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Meta-Analysis
. 2024 Aug 30:12:e56972.
doi: 10.2196/56972.

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis

Ben Singh  1 , Sebastien Chastin  2 , Aaron Miatke  2 , Rachel Curtis  2 , Dorothea Dumuid  2 , Jacinta Brinsley  2 , Ty Ferguson  2 , Kimberley Szeto  2 , Catherine Simpson  2 , Emily Eglitis  2 , Iris Willems  2 , Carol Maher  2
Affiliations
Meta-Analysis

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis

Ben Singh et al. JMIR Mhealth Uhealth. .

Abstract

Background: Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain.

Objective: This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events.

Methods: Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies.

Results: A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%).

Conclusions: Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability.

Trial registration: Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.

Keywords: COVID-19; COVID-19 diagnosis; Fitbit; atrial; atrial fibrillation; detection; diagnostic tool; disease detection; meta-analysis; monitoring; physiological; tool; tools; tracker; wearable; wearable activity trackers; wearable tracker; wearables.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.
Figure 2
Figure 2
Meta-analysis of accuracy, sensitivity, AUC, and specificity of wearable activity trackers for detection of COVID-19. AUC: area under the curve.
Figure 3
Figure 3
Overview of sensitivity and specificity for the different devices for COVID-19 detection.
Figure 4
Figure 4
Meta-analysis of PPV, sensitivity, and specificity of wearable activity trackers for detection of AF and AR. AF: atrial fibrillation; AR: arrhythmia; PPV: positive predictive value.
Figure 5
Figure 5
Meta-analyses of sensitivity and specificity of wearable activity trackers for detection for fall detection.
See this image and copyright information in PMC

References

    1. Watson SI, Sahota H, Taylor CA, Chen YF, Lilford RJ. Cost-effectiveness of health care service delivery interventions in low and middle income countries: a systematic review. Glob Health Res Policy. 2018;3:17. doi: 10.1186/s41256-018-0073-z. https://ghrp.biomedcentral.com/articles/10.1186/s41256-018-0073-z 73 - DOI - DOI - PMC - PubMed
    1. Wang W, Yan Y, Guo Z, Hou H, Garcia M, Tan X, Anto EO, Mahara G, Zheng Y, Li B, Kang T, Zhong Z, Wang Y, Guo X, Golubnitschaja O. All around suboptimal health—a joint position paper of the suboptimal health study consortium and European association for predictive, preventive and personalised medicine. EPMA J. 2021;12(4):403–433. doi: 10.1007/s13167-021-00253-2. https://europepmc.org/abstract/MED/34539937 253 - DOI - PMC - PubMed
    1. Natalucci V, Marmondi F, Biraghi M, Bonato M. The effectiveness of wearable devices in non-communicable diseases to manage physical activity and nutrition: where we are? Nutrients. 2023;15(4):913. doi: 10.3390/nu15040913. https://www.mdpi.com/resolver?pii=nu15040913 nu15040913 - DOI - PMC - PubMed
    1. Shin G, Jarrahi MH, Fei Y, Karami A, Gafinowitz N, Byun A, Lu X. Wearable activity trackers, accuracy, adoption, acceptance and health impact: a systematic literature review. J Biomed Inform. 2019;93:103153. doi: 10.1016/j.jbi.2019.103153. https://linkinghub.elsevier.com/retrieve/pii/S1532-0464(19)30071-1 S1532-0464(19)30071-1 - DOI - PubMed
    1. Kooiman TJM, Dontje ML, Sprenger SR, Krijnen WP, van der Schans CP, de Groot M. Reliability and validity of ten consumer activity trackers. BMC Sports Sci Med Rehabil. 2015;7:24. doi: 10.1186/s13102-015-0018-5. https://bmcsportsscimedrehabil.biomedcentral.com/articles/10.1186/s13102... 18 - DOI - DOI - PMC - PubMed

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