ReviewJournal of the American Medical Informatics Association : JAMIA2018
Using wearable technology to predict health outcomes: a literature review.
Review in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed.
- Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A machine learning model for frailty based on wearable device measurements.Communications medicine · 2026Article
- A Digital Health Framework to Assess Glycemia and Physical Activity in Kidney Transplant Candidates: A Pilot Study.Transplantation direct · 2026Article
- Investigating the Role of Wearable Devices in Facilitating Telehealth Adoption Among the Aging Population: Mediation Analysis of US National Data.JMIR medical informatics · 2025Article
- Advancing cardiac arrhythmia management: The integration of wearable technology and remote monitoring.World journal of cardiology · 2025Review
- Feasibility of Data Collection Via Consumer-Grade Wearable Devices in Adolescent Student Athletes: Prospective Longitudinal Cohort Study.JMIR formative research · 2025Article
- Leveraging Mobile Health and Wearable Technologies for the Prevention and Management of Atherosclerotic Cardiovascular Disease.Current atherosclerosis reports · 2025Review
- Observational
- Validation of Aerobic Capacity (VO2max) and Pulse Oximetry in Wearable Technology.Sensors (Basel, Switzerland) · 2025Article
- App Engagement as a Predictor of Weight Loss in Blended-Care Interventions: Retrospective Observational Study Using Large-Scale Real-World Data.Journal of medical Internet research · 2024Observational
- Patterns of digital health access and use among US adults: A latent class analysis.BMC digital health · 2024Article
- Monitoring External Workload With Wearable Technology After Anterior Cruciate Ligament Reconstruction: A Scoping Review.Orthopaedic journal of sports medicine · 2023Article
- Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation.NPJ digital medicine · 2023Article
- Article
- Data Quality Degradation on Prediction Models Generated From Continuous Activity and Heart Rate Monitoring: Exploratory Analysis Using Simulation.JMIR cardio · 2023Article
- Factors associated with use of wearable technology to support activity, well-being, or a healthy lifestyle in the adult population and among older adults.PLOS digital health · 2023Article
- Article
- An Algorithm to Classify Real-World Ambulatory Status From a Wearable Device Using Multimodal and Demographically Diverse Data: Validation Study.JMIR biomedical engineering · 2023Article
- Willingness to Use Mobile Health Devices in the Post-COVID-19 Era: Nationwide Cross-sectional Study in China.Journal of medical Internet research · 2023Article
- Physics-Informed Neural Networks for Modeling Physiological Time Series: A Case Study with Continuous Blood Pressure.Research square · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.