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Are Currently Available Wearable Devices for Activity Tracking and Heart Rate Monitoring Accurate, Precise, and Medically Beneficial?
Open accessAbstract
OBJECTIVES: The new wave of wireless technologies, fitness trackers, and body sensor devices can have great impact on healthcare systems and the quality of life. However, there have not been enough studies to prove the accuracy and precision of these trackers. The objective of this study was to evaluate the accuracy, precision, and overall performance of seventeen wearable devices currently available compared with direct observation of step counts and heart rate monitoring. METHODS: Each participant in this study used three accelerometers at a time, running the three corresponding applications of each tracker on an Android or iOS device simultaneously. Each participant was instructed to walk 200, 500, and 1,000 steps. Each set was repeated 40 times. Data was recorded after each trial, and the mean step count, standard deviation, accuracy, and precision were estimated for each tracker. Heart rate was measured by all trackers (if applicable), which support heart rate monitoring, and compared to a positive control, the Onyx Vantage 9590 professional clinical pulse oximeter. RESULTS: The accuracy of the tested products ranged between 79.8% and 99.1%, while the coefficient of variation (precision) ranged between 4% and 17.5%. MisFit Shine showed the highest accuracy and precision (along with Qualcomm Toq), while Samsung Gear 2 showed the lowest accuracy, and Jawbone UP showed the lowest precision. However, Xiaomi Mi band showed the best package compared to its price. CONCLUSIONS: The accuracy and precision of the selected fitness trackers are reasonable and can indicate the average level of activity and thus average energy expenditure.
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“The accuracy and precision of wearable devices typically range from 92% to 99% [110].”
From Review of Wearable Devices and Data Collection Considerations for Connected Health (2021) · cites this paper as [110]
Experimental — accuracy not yet measuredIn the citing paper
“Smartwatches such as the Apple Watch, Samsung Gear S2, and Pebble Watch are also reported to have acceptable validity and reliability, at least for measuring steps during overground walking [29].”
From Step Counting: A Review of Measurement Considerations and Health-Related Applications (2017) · cites this paper as [29]
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“other studies have used suboptimal reference methods, such as the chest straps45–47 or pulse oximeters48, which themselves have a degree of error (varying between 0 and 40 bpm when compared to ECGs44).”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [48]
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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
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
Non-contact, automated cardiac pulse measurements using video imaging and blind source separation
Poh, M., McDuff, D., & Picard, R. W. (2010) · Optics Express · doi:10.1364/oe.18.010762 · Cited by 1,652
Smart wearable systems: Current status and future challenges
Chan, M., Estève, D., Fourniols, J.-Y., Escriba, C., & Campo, E. (2012) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2012.09.003 · Cited by 953
Wearable Devices as Facilitators, Not Drivers, of Health Behavior Change
Patel, M. S., Asch, D. A., & Volpp, K. G. (2015) · JAMA · doi:10.1001/jama.2014.14781 · Cited by 808
Accuracy of Smartphone Applications and Wearable Devices for Tracking Physical Activity Data
Case, M. A., Burwick, H. A., Volpp, K. G., & Patel, M. S. (2015) · JAMA · doi:10.1001/jama.2014.17841 · Cited by 694
Wearable and implantable wireless sensor network solutions for healthcare monitoring
Darwish, A., & Hassanien, A. E. (2011) · Sensors · doi:10.3390/s110605561 · Cited by 556
Detecting Vital Signs with Wearable Wireless Sensors
Yilmaz, T., Foster, R., & Hao, Y. (2010) · Sensors · doi:10.3390/s101210837 · Cited by 341
McGrath, M. J., & Scanaill, C. N. (2013) · Apress eBooks · doi:10.1007/978-1-4302-6014-1 · Cited by 230
Factors Affecting Acceptance of Smartphone Application for Management of Obesity
Jeon, E., & Park, H. (2015) · Healthcare Informatics Research · doi:10.4258/hir.2015.21.2.74 · Cited by 74
Research works printed but not linked: 1
- Wagenaar, 2011, Conf Proc IEEE Eng Med Biol Soc, 2011, 4844
Cited by
Sorted by citations
Henriksen, A., Haugen Mikalsen, M., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9157 · Cited by 542
A Survey on Wearable Technology: History, State-of-the-Art and Current Challenges
Ometov, A., Shubina, V., Klus, L., Skibińska, J., Saafi, S., Pascacio, P., Flueratoru, L., Quezada-Gaibor, D., Chukhno, N., Chukhno, O., et al. (2021) · Computer Networks · doi:10.1016/j.comnet.2021.108074 · Cited by 572
Step Counting: A Review of Measurement Considerations and Health-Related Applications
Bassett, D. R., Toth, L. P., LaMunion, S. R., & Crouter, S. E. (2017) · Sports Medicine · doi:10.1007/s40279-016-0663-1 · Cited by 460
Review of Wearable Devices and Data Collection Considerations for Connected Health
Vijayan, V., Connolly, J. P., Condell, J., McKelvey, N., & Gardiner, P. (2021) · Sensors · doi:10.3390/s21165589 · Cited by 435
Accuracy of wrist-worn heart rate monitors
Wang, R., Blackburn, G., Desai, M., Phelan, D., Gillinov, L., Houghtaling, P., & Gillinov, M. (2017) · JAMA Cardiology · doi:10.1001/jamacardio.2016.3340 · Cited by 386
Review on Wearable Technology Sensors Used in Consumer Sport Applications
Aroganam, G., Manivannan, N., & Harrison, D. (2019) · Sensors · doi:10.3390/s19091983 · Cited by 416
Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise
GILLINOV, S., ETIWY, M., WANG, R., BLACKBURN, G., PHELAN, D., GILLINOV, A. M., HOUGHTALING, P., JAVADIKASGARI, H., & DESAI, M. Y. (2017) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001284 · Cited by 345
Nelson, B. W., & Allen, N. B. (2019) · JMIR mHealth and uHealth · doi:10.2196/10828 · Cited by 310
Sensors and Functionalities of Non-Invasive Wrist-Wearable Devices: A Review
Kamišalić, A., Fister, I., Turkanović, M., & Karakatič, S. (2018) · Sensors · doi:10.3390/s18061714 · Cited by 272
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
How consumer physical activity monitors could transform human physiology research
Wright, S. P., Hall Brown, T. S., Collier, S. R., & Sandberg, K. (2017) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00349.2016 · Cited by 234
Wearable activity trackers–advanced technology or advanced marketing?
Shei, R., Holder, I. G., Oumsang, A. S., Paris, B. A., & Paris, H. L. R. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-022-04951-1 · Cited by 182
Su, J. J., & Yu, D. S. F. (2021) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2021.104040 · Cited by 157
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 158
Accuracy of wearable heart rate monitors in cardiac rehabilitation
Etiwy, M., Akhrass, Z., Gillinov, L., Alashi, A., Wang, R., Blackburn, G., Gillinov, S. M., Phelan, D., Gillinov, A. M., Houghtaling, P. L., et al. (2019) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2019.04.08 · Cited by 143
Chow, H.-W., & Yang, C.-C. (2020) · JMIR mHealth and uHealth · doi:10.2196/14707 · Cited by 140
“Wearable fitness trackers have gained popularity worldwide, and their annual sales continue to grow [1,2].”
Accuracy of commercially available heart rate monitors in athletes: a prospective study
Pasadyn, S. R., Soudan, M., Gillinov, M., Houghtaling, P., Phelan, D., Gillinov, N., Bittel, B., & Desai, M. Y. (2019) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2019.06.05 · Cited by 135
Diraco, G., Leone, A., & Siciliano, P. A. (2017) · Biosensors · doi:10.3390/bios7040055 · Cited by 135
Wahl, Y., Düking, P., Droszez, A., Wahl, P., & Mester, J. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00725 · Cited by 133
Soon, S., Svavarsdottir, H. S., Downey, C. L., & Jayne, D. G. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000354 · Cited by 137
Kroll, R. R., Boyd, J. G., & Maslove, D. M. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.6025 · Cited by 123
Cognitive pilot-aircraft interface for single-pilot operations
Liu, J., Gardi, A., Ramasamy, S., Lim, Y., & Sabatini, R. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.08.031 · Cited by 120
Cosoli, G., Spinsante, S., & Scalise, L. (2020) · Measurement · doi:10.1016/j.measurement.2020.107789 · Cited by 131
Review of Validity and Reliability of Garmin Activity Trackers
Evenson, K. R., & Spade, C. L. (2020) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2019-0035 · Cited by 122
Scardulla, F., Cosoli, G., Spinsante, S., Poli, A., Iadarola, G., Pernice, R., Busacca, A., Pasta, S., Scalise, L., & D’Acquisto, L. (2023) · Measurement · doi:10.1016/j.measurement.2023.113150 · Cited by 134
Sahoo, D., Wang, H., Ke, S., Xiongwei, W., Le, H., Achananuparp, P., Lim, E., & Hoi, S. C. H. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330734 · Cited by 121
Zhou, M., Fukuoka, Y., Mintz, Y., Goldberg, K., Kaminsky, P., Flowers, E., & Aswani, A. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9117 · Cited by 111
Zhao, J., Zha, J., Zeng, Z., & Tan, C. (2021) · Journal of Materials Chemistry A · doi:10.1039/d1ta02493k · Cited by 112
Nguyen, N. H., Hadgraft, N. T., Moore, M. M., Rosenberg, D. E., Lynch, C., Reeves, M. M., & Lynch, B. M. (2017) · Supportive Care in Cancer · doi:10.1007/s00520-017-3756-y · Cited by 107
Servati, A., Zou, L., Wang, Z. J., Ko, F., & Servati, P. (2017) · Sensors · doi:10.3390/s17071622 · Cited by 112
Wearables and Location Tracking Technologies for Mental-State Sensing in Outdoor Environments
Birenboim, A., Dijst, M., Scheepers, F. E., Poelman, M. P., & Helbich, M. (2019) · The Professional Geographer · doi:10.1080/00330124.2018.1547978 · Cited by 109
Nurmi, J., Knittle, K. P., Ginchev, T., Khattak, F., Helf, C., Zwickl, P., Castellano‐Tejedor, C., Lusilla, P., Costa‐Requena, J., Ravaja, N., et al. (2019) · JMIR mhealth and uhealth · doi:10.2196/12884 · Cited by 110
Storywell: Designing for Family Fitness App Motivation by Using Social Rewards and Reflection
Saksono, H., Castaneda‐Sceppa, C., Hoffman, J. A., Morris, V., El‐Nasr, M. S., & Parker, A. G. (2020) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376686 · Cited by 92
Wang, W., Cheng, J., Song, W., & Shen, Y. (2022) · JMIR mhealth and uhealth · doi:10.2196/32435 · Cited by 89
Promise of Wearable Physical Activity Monitors in Oncology Practice
Beg, M. S., Gupta, A., Stewart, T. F., & Rethorst, C. D. (2017) · Journal of Oncology Practice · doi:10.1200/jop.2016.016857 · Cited by 85
Communication Requirements in 5G-Enabled Healthcare Applications: Review and Considerations
Qureshi, H. N., Manalastas, M., Ijaz, A., Imran, A., Liu, Y., & Kalaa, M. O. A. (2022) · Healthcare · doi:10.3390/healthcare10020293 · Cited by 84
McMahon, D. D., Barrio, B., McMahon, A. K., Tutt, K., & Firestone, J. (2020) · Journal of Special Education Technology · doi:10.1177/0162643419836416 · Cited by 80
Birrenkott, D. A., Pimentel, M. A. F., Watkinson, P. J., & Clifton, D. A. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2778265 · Cited by 71
Kano, S., & Fujii, M. (2018) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.8b02550 · Cited by 70
Kim, T. B., & Ho, C. B. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2021.101603 · Cited by 74
Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study
for the WEARable Information Technology for hospital INpatients (WEARIT-IN) study group, Kroll, R. R., McKenzie, E. D., Boyd, J. G., Sheth, P., Howes, D., Wood, M., & Maslove, D. M. (2017) · Journal of Intensive Care · doi:10.1186/s40560-017-0261-9 · Cited by 69
Nissen, M., Slim, S., Jaeger, K. M., Flaucher, M., Huebner, H., Danzberger, N., Fasching, P. A., Beckmann, M. W., Gradl, S., & Eskofier, B. M. (2022) · JMIR Formative Research · doi:10.2196/33635 · Cited by 69
Heart Rate Recovery 10 Seconds After Cessation of Exercise Predicts Death
van de Vegte, Y. J., van der Harst, P., & Verweij, N. (2018) · Journal of the American Heart Association · doi:10.1161/jaha.117.008341 · Cited by 69
Wearable sensors: can they benefit patients with chronic kidney disease?
Wieringa, F. P., Broers, N. J. H., Kooman, J. P., SANDE, F. M. V. D., & Hoof, C. A. V. (2017) · Expert Review of Medical Devices · doi:10.1080/17434440.2017.1342533 · Cited by 57
Harju, J., Tarniceriu, A., Parák, J., Vehkaoja, A. T., Yli‐Hankala, A., & Korhonen, I. (2018) · Physiological Measurement · doi:10.1088/1361-6579/aac9a9 · Cited by 55
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
Ludwig, M., Hoffmann, K., Endler, S., Asteroth, A., & Wiemeyer, J. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.00778 · Cited by 55
Pérez, A. D. L. C., Román, P. Á. L., Jiménez, M. M., Zurita, M. L., Laredo‐Aguilera, J. A., MONTILLA, J. A. P., & Linares, J. C. C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031593 · Cited by 53
Huang, Z., Xu, Y., Cheng, Y., Xue, M., Deng, M., Jaffrezic-Renault, N. J., & Guo, Z. (2022) · Sensors & Diagnostics · doi:10.1039/d2sd00037g · Cited by 53
Pino‐Ortega, J., Gómez‐Carmona, C. D., & Rico-González, M. (2021) · Gait & Posture · doi:10.1016/j.gaitpost.2021.04.015 · Cited by 48
Tracking Steps on Apple Watch at Different Walking Speeds
Veerabhadrappa, P., Moran, M. D., Renninger, M. D., Rhudy, M. B., Dreisbach, S. B., & Gift, K. M. (2018) · Journal of General Internal Medicine · doi:10.1007/s11606-018-4332-y · Cited by 48
Akinola, S., & Telukdarie, A. (2023) · Sustainability · doi:10.3390/su151310417 · Cited by 52
Amani-Shalamzari, S., Khoshghadam, E., Donyaei, A., Parnow, A. H., Bayati, M., & Clemente, F. M. (2019) · Physiology & Behavior · doi:10.1016/j.physbeh.2019.03.017 · Cited by 48
Fasel, B., Duc, C., Dadashi, F., Bardyn, F., Savary, M., Farine, P.-A., & Aminian, K. (2017) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-017-1621-2 · Cited by 46
Su, J. J., & Yu, D. S. F. (2019) · BMC Cardiovascular Disorders · doi:10.1186/s12872-019-1262-5 · Cited by 46
Müller, J., Hoch, A.-M., Zoller, V., & Oberhoffer, R. (2018) · Frontiers in Pediatrics · doi:10.3389/fped.2018.00005 · Cited by 44
Domingos, C., Costa, P., Santos, N. C., & Pêgo, J. M. (2021) · Journal of Medical Internet Research · doi:10.2196/26652 · Cited by 47
Qiu, S., Jiang, C., & Zhou, L. (2019) · European Journal of Cancer Prevention · doi:10.1097/cej.0000000000000511 · Cited by 42
An Independent Component Analysis Approach to Motion Noise Cancelation of Cardio-Mechanical Signals
Yang, C., & Tavassolian, N. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2856700 · Cited by 41
Is Continuous Heart Rate Monitoring of Livestock a Dream or Is It Realistic? A Review
Nie, L., Berckmans, D., Wang, C., & Li, B. (2020) · Sensors · doi:10.3390/s20082291 · Cited by 46
Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning
Scirè, A., Tropeano, F., Anagnostopoulos, A., & Chatzigiannakis, I. (2019) · Algorithms · doi:10.3390/a12020032 · Cited by 40
Wearables and Location Tracking Technologies for Mental-State Sensing in Outdoor Environments
Birenboim, A., Dijst, M., Scheepers, F. E., Poelman, M. P., Helbich, M., Transitions, S. U., & Transitions, S. U. (2019) · Utrecht University Repository (Utrecht University) · Cited by 39
Chacon, P. J., Pu, L., da Costa, T. H., Shin, Y.-H., Ghomian, T., Shamkhalichenar, H., Wu, H., Irving, B. A., & Choi, J. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2874885 · Cited by 39
Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements
Piccinini, F., Martinelli, G., & Carbonaro, A. (2020) · Sensors · doi:10.3390/s20216293 · Cited by 38
Interrogation of Patient Smartphone Activity Tracker to Assist Arrhythmia Management
Rudner, J., McDougall, C., Sailam, V., Smith, M., & Sacchetti, A. D. (2016) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2016.02.039 · Cited by 38
Innovative Use of Wrist-Worn Wearable Devices in the Sports Domain: A Systematic Review
Santos-Gago, J. M., Merino, M. R., Vallarades-Rodriguez, S., Sabucedo, L. M. Á., Iglesias, M. J. F., & Soidán, J. L. G. (2019) · Electronics · doi:10.3390/electronics8111257 · Cited by 44
Poh, M., & Poh, Y. C. (2017) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2016.0230 · Cited by 36
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
Developing a Smartwatch-Based Healthcare Application: Notes to Consider
Ramezani, R., Cao, M., Earthperson, A., & Naeim, A. (2023) · Sensors · doi:10.3390/s23156652 · Cited by 36
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
Montes, J., Tandy, R. D., Young, J., Lee, S., & Navalta, J. W. (2020) · International journal of exercise science · doi:10.70252/izmd1804 · Cited by 34
The Emergence of Personalized Health Technology
Allen, L. N., & Christie, G. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5357 · Cited by 34
Parák, J., Uuskoski, M., Machek, J., & Korhonen, I. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7437 · Cited by 34
Nation-Grainger, S. (2017) · Research Papers in Education · doi:10.1080/02671522.2017.1319590 · Cited by 44
Castro-García, J. A., Molina-Cantero, A. J., Gómez, I., Lafuente-Arroyo, S., & Merino-Monge, M. (2022) · Electronics · doi:10.3390/electronics11010155 · Cited by 36
Extending a Gamified Mobile App with a Public Display to Encourage Walking
Altmeyer, M., Lessel, P., Sander, T., & Krüger, A. (2018) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3275116.3275135 · Cited by 32
Baril, J.-F., Bromberg, S., Moayedi, Y., Taati, B., Manlhiot, C., Ross, H. J., & Cafazzo, J. A. (2019) · JMIR Cardio · doi:10.2196/12122 · Cited by 31
Heart rate monitoring, activity recognition, and recommendation for e-coaching
Pessemier, T. D., & Martens, L. (2018) · Multimedia Tools and Applications · doi:10.1007/s11042-018-5640-2 · Cited by 35
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
I am a "Smart" watch, Smart Enough to Know the Accuracy of My Own Heart Rate Sensor
Ra, H.-K., Ahn, J., Yoon, H. J., Yoon, D., Son, S. H., & Ko, J. (2017) · Proceedings of the 18th International Workshop on Mobile Computing Systems and Applications · doi:10.1145/3032970.3032977 · Cited by 29
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
Kim, C., Song, J. H., & Kim, S. H. (2023) · Annals of Rehabilitation Medicine · doi:10.5535/arm.23019 · Cited by 29
Domingos, C., Santos, N. C., & Pêgo, J. M. (2021) · Sensors · doi:10.3390/s21072258 · Cited by 32
Breteler, M. J. M., Janssen, J. H., Spiering, W., Kalkman, C. J., van Solinge, W. W., & Dohmen, D. (2019) · JMIR Formative Research · doi:10.2196/11489 · Cited by 29
Malik, A. R., & Boger, J. N. (2021) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/25996 · Cited by 29
Viciana, J., Casado-Robles, C., Guijarro-Romero, S., & Mayorga-Vega, D. (2022) · Journal of Sports Science and Medicine · doi:10.52082/jssm.2022.356 · Cited by 30
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Slepian, R. C., Vincent, A. C., Patterson, H. L., & Furman, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824010-6.00072-1 · Cited by 66
Investigating Adoption Factors of Wearable Technology in Health and Fitness
Rubin, A., & Ophoff, J. (2018) · Open Innovations Conference (OI) · doi:10.1109/oi.2018.8535831 · Cited by 26
Rahman, M. J., Morshed, B. I., Harmon, B. E., & Rahman, M. (2021) · Smart Health · doi:10.1016/j.smhl.2021.100249 · Cited by 25
Accurate Step Count with Generalized and Personalized Deep Learning on Accelerometer Data
Luu, L., Pillai, A., Lea, H., Buendía, R., Khan, F. M., & Dennis, G. (2022) · Sensors · doi:10.3390/s22113989 · Cited by 26
Assessing Changes in the Activity Levels of Breast Cancer Patients During Radiation Therapy
Champ, C. E., Ohri, N., Klement, R. J., Cantor, M., Beriwal, S., Glaser, S., & Smith, R. P. (2017) · Clinical Breast Cancer · doi:10.1016/j.clbc.2017.08.009 · Cited by 25
Dry electrode made from carbon nanotubes for continuous recording of bio-signals
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Giurgiu, M., Ketelhut, S., Kubica, C., Nissen, R., Doster, A.-K., Thron, M., Timm, I., Giurgiu, V., Nigg, C. R., Wöll, A., et al. (2023) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-023-01473-7 · Cited by 29
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Sadeghi, M., Sasangohar, F., McDonald, A. D., & Hegde, S. (2021) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/00187208211034024 · Cited by 23
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Metadata
- Authors
- El-Amrawy, F., & Nounou, M. I.
- Affiliations
- Department of Pharmaceutics, Faculty of Pharmacy, Alexandria University, Alexandria, Egypt.
- Alexandria University (EG)
- Corresponding author
- Nounou, Mohamed Ismail
- Venue
- Healthcare Informatics Research
- Published
- 2015
- Volume
- 21
- Issue
- 4
- Pages
- 315
- ISSN
- 2093-3681 (ISSN-L)
- 2093-369X
- PMC
- PMC4659890
- OpenAlex
- W2175170030
- Cited by
- 287
- References
- 14
- Citations by year
- 2026: 9 · 2025: 11 · 2024: 23 · 2023: 24 · 2022: 28 · 2021: 37 · 2020: 30 · 2019: 43 · 2018: 39 · 2017: 28 · 2016: 15
- FWCI
- 23.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Healthcare Informatics Research (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Alexandria University
- Topics
- 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
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Keywords
- wearable devices, wearable activity trackers, activity monitoring, step count accuracy, heart rate monitoring, accelerometer, pulse oximeter, energy expenditure estimation
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