Evaluation of Wearable Technology in Dementia: A Systematic Review and Meta-Analysis
Overview
Affiliations
The objective of this analysis was to systematically review studies employing wearable technology in patients with dementia by quantifying differences in digitally captured physiological endpoints. This systematic review and meta-analysis was based on web searches of Cochrane Database, PsycInfo, Pubmed, Embase, and IEEE between October 25-31st, 2017. Observational studies providing physiological data measured by wearable technology on participants with dementia with a mean age ≥50. Data were extracted according to PRISMA guidelines and methodological quality assessed independently using Downs and Black criteria. Standardized mean differences between cases and controls were estimated using random-effects models. Forty-eight studies from 18,456 screened abstracts (Dementia: = 2,516, Control: = 1,224) met inclusion criteria for the systematic review. Nineteen of these studies were included in one or multiple meta-analyses (Dementia: = 617, Control: = 406). Participants with dementia demonstrated lower levels of daily activity (standardized mean difference (SMD), -1.60; 95% CI, -2.66 to -0.55), decreased sleep efficiency (SMD, -0.52; 95% CI, -0.89 to -0.16), and greater intradaily circadian variability (SMD, 0.46; 95% CI, 0.27 to 0.65) than controls, among other measures. Statistical between-study heterogeneity was observed, possibly due to variation in testing duration, device type or patient setting. Digitally captured data using wearable devices revealed that adults with dementia were less active, demonstrated increased fragmentation of their sleep-wake cycle and a loss of typical diurnal variation in circadian rhythm as compared to controls.
Wearables in Chronomedicine and Interpretation of Circadian Health.
Gubin D, Weinert D, Stefani O, Otsuka K, Borisenkov M, Cornelissen G Diagnostics (Basel). 2025; 15(3).
PMID: 39941257 PMC: 11816745. DOI: 10.3390/diagnostics15030327.
Wearable sensors for monitoring caregivers of people with dementia: a scoping review.
Palmese F, Druda Y, Benintende V, Fuda D, Sicbaldi M, Di Florio P Eur Geriatr Med. 2024; .
PMID: 39625554 DOI: 10.1007/s41999-024-01113-8.
In-Home Positioning for Remote Home Health Monitoring in Older Adults: Systematic Review.
Chan A, Cai J, Qian L, Coutts B, Phan S, Gregson G JMIR Aging. 2024; 7:e57320.
PMID: 39622026 PMC: 11661402. DOI: 10.2196/57320.
Caregiver perspectives enable accurate diagnosis of neurodegenerative disease.
Murley A, Bowns L, Camacho M, Williams-Gray C, Tsvetanov K, Rittman T Alzheimers Dement. 2024; 21(1):e14377.
PMID: 39559925 PMC: 11772714. DOI: 10.1002/alz.14377.
Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care.
Acosta C, Ye Y, Wong K, Zhao Y, Lawrence J, Towell M Sensors (Basel). 2024; 24(21).
PMID: 39517699 PMC: 11548467. DOI: 10.3390/s24216803.