Keeping Pace with Wearables: A Living Umbrella Review of Systematic Reviews Evaluating the Accuracy of Consumer Wearable Technologies in Health Measurement.


Journal

Sports medicine (Auckland, N.Z.)
ISSN: 1179-2035
Titre abrégé: Sports Med
Pays: New Zealand
ID NLM: 8412297

Informations de publication

Date de publication:
30 Jul 2024
Historique:
accepted: 10 07 2024
medline: 31 7 2024
pubmed: 31 7 2024
entrez: 30 7 2024
Statut: aheadofprint

Résumé

Consumer wearable technologies have become ubiquitous, with clinical and non-clinical populations leveraging a variety of devices to quantify various aspects of health and wellness. However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear. To conduct a 'living' (i.e. ongoing) evaluation of the accuracy of consumer wearable technologies in measuring various physiological outcomes. A systematic search of the literature was conducted in the following scientific databases: MEDLINE via PubMed, Embase, Cinahl and SPORTDiscus via EBSCO. The inclusion criteria required systematic reviews or meta-analyses that evaluated the validation of consumer wearable devices against accepted reference standards. In addition to publication details, review protocol, device specifics and a summary of the authors' results, we extracted data on mean absolute percentage error (MAPE), pooled absolute bias, intraclass correlation coefficients (ICCs) and mean absolute differences. Of 904 identified studies through the initial search, 24 systematic reviews met our inclusion criteria; these systematic reviews included 249 non-duplicate validation studies of consumer wearable devices involving 430,465 participants (43% female). Of the commercially available wearable devices released to date, approximately 11% have been validated for at least one biometric outcome. However, because a typical device can measure a multitude of biometric outcomes, the number of validation studies conducted represents just 3.5% of the total needed for a comprehensive evaluation of these devices. For heart rate, wearables showed a mean bias of ± 3%. In arrhythmia detection, wearables exhibited a pooled sensitivity and specificity of 100% and 95%, respectively. For aerobic capacity, wearables significantly overestimated VO While consumer wearables show promise in health monitoring, a conclusive assessment of their accuracy is impeded by pervasive heterogeneity in research outcomes and methodologies. There is a need for standardised validation protocols and collaborative industry partnerships to enhance the reliability and practical applicability of wearable technology assessments. CRD42023402703.

Sections du résumé

BACKGROUND BACKGROUND
Consumer wearable technologies have become ubiquitous, with clinical and non-clinical populations leveraging a variety of devices to quantify various aspects of health and wellness. However, the accuracy with which these devices measure biometric outcomes such as heart rate, sleep and physical activity remains unclear.
OBJECTIVE OBJECTIVE
To conduct a 'living' (i.e. ongoing) evaluation of the accuracy of consumer wearable technologies in measuring various physiological outcomes.
METHODS METHODS
A systematic search of the literature was conducted in the following scientific databases: MEDLINE via PubMed, Embase, Cinahl and SPORTDiscus via EBSCO. The inclusion criteria required systematic reviews or meta-analyses that evaluated the validation of consumer wearable devices against accepted reference standards. In addition to publication details, review protocol, device specifics and a summary of the authors' results, we extracted data on mean absolute percentage error (MAPE), pooled absolute bias, intraclass correlation coefficients (ICCs) and mean absolute differences.
RESULTS RESULTS
Of 904 identified studies through the initial search, 24 systematic reviews met our inclusion criteria; these systematic reviews included 249 non-duplicate validation studies of consumer wearable devices involving 430,465 participants (43% female). Of the commercially available wearable devices released to date, approximately 11% have been validated for at least one biometric outcome. However, because a typical device can measure a multitude of biometric outcomes, the number of validation studies conducted represents just 3.5% of the total needed for a comprehensive evaluation of these devices. For heart rate, wearables showed a mean bias of ± 3%. In arrhythmia detection, wearables exhibited a pooled sensitivity and specificity of 100% and 95%, respectively. For aerobic capacity, wearables significantly overestimated VO
CONCLUSIONS CONCLUSIONS
While consumer wearables show promise in health monitoring, a conclusive assessment of their accuracy is impeded by pervasive heterogeneity in research outcomes and methodologies. There is a need for standardised validation protocols and collaborative industry partnerships to enhance the reliability and practical applicability of wearable technology assessments.
PROSPERO ID UNASSIGNED
CRD42023402703.

Identifiants

pubmed: 39080098
doi: 10.1007/s40279-024-02077-2
pii: 10.1007/s40279-024-02077-2
doi:

Types de publication

Journal Article Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : Science Foundation Ireland
ID : 22/NCF/FD/10949
Pays : Ireland

Informations de copyright

© 2024. The Author(s).

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Auteurs

Cailbhe Doherty (C)

School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland. cailbhe.doherty@ucd.ie.
Insight SFI Research Centre for Data Analytics, University College Dublin, Dublin, Ireland. cailbhe.doherty@ucd.ie.

Maximus Baldwin (M)

School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
Insight SFI Research Centre for Data Analytics, University College Dublin, Dublin, Ireland.
Institute for Sport and Health, University College Dublin, Dublin, Ireland.

Alison Keogh (A)

Insight SFI Research Centre for Data Analytics, University College Dublin, Dublin, Ireland.
School of Medicine, Trinity College Dublin, Dublin, Ireland.

Brian Caulfield (B)

School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
Insight SFI Research Centre for Data Analytics, University College Dublin, Dublin, Ireland.

Rob Argent (R)

Insight SFI Research Centre for Data Analytics, University College Dublin, Dublin, Ireland.
School of Pharmacy and Biomolecular Sciences, Royal College of Surgeons (RCSI), University of Medicine and Health Sciences, Dublin, Ireland.

Classifications MeSH