Evaluating the Validity of Current Mainstream Wearable Devices in Fitness Tracking Under Various Physical Activities: Comparative Study
Open accessPublished version of a preprint
Abstract
BACKGROUND: Wearable devices have attracted much attention from the market in recent years for their fitness monitoring and other health-related metrics; however, the accuracy of fitness tracking results still plays a major role in health promotion. OBJECTIVE: The aim of this study was to evaluate the accuracy of a host of latest wearable devices in measuring fitness-related indicators under various seminatural activities. METHODS: A total of 44 healthy subjects were recruited, and each subject was asked to simultaneously wear 6 devices (Apple Watch 2, Samsung Gear S3, Jawbone Up3, Fitbit Surge, Huawei Talk Band B3, and Xiaomi Mi Band 2) and 2 smartphone apps (Dongdong and Ledongli) to measure five major health indicators (heart rate, number of steps, distance, energy consumption, and sleep duration) under various activity states (resting, walking, running, cycling, and sleeping), which were then compared with the gold standard (manual measurements of the heart rate, number of steps, distance, and sleep, and energy consumption through oxygen consumption) and calculated to determine their respective mean absolute percentage errors (MAPEs). RESULTS: Wearable devices had a rather high measurement accuracy with respect to heart rate, number of steps, distance, and sleep duration, with a MAPE of approximately 0.10, whereas poor measurement accuracy was observed for energy consumption (calories), indicated by a MAPE of up to 0.44. The measurements varied for the same indicator measured by different fitness trackers. The variation in measurement of the number of steps was the highest (Apple Watch 2: 0.42; Dongdong: 0.01), whereas it was the lowest for heart rate (Samsung Gear S3: 0.34; Xiaomi Mi Band 2: 0.12). Measurements differed insignificantly for the same indicator measured under different states of activity; the MAPE of distance and energy measurements were in the range of 0.08 to 0.17 and 0.41 to 0.48, respectively. Overall, the Samsung Gear S3 performed the best for the measurement of heart rate under the resting state (MAPE of 0.04), whereas Dongdong performed the best for the measurement of the number of steps under the walking state (MAPE of 0.01). Fitbit Surge performed the best for distance measurement under the cycling state (MAPE of 0.04), and Huawei Talk Band B3 performed the best for energy consumption measurement under the walking state (MAPE of 0.17). CONCLUSIONS: At present, mainstream devices are able to reliably measure heart rate, number of steps, distance, and sleep duration, which can be used as effective health evaluation indicators, but the measurement accuracy of energy consumption is still inadequate. Fitness trackers of different brands vary with regard to measurement of indicators and are all affected by the activity state, which indicates that manufacturers of fitness trackers need to improve their algorithms for different activity states.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice.
“Wearable and mobile sensor technologies can be useful tools in precision nutrition research and practice”
From Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study (2020) · cites this paper as [15]
The Introduction describes wearable sensors as potentially useful for health monitoring, including measuring energy consumption. However, it does not discuss their use in precision nutrition research or practice, or establish the usefulness of mobile sensor technologies for that purpose. The Results (Calorie Accuracy for Different Devices) also report poor accuracy for energy-consumption measurements. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA second study found that accuracy of heart rate measurement was highest during running.
“a second study found that the accuracy of HR measurement was highest during running51”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [51]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
‘Small Changes' to Diet and Physical Activity Behaviors for Weight Management
Hills, A. P., Byrne, N. M., Lindstrom, R. C., & Hill, J. O. (2013) · Obesity Facts · doi:10.1159/000345030 · Cited by 9,526 · see citing works
Using Pedometers to Increase Physical Activity and Improve Health: A Systematic Review
Bravata, D. M., Smith-Spangler, C., Sundaram, V., Gienger, A. L., Lin, N., Lewis, R., Stave, C. D., Olkin, I., & Sirard, J. R. (2007) · JAMA · doi:10.1001/jama.298.19.2296 · Cited by 2,064 · see citing works
Systematic review of the validity and reliability of consumer-wearable activity trackers
Evenson, K. R., Goto, M. M., & Furberg, R. D. (2015) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-015-0314-1 · Cited by 1,232 · see citing works
Sullivan, A. N., & Lachman, M. E. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2016.00289 · Cited by 458 · see citing works
Validity of Consumer-Based Physical Activity Monitors
LEE, J.-M., KIM, Y., & WELK, G. J. (2014) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000287 · Cited by 401 · cited 2× · see citing works
“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…”
MethodInfluentialValidation of the Fitbit One activity monitor device during treadmill walking
Takacs, J., Pollock, C. L., Guenther, J. R., Bahar, M., Napier, C., & Hunt, M. A. (2013) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2013.10.241 · Cited by 322 · see citing works
Movement toward a novel activity monitoring device
Montgomery-Downs, H. E., Insana, S. P., & Bond, J. A. (2012) · Sleep and Breathing · doi:10.1007/s11325-011-0585-y · Cited by 301 · see citing works
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 · see citing works
Feasibility and Effectiveness of Using Wearable Activity Trackers in Youth: A Systematic Review
Ridgers, N. D., McNarry, M. A., & Mackintosh, K. A. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6540 · Cited by 245 · see citing works
Dooley, E. E., Golaszewski, N. M., & Bartholomew, J. B. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.7043 · Cited by 242 · see citing works
Meltzer, L. J., Hiruma, L., Avis, K. T., Montgomery‐Downs, H. E., & Valentin, J. (2015) · SLEEP · doi:10.5665/sleep.4918 · Cited by 228 · see citing works
Validity 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 · see citing works
Adam Noah, J., Spierer, D. K., Gu, J., & Bronner, S. (2013) · Journal of Medical Engineering & Technology · doi:10.3109/03091902.2013.831135 · Cited by 202 · see citing works
Tully, M. A., McBride, C., Heron, L., & Hunter, R. F. (2014) · BMC Research Notes · doi:10.1186/1756-0500-7-952 · Cited by 172 · see citing works
Validation of Biofeedback Wearables for Photoplethysmographic Heart Rate Tracking.
(2016) · PubMed Central · Cited by 150 · see citing works
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 · see citing works
Opportunities and challenges in the use of personal health data for health research
Bietz, M. J., Bloss, C. S., Calvert, S., Godino, J. G., Gregory, J., Claffey, M. P., Sheehan, J., & Patrick, K. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv118 · Cited by 148 · see citing works
Evaluation of wearable consumer heart rate monitors based on photopletysmography
Parak, J., & Korhonen, I. (2014) · 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc.2014.6944419 · Cited by 148 · see citing works
How valid are wearable physical activity trackers for measuring steps?
An, H., Jones, G. C., Kang, S., Welk, G. J., & Lee, J. M. (2016) · European Journal of Sport Science · doi:10.1080/17461391.2016.1255261 · Cited by 131 · see citing works
A Comparison of Energy Expenditure Estimation of Several Physical Activity Monitors
Dannecker, K. L., Sazonova, N. A., Melanson, E. L., Sazonov, E. E., & Browning, R. C. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e318299d2eb · Cited by 124 · see citing works
Validation of the SenseWear Armband at high intensity exercise
Drenowatz, C. J., & Eisenmann, J. C. (2010) · European Journal of Applied Physiology · doi:10.1007/s00421-010-1695-0 · Cited by 123 · see citing works
Hardman, A. E., & Stensel, D. J. (2009) · Routledge eBooks · doi:10.4324/9780203890714 · Cited by 112 · see citing works
Poirier, J., Bennett, W. L., Jerome, G. J., Shah, N. G., Lazo, M., Yeh, H.-C., Clark, J. M., & Cobb, N. K. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5295 · Cited by 111 · see citing works
Bonomi, A. G., Plasqui, G., Goris, A. H. C., & Westerterp, K. R. (2010) · Obesity · doi:10.1038/oby.2010.34 · Cited by 100 · see citing works
Gualtieri, L. N., Rosenbluth, S., & Phillips, J. (2016) · JMIR Research Protocols · doi:10.2196/resprot.6534 · Cited by 86 · see citing works
Consumers' perceived attitudes to wearable devices in health monitoring in China: A survey study
Wen, D., Zhang, X.-T., & Lei, J. (2017) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2016.12.009 · Cited by 78 · see citing works
Self-citationValidation of Pattern-Recognition Monitors in Children Using Doubly Labeled Water
Calabro, M. A., STEWART, J. M., & Welk, G. J. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31828579c3 · Cited by 53 · see citing works
Abedtash, H., & Holden, R. J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx006 · Cited by 35 · see citing works
Cohen, A. B., & Mathews, S. C. (2018) · Digital Biomarkers · doi:10.1159/000492396 · Cited by 24 · see citing works
Validation of Youth Oriented Activity Trackers: Comprehensive Laboratory- and Field-Based Validation
Sirard, J. R., Masteller, B. R., Freedson, P. S., Mendoza, A., & Hickey, A. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6360 · Cited by 15 · see citing works
Evaluation of the Basis Band Fitness Tracker
Desilets, P.-A., & Mahar, M. T. (2016) · International journal of exercise science · doi:10.70252/lbkh4812 · Cited by 4 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [3]Global Recommendations on Physical Activity for Health 2010 Geneva WHO Press, World Health Organization 26180873
- [11]Abedtash H Holden RJ Systematic review of the effectiveness of health-related behavioral interventions using portable activity sensing devices (PASDs) J Am Med Inform Assoc 2017 09 1 24 5 1002 13 10.1093/jamia/ocx006 28339620 2987476 28339620 PMC6259669 · DOI
Cited by
Sorted by citations
Fuller, 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 · see citing works
Nano Biosensors: Properties, applications and electrochemical techniques
Huang, X., Zhu, Y., & Kianfar, E. (2021) · Journal of Materials Research and Technology · doi:10.1016/j.jmrt.2021.03.048 · Cited by 493 · see citing works
Review on Wearable Technology Sensors Used in Consumer Sport Applications
Aroganam, G., Manivannan, N., & Harrison, D. (2019) · Sensors · doi:10.3390/s19091983 · Cited by 370 · see citing works
Nelson, B. W., & Allen, N. B. (2019) · JMIR mHealth and uHealth · doi:10.2196/10828 · Cited by 310 · see citing works
Zhao, D. X., Liu, L., Yu, F., Heidari, A. A., Wang, M., Liang, G., Muhammad, K., & Chen, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106510 · Cited by 308 · see citing works
Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research
Nelson, B. W., Low, C. A., Jacobson, N., Areán, P., Torous, J., & Allen, N. B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0297-4 · Cited by 241 · see citing works
MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting
Premkumar, M., Jangir, P., Sowmya, R., Alhelou, H. H., Heidari, A. A., & Chen, H. (2020) · IEEE Access · doi:10.1109/access.2020.3047936 · Cited by 235 · see citing works
Nanoparticle enhanced PCM exergy loss and thermal behavior by means of FVM
Chu, Y., Abu‐Hamdeh, N. H., Ben-Beya, B., Hajizadeh, M. R., Li, Z., & Bach, Q. (2020) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.114457 · Cited by 225 · see citing works
Fitbit-based interventions for healthy lifestyle outcomes: systematic review and meta-analysis
Ringeval, M., Wagner, G., Denford, J., Paré, G., & Kitsiou, S. (2020) · Journal of Medical Internet Research · doi:10.2196/23954 · Cited by 225 · see citing works
“We focused on Fitbit devices because they are among the most accurate commercially available wearables [39-43] and are, in some cases, comparable to research-grade monitors [44].”
ResultShan, W., Qiao, Z., Heidari, A. A., Chen, H., Turabieh, H., & Teng, Y. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106728 · Cited by 210 · see citing works
Radio-Frequency and Microwave Techniques for Non-Invasive Measurement of Blood Glucose Levels
Yilmaz, T., Foster, R., & Hao, Y. (2019) · Diagnostics · doi:10.3390/diagnostics9010006 · Cited by 195 · see citing works
Song, S., Wang, P., Heidari, A. A., Wang, M., Zhao, X., Chen, H., He, W., & Xu, S. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106425 · Cited by 193 · see citing works
Sañudo, B., Fennell, C., & Sánchez-Oliver, A. J. (2020) · Sustainability · doi:10.3390/su12155890 · Cited by 189 · see citing works
Attig, C., & Franke, T. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.025 · Cited by 187 · see citing works
Approaches for expedition of discharging of PCM involving nanoparticles and radial fins
Wang, T., Almarashi, A., Al‐Turki, Y. A., Abu‐Hamdeh, N. H., Hajizadeh, M. R., & Chu, Y. (2020) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.115052 · Cited by 187 · see citing works
Melting process of nanoparticle enhanced PCM through storage cylinder incorporating fins
Li, F., Almarashi, A., Jafaryar, M., Hajizadeh, M. R., & Chu, Y.-M. (2021) · Powder Technology · doi:10.1016/j.powtec.2020.12.026 · Cited by 184 · see citing works
Windows into human health through wearables data analytics
Witt, D. R., Kellogg, R., Snyder, M. P., & Dunn, J. (2019) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2019.01.001 · Cited by 160 · see citing works
Industry 4.0 and Digitalisation in Healthcare
Popov, V. V., Kudryavtseva, E. V., Katiyar, N. K., Shishkin, A., Stepanov, S. I., & Goel, S. (2022) · Materials · doi:10.3390/ma15062140 · Cited by 150 · see citing works
Application of Internet of Things and artificial intelligence for smart fitness: A survey
Farrokhi, A., Farahbakhsh, R., Rezazadeh, J., & Minerva, R. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.107859 · Cited by 153 · see citing works
Tedesco, S., Sica, M., Ancillao, A., Timmons, S., Barton, J., & O'Flynn, B. (2019) · JMIR mHealth and uHealth · doi:10.2196/13084 · Cited by 139 · see citing works
A collaborative healthcare framework for shared healthcare plan with ambient intelligence
Javed, A. R., Sarwar, M. U., Beg, M. O., Asim, M., Baker, T., & Tawfik, H. (2020) · Human-centric Computing and Information Sciences · doi:10.1186/s13673-020-00245-7 · Cited by 142 · see citing works
Liang, J., Xian, D., Liu, X., Fu, J., Zhang, X., Tang, B., & Lei, J. (2018) · JMIR mhealth and uhealth · doi:10.2196/11066 · Cited by 145 · see citing works
Self-citationStandardizing Analytic Methods and Reporting in Activity Monitor Validation Studies
Welk, G. J., Bai, Y., Lee, J. M., Godino, J., Saint‐Maurice, P. F., & Carr, L. J. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001966 · Cited by 139 · see citing works
Nanoparticles for phase change process of water utilizing FEM
Xiong, P.-Y., Almarashi, A., Dhahad, H. A., Alawee, W. H., Issakhov, A., & Chu, Y. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.116096 · Cited by 134 · see citing works
Soon, S., Svavarsdottir, H. S., Downey, C. L., & Jayne, D. G. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000354 · Cited by 133 · see citing works
Qi, J., Yang, P., Newcombe, L., Peng, X., Yang, Y., & Zhao, Z. (2019) · Information Fusion · doi:10.1016/j.inffus.2019.09.002 · Cited by 133 · see citing works
River Water Salinity Prediction Using Hybrid Machine Learning Models
Melesse, A. M., Khosravi, K., Tiefenbacher, J. P., Heddam, S., Kim, S., Mosavi, A. H., & Pham, B. T. (2020) · Water · doi:10.3390/w12102951 · Cited by 118 · see citing works
Tedesco, S., Sica, M., Ancillao, A., Timmons, S., Barton, J., & O’Flynn, B. (2019) · PLOS ONE · doi:10.1371/journal.pone.0216891 · Cited by 116 · see citing works
Heat recovery application of nanomaterial with existence of turbulator
Jun-hua, L., Alawee, W. H., Rawa, M. J. H., Dhahad, H. A., Chu, Y., Issakhov, A., Abu‐Hamdeh, N. H., & Hajizadeh, M. R. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2020.115268 · Cited by 114 · see citing works
Wang, T., Fan, L., Zheng, X., Wang, W., Liang, J., An, K., Ju, M., & Lei, J. (2021) · Journal of Medical Internet Research · doi:10.2196/24546 · Cited by 120 · see citing works
Self-citationWearable devices for continuous monitoring of biosignals: Challenges and opportunities
Stuart, T., Hanna, J., & Gutruf, P. (2022) · APL Bioengineering · doi:10.1063/5.0086935 · Cited by 114 · see citing works
Weichenthal, S., Hatzopoulou, M., & Brauer, M. D. (2018) · Environment International · doi:10.1016/j.envint.2018.11.042 · Cited by 102 · see citing works
Liang, J., Li, Y., Zhang, Z., Shen, D., Jie, X., Zheng, X., Wang, T., Tang, B., Lei, J., & Zhang, J. (2021) · Journal of Medical Internet Research · doi:10.2196/24813 · Cited by 106 · see citing works
Self-citationCao, J., Yang, X., Rao, J., Mitriashkin, A., Fan, X., Chen, R., Cheng, H., Wang, X., Goh, J. C., Leo, H. L., et al. (2022) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.2c11921 · Cited by 98 · see citing works
Li, C., Li, J., Chen, H., & Heidari, A. A. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114529 · Cited by 80 · see citing works
Random learning gradient based optimization for efficient design of photovoltaic models
Zhou, W. H., Wang, P., Heidari, A. A., Zhao, X., Turabieh, H., & Chen, H. (2021) · Energy Conversion and Management · doi:10.1016/j.enconman.2020.113751 · Cited by 79 · see citing works
Chevance, G., Golaszewski, N. M., Tipton, E., Hekler, E. B., Buman, M. P., Welk, G. J., Patrick, K., & Godino, J. (2022) · JMIR mhealth and uhealth · doi:10.2196/35626 · Cited by 77 · see citing works
Enhanced Harris hawks optimization with multi-strategy for global optimization tasks
Li, C., Li, J., Chen, H., Jin, M. S., & Ren, H. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115499 · Cited by 76 · see citing works
Injection of hydrogen sonic multi-jet on inclined surface at supersonic flow
Hassanvand, A., Gerdroodbary, M. B., & Abazari, A. M. (2020) · International Journal of Modern Physics C · doi:10.1142/s0129183121500431 · Cited by 74 · see citing works
Nanomaterial transportation and exergy loss modeling incorporating CVFEM
Xiong, P.-Y., Almarashi, A., Dhahad, H. A., Alawee, W. H., Abusorrah, A. M., Issakhov, A., Abu‐Hamdeh, N. H., Shafee, A., & Chu, Y. (2021) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2021.115591 · Cited by 71 · see citing works
Sunny, J. S., Patro, C. P. K., Karnani, K., Pingle, S. C., Lin, F., Anekoji, M., Jones, L., Kesari, S., & Ashili, S. (2022) · Sensors · doi:10.3390/s22030756 · Cited by 73 · see citing works
A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications
Wang, W. K., Chen, I.-Y., Hershkovich, L., Yang, J., Shetty, A., Singh, G., Jiang, Y., Kotla, A. V., Shang, Z., Yerrabelli, R., et al. (2022) · Sensors · doi:10.3390/s22208016 · Cited by 70 · see citing works
Chu, Y., Bilal, S. M., & Hajizadeh, M. R. (2020) · Mathematical Methods in the Applied Sciences · doi:10.1002/mma.6937 · Cited by 69 · see citing works
Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting
Bai, Y., Tompkins, C. L., Gell, N., Dione, D. R., Zhang, T., & Byun, W. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251975 · Cited by 67 · see citing works
Bai, Y., Alzahrani, B., Baharom, S., & Habibi, M. (2020) · Engineering With Computers · doi:10.1007/s00366-020-01191-9 · Cited by 63 · see citing works
On the chaotic behavior of graphene-reinforced annular systems under harmonic excitation
Ma, L., Liu, X., & Moradi, Z. (2021) · Engineering With Computers · doi:10.1007/s00366-020-01210-9 · Cited by 62 · see citing works
Ye, H., Wu, P., Zhu, T., Xiao, Z., Zhang, X., Zheng, L., Zheng, R., Sun, Y., Zhou, W., Fu, Q., et al. (2021) · IEEE Access · doi:10.1109/access.2021.3052835 · Cited by 62 · see citing works
Degroote, L., Hamerlinck, G., Poels, K., Maher, C., Crombez, G., De Bourdeaudhuij, I., Vandendriessche, A., Curtis, R. G., & DeSmet, A. (2020) · JMIR mhealth and uhealth · doi:10.2196/16674 · Cited by 60 · see citing works
Johnson, A. K., Yang, F., Gollarahalli, S., Banerjee, T., Abrams, D. M., Jonassaint, J., Jonassaint, C. R., & Shah, N. (2019) · JMIR mhealth and uhealth · doi:10.2196/13671 · Cited by 60 · see citing works
A personal health large language model for sleep and fitness coaching
Khasentino, J., Belyaeva, A., Liu, X., Yang, Z., Furlotte, N. A., Lee, C., Schenck, E., Patel, Y., Cui, J. H., Schneider, L. D., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03888-0 · Cited by 104 · see citing works
Müller, A. M., Wang, N. X., Yao, J., Tan, C. S., Low, I. C. C., Lim, N., Tan, J., Tan, A. W. H., & Müller‐Riemenschneider, F. (2019) · JMIR mhealth and uhealth · doi:10.2196/14120 · Cited by 59 · see citing works
A Guide to Nature Immersion: Psychological and Physiological Benefits
Lim, P. Y., Dillon, D., & Chew, P. K. H. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17165989 · Cited by 58 · see citing works
Accuracy and Acceptability of Wearable Motion Tracking for Inpatient Monitoring Using Smartwatches
Auepanwiriyakul, C., Waibel, S., Songa, J., Bentley, P., & Faisal, A. A. (2020) · Sensors · doi:10.3390/s20247313 · Cited by 56 · see citing works
Does Dog Ownership Affect Physical Activity, Sleep, and Self-Reported Health in Older Adults?
Mičková, E., Machová, K., Daďová, K., & Svobodová, I. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16183355 · Cited by 55 · see citing works
Wang, X., Chen, H., Heidari, A. A., Zhang, X., Xu, J., Xu, Y., & Huang, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106437 · Cited by 54 · see citing works
Willingham, T. B., Stowell, J. F., Collier, G., & Backus, D. A. (2024) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph21010079 · Cited by 54 · see citing works
RETRACTED ARTICLE: Comparison and evaluation of the performance of graphene-based biosensors
Abdelbasset, W. K., Jasim, S. A., Bokov, D. O., Oleneva, M. S., Islamov, A., Hammid, A. T., Mustafa, Y. F., Yasin, G., Alguno, A. C., & Kianfar, E. (2022) · Carbon letters · doi:10.1007/s42823-022-00338-6 · Cited by 53 · see citing works
RetractedChristensen, J. C., Blackburn, B. E., Anderson, L. A., Gililland, J. M., Peters, C. L., Archibeck, M. J., & Pelt, C. E. (2023) · The Journal of Arthroplasty · doi:10.1016/j.arth.2023.03.060 · Cited by 53 · see citing works
Xintarakou, A., Sousonis, V., Asvestas, D., Vardas, P. E., & Tzeis, S. E. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.853614 · Cited by 52 · see citing works
Emami-Nejad, H., Mir, A., & Farmani, A. (2021) · Plasmonics · doi:10.1007/s11468-021-01399-5 · Cited by 51 · see citing works
Argent, R., Hetherington‐Rauth, M., Stang, J. S., Tarp, J., Ortega, F. B., Molina‐García, P., Schumann, M., Bloch, W., Cheng, S., Grøntved, A., et al. (2022) · Sports Medicine · doi:10.1007/s40279-022-01665-4 · Cited by 49 · see citing works
Navalta, J. W., Ramirez, G. G., Maxwell, C., Radzak, K. N., & McGinnis, G. R. (2020) · Scientific Reports · doi:10.1038/s41598-020-64185-z · Cited by 49 · see citing works
Wu, J., Ho, T., Chang, Y., Hsu, C., Tsai, C. J., Lai, F., & Lin, M. (2019) · JMIR mhealth and uhealth · doi:10.2196/11989 · Cited by 46 · see citing works
Pino‐Ortega, J., Gómez‐Carmona, C. D., & Rico-González, M. (2021) · Gait & Posture · doi:10.1016/j.gaitpost.2021.04.015 · Cited by 48 · see citing works
Validity of the Wrist-Worn Polar Vantage V2 to Measure Heart Rate and Heart Rate Variability at Rest
Nuuttila, O., Korhonen, E., Laukkanen, J. A., & Kyröläinen, H. (2021) · Sensors · doi:10.3390/s22010137 · Cited by 47 · see citing works
Jia, Y., Wang, W., Wen, D., Liang, L., Gao, L., & Lei, J. (2018) · PeerJ · doi:10.7717/peerj.5350 · Cited by 47 · see citing works
Self-citationHybrid nanomaterial treatment within a permeable tank considering irreversibility
Chu, Y., Almusawi, M. B., Hajizadeh, M. R., Yao, S.-W., & Bach, Q. (2020) · International Journal of Modern Physics C · doi:10.1142/s0129183121500613 · Cited by 47 · see citing works
Liao, Y., & Schembre, S. M. (2018) · JMIR mhealth and uhealth · doi:10.2196/11181 · Cited by 45 · see citing works
A systematic review of mobile health interventions in China: Identifying gaps in care
Yang, X., & Kovarik, C. (2019) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x19856746 · Cited by 45 · see citing works
Modern Technologies for Personalized Nutrition
Boland, M., Alam, F., & Bronlund, J. E. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816403-7.00006-4 · Cited by 44 · see citing works
Wearable Artificial Intelligence for Detecting Anxiety: Systematic Review and Meta-Analysis
Abd-Alrazaq, A. A., AlSaad, R., Harfouche, M., Aziz, S., Ahmed, A., Damseh, R., & Sheikh, J. I. (2023) · Journal of Medical Internet Research · doi:10.2196/48754 · Cited by 48 · see citing works
Domingos, C., Costa, P., Santos, N. C., & Pêgo, J. M. (2021) · Journal of Medical Internet Research · doi:10.2196/26652 · Cited by 47 · see citing works
Liu, W., Deng, L., Cai, Z., Li, D., & Rahimi, A. (2021) · Engineering With Computers · doi:10.1007/s00366-020-01215-4 · Cited by 41 · see citing works
Liu, J.-C., Wei, J., Heidari, A. A., Kuang, F., Zhang, S., Gui, W., Chen, H., & Pan, Z. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105356 · Cited by 41 · see citing works
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 40 · see citing works
Silva, A. G., Simões, P., Queirós, A., Rodrigues, M. J. F., & Rocha, N. P. (2020) · Journal of Medical Systems · doi:10.1007/s10916-019-1506-z · Cited by 40 · see citing works
Carrier, B., Barrios, B., Jolley, B. D., & Navalta, J. W. (2020) · Technologies · doi:10.3390/technologies8040070 · Cited by 40 · see citing works
Delayed dynamic step shuffling frog-leaping algorithm for optimal design of photovoltaic models
Fan, Y., Wang, P., Heidari, A. A., Zhao, X., Turabieh, H., & Chen, H. (2020) · Energy Reports · doi:10.1016/j.egyr.2020.12.013 · Cited by 38 · see citing works
Overview of Physical Activity Counseling in Primary Care
Wattanapisit, A., Wattanapisit, S., & Wongsiri, S. (2020) · Korean Journal of Family Medicine · doi:10.4082/kjfm.19.0113 · Cited by 38 · see citing works
Jiang, Y. Y., Moradi, R., Abusorrah, A. M., Hajizadeh, M. R., & Li, Z. (2020) · Aerospace Science and Technology · doi:10.1016/j.ast.2020.106410 · Cited by 36 · see citing works
Bai, Y., Suhatril, M., Cao, Y., Forooghi, A., & Assilzadeh, H. (2021) · Engineering With Computers · doi:10.1007/s00366-020-01218-1 · Cited by 36 · see citing works
Tan, C., & Lin, J. (2021) · Soft Computing · doi:10.1007/s00500-021-05932-w · Cited by 35 · see citing works
Sun, X., Bi, Y., Guo, Y. F., Ghadiri, M., & Mohammadinia, S. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.124662 · Cited by 34 · see citing works
Hinze, A., Bowen, J., & König, J. L. (2021) · Smart Health · doi:10.1016/j.smhl.2021.100225 · Cited by 33 · see citing works
Editorial: A Good Sleep: The Role of Factors in Psychosocial Health
Pakpour, A. H., Griffiths, M. D., Ohayon, M. M., Broström, A., & Lin, C. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00520 · Cited by 31 · see citing works
Budig, M., Keiner, M., Stoohs, R. A., Hoffmeister, M., & Höltke, V. (2021) · Sensors · doi:10.3390/s22010180 · Cited by 29 · see citing works
Atkins, R. L., Lam, W., Scanlan, A. T., Beaven, C. M., & Driller, M. W. (2020) · Journal of Sports Sciences · doi:10.1080/02640414.2020.1737387 · Cited by 30 · see citing works
Topalidis, P. I., Florea, C., Eigl, E., Kurapov, A., Leon, C. A. B., & Schabus, M. (2021) · Sensors · doi:10.3390/s21113774 · Cited by 30 · see citing works
Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study
Concheiro-Moscoso, P., Groba, B., Álvarez-Estévez, D., Miranda-Duro, M. D. C., Pousada, T., Nieto-Riveiro, L., Mejuto-Muiño, F. J., & Pereira, J. (2023) · Journal of Medical Internet Research · doi:10.2196/42073 · Cited by 29 · see citing works
Doherty, C., Lambe, R., O’Grady, B., O’Reilly-Morgan, D., Smyth, B., Lawlor, A., Hurley, N., & Τράγος, Η. (2024) · JMIR mhealth and uhealth · doi:10.2196/49443 · Cited by 29 · see citing works
Wearable bioelectronics based on emerging nanomaterials for telehealth applications
Ren, Y., Zhang, F., Yan, Z., & Chen, P. (2025) · Device · doi:10.1016/j.device.2024.100676 · Cited by 32 · see citing works
Domingos, C., Santos, N. C., & Pêgo, J. M. (2021) · Sensors · doi:10.3390/s21072258 · Cited by 32 · see citing works
Ronghui, S., & Liangrong, N. (2021) · Engineering With Computers · doi:10.1007/s00366-021-01420-9 · Cited by 28 · see citing works
Nuske, H. J., Goodwin, M. S., Kushleyeva, Y., Forsyth, D., Pennington, J. W., Masino, A. J., Finkel, E., Bhattacharya, A., Tan, J., Tai, H., et al. (2021) · Autism Research · doi:10.1002/aur.2633 · Cited by 28 · see citing works
Wearable Technology to Quantify the Nutritional Intake of Adults: Validation Study
Dimitratos, S. M., German, J. B., & Schaefer, S. E. (2020) · JMIR mHealth and uHealth · doi:10.2196/16405 · Cited by 27 · see citing works
Recent Advances in Nanomaterials Used for Wearable Electronics
Yang, M., Ye, Z., Ren, Y., Farhat, M., & Chen, P. (2023) · Micromachines · doi:10.3390/mi14030603 · Cited by 29 · see citing works
A field experiment on gamification of physical activity – Effects on motivation and steps
Grech, E. M., Briguglio, M., & Said, E. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103205 · Cited by 28 · see citing works
Sañudo, B., Sánchez-Trigo, H., Domínguez, R., Aguilar, G. F., Sánchez-Oliver, A. J., del Moral, J. E. V., & Oviedo‐Caro, M. Á. (2024) · Psychology of sport and exercise · doi:10.1016/j.psychsport.2024.102590 · Cited by 35 · see citing works
Kwon, K. Y., Shin, Y. J., Shin, J. H., Jeong, C., Jung, Y. H., Park, B., & Kim, T. (2019) · Advanced Healthcare Materials · doi:10.1002/adhm.201801593 · Cited by 27 · see citing works
Belabbaci, N. A., Anaadumba, R., & Alam, M. A. U. (2025) · JMIR mhealth and uhealth · doi:10.2196/60569 · Cited by 28 · see citing works
- Authors
- Xie, J., Wen, D., Liang, L., Jia, Y., Gao, L., & Lei, J.
- Affiliations
- Peking University (CN)
- Guangdong Medical College (CN)
- Jilin University (CN)
- Corresponding author
- Lei, Jianbo
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2018-04-12
- Volume
- 6
- Issue
- 4
- Pages
- e94
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC5920198
- OpenAlex
- W2798041585
- Cited by
- 310
- References
- 31
- Citations by year
- 2026: 17 · 2025: 31 · 2024: 38 · 2023: 36 · 2022: 35 · 2021: 63 · 2020: 55 · 2019: 27 · 2018: 8
- FWCI
- 19.13
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Keywords
- wearable activity trackers, heart rate monitoring, step counting, distance measurement, energy expenditure estimation, Apple Watch, mean absolute percentage error, oxygen consumption, mobile health apps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
