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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pace of technological change, these existing studies have largely been completed on devices that would be considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and the Nike FuelBand.”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]
Citations
Cites
Compher, C., Frankenfield, D., Keim, N., & Roth-Yousey, L. (2006) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2006.02.009 · Cited by 923 · cited 1×
“after a 10-h fast, following previously published guidelines (6).”
MethodValidity of four motion sensors in measuring moderate intensity physical activity
Bassett, D. R. (2000) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200009001-00006 · Cited by 533
Accuracy of Armband Monitors for Measuring Daily Energy Expenditure in Healthy Adults
Johannsen, D. L., Calabro, M. A., Stewart, J. W., Franke, W. D., Rood, J. C., & Welk, G. J. (2010) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181e0b3ff · Cited by 433
Self-citationReliability of accelerometry-based activity monitors: a generalizability study.
Welk, G. J., Schaben, J. A., & Morrow, J. R. (2004) · PubMed · Cited by 394
Self-citationStaudenmayer, J., Pober, D., Crouter, S., Bassett, D., & Freedson, P. (2009) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00465.2009 · Cited by 361
Validity and Reliability issues in Objective Monitoring of Physical Activity
Bassett, D. R. (2000) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.2000.11082783 · Cited by 330
Testing Statistical Hypotheses of Equivalence
Wellek, S. (2002) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420035964 · Cited by 323
Predictive Validity of Three ActiGraph Energy Expenditure Equations for Children
Trost, S. G., Way, R., & Okely, A. D. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000183848.25845.e0 · Cited by 220
A new statistical procedure for testing equivalence in two-group comparative bioavailability trials
Hauck, W. W., & Anderson, S. (1984) · Journal of Pharmacokinetics and Biopharmaceutics · doi:10.1007/bf01063612 · Cited by 209
Comparison of Four ActiGraph Accelerometers during Walking and Running
John, D., Tyo, B. M., & Bassett, D. R. (2010) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181b3af49 · Cited by 183
Cited by
Sorted by citations
Items 301–400 of 401
Impact of EPOC adjustment on estimation of energy expenditure using activity monitors
Přibyslavská, V., Caputo, J. L., Coons, J. M., & Barry, V. (2018) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2018.1472823 · Cited by 5
“5% across several consumer-based activity monitors was reported during free-living conditions [9].”
BackgroundModeling energy balance while correcting for measurement error via free knot splines
Ries, D., Carriquiry, A., & Shook, R. P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201892 · Cited by 5
Sollanek, K. J., Liu, M., Carballo, A., Caldwell, A. R., & Cheuvront, S. N. (2020) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2020-0236 · Cited by 5
Kourbelis, C., Franzon, J., Foote, J. W., Brown, A. D. H., Daniel, M., Coffee, N. T., Newman, P. K., Ganesan, A. N., Nicholls, S. J., & Clark, R. A. (2018) · The JBI Database of Systematic Reviews and Implementation Reports · doi:10.11124/jbisrir-2017-003584 · Cited by 5
Engel, F. A., Masur, L., Sperlich, B., & Düking, P. (2025) · European Journal of Applied Physiology · doi:10.1007/s00421-025-05923-x · Cited by 4
“To provide an indicator of the overall measurement error, mean absolute percent errors (MAPE) were calculated as previously performed (Lee et al. 2014).”
Analysing longitudinal wearable physical activity data using non-stationary time series models
Angel, M. D., Nunes, M. A., Peacock, O. J., Cranwell, E. J., & Thompson, D. (2025) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-025-01779-8 · Cited by 4
Validity of Smartwatches for Estimating Energy Expenditure During Aerobic Dance
Zhao, X., Ding, X., Yang, J., Yan, S., Wang, H., Tao, K., & Qiu, J. (2025) · Journal of Science in Sport and Exercise · doi:10.1007/s42978-025-00339-7 · Cited by 4
Tracking energy balance in adolescents: Levels of compliance, energy flux, and learning
Chen, S., Zhu, X., Welk, G. J., & Kim, Y. (2015) · Journal of Exercise Science & Fitness · doi:10.1016/j.jesf.2015.01.001 · Cited by 4
Self-citationAccuracy and Metrological Characteristics of Wearable Devices: A Systematic Review
Cosoli, G., & Scalise, L. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-04324-7_47 · Cited by 4
Delgado-Gonzalo, R., Renevey, P., Lemkaddem, A., Lemay, M., Solà, J., Korhonen, I., & Bertschi, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-69362-0_14 · Cited by 4
Espinoza, J., Chen, A. M., Deavenport‐Saman, A., Solomon, O., Ponce, A., Sikder, A. T., Castillo, P., Kreutzer, C. B., & Yin, L. (2021) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2021.0048 · Cited by 4
Accuracy of Wearable Devices for Estimating Energy Expenditure and Heart Rate During Golf
Lee, H., Lee, M., & Park, S. (2022) · Exercise Science · doi:10.15857/ksep.2022.00178 · Cited by 4
“동등성 검증은 웨어러블 기기로부터 측정된 값의 평균에 대한 90%신 뢰구간(confidence intervals, CI)이 준거측정치의 제안된 동등성 한계 (±10%) 영역에 속하면 준거측정치와 동등한 것으로 간주하였다[23].”
Woo, J. L., DiLorenzo, M. P., Rosenzweig, E., Pasumarti, N., Villeda, G. V., Rosenzweig, E. B., & Krishnan, U. (2022) · Texas Heart Institute Journal · doi:10.14503/thij-21-7719 · Cited by 4
Mobile Sensors and Wearable Technology
Yao, C. A., & Ho, K. (2017) · Springer eBooks · doi:10.1007/978-3-319-56103-5_7 · Cited by 4
Schwartz, M., Oppold, P. J., & Hancock, P. A. (2019) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-5225-7949-6.ch007 · Cited by 4
Cabrita, M., Tabak, M., & Vollenbroek-Hutten, M. M. R. (2017) · International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE) · doi:10.5220/0006321101640171 · Cited by 4
Taherzadeh, G., Reid, R. D., Prince, S. A., Blanchard, C. M., Chessex, C., Harris, J., Pipe, A., & Grace, S. L. (2018) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/phm.0000000000000972 · Cited by 4
“Patients could also use a commercially available activity monitor, which have recently grown in accessibility and have been shown to be valid and reliable.(38,39) There are also structured internet programs with social components that are designed for eliciting social support and reinforcement, as well as promoting ti…”
Almeida, G. J., Terhorst, L. A., James, J. A., Irrgan, G., Fitzgerald, G. K., Jakicic, J. M., & Piva, S. R. (2017) · Journal of Exercise Sports & Orthopedics · doi:10.15226/2374-6904/4/3/00164 · Cited by 4
“[13-15]These three instruments have the advantage to measure light-intensity PA(i.e., household chores and slow-walking) that are mostly performed by older adults who undergo TKA.”
InfluentialCai, M., Tan, K. H., & Ang, S. B. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025970 · Cited by 4
Validity of Tools to Measure Physical Activity in Older Adults Following Total Knee Arthroplasty
Jasper, L., Beaupré, L. A., Spence, J. C., & Jones, C. (2020) · Journal of Aging and Physical Activity · doi:10.1123/japa.2020-0240 · Cited by 5
Leung, W. C. W., Case, L., Jung, J., & Yun, J. (2021) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2021.1893395 · Cited by 4
Mežan, L. Ž. (2025) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2025.1707991 · Cited by 5
“MAPE is often calculated because it provides a more conservative estimate of error, accounting for both overestimation and underestimation (22).”
Zhao, H., Guo, X., Li, Y., Wang, D., Pesola, A. J., & Gao, Y. (2025) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2024-0036 · Cited by 3
Quittner, A. L., Varley, C. D., & Henkle, E. M. (2022) · Respiratory medicine · doi:10.1007/978-3-031-12926-1_10 · Cited by 3
Accuracy of Wearable Devices to Estimate Physical Activity Levels
이미영, & 최지엽 (2015) · The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science · doi:10.21797/ksme.2015.17.2.004 · Cited by 3
Moore, L. D., McDevitt, B., Akhtar, N., Doherty, R., Scott, W. N., & Connolly, J. (2020) · Irish Signals and Systems Conference · doi:10.1109/issc49989.2020.9180169 · Cited by 3
“Wearable devices for the recording of various components of daily activity have been proliferating, but little research has examined how these devices compare with each other and how accurate and effective they are for research and clinical purposes [1].”
BackgroundQualified Self - Schnittstelle zwischen Lifestyle und Medizin
Becker, H. K. (2018) · doi:10.3218/3892-7 · Cited by 3
Liu, H., Li, Q., Li, Y., Wang, Y., Huang, Y., Bao, D., Liu, H., & Cui, Y. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.950074 · Cited by 3
Scalable Mining of Daily Behavioral Patterns in Context Sensing Life-Log DataPreprint
Rawassizadeh, R., Momeni, E., & Shetty, P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1411.4726 · Cited by 3
[Pleural eosinophilia: its diagnostic and prognostic significance].
Nuevo, G. D., Castro, D. J., Perez-Rodriguez, E., Yaya, E. P., & Bendito, A. S. (1999) · PubMed · Cited by 3
Physiological outcomes from mind-body resiliency programs in healthcare workers: A scoping review
Kronenberg, J., Merrigan, J. J., Quatman‐Yates, C. C., Emerson, A. M., Orr, M. L., Summers, R., Hagen, J. A., & Klatt, M. D. (2025) · PLOS mental health. · doi:10.1371/journal.pmen.0000332 · Cited by 3
Ishikawa, Y., Svensson, T., Madhawa, K., Hoang, N. T., Chung, U., & Svensson, A. K. (2025) · Journal of Occupational Health · doi:10.1093/joccuh/uiaf062 · Cited by 2
“17,18”
Using Wireless Pedometers to Measure Children’s Physical Activity: How Reliable is the Fitbit Zip?
Xu, T., Byker, E. J., & Gonzales, M. R. (2017) · Movement Health & Exercise · doi:10.15282/mohe.v6i2.157 · Cited by 2
Choi, J.-H., & Kim, H.-J. (2019) · Journal of people, plants, and environment · doi:10.11628/ksppe.2019.22.2.219 · Cited by 1
“The amount of exercises, that is, energy consumption, during forest exercises have been hardly measured, but energy consumption during general exercises has been widely measured using Actograph’s validated 3-axis accelerometer (Sasaki et al., 2011; Lee, 2012; Lee et al., 2014; Lee and Choi, 2015).”
BackgroundMethodResultInfluentialJames, P., & Redline, S. S. (2019) · Springer eBooks · doi:10.1007/978-3-030-20992-6_3 · Cited by 2
Miller, C. K., King, D., Nagaraja, H. N., Fujita, K., & Cheavens, J. S. (2024) · Health Psychology and Behavioral Medicine · doi:10.1080/21642850.2024.2385490 · Cited by 2
“Prior research found the Fitbit device has similar accuracy for PA assessment compared to research grade accelerometers (Imboden et al., 2018; Lee et al., 2014).”
MethodHoo, W. C., Guodian, W., Wolor, C. W., Ling, Z., & Prompanyo, M. (2024) · Journal of Ecohumanism · doi:10.62754/joe.v3i4.3768 · Cited by 2
“According to Lee, Kim, and Welk (2014), it presents a challenge for companies to simultaneously provide multiple values, as different individuals may have varying perceptions of the defined value.”
BackgroundSchneider, C., Stuetz, L., Dierks, A., Campbell, K. L., Wilhelm, M., & Eser, P. (2024) · JSAMS Plus · doi:10.1016/j.jsampl.2024.100075 · Cited by 2
“Fitbit Zip devices contain a tri-axial accelerometer to capture body motion in 3 dimensions and were shown to be within the 10% equivalence zone around the indirect calorimetry estimate [23].”
Implantable gastric electric stimulator with automatic daily activity compliance reporting
Takami, E., Sánchez, J., Province, R. A., & Torres, A. J. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591912 · Cited by 2
“Because the system is semipermanently implanted into the patient, we can evade the user compliance issue that causes most Fitbit or Jawbone users to stop wearing their device after six months [10].”
BackgroundMethodValidity of Fitbit Flex® consumer device on the sleep evaluation
López-Flores, M., Fernandez, A. R., Villa-Vicente, J., Iglesias, D. S., & Marroyo, J. A. R. (2020) · Cultura Ciencia y Deporte · doi:10.12800/ccd.v15i43.1397 · Cited by 2
“…si bien este instrumento ya se había validado anteriormente en su función de cuenta pasos y estimación del gasto energético (Baroni et al., 2016; Lee et al., 2014; Mammen et al., 2012), así como para la valoración de los indicadores de calidad del sueño comparándolo con la polisomnografía…”
BackgroundSmart-Home Energy Management in the Context of Occupants' Activity
Zupan, D., & Stefan, J. (2014) · Cited by 2
Quantified Self in de huisartsenpraktijk
de Groot, M., Timmers, B., Kooiman, T. J. M., & van Ittersum, M. (2015) · Bijblijven · doi:10.1007/s12414-015-0077-2 · Cited by 2
“De duidelijke onderschatting van het aantal stappen van de Nike + Fuelband kwam overeen met andere studies [6, 8, 9].”
BackgroundBuchholz, S. W., & Edmunds, M. W. (2017) · The Journal for Nurse Practitioners · doi:10.1016/j.nurpra.2016.06.002 · Cited by 2
Dustin, I. M., Resnick, B. M., Galik, E. M., Klinedinst, N. J., Michael, K., & Wiggs, E. A. (2017) · Journal of Nursing Measurement · doi:10.1891/1061-3749.25.1.22 · Cited by 2
Rovniak, L. S., & King, A. C. (2017) · Transport and sustainability · doi:10.1108/s2044-994120170000009015 · Cited by 2
Measurement of Physical Activity Among Older People
Aguilar-Farías, N., & Hopman-Rock, M. (2018) · Springer eBooks · doi:10.1007/978-3-319-71291-8_31 · Cited by 2
Nazirizadeh, S., Stokes, M. J., Arden, N. K., & Forrester, A. I. J. (2021) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668320929551 · Cited by 2
Exploring the Potential of Wearable Electronics for Healthcare Monitoring and Diagnosis
Kandpal, J. (2022) · Mathematical Statistician and Engineering Applications · doi:10.17762/msea.v71i2.2195 · Cited by 2
The Impact of Persuasive Messages on the Disclosure of Personal Health Information
Becker, M. Y., & Matt, C. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.514 · Cited by 2
“Since HWs are very diverse, we had to create a similar product and provider vision by concentrating on a specific subgroup of the examined technology.”
MethodInfluentialA Novel Method to Characterize Walking and Running Energy Expenditure
Evans, A., Collins, G. Q., Rosquist, P. G., Tuttle, N. J., Morrin, S. J., Tracy, J., Merrell, A. J., Christensen, W. F., Fullwood, D. T., Bowden, A. E., et al. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0008 · Cited by 2
Health Information Technologies in Diabetes Management
Yoshida, Y., & Simões, E. J. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.83693 · Cited by 2
Výzkum orientovaný na pohybovou aktivitu: metodologické ukotvení
Cuberek, R. (2019) · doi:10.5507/ftk.19.24455976 · Cited by 2
Investigating the use of Activity Trackers to Observe High-Risk Work Environments
Bowen, J., Hinze, A., Cunningham, S. J., & Parker, R. (2015) · Research Commons (University of Waikato) · Cited by 2
Zanin, N. B., Watanabe, W. T., Rescigno, W. R., Ishizaki, M. T., da Silva, R. R., & Goroso, D. G. (2021) · Research Society and Development · doi:10.33448/rsd-v10i13.21399 · Cited by 1
“(Lee et al., 2014) investigated the accuracy of eight types of activity monitors, which were used simultaneously to analyze the EE estimate in healthy adults, the values obtained were compared with a portable metabolic analyzer, Oxycon mobile 5.0.”
BackgroundMethodInfluentialWallace, T., Koebbel, C., & Heath, J. (2025) · Journal of Health Psychology · doi:10.1177/13591053251351222 · Cited by 1
“The FitBit is an activity monitor that has demonstrated high validity and reliability for energy expenditure estimates (Lee et al., 2014) and the companion app allows users to log food intake and access visual feedback.”
Hyde, E. T., Hayes, H. A., Matthews, C. E., Shreves, A. H., Ylarregui, K., Brennan, S., & Keadle, S. K. (2026) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003989 · Cited by 2
User preference-based human-in-the-loop tuning of exoskeleton assistance during walking
Schäfer, N., Zhao, G., Li, B., Kupnik, M., Andre Seyfarth, A., Beckerle, P., & Grimmer, M. (2026) · npj Biomedical Innovations. · doi:10.1038/s44385-026-00085-7 · Cited by 2
“There is no established consensus in the literature regarding equivalence bounds for metabolic cost, but some authors have suggested ±10% 66 – 68 .”
Woodman, J. A. (2015) · Cited by 1
Heart Rate Equivalency of the Fitbit Charge HR During Continuous Aerobic Exercise
Stock, J. M., Pohlig, R. T., Botieri, M. J., Edwards, D. G., & Dominick, G. M. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0009 · Cited by 1
Convergent Validity Evidence of Smartphone-based Physical Activity Measures in Free-living
이미영, & 최지엽 (2017) · The Korean Journal of Measurement and Evaluation in Physical Education and Sports Science · doi:10.21797/ksme.2017.19.4.006 · Cited by 1
Vandoni, M., Pellino, V. C., Dell’Anna, S., Ricagno, E., Liberali, G., Bonfanti, C., & Correale, L. (2019) · International Journal of Physical Education Fitness and Sports · doi:10.26524/ijpefs1945 · Cited by 1
Wearable Electronics and Embedded Computing Systems for Biomedical Applications
Scilingo, E. P., & Valenza, G. (2017) · MDPI eBooks · doi:10.3390/books978-3-03842-387-4 · Cited by 1
Wearable devices and the Internet of Things
Collecchia, G., & Gobbi, R. D. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-14054-9.00024-7 · Cited by 1
Maylor, B. D., Rowlands, A. V., Daley, A., Sanders, J. P., Yates, T., & Edwardson, C. L. (2024) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2023-0056 · Cited by 1
Ventura, L., Martinez, G., Morrone, M., Boi, A., Meloni, M., Perroni, F., Donadu, M. G., Deriu, F., & Manca, A. (2025) · The Journal of Sports Medicine and Physical Fitness · doi:10.23736/s0022-4707.24.16556-5 · Cited by 1
Buresh, R. J., KLISZCZEWICZ, B., HAYES, K., & JULIAN, J. (2022) · The Journal of Sports Medicine and Physical Fitness · doi:10.23736/s0022-4707.21.12038-9 · Cited by 1
Implementation of nutrition care process in nephrology practice: A mini review
Ookalkar, D. S., Saxena, A., Puri, H., Ookalkar, A., & Fulzade, S. (2020) · Journal of Renal Nutrition and Metabolism · doi:10.4103/jrnm.jrnm_28_20 · Cited by 1
Performance of energy expenditure assessment using a chest-worn wireless patch sensor
Selvaraj, N., & Doan, T. (2014) · Wireless Mobile Communication and Healthcare · doi:10.4108/icst.mobihealth.2014.257292 · Cited by 1
“The EE prediction of [2] and few other previous studies could be too optimistic due to the reasons including: (i) the resting/no activity time period was very low compared to the total time duration of”
BackgroundMethodResultInfluentialKulik, A., Rosłoniec, E., Madejski, P., Spannbauer, A., Mika, P., Różycka, I., Weber‐Nowakowska, K., & Trzeciak, J. (2023) · Annals of Agricultural and Environmental Medicine · doi:10.26444/aaem/159686 · Cited by 1
“They can find application in research on the evaluation of physical activity or interventions for its promotion [9].”
BackgroundAccuracy and stability testing of a 'smart dresser' for persons with dementia.
Mahoney, D. F., Burleson, W., Rowe, J., & Mahoney, E. (2016) · PubMed · Cited by 1
Western, M. J. (2016) · The University of Bath Online Publications Store (The University of Bath) · Cited by 1
Giordanengo, A., Torsvik, T., Woldaregay, A. Z., Grøttland, A., Årsand, E., Hartvigsen, G., & Hansen, A. H. (2019) · doi:10.2196/preprints.14002 · Cited by 1
How people interpret and react to everyday automation issues
Preusse, K. C. (2016) · SMARTech Repository (Georgia Institute of Technology)
Walking and Physical Activity Monitoring in Children with Cerebral Palsy
Bjornson, K. F., & Lennon, N. (2017) · Springer eBooks · doi:10.1007/978-3-319-30808-1_59-1
Validity of energy expenditure for the Garmin Vivofit during a week of physical activity
Webb, K. M. (2017) · JMU Scholoraly Commons (James Madison University)
Web-Based Workshop & Webinar Series: Obesity Education and Resources for Medical Students
Shroads, L. (2015) · OakTrust (Texas A&M University Libraries)
Kim, J.-H., Chung, D.-H., & Lee, B.-K. (2018) · Exercise Science · doi:10.15857/ksep.2018.27.4.274
PERFORMANCE OF AN ACTIVITY MONITOR INTEGRATED INTO A MICROPROCESSOR KNEE
Sykes, A., Stech, N., Laszczak, P., McGrath, M., Kercher, A., Zahedi, S., & Moser, D. (2018) · Canadian Prosthetics & Orthotics Journal · doi:10.33137/cpoj.v1i2.32031
The KOOLO app - A case study of self-tracking, visualization, and organizing personal moods
van der Velden, M., & Sommervold, M. M. (2016) · International journal on advances in intelligent systems
Validity of Heart Rate Measurements in a Fitbit Charge 2 and Apple iWatch in a Free-Living Setting
Dione, D. R. (2019) · ScholarWorks -A service of University of Vermont Libraries (University of Vermont)
Tuisku, K., Tanskanen, T., Hällfors, S., Härkönen, T., & Alakuijala, A. (2018) · DOAJ (DOAJ: Directory of Open Access Journals)
Design and validation of wearable technologies for children with physical disabilities
Greenspan, B. (2024) · Library, Museums and Press - UDSpace (University of Delaware) · doi:10.58088/4x9w-d025
Smart material process in additive healthcare manufacturing
Deepa, C., & Tharageswari, K. (2021) · AIP conference proceedings · doi:10.1063/5.0039749
Roy, M., Trudeau, F., Díaz, A., & Houle, J. (2021) · Science & Sports · doi:10.1016/j.scispo.2021.02.005
Klein, M., Kouwer, K., Leen, N. A., ten Noort, C. B. O., Geuze, S. G., & de Weijer, A. D. (2021) · doi:10.2196/preprints.32720
Latest Clinical Research Published by ACSM
Jaworski, C. A. (2014) · Current Sports Medicine Reports · doi:10.1249/jsr.0000000000000091
O’Driscoll, R., & Stubbs, R. J. (2018) · doi:10.26226/morressier.5b1a467c3b823805c63c0048
Gladden, L. B. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001488
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Yao, C. A., & Ho, K. (2021) · Springer eBooks · doi:10.1007/978-3-030-69262-9_30
Walking and Physical Activity Monitoring in Children with Cerebral Palsy
Bjornson, K. F., & Lennon, N. (2018) · Springer eBooks · doi:10.1007/978-3-319-14418-4_59
อัศวศักดิ์สกุล, น., ตีรณธนากุล, ข., & ศิริชนะ, ว. (2017) · Chula Digiverse · doi:10.58837/chula.the.2017.1618
Forecasting individual exercise sweat losses from forecast air temperature and energy expenditure
Cheuvront, S. N., Sollanek, K. J., & Kenefick, R. W. (2023) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2023.1277070
“The average energy expenditure error was <1%, which is well below the ∼ 10% commonly reported for many types of wearable technologies (27) and when compared with an indirect calorimetry gold standard such as the one used by Sollanek et al. (11).”
BackgroundZhang, Y., Bornkamp, N., Hivert, M., Oken, E., & James, P. (2024) · doi:10.2196/preprints.59159
Beatty, P., Tumperi, M., Nguyen, H. H., Howe, D., Hage, P., Wilson, R., Lindsey, B., Kandel, J., Collar, B., Gravelyn, S., et al. (2026) · Frontiers in Physiology · doi:10.3389/fphys.2026.1759280
“Commercial off-the-shelf (COTS) and medical-grade devices feature sensors that are capable of measuring these biosignals (Merrigan et al., 2023; Lee et al., 2014; Murdock and Hagen, 2018; Seshadri et al., 2019; Li et al., 2016).”
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Nagrale, S., & Jiandani, M. (2026) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_1909_24
Estimation de la sollicitation énergétique des patients AVC en situation réelle de vie
Compagnat, M. (2020) · theses.fr (ABES) · doi:10.70675/0e7cd6f4z11f2z4a5dz906fz99e3957dc846
Nightingale, T. E., Hodgkiss, D. D., Farrow, M. T., MAHER, J. L., Thompson, D., & Bilzon, J. (2026) · Research Square · doi:10.21203/rs.3.rs-9707223/v1
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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