The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Fitbit®: An accurate and reliable device for wireless physical activity tracking
Open accessOverview
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
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?
In the citing paper
“Five studies (50 comparisons) reported on interdevice reliability [85,113,116,117,166], with analyzed sample sizes ranging from 13 [117] to 29 [113].”
From Reliability and Validity of Commercially Available Wearable Devices for Measuring Steps, Energy Expenditure, and Heart Rate: Systematic Review (2020) · cites this paper as [116]
In the citing paper
“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pace of technological change, these existing studies have largely been completed on devices that would be considered outdated, such as earlier Fitbit models (One, Zip, Flex, Ultra), Jawbone Up, and the Nike FuelBand.”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 [8]
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 [8]
In the citing paper
“In regard to specific exercise intensity, this study supports previous research that found the Fitbit device to overestimate EE during moderate activity [8].”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [8]
In the citing paper
“The increased error from previous studies for the Fitbit device [8,10] suggests that the Fitbit Charge HR may utilize heart rate in determining EE.”
From Estimating Accuracy at Exercise Intensities: A Comparative Study of Self-Monitoring Heart Rate and Physical Activity Wearable Devices (2017) · cites this paper as [8]
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 [8]
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
Hébert, E. T., Caughy, M. O., & Shuval, K. (2012) · British Journal of Sports Medicine · doi:10.1136/bjsports-2011-090734 · Cited by 418
Physical Activity Promotion in the Health Care System
VUORI, I. M., Lavie, C. J., & Blair, S. N. (2013) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2013.08.020 · Cited by 344
VanWormer, J. J., Pronk, N. P., & Kroeninger, G. J. (2009) · Diabetes Spectrum · doi:10.2337/diaspect.22.1.48 · Cited by 35
Cited by
Sorted by citations
Items 201–300 of 406
Does stress induction affect cognitive performance or avoidance of cognitive effort?
Picciotto, G., & Fabio, R. A. (2023) · Stress and Health · doi:10.1002/smi.3280 · Cited by 18
Healthy lifestyle intervention for adult clinic patients with type 2 diabetes mellitus.
Coughlin, S. S., Hatzigeorgiou, C., Anglin, J. C., Xie, D., Besenyi, G. M., Leo, G. D., Stewart, J. L., & Wilkins, T. (2017) · PubMed · Cited by 12
Towards a multi-scalar framework for smart healthcare
Dritsa, D., & Biloria, N. M. (2018) · Smart and Sustainable Built Environment · doi:10.1108/sasbe-10-2017-0057 · Cited by 12
Sala, D. A., Grissom, H., DelSole, E. M., Chu, M. L., Godfried, D. H., Bhattacharyya, S., Karamitopoulos, M. S., & Chu, A. (2019) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.14207 · Cited by 12
The validity and accuracy of wrist-worn activity monitors in lower-limb prosthesis users
Smith, J. D., Guerra, G., & Burkholder, B. G. (2019) · Disability and Rehabilitation · doi:10.1080/09638288.2019.1587792 · Cited by 12
Ababneh, A., Finlayson, K. J., Edwards, H. E., van Netten, J. J., & Lazzarini, P. A. (2023) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/20420188221142457 · Cited by 12
Angeles, W. W. L.-D. L. (2023) · Academic Pediatrics · doi:10.1016/j.acap.2023.09.014 · Cited by 15
Chen, Z., Wang, C. T., Hu, C. J., Ward, K. M., Kho, A., & Webster, G. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.124.036801 · Cited by 12
Cheung, Y. K., Hsueh, P.-Y. S., Ensari, I., Willey, J. Z., & Diaz, K. M. (2018) · Sensors · doi:10.3390/s18093056 · Cited by 11
Self-citationEffect of Auricular Acupressure on Sleep and Pruritus in Patients Undergoing Hemodialysis
Bang, Y. Y., & Park, H. (2020) · Journal of Korean Academy of Fundamentals of Nursing · doi:10.7739/jkafn.2020.27.2.141 · Cited by 11
Liu, H.-P., Chuang, Y.-M., Liu, C.-H., Yang, P. C.-M., & Fuh, C. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3015392 · Cited by 11
McMahon, S., Lewis, B. A., Guan, W., Wyman, J. F., & Rothman, A. J. (2020) · Contemporary Clinical Trials · doi:10.1016/j.cct.2020.106238 · Cited by 11
Sawani, S., Siddiqui, A. R., Azam, S. I., Humayun, K. N., Ahmed, A., Habib, A., Naz, S., Tufail, M., & Iqbal, R. (2020) · Trials · doi:10.1186/s13063-020-4205-7 · Cited by 11
An interpretable health behavioral intervention policy for mobile device users
Hu, X., Hsueh, P.-Y. S., Chen, C.-H., Diaz, K. M., Parsons, F., Ensari, I., Qian, M., & Cheung, Y. K. (2018) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2769320 · Cited by 10
Self-citationCole, T. S., Jahnke, H. K., Godzik, J., Morgan, C. D., Nakaji, P., & Little, A. S. (2019) · Pituitary · doi:10.1007/s11102-019-00946-y · Cited by 10
Martins, N., Campos, J., & Simões, R. (2020) · Advances in Science Technology and Engineering Systems Journal · doi:10.25046/aj050279 · Cited by 10
Celedonia, K. L., Valenti, M. W., Compagnucci, M. C., & Wilson, M. L. (2020) · Research Ethics · doi:10.1177/1747016120980560 · Cited by 10
The Medical Internet of Things: applications in respiratory medicine
Angelucci, A., & Aliverti, A. (2023) · European Respiratory Society eBooks · doi:10.1183/2312508x.10000423 · Cited by 11
Occupational physical activity in brewery and office workers
Thompson, J. F., Severson, R. L., & Rosecrance, J. C. (2018) · Journal of Occupational and Environmental Hygiene · doi:10.1080/15459624.2018.1492136 · Cited by 9
Masuda, H., & Okada, S. (2023) · Frontiers in Global Women s Health · doi:10.3389/fgwh.2023.1260645 · Cited by 9
Lee, S.-Y., Choi, J., Lee, J., Lee, Y., Park, J. M., & Hong, K. (2024) · Korean Journal of Family Medicine · doi:10.4082/kjfm.23.0166 · Cited by 10
Triantafyllidis, A. K., Alexiadis, A., Soutos, K., Fischer, T., Votis, K., & Tzovaras, D. K. (2021) · Digital · doi:10.3390/digital1020008 · Cited by 13
Mechanical Observation Research in Social Marketing and Beyond
Bogomolova, S. (2016) · Springer eBooks · doi:10.1007/978-981-10-1829-9_8 · Cited by 9
Ho, B.-J., Liu, R., Tseng, H.-Y., & Srivastava, M. B. (2017) · doi:10.1145/3089341.3089346 · Cited by 9
Using Commercial Physical Activity Trackers for Health Promotion Research: Four Case Studies
Turner‐McGrievy, G. M., Jake‐Schoffman, D. E., Singletary, C. R., Wright, M., Crimarco, A. E., Wirth, M. D., Shivappa, N., Mandes, T., West, D. S., Wilcox, S., et al. (2018) · Health Promotion Practice · doi:10.1177/1524839918769559 · Cited by 9
Pathare, N. C., Conroy, J. P., Gillard, A., & Hansen, M. (2019) · Cardiopulmonary Physical Therapy Journal · doi:10.1097/cpt.0000000000000115 · Cited by 9
Swift, D. L., Nevels, T. R., Solar, C. A., Brophy, P. M., McGee, J. E., Brewer, S. B., Clark, A., Houmard, J. A., & Lutes, L. D. (2021) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002675 · Cited by 10
Mancuso, M. E., Biasoli, C., Marino, R., Buzzi, A., Preti, D., Sannino, L., Tempre, R., Bendinelli, S., Pompeo, E., Siri, G., et al. (2024) · Journal of Clinical Medicine · doi:10.3390/jcm13113036 · Cited by 8
Jiang, Z., Chan, A. Y. L., Lum, D. H., Wong, K. H. T. W., Leung, J. C. N., Ip, P., Coghill, D., Wong, R. S. M., Ngai, E. C. ‐., & Wong, I. C. K. (2024) · JAACAP Open · doi:10.1016/j.jaacop.2024.11.003 · Cited by 9
Towards Real-Time Patient Prioritization in Hospital Emergency Services
Lima, B., & Faria, J. P. (2018) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom.2018.8531089 · Cited by 8
Futami, K., Terada, T., & Tsukamoto, M. (2021) · Sensors · doi:10.3390/s21238016 · Cited by 9
Paromita, P., Mundnich, K., Nadarajan, A., Booth, B. M., Narayanan, S. S., & Chaspari, T. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1195795 · Cited by 9
Sears, T., Alvalos, E., Lawson, S., McAlister, I. P., Eschbach, L. C., & Bunn, J. A. (2017) · International journal of exercise science · Cited by 8
Modeling Clinically Validated Physical Activity Assessments Using Commodity Hardware
Winfree, K. N., & Dominick, G. M. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2787461 · Cited by 8
Wilbur, R. E., Griffin, J. S., Sorensen, M., & Furberg, R. (2018) · JMIR Research Protocols · doi:10.2196/10215 · Cited by 7
Joyner, D., Wengreen, H. J., Aguilar, S., & Madden, G. J. (2019) · JMIR Serious Games · doi:10.2196/13051 · Cited by 8
Nicholson, E., Allison, D. J., Bullock, A., & Heisz, J. J. (2020) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2020.111406 · Cited by 9
The Effect of Auricular Acupressure on Sleep in Older Adults with Sleep Disorders
Lee, J. H., & Park, H. (2021) · Journal of Korean Gerontological Nursing · doi:10.17079/jkgn.2021.23.2.117 · Cited by 8
ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Computing at the Edge
Kanani, A., Bhattacharjya, R., & Banerjee, D. S. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630165 · Cited by 8
Park, J. M., Choi, J., Lee, H. S., Jeon, S., Lee, J., & Hong, K. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19095433 · Cited by 8
Effects of the COVID‐19 pandemic on recruitment for the working women walking program
Daniel, M. N., Buchholz, S. W., Schoeny, M. E., Halloway, S., Kitsiou, S., Johnson, T. J., Vispute, S., Kapp, M., & Wilbur, J. (2022) · Research in Nursing & Health · doi:10.1002/nur.22258 · Cited by 7
Personalized Wellness Past and Future
Kanter, M., & Desrosiers, A. (2019) · Nutrition Today · doi:10.1097/nt.0000000000000354 · Cited by 8
A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
Dor‐Haim, H., Katzburg, S., & Leibowitz, D. W. (2019) · Journal of Visualized Experiments · doi:10.3791/59019-v · Cited by 8
Núñez-Gaunaurd, A., & Tovin, M. M. (2024) · Journal of Developmental and Physical Disabilities · doi:10.1007/s10882-024-09951-x · Cited by 7
“Because i choose to”: reasons for women to choose boutique fitness centers
Yıldız, K., Yumuk, E. D., Ekim, N., & Fernández, J. G. (2021) · Podium Sport Leisure and Tourism Review · doi:10.5585/podium.v10i1.17616 · Cited by 7
Healthy lifestyle intervention for African American uterine cancer survivors: Study protocol
Stewart, J. L., Besenyi, G. M., Williams, L. B., Burt, V., Anglin, J. C., Ghamande, S. A., & Coughlin, S. S. (2017) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2017.08.011 · Cited by 7
Examining Potential Psychosocial Mediators in a Physical Activity Intervention for Older Adults
McMahon, S., Lewis, B. A., Oakes, J. M., Wyman, J. F., Guan, W., & Rothman, A. J. (2019) · Western Journal of Nursing Research · doi:10.1177/0193945919871697 · Cited by 7
Moitra, E., Stein, M. D., Busch, A. M., Pinkston, M. M., Abrantes, A. M., Baker, J. V., Weisberg, R. B., Anderson, B. J., & Uebelacker, L. A. (2021) · AIDS Care · doi:10.1080/09540121.2021.1981819 · Cited by 8
Forman, E. M., Butryn, M. L., Chwyl, C., Crane, M. M., Dart, H., Hagerman, C. J., Manasse, S. M., Onu, M. C., Sun, J., Veling, H., et al. (2022) · Contemporary Clinical Trials · doi:10.1016/j.cct.2022.107010 · Cited by 9
Yang, P. C.-M., Jha, A., Xu, W., Song, Z., Jamp, P., & Teuteberg, J. J. (2023) · JMIR Cardio · doi:10.2196/45130 · Cited by 9
Swift, D. L., Dover, S. E., Nevels, T. R., Solar, C. A., Brophy, P. M., Hall, T. R., Houmard, J. A., & Lutes, L. D. (2015) · Contemporary Clinical Trials · doi:10.1016/j.cct.2015.11.005 · Cited by 7
Silverman-Lloyd, L. G., Kianoush, S., Blaha, M. J., Sabina, A. B., Graham, G. N., & Martin, S. S. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.9292 · Cited by 7
Feasibility of a Latin Dance Program with mHealth for Middle-Aged and Older Latinxs (BAILA TECH)
Marques, I. G., Kitsiou, S., Gerber, B. S., Buchholz, S. W., Bustamante, E. E., & Márquez, D. X. (2021) · Translational Journal of the American College of Sports Medicine · doi:10.1249/tjx.0000000000000143 · Cited by 7
EMVE-DeCK: A Theory-Based Framework for Designing and Tailoring Persuasive Technology
Oyibo, K. (2021) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3450614.3464617 · Cited by 9
Clubfoot Activity and Recurrence Exercise Study (CARES)
El-Banna, G., Baskar, D., Segovia, N., & Frick, S. (2021) · Journal of pediatric orthopedics · doi:10.1097/bpo.0000000000001973 · Cited by 9
Cavanna, F., Muller, S. L., de la Fuente, L. A., Zamberlán, F., Palmucci, M., Janeckova, L., Kuchař, M., Pallavicini, C., & Tagliazucchi, E. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.30.470657 · Cited by 7
Mishra, S. K., Hamadou, M., Gurung, K., Subba, A., Mukhia, P., Chhetri, T., Roy, A., Rai, P., & Georrge, J. J. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10617-4 · Cited by 6
Zhang, Y., Bornkamp, N., Hivert, M., Oken, E., & James, P. (2024) · JMIR Pediatrics and Parenting · doi:10.2196/59159 · Cited by 6
Büsching, F., Holzhauser, N., Knapp, P., & Wolf, L. C. (2016) · doi:10.1145/2980147.2980149 · Cited by 6
Hu, X., Hsueh, P.-Y. S., Chen, C.-H., Diaz, K. M., Cheung, Y.-K. K., & Qian, M. (2018) · PubMed Central · doi:10.7916/d82531qb · Cited by 6
Self-citationAssessment of Cardiometabolic Health, Diet and Physical Activity in Helicopter Rescue Paramedics
Meadley, B., Perraton, L. G., Smith, K. L., Bonham, M. P., & Bowles, K. (2021) · Prehospital Emergency Care · doi:10.1080/10903127.2021.1907492 · Cited by 7
The Real-Time Classification of Competency Swimming Activity Through Machine Learning
Powell, L., Polsley, S., Casey, D., & Hammond, T. (2023) · International Journal of Aquatic Research and Education · doi:10.25035/ijare.14.01.06 · Cited by 6
Youngmoon, K., & Oh, S. (2017) · 한국체육학회지 · doi:10.23949/kjpe.2017.03.56.2.45 · Cited by 6
Drake, J., Cain, R., & Lee, V. R. (2017) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-2528-8.ch001 · Cited by 6
Die postoperative Mobilisation nach Hüfttotalendoprothesenimplantation
Doppelbauer, M., Schüler, M., & Sauter, D. (2019) · Der Orthopäde · doi:10.1007/s00132-019-03768-9 · Cited by 6
Clark, M., & Driller, M. W. (2020) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2019-0034 · Cited by 7
Cornelius, T. M., Denes, A., Webber, K. T., Guest, C., Goldsmith, J., Schwartz, J. E., & Gorin, A. A. (2021) · Journal of Health Psychology · doi:10.1177/13591053211042075 · Cited by 6
Self-citationLajevardi-Khosh, A., Tresco, B. I. C., Stuart, A. R., Sinclair, S. K., Ackerman, M., Kubiak, E. N., Petelenz, T. J., & Hitchcock, R. W. (2018) · Journal of Rehabilitation and Assistive Technologies Engineering · doi:10.1177/2055668318804974 · Cited by 6
Accuracy of Physical Activity Monitors for Steps and Calorie Measurement During Pregnancy Walking
Montoye, A. H. K., Dahmen, J. B., Campbell, N., & Connolly, C. P. (2019) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2018-0067 · Cited by 6
Waller, G., Kim, T. G., Perez, S. D., Esper, G. J., Srinivasan, J. K., Shaffer, V. O., Staley, C. A., & Sullivan, P. S. (2020) · The American Surgeon · doi:10.1177/0003134820973364 · Cited by 6
Abel, W. M., & DeHaven, M. J. (2020) · Research in Nursing & Health · doi:10.1002/nur.22090 · Cited by 7
Jeong, S., Cha, C., Nam, S., & Song, J. (2024) · Medicine · doi:10.1097/md.0000000000036748 · Cited by 6
Barua, S., Upadhyay, D., Surapaneni, A., Grams, M. E., Jankelson, L., & Heffron, S. P. (2025) · American Heart Journal · doi:10.1016/j.ahj.2025.05.003 · Cited by 6
Higher number of steps is related to lower endogenous progesterone but not estradiol levels in women
Słojewska, K., Galbarczyk, A., Klimek, M., Tubek-Krokosz, A., Miłkowska, K., Szklarczyk, J., Mijas, M. E., Ścibor, M., & Grazyna Jasienska, A. (2024) · PLoS ONE · doi:10.1371/journal.pone.0299580 · Cited by 5
Determining the Reliability of Several Consumer-Based Physical Activity Monitors
Bock, J. M., Kaminsky, L. A., Harber, M. P., & Montoye, A. H. K. (2017) · Technologies · doi:10.3390/technologies5030047 · Cited by 5
DeepVentilation: Learning to Predict Physical Effort from Breathing
Sen, S., Bernabé, P., & Husom, E. J. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/753 · Cited by 5
Junghans-Rutelonis, A. N., Sim, L. A., Harbeck‐Weber, C., Dresher, E. R., Timm, W., & Weiss, K. E. (2024) · Frontiers in Pain Research · doi:10.3389/fpain.2023.1325270 · Cited by 4
He, Z., Zhao, G., Li, C., Xing, Y., Xu, A., Yang, J., & Wang, R. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-080186 · Cited by 6
Exercise monitoring of young adults using a Facebook application
Pagkalos, I. G., Κοκκινοπούλου, Ά., Weal, M. J., Petrou, L., & Hassapidou, M. N. (2017) · Digital Health · doi:10.1177/2055207617711286 · Cited by 5
Klugman, N., Jacome, V., Clark, M., Podolsky, M., Pannuto, P., Jackson, N., Nassor, A. S., Wolfram, C., Callaway, D. S., Taneja, J., et al. (2018) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3241539.3241583 · Cited by 5
Assessing the Measurement Properties of the Fitbit Zip® Among Adults Living With HIV
Dagenais, M., Salbach, N. M., Brooks, D., & O’Brien, K. K. (2020) · Journal of Physical Activity and Health · doi:10.1123/jpah.2019-0242 · Cited by 5
Activity Recognition in the City using Embedded Systems and Anonymous Sensors
Baghezza, R., Bouchard, K., Bouzouane, A., & Gouin-Vallerand, C. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.140 · Cited by 5
A Novel Re-Targetable Application Development Platform for Healthcare Mobile ApplicationsPreprint
Cho, C. H., Tabei, F., Phan, T. N., Kim, Y., & Chong, J. W. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.05783 · Cited by 5
Bruna, C. L., Emmen, A. M. L. H., de Graaf, N., Ramera, M., van Hilst, J., Björnsson, B., Boggi, U., Busch, O. R., Daams, F. D., D’Hondt, M. A., et al. (2026) · JAMA Surgery · doi:10.1001/jamasurg.2026.1503 · Cited by 4
Plasma-ionization-based breath sensing to prevent overexercise and visualize workout intensity
Zhang, Z., Sun, X., Xiao, M., Wang, T., Si, T., & Jin, H. (2026) · Cell Reports Physical Science · doi:10.1016/j.xcrp.2026.103493 · Cited by 4
Benmaamar, S., Oulhajene, Z., Baldi, M. E., Harch, I. E., & Rhazi, K. E. (2024) · Sport Sciences for Health · doi:10.1007/s11332-024-01239-z · Cited by 4
Ciubotaru, B.-I., Sasu, G.-V., Goga, N., Vasilăţeanu, A., Marin, I., Păvăloiu, I.-B., & Gligore, C. T. I. (2024) · Applied Sciences · doi:10.3390/app14167180 · Cited by 4
Hong, Y. A., Forjuoh, S. N., Ory, M. G., Reis, M. D., & Sang, H. (2017) · JMIR Research Protocols · doi:10.2196/resprot.8220 · Cited by 5
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
SEMİZ, K., Baran, E., & İnce, M. L. (2022) · Pamukkale Journal of Sport Sciences · doi:10.54141/psbd.1166034 · Cited by 4
Self-expansion is positively associated with Fitbit-measured daily steps across 4-weeks
Xu, X., Tupy, S., Sharp, J. L., Miller, A. L., Correll, D., & Nigg, C. R. (2022) · Scientific Reports · doi:10.1038/s41598-022-24576-w · Cited by 4
Flexible PCB Connection Methods for Wearable Energy Harvesting Applications
Bagci, F. S., Angsetya, R., Logi, S., & Kim, K. A. (2023) · Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec43580.2023.10131451 · Cited by 4
A Low-Cost Portable Health Platform for Monitoring of Human Physiological Signals
Brown, K., Secco, E. L., & Nagar, A. K. (2019) · Lecture notes in electrical engineering · doi:10.1007/978-3-030-02242-6_16 · Cited by 4
Intention mining: A deep learning-based approach for smart devices
Muzaffar, S. I., Shahzad, K., Malik, M. K., & Mahmood, K. (2020) · Journal of Ambient Intelligence and Smart Environments · doi:10.3233/ais-200545 · Cited by 4
Cai, M., Tan, K. H., & Ang, S. B. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025970 · Cited by 4
Kim, S. H., Kim, J. W., Park, J.-J., Shin, M., & Choi, M. Y. (2019) · JMIR mhealth and uhealth · doi:10.2196/12335 · Cited by 4
Connolly, C. P., Dahmen, J. B., Catena, R. D., Campbell, N., & Montoye, A. H. K. (2020) · Journal for the Measurement of Physical Behaviour · doi:10.1123/jmpb.2019-0059 · Cited by 5
R-Mon: An mhealth Tool for Real-time Respiratory Monitoring During Pandemics and Self-Isolation
Valero, M., Shahriar, H., & Ahamed, S. I. (2020) · IEEE World Congress on Services (SERVICES) · doi:10.1109/services48979.2020.00017 · Cited by 4
Leung, W. C. W., Case, L., Jung, J., & Yun, J. (2021) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2021.1893395 · Cited by 4
Liu, H., Zhan, L., & Lin, X. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112664 · Cited by 3
Metadata
- Authors
- Diaz, K. M., Krupka, D. J., Chang, M. J., Peacock, J., Ma, Y., Goldsmith, J., Schwartz, J. E., & Davidson, K. W.
- Affiliations
- Columbia University Irving Medical Center (US)
- Columbia University (US)
- Corresponding author
- Diaz, Keith M.
- Venue
- International Journal of Cardiology
- Publisher
- Elsevier BV
- Published
- 2015-04
- Volume
- 185
- Pages
- 138-140
- ISSN
- 0167-5273 (ISSN-L)
- PMC
- PMC4406840
- OpenAlex
- W2076953400
- Cited by
- 405
- References
- 3
- Citations by year
- 2026: 13 · 2025: 18 · 2024: 24 · 2023: 20 · 2022: 19 · 2021: 42 · 2020: 61 · 2019: 61 · 2018: 52 · 2017: 54 · 2016: 32 · 2015: 9
- FWCI
- 36.10
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- International Journal of Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Columbia Academic Commons (Columbia University)
- Funders
- National Heart, Lung, and Blood Institute (R01-HL115941)
- NIH Diversity Supplement (R01-HL116470-02S1)
- 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
- Fitbit, wearable activity trackers, wearable devices, physical activity monitoring, device validity, device reliability, primary care, physical activity counseling
- MeSH terms
- Adult, Equipment Design, Female, Humans, Male, Middle Aged, Motor Activity, Reproducibility of Results, Monitoring, Ambulatory, Young Adult, Accelerometry, Healthy Volunteers
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