Wireless and continuous monitoring of vital signs in patients at the general ward



      Clinical deterioration regularly occurs in hospitalized patients potentially resulting in life threatening events. Early warning scores (EWS), like the Modified Early Warning Score (MEWS), assist care givers in assessing patients’ clinical situation, but cannot alert for deterioration between measurements. New devices, like the ViSi Mobile (VM) and HealthPatch (HP) allow for continuous monitoring and can alert deterioration in an earlier phase. VM and HP were tested regarding MEWS calculation compared to nurse measurements, and detection of high MEWS in periods between nurse observations.


      This quantitative study was part of a randomized controlled trial. Sixty patients of the surgical and internal medicine ward with a minimal expected hospitalization time of three days were randomized to VM or HP continuous monitoring in addition to regular nurse MEWS measurements for 24–72 h.


      Median VM and HP MEWS were higher than nurse measurements (2.7 vs. 1.9 and 1.9 vs. 1.3, respectively), predominantly due to respiratory rate measurement differences. During 1282 h VM and 1886 h HP monitoring, 71 (14 patients) and 32 (7 patients) high MEWS periods were detected during the non-observed periods. Time between VM or HP based high MEWS and next regular nurse measurement ranged from 0 to 9 (HP) and 10 (VM) hours.


      Both VM and HP are promising for continuous vital sign monitoring and may be more accurate than nurses. High MEWS can be detected in hospitalized patients around the clock and clinical deterioration at an earlier phase during unobserved periods.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Resuscitation
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Churpek M.M.
        • Yuen T.C.
        • Edelson D.P.
        Predicting clinical deterioration in the hospital: the impact of outcome selection.
        Resuscitation. 2013; 84: 564-568
        • Ludikhuize J.
        • Smorenburg S.M.
        • de Rooij S.E.
        • de Jonge E.
        Identification of deteriorating patients on general wards; measurement of vital parameters and potential effectiveness of the Modified Early Warning Score.
        J Crit Care. 2012; 27 (424.e7–13)
        • Churpek M.M.
        • Wendlandt B.
        • Zadravecz F.J.
        • Adhikari R.
        • Winslow C.
        • Edelson D.P.
        Association between intensive care unit transfer delay and hospital mortality: a multicenter investigation.
        J Hosp Med. 2016; 11: 757-762
        • Vlayen A.
        • Verelst S.
        • Bekkering G.E.
        • Schrooten W.
        • Hellings J.
        • Claes N.
        Incidence and preventability of adverse events requiring intensive care admission: a systematic review.
        J Eval Clin Pract. 2012; 18: 485-497
        • Calzavacca P.
        • Licari E.
        • Tee A.
        • et al.
        The impact of Rapid Response System on delayed emergency team activation patient characteristics and outcomes—a follow-up study.
        Resuscitation. 2010; 81: 31-35
        • Frost S.A.
        • Alexandrou E.
        • Bogdanovski T.
        • Salamonson Y.
        • Parr M.J.
        • Hallman K.M.
        Unplanned admission to intensive care after emergency hospitalisation: risk factors and development of a nomogram for individualising risk.
        Resuscitation. 2009; 80: 224-230
        • Haller G.
        • Myles P.S.
        • Wolfe R.
        • Weeks A.M.
        • Stoelwinder J.
        • McNeil J.
        Validity of unplanned admission to an intensive care unit as a measure of patient safety in surgical patients.
        Anesthesiology. 2005; 103: 1121-1129
        • van Zanten A.R.
        • Brinkman S.
        • Arbous M.S.
        • et al.
        Guideline bundles adherence and mortality in severe sepsis and septic shock.
        Crit Care Med. 2014; 42: 1890-1898
        • Young M.P.
        • Gooder V.J.
        • McBride K.
        • James B.
        • Fisher E.S.
        Inpatient transfers to the intensive care unit.
        J Gen Intern Med. 2003; 18: 77-83
        • Morgan R.J.M.
        • Williams F.
        • Wright M.
        An early warning scoring system for detecting developing critical illness.
        J Gen Intern Med. 1997; 8: 100
      1. Centre for Clinical Practice at NICE (UK): acutely ill patients in hospital: recognition of and response to acute illness in adults in hospital. National Institute for Health and Clinical Excellence: guidance.
        • Alam N.
        • Hobbelink E.L.
        • van Tienhoven A.J.
        • van de Ven P.M.
        • Jansma E.P.
        • Nanayakkara P.W.
        The impact of the use of the Early Warning Score (EWS) on patient outcomes: a systematic review.
        Resuscitation. 2014; 85: 587-594
        • McNeill G.
        • Bryden D.
        Do either early warning systems or emergency response teams improve hospital patient survival? A systematic review.
        Resuscitation. 2013; 84: 1652-1667
        • De Meester K.
        • Haegdorens F.
        • Monsieurs K.G.
        • Verpooten G.A.
        • Holvoet A.
        • Van Bogaert P.
        Six-day postoperative impact of a standardized nurse observation and escalation protocol: a preintervention and postintervention study.
        J Crit Care. 2013; 28: 1068-1074
        • Evans D.
        • Hodgkinson B.
        • Berry J.
        Vital signs in hospital patients: a systematic review.
        Int J Nurs Stud. 2001; 38: 643-650
        • Lockwood C.
        • Conroy-Hiller T.
        • Page T.
        Vital signs.
        JBI Libr Syst Rev. 2004; 2: 1-38
        • Cardona-Morrell M.
        • Prgomet M.
        • Turner R.M.
        • Nicholson M.
        • Hillman K.
        Effectiveness of continuous or intermittent vital signs monitoring in preventing adverse events on general wards: a systematic review and meta-analysis.
        Int J Clin Pract. 2016; 70: 806-824
        • Watkins T.
        • Whisman L.
        • Booker P.
        Nursing assessment of continuous vital sign surveillance to improve patient safety on the medical/surgical unit.
        J Clin Nurs. 2016; 25: 278-281
        • Boatin A.A.
        • Wylie B.J.
        • Goldfarb I.
        • et al.
        Wireless vital sign monitoring in pregnant women: a functionality and acceptability study.
        Telemed J E Health. 2016; 22: 564-571
        • Sahandi R.
        • Noroozi S.
        • Roushan G.
        • Heaslip V.
        • Liu Y.
        Wireless technology in the evolution of patient monitoring on general hospital wards.
        J Med Eng Technol. 2010; 34: 51-63
        • Zubiete E.D.
        • Luque L.F.
        • Rodriguez A.V.
        • González I.G.
        Review of wireless sensors networks in health applications.
        Conf Proc IEEE Eng Med Biol Soc. 2011; 2011: 1789-1793
        • Weenk M.
        • van Goor H.
        • Frietman B.
        • et al.
        Continuous monitoring of vital signs using wearable devices on the general ward: pilot study.
        JMIR Mhealth Uhealth. 2017; 5: e91
        • Selvaraj N.
        Long-term remote monitoring of vital signs using a wireless patch sensor.
        IEEE healthcare innovation conference. 2014
        • Weenk M.
        • van Goor H.
        • van Acht M.
        • Engelen L.J.
        • van de Belt T.H.
        • Bredie S.J.
        A smart all-in-one device to measure vital signs in admitted patients.
        PloS One. 2018; 13 (e0190138)
        • Taenzer A.H.
        • Pyke J.B.
        • McGrath S.P.
        • Blike G.T.
        Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before-and-after concurrence study.
        Anesthesiology. 2010; 112: 282-287
        • Fieselmann J.F.
        • Hendryx M.S.
        • Helms C.M.
        • Wakefield D.S.
        Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients.
        J Gen Intern Med. 1993; 8: 354-360
        • Edmonds Z.V.
        • Mower W.R.
        • Lovato L.M.
        • Lomeli R.
        The reliability of vital sign measurements.
        Ann Emerg Med. 2002; 39: 233-237
        • Beckett D.
        • Gordon C.
        • Paterson R.
        • Chalkley S.
        • Macleod D.
        • Bell D.
        Assessment of clinical risk in the out of hours hospital prior to the introduction of Hospital at Night.
        Acute Med. 2009; 8: 33-38
        • Mancia G.
        Short- and long-term blood pressure variability: present and future.
        Hypertension. 2012; 60: 512-517
        • Clifton L.
        • Clifton D.A.
        • Pimentel M.A.
        • Watkinson P.J.
        • Tarassenko L.
        Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors.
        IEEE J Biomed Health Inform. 2014; 18: 722-730
        • Welch J.
        • Kanter B.
        • Skora B.
        • et al.
        Multi-parameter vital sign database to assist in alarm optimization for general care units.
        J Clin Monit Comput. 2016; 30: 895-900
        • Gross B.
        • Dahl D.
        • Nielsen L.
        Physiologic monitoring alarm load on medical/surgical floors of a community hospital.
        Biomed Instrum Technol. 2011; : 29-36
        • Eerikainen L.M.
        • Vanschoren J.
        • Rooijakkers M.J.
        • Vullings R.
        • Aarts R.M.
        Reduction of false arrhythmia alarms using signal selection and machine learning.
        Physiol Meas. 2016; 37: 1204-1216
        • Antink C.H.
        • Leonhardt S.
        • Walter M.
        Reducing false alarms in the ICU by quantifying self-similarity of multimodal biosignals.
        Physiol Meas. 2016; 37: 1233-1252
        • Luo W.
        • Phung D.
        • Tran T.
        • Gupta S.
        • Rana S.
        • Karmakar C.
        • et al.
        Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view.
        J Med Internet Res. 2016; 18: e323
        • Bellomo R.
        • Ackerman M.
        • Bailey M.
        • et al.
        A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards.
        Crit Care Med. 2012; 40: 2349-2361
        • Paterson R.
        • MacLeod D.C.
        • Thetford D.
        • et al.
        Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit.
        Clin Med (Lond). 2006; 6: 281-284
        • Ohashi K.
        • Kurihara Y.
        • Watanabe K.
        • Ohno-Machado L.
        • Tanaka H.
        Feasibility evaluation of Smart Stretcher to improve patient safety during transfers.
        Methods Inf Med. 2011; 50: 253-264
        • Slight S.P.
        • Franz C.
        • Olugbile M.
        • Brown H.V.
        • Bates D.W.
        • Zimlichman E.
        The return on investment of implementing a continuous monitoring system in general medical-surgical units.
        Crit Care Med. 2014; 42: 1862-1868
        • Giusti G.D.
        • Tuteri D.
        • Giontella M.
        Nursing interactions with intensive care unit patients affected by sleep deprivation: an observational study.
        Dimens Crit Care Nurs. 2016; 35: 154-159
        • Patel M.
        • Chipman J.
        • Carlin B.W.
        • Shade D.
        Sleep in the intensive care unit setting.
        Crit Care Nurs Q. 2008; 31: 309-318
        • Delaney L.J.
        The role of sleep in patient recovery.
        Aust Nurs Midwifery J. 2016; 23: 26-29