and funders. Outlining how to use video cameras responsibly will enable more research to be conducted that, in turn, will build the case for its benefits to researchers and society.

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Moving Beyond the Characterization of Activity Intensity Bouts as Square Waves Signals

Myles W. O’Brien, Jennifer L. Petterson, Liam P. Pellerine, Madeline E. Shivgulam, Derek S. Kimmerly, Ryan J. Frayne, Pasan Hettiarachchi, and Peter J. Johansson

Wearable activity monitors provide objective estimates of time in different physical activity intensities. Each continuous stepping period is described by its length and a corresponding single intensity (in metabolic equivalents of task [METs]), creating square wave–shaped signals. We argue that physiological responses do not resemble square waves, with the purpose of this technical report to challenge this idea and use experimental data as a proof of concept and direct potential solutions to better characterize activity intensity. Healthy adults (n = 43, 19♀; 23 ± 5 years) completed 6-min treadmill stages (five walking and five jogging/running) where oxygen consumption (3.5 ml O2·kg−1·min−1 = 1 MET) was recorded throughout and following the cessation of stepping. The time to steady state was ∼1–1.5 min, and time back to baseline following exercise was ∼1–2 min, with faster stepping stages generally exhibiting longer durations. Instead of square waves, the duration intensity signal reflected a trapezoid shape for each stage. The METs per minute during the rise to steady state (upstroke slopes; average: 1.7–6.3 METs/min for slow walking to running) may be used to better characterize activity intensity for shorter activity bouts where steady state is not achieved (within ∼90 s). While treating each activity bout as a single intensity is a much simpler analytical procedure, characterizing each bout in a continuous manner may better reflect the true physiological responses to movement. The information provided herein may be used to improve the characterization of activity intensity, definition of bout breaks, and act as a starting point for researchers and software developers interested in using wearables to measure activity intensity.

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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)

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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.