Cited by 670Non-Invasive Vital Sign MonitoringContext-Aware Activity Recognition SystemsWireless Body Area Networks

The DOI matches its Crossref record; 156 of the 156 references it cites were found in a registry or an index; none of the 120 sources its claims cite is checked for support yet.

Wearables and the Medical Revolution

Open access
Dunn, Jessilyn, Runge, Ryan, Snyder, Michael·2018·Personalized Medicine

Abstract

Wearable sensors are already impacting healthcare and medicine by enabling health monitoring outside of the clinic and prediction of health events. This paper reviews current and prospective wearable technologies and their progress toward clinical application. We describe technologies underlying common, commercially available wearable sensors and early-stage devices and outline research, when available, to support the use of these devices in healthcare. We cover applications in the following health areas: metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. Finally, we discuss challenges associated with the adoption of wearable sensors in the current healthcare ecosystem and discuss areas for future research and development.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review surveys wearable sensors for healthcare, covering mechanical, physiological, and biochemical devices and applications from hospital monitoring to chronic disease, maternal, pulmonary, and mental health. It argues wearables can enable continuous longitudinal monitoring and prediction but require validation, standards, integration, and regulatory clarity before broad clinical adoption.

Key findings

  • Wearables span mechanical, physiological, and biochemical sensors, with applications including glucose monitoring, arrhythmia detection, sleep, maternal/fetal care, pulmonary health, and environmental exposures.
  • In a partnership with the UCSF Health eHeart study, Cardiogram’s DeepHeart platform used data from 14,011 wearable-equipped participants and detected Type II diabetes at 85% accuracy, atrial fibrillation at 97%, sleep apnea at 90%, and hypertension at 82%. A trial comparing AliveCor’s technology with routine care (n = 1001) resulted in a nearly fourfold increase in atrial fibrillation diagnosis.
  • Other reported performance examples include Fitbit Charge post-ICU tachycardia detection with 69.5% sensitivity and 98.8% specificity (n = 50); Oura ring sleep detection with 96% sensitivity, 48% wake specificity, and 65/51/61% agreement for light/deep/REM sleep; Empatica Embrace detecting 94% of GTC seizures in 80 pediatric patients with less than one false alarm per 24 h; ADAMM asthma detection accuracy of 71% in 84 teens; and VitalConnect HealthPatch fall detection with 99% sensitivity and 100% specificity (n = 25).
  • The global wearable devices market was anticipated to reach $19.5 billion in 2021; replacing one in five outpatient consultations and home health visits with digital visits was estimated to save $40 billion a year, and remote monitoring in patients with implantable cardiovascular devices (n = 43) decreased healthcare costs by 25%.

Methods

  • Narrative review of current and prospective wearable technologies and their progress toward clinical application, organized by health areas: metabolic, cardiovascular, and gastrointestinal monitoring; sleep, neurology, movement disorders, and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures.
  • No primary data collection; the paper synthesizes published studies, commercially available devices, early-stage devices, and examples from the literature.
  • No systematic search strategy, inclusion criteria, or meta-analysis is stated; the review also discusses regulatory oversight, economic impact, challenges, and future directions.

Limitations

  • The paper states limitations including high device cost, cross-platform incompatibility, lack of data standards, setup and charging burden, variable acceptability and stigma, need for large clinical and field trials, unsubstantiated commercial claims, false negatives and false positives, inaccurate activity tracking, need for clinician training, and potential emotional burden or technology addiction.
  • Evident from the text: as a narrative review, it lacks a systematic search or formal quality appraisal, and many cited studies are small, early-stage, feasibility, pilot, or not yet peer-reviewed; several device examples are commercial or under development.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.2217/pme-2018-0044

Cites

156 of 156 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Figures

Figure 1. A wide variety of wearable devices facilitate personalized healthcare. (A) Overview diagram of review paper showing major topics covered. (B) The three major wearable sensor types (red) and sensor grades (blue). (C) Wearable devices for use in metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. IMU: Inertial measurement unit; CGM: Continuous glucose monitoring.
Figure 1. A wide variety of wearable devices facilitate personalized healthcare. (A) Overview diagram of review paper showing major topics covered. (B) The three major wearable sensor types (red) and sensor grades (blue). (C) Wearable devices for use in metabolic, cardiovascular and gastrointestinal monitoring; sleep, neurology, movement disorders and mental health; maternal, pre- and neo-natal care; and pulmonary health and environmental exposures. IMU: Inertial measurement unit; CGM: Continuous glucose monitoring.CC BY-NC-ND · PubMed Central
Figure 2. The wearables landscape may impact their adoption in clinical care. (A) Settings where wearables can provide or improve healthcare. (B) Flow of data from wearables to health decision-makers. (C) Areas of healthcare that currently existing wearables can target (some with strong evidence supporting their use and others are newer). (D) Challenges and limitations for the adoption of wearable technology in healthcare. (E) A single platform integrating wearable device data collection, analytics and intervention delivery will constitute a complete operating healthcare monitoring system.
Figure 2. The wearables landscape may impact their adoption in clinical care. (A) Settings where wearables can provide or improve healthcare. (B) Flow of data from wearables to health decision-makers. (C) Areas of healthcare that currently existing wearables can target (some with strong evidence supporting their use and others are newer). (D) Challenges and limitations for the adoption of wearable technology in healthcare. (E) A single platform integrating wearable device data collection, analytics and intervention delivery will constitute a complete operating healthcare monitoring system.CC BY-NC-ND · PubMed Central

Verification

Claims the paper makes

73 Not checked yet · 38 Own interpretation · 13 No basis given

Verify all 120 references — free account

Show all 58 claims

  • Probably not a substantial claim. 31
  • It may state two separate claims as one. 42
66 of 66
  • The paper relies on this without support.

    Most airway abnormalities can be characterized by abnormal sounds (adventitious sounds).

    “Pulmonary disease wearables take advantage of the fact that most airway abnormalities can be characterized by the abnormal sounds (adventitious sounds).”
1 of 1

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Each contradiction between two passages listed here was confirmed by an independent check.

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Citations

Cites

Show all 154 references

Research works printed but not linked: 4

  1. 10.1177/19322968187632281932296818763228 · DOI
  2. Nandakumar S . Long-term remote monitoring of vital signs using a wireless patch sensor . Presented at: IEEE Health Innovations and Point-of-Care Technologies Conference . Seattle, WA, USA ( 8–10 October 2014 ).
  3. Semi Supervised Sequence Learning for Continuous Digital Biomarkers. Neural information processing systems machine learning for healthcare workshop ( 2016 ). www.nipsml4hc.ws/posters .
  4. WHO . Improving access to epilepsy care . WHO/entity/mental_health/neurology/epilepsy/en/index.html( 2018 ) ( Epub ahead of print ).

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Metadata

Authors
Dunn, J., Runge, R., & Snyder, M.
Affiliations
Stanford University (US)
Corresponding author
Snyder, Michael
Venue
Personalized Medicine
Published
2018-09
Volume
15
Issue
5
Pages
429-448
ISSN
  • 1741-0541 (ISSN-L)
  • 1744-828X
OpenAlex
W2893814198
Cited by
654
References
154
Citations by year
2026: 99 · 2025: 88 · 2024: 101 · 2023: 94 · 2022: 94 · 2021: 95 · 2020: 56 · 2019: 22 · 2018: 5
FWCI
3.64
Type
journal article
Language
en
Open access
Locations
  • Personalized Medicine (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
Mobilize Center (U54 EB020405)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
Keywords
wearable sensors, remote patient monitoring, clinical translation, metabolic monitoring, cardiovascular monitoring, gastrointestinal monitoring, sleep monitoring, neurology, movement disorders, mental health, maternal and newborn care, respiratory health, environmental exposure monitoring
MeSH terms
Wearable Electronic Devices, Delivery of Health Care, Humans, Monitoring, Physiologic, Telemetry, Monitoring, Ambulatory, Wireless Technology

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