TY - JOUR
T1 - Using wearable technology to predict health outcomes
T2 - A literature review
AU - Burnham, Jason P.
AU - Lu, Chenyang
AU - Yaeger, Lauren H.
AU - Bailey, Thomas C.
AU - Kollef, Marin H.
N1 - Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Objective: To review and analyze the literature to determine whether wearable technologies can predict health outcomes. Materials and methods: We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library and Engineering Village through April 18, 2018, for studies utilizing wearable technology in clinical outcome prediction. Studies were deemed relevant to the research question if they involved human subjects, used wearable technology that tracked a health-related parameter, and incorporated data from wearable technology into a predictive model of mortality, readmission, and/or emergency department (ED) visits. Results: Eight unique studies were directly related to the research question, and all were of at least moderate quality. Six studies developed models for readmission and two for mortality. In each of the eight studies, data obtained from wearable technology were predictive of or significantly associated with the tracked outcome. Discussion: Only eight unique studies incorporated wearable technology data into predictive models. The eight studies were of moderate quality or higher and thereby provide proof of concept for the use of wearable technology in developing models that predict clinical outcomes. Conclusion: Wearable technology has significant potential to assist in predicting clinical outcomes, but needs further study. Well-designed clinical trials that incorporate data from wearable technology into clinical outcome prediction models are required to realize the opportunities of this advancing technology.
AB - Objective: To review and analyze the literature to determine whether wearable technologies can predict health outcomes. Materials and methods: We queried Ovid Medline 1946 -, Embase 1947 -, Scopus 1823 -, the Cochrane Library, clinicaltrials.gov 1997 – April 17, 2018, and IEEE Xplore Digital Library and Engineering Village through April 18, 2018, for studies utilizing wearable technology in clinical outcome prediction. Studies were deemed relevant to the research question if they involved human subjects, used wearable technology that tracked a health-related parameter, and incorporated data from wearable technology into a predictive model of mortality, readmission, and/or emergency department (ED) visits. Results: Eight unique studies were directly related to the research question, and all were of at least moderate quality. Six studies developed models for readmission and two for mortality. In each of the eight studies, data obtained from wearable technology were predictive of or significantly associated with the tracked outcome. Discussion: Only eight unique studies incorporated wearable technology data into predictive models. The eight studies were of moderate quality or higher and thereby provide proof of concept for the use of wearable technology in developing models that predict clinical outcomes. Conclusion: Wearable technology has significant potential to assist in predicting clinical outcomes, but needs further study. Well-designed clinical trials that incorporate data from wearable technology into clinical outcome prediction models are required to realize the opportunities of this advancing technology.
KW - Emergency department
KW - Mortality
KW - Predictive modeling
KW - Readmission
KW - Wearable technology
UR - http://www.scopus.com/inward/record.url?scp=85055170413&partnerID=8YFLogxK
U2 - 10.1093/jamia/ocy082
DO - 10.1093/jamia/ocy082
M3 - Article
C2 - 29982520
AN - SCOPUS:85055170413
SN - 1067-5027
VL - 25
SP - 1221
EP - 1227
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 9
ER -