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

Wearable activity trackers, devices that measure physical activity under free-living conditions, are part of a rapidly growing trend in medicine. In 2016, Fitbit Inc. was reported to have a 79% market share in wearable activity trackers ( The NPD Group, 2016 ) and shipped 22.5 million units

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Kelly R. Evenson and Camden L. Spade

wearables, based on a 2018 survey of more than 2,000 health professionals from around the world, “wearable technology” was considered the leading fitness trend ( Thompson, 2019 ). Activity trackers, a subset of wearables, have quickly caught on for personal use, such as to promote changes in physical

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

, Collier, & Sandberg, 2017 ). While the model below applies to all wearable technology, this review focuses on consumer grade products. Smart wristbands or activity tracking wristbands (ATWs) constitute the largest market segment of wearable trackers ( Statt, 2015 ). In general, ATWs are designed to

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Albert R. Mendoza, Kate Lyden, John Sirard, John Staudenmayer, Catrine Tudor-Locke and Patty S. Freedson

, Chansin, & Zervos, 2017 ). Of the numerous wearable activity tracker manufacturers, Fitbit was the leading brand of activity tracker in 2015, accounting for 79 percent of sales ( NPD, 2016 ). The enormous market for wearable activity trackers and pedometers (ATPs) is driven in part by lower cost, longer

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Charlotte L. Edwardson, Melanie Davies, Kamlesh Khunti, Thomas Yates and Alex V. Rowlands

, with the market share increasing to ∼75% when including smartwatches (Statista Website [Internet], 2017a ). These activity trackers provide consumers with information on their health behavior, most commonly in the form of steps taken, distance walked, calories expended, and sleep duration. Multiple

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Danielle R. Madden, Chun Nok Lam, Brian Redline, Eldin Dzubur, Harmony Rhoades, Stephen S. Intille, Genevieve F. Dunton and Benjamin Henwood

collection strategies, such as ecological momentary assessments (EMA) and activity trackers (e.g., accelerometers; Fisher, 2008 ; Haskell, 2012 ), the findings point to the importance of within-person variability. Essentially, one’s feeling states (e.g., mood or affect) fluctuate over time, and this

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Jeffer Eidi Sasaki, Amanda Hickey, Marianna Mavilia, Jacquelynne Tedesco, Dinesh John, Sarah Kozey Keadle and Patty S. Freedson


The purpose of this study was to examine the accuracy of the Fitbit wireless activity tracker in assessing energy expenditure (EE) for different activities.


Twenty participants (10 males, 10 females) wore the Fitbit Classic wireless activity tracker on the hip and the Oxycon Mobile portable metabolic system (criterion). Participants performed walking and running trials on a treadmill and a simulated free-living activity routine. Paired t tests were used to test for differences between estimated (Fitbit) and criterion (Oxycon) kcals for each of the activities.


Mean bias for estimated energy expenditure for all activities was −4.5 ± 1.0 kcals/6 min (95% limits of agreement: −25.2 to 15.8 kcals/6 min). The Fitbit significantly underestimated EE for cycling, laundry, raking, treadmill (TM) 3 mph at 5% grade, ascent/descent stairs, and TM 4 mph at 5% grade, and significantly overestimated EE for carrying groceries. Energy expenditure estimated by the Fitbit was not significantly different than EE calculated from the Oxycon Mobile for 9 activities.


The Fitbit worn on the hip significantly underestimates EE of activities. The variability in underestimation of EE for the different activities may be problematic for weight loss management applications since accurate EE estimates are important for tracking/monitoring energy deficit.

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Abby Haynes, Catherine Sherrington, Geraldine Wallbank, David Lester, Allison Tong, Dafna Merom, Chris Rissel and Anne Tiedemann

effectiveness of health coaching provided specifically by physiotherapists is nascent and mixed ( Rethorn & Pettitt, 2019 ). Activity trackers increase PA in adults of all ages, including older adults ( Mercer, Li, Giangregorio, Burns, & Grindrod, 2016 ; Oliveira, Sherrington, Zheng, et al., 2019 ) and

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Patty Freedson

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Adam Šimůnek, Jan Dygrýn, Lukáš Jakubec, Filip Neuls, Karel Frömel and Gregory John Welk

, are inexpensive and have been widely used in PE settings, but they are limited by the inability to segment data by time or data to be downloaded for processing. A reasonable compromise for future applications is the coordinated use of consumer-based activity trackers. These devices share elements of