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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.
The objective of this study was to prove that using a wearable device for assessing frailty in older home care clients could be possible.
From June 2018 to September 2019, we recruited home care clients aged 55 years and older to be monitored over a minimum 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 were 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 to predict frailty.
A total of 37 older home care clients completed the study. The mean age was 82.27 (SD 10.84) years, and 76% (28/37) were female; 13 participants were frail, significantly older (
We proved that a wearable device could be used 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.
Frailty has detrimental health impacts among community-dwelling older adults. Frailty is associated with higher mortality [
The prevalence of frailty among community-dwelling older adults is poorly understood. A systematic review [
Both home and community health care are challenged with increased demand, primarily due to the aging population and emphasis on aging-in-place [
Tools to screen community-dwelling older adults for frailty exist, but they have been used inconsistently and are often impractical or have been invalidated [
Recognizing the need for an innovative solution to measure frailty in community-dwelling older adults, we set out to investigate the possibility of using consumer-grade wearable devices. We examined the data generated from a wearable device worn by home care clients to identify associations with frailty. We also aimed to identify key wearable device measures that can predict the status of frailty. Study procedure, tools, and statistical analyses are described. The results of the study are then presented, followed by a discussion where new findings are interpreted and compared to existing knowledge. The implications for frailty research studies, for wearable device research studies, and in home and community health care sectors, as well as the limitations of the study are presented.
A prospective observational study was conducted to meet the study objectives. Participants were asked to wear a wearable device for a minimum of 8 days. At the end of the study, participants were assessed for frailty, activities of daily living, and level of comorbidity.
Home care clients in the Greater Toronto Area were recruited through VHA Home Healthcare from August 2018 to September 2019. VHA Home Healthcare is a home care agency that serves over 3000 clients throughout the Greater Toronto Area and other metropolitan areas in Ontario, Canada. Patients 55 years or older who had been receiving personal support service for more than 3 months were eligible for the study. Patients who were diagnosed with primary neuromuscular pathology, dependent on wheelchair, in an end-of-life program, or had cognitive impairments that could interfere with the use of wearable devices were excluded. Eligible home care patients were identified using VHA’s electronic medical record system.
The Xiaomi Mi Band Pulse 1S (Mi Band, hereafter) is a commercially available wearable device that is worn on the wrist. It uses a triaxial accelerometer to capture motions to approximate step count and sleep events. It is equipped with an optical heart rate sensor (photoplethysmography) to measure minute-by-minute heart rate. While the Mi Band can be worn on either the wrist or neck (as a pendant), its placement was limited to the wrist for the study. The reliability and internal consistency of Mi Band’s performance for measuring step count when walking and jogging has been validated [
We collected daily step count, light sleep time, deep sleep time, total sleep time, awake time, sleep quality, mean heart rate, and heart rate standard deviation. Sleep quality was calculated as the percentage of sleep duration over total sleep time; sleep duration was determined by subtracting awake time from total sleep time [
Frailty was assessed using the Fried Frailty Index, a tool that has been developed for and used widely with community-dwelling older adults [
Sociodemographic variables were collected using a short background questionnaire and through review of the patient’s medical chart. These sociodemographic variables included age, sex, weight, height, ethnicity, level of education, income, and marital status. The level of comorbidity was assessed using the Charlson Comorbidity Index (CCI) [
Descriptive statistics and univariate comparisons of means, medians, and proportions were performed to describe the sociodemographic information and patient assessments according to their frailty status. The level of education was condensed into 2 levels: high school (some or completed) and postsecondary. Household income was categorized into a lower income, those who earned $30,000 (approximately US $22,653) per year or less, and higher income, those who earned $30,000 or higher per year. Ethnicity was categorized into 2 levels: Caucasian and others which included aboriginal identity, Latin American, African American, South Asian, Southeast Asian, East Asian, Filipino, Arab, and West Asian.
Wearable device data were examined for participants adherence level, and days with less than 10 hours of wear time were excluded. Heart rate measurements of zero were generated when the device failed to have good skin contact. Such measurements were treated as missing and were removed from the analyses.
The Shapiro-Wilk test was performed to check for normality. To check for significant differences between patients who were frail and patients who were nonfrail, when the assumption of normal distribution was met, a two-tailed independent
Pearson and Spearman correlation statistics were used to examine the relationship between frailty, sociodemographic information, patient assessments, and the data collected from the wearable devices.
Multiple logistic regression models were generated to predict frailty status. A sequential stepwise feature selection method was used to select the variables to be fitted into the models. The feature selection was used on the pool of sociodemographic and patient assessment variables to determine the features to be included in model 1. Model 2 was built by applying feature selection to the variables derived from the wearable device data. Model 3 used all available variables in a feature selection algorithm; the selected variables were used to build the logistic regression model. The Hosmer-Lemeshow test was performed to test the goodness-of-fit for each model. The predictive performance of each model was evaluated and compared using the area under the receiver operating characteristics curve (AUROC).
Statistical significance was set at α=.05 for all statistical results. The significance level for posthoc tests was corrected using the Bonferroni method. All statistical analyses were performed using R (version 3.6.0) in R studio (version 1.2.1335; R Studio Inc). Stepwise feature selection was performed using the function (stepAIC, version 7.3-51.4) from the MASS library [
This study received ethics approval from the Office of Research Ethics Board at the University of Waterloo (ORE22842).
A total of 72 older adults responded to the mailed recruitment brochure. All 72 older adults were contacted, and 45 agreed to participate in the study; 4 participants withdrew before completion of the 8-day study period. Data attrition due to technical issues resulted in data from 4 participants not being included. In total, 37 older home care clients were included in the study.
Participants were 57 to 96 years of age, with a mean age of 82.23 (SD 10.84) years and 76% (28/37) were female (
Baseline sociodemographic and patient characteristics stratified by frailty status.
Characteristics | Frail (n=13) | Nonfrail (n=24) | ||
Age (years), mean (SD) | 83.92 (9.66) | 80.61 (13.96) | <.001a | |
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>.999b | |
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Male | 3 (23) | 6 (25) |
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Female | 10 (77) | 18 (75) |
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BMI (kg/m2), mean (SD) | 26.96 (6.70) | 28.54 (5.43) | .44c | |
ADLd score, mean (SD) | 4.62 (1.45) | 5.08 (0.88) | .43a | |
CCIe score, mean (SD) | 1.92 (1.26) | 1.25 (1.11) | .11a | |
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.29b | |
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Single | 1 (8) | 7 (29) |
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Divorced or separated | 2 (15) | 5 (21) |
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Widowed | 4 (31) | 7 (29) |
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Currently married | 6 (46) | 5 (21) |
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.12b | |
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High school or less | 8 (62) | 7 (29) |
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Postsecondary or higher | 5 (38) | 17 (71) |
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.03b | |
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Prefer not to answer | 7 (54) | 3 (12) | .06f |
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Low income | 4 (31) | 13 (54) | .93f |
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Mid to high income | 2 (15) | 8 (33) | >.999f |
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.71b | |
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White | 10 (77) | 21 (88) |
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Other | 3 (23) | 3 (12) |
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Personal support service, hours per week | 5.15 (3.51) | 2.77 (1.85) | .01a |
aMann-Whitney
bChi-square test was used.
cAn independent
dADL: activities of daily living; Katz index of independence was used.
eCCI: Charlson Comorbidity Index.
fPosthoc chi-square test was used.
On average, older adults wore the device for 20.03 (1.64) hours per day (
Difference in the data collected from the wearable device between frail and nonfrail participants.
Measures | Frail (n=13), mean (SD) |
Nonfrail (n=24), mean (SD) |
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Worn time (hours per day) | 20.66 (1.03) | 19.69 (1.82) | .16a | |
Daily step count | 367.11 (272.63) | 1023.95 (863.83) | .04a | |
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Deep sleep time (minutes) | 138.90 (64.00) | 75.65 (39.12) | <.001a |
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Light sleep time (minutes) | 350.88 (130.56) | 312.78 (82.32) | .35b |
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Total sleep time (minutes) | 489.78 (139.54) | 388.44 (93.28) | .01a |
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Awake time (minutes) | 36.03 (24.27) | 65.05 (57.97) | .17a |
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Sleep quality (%) | 92.48 (5.62) | 78.95 (26.53) | .08a |
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Heart rate (bpm) | 82.77 (10.25) | 77.43 (8.66) | .13b |
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Heart rate SD (bpm) | 22.12 (7.61) | 18.78 (4.54) | .17b |
aMann-Whitney
bAn independent
The correlation between wearable data and frailty is summarized in
Correlations between wearable device data, patient characteristics, and frailty.
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Frailty | ||
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Correlation coefficient | ||
Daily step count | –0.52 | .001 | |
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Total sleep time | 0.52 | .001 |
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Deep sleep time | 0.47 | .003 |
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Light sleep time | 0.35 | .03 |
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Sleep quality | 0.56 | <.001 |
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Awake time | –0.54 | <.001 |
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Mean heart rate | 0.11 | .54 |
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Heart rate SD | –0.25 | .16 |
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Age | 0.29 | .08 |
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Sex | 0.074 | .66 |
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BMI | –0.068 | .69 |
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Income level | –0.066 | .74 |
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Education level | –0.40 | .02 |
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ADLa score | –0.18 | .27 |
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CCIb score | 0.16 | .33 |
Personal support hours | 0.23 | .17 |
aADL: activities of daily living; Katz index of independence was used.
bCCI: Charlson Comorbidity Index.
A total of 3 multiple variable logistic regression models were fitted to predict frailty with the sociodemographic variables, patient assessments, and wearable data (
Three frailty prediction models and the variables selected by the stepwise feature selection method.
Models | Variable pool | Selected variables |
Model 1 | Sociodemographic and patient assessment variables | CCIa, education level |
Model 2 | Wearable device–derived variables | Step count, deep sleep time, light sleep time, heart rate standard deviation |
Model 3 | Sociodemographic, patient assessment, and wearable device–derived variables | Deep sleep time, step count, age, education level |
aCCI: Charlson Comorbidity Index.
All 3 models were evaluated for their goodness of fit using the Hosmer-Lemeshow statistic. Overall, no model showed statistical significance on this test, indicating they had acceptable goodness-of-fit, and the predicted frailty matched the observed frailty status (
When the predictive performance was evaluated by AUROC, all 3 models showed medium to high values. Model 1 (AUROC 0.77), based on sociodemographic and patient assessment variables, was outperformed by model 2 (AUROC 0.88), which was fitted with wearable device variables. Model 3 (AUROC 0.90) had the best predictive performance (
Multiple logistic regression of factors associated with frailty.
Model and variables |
Adjusted ORa (95 % CI) | ||||
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CCIb | 1.78 (0.95, 3.66) | .09 | ||
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Education level—high school or below | reference | — | ||
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Education level—postsecondary education or higher | 0.22 (0.04, 0.96) | .05 | ||
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Step count | 1.00 (1.00, 1.00) | .17 | ||
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Deep sleep time | 1.02 (1.01, 1.05) | .02 | ||
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Awake time | 0.97 (0.93, 1.01) | .18 | ||
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Heart rate standard deviation | 1.17 (0.99, 1.46) | .10 | ||
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Deep sleep time | 1.03 (1.01, 1.07) | .04 | ||
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Step count | 1.00 (1.00, 1.00) | .06 | ||
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Age | 0.90 (0.80, 0.99) | .04 | ||
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Education level—high school or less | reference | — | ||
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Education level—postsecondary education or higher | 0.11 (0.01, 0.94) | .06 |
aOR: odds ratio.
bCCI: Charlson Comorbidity Index.
Summary of model performance in predicting frailty status.
Models | Accuracy | Sensitivity | Specificity | AUROCa | Hosmer-Lemeshow test |
Model 1: Sociodemographic and patient assessment variables | 0.76 | 0.46 | 0.92 | 0.77 | 0.73 |
Model 2:Wearable device derived variables | 0.81 | 0.69 | 0.88 | 0.88 | 0.95 |
Model 3: All variables from models 1 and 2 | 0.81 | 0.69 | 0.88 | 0.90 | 0.85 |
aAUROC: area under the receiver operating characteristics curve.
The receiver operating characteristics curves (with area under the curve) for all models fitted to predict frailty. AUC: area under the curve.
The growing aging population in Canada and the emphasis on aging-in-place call for innovative ways to improve efficiency in the home and community health care sector. There is an increasing interest in integrating information and communication technology such as consumer-grade wearable devices into health care delivery due to their rising popularity, ease-of-use, and the potential usefulness of continuously collected data [
We observed 37 older home care clients for a minimum of 8 days. The prevalence of frailty in the study sample, 35% (13/37), was similar to that found in other research studies examining home care clients [
Our study found a significantly higher utilization of home care service by older adults who were frail compared to utilization by older adults who were nonfrail (mean hours per week: 5.15 vs 2.77;
In our study sample, older adults who were nonfrail walked significantly more than the older adults who were frail. This result is in line with the findings of previous research studies where reduced daily step count and physical activity were observed for frail community-dwelling older adults [
Sleep measures including longer total sleep, deep sleep, and light sleep durations; awake time; and sleep quality were shown to be related to more severe frailty. This is contrary to the common knowledge of deterioration of sleep quality and quantity with aging [
In this study, we built logistic regression models using a sequential stepwise feature selection method. Feature selection in general can help improve predictive performance [
Many mobile health and telehealth apps have been successful at delivering health care while improving efficiency [
Future research should confirm the predictive power of data derived from wearable devices and extend it beyond the home and community care sector. Our results indicated that wearable devices are a valid tool when an adequate analytical process is used. We recommend that future home care research studies leverage the potential of consumer-grade wearable devices to help identify vulnerable and frail groups who may benefit from additional home care services and increased access to health care.
Our study has several limitations. First, the small study sample prevented us from stratifying patients into nonfrail, prefrail, and frail groups. A third frailty state could have helped us demonstrate gradient measures of wearable data. The small sample size also limited the number of variables that could be used in developing multiple logistic regression models. The 3 logistic regression models were each fitted with 2 to 4 features. They exceeded the common rule of 1-in-10 and which may have increased the risk of overfitting [
Our research used an 8-day observation period. While this was longer than the observation periods of most other studies using wearable devices, an even longer observational period may be required to reveal new patterns that are not observable within 8 days such as weekdays versus weekends and seasonal differences. Lastly, the validation studies that examined the Mi Band [
In this study, we proved the concept of using a wrist-worn consumer-grade wearable device to assess frailty among older home care clients. Data collected from the wearable device, such as total sleep time and deep sleep time, were associated with frailty. The frailty prediction model based on variables selected from wearable devices, sociodemographic variable, and patient assessment variables achieved the highest AUROC of 0.90, compared to the AUROC of the other predictive models that either used only sociodemographic and assessment variables or only wearable device–derived variables.
The results of Shapiro-Wilk normality tests for all continuous variables.
T test statistics and chi-square test statistics for comparisons between the frail and nonfrail participants with respect to baseline sociodemographic and patient characteristics (n=37).
Boxplots of the wearable device data comparing frail and nonfrail participants.
area under the receiver operating characteristics curve
Charlson comorbidity index
This work was supported by a Core Research Project Grant from the AGE-WELL Network of Centres of Excellence (WP7.3).
None declared.