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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pace of technological change, these existing studies have largely been completed on devices that would be considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and the Nike FuelBand.”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 101–200 of 401
Ridgers, N. D., Timperio, A. F., Brown, H. L., Ball, K., Macfarlane, S., Lai, S. K., Richards, K., Ngan, W. W., & Salmon, J. (2017) · BMC Public Health · doi:10.1186/s12889-016-3945-5 · Cited by 57
“The Fitbit® was selected as it accounts for a large proportion of activity tracker sales [45], performs well in comparison to research grade monitors [46], and the accompanying app is free to use.”
MethodImproving energy expenditure estimates from wearable devices: A machine learning approach
O’Driscoll, R., Turicchi, J., Hopkins, M., Horgan, G. W., Finlayson, G., & Stubbs, R. J. (2020) · Journal of Sports Sciences · doi:10.1080/02640414.2020.1746088 · Cited by 60
“To be considered equivalent, the 90% con fi dence interval of the estimate must fall within ± 10% of the criterion mean (Lee et al., 2014).”
BackgroundValidation of the Fitbit Zip for monitoring physical activity among free-living adolescents
Schneider, M. L., & Chau, L. (2016) · BMC Research Notes · doi:10.1186/s13104-016-2253-6 · Cited by 56
“The Fitbit Zip has been validated within a laboratory situation and has been found to be quite accurate in detecting number of steps when an adult is walking on a treadmill [2, 3], engaging in a variety of activities in a supervised setting [4], or participating in free-living activities [3].”
BackgroundMotion sensors in multiple sclerosis: Narrative review and update of applications
Sasaki, J. E., Sandroff, B. M., Bamman, M. M., & Motl, R. W. (2017) · Expert Review of Medical Devices · doi:10.1080/17434440.2017.1386550 · Cited by 53
Context-based ensemble method for human energy expenditure estimation
Gjoreski, H., Kaluža, B., Gams, M., Milić, R., & Luštrek, M. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.05.001 · Cited by 53
“In EE estimation and other tasks dealing with a human in an environment, the context is known to be very important [23] and is also close to how humans reason about”
BackgroundInfluentialPolgreen, L. A., Anthony, C., Carr, L., Simmering, J. E., Evans, N. J., Foster, E. D., Segre, A. M., Cremer, J. F., & Polgreen, P. M. (2018) · PLOS ONE · doi:10.1371/journal.pone.0195797 · Cited by 52
Rowlands, A. V., Plekhanova, T., Yates, T. E., Mirkes, E. M., Davies, M. J., Khunti, K., & Edwardson, C. L. (2019) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0073 · Cited by 51
Støve, M. P., Haucke, E., Nymann, M. L., Sigurdsson, T., & Larsen, B. T. (2018) · Journal of Sports Sciences · doi:10.1080/02640414.2018.1535563 · Cited by 51
“…wrist-worn activity monitors demonstrate good potential (Lee, Kim, & Welk, 2014) studies show somewhat discrepant findings regarding its validity (Lee et al., 2014; Stahl, An, Dinkel, Noble, & Lee, 2016; Wallen et al., 2016) depending on the devices used and the type and intensity of activity…”
BackgroundLesson Learned from Collecting Quantified Self Information via Mobile and Wearable Devices
Rawassizadeh, R., Momeni, E., Dobbins, C., Mirza-Babaei, P., & Rahnamoun, R. (2015) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan4040315 · Cited by 50
The potential and challenges of inferring thermal comfort at home using commodity sensors
Huang, C.-C., Yang, R., & Newman, M. (2015) · ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) · doi:10.1145/2750858.2805831 · Cited by 50
Cellini, N., McDevitt, E. A., Mednick, S. C., & Buman, M. P. (2016) · Physiology & Behavior · doi:10.1016/j.physbeh.2016.01.034 · Cited by 48
“[40], for each parameter we computed the equivalence zone (EZ) as the ±10% of the mean of our criterion (e.”
MethodMerrigan, J. J., Stovall, J. H., Stone, J. D., Stephenson, M., Finomore, V. S., & Hagen, J. A. (2022) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2022.2161820 · Cited by 49
“However, regardless of their potential for producing low-fidelity data (Lee et al., 2014), these devices are being implemented to inform decisions in training and free-living conditions for soldiers, athletes, general, and clinical populations (Li et al., 2016; Murdock & Hagen, 2018; Seshadri et…”
BackgroundO'Brien, M. W. (2021) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2021-0021 · Cited by 46
The Validity of Consumer-Level Activity Monitors in Healthy Older Adults in Free-Living Conditions
Farina, N., & Lowry, R. (2017) · Journal of Aging and Physical Activity · doi:10.1123/japa.2016-0344 · Cited by 45
Validity of sports watches when estimating energy expenditure during running
Roos, L., Taube, W., Beeler, N., & Wyss, T. (2017) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-017-0089-6 · Cited by 45
“The same device was validated in another study on daily life activities; an overall MAPE value of 9% was observed [11].”
BackgroundMethodResultInfluentialYang, Y., Schumann, M., Le, S., & Cheng, S. (2018) · PeerJ · doi:10.7717/peerj.5775 · Cited by 45
Schrack, J. A., Gresham, G. K., & Wanigatunga, A. A. (2017) · Molecular Case Studies · doi:10.1101/mcs.a001933 · Cited by 43
Prehabilitation in our most frail surgical patients
Rumer, K. K., Saraswathula, A., & Melcher, M. L. (2016) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0000000000000295 · Cited by 42
“WFTs vary in error between 9 and 25% compared to a portable metabolic system [57].”
BackgroundMyths and methodologies: Making sense of exercise mass and water balance
Cheuvront, S. N., & Montain, S. J. (2017) · Experimental Physiology · doi:10.1113/ep086284 · Cited by 42
“A number of wearable technologies can provide reasonable estimates of energy expenditure in the field (Lee et al. 2014), thus WBSL can be estimated more simply using eqn (9), as follows: WSBL = body mass − 0.20 g kcal−1 (9) It follows that differences in exercise mass and water balance are equal to…”
MethodInfluentialAlbergoni, A., Hettinga, F. J., La Torre, A., Bonato, M., & Sartor, F. (2019) · Sports Medicine - Open · doi:10.1186/s40798-019-0214-z · Cited by 39
Are Activity Wrist-Worn Devices Accurate for Determining Heart Rate during Intense Exercise?
Martín-Escudero, P., Cabanas, A. M., Dotor-Castilla, M. L., Galindo-Canales, M., Miguel‐Tobal, F., Fernández, C., Fuentes, M. E., & Giannetti, R. (2023) · Bioengineering · doi:10.3390/bioengineering10020254 · Cited by 41
“Earlier models of wearable devices relied solely on movement sensors such as accelerometers to estimate energy expenditure [26].”
Criterion Validity of Competing Accelerometry-Based Activity Monitoring Devices
Kim, Y., & Welk, G. J. (2015) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000691 · Cited by 41
“Greater detail on equivalence testing is provided in our previous articles using this method (22,39).”
MethodSelf-citationEconomic Perspective of the Use of Wearables in Health Care: A Systematic Review
Velasquez, G. D. D. S., Borna, S., Maniaci, M. J., Coffey, J. D., Haider, C. R., Demaerschalk, B. M., & Forte, A. J. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.05.003 · Cited by 50
“38 This evidence highlights the importance of pedometers in health promotion and the evolving capabilities of wearable technology to support active lifestyles.”
weSport: Utilising wrist-band sensing to detect player activities in basketball games
Bai, L., Efstratiou, C., & Ang, C. S. (2016) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2016.7457167 · Cited by 40
Validity of energy expenditure estimation methods during 10 days of military training
Siddall, A. G., Powell, S. D., Needham‐Beck, S. C., Edwards, V. C., Thompson, J. E. S., Kefyalew, S. S., Singh, P., Orford, E. R., Venables, M., Jackson, S., et al. (2019) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.13488 · Cited by 44
Validity of Wrist-Worn Activity Trackers for Estimating VO2max and Energy Expenditure
Passler, S., Bohrer, J., Blöchinger, L., & Senner, V. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16173037 · Cited by 43
Physical activity intervention using Fitbits in an introductory college health course
Rote, A. E. (2016) · Health Education Journal · doi:10.1177/0017896916674505 · Cited by 39
Montoye, A. H. K., Mitrzyk, J. R., & Molesky, M. J. (2017) · Measurement in Physical Education and Exercise Science · doi:10.1080/1091367x.2017.1331166 · Cited by 37
Data quality challenges for person-generated health and wellness data
Codella, J., Partovian, C., Chang, H.-Y., & Chen, C.-H. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2762218 · Cited by 37
Tracking steps in oncology: the time is now
Purswani, J., Ohri, N., & Champ, C. E. (2018) · Cancer Management and Research · doi:10.2147/cmar.s148710 · Cited by 40
“This fitness tracker has been demonstrated to be reliable in healthy adults 42 and people with stroke and traumatic brain injury.”
Fabre, N., Lhuisset, L., Bernal, C. M. M., & Bois, J. E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0227740 · Cited by 37
“5-min for intermittent conditions) divided by the reference value, multiplied by 100 [24].”
MethodPrehabilitation Interventions for Older Adults
Halloway, S., Buchholz, S. W., Wilbur, J., & Schoeny, M. E. (2014) · Western Journal of Nursing Research · doi:10.1177/0193945914551006 · Cited by 35
Remmert, J. E., Woodworth, A., Chau, L., Schumacher, L. M., Butryn, M. L., & Schneider, M. L. (2018) · The Journal of School Nursing · doi:10.1177/1059840518786782 · Cited by 36
Costantino, A., Ferrara, M., Arnesano, M., & Fabrizio, E. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.106379 · Cited by 40
Wearables for health and fitness: Measurement characteristics and accuracy
Scalise, L., & Cosoli, G. (2018) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2018.8409635 · Cited by 35
Marin, T. S., Kourbelis, C., Foote, J., Newman, P., Brown, A., Daniel, M., Coffee, N. T., Nicholls, S. J., Ganesan, A., Versace, V. L., et al. (2019) · European Journal of Preventive Cardiology · doi:10.1177/2047487318805580 · Cited by 33
Rowlands, A. V., Maylor, B. D., Dawkins, N. P., Dempsey, P. C., Edwardson, C. L., Soczawa-Stronczyk, A. A., Bocian, M., Patterson, M., & Yates, T. (2022) · Journal of Sports Sciences · doi:10.1080/02640414.2022.2147134 · Cited by 36
“We selected ±10% as our proposed equivalence zone as has been used in previous studies comparing activity monitors (e.g., Lee et al., 2014).”
MethodBittel, A. J., Elazzazi, A. M., & Bittel, D. C. (2015) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2015.0063 · Cited by 36
“Examples include the DirectLife, Fitbit, Jawbone, Nike FuelBand, and Actigraph consumer-based physical activity monitors.(5) Wearable sensors may also offer unique opportunities to monitor various aspects of patient health in the clinic and at home through telehealth initiatives.”
Validity and reliability of Nike + Fuelband for estimating physical activity energy expenditure
Tucker, W. J., Bhammar, D. M., Sawyer, B. J., Buman, M. P., & Gaesser, G. A. (2015) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-015-0008-7 · Cited by 33
“[16] estimated TEE by adding REE from the criterion measure (OM) to the Nike + Fuelband.”
BackgroundMethodResultInfluentialAlmeida, G. J., Irrgang, J. J., Fitzgerald, G. K., Jakicic, J. M., & Piva, S. R. (2016) · Physical Therapy · doi:10.2522/ptj.20150407 · Cited by 33
Acceptability of mHealth Technology for Self‐Monitoring Eating and Activity among Rural Men
Eisenhauer, C. M., Hageman, P. A., Rowland, S. A., Becker, B. J., Barnason, S. A., & Pullen, C. H. (2016) · Public Health Nursing · doi:10.1111/phn.12297 · Cited by 32
Kang, S., Kim, Y., Byun, W., Suk, J. S., & Lee, J. M. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16152663 · Cited by 32
Self-citationEstimating Energy Expenditure With Multiple Models Using Different Wearable Sensors
Cvetković, B., Milić, R., & Luštrek, M. (2015) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2015.2432911 · Cited by 31
“[15] compared several single- and multisensor devices in free-living conditions.”
BackgroundThe Wearing Comfort and Acceptability of Ambulatory Physical Activity Monitoring Devices in Soldiers
Beeler, N., Roos, L., Delves, S. K., Veenstra, B., Friedl, K. E., Buller, M. J., & Wyss, T. (2018) · IISE Transactions on Occupational Ergonomics and Human Factors · doi:10.1080/24725838.2018.1435431 · Cited by 31
Comparison of wrist-worn and hip-worn activity monitors under free living conditions
Hargens, T. A., Deyarmin, K. N., Snyder, K. M., Mihalik, A. G., & Sharpe, L. E. (2017) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2016.1271046 · Cited by 30
Xu, Z., Liu, Y., Williams, I., Li, Y., Qian, F., Wang, L., Lei, Y., & Li, B. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.01.104 · Cited by 30
Compagnat, M., Mandigout, S., Chaparro, D., Daviet, J., & Salle, J. (2018) · Clinical Rehabilitation · doi:10.1177/0269215518788116 · Cited by 30
Accuracy of Consumer Monitors for Estimating Energy Expenditure and Activity Type
WOODMAN, J. A., CROUTER, S. E., BASSETT, D. R., FITZHUGH, E. C., & BOYER, W. R. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001090 · Cited by 29
“To date, numerous consumer-based monitors have been validated in laboratory settings, including the Fitbit (7,15,17,18,22,25,28), Nike FuelBand (15,25,26), Jawbone Up (3,9,15,17), and Misfit Shine (3,9,17).”
BackgroundInfluentialClassification and suitability of sensing technologies for activity recognition
Woznowski, P., Kaleshi, D., Oikonomou, G., & Craddock, I. J. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.03.006 · Cited by 29
Kendall, B., Bellovary, B. N., & Gothe, N. P. (2018) · Journal of Sports Sciences · doi:10.1080/02640414.2018.1481723 · Cited by 29
“…high intensity activities, some studies have only examined a single device while others have examined the accuracy of AMs across a number of activities making it unclear how accurate AMs are during each specific form or intensity of activity (Drenowatz & Eisenmann, 2011; Lee et al., 2014).”
BackgroundMethodInfluentialAssawasaksakul, N., Sirichana, W., Joosri, W., Kulaputana, O., Eksakulkla, S., Ketanun, C., Kittiskulnam, P., Chantadisai, M., Takkavatakarn, K., Susantitaphong, P., et al. (2020) · International Urology and Nephrology · doi:10.1007/s11255-020-02677-7 · Cited by 31
Wearable Inertial Sensor for Jump Performance Analysis
Milosevic, B., & Farella, E. (2015) · Workshop on Wearable Systems and Applications (WearSys) · doi:10.1145/2753509.2753512 · Cited by 29
Peacock, O. J., Western, M. J., Batterham, A. M., Stathi, A., Standage, M., Tapp, A., Bennett, P., & Thompson, D. (2015) · Trials · doi:10.1186/s13063-015-0892-x · Cited by 30
Ng, K., Tynjälä, J., & Kokko, S. P. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6940 · Cited by 28
“Recent test results of commercial products have demonstrated some level of acceptability as well as room for improvement when reporting overall PA levels [15-18].”
BackgroundJayaraman, C., Mummidisetty, C. K., Mannix-Slobig, A., Koch, L. L. M., & Jayaraman, A. (2018) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-018-0358-y · Cited by 27
“Devices used Currently, a plethora of commercial and research grade wearable devices are available for clinicians to choose from [17].”
Sasai, H., Nakata, Y., Murakami, H., Kawakami, R., Nakae, S., Tanaka, S., Ishikawa‐Takata, K., Yamada, Y., & Miyachi, M. (2018) · Journal of Epidemiology · doi:10.2188/jea.je20170129 · Cited by 28
Methods for Activity Monitor Validation Studies: An Example With the Fitbit Charge
DeShaw, K. J., Ellingson, L. D., Bai, Y., Lansing, J. E., Perez, M., & Welk, G. J. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0017 · Cited by 29
Self-citationValidity of Garmin Vívofit 1 and Garmin Vívofit 3 for School-Based Physical Activity Monitoring
Šimůnek, A., Dygrýn, J., Jakubec, L., Neuls, F., Frömel, K., & Welk, G. J. (2018) · Pediatric Exercise Science · doi:10.1123/pes.2018-0019 · Cited by 27
Self-citationGiordanengo, A., Årsand, E., Woldaregay, A. Z., Bradway, M., Grøttland, A., Hartvigsen, G., Granja, C., Torsvik, T., & Hansen, A. H. (2019) · JMIR Diabetes · doi:10.2196/14002 · Cited by 28
“of the main barriers of acceptance of introducing self-collected health data into consultation, namely, the distrust of this source of data [53-56] and a time increase in consultation.”
BackgroundA Validation Study of the Fitbit One in Daily Life Using Different Time Intervals
Middelweerd, A., van der Ploeg, H. P., van Halteren, A., Twisk, J. W. R., Brug, J., & Velde, S. J. T. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001225 · Cited by 28
“(16)—in a laboratory setting—lower mean absolute error percentages were found between energy expenditure assessed by indirect calorimetry and estimated energy expenditure by”
BackgroundMethodMorris, C. E., Wessel, P., Tinius, R. A., Schafer, M. A., & Maples, J. M. (2019) · Research Quarterly for Exercise and Sport · doi:10.1080/02701367.2019.1603989 · Cited by 27
“The FBO had a MAPE of 17.5% in the current study during the HIFT bout and a value of 10.4% in the study conducted by Lee et al. (2014).”
BackgroundMethodResultInfluentialGilgen-Ammann, R., Schweizer, T., & Wyss, T. (2019) · JMIR mhealth and uhealth · doi:10.2196/14534 · Cited by 27
“Previous investigations demonstrated higher EE estimation errors of 14% to 210% during walking, running, and sitting [10]; 9% to 43% during lying, sitting, walking, running, and cycling [13]; and 9% to 24% during 13 activities ranging from those of low intensity (eg, writing at a computer) to those of vigorous intensi…”
BackgroundMethodEquivalency of Sleep Estimates: Comparison of Three Research-Grade Accelerometers
Plekhanova, T., Rowlands, A. V., Yates, T. E., Hall, A. P., Brady, E. M., Davies, M. J., Khunti, K., & Edwardson, C. L. (2020) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2019-0047 · Cited by 26
Greenspan, B., Cunha, A. B., & Lobo, M. A. (2021) · Infant Behavior and Development · doi:10.1016/j.infbeh.2021.101530 · Cited by 28
Western, M. J., Peacock, O. J., Stathi, A., & Thompson, D. (2015) · PLoS ONE · doi:10.1371/journal.pone.0126156 · Cited by 29
“0, Pittsburgh, USA), which accurately estimates energy expenditure [15-17].”
BackgroundTechnology to Promote and Increase Physical Activity in Heart Failure
Franklin, N. C. (2014) · Heart Failure Clinics · doi:10.1016/j.hfc.2014.08.006 · Cited by 26
Faria, G. S. E., Polese, J. C., Ribeiro-Samora, G. A., Scianni, A. A., Faria, C. D. C. D. M., & Teixeira‐Salmela, L. F. (2018) · Brazilian Journal of Physical Therapy · doi:10.1016/j.bjpt.2018.08.003 · Cited by 26
“It was also observed that smartphone applications estimated energy expenditure of healthy young individuals during walking and running on a treadmill with better accuracy than the GT3X+ ® ActiGraph accelerometer (a newer version of the GT3X Actigraph accelerometer).(15) Generally, several smartphone applications have…”
BackgroundThe Past, Present, and Future of Patient-Reported Outcomes in Oncology
Strong, L. E. (2015) · American Society of Clinical Oncology Educational Book · doi:10.14694/edbook_am.2015.35.e616 · Cited by 26
Motion Sensor Use for Physical Activity Data
McCarthy, M. M., & Grey, M. J. (2015) · Nursing Research · doi:10.1097/nnr.0000000000000098 · Cited by 25
Wearable Technology Reduces Prolonged Bouts of Sedentary Behavior
Ellingson, L. D., Meyer, J. D., & Cook, D. B. (2016) · Translational Journal of the American College of Sports Medicine · doi:10.1249/tjx.0000000000000001 · Cited by 24
“, goal setting and social support) (33,50) and a growing body of literature has demonstrated their use for promoting behavior change (3,10,49), few studies have assessed their value for the improvement in sedentary behaviors (2,6).”
BackgroundO’Connell, S., OLaighin, G. M., Kelly, L. S., Murphy, E., Beirne, S., Burke, N., Kilgannon, O., & Quinlan, L. R. (2016) · PLoS ONE · doi:10.1371/journal.pone.0154956 · Cited by 24
O’Driscoll, R., Turicchi, J., Hopkins, M., Gibbons, C., Larsen, S. C., Palmeira, A. L., Heitmann, B. L., Horgan, G. W., Finlayson, G., & Stubbs, R. J. (2019) · Health and Technology · doi:10.1007/s12553-019-00392-7 · Cited by 22
Šimůnek, A., Dygrýn, J., Gába, A., Jakubec, L., Stelzer, J., & Chmelík, F. (2016) · Acta Gymnica · doi:10.5507/ag.2016.014 · Cited by 28
“These functions have previously been, in a different type of trackers, subject to validation studies (Bai et al., 2016; Ferguson et al., 2015; Kooiman et al., 2015; J. M. Lee et al., 2014).”
BackgroundYavelberg, L., Zaharieva, D. P., Çınar, A., Riddell, M. C., & Jamnik, V. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817750401 · Cited by 23
“Despite the statistical difference compared to the criterion measures for EE and HR, the estimates of the research-grade devices should be deemed acceptable for clinical use, given that the discrepancies were all well below 10%.(8-10,24,25) As shown in Table 2, the EE as measured by the Metria had a mean error of 1.”
BackgroundMethodResultInfluentialHashimoto, Y., Sato, R., Takagahara, K., Ishihara, T., Watanabe, K., & Togo, H. (2022) · Sensors · doi:10.3390/s22239241 · Cited by 24
“One of the most widespread wearable heart rate monitors for consumers is the wristband-type sensor [8,9].”
BackgroundDevelopment of a real time activity monitoring Android application utilizing SmartStep
Hegde, N., Melanson, E. L., & Sazonov, E. E. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591089 · Cited by 23
“Due to the boom in the wearables systems market in the recent years, there are many wearable systems that are used for activity monitoring [6], [7] but most of them struggle with low accuracies for daily living activities classification [8].”
BackgroundWearable monitors criterion validity for energy expenditure in sedentary and light activities
Flórez-Pregonero, A., Meckes, N., Buman, M. P., & Ainsworth, B. E. (2016) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2016.10.005 · Cited by 25
Comparative Effectiveness of Three Occupational Therapy Sleep Interventions
Gutman, S. A., Gregory, K. A., Sadlier-Brown, M. M., Schlissel, M. A., Schubert, A. M., Westover, L. A., & Miller, R. C. (2016) · OTJR Occupational Therapy Journal of Research · doi:10.1177/1539449216673045 · Cited by 24
Heydenreich, J., Schütz, Y., Melzer, K., & Kayser, B. (2019) · Nutrients · doi:10.3390/nu11020458 · Cited by 22
A Survey on Energy Expenditure Estimation Using Wearable Devices
Álvarez-García, J. A., Cvetković, B., & Luštrek, M. (2020) · ACM Computing Surveys · doi:10.1145/3404482 · Cited by 27
Assessment of Peak Oxygen Uptake with a Smartwatch and its Usefulness for Training of Runners
Düking, P., Hooren, B. V., & Sperlich, B. (2022) · International Journal of Sports Medicine · doi:10.1055/a-1686-9068 · Cited by 22
“As previously performed, mean absolute percent errors (MAPE) were calculated to provide an indicator of overall measurement error [24].”
BackgroundMethodValidity of three smartwatches in estimating energy expenditure during outdoor walking and running
Le, S., Wang, X., Zhang, T., Lei, S., Cheng, S., Yao, W., & Schumann, M. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.995575 · Cited by 21
“There is considerable variability of device accuracy for predicting EE of different types of activities (O’driscoll et al., 2020; Lee et al., 2014).”
BackgroundSchneider, M. L., Schmalbach, P., & Godkin, S. (2016) · Journal of Behavioral Medicine · doi:10.1007/s10865-016-9776-0 · Cited by 35
The Role of Remote Monitoring in Evaluating Fatigue in Multiple Sclerosis: A Review
Block, V. J., Bove, R. M., & Nourbakhsh, B. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.878313 · Cited by 22
Setting the scene: Mobile and wearable technology for managing healthcare and wellbeing
Amor, J. D., & James, C. J. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320189 · Cited by 21
“D. Consumer Devices Consumer devices are a broad class of device that encompass devices provided by 3 rd party suppliers that have their own ecosystem in terms of data infrastructure, processing and feedback mechanisms and include devices such as the Jawbone UP and Fit-Bit [5], as well as others.”
Barkley, J. E., Glickman, E. L., Fennell, C., Kobak, M. S., Frank, M. L., & Farnell, G. (2018) · Journal of Sports Sciences · doi:10.1080/02640414.2018.1523039 · Cited by 21
“This compares favorably with other, previously-evaluated, commercially-available accelerometers and their associations with variables measured via indirect calorimetery (i.e., caloric expenditure estimated fromVO2) (Lee et al., 2014).”
BackgroundMethodResultInfluentialMiddelweerd, A., Mollee, J. S., Klein, M. C. A., Manzoor, A. F., Brug, J., & Velde, S. J. T. (2019) · JMIR Formative Research · doi:10.2196/12538 · Cited by 21
“Detailed information on the development and technical design of the Active2Gether intervention can be found elsewhere [19,20].”
InfluentialExercise as therapy in congenital heart disease — A gamification approach
Stuart, G. (2014) · Progress in Pediatric Cardiology · doi:10.1016/j.ppedcard.2014.12.008 · Cited by 21
“Lee compared the FuelBand with 6 other activity monitors and found a 10% equivalence zone around the indirect calorimetry estimate suggesting that the estimate of calorie consumption was reasonable for a non-clinical instrument in comparison to indirect calorimetry [119].”
MethodFeehan, L. M., Goldsmith, C. H., Leung, A. Y. F., & Li, L. C. (2016) · Physiotherapy Canada · doi:10.3138/ptc.2015-12 · Cited by 20
Dustin, I. H., Resnick, B. M., Galik, E. M., Klinedinst, N. J., Michael, K., Wiggs, E. A., & Theodore, W. H. (2019) · Journal of Neuroscience Nursing · doi:10.1097/jnn.0000000000000425 · Cited by 20
“Evidence of validity was based on significant correlations between the step counts obtained using the Jawbone UP relative to research-grade accelerometers.(15) Following baseline measures, the Jawbone idle alert was set to vibrate after 1 hour of inactivity.”
Hendrikx, J., Ruijs, L. S., Cox, L. G. E., Lemmens, P. M. C., Schuijers, E. G., & Goris, A. H. C. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6893 · Cited by 19
“For instance, Lee et al [30] observed a mean absolute percentage error (MAPE) of 23.”
InfluentialArch, E. S., Sions, J. M., Horne, J. R., & Bodt, B. (2018) · Prosthetics and Orthotics International · doi:10.1177/0309364618767138 · Cited by 20
Validation of Four Smartwatches in Energy Expenditure and Heart Rate Assessment During Exergaming
Pope, Z., Lee, J. E., Zeng, N., & Gao, Z. (2019) · Games for Health Journal · doi:10.1089/g4h.2018.0087 · Cited by 20
“13,14 Although smartwatches’ health metrics generally demonstrated high moderate-to-strong validity, limitations among the current literature are present.”
Self-citationUse of consumer monitors for estimating energy expenditure in youth
LaMunion, S. R., Blythe, A. L., Hibbing, P. R., Kaplan, A. S., Clendenin, B. J., & Crouter, S. E. (2019) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2019-0129 · Cited by 20
Farnell, G., & Barkley, J. E. (2017) · Journal of Human Sport and Exercise · doi:10.14198/jhse.2017.124.09 · Cited by 20
Fore, R., Hart, J. E., Choirat, C., Thompson, J., Lynch, K. A., Laden, F., Chavarro, J. E., & James, P. (2020) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-1386 · Cited by 21
“Fitbits measure physical activity aswell as research-grade accelerometry in lab settings (37), are comparablewith electrocardiography (ECG) for heart rate during routine physical activity (38–42), and have been tested for validity and usability in free-living subjects (43–47), although it is (1)Harvard Medical School…”
Lloyd, G. R., Oza, S., Keadle, S. K., Pellegrini, C. A., Conroy, D. E., Penedo, F. J., Spring, B. J., & Phillips, S. M. (2016) · AIMS Public Health · doi:10.3934/publichealth.2016.3.592 · Cited by 21
“However, while commercially available activity monitors have demonstrated adequate validity for physical activity assessment [61,62], they have not been well-validated to assess or monitor sedentary behavior.”
BackgroundEnergy expenditure estimation from respiration variables
Gilgen-Ammann, R., Koller, M., Huber, C., Ahola, R., Korhonen, T., & Wyss, T. (2017) · Scientific Reports · doi:10.1038/s41598-017-16135-5 · Cited by 19
Validation of the Fitbit Zip and Fitbit Flex with pregnant women in free-living conditions
St-Laurent, A., Mony, M., Mathieu, M.-E., & Ruchat, S.-M. (2018) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2018.1472822 · Cited by 23
Nogic, J., Thein, P. M., Cameron, J. D., Mirzaee, S., Ihdayhid, A. R., & Nasis, A. (2017) · BMC Cardiovascular Disorders · doi:10.1186/s12872-017-0726-8 · Cited by 18
“These devices, offering sophisticated features such as GPS tracking, heart rate monitoring, activity and sleep tracking with smart phone application integration, give individuals an easy and affordable way to monitor physical activity and energy expenditure with the majority of devices showing acceptable validity of t…”
BackgroundMethod
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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