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Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition.
Gerontology. 2017; 63(5):479-487.G

Abstract

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.

Authors+Show Affiliations

Interdisciplinary Consortium on Advanced Motion Performance (iCAMP), Department of Surgery, College of Medicine, University of Arizona, Tucson, AZ, USA.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

28285311

Citation

Parvaneh, Saman, et al. "Postural Transitions During Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty During Unsupervised Condition." Gerontology, vol. 63, no. 5, 2017, pp. 479-487.
Parvaneh S, Mohler J, Toosizadeh N, et al. Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition. Gerontology. 2017;63(5):479-487.
Parvaneh, S., Mohler, J., Toosizadeh, N., Grewal, G. S., & Najafi, B. (2017). Postural Transitions during Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty during Unsupervised Condition. Gerontology, 63(5), 479-487. https://doi.org/10.1159/000460292
Parvaneh S, et al. Postural Transitions During Activities of Daily Living Could Identify Frailty Status: Application of Wearable Technology to Identify Frailty During Unsupervised Condition. Gerontology. 2017;63(5):479-487. PubMed PMID: 28285311.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Postural Transitions during Activities of Daily Living Could Identify Frailty Status: 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, Y1 - 2017/03/11/ PY - 2016/08/06/received PY - 2017/02/08/accepted PY - 2017/3/13/pubmed PY - 2018/5/10/medline PY - 2017/3/13/entrez KW - Cautious sitting KW - Frailty KW - Physical activity KW - Postural transition KW - Quick sitting KW - Telehealth KW - Walking to sitting KW - Wearable sensor SP - 479 EP - 487 JF - Gerontology JO - Gerontology VL - 63 IS - 5 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. SN - 1423-0003 UR - https://www.unboundmedicine.com/medline/citation/28285311/Postural_Transitions_during_Activities_of_Daily_Living_Could_Identify_Frailty_Status:_Application_of_Wearable_Technology_to_Identify_Frailty_during_Unsupervised_Condition_ L2 - https://www.karger.com?DOI=10.1159/000460292 DB - PRIME DP - Unbound Medicine ER -