Evaluation of Wearable Technology in Dementia: A Systematic Review and Meta-Analysis

Abstract

Background: 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. Methods: 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. Results: Forty-eight studies from 18,456 screened abstracts (Dementia: n = 2,516, Control: n = 1,224) met inclusion criteria for the systematic review. Nineteen of these studies were included in one or multiple meta-analyses (Dementia: n = 617, Control: n = 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. Conclusions and Relevance: 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.

Keywords: cognition; geriatrics; sleep; technology; wearable.

Figure 1

Literature search flow diagram.

Figure 2

Actigraphy outcomes in observational case control studies of wearable technology. (A) Interdaily stability, (B) interdaily variability, (C) relative amplitude, (D) activity of least active 5 h, (E) Activity of most active 10 h, (F) total sleep time, (G) sleep efficiency, (H) amplitude, and (I) daytime activity.

Figure 3

Funnel plots with pseudo 95 and 99.7% confidence intervals assessing publication bias of included studies for nine actigraphy measures. (A) Interdaily stability, (B) intradaily variability, (C) relative amplitude, (D) activity of most active 10h, (E) total sleep time, (F) sleep efficiency, (G) activity of least active 5h, (H) daytime activity, (I) amplitude.

Figure 4

Influence analysis. (A) Interdaily stability, (B) intradaily variability, (C) relative amplitude, (D) activity of most active 10 h, (E) total sleep time, (F) sleep efficiency, (G) activity of least active 5 h, (H) daytime activity, and (I) amplitude.