The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
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.
Overview
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
Figures


Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Items 101–200 of 287
Sheng, B., Moosman, O. M., Cruz, B. D. P., del Pozo-Cruz, J., Alfonso‐Rosa, R. M., & Zhang, Y. (2020) · Measurement · doi:10.1016/j.measurement.2020.107480 · Cited by 22
Let's (Tik) Talk About Fitness Trends
Štajer, V., Milovanović, I., Todorović, N., Ranisavljev, M., Pišot, S., & Drid, P. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.899949 · Cited by 33
Kunkels, Y. K., van Roon, A. M., Wichers, M., & Riese, H. (2021) · Psychophysiology · doi:10.1111/psyp.13898 · Cited by 20
Automated assessment of pulmonary patients using heart rate variability from everyday wearables
Rahman, M. J., Nemati, E., Rahman, M. M., Nathan, V., Vatanparvar, K., & Kuang, J. (2019) · Smart Health · doi:10.1016/j.smhl.2019.100081 · Cited by 21
Double Authentication Model based on PPG and ECG Signals
Bastos, L., Tavares, T., Rosário, D., Cerqueira, E., dos Santos, A. L., & Nogueira, M. (2020) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc48107.2020.9148521 · Cited by 22
Kutt, K., Nalepa, G. J., Giżycka, B., Jemioło, P., & Adamczyk, M. (2018) · Conference on Human System Interactions · doi:10.1109/hsi.2018.8431271 · Cited by 20
Use 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
Continuous heart rhythm monitoring using mobile photoplethysmography in ambulatory patients
Hochstadt, A., Havakuk, O., Chorin, E., Schwartz, A. L., Merdler, I., Laufer, M., Lubman, N., Ghantous, E., Viskin, S., & Rosso, R. (2020) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2020.04.017 · Cited by 18
Non-invasive technique for real-time myocardial infarction detection using faster R-CNN
Mohan, H., Rao, P. V., Kumara, H. C. S., & Manasa, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10957-2 · Cited by 19
Hänsel, K., Poguntke, R., Haddadi, H., Alomainy, A., & Schmidt, A. (2018) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3173574.3173719 · Cited by 19
Choi, J. Y., Chang, A. K., & Choi, E.-J. (2018) · Holistic Nursing Practice · doi:10.1097/hnp.0000000000000282 · Cited by 21
Detecting Heart Rate Variability using Millimeter-Wave Radar Technology
Al‐Masri, E., & Momin, M. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622096 · Cited by 19
Genes for Good: Engaging the Public in Genetics Research via Social Media.
Brieger, K., Zajac, G., Pandit, A., Foerster, J. R., Li, K. W., Annis, A. C., Schmidt, E. M., Clark, C. P., McMorrow, K., Zhou, W., et al. (2019) · American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.05.006 · Cited by 18
Validity of the Polar Vantage M watch when measuring heart rate at different exercise intensities
Shumate, T., Link, M., Furness, J. W., Kemp‐Smith, K., Simas, V., & Climstein, M. (2021) · PeerJ · doi:10.7717/peerj.10893 · Cited by 19
Study Protocol on the Validation of the Quality of Sleep Data from Xiaomi Domestic Wristbands
Concheiro-Moscoso, P., Martínez-Martínez, F. J., Miranda-Duro, M. D. C., Pousada, T., Nieto-Riveiro, L., Groba, B., Mejuto-Muiño, F. J., & Pereira, J. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18031106 · Cited by 17
Investing in college students: the role of the fitness tracker
Schaben, J. A., & Furness, S. (2018) · Digital Health · doi:10.1177/2055207618766800 · Cited by 20
Accelerating Research With Technology: Rapid Recruitment for a Large-Scale Web-Based Sleep Study
Deering, S., Grade, M. M., Uppal, J., Foschini, L., Juusola, J. L., Amdur, A., & Stepnowsky, C. J. (2019) · JMIR Research Protocols · doi:10.2196/10974 · Cited by 16
Miranda-Duro, M. D. C., Nieto-Riveiro, L., Concheiro-Moscoso, P., Groba, B., Pousada, T., Canosa, N., & Pereira, J. (2021) · Sensors · doi:10.3390/s21103341 · Cited by 16
Hahm, J., Choi, H., Matsuoka, H., Kim, J., & Byon, K. K. (2023) · International Journal of Sports Marketing and Sponsorship · doi:10.1108/ijsms-08-2022-0163 · Cited by 14
Domingos, C., Picó‐Pérez, M., Magalhães, R. J. D. S., Moreira, M., Sousa, N., Pêgo, J. M., & Santos, N. C. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.729060 · Cited by 18
Liu, J., Wong, W. T., Zwetsloot, I. M., Hsu, Y., & Tsui, K. (2019) · Telemedicine Journal and e-Health · doi:10.1089/tmj.2018.0202 · Cited by 15
Detection of Walk Tests in Free-Living Activities Using a Wrist-Worn Device
Sokas, D., Paliakaitė, B., Rapalis, A., Marozas, V., Bailón, R., & Petrėnas, A. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.706545 · Cited by 15
Ráthonyi, G., Takács, V. L., Szilágyi, R., Bába, É. B., Müller, A., Bács, Z., Harangi–Rákos, M., Balogh, L., & Ráthonyi-Ódor, K. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.661471 · Cited by 18
Shook, R. P., Yeh, H., Welk, G. J., Davis, A. M., & Ries, D. (2021) · Journal of Nutrition · doi:10.1093/jn/nxab317 · Cited by 13
Use of Wearable Devices to Study Physical Activity in Early Childhood Education
Díaz-Quesada, G., Bahamonde, C., Giménez‐Egido, J. M., & Torres‐Luque, G. (2021) · Sustainability · doi:10.3390/su132413998 · Cited by 18
“Big data” and “open data”: What kind of access should researchers enjoy?
Châtellier, G., Varlet, V., Blachier, C., Beslay, N., Béhier, J., Braunstein, D., Caralp, M., Congard-Chassol, B., Diaz, I., Fournier, L. S., et al. (2016) · Therapies · doi:10.1016/j.therap.2016.01.005 · Cited by 13
Wireless Sensor-Dependent Ecological Momentary Assessment for Pediatric Asthma mHealth Applications
Buonocore, C. M., Rocchio, R. A., Roman, A., King, C., & Sarrafzadeh, M. (2017) · IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies · doi:10.1109/chase.2017.72 · Cited by 13
Almas, A., Iqbal, R., Sabir, S., Ghani, A., & Kazmi, K. A. (2020) · Pilot and Feasibility Studies · doi:10.1186/s40814-020-00625-x · Cited by 12
Study for the Design of a Protocol to Assess the Impact of Stress in the Quality of Life of Workers
Concheiro-Moscoso, P., Groba, B., Martínez-Martínez, F. J., Miranda-Duro, M. D. C., Nieto-Riveiro, L., Pousada, T., Queirós, C. M. L., & Pereira, J. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18041413 · Cited by 18
Multi-Device Nutrition Control
Cunha, C. A. D. S., & Duarte, R. P. (2022) · Sensors · doi:10.3390/s22072617 · Cited by 12
Wearable Device Validity in Determining Step Count During Hiking and Trail Running
Navalta, J. W., Montes, J., Bodell, N. G., Aguilar, C. D., Lujan, A., Guzman, G., Kam, B. K., Manning, J. W., & DeBeliso, M. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0010 · Cited by 12
Marín‐Jiménez, N., Sánchez‐Parente, S., Expósito-Carrillo, P., Jiménez-Iglesias, J., Álvarez‐Gallardo, I. C., Cuenca‐García, M., & Castro‐Piñero, J. (2023) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2023.03.005 · Cited by 17
Almas, A., Iqbal, R., Ghani, A., Samad, Z. Z., Sabir, S., & Kazmi, K. A. (2023) · Pilot and Feasibility Studies · doi:10.1186/s40814-023-01344-9 · Cited by 11
Su, J. J., Wong, A. K. C., ZHANG, L.-P., Bayuo, J., Lin, R. S. Y., Abu‐Odah, H., & Baťalík, L. (2023) · European Journal of Physical and Rehabilitation Medicine · doi:10.23736/s1973-9087.23.08111-x · Cited by 11
« Big data » et « open data » : quel accès pour la recherche ?
Châtellier, G., Varlet, V., Blachier, C., Beslay, N., Béhier, J., Braunstein, D., Caralp, M., Congard-Chassol, B., Diaz, I., Fournier, L. S., et al. (2016) · Therapies · doi:10.1016/j.therap.2016.01.004 · Cited by 11
Improving Heart Rate Variability Measurements from Consumer Smartwatches with Machine Learning
Maritsch, M., Bérubé, C., Kraus, M., Lehmann, V. F., Züger, T., Feuerriegel, S., Kowatsch, T., & Wortmann, F. (2019) · Alexandria (UniSG) (University of St.Gallen) · doi:10.7892/boris.138443 · Cited by 12
Montes, J., Tandy, R. D., Young, J., Lee, S., & Navalta, J. W. (2019) · International Journal of Kinesiology and Sports Science · doi:10.7575/aiac.ijkss.v.7n.2p.30 · Cited by 10
Wang, Y., Tarbox, J., Chastain, A. N., & Cameron, M. J. (2020) · Journal of Contextual Behavioral Science · doi:10.1016/j.jcbs.2020.08.002 · Cited by 11
Measuring Physical Activity Levels in People With Mild Cognitive Impairment or Mild Dementia
van der Wardt, V., Hancox, J. E., Burgon, C., Bajwa, R. K., Goldberg, S. E., & Harwood, R. (2020) · Journal of Aging and Physical Activity · doi:10.1123/japa.2019-0234 · Cited by 11
Li, Y. N., Cheng, B. H., Yu, B., & Zhu, J. N. Y. (2024) · Personnel Psychology · doi:10.1111/peps.12585 · Cited by 13
Domingos, C., Costa, P., Santos, N. C., & Pêgo, J. M. (2020) · JMIR mhealth and uhealth · doi:10.2196/19245 · Cited by 11
Data-Driven Healthcare: The Role of Computational Methods in Medical Innovation
Hariharasakthisudhan, P., Rajendran, S., Khalkar, V., Devarajan, G., & Kamaraj, L. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.056605 · Cited by 14
Weaver, L., Wooden, T., & Grazer, J. (2019) · Journal of Student Research · doi:10.47611/jsr.vi.662 · Cited by 10
Development of a Smart Seat Cushion for Heart Rate Monitoring Using Ballistocardiography
Malik, A. R., Pilon, L., & Boger, J. N. (2019) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/cse/euc.2019.00078 · Cited by 10
Wearable Activity Trackers Usage among University Students
Ráthonyi, G., Ráthonyi-Ódor, K., Bendíkova, E., & Bába, É. B. (2019) · European Journal of Contemporary Education · doi:10.13187/ejced.2019.3.600 · Cited by 11
Accuracy of Heart Rate Measurements of Three Smartwatch Models in Dogs
Yanmaz, L. E., Okur, S., Ersöz, U., Şenocak, M. G., & Turgut, F. (2022) · Topics in companion animal medicine · doi:10.1016/j.tcam.2022.100654 · Cited by 10
Hsiao, W.-H., Paterno, M. T., Iradukunda, F., & Hawkins, M. (2020) · Behavioral Sleep Medicine · doi:10.1080/15402002.2020.1851230 · Cited by 9
Data collection and analysis of smartphone use and sleep of secondary school children
Ahn, H., Nguyen, G. T., Lee, H., Jo, S., Kim, E. A., & Yim, H. W. (2017) · IEEE International Conference on Big Data and Smart Computing (BigComp) · doi:10.1109/bigcomp.2017.7881746 · Cited by 11
Overbeek, D. L., Hogikyan, E. M., Davis, M. G., & McGillicuddy, D. C. (2019) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2019.158357 · Cited by 9
Ricchio, K., Lyter-Antonneau, P., & Andrés, J. M. P. (2018) · Central European Journal of Sport Sciences and Medicine · doi:10.18276/cej.2018.4-06 · Cited by 9
Alonso‐Domínguez, R., Sánchez‐Aguadero, N., Llamas‐Ramos, I., Lugones‐Sánchez, C., González‐Sánchez, S., Gómez-Marcos, M. Á., García‐Ortiz, L., & Ewomen Investigators (2021) · Journal of Advanced Nursing · doi:10.1111/jan.14737 · Cited by 8
Snoozing: an examination of a common method of waking
Mattingly, S. M., Martinez, G. J., Young, J., Cain, M. K., & Striegel, A. (2022) · SLEEP · doi:10.1093/sleep/zsac184 · Cited by 8
Collombon, E. H. G. M., Peels, D. A., Bolman, C. A. W., de Bruijn, G., & Lechner, L. (2022) · JMIR Formative Research · doi:10.2196/42394 · Cited by 9
Kaufmann, E. E., Tackie, R. G., Pitt, J. B., Mba, S., Akwetey, B. Y., Quaye, D., Mills, G. A., Nyame, C. A., Bulley, H. N. N., Glucksberg, M. R., et al. (2024) · International Journal of Telemedicine and Applications · doi:10.1155/2024/8906413 · Cited by 8
Nudging vs. Budging – Users’ Acceptance of Nudging for More Physical Activity
Lidynia, C., Offermann, J., & Ziefle, M. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-20476-1_3 · Cited by 7
Reliability of a smartphone application to measure physical activity
Caputo, E. L., Feter, N., Alberton, C. L., Leite, J. S., Rodrigues, A. N., Dumith, S. C., & da Silva, M. C. (2021) · Research in Sports Medicine · doi:10.1080/15438627.2021.1899919 · Cited by 7
Electrospun materials for wearable sensor applications in healthcare
Angkawinitwong, U., & Williams, G. R. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819611-3.00013-3 · Cited by 8
A comparison between heart-rate monitoring smart devices for ambient assisted living
Alsulami, M. H., Almuayqil, S. N., & Atkins, A. S. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03025-y · Cited by 7
Zhuo, L. X., & Macedo, L. G. (2021) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/18942 · Cited by 6
Kontaxis, S., Laporta, E., Pagès, E. G., Martinis, M., Leocani, L., Roselli, L., Buron, M. D., Guerrero, A. I., Zabalza, A., Cummins, N., et al. (2023) · Sensors · doi:10.3390/s23136017 · Cited by 6
Pino‐Ortega, J., Bastida‐Castillo, A., Lozano, J. M. O., Rojas‐Valverde, D., Reche-Soto, P., & Gómez‐Carmona, C. D. (2019) · Retos · doi:10.47197/retos.v36i36.68676 · Cited by 6
An Extensible Approach for Integrating Health and Activity Wearables in Mobile IoT Apps
Bohé, I., Willocx, M., & Naessens, V. (2019) · IEEE International Congress on Internet of Things (ICIOT) · doi:10.1109/iciot.2019.00023 · Cited by 6
Reviewing Mobile Apps to Control Heart Rate in Literature and Virtual Stores
García, J. S., Alonso, S. G., Díez, I. D. L. T., García-Zapirain, B., Castillo-Olea, C., Coronado, M. L., & Salvador, J. C. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1202-z · Cited by 6
Deriving Information Requirements for a Smart Nursing System for Intensive Care Units
Khanade, K., Sasangohar, F., Sutherland, S. C., & Alexander, K. E. (2017) · Critical Care Nursing Quarterly · doi:10.1097/cnq.0000000000000183 · Cited by 7
Resting heart rate and wearable technology
Khan, J. S., Sharma, A., & Seth, P. (2016) · Canadian Medical Association Journal · doi:10.1503/cmaj.1150109 · Cited by 6
Polo-Rodríguez, A., Rotbei, S., Amador, S., Baños, O., Gil, D., & Medina-Quero, J. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822822-7.00005-3 · Cited by 5
Spartano, N. L., Zhang, Y., Liu, C., Chernofsky, A., Lin, H., Trinquart, L., Borrelli, B., Pathiravasan, C. H., Kheterpal, V., Nowak, C., et al. (2024) · JMIR Biomedical Engineering · doi:10.2196/54631 · Cited by 6
Obesity and risk of hypertension in preadolescent urban school children: insights from Pakistan
Akhtar, S., Khan, S. A., Aziz, N., Imran, M., Samad, Z. Z., Iqbal, R., & Almas, A. (2024) · Journal of Health Population and Nutrition · doi:10.1186/s41043-024-00585-5 · Cited by 5
Edwardson, C. L., Davies, M. J., Khunti, K., Yates, T. E., & Rowlands, A. V. (2018) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2017-0001 · Cited by 6
Enciso, J., Variya, D., Sunthonlap, J., Sarmiento, T., Lee, K. M., Velasco, J., Pebdani, R. N., de Leon, R. D., Dy, C. J., Keslacy, S., et al. (2020) · JMIR Rehabilitation and Assistive Technologies · doi:10.2196/16054 · Cited by 5
Díaz-Quesada, G., Puga-González, E., & Galiano, I. M. M. (2021) · JUMP · doi:10.17561/jump.n3.2 · Cited by 5
Cárthaigh, S. M., Perry, J. L., & Griffin, C. P. (2022) · Behaviour and Information Technology · doi:10.1080/0144929x.2022.2136533 · Cited by 6
Scalise, F., Cavanna, F., Godio, C., & Beretta, E. P. (2023) · Sports Health A Multidisciplinary Approach · doi:10.1177/19417381231175149 · Cited by 5
Zameni, L., Bahram, A., Khalaji, H., Ghadiri, F., & Hasani, G. (2018) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 5
Modeling energy balance while correcting for measurement error via free knot splines
Ries, D., Carriquiry, A., & Shook, R. P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0201892 · Cited by 5
Lossy Compression Methods for Performance-Restricted Wearable Devices
Klus, L., Lohan, E. S., Granell, C., & Nurmi, J. (2020) · Repositori UJI (Universitat Jaume I) · doi:10.5281/zenodo.3908173 · Cited by 5
Khushhal, A. A., Abdelaal, A. A. M., Alsegame, M. M., & Alsaedi, A. (2025) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s507504 · Cited by 4
Nurmi, J., Knittle, K. P., Naughton, F., Sutton, S. R., Ginchev, T., Khattak, F., Castellano‐Tejedor, C., Lusilla, P., Ravaja, N., & Haukkala, A. (2023) · JMIR Formative Research · doi:10.2196/34232 · Cited by 6
Accuracy and Metrological Characteristics of Wearable Devices: A Systematic Review
Cosoli, G., & Scalise, L. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-04324-7_47 · Cited by 4
Preview investigators, Öhlin, A., Simunovic, N., Duong, A., & Ayeni, O. R. (2020) · BMC Musculoskeletal Disorders · doi:10.1186/s12891-020-03220-6 · Cited by 7
Marchand, D. L. P., Carvalho, L. S. R., Leal, D. D. S., Câmara, S. G., & Cassol, M. (2024) · Logopedics Phoniatrics Vocology · doi:10.1080/14015439.2024.2303633 · Cited by 6
Evaluation of the IngVaL Pedobarography System for Monitoring of Walking Speed
Hellström, P. A. R., Åkerberg, A., Ekstrom, M. C., & Folke, M. (2018) · Healthcare Informatics Research · doi:10.4258/hir.2018.24.2.118 · Cited by 4
Rahman, M. J., Morshed, B. I., & Harmon, B. E. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9175771 · Cited by 4
Smart-Wearables and Heart-Rate Assessment Accuracy
Manso, A. F., Fred, A. L. N., das Neves, R. C., & Ferreira, R. C. (2018) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea.2018.8438812 · Cited by 4
Nguyen, H. V., Ivanov, R., DeMauro, S. B., & Weimer, J. E. (2019) · ACM SIGBED Review · doi:10.1145/3357495.3357501 · Cited by 4
Gómez, A. G., Morillo, M. M., Sánchez, J. C. Z., & Santano, L. J. C. (2020) · Retos · doi:10.47197/retos.v38i38.74586 · Cited by 4
Bailey, J. (2016) · Canadian Medical Association Journal · doi:10.1503/cmaj.1150107 · Cited by 4
Sundar, S. S., Cho, E., & Wang, J. (2017) · doi:10.1002/9781118976005.ch27 · Cited by 4
Lucot, Q., Beurienne, E., & Behr, M. (2024) · Sensors · doi:10.3390/s24216927 · Cited by 3
Khusial, R., Sont, J. K., Usmani, O. S., Bonini, M., Chung, K. F., Fowler, S. J., & Honkoop, P. (2024) · JMIR Cardio · doi:10.2196/56848 · Cited by 4
Remote Sensing in Public Health Environment: A Review
Das, P., Sagayam, K. M., Jamader, A. R., & Acharya, B. (2022) · Smart computing and intelligence · doi:10.1007/978-981-19-1408-9_17 · Cited by 3
Salas-Monedero, M., Lozano-Berrio, V., Cazorla-Martínez, M.-J., Ceruelo-Abajo, S., Gil-Agudo, A. M., Hernández-Sánchez, S., Díaz, J. F. J., & Reyes-Guzmán, A. D. L. (2022) · Bioengineering · doi:10.3390/bioengineering9120763 · Cited by 3
Babinski, M., Duperval, R., Altenor, K., & Oettingen, J. E. V. (2023) · Pediatric Diabetes · doi:10.1155/2023/4289288 · Cited by 4
Digital Technology and Health: A Theoretical Framework
Šmahel, D., Macháčková, H., Šmahelová, M., Čevelíček, M., Almenara, C. A., & Holubčíková, J. (2018) · Springer eBooks · doi:10.1007/978-3-319-93221-7_2 · Cited by 3
Willemse, B. J. P. C., Kaptein, M. C., & Hasaart, F. (2020) · JMIR Research Protocols · doi:10.2196/16471 · Cited by 3
Heart Rate Monitoring System in Virtual Environment: The Case of Daily Exercise with Virtual Reality
Setiawan, A., Agiwahyuanto, F., & Arsiwi, P. (2018) · International Seminar on Application for Technology of Information and Communication (iSemantic) · doi:10.1109/isemantic.2018.8549776 · Cited by 3
Simple Wearable Device to Reduce Stress When Delivering a Speech without Pre-training
Yamane, T., Nakadoi, Y., Takagi, M., & Morita, M. (2021) · Healthcare Informatics Research · doi:10.4258/hir.2021.27.3.231 · Cited by 3
Bello, M. L., Anglin, D. A., Gillen, Z. M., & Smith, J. W. (2022) · International Journal of Kinesiology and Sports Science · doi:10.7575/aiac.ijkss.v.10n.2p.11 · Cited by 3
Sudaryono, N. G. P., Fadhiil, M., Syarifah, S., & Simanjuntak, E. R. (2023) · Journal Of World Science · doi:10.58344/jws.v2i7.311 · Cited by 3
Prevention better than harm reduction
Oyston, J. (2016) · Canadian Medical Association Journal · doi:10.1503/cmaj.1150108 · Cited by 3
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
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