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Patty Freedson

In the second issue of the Journal for the Measurement of Physical Behaviour , we feature a large surveillance study ( Lee et al., 2018 ) that used a wearable device to characterize physical activity and sedentary behavior in over 16,500 older women who participated in the Women’s Health Study. I

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Stephanie J. Shell, Brad Clark, James R. Broatch, Katie Slattery, Shona L. Halson, and Aaron J. Coutts

wearable device which is capable of measuring swim training and performance metrics. 5 However, this study only assessed the validity of freestyle and breaststroke over a distance of 100 m in a 25-m pool. Considering swimmers are typically required to complete a range of swim strokes (and modified strokes

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James W. Navalta, Jeffrey Montes, Nathaniel G. Bodell, Charli D. Aguilar, Ana Lujan, Gabriela Guzman, Brandi K. Kam, Jacob W. Manning, and Mark DeBeliso

activity tracking devices becomes more pervasive. A number of recent investigations have determined the accuracy of wearable devices in determining step count in the laboratory ( An, Jones, Kang, Welk, & Lee, 2017 ; Chen, Kuo, Pellegrini, & Hsu, 2016 ; Fokkema, Kooiman, Krijnen, Van Der Schans, & De

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Lindsay P. Toth, Susan Park, Whitney L. Pittman, Damla Sarisaltik, Paul R. Hibbing, Alvin L. Morton, Cary M. Springer, Scott E. Crouter, and David R. Bassett

Purpose: To examine the effect of brief, intermittent stepping bouts on step counts from 10 physical activity monitors (PAMs). Methods: Adults (N = 21; M ± SD, 26 ± 9.0 yr) wore four PAMs on the wrist (Garmin Vivofit 2, Fitbit Charge, Withings Pulse Ox, and ActiGraph wGT3X-BT [AG]), four on the hip (Yamax Digi-Walker SW-200 [YX], Fitbit Zip, Omron HJ-322U, and AG), and two on the ankle (StepWatch [SW] with default and modified settings). AG data were processed with and without the low frequency extension (AGL) and with the Moving Average Vector Magnitude algorithm. In Part 1 (five trials), walking bouts were varied (4–12 steps) and rest intervals were held constant (10 s). In Part 2 (six trials), walking bouts were held constant (4 steps) and rest intervals were varied (1–10 s). Percent of hand-counted steps and mean absolute percentage error were calculated. One sample t-test was used to compare percent of hand-counted steps to 100%. Results: In Parts 1 and 2, the SWdefault, SWmodified, YX, and AGLhip captured within 10% of hand-counted steps across nearly all conditions. In Part 1, estimates of most methods improved as the number of steps per bout increased. In Part 2, estimates of most methods decreased as the rest duration increased. Conclusion: Most methods required stepping bouts of >6–10 consecutive steps to record steps. Rest intervals of 1–2 seconds were sufficient to break up walking bouts in many methods. The requirement for several consecutive steps in some methods causes an underestimation of steps in brief, intermittent bouts.

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Alanna Weisberg, Alexandre Monte Campelo, Tanzeel Bhaidani, and Larry Katz

this population is critically important for preventing chronic disease, maintaining autonomy, and improving quality of life. Technological tools, such as wearable sensors, have been used to assess objective wellness and health-related variables. Specifically, current wearable devices are able to assess

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I-Min Lee, Eric J. Shiroma, Kelly R. Evenson, Masamitsu Kamada, Andrea Z. LaCroix, and Julie E. Buring

participation. The present study is one of the “next generation” epidemiologic studies of physical activity, utilizing wearable devices—instead of self-report—for measurement and with anticipated long-term follow-up of participants after assessment ( Bassett, Toth, LaMunion, & Crouter, 2017 ; Troiano et

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Jennifer L. Huberty, Jeni L. Matthews, Meynard Toledo, Lindsay Smith, Catherine L. Jarrett, Benjamin Duncan, and Matthew P. Buman

Purpose: To estimate the energy expenditure (EE) of Vinyasa Flow and validate the Actigraph (AG) and GENEActiv (GA) for measuring EE in Vinyasa Flow. Methods: Participants (N = 22) were fitted to a mask attached to the Oxycon. An AG was placed on the left hip and a GA was placed on the non-dominant wrist. Participants were randomized to an initial resting activity before completing a 30-minute Vinyasa Flow video. AG data was scored using the Freedson VM3 (2011) and the Freedson Adult (1998) algorithms in the Actilife software platform. EE from GA were derived using cut points from a previous study. Date and time filters were added corresponding to the time stamps recorded by the tablet video files of each yoga session. Kcals and METs expended by participants were calculated using bodyweight measured during their visit. Data was analyzed using SPSS. A dependent samples t-test, an intraclass correlation coefficient (ICC), and mean absolute difference were used to determine agreement between variables. Results: According to the Oxycon, participation in Vinyasa Flow required an average EE of 3.2 ± 0.4 METs. The absolute agreement between the Oxycon, AG, or GA was poor (ICC < .20). The mean difference in METs for the AG was −2.1 ± 0.6 and GA was −1.4 ± 0.6 (all p < .01). Conclusion: According to the Oxycon, participation in Vinyasa Flow met the criteria for moderate-intensity physical activity. The AG and GA consistently underestimated EE. More research is needed to determine an accurate measurement for EE during yoga using a wearable device appropriate for free-living environments.

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Kayla J. Nuss, Joseph L. Sanford, Lucas J. Archambault, Ethan J. Schlemer, Sophie Blake, Jimikaye Beck Courtney, Nicholas A. Hulett, and Kaigang Li

Over the last several years, wearable devices, such as the Apple Watch, have become ubiquitous. It was estimated that worldwide sales will exceed 14.7 billion U.S. dollars by 2026 ( “Fitness App 2019 Global Market Net Worth US$ 14.7 billion Forecast By 2026 - MarketWatch,” 2019 ). Two features that

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David R. Bassett, Patty S. Freedson, and Dinesh John

wearable devices, mobile health, health information technology, telemedicine, and personalized medicine ( FDA, 2017 ). Physicians and other stakeholders view digital health as a set of tools to improve the efficiency, access, and quality of health care while reducing medical costs. Patients and the general

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Alyssa Evans, Gavin Q. Collins, Parker G. Rosquist, Noelle J. Tuttle, Steven J. Morrin, James B. Tracy, A. Jake Merrell, William F. Christensen, David T. Fullwood, Anton E. Bowden, and Matthew K. Seeley

.g., cardiovascular disease, cancer, and diabetes) ( Alves et al., 2016 ; Beavis, Smith, & Fader, 2016 ; Hamasaki, 2016 ). Commercially available wearable devices that can estimate activity-based outcomes (e.g., energy expenditure) in real time have been shown to (a) increase exercise motivation, physical activity levels