scholarly journals Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study (Preprint)

2020 ◽  
Author(s):  
Ben Kim ◽  
Sandra M McKay ◽  
Joon Lee
Keyword(s):  
Older Adults ◽  
Heart Rate ◽  
Logistic Regression ◽  
Home Care ◽  
Total Sleep Time ◽  
Care Service ◽  
Sleep Time ◽  
Wearable Devices ◽  
Wearable Device ◽  
Deep Sleep

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 was to prove that using a wearable device for assessing frailty in older home care clients could be possible. METHODS 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. RESULTS 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 (<i>P</i>&lt;.01), utilized more home care service (<i>P</i>=.01), walked less (<i>P</i>=.04), slept longer (<i>P</i>=.01), and had longer deep sleep time (<i>P</i>&lt;.01). Total sleep time (r=0.41, <i>P</i>=.01) and deep sleep time (r=0.53, <i>P</i>&lt;.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow <i>P</i>=.88). CONCLUSIONS 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.

scholarly journals Consumer-Grade Wearable Device for Predicting Frailty in Canadian Home Care Service Clients: Prospective Observational Proof-of-Concept Study

10.2196/19732 ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. e19732
Author(s):  
Ben Kim ◽  
Sandra M McKay ◽  
Joon Lee
Keyword(s):  
Older Adults ◽  
Heart Rate ◽  
Logistic Regression ◽  
Home Care ◽  
Total Sleep Time ◽  
Care Service ◽  
Sleep Time ◽  
Wearable Devices ◽  
Wearable Device ◽  
Deep Sleep

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 was to prove that using a wearable device for assessing frailty in older home care clients could be possible. Methods 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. Results 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 (P<.01), utilized more home care service (P=.01), walked less (P=.04), slept longer (P=.01), and had longer deep sleep time (P<.01). Total sleep time (r=0.41, P=.01) and deep sleep time (r=0.53, P<.01) were moderately correlated with frailty. The logistic regression model fitted with deep sleep time, step count, age, and education level yielded the best predictive performance with an area under the receiver operating characteristics curve value of 0.90 (Hosmer-Lemeshow P=.88). Conclusions 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.

250 AI-Supported Sleep Staging from Activity and Heart Rate

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A101-A101
Author(s):  
Samadrita Chowdhury ◽  
TzuAn Song ◽  
Richa Saxena ◽  
Shaun Purcell ◽  
Joyita Dutta
Keyword(s):  
Heart Rate ◽  
Network Architecture ◽  
Class Imbalance ◽  
Nrem Sleep ◽  
Sleep Time ◽  
Deep Sleep ◽  
Sleep Staging ◽  
Temporal Correlations ◽  
Stage Classification ◽  
Small Form Factor

Abstract Introduction Polysomnography (PSG) is considered the gold standard for sleep staging but is labor-intensive and expensive. Wrist wearables are an alternative to PSG because of their small form factor and continuous monitoring capability. In this work, we present a scheme to perform such automated sleep staging via deep learning in the MESA cohort validated against PSG. This scheme makes use of actigraphic activity counts and two coarse heart rate measures (only mean and standard deviation for 30-s sleep epochs) to perform multi-class sleep staging. Our method outperforms existing techniques in three-stage classification (i.e., wake, NREM, and REM) and is feasible for four-stage classification (i.e., wake, light, deep, and REM). Methods Our technique uses a combined convolutional neural network coupled and sequence-to-sequence network architecture to appropriate the temporal correlations in sleep toward classification. Supervised training with PSG stage labels for each sleep epoch as the target was performed. We used data from MESA participants randomly assigned to non-overlapping training (N=608) and validation (N=200) cohorts. The under-representation of deep sleep in the data leads to class imbalance which diminishes deep sleep prediction accuracy. To specifically address the class imbalance, we use a novel loss function that is minimized in the network training phase. Results Our network leads to accuracies of 78.66% and 72.46% for three-class and four-class sleep staging respectively. Our three-stage classifier is especially accurate at measuring NREM sleep time (predicted: 4.98 ± 1.26 hrs. vs. actual: 5.08 ± 0.98 hrs. from PSG). Similarly, our four-stage classifier leads to highly accurate estimates of light sleep time (predicted: 4.33 ± 1.20 hrs. vs. actual: 4.46 ± 1.04 hrs. from PSG) and deep sleep time (predicted: 0.62 ± 0.65 hrs. vs. actual: 0.63 ± 0.59 hrs. from PSG). Lastly, we demonstrate the feasibility of our method for sleep staging from Apple Watch-derived measurements. Conclusion This work demonstrates the viability of high-accuracy, automated multi-class sleep staging from actigraphy and coarse heart rate measures that are device-agnostic and therefore well suited for extraction from smartwatches and other consumer wrist wearables. Support (if any) This work was supported in part by the NIH grant 1R21AG068890-01 and the American Association for University Women.

scholarly journals Impact of a Wearable Device-Based Walking Programs in Rural Older Adults on Physical Activity and Health Outcomes: Cohort Study (Preprint)

2018 ◽  
Author(s):  
Il-Young Jang ◽  
Hae Reong Kim ◽  
Eunju Lee ◽  
Hee-Won Jung ◽  
Hyelim Park ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Health Care ◽  
Health Outcomes ◽  
Rural Areas ◽  
Wearable Devices ◽  
Community Dwelling ◽  
Wearable Device ◽  
Self Management ◽  
Community Dwelling Older Adults

BACKGROUND Community-dwelling older adults living in rural areas are in a less favorable environment for health care compared with urban older adults. We believe that intermittent coaching through wearable devices can help optimize health care for older adults in medically limited environments. OBJECTIVE We aimed to evaluate whether a wearable device and mobile-based intermittent coaching or self-management could increase physical activity and health outcomes of small groups of older adults in rural areas. METHODS To address the above evaluation goal, we carried out the “Smart Walk” program, a health care model wherein a wearable device is used to promote self-exercise particularly among community-dwelling older adults managed by a community health center. We randomly selected older adults who had enrolled in a population-based, prospective cohort study of aging, the Aging Study of Pyeongchang Rural Area. The “Smart Walk” program was a 13-month program conducted from March 2017 to March 2018 and included 6 months of coaching, 1 month of rest, and 6 months of self-management. We evaluated differences in physical activity and health outcomes according to frailty status and conducted pre- and postanalyses of the Smart Walk program. We also performed intergroup analysis according to adherence of wearable devices. RESULTS We recruited 22 participants (11 robust and 11 prefrail older adults). The two groups were similar in most of the variables, except for age, frailty index, and Short Physical Performance Battery score associated with frailty criteria. After a 6-month coaching program, the prefrail group showed significant improvement in usual gait speed (mean 0.73 [SD 0.11] vs mean 0.96 [SD 0.27], P=.02), International Physical Activity Questionnaire scores in kcal (mean 2790.36 [SD 2224.62] vs mean 7589.72 [SD 4452.52], P=.01), and European Quality of Life-5 Dimensions score (mean 0.84 [SD 0.07] vs mean 0.90 [SD 0.07], P=.02), although no significant improvement was found in the robust group. The average total step count was significantly different and was approximately four times higher in the coaching period than in the self-management period (5,584,295.83 vs 1,289,084.66, P<.001). We found that participants in the “long-self” group who used the wearable device for the longest time showed increased body weight and body mass index by mean 0.65 (SD 1.317) and mean 0.097 (SD 0.513), respectively, compared with the other groups. CONCLUSIONS Our “Smart Walk” program improved physical fitness, anthropometric measurements, and geriatric assessment categories in a small group of older adults in rural areas with limited resources for monitoring. Further validation through various rural public health centers and in a large number of rural older adults is required.

scholarly journals Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Isaac Moshe ◽  
Yannik Terhorst ◽  
Kennedy Opoku Asare ◽  
Lasse Bosse Sander ◽  
Denzil Ferreira ◽  
...  
Keyword(s):  
Multilevel Models ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Wearable Devices ◽  
Screening Methods ◽  
Sleep Onset Latency ◽  
Depression And Anxiety ◽  
Symptoms Of Depression ◽  
Time In Bed

Background: Depression and anxiety are leading causes of disability worldwide but often remain undetected and untreated. Smartphone and wearable devices may offer a unique source of data to detect moment by moment changes in risk factors associated with mental disorders that overcome many of the limitations of traditional screening methods.Objective: The current study aimed to explore the extent to which data from smartphone and wearable devices could predict symptoms of depression and anxiety.Methods: A total of N = 60 adults (ages 24–68) who owned an Apple iPhone and Oura Ring were recruited online over a 2-week period. At the beginning of the study, participants installed the Delphi data acquisition app on their smartphone. The app continuously monitored participants' location (using GPS) and smartphone usage behavior (total usage time and frequency of use). The Oura Ring provided measures related to activity (step count and metabolic equivalent for task), sleep (total sleep time, sleep onset latency, wake after sleep onset and time in bed) and heart rate variability (HRV). In addition, participants were prompted to report their daily mood (valence and arousal). Participants completed self-reported assessments of depression, anxiety and stress (DASS-21) at baseline, midpoint and the end of the study.Results: Multilevel models demonstrated a significant negative association between the variability of locations visited and symptoms of depression (beta = −0.21, p = 0.037) and significant positive associations between total sleep time and depression (beta = 0.24, p = 0.023), time in bed and depression (beta = 0.26, p = 0.020), wake after sleep onset and anxiety (beta = 0.23, p = 0.035) and HRV and anxiety (beta = 0.26, p = 0.035). A combined model of smartphone and wearable features and self-reported mood provided the strongest prediction of depression.Conclusion: The current findings demonstrate that wearable devices may provide valuable sources of data in predicting symptoms of depression and anxiety, most notably data related to common measures of sleep.

scholarly journals Caudate Volume Mediates the Interaction between Total Sleep Time and Executive Function after Acute Exercise in Healthy Older Adults

2019 ◽  
Vol 5 (1) ◽  
pp. 69-82 ◽  
Author(s):  
Junyeon Won ◽  
Alfonso J. Alfini ◽  
Lauren R. Weiss ◽  
Casandra C. Nyhuis ◽  
Adam P. Spira ◽  
...  
Keyword(s):  
Older Adults ◽  
Executive Function ◽  
Acute Exercise ◽  
Total Sleep Time ◽  
Sleep Time ◽  
Healthy Older Adults

scholarly journals Sleep continuity and total sleep time are associated with task-switching and preparation in young and older adults

2014 ◽  
Vol 23 (5) ◽  
pp. 508-516 ◽  
Author(s):  
Kristine A. Wilckens ◽  
Sarah G. Woo ◽  
Kirk I. Erickson ◽  
Mark E. Wheeler
Keyword(s):  
Older Adults ◽  
Task Switching ◽  
Total Sleep Time ◽  
Sleep Time ◽  
Sleep Continuity

scholarly journals Impact of Sleep on Medical Residents’ Emotions: A Cross-Sectional Study Using a Wearable Device

2021 ◽  
Vol 53 (4) ◽  
pp. 275-281
Author(s):  
Sarah El Baba ◽  
Jumana Antoun ◽  
Andrea Mladenovic ◽  
Hani Tamim ◽  
Jihane Naous
Keyword(s):  
Negative Emotions ◽  
Sleep Time ◽  
Cross Sectional Study ◽  
Wearable Device ◽  
Medical Residents ◽  
Deep Sleep ◽  
Sectional Study ◽  
Cross Sectional ◽  
Residency Programs ◽  
Sleep Quantity

Background and Objectives: Controlling negative emotions and getting sufficient sleep are key factors in reducing medical errors and optimizing quality of care. The objective of this study was to measure the relationship between the emotions of medical residents and sleep as measured by a wearable device. Methods: We conducted a cross-sectional study addressing all residents of all postgraduation years and specialties at an Accreditation for Graduate Medical Educations-I accredited institution over 6 months. Sleep quantity and quality were measured by Fitbit Charge 2 device, and daily emotions by the Positive and Negative Affect Schedule questionnaire. Results: We included a total of 45 participants with a total of 1,112 observations (response rate=19.3%). The mean duration of total daily sleep was 5.9±1.6 hours, with a deep sleep time of 1.1±0.4 hours. We found a negative association between negative emotions and total sleep (rrm=-0.14, P&lt;.0001) and deep sleep (rrm=-0.11, P=.0005) using repetitive measures correlation. A linear regression model to predict the negative emotions of the residents revealed additional determinants beyond deep sleep. Conclusions: Our findings provide a further understanding of the importance of sleep quality on emotions by emphasizing deep sleep as a predictor of the second-day affect. Residency programs should strive to provide an ideal sleep environment to their residents and deliver workshops to deal with negative emotions.

scholarly journals Home-Based Sleep Sensor Measurements in an Older Australian Population: Before and during a Pandemic

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 5993
Author(s):  
Mahnoosh Kholghi ◽  
Claire M. Ellender ◽  
Qing Zhang ◽  
Yang Gao ◽  
Liesel Higgins ◽  
...  
Keyword(s):  
Older Adults ◽  
Time Course ◽  
Total Sleep Time ◽  
Sleep Onset ◽  
Sleep Time ◽  
Community Dwelling ◽  
Sleep Efficiency ◽  
Sleep Onset Latency ◽  
Onset Latency ◽  
Short Sleep Duration

Older adults are susceptible to poor night-time sleep, characterized by short sleep duration and high sleep disruptions (i.e., more frequent and longer awakenings). This study aimed to longitudinally and objectively assess the changes in sleep patterns of older Australians during the 2020 pandemic lockdown. A non-invasive mattress-based device, known as the EMFIT QS, was used to continuously monitor sleep in 31 older adults with an average age of 84 years old before (November 2019–February 2020) and during (March–May 2020) the COVID-19, a disease caused by a form of coronavirus, lockdown. Total sleep time, sleep onset latency, wake after sleep onset, sleep efficiency, time to bed, and time out of bed were measured across these two periods. Overall, there was no significant change in total sleep time; however, women had a significant increase in total sleep time (36 min), with a more than 30-min earlier bedtime. There was also no increase in wake after sleep onset and sleep onset latency. Sleep efficiency remained stable across the pandemic time course between 84–85%. While this sample size is small, these data provide reassurance that objective sleep measurement did not deteriorate through the pandemic in older community-dwelling Australians.

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