JMIR Publications

ABSTRACT

Background:

Frailty has detrimental health impacts on older home care clients and is associated with increased hospitalization and long-term care admission. The prevalence of frailty among home care clients is poorly understood and ranges from 4.0% to 59.1%. Although frailty screening tools exist, their inconsistent use in practice calls for more innovative and easier-to-use tools. Owing to increases in the capacity of wearable devices, as well as in technology literacy and adoption in Canadian older adults, wearable devices are emerging as a viable tool to assess frailty in this population.

Objective:

The objective of this study is to prove the concept of using a wearable device for assessing frailty for older home care clients.

Methods:

We recruited home care clients from June 2018 to September 2019 aged 55 years and older to be monitored over the course of 8 days using a wearable device. Detailed sociodemographic information, and patient assessments including degree of comorbidity and activities of daily living were collected. Frailty was measured using the Fried Frailty Index. Data collected from the wearable device was used to derive variables including daily step count, total sleep time, deep sleep time, light sleep time, awake time, sleep quality, heart rate, and heart rate standard deviation. Using both wearable and conventional assessment data, multiple logistic regression models were fitted via a sequential stepwise feature selection method to predict frailty.

Results:

A total of 37 older home care clients completed the study. The mean age was 82.27 (SD: 10.84) years and 75.68% were female. Frail participants were significantly older (p<0.01), utilized more homecare service (p=0.012), walked less (p=0.04), slept longer (p=0.010) and had longer deep sleep time (p<0.01). Total sleep time (r=0.41, p=0.012) and deep sleep time (r=0.53, p<0.01) were moderately correlated with the frailty. The logistic regression model fitted with deep sleep time, age, education level, and sleep quality yielded the best predictive performance with an AUC of 0.90 (Hosmer-Lemeshow p=0.88).

Conclusions:

We proved the concept of using a wearable device to assess frailty for older home care clients. Wearable data complemented the existing assessments and enhanced predictive power. Wearable technology can be used to identify vulnerable older adults who may benefit from additional home care services.