Validity of Consumer-Based Physical Activity Monitors
Abstract
BACKGROUND: Many consumer-based monitors are marketed to provide personal information on the levels of physical activity and daily energy expenditure (EE), but little or no information is available to substantiate their validity. PURPOSE: This study aimed to examine the validity of EE estimates from a variety of consumer-based, physical activity monitors under free-living conditions. METHODS: Sixty (26.4 ± 5.7 yr) healthy males (n = 30) and females (n = 30) wore eight different types of activity monitors simultaneously while completing a 69-min protocol. The monitors included the BodyMedia FIT armband worn on the left arm, the DirectLife monitor around the neck, the Fitbit One, the Fitbit Zip, and the ActiGraph worn on the belt, as well as the Jawbone Up and Basis B1 Band monitor on the wrist. The validity of the EE estimates from each monitor was evaluated relative to criterion values concurrently obtained from a portable metabolic system (i.e., Oxycon Mobile). Differences from criterion measures were expressed as a mean absolute percent error and were evaluated using 95% equivalence testing. RESULTS: For overall group comparisons, the mean absolute percent error values (computed as the average absolute value of the group-level errors) were 9.3%, 10.1%, 10.4%, 12.2%, 12.6%, 12.8%, 13.0%, and 23.5% for the BodyMedia FIT, Fitbit Zip, Fitbit One, Jawbone Up, ActiGraph, DirectLife, NikeFuel Band, and Basis B1 Band, respectively. The results from the equivalence testing showed that the estimates from the BodyMedia FIT, Fitbit Zip, and NikeFuel Band (90% confidence interval = 341.1-359.4) were each within the 10% equivalence zone around the indirect calorimetry estimate. CONCLUSIONS: The indicators of the agreement clearly favored the BodyMedia FIT armband, but promising preliminary findings were also observed with the Fitbit Zip.
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Existing validation studies have largely been completed on devices considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and Nike FuelBand.
“However, given the pace of technological change, these existing studies have largely been completed on devices that would be considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and the Nike FuelBand.”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
Most research on wearable device accuracy has primarily focused on estimates of energy expenditure.
“Most research has primarily focused on the accuracy of estimates of EE [7-12].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
One previous study [10] found MAPE for Fitbit Zip (10.1%) and Fitbit One (10.4%) to be much lower than the results from this study, where MAPE was between 16.85% and 84.98%.
“One previous study [10] found MAPE for Fitbit Zip (10.1%) and Fitbit One (10.4%) to be much lower than the results from this study, where MAPE was between 16.85% and 84.98%.”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
While Fitbit models have changed over time, the basic error rate has been similar across studies and devices [8,10].
“For example, while Fitbit models have changed over time, the basic error rate has been similar across studies and devices [8,10].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [10]
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InfluentialHow consumer physical activity monitors could transform human physiology research
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BackgroundA Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement
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InfluentialValidation of the Instant Blood Pressure Smartphone App
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“In the earlier models of activity monitors, only accelerometers were used to estimate EE [11], but in more recent multisensor models, photoplethysmography (PPG) is being used to estimate HR [12] and, potentially, to improve the accuracy in estimating EE [13].”
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“The larger variation in sample characteristics may explain the larger measurement error compared to other studies (13,22), but it provides more confidence that the results can be generalized to a broader population.”
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Phillips, S. M., Cadmus‐Bertram, L., Rosenberg, D. E., Buman, M. P., & Lynch, B. M. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2017.08.015 · Cited by 148 · see citing works
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“A number of past studies have provided support for the relatively good performance of earlier Fitbit products, such as Fitbit One™ (Lauritzen, Munoz, Luis Sevillano, & Civit, 2013), Fitbit Zip™(Lee et al., 2014), and Fitbit Flex™ (Bai et al., 2016), none of which has the built-in heart rate feature.”
ResultSelf-citationMontoye, A. H. K., Moore, R. W., Bowles, H. R., Korycinski, R. W., & Pfeiffer, K. A. (2016) · British Journal of Sports Medicine · doi:10.1136/bjsports-2015-095947 · Cited by 141 · see citing works
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“…to describe physical activity patterns across diverse population subgroups (e.g., healthy adults and children, and adults with diabetes, chronic obstructive pulmonary disease [COPD], and arthritis) (Troiano et al., 2014; Troiano, 2005; Loprinzi et al., 2014; Lee et al., 2013; Colley et al., 2011).”
BackgroundStandardizing Analytic Methods and Reporting in Activity Monitor Validation Studies
Welk, G. J., Bai, Y., Lee, J. M., Godino, J., Saint‐Maurice, P. F., & Carr, L. J. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001966 · Cited by 139 · see citing works
Self-citationDevices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review
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“Validity data on each device was not extracted; instead, articles with relevant validity data, where available [19-38], were referenced in the data table because the authors chose to focus this review on the characteristics of the devices to allow the reader to make a judgment about their efficacy as self-monitoring t…”
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How valid are wearable physical activity trackers for measuring steps?
An, H., Jones, G. C., Kang, S., Welk, G. J., & Lee, J. M. (2016) · European Journal of Sport Science · doi:10.1080/17461391.2016.1255261 · Cited by 131 · see citing works
“Previous studies have examined the validity of these devices for estimating energy expenditure (Bai et al., 2015; Lee et al., 2014b); however, this is one of the only studies to examine the relative validity for counting steps, an outcome measure of greater interest to consumers.”
BackgroundResultSelf-citationWearable Activity Tracker Use Among Australian Adolescents: Usability and Acceptability Study
Ridgers, N. D., Timperio, A., Brown, H., Ball, K., Macfarlane, S., Lai, S. K., Richards, K., Mackintosh, K. A., McNarry, M. A., Foster, M., et al. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9199 · Cited by 125 · see citing works
“To date, researchers have tended to focus on the validity and/or reliability of such wearable devices for measuring a range of outcomes in laboratory and free-living settings, including steps, distance traveled, energy expenditure, and sleep [3,4].”
BackgroundValidity of FitBit, Jawbone UP, Nike+ and other wearable devices for level and stair walking
Huang, Y., Xu, J., Yu, B., & Shull, P. B. (2016) · Gait & Posture · doi:10.1016/j.gaitpost.2016.04.025 · Cited by 122 · see citing works
“Each walking trial lasted 3 min [21] with a 3 min rest between each trial.”
MethodCognitive pilot-aircraft interface for single-pilot operations
Liu, J., Gardi, A., Ramasamy, S., Lim, Y., & Sabatini, R. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.08.031 · Cited by 121 · see citing works
Cosoli, G., Spinsante, S., & Scalise, L. (2020) · Measurement · doi:10.1016/j.measurement.2020.107789 · Cited by 122 · see citing works
Chowdhury, E. A., Western, M. J., Nightingale, T. E., Peacock, O. J., & Thompson, D. (2017) · PLOS ONE · doi:10.1371/journal.pone.0171720 · Cited by 118 · see citing works
“Previous research has shown that BodyMedia SenseWear accurately measures energy expenditure relative to criterion measures [11,20,21] and has been used to quantify physical activity and energy expenditure in experimental trials [37,38].”
BackgroundMethodInfluentialImboden, M. T., Nelson, M. B., Kaminsky, L. A., & Montoye, A. H. K. (2017) · British Journal of Sports Medicine · doi:10.1136/bjsports-2016-096990 · Cited by 119 · see citing works
Smartphone apps and the nutrition care process: Current perspectives and future considerations
Chen, J., Gemming, L., Hanning, R. M., & Allman-Farinelli, M. A. (2017) · Patient Education and Counseling · doi:10.1016/j.pec.2017.11.011 · Cited by 116 · see citing works
Pinto, A. J., Roschel, H., Pinto, A. L. D. S., Lima, F. R., Pereira, R. M. R., Silva, C. A., Bonfá, E., & Gualano, B. (2017) · Autoimmunity Reviews · doi:10.1016/j.autrev.2017.05.001 · Cited by 112 · see citing works
Zhou, M., Fukuoka, Y., Mintz, Y., Goldberg, K., Kaminsky, P., Flowers, E., & Aswani, A. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9117 · Cited by 111 · see citing works
“The built-in health chip in the iPhone collects the step data, and the accuracy of step counts collected by the iPhone health chip has been validated in a number of studies to have comparable accuracy to an ActiGraph [50-55].”
Lobelo, F., Kelli, H. M., Tejedor, S. C., Pratt, M., McConnell, M. V., Martin, S. S., & Welk, G. J. (2016) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2016.02.007 · Cited by 113 · see citing works
“on a cancellation of over and underestimation from various activities [27, 28].”
BackgroundSelf-citationWen, D., Zhang, X., Liu, X., & Lei, J. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6874 · Cited by 111 · see citing works
“LEE et al [15] investigated the accuracy of EE measurements on 60 subjects wearing 8 types of wearable devices performing activities at different intensities and showed that the mean absolute percent error of different devices on EE measurements varied between 9.3% and 23.5%; moreover, the error…”
InfluentialFloegel, T. A., Flórez-Pregonero, A., Hekler, E. B., & Buman, M. P. (2016) · The Journals of Gerontology Series A · doi:10.1093/gerona/glw098 · Cited by 108 · see citing works
“An equivalence zone of ±10%, while arbitrary, is consistent with other activity monitor validation studies (8,19).”
Godino, J. G., Wing, D., de Zambotti, M., Baker, F. C., Bagot, K., Inkelis, S., Pautz, C., Higgins, M., Nichols, J., Brumback, T., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237719 · Cited by 107 · see citing works
Peart, D. J., Balsalobre‐Fernández, C., & Shaw, M. P. (2017) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000002344 · Cited by 105 · see citing works
“For this, many of the most popular wearable devices available in the market are focused in steps tracking using acceleration data to provide users with information about their step count (11,19,60).”
BackgroundGPS-Based Exposure to Greenness and Walkability and Accelerometry-Based Physical Activity
James, P., Hart, J. E., Hipp, J. A., Mitchell, J. A., Kerr, J., Hurvitz, P. M., Glanz, K., & Laden, F. (2017) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-16-0925 · Cited by 104 · see citing works
Dominick, G. M., Winfree, K. N., Pohlig, R. T., & Papas, M. A. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6281 · Cited by 102 · see citing works
“In general, these studies indicate that Fitbit underestimates EE across different modes of activity [4,16,18] and overestimates EE when activities are combined [15].”
BackgroundLee, H. A., Lee, H., Moon, J.-H., Lee, T., Kim, M.-G., In, H., Cho, C., & Kim, L. (2017) · Psychiatry Investigation · doi:10.4306/pi.2017.14.2.179 · Cited by 102 · see citing works
Exercise and the Development of the Artificial Pancreas
Riddell, M. C., Zaharieva, D. P., Yavelberg, L., Çınar, A., & Jamnik, V. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815609370 · Cited by 102 · see citing works
“To date, HR monitoring provides one of the most efficient means of estimating energy expenditure.(52) Other technologies that may turn out to be useful for AP development capture variables such as skin electrical conductance, skin temperature, near body ambient temperature, breathing rates/ frequencies, heat flux, swe…”
BackgroundA Comparative Review of Footwear-Based Wearable Systems
Hegde, N., Bries, M., & Sazonov, E. E. (2016) · Electronics · doi:10.3390/electronics5030048 · Cited by 99 · see citing works
How Sensors Might Help Define the External Exposome
Loh, M. M., Sarigiannis, D. A., Gotti, A., Karakitsios, S., Pronk, A., Kuijpers, E., Annesi‐Maesano, I., Baïz, N., Madureira, J., Fernandes, E. O., et al. (2017) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph14040434 · Cited by 95 · see citing works
der Walt, N. V., Salmon, L. J., Gooden, B. R., Lyons, M. C., O'Sullivan, M., Martina, K., Pinczewski, L. A., & Roe, J. P. (2018) · The Journal of Arthroplasty · doi:10.1016/j.arth.2018.06.024 · Cited by 92 · see citing works
“It has also been identified that these activity trackers have a great potential to accurately assess activity level before and after joint arthroplasty [9].”
BackgroundRiedl, R., Fischer, T., Léger, P., & Davis, F. D. (2020) · ACM SIGMIS Database the DATABASE for Advances in Information Systems · doi:10.1145/3410977.3410980 · Cited by 94 · see citing works
Gomersall, S. R., Ng, N., Burton, N. W., Pavey, T., Gilson, N. D., & Brown, W. J. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5531 · Cited by 90 · see citing works
The Use of Wearable Devices in Oncology Patients: A Systematic Review
Chow, R., Drkulec, H., Im, J. H. B., Tsai, J., Nafees, A., Kumar, S., Hou, T., Fazelzad, R., Leighl, N. B., Krzyzanowska, M., et al. (2024) · The Oncologist · doi:10.1093/oncolo/oyad305 · Cited by 85 · see citing works
Price, K. J., Bird, S. R., Lythgo, N., Raj, I. S., Wong, J., & Lynch, C. (2016) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2016.1253795 · Cited by 87 · see citing works
Use of Wearable, Mobile, and Sensor Technology in Cancer Clinical Trials
Cox, S. M., Lane, A., & Volchenboum, S. L. (2018) · JCO Clinical Cancer Informatics · doi:10.1200/cci.17.00147 · Cited by 86 · see citing works
Murakami, H., Kawakami, R., Nakae, S., Yamada, Y., Nakata, Y., Ohkawara, K., Sasai, H., Ishikawa‐Takata, K., Tanaka, S., & Miyachi, M. (2019) · JMIR mhealth and uhealth · doi:10.2196/13938 · Cited by 84 · see citing works
“Several studies have evaluated the validity of EE estimated by wearable devices during some activities [7-14].”
BackgroundResultInfluentialConcurrent Validity of Wearable Activity Trackers Under Free-Living Conditions
Brooke, S. M., An, H.-S., Kang, S.-K., Noble, J. M., Berg, K. E., & Lee, J.-M. (2017) · Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000001571 · Cited by 79 · see citing works
“Many of the wearable activity trackers have been validated during specific activities in laboratory settings (2,14), but to our best knowledge, although Ferguson et al.”
BackgroundMethodSelf-citationJames, P., Jankowska, M. M., Marx, C. M., Hart, J. E., Berrigan, D. A., Kerr, J., Hurvitz, P. M., Hipp, J. A., & Laden, F. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.06.006 · Cited by 78 · see citing works
Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study
Pobiruchin, M., Suleder, J., Zowalla, R., & Wiesner, M. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6395 · Cited by 78 · see citing works
“In theory, the measurements obtained by different vendors and device categories (ie, GPS-enabled system vs accelerometer-based) should be comparable with each other [11].”
Redenius, N., Kim, Y., & Byun, W. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0668-1 · Cited by 78 · see citing works
Self-citationHickey, A. M., & Freedson, P. S. (2016) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2016.02.006 · Cited by 77 · see citing works
Real-time activity monitoring with a wristband and a smartphone
Cvetković, B., Szeklicki, R., Janko, V., Lutomski, P., & Luštrek, M. (2017) · Information Fusion · doi:10.1016/j.inffus.2017.05.004 · Cited by 77 · see citing works
Alinia, P., Cain, C. R., Fallahzadeh, R., Shahrokni, A., Cook, D. J., & Ghasemzadeh, H. G. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6321 · Cited by 75 · see citing works
Abrantes, A. M., Blevins, C. E., Battle, C. L., Read, J. P., Gordon, A. L., & Stein, M. D. (2017) · Journal of Substance Abuse Treatment · doi:10.1016/j.jsat.2017.07.006 · Cited by 78 · see citing works
Wearables for Promoting Physical Activity
Strath, S. J., & Rowley, T. W. (2017) · Clinical Chemistry · doi:10.1373/clinchem.2017.272369 · Cited by 74 · see citing works
Lucan, S. C., & DiNicolantonio, J. J. (2014) · Public Health Nutrition · doi:10.1017/s1368980014002559 · Cited by 73 · see citing works
Apovian, C. M., Garvey, W. T., & Ryan, D. H. (2015) · Obesity · doi:10.1002/oby.21140 · Cited by 72 · see citing works
“Many resources and tools have become available to help individuals with weight loss efforts, including a wide range of diet options, activity monitors, social networks, and structured weight loss programs (3,24-29).”
BackgroundPractical Approaches to Prescribing Physical Activity and Monitoring Exercise Intensity
Reed, J. L., & Pipe, A. L. (2016) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2015.12.024 · Cited by 100 · see citing works
Assessing Physical Activity as a Core Component in Cardiac Rehabilitation
Kaminsky, L. A., Brubaker, P. H., Guazzi, M., Lavie, C. J., Montoye, A. H. K., Sanderson, B., & Savage, P. D. (2016) · Journal of Cardiopulmonary Rehabilitation and Prevention · doi:10.1097/hcr.0000000000000191 · Cited by 72 · see citing works
Burton, E., Hill, K., Lautenschlager, N. T., Thøgersen‐Ntoumani, C., Lewin, G. F., Boyle, E., & Howie, E. K. (2018) · BMC Geriatrics · doi:10.1186/s12877-018-0793-4 · Cited by 71 · see citing works
“The distribution of error and testing for proportional bias is shown in the Bland-Altman plot analyses [26].”
MethodCORP: Improving the status quo for measuring whole body sweat losses
Cheuvront, S. N., & Kenefick, R. W. (2017) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00433.2017 · Cited by 69 · see citing works
“The sum of H2Oresp and gasesin – gasesout can also be estimated more conveniently from energy expenditure measured using any number of wearable technologies (12), or estimated from standard equations relating energy expenditure to locomotion (7), with the equation (5):”
BackgroundMork, P. J., & Bach, K. (2018) · JMIR Research Protocols · doi:10.2196/resprot.9379 · Cited by 64 · see citing works
“Although recent studies have indicated that wearables have poor validity in estimating the energy expenditure [58,59], they have acceptable reliability [60] and validity for the step detection [61], thereby fulfilling our requirement, that is, repeated daily measurements of steps in the same individual.”
An affordance lens for wearable information systems
Benbunan‐Fich, R. (2018) · European Journal of Information Systems · doi:10.1080/0960085x.2018.1512945 · Cited by 65 · see citing works
“…focused on gathering quantitative or qualitative data about device usability (Altenho ff et al. 2015; Mackinlay, 2013); accuracy of monitoring data (Lee, Kim, & Welk, 2014; Patel et al., 2015); and motivational e ff ects resulting from data awareness and quanti fi cation (Etkin, 2016; James et al.,…”
BackgroundBennett, A. V., Reeve, B. B., Basch, E., Mitchell, S. A., Meeneghan, M., Battaglini, C. L., Smith‐Ryan, A. E., Phillips, B., Shea, T. C., & Wood, W. A. (2015) · Quality of Life Research · doi:10.1007/s11136-015-1179-0 · Cited by 65 · see citing works
“The Fitbit TM Flex uses a triaxial accelerometer and proprietary algorithm to estimate steps, which has been found to be reliable [16–18].”
Accuracy of inclinometer functions of the activPAL and ActiGraph GT3X+: A focus on physical activity
An, H.-S., Kim, Y., & Lee, J. M. (2016) · Gait & Posture · doi:10.1016/j.gaitpost.2016.10.014 · Cited by 62 · see citing works
“Physical activity (PA) monitors have been usually used for estimating PA energy expenditure and classifying different intensities of PA [13].”
BackgroundSelf-citationClaes, J., Cornelissen, V., McDermott, C. M., Moyna, N. M., Pattyn, N., Cornelis, N., Gallagher, A., McCormack, C. M., Newton, H., Gillain, A., et al. (2019) · Journal of Medical Internet Research · doi:10.2196/14221 · Cited by 60 · see citing works
“Wearable sensors, often worn as a wristband or embedded in a smartwatch or mobile phone, are now ubiquitous and provide real-time activity and physiological information that allows patients to monitor and adjust physical activity (PA) [13] levels and exercise intensity [14] to meet their rehabilitation goals.”
BackgroundSmartphone applications to support weight loss: current perspectives
Pellegrini, C. A., Pfammatter, A. F., Conroy, D. E., & Spring, B. J. (2015) · Advanced Health Care Technologies · doi:10.2147/ahct.s57844 · Cited by 62 · see citing works
“Lee et al 53 recently examined a sample of consumer-based physical activity monitors and found that the error in estimation of energy expenditure ranged from 9.3% to 23.5%.”
BackgroundInfluentialAn Evaluation of Commercial Pedometers for Monitoring Slow Walking Speed Populations
Beevi, F. H. A., Miranda, J., Pedersen, C. F., & Wagner, S. (2015) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2015.0120 · Cited by 62 · see citing works
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Phillips, L. A., Chamberland, P.-É., Hekler, E. B., Abrams, J., & Eisenberg, M. H. (2016) · Sport Exercise and Performance Psychology · doi:10.1037/spy0000071 · Cited by 58 · see citing works
- Authors
- LEE, J.-M., KIM, Y., & WELK, G. J.
- Affiliations
- University of Nebraska at Omaha (US)
- Iowa State University (US)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2014-09
- Volume
- 46
- Issue
- 9
- Pages
- 1840-1848
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2095561638
- Cited by
- 401
- References
- 22
- Citations by year
- 2026: 6 · 2025: 11 · 2024: 10 · 2023: 13 · 2022: 17 · 2021: 19 · 2020: 29 · 2019: 51 · 2018: 68 · 2017: 67 · 2016: 61 · 2015: 39 · 2014: 9
- FWCI
- 39.04
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- wearable activity trackers, daily energy expenditure, free-living conditions, Fitbit, ActiGraph, indirect calorimetry, equivalence testing
- MeSH terms
- Adult, Basketball, Energy Metabolism, Exercise Test, Female, Humans, Male, Motor Activity, Oxygen Consumption, Running, Walking, Monitoring, Ambulatory, Young Adult, Accelerometry
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