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Wearable Sensor-Based In-Home Assessment of Gait, Balance, and Physical Activity for Discrimination of Frailty Status: Baseline Results of the Arizona Frailty Cohort Study
Open accessAbstract
Background: Frailty is a geriatric syndrome resulting from age-related cumulative decline across multiple physiologic systems, impaired homeostatic reserve, and reduced capacity to resist stress. Based on recent estimates, 10% of community-dwelling older individuals are frail and another 41.6% are prefrail. Frail elders account for the highest health care costs in industrialized nations. Impaired physical function is a major indicator of frailty, and functional performance tests are useful for the identification of frailty. Objective instrumented assessments of physical functioning that are feasible for home frailty screening have not been adequately developed. Objective: To examine the ability of wearable sensor-based in-home assessment of gait, balance, and physical activity (PA) to discriminate between frailty levels (nonfrail, prefrail, and frail). Methods: In an observational cross-sectional study, in-home visits were completed in 125 older adults (nonfrail: n = 44, prefrail: n = 60, frail: n = 21) living in Tucson, Ariz., USA, between September 2012 and November 2013. Temporal-spatial gait parameters (speed, stride length, stride time, double support, and variability of stride velocity), postural balance (sway of hip, ankle, and center of mass), and PA (percentage of walking, standing, sitting, and lying; mean duration and variability of single walking, standing, sitting, and lying bouts) were measured in the participant's home using validated wearable sensor technology. Logistic regression was used to assess the most sensitive gait, balance, and PA variables for identifying prefrail participants (vs. nonfrail). Multinomial logistic regression was used to identify variables sensitive to discriminate between three frailty levels. Results: Gait speed (area under the curve, AUC = 0.802), hip sway (AUC = 0.734), and steps/day (AUC = 0.736) were the most sensitive parameters for the identification of prefrailty. Multinomial regression revealed that stride length (AUC = 0.857) and double support (AUC = 0.841) were the most sensitive gait parameters for discriminating between three frailty levels. Interestingly, walking bout duration variability was the most sensitive PA parameter for discriminating between three frailty levels (AUC = 0.818). No balance parameter discriminated between three frailty levels. Conclusion: Our results indicate that unique parameters derived from objective assessment of gait, balance, and PA are sensitive for the identification of prefrailty and the classification of a subject's frailty level. The present findings highlight the potential of wearable sensor technology for in-home assessment of frailty status.
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“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 [103]
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Metadata
- Authors
- Schwenk, M., Mohler, J., Wendel, C., D''Huyvetter, K., Fain, M., Taylor-Piliae, R., & Najafi, B.
- Affiliations
- University of Arizona (US)
- Venue
- Gerontology
- Publisher
- S. Karger AG
- Published
- 2014-12-24
- Volume
- 61
- Issue
- 3
- Pages
- 258-267
- ISSN
- 0304-324X (ISSN-L)
- 1423-0003
- PMC
- PMC4452118
- OpenAlex
- W2001286221
- Cited by
- 184
- References
- 55
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 16 · 2023: 20 · 2022: 15 · 2021: 23 · 2020: 22 · 2019: 23 · 2018: 20 · 2017: 8 · 2016: 14 · 2015: 1
- FWCI
- 3.91
- 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 Institute on Aging (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, pre-frailty, wearable sensors, home-based assessment, gait analysis, balance, physical activity, walking speed, stride length, double support, older adults, physical function, functional performance tests, community-dwelling older adults
- MeSH terms
- Aged, Aged, 80 and over, Aging, Arizona, Cross-Sectional Studies, Postural Balance, Female, Gait, Geriatrics, Humans, Male, Motor Activity, Cohort Studies, Geriatric Assessment, Walking, Frail Elderly
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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