TY - JOUR
T1 - Toward using wearables to remotely monitor cognitive frailty in community-living older adults
T2 - An observational study
AU - Razjouyan, Javad
AU - Najafi, Bijan
AU - Horstman, Molly
AU - Sharafkhaneh, Amir
AU - Amirmazaheri, Mona
AU - Zhou, He
AU - Kunik, Mark E.
AU - Naik, Aanand
N1 - Funding Information:
Funding: This research was funded partly by the National Institutes of Health/National Institute on Aging (award number 1R42AG060853-01), the National Institutes of Health/National Cancer Institute (award number 1R21CA190933-01A1), and the Center for Innovations in Quality, Effectiveness and Safety (CIN 13-413) at the Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX. J.R. receives support from the Big Data-Scientist Training Enhancement Program (BD-STEP) at the Department of Veterans Affairs and the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/4/2
Y1 - 2020/4/2
N2 - Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog-individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog-(p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty.
AB - Physical frailty together with cognitive impairment (Cog), known as cognitive frailty, is emerging as a strong and independent predictor of cognitive decline over time. We examined whether remote physical activity (PA) monitoring could be used to identify those with cognitive frailty. A validated algorithm was used to quantify PA behaviors, PA patterns, and nocturnal sleep using accelerometer data collected by a chest-worn sensor for 48-h. Participants (N = 163, 75 ± 10 years, 79% female) were classified into four groups based on presence or absence of physical frailty and Cog: PR-Cog-, PR+Cog-, PR-Cog+, and PR+Cog+. Presence of physical frailty (PR-) was defined as underperformance in any of the five frailty phenotype criteria based on Fried criteria. Presence of Cog (Cog-) was defined as a Mini-Mental State Examination (MMSE) score of less than 27. A decision tree classifier was used to identify the PR-Cog-individuals. In a univariate model, sleep (time-in-bed, total sleep time, percentage of sleeping on prone, supine, or sides), PA behavior (sedentary and light activities), and PA pattern (percentage of walk and step counts) were significant metrics for identifying PR-Cog-(p < 0.050). The decision tree classifier reached an area under the curve of 0.75 to identify PR-Cog-. Results support remote patient monitoring using wearables to determine cognitive frailty.
KW - Cognitive frailty
KW - Cognitive impairment
KW - Digital health
KW - Motoric cognitive risk syndrome
KW - Remote patient monitoring
KW - Telehealth
KW - Wearable
UR - http://www.scopus.com/inward/record.url?scp=85083392671&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85083392671&partnerID=8YFLogxK
U2 - 10.3390/s20082218
DO - 10.3390/s20082218
M3 - Article
C2 - 32295301
AN - SCOPUS:85083392671
SN - 1424-8220
VL - 20
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 8
M1 - 2218
ER -