TY - JOUR
T1 - Postural Transitions during Activities of Daily Living Could Identify Frailty Status
T2 - Application of Wearable Technology to Identify Frailty during Unsupervised Condition
AU - Parvaneh, Saman
AU - Mohler, Jane
AU - Toosizadeh, Nima
AU - Grewal, Gurtej Singh
AU - Najafi, Bijan
N1 - Publisher Copyright: © 2017 S. Karger AG, Basel.
PY - 2017/8/1
Y1 - 2017/8/1
N2 - Background: Impairment of physical function is a major indicator of frailty. Functional performance tests have been shown to be useful for identification of frailty in older adults. However, these tests are often not translatable into unsupervised and remote monitoring of frailty status at home and/or community settings. Objective: In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults. Methods: Spontaneous daily physical activity was monitored over 24 h in 120 community-dwelling elderly (age: 78 ± 8 years) using an unobtrusive wearable sensor (PAMSys™, BioSensics LLC, Watertown, MA, USA). Participants were classified as non-frail and pre-frail/frail using Fried's criteria. A validated software package was used to identify body postures and postural transition between each independent postural activity such as sit-To-stand, stand-To-sit, stand-To-walk, and walk-To-stand. The transition from walking to sitting was further classified as quick sitting and cautious sitting based on presence/absence of a standing posture pause between sitting and walking. A general linear model univariate test was used for between-group comparison. Pearson's correlation was used to determine the association between sensor-derived parameters and age. Logistic regression model was used to identify independent predictors of frailty. Results: According to Fried's criteria, 63% of participants were pre-frail/frail. The total number of postural transitions, stand-To-walk, and walk-To-stand were, respectively, 25.2, 30.2, and 30.6% lower in the pre-frail/frail group when compared to the non-frail group (p < 0.05, Cohen's d = 0.73-0.79). Furthermore, the ratio of cautious sitting was significantly higher by 6.2% in pre-frail/frail compared to non-frail (p = 0.025, Cohen's d = 0.22). Total number of postural transitions and the ratio of cautious sitting also showed significant negative and positive correlations with age, respectively (r =-0.51 and 0.29, p < 0.05). After applying a logistic regression model, among tested parameters, walk-To-stand (odds ratio [OR] = 0.997 p = 0.013), quick sitting (OR = 1.036, p = 0.05), and age (OR = 1.073, p = 0.016) were recognized as independent variables to identify frailty status. Conclusions: This study demonstrated that daily number of specific postural transitions such as walk-To-stand and quick sitting could be used for monitoring frailty status by unsupervised monitoring of daily physical activity. Further study is warranted to explore whether tracking the daily number of specific postural transitions is also sensitive to track change in the status of frailty over time.
AB - Background: Impairment of physical function is a major indicator of frailty. Functional performance tests have been shown to be useful for identification of frailty in older adults. However, these tests are often not translatable into unsupervised and remote monitoring of frailty status at home and/or community settings. Objective: In this study, we explored daily postural transition quantified using a chest-worn wearable technology to identify frailty in community-dwelling older adults. Methods: Spontaneous daily physical activity was monitored over 24 h in 120 community-dwelling elderly (age: 78 ± 8 years) using an unobtrusive wearable sensor (PAMSys™, BioSensics LLC, Watertown, MA, USA). Participants were classified as non-frail and pre-frail/frail using Fried's criteria. A validated software package was used to identify body postures and postural transition between each independent postural activity such as sit-To-stand, stand-To-sit, stand-To-walk, and walk-To-stand. The transition from walking to sitting was further classified as quick sitting and cautious sitting based on presence/absence of a standing posture pause between sitting and walking. A general linear model univariate test was used for between-group comparison. Pearson's correlation was used to determine the association between sensor-derived parameters and age. Logistic regression model was used to identify independent predictors of frailty. Results: According to Fried's criteria, 63% of participants were pre-frail/frail. The total number of postural transitions, stand-To-walk, and walk-To-stand were, respectively, 25.2, 30.2, and 30.6% lower in the pre-frail/frail group when compared to the non-frail group (p < 0.05, Cohen's d = 0.73-0.79). Furthermore, the ratio of cautious sitting was significantly higher by 6.2% in pre-frail/frail compared to non-frail (p = 0.025, Cohen's d = 0.22). Total number of postural transitions and the ratio of cautious sitting also showed significant negative and positive correlations with age, respectively (r =-0.51 and 0.29, p < 0.05). After applying a logistic regression model, among tested parameters, walk-To-stand (odds ratio [OR] = 0.997 p = 0.013), quick sitting (OR = 1.036, p = 0.05), and age (OR = 1.073, p = 0.016) were recognized as independent variables to identify frailty status. Conclusions: This study demonstrated that daily number of specific postural transitions such as walk-To-stand and quick sitting could be used for monitoring frailty status by unsupervised monitoring of daily physical activity. Further study is warranted to explore whether tracking the daily number of specific postural transitions is also sensitive to track change in the status of frailty over time.
KW - Cautious sitting
KW - Frailty
KW - Physical activity
KW - Postural transition
KW - Quick sitting
KW - Telehealth
KW - Walking to sitting
KW - Wearable sensor
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U2 - 10.1159/000460292
DO - 10.1159/000460292
M3 - Article
C2 - 28285311
SN - 0304-324X
VL - 63
SP - 479
EP - 487
JO - Gerontology
JF - Gerontology
IS - 5
ER -