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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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“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pace of technological change, these existing studies have largely been completed on devices that would be considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and the Nike FuelBand.”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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Systematic review of the validity and reliability of consumer-wearable activity trackers
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“One study also reported entering stride length [34], another study input handedness and smoking status [35], and another study used event markers to denote when an activity started and ended [39].”
BackgroundMethodResultInfluentialFuller, D., Colwell, E., Low, J., Orychock, K., Tobin, M. A., Simango, B., Buote, R., Van Heerden, D., Luan, H., Cullen, K., et al. (2020) · JMIR mHealth and uHealth · doi:10.2196/18694 · Cited by 715
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MethodAccuracy of Fitbit Devices: Systematic Review and Narrative Syntheses of Quantitative Data
Feehan, L. M., Geldman, J., Sayre, E. C., Park, C., Ezzat, A. M., Yoo, J. Y., Hamilton, C. B., & Li, L. C. (2018) · JMIR mHealth and uHealth · doi:10.2196/10527 · Cited by 616
“or indirect (16 studies) calorimetry (Multimedia Appendix 3) [29,34,37,39,46,68-80].”
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“ing of energy expenditure, within about 10% to 15% error, compared with a portable metabolic analyzer.(116) Equivalence testing”
MethodFerguson, T., Rowlands, A. V., Olds, T., & Maher, C. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0201-9 · Cited by 468
“Lee, Kim and Welk [16] examined the validity of eight consumer-level devices for”
BackgroundResultInfluentialMercer, K., Giangregorio, L., Schneider, E., Chilana, P., Li, M., & Grindrod, K. (2016) · JMIR mHealth and uHealth · doi:10.2196/mhealth.4225 · Cited by 419
“The BodyMedia FIT and Fitbit Zip trackers have been shown to provide valid measures of daily energy expenditures [27-30].”
Lobelo, F., Rohm Young, D., Sallis, R., Garber, M. D., Billinger, S. A., Duperly, J., Hutber, A., Pate, R. R., Thomas, R. J., Widlansky, M. E., et al. (2018) · Circulation · doi:10.1161/cir.0000000000000559 · Cited by 402
Reliability and validity of ten consumer activity trackers
Kooiman, T. J. M., Dontje, M. L., Sprenger, S. R., Krijnen, W. P., van der Schans, C. P., & de Groot, M. (2015) · BMC Sports Science, Medicine and Rehabilitation · doi:10.1186/s13102-015-0018-5 · Cited by 360
“[12] indicated similar results for the Fitbit Zip, Jawbone Up, and the Nike+ Fuelband, i.”
BackgroundResultInfluentialWearable Performance Devices in Sports Medicine
Li, R. T., Kling, S. R., Salata, M. J., Cupp, S. A., Sheehan, J., & Voos, J. E. (2015) · Sports Health A Multidisciplinary Approach · doi:10.1177/1941738115616917 · Cited by 363
“19,32 Energy expenditure is a crucial parameter for assessing the intensity of a training regimen.”
BackgroundVariable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise
GILLINOV, S., ETIWY, M., WANG, R., BLACKBURN, G., PHELAN, D., GILLINOV, A. M., HOUGHTALING, P., JAVADIKASGARI, H., & DESAI, M. Y. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001284 · Cited by 345
Patient-centered activity monitoring in the self-management of chronic health conditions
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“, Oxycon Mobile®) found a mean percent error range from 9% to 24%, with BodyMedia FIT® on the low end and the Basis B1 Band® on the high end [22].”
MethodAccuracy of Heart Rate Watches: Implications for Weight Management
Wallen, M. P., Gomersall, S. R., Keating, S. E., Wisløff, U., & Coombes, J. S. (2016) · PLOS ONE · doi:10.1371/journal.pone.0154420 · Cited by 336
Shin, G., Jarrahi, M. H., Fei, Y., Karami, A., Gafinowitz, N., Byun, A., & Lu, X. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103153 · Cited by 326
Sullivan, A. N., & Lachman, M. E. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2016.00289 · Cited by 458
Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L., & Lei, J. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9754 · Cited by 310
“Lee et al [34] asked 60 subjects to simultaneously wear eight types of wearable devices in a natural state of life for 69 min and used the Oxycon mobile 5.0 as the gold standard of energy consumption measurement and found that, except for the Basis Band (MAPE=0.23), the majority of the devices had good accuracy, maint…”
MethodInfluentialTwenty-four Hours of Sleep, Sedentary Behavior, and Physical Activity with Nine Wearable Devices
Rosenberger, M. E., Buman, M. P., Haskell, W. L., McConnell, M. V., & Carstensen, L. L. (2015) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000778 · Cited by 314
“In recent evaluations of these devices for predicting energy expenditure, Jawbone and Fitbit were more accurate than the GT3X+ (25).”
Stahl, S. E., An, H.-S., Dinkel, D. M., Noble, J. M., & Lee, J.-M. (2016) · BMJ Open Sport & Exercise Medicine · doi:10.1136/bmjsem-2015-000106 · Cited by 276
“spent on these small, non-invasive, and easy-to-use activity monitors, validated research is needed to ensure the activity monitors accurately project HR under resting, light, moderate, and vigorous intensity conditions.(3) The six newly developed wearable activity trackers that are currently popular in the market and…”
Self-citationDooley, E. E., Golaszewski, N. M., & Bartholomew, J. B. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.7043 · Cited by 242
“For example, while Fitbit models have changed over time, the basic error rate has been similar across studies and devices [8,10].”
InfluentialHow consumer physical activity monitors could transform human physiology research
Wright, S. P., Hall Brown, T. S., Collier, S. R., & Sandberg, K. (2017) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00349.2016 · Cited by 234
Moving beyond Karnofsky and ECOG Performance Status Assessments with New Technologies
Kelly, C. M., & Shahrokni, A. (2016) · Journal of Oncology · doi:10.1155/2016/6186543 · Cited by 229
“, Bluetooth), and (iii) the increased sophistication of personalized social media applications [62].”
BackgroundA Primer on the Use of Equivalence Testing for Evaluating Measurement Agreement
DIXON, P. M., SAINT-MAURICE, P. F., KIM, Y., HIBBING, P., BAI, Y., & WELK, G. J. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001481 · Cited by 227
“of the specific advantages and applications of equivalence tests for methodological comparisons (7,8,10,12,13,15,17,22,24),”
BackgroundSelf-citationPhysical Activity, Sedentary Time, and Obesity in an International Sample of Children
KATZMARZYK, P. T., BARREIRA, T. V., BROYLES, S. T., CHAMPAGNE, C. M., CHAPUT, J.-P., FOGELHOLM, M., HU, G., JOHNSON, W. D., KURIYAN, R., KURPAD, A., et al. (2015) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000649 · Cited by 222
“commercial devices such as wrist band physical activity monitors have proliferated in the marketplace (15).”
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“Recent attempts to validate consumer grade devices against energy expenditure or step counts are somewhat problematic because each device has its own method of calculating the desired output metrics (24–26).”
MethodValidity of Consumer-Based Physical Activity Monitors for Specific Activity Types
Nelson, M. B., Kaminsky, L. A., Dickin, D. C., & Montoye, A. H. K. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000933 · Cited by 206
“(15) showed that the Fitbit Zip (FZ) was accurate for estimating total EE within a margin of 10% error using equivalence testing analyses; the mean absolute percent error (MAPE) of monitors ranged from 9% to 24%.”
Validity of Wearable Activity Monitors during Cycling and Resistance Exercise
BOUDREAUX, B. D., HEBERT, E. P., HOLLANDER, D. B., WILLIAMS, B. M., CORMIER, C. L., NAQUIN, M. R., GILLAN, W. W., GUSEW, E. E., & KRAEMER, R. R. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001471 · Cited by 205
“Findings from this study support previous research (11,22,23,29) that wearable devices may not be accurate for measurement of EE during physical activity.”
ResultMurakami, H., Kawakami, R., Nakae, S., Nakata, Y., Ishikawa-Takata, K., Tanaka, S., & Miyachi, M. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0152 · Cited by 204
Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent?
ROWLANDS, A. V., MIRKES, E. M., YATES, T., CLEMES, S., DAVIES, M., KHUNTI, K., & EDWARDSON, C. L. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001435 · Cited by 187
“We selected ±10% of the mean as our proposed equivalence zone as in previous studies comparing activity monitors (31, 32).”
MethodO’Driscoll, R., Turicchi, J., Beaulieu, K., Scott, S., Matu, J., Deighton, K., Finlayson, G., & Stubbs, R. J. (2018) · British Journal of Sports Medicine · doi:10.1136/bjsports-2018-099643 · Cited by 202
Using an electronic activity monitor system as an intervention modality: A systematic review
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“Validation studies have shown that some EAMS can produce valid measures of energy expenditure [43–47].”
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“Impaired sleep disrupts cognitive and immune functions, enhances daytime sleepiness, and reduces overall performance (Leeder et al., 2012; Halson, 2014b).”
BackgroundGresham, G. K., Hendifar, A. E., Spiegel, B. M. R., Neeman, E., Tuli, R., Rimel, B. J., Figlin, R. A., Meinert, C. L., Piantadosi, S., & Shinde, A. M. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0032-6 · Cited by 172
Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors
Storm, F. A., Heller, B. W., & Mazzà, C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0118723 · Cited by 168
Systems and WBANs for Controlling Obesity
Mohammed, M. S., Sendra, S., Lloret, J., & Reig, I. B. (2018) · Journal of Healthcare Engineering · doi:10.1155/2018/1564748 · Cited by 172
“Participants can track their EE estimation data through a personal website which provides them with tips, activity ideas, and statics [5].”
InfluentialValidation of the Instant Blood Pressure Smartphone App
Plante, T. B., Urrea, B., MacFarlane, Z. T., Blumenthal, R. S., Miller, E. R., Appel, L. J., & Martin, S. S. (2016) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.0157 · Cited by 159
Reddy, R. K., Pooni, R., Zaharieva, D. P., Senf, B., El Youssef, J., Dassau, E., Doyle III, F. J., Clements, M. A., Rickels, M. R., Patton, S. R., et al. (2018) · JMIR mHealth and uHealth · doi:10.2196/10338 · Cited by 155
“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].”
Comparison of Consumer and Research Monitors under Semistructured Settings
BAI, Y., WELK, G. J., NAM, Y. H., LEE, J. A., LEE, J.-M., KIM, Y., MEIER, N. F., & DIXON, P. M. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000727 · Cited by 149
“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
Comparative evaluation of heart rate-based monitors: Apple Watch vs Fitbit Charge HR
Bai, Y., Hibbing, P. R., Mantis, K., & Welk, G. J. (2017) · Journal of Sports Sciences · doi:10.1080/02640414.2017.1412235 · Cited by 147
“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 147
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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 142
Self-citationDevices for Self-Monitoring Sedentary Time or Physical Activity: A Scoping Review
Sanders, J. P., Loveday, A., Pearson, N., Edwardson, C. L., Yates, T. E., Biddle, S., & Esliger, D. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5373 · Cited by 148
“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…”
Reid, R. E. R., Insogna, J. A., Carver, T. E., Comptour, A. M., Bewski, N. A., Sciortino, C. D., & Andersen, R. E. (2016) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2016.10.015 · Cited by 143
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
“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
“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 129
“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 120
Cosoli, G., Spinsante, S., & Scalise, L. (2020) · Measurement · doi:10.1016/j.measurement.2020.107789 · Cited by 131
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
“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
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 120
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
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
“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 120
“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
“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
“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
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
“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
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
“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
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
“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
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 101
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
“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 97
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 96
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
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
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
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 91
“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
“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
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
“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 80
Self-citationHickey, A. M., & Freedson, P. S. (2016) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2016.02.006 · Cited by 77
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
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
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 80
Wearables for Promoting Physical Activity
Strath, S. J., & Rowley, T. W. (2017) · Clinical Chemistry · doi:10.1373/clinchem.2017.272369 · Cited by 74
Lucan, S. C., & DiNicolantonio, J. J. (2014) · Public Health Nutrition · doi:10.1017/s1368980014002559 · Cited by 76
Apovian, C. M., Garvey, W. T., & Ryan, D. H. (2015) · Obesity · doi:10.1002/oby.21140 · Cited by 72
“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
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 76
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 74
“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
“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 69
“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 75
“…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
“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
“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
“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 67
“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
Thompson, D., Batterham, A. M., Peacock, O. J., Western, M. J., & Booso, R. (2016) · Preventive Medicine · doi:10.1016/j.ypmed.2016.06.017 · Cited by 59
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 63
Metadata
- 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 › Public Health, Environmental and Occupational Health › 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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