Postural transitions during activities of daily living could identify frailty status: application of wearable technology to identify frailty during unsupervised condition
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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.
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Some groups have started identifying wearable sensor features from daily functional tasks that are associated with the overall frailty level of older individuals.
“Nonetheless, some groups have started considering this by identifying wearable sensor features from daily functional tasks that are associated with the overall frailty level of older individuals (103, 104).”
From Could Wearable and Mobile Technology Improve the Management of Essential Tremor? (2018) · cites this paper as [104]
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- Authors
- Parvaneh, S., Mohler, J., Toosizadeh, N., Grewal, G. S., & Najafi, B.
- Affiliations
- University of Arizona (US)
- Venue
- Gerontology
- Publisher
- S. Karger AG
- Published
- 2017
- Volume
- 63
- Issue
- 5
- Pages
- 479-487
- ISSN
- 0304-324X (ISSN-L)
- 1423-0003
- PMC
- PMC5561495
- OpenAlex
- W2591841663
- Cited by
- 67
- References
- 52
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 11 · 2023: 6 · 2022: 7 · 2021: 21 · 2020: 6 · 2019: 2 · 2018: 4
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Gerontology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute on Aging (R41AG032748, R42AG032748)
- Topics
- Health Sciences › Medicine › Geriatrics and Gerontology › Frailty in Older Adults
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- frailty, postural transitions, activities of daily living, wearable sensors, chest-worn sensors, community-dwelling older adults, Fried criteria, pre-frailty, sit-to-stand, physical function, non-intrusive monitoring, physical activity monitoring, predictors of frailty
- MeSH terms
- Frailty, Wearable Electronic Devices, Activities of Daily Living, Aged, Aged, 80 and over, Postural Balance, Female, Humans, Male, Movement, Exercise, Geriatric Assessment, Frail Elderly, Monitoring, Ambulatory, Independent Living
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