Use of wearable devices for post-discharge monitoring of ICU patients: a feasibility study
Open accessAbstract
Wearable devices generate signals detecting activity, sleep, and heart rate, all of which could enable detailed and near-continuous characterization of recovery following critical illness. To determine the feasibility of using a wrist-worn personal fitness tracker among patients recovering from critical illness, we conducted a prospective observational study of a convenience sample of 50 stable ICU patients. We assessed device wearability, the extent of data capture, sensitivity and specificity for detecting heart rate excursions, and correlations with questionnaire-derived sleep quality measures. Wearable devices were worn over a 24-h period, with excellent capture of data. While specificity for the detection of tachycardia was high (98.8%), sensitivity was low to moderate (69.5%). There was a moderate correlation between wearable-derived sleep duration and questionnaire-derived sleep quality (r = 0.33, P = 0.03). Devices were well-tolerated and demonstrated no degradation in quality of data acquisition over time. We found that wearable devices could be worn by patients recovering from critical illness and could generate useful data for the majority of patients with little adverse effect. Further development and study are needed to better define and enhance the role of wearables in the monitoring of post-ICU recovery. Clinicaltrials.gov, NCT02527408
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In a recent feasibility study, Fitbit Charge monitoring of post-ICU convalescence in 50 stable ICU patients over 24 h had tachycardia detection sensitivity 69.5% and specificity 98.8%.
“Tachycardia detection had 69.5% sensitivity and 98.8% specificity [22].”
From Wearables and the Medical Revolution (2018) · cites this paper as [22]
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A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis
Pantelopoulos, A., & Bourbakis, N. G. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/tsmcc.2009.2032660 · Cited by 2,296 · see citing works
Wearable photoplethysmographic sensors—past and present
Tamura, T., Maeda, Y., Sekine, M., & Yoshida, M. (2014) · Electronics · doi:10.3390/electronics3020282 · Cited by 912 · cited 1× · see citing works
“These include photoplethysmography (PPG) sensors to detect heart rate [6, 7], as well as accelerometers to track activity and movement [3, 8, 9].”
BackgroundAccuracy 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 · see citing works
Churpek, M. M., Yuen, T. C., Winslow, C., Meltzer, D. O., Kattan, M. W., & Edelson, D. P. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001571 · Cited by 638 · see citing works
Jakicic, J. M., Davis, K. K., Rogers, R. J., King, W. C., Marcus, M. D., Helsel, D., Rickman, A. D., Wahed, A. S., & Belle, S. H. (2016) · JAMA · doi:10.1001/jama.2016.12858 · Cited by 621 · see citing works
Mobile monitoring with wearable photoplethysmographic biosensors
Asada, H. H., Shaltis, P., Reisner, A., Rhee, S., & Hutchinson, R. C. (2003) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2003.1213624 · Cited by 466 · see citing works
Sleep Deprivation in Critical Illness
Kamdar, B. B., Needham, D. M., & Collop, N. A. (2011) · Journal of Intensive Care Medicine · doi:10.1177/0885066610394322 · Cited by 456 · see citing works
Measurement of Sleep in Critically Ill Patients
Richards, K. C., O’Sullivan, P., & Phillips, R. L. (2000) · Journal of Nursing Measurement · doi:10.1891/1061-3749.8.2.131 · Cited by 438 · see citing works
Herridge, M. S., Chu, L. M., Matte, A. L., TOMLINSON, G. A., Chan, L. S., Thomas, C., Friedrich, J. O., Mehta, S., Lamontagne, F., Levasseur, M., et al. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201512-2343oc · Cited by 416 · see citing works
Early Warning System Scores for Clinical Deterioration in Hospitalized Patients: A Systematic Review
Smith, M. E. B., Chiovaro, J. C., O'Neil, M. E., Kansagara, D. L., Quiñones, A. R., Freeman, M., Motu’apuaka, M. L., & Slatore, C. G. (2014) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201403-102oc · Cited by 388 · see citing works
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 · see citing works
Characterisation of sleep in intensive care using 24-hour polysomnography: anobservational study
Elliott, R. M., McKinley, S., Cistulli, P. A., & Fien, M. V. (2013) · Critical Care · doi:10.1186/cc12565 · Cited by 338 · see citing works
Mantua, J., Gravel, N., & Spencer, R. (2016) · Sensors · doi:10.3390/s16050646 · Cited by 314 · see citing works
El-Amrawy, F., & Nounou, M. I. (2015) · Healthcare Informatics Research · doi:10.4258/hir.2015.21.4.315 · Cited by 287 · see citing works
de Zambotti, M., Baker, F. C., Willoughby, A. R., Godino, J. G., Wing, D., Patrick, K., & Colrain, I. M. (2016) · Physiology & Behavior · doi:10.1016/j.physbeh.2016.03.006 · Cited by 222 · see citing works
Rienzo, M. D., Rizzo, F., Parati, G., Brambilla, G., Ferratini, M., & Castiglioni, P. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1616161 · Cited by 200 · see citing works
Derivation of a cardiac arrest prediction model using ward vital signs*
Churpek, M. M., Yuen, T. C., Park, S. Y., Meltzer, D. O., Hall, J. B., & Edelson, D. P. (2012) · Critical Care Medicine · doi:10.1097/ccm.0b013e318250aa5a · Cited by 197 · see citing works
The Efficacy of Earplugs as a Sleep Hygiene Strategy for Reducing Delirium in the ICU
Litton, E., Carnegie, V., Elliott, R. M., & Webb, S. A. R. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001557 · Cited by 170 · see citing works
Patient-Nurse Interrater Reliability and Agreement of the Richards-Campbell Sleep Questionnaire
Kamdar, B. B., Shah, P. A., King, L., Kho, M. E., Zhou, X. J., Colantuoni, E., Collop, N. A., & Needham, D. M. (2012) · American Journal of Critical Care · doi:10.4037/ajcc2012111 · Cited by 162 · see citing works
Churpek, M. M., Yuen, T. C., Park, S. Y., Gibbons, R. D., & Edelson, D. P. (2013) · Critical Care Medicine · doi:10.1097/ccm.0000000000000038 · Cited by 138 · see citing works
Kroll, R. R., Boyd, J. G., & Maslove, D. M. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.6025 · Cited by 123 · see citing works
Self-citationSleep in hospitalized medical patients, Part 1: Factors affecting sleep
Young, J. S., Bourgeois, J. A., Hilty, D. M., & Hardin, K. A. (2008) · Journal of Hospital Medicine · doi:10.1002/jhm.372 · Cited by 118 · see citing works
Differences in Vital Signs Between Elderly and Nonelderly Patients Prior to Ward Cardiac Arrest
Churpek, M. M., Yuen, T. C., Winslow, C. J., Hall, J. B., & Edelson, D. P. (2015) · Critical Care Medicine · doi:10.1097/ccm.0000000000000818 · Cited by 100 · see citing works
Flannery, A. H., Oyler, D. R., & Weinhouse, G. L. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001952 · Cited by 88 · see citing works
McNelly, A. S., Rawal, J., Shrikrishna, D., Hopkinson, N. S., Moxham, J., Harridge, S. D. R., Hart, N., Montgomery, H., & Puthucheary, Z. (2016) · Critical Care Medicine · doi:10.1097/ccm.0000000000001645 · Cited by 69 · see citing works
The Influence of Environmental Factors on Sleep Quality in Hospitalized Medical Patients
Bano, M., Chiaromanni, F., Corrias, M., Turco, M., Rui, M. D., Amodio, P., Merkel, C., Gatta, A., Mazzotta, G. M., Costa, R., et al. (2014) · Frontiers in Neurology · doi:10.3389/fneur.2014.00267 · Cited by 67 · see citing works
Roney, J. K., Whitley, B. E., Maples, J., Futrell, L. S., Stunkard, K. A., & Long, J. D. (2015) · Journal of Clinical Nursing · doi:10.1111/jocn.12952 · Cited by 58 · see citing works
Mobile applications for postoperative monitoring after discharge
Semple, J. L., & Armstrong, K. (2016) · Canadian Medical Association Journal · doi:10.1503/cmaj.160195 · Cited by 48 · see citing works
Savage, N. J. (2015) · Nature · doi:10.1038/527s12a · Cited by 33 · see citing works
Flagged first, then by citations
1 of the 30 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Wearables and the Medical Revolution
Dunn, J., Runge, R., & Snyder, M. (2018) · Personalized Medicine · doi:10.2217/pme-2018-0044 · Cited by 654 · see citing works
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 · see citing works
Achieving affordable critical care in low-income and middle-income countries
Turner, H. C., Hảo, N. V., Yacoub, S., Hoang, V. M. T., Clifton, D. A., Thwaites, G. E., Dondorp, A. M., Thwaites, L., & Châu, N. V. V. (2019) · BMJ Global Health · doi:10.1136/bmjgh-2019-001675 · Cited by 147 · 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
Lee, S. Y., & Kang, J. (2020) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2020.102849 · Cited by 121 · see citing works
A Novel Non-contact Heart Rate Monitor Using Impulse-Radio Ultra-Wideband (IR-UWB) Radar Technology
Lee, Y., Park, J.-Y., Choi, Y.-W., Park, H., Cho, S. H., Cho, S. H., & Lim, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-31411-8 · Cited by 117 · see citing works
Wearable Sensing Devices for Point of Care Diagnostics
Mondal, S., Zehra, N., Choudhury, A., & Iyer, P. K. (2020) · ACS Applied Bio Materials · doi:10.1021/acsabm.0c00798 · Cited by 100 · see citing works
Koerber, D., Khan, S., Shamsheri, T., Kirubarajan, A., & Mehta, S. (2023) · Journal of Racial and Ethnic Health Disparities · doi:10.1007/s40615-022-01446-9 · Cited by 91 · see citing works
Mizuno, A., Changolkar, S., & Patel, M. S. (2020) · Annual Review of Medicine · doi:10.1146/annurev-med-050919-031534 · Cited by 80 · see citing works
Patel, V., Orchanian‐Cheff, A., & Wu, R. C. (2021) · JMIR mhealth and uhealth · doi:10.2196/17411 · Cited by 65 · see citing works
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 62 · see citing works
Challenges and Solutions in translating sepsis guidelines into practice in resource-limited settings
Ranjit, S., & Kissoon, N. “. (2021) · Translational Pediatrics · doi:10.21037/tp-20-310 · Cited by 61 · see citing works
Tocco, J. D., Raiano, L., Sabbadini, R., Massaroni, C., Formica, D., & Schena, E. (2021) · Sensors · doi:10.3390/s21093018 · Cited by 51 · see citing works
Sleep assessment in critically ill adults: A systematic review and meta-analysis
Kakar, E., Priester, M., Wessels, P., Slooter, A. J. C., Louter, M., & van der Jagt, M. (2022) · Journal of Critical Care · doi:10.1016/j.jcrc.2022.154102 · Cited by 42 · see citing works
Wearable devices for patient monitoring in the intensive care unit
Angelucci, A., Greco, M., Cecconi, M., & Aliverti, A. (2025) · Intensive Care Medicine Experimental · doi:10.1186/s40635-025-00738-8 · Cited by 45 · see citing works
Economic Perspective of the Use of Wearables in Health Care: A Systematic Review
Velasquez, G. D. D. S., Borna, S., Maniaci, M. J., Coffey, J. D., Haider, C. R., Demaerschalk, B. M., & Forte, A. J. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.05.003 · Cited by 48 · see citing works
Xu, H., Li, P., Yang, Z., Liu, X., Wang, Z., Yan, W., He, M., Chu, W., She, Y., Li, Y., et al. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01653-z · Cited by 41 · see citing works
Critical Bias in Critical Care Devices
Charpignon, M.-L., Byers, J., Cabral, S., Celi, L. A., Fernandes, C., Gallifant, J., Lough, M. E., Mlombwa, D., Moukheiber, L., Ong, B. A., et al. (2023) · Critical Care Clinics · doi:10.1016/j.ccc.2023.02.005 · Cited by 35 · see citing works
Sleep Fragmentation and Cognitive Trajectories After Critical Illness
Wilcox, M. E., McAndrews, M. P., Van, J., Jackson, J. C., Pinto, R. L., Black, S. E., Lim, A. S. P., Friedrich, J. O., & Rubenfeld, G. D. (2020) · CHEST Journal · doi:10.1016/j.chest.2020.07.036 · Cited by 29 · see citing works
Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses
Starliper, N., Mohammadzadeh, F., Songkakul, T., Hernandez, M. L., Bozkurt, A. Y., & Lobaton, E. J. (2019) · Sensors · doi:10.3390/s19030441 · Cited by 26 · see citing works
Advances in anesthesia technology are improving patient care, but many challenges remain
Doyle, D. J., Dahaba, A. A., & Manach, Y. L. (2018) · BMC Anesthesiology · doi:10.1186/s12871-018-0504-x · Cited by 25 · see citing works
Liu, X., Liu, T., Zhang, Z., Kuo, P., Xu, H., Yang, Z., Lan, K., Li, P., Ouyang, Z., Ng, Y. L., et al. (2021) · JMIR Medical Informatics · doi:10.2196/18803 · Cited by 23 · see citing works
Characterization and Validation of Flexible Dry Electrodes for Wearable Integration
Nunes, T., & Silva, H. (2023) · Sensors · doi:10.3390/s23031468 · Cited by 22 · see citing works
Talboom, J. S., & Huentelman, M. J. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy092 · Cited by 23 · see citing works
Kim, B. Y., McKay, S. M., & Lee, J. (2020) · Journal of Medical Internet Research · doi:10.2196/19732 · Cited by 19 · see citing works
Kim, B. Y., Hunt, M., Muscedere, J., Maslove, D. M., & Lee, J. (2020) · JMIR Aging · doi:10.2196/19859 · Cited by 12 · see citing works
Self-citationUm, J., Park, J., Lee, D. E., Ahn, J. E., & Baek, J. H. (2025) · Psychiatry Investigation · doi:10.30773/pi.2024.0257 · Cited by 11 · see citing works
Mestrom, E. H. J., Deneer, R., Bonomi, A., Margarito, J., Gelissen, J., Haakma, R., Korsten, H. H. M., Scharnhorst, V., & Bouwman, R. A. (2021) · JMIR Cardio · doi:10.2196/27765 · Cited by 10 · see citing works
Efficient and Compressive IoT based Health Care System for Parkinson’s Disease Patient
Pathak, V., Singh, K., Aziz, A., & Dhoot, A. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.441 · Cited by 8 · see citing works
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Lan, L. Y.-L., Kumar, W. M., Liu, L., Roberts, A. K., Chen, S., & Snyder, M. P. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15380-8.00003-5 · Cited by 9 · see citing works
Zhou, S., Li, K., Ogihara, A., & Wang, X. (2022) · Digital Health · doi:10.1177/20552076221102246 · Cited by 11 · see citing works
Ma, X., Wang, L., Meng, S., Xu, R., Chen, R., Zhang, X., Xin, M., Zhang, J., Chong, Y., Luo, P., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01072-6 · Cited by 8 · see citing works
Wearable electronic devices in the intensive care units
Chen, J., Jiang, W., & Song, Y. (2024) · Clinical eHealth · doi:10.1016/j.ceh.2024.02.001 · Cited by 7 · see citing works
Hendriks, M. M. S., der Hulst, M. V.-V., & Keijsers, N. (2020) · Sensors · doi:10.3390/s20102748 · Cited by 7 · see citing works
Kitisin, N., Lao-amornphunkul, S., Hemtanon, N., Thikom, N., Phochan, N., Thanakiattiwibun, C., Chaiwat, O., Wongtangman, K., Trachuthamcharoen, P., Piriyapatsom, A., et al. (2025) · Journal of Intensive Care · doi:10.1186/s40560-025-00827-6 · Cited by 6 · see citing works
Multicenter Evaluation of Machine-Learning Continuous Pulse Rate Algorithm on Wrist-Worn Device
Chen, W. '., Cordero, R., Taylor, J. L., Pangallo, D. R., Picard, R. W., Cruz, M. L., & Regalia, G. (2024) · Digital Biomarkers · doi:10.1159/000542615 · Cited by 6 · see citing works
Fortunato, M. P., Adusumalli, S., Chokshi, N. P., Harrison, J. D., Rareshide, C. A. L., & Patel, M. S. (2020) · JMIR Formative Research · doi:10.2196/14508 · Cited by 6 · see citing works
Personalized Treatment for Opioid Use DisorderPreprint
Gan, K., Scheller-Wolf, A. A., & TAYUR, S. R. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3389539 · Cited by 5 · see citing works
Cotton and other cellulose fibres for comfort smart clothing
Nuruzzaman, M., Ahmed, F., Kadri, H. J., & Mondal, M. I. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15471-3.00014-5 · Cited by 4 · see citing works
Bhalsod, D., Taksande, K., Paul, A., & Patel, V. (2025) · Multidisciplinary Reviews · doi:10.31893/multirev.2025256 · Cited by 4 · see citing works
A novel mobile biobehavioral regulation system for personalized trauma recovery support
Matto, H. C., Seshaiyer, P., Newcomb, A. B., Rothberg, S., & Lopez-Piper, A. (2019) · Patient Experience Journal · doi:10.35680/2372-0247.1369 · Cited by 4 · see citing works
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 · see citing works
Biomedical Signal Processing: The Cornerstone of Artificial Intelligence in Healthcare Wearables
Valenza, G. (2022) · Biomedical Materials & Devices · doi:10.1007/s44174-022-00051-y · Cited by 3 · see citing works
Do Patients Sleep Better at Home or in the Hospital Following Primary Total Joint Arthroplasty?
Liao, G. Y., Chansky, H. A., Manner, P. A., Brinkmann, E. J., Fernando, N. D., & Hernandez, N. M. (2025) · The Journal of Arthroplasty · doi:10.1016/j.arth.2025.05.020 · Cited by 3 · see citing works
Patient wearable technology in the ICU
Coffman, L. (2018) · Nursing Critical Care · doi:10.1097/01.ccn.0000534929.31666.37 · Cited by 3 · see citing works
Review of developments in sensor technology for monitoring of health-related conditions
Rahman, A., Kafy, A., Kabir, J. F., Pranto, M. T. A., Akther, A., & Choudhury, I. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96020-5.00062-5 · Cited by 3 · see citing works
Kang, J., & Cho, H. K. (2026) · Intensive and Critical Care Nursing · doi:10.1016/j.iccn.2026.104380 · Cited by 2 · see citing works
Santos, M. M., Peyroteo, M., & Lapão, L. V. (2024) · IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) · doi:10.1109/ice/itmc61926.2024.10794414 · Cited by 2 · see citing works
Anti-lost Intelligent Tracker Based on NB-IoT Technology for the Elderly
Tao, J., Xie, S., Jiang, J., & Wei, W. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-5577-0_57 · Cited by 2 · see citing works
The energy requirement after ICU discharge and strategies to improve nutritional adequacy
Scarcella, M., D’Alessandro, L., Scarpellini, E., Abenavoli, L., Charitos, I. A., ROBERTIS, E. D., & Cotoia, A. (2025) · Clinical Nutrition Open Science · doi:10.1016/j.nutos.2025.07.004 · Cited by 1 · see citing works
Perception of Wearable Intelligent Devices: A Case of Fitbit-Alta-HR
Shahzad, K., Malik, M. K., & Mehmood, K. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-5232-8_4 · Cited by 1 · see citing works
Biophysiologic Monitoring for the Neurosurgical Patient
Cole, K. L., Gautam, D., Findlay, M. C., & Lucke-Wold, B. P. (2023) · Future Integrative Medicine · doi:10.14218/fim.2023.00018 · Cited by 1 · see citing works
PUSHING THE BOUNDARIES OF CONSUMER GRADE WEARABLE DEVICES IN HEALTH CARE FOR OLDER ADULTS
Kim, B. Y. (2020) · UWSpace (University of Waterloo)
Impact of COVID-19 on Health Infrastructure and Wellness
Bithal, P. K., & Rath, G. P. (2020) · Journal of Neuroanaesthesiology and Critical Care · doi:10.1055/s-0040-1721520
Physical Rehabilitation Programmes Following ICU Discharge
Skinner, E. H., Jones, J. R. A., & Berney, S. C. (2021) · Springer eBooks · doi:10.1007/978-3-030-68680-2_9
Wu, R. C., Patel, V., Moosa, S., Langer, L. K., MacMillan, T. E., Cavalcanti, R. B., Cram, P. M., Gunaratne, K., & Bayley, M. T. (2020) · Research Square · doi:10.21203/rs.3.rs-61615/v2
Use of fitness trackers in patient-centred healthcare research: a systematic review (Preprint)Preprint
Khetrapal, P., Ang, K. M., Chau, M., Scanaill, P. Ó., Mcalister, J., & Kelly, J. D. (2019) · doi:10.2196/preprints.17094
Wu, R. C., Patel, V., Moosa, S., Langer, L. K., MacMillan, T. E., Cavalcanti, R. B., Cram, P. M., Gunaratne, K., & Bayley, M. T. (2020) · Research Square · doi:10.21203/rs.3.rs-61615/v1
Wu, R. C., Patel, V., Moosa, S., Langer, L. K., MacMillan, T. E., Cavalcanti, R. B., Cram, P. M., Gunaratne, K., & Bayley, M. T. (2022) · Canadian Journal of General Internal Medicine · doi:10.22374/cjgim.v17i4.616
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Arora, S., Chopra, H., Chopra, S., & Emran, T. B. (2024) · International Journal of Surgery Open · doi:10.1097/io9.0000000000000232
Nunes, T., Silva, H., & Gamboa, H. F. S. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-96899-0_7
Telemedicine-Based ICU Follow-Up Clinics and Digital Health
Dangayach, N. S., & Tosto-Mancuso, J. M. (2026) · Cambridge University Press eBooks · doi:10.1017/9781009501477.030
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Khinvasara, T. (2026) · Springer handbooks · doi:10.1007/978-3-032-01636-2_40
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Trieu, M., Fortich, M., Harrington, N., Fuentes, A. L., Haleem, M., Bellinghausen, A. L., King, K. R., & Owens, R. L. (2026) · CHEST Critical Care · doi:10.1016/j.chstcc.2026.100322
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- Authors
- 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.
- Affiliations
- Queen's University (CA)
- Corresponding author
- Maslove, David M.
- Venue
- Journal of Intensive Care
- Published
- 2017-11-21
- Volume
- 5
- Issue
- 1
- Pages
- 64-64
- ISSN
- 2052-0492 (ISSN-L)
- PMC
- PMC5698959
- OpenAlex
- W2769216997
- Cited by
- 69
- References
- 30
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 7 · 2023: 5 · 2022: 6 · 2021: 6 · 2020: 19 · 2019: 5 · 2018: 6
- FWCI
- 3.69
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Intensive Care (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Southeastern Ontario Academic Medical Organization (AHSC AFP Innovation Fund)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Surgery › Healthcare Technology and Patient Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- wearable devices, wearable activity trackers, post-ICU recovery, critical illness, intensive care unit patients, tachycardia detection, heart rate monitoring, sleep quality, sleep duration, post-discharge monitoring
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 ›
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