Comparison of Consumer and Research Monitors under Semistructured Settings YANG BAI 1 , GREGORY J. WELK 1 , YOON HO NAM 1 , JOEY A. LEE 1 , JUNG-MIN LEE 2 , YOUNGWON KIM 1,3 , NATHAN F. MEIER 1 , and PHILIP M. DIXON 4 1 Department of Kinesiology, Iowa State University, Ames, IA; 2 School of Health, Physical Education, and Recreation, University of Nebraska at Omaha, Omaha, NE; 3 MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UNITED KINGDOM; and 4 Department of Statistics, Iowa State University, Ames, IA ABSTRACT BAI, Y., G. J. WELK, Y. H. NAM, J. A. LEE, J.-M. LEE, Y. KIM, N. F. MEIER, and P. M. DIXON. Comparison of Consumer and Research Monitors under Semistructured Settings. Med. Sci. Sports Exerc., Vol. 48, No. 1, pp. 151–158, 2016. Purpose: This study evaluated the relative validity of different consumer and research activity monitors during semistructured periods of sedentary activity, aerobic exercise, and resistance exercise. Methods: Fifty-two (28 male and 24 female) participants age 18–65 yr performed 20 min of self-selected sedentary activity, 25 min of aerobic exercise, and 25 min of resistance exercise, with 5 min of rest between each activity. Each participant wore five wrist-worn consumer monitors [Fitbit Flex, Jawbone Up24, Misfit Shine (MS), Nike+ Fuelband SE (NFS), and Polar Loop] and two research monitors [ActiGraph GT3X+ on the waist and BodyMedia Core (BMC) on the arm] while being concurrently monitored with Oxycon Mobile (OM), a portable metabolic measuring system. Energy expenditure (EE) on different activity sessions was measured by OM and estimated by all monitors. Results: Mean absolute percent error (MAPE) values for the full 80-min protocol ranged from 15.3% (BMC) to 30.4% (MS). EE estimates from ActiGraph GT3X+ were found to be equivalent to those from OM (T10% equivalence zone, 285.1–348.5). Correlations between OM and the various monitors were generally high (ranged between 0.71 and 0.90). Three monitors had MAPE values lower than 20% for sedentary activity: BMC (15.7%), MS (18.2%), and NFS (20.0%). Two monitors had MAPE values lower than 20% for aerobic exercise: BMC (17.2%) and NFS (18.5%). None of the monitors had MAPE values lower than 25% for resistance exercise. Conclusion: Overall, the research monitors and Fitbit Flex, Jawbone Up24, and NFS provided reasonably accurate total EE estimates at the individual level. However, larger error was evident for individual activities, especially resistance exercise. Further research is needed to examine these monitors across various activities and intensities as well as under real-world conditions. Key Words: ENERGY EXPENDITURE, WRIST-WORN MONITORS, PHYSICAL ACTIVITY, FREE-LIVING I n recent years, the consumer marketplace has been flooded with an array of activity monitors designed to enhance self-monitoring and behavior change. Accelerometry- based activity monitors have been widely used in research applications for many years (11,30), but the availability of these devices to consumers is a relatively recent phenomenon. According to ABI Research, a market research and intelli- gence firm, more than 32 million wearable activity and health devices were sold in 2013, and the number will increase to approximately 42 million in 2014. In addition, several re- search studies have adopted consumer monitors to serve as self-assessment and monitoring tools for clinical and research purposes (3,4,12,25). The trend has been toward wrist-worn activity tracking devices that link to cell phone and social media applications for personalized monitoring. Some con- sumer activity monitors utilize online platforms that allow users to share progress with friends as a means of peer support, but nearly all provide direct estimates of time spent in physical activity (e.g., minutes of moderate and vigorous activities) and estimates of total energy expenditure (EE), physical activity EE, or steps. Some monitors [e.g., BodyMedia Core (BMC) and Jawbone Up24 (JU24)] segment data into different inten- sity zones, whereas others include ancillary outcome measures, including sleep time, distance, physical activity time, sedentary time, and heart rate. Wearable monitors have been shown to have utility as effective assessment and monitoring tools in a variety of set- tings (2,3,4,12,21,23–25). Bassett and John (5) highlighted the monitors_ convenient display and utility as key features for clinical populations. Appelboom et al. (3) recently sum- marized key features of smart wearable sensors in a variety of clinical applications (patients with cardiopulmonary, vas- cular, and neurological dysfunctions, as well as physical therapy and rehabilitation). The rapid technological advance- ments in consumer-based monitors offer considerable po- tential for clinical applications and may also serve as more cost-effective and appealing intervention methods for be- havior change applications. Address for correspondence: Yang Bai, M.S., Department of Kinesiology, College of Human Sciences, Iowa State University, 283 Forker Building, Ames, IA 50010; E-mail: ybai@iastate.edu. Submitted for publication February 2015. Accepted for publication June 2015. 0195-9131/16/4801-0151/0 MEDICINE & SCIENCE IN SPORTS & EXERCISE Ò Copyright Ó 2015 by the American College of Sports Medicine DOI: 10.1249/MSS.0000000000000727 151 APPLIED SCIENCES Copyright © 2015 by the American College of Sports Medicine. 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