Motor Performance and Physical Activity as Predictors of Prospective Falls in Community-Dwelling Older Adults by Frailty Level: Application of Wearable Technology

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Abstract

Background: Few studies of the association between prospective falls and sensor-based measures of motor performance and physical activity (PA) have evaluated subgroups of frailty status separately. Objective: To evaluate wearable sensor-based measures of gait, balance, and PA that are predictive of future falls in community-dwelling older adults. Methods: The Arizona Frailty Cohort Study in Tucson, Arizona, followed community-dwelling adults aged 65 years and over (without baseline cognitive deficit, severe movement disorders, or recent stroke) for falls over 6 months. Baseline measures included Fried frailty criteria: in-home and sensor-based gait (normal and fast walk), balance (bipedal eyes open and eyes closed), and spontaneous daily PA over 48 h, measured using validated wearable technologies. Results: Of the 119 participants (36% non-frail, 48% pre-frail, and 16% frail), 48 reported one or more fall (47% of non-frail, 33% of pre-frail, and 47% of frail). Although balance deficit and PA were independent fall predictors in pre-frail and frail groups, they were not sensitive to predict prospective falls in the non-frail group. Even though gait performance deteriorated as frailty increased, gait was not a predictor of prospective falls when participants were stratified based on frailty status. In pre-frail and frail participants combined, center of mass sway [odds ratio (OR) = 5.9, 95% confidence interval (CI) 2.6-13.7], PA mean walking bout duration (OR = 1.1, 95% CI 1.0-1.2), PA mean standing bout duration (OR = 0.94, 95% CI 0.91-0.99), and a fall in previous 6 months (OR = 7.3, 95% CI 1.5-36.4) were independent predictors of prospective falls (area under the curve: 0.882). Conclusion: This study suggests that independent predictors of falls are dependent on frailty status. Among sensor-derived parameters, balance deficit, longer typical walking episodes, and shorter typical standing episodes were the most sensitive predictors of prospective falls in the combined pre-frail and frail sample. Gait deficit was not a sensitive fall predictor in the context of frailty status.

Original languageEnglish (US)
JournalGerontology
DOIs
StateAccepted/In press - Apr 30 2016

Fingerprint

Independent Living
Gait
Motor Activity
Technology
Odds Ratio
Confidence Intervals
Walking
Movement Disorders
Area Under Curve
Cohort Studies
Stroke

Keywords

  • Balance
  • Falls
  • Frailty
  • Gait
  • Monitoring
  • Physical activity
  • Physical function
  • Wearable sensors

ASJC Scopus subject areas

  • Aging
  • Geriatrics and Gerontology

Cite this

@article{eaa2c898128a45b892924904af1c03c2,
title = "Motor Performance and Physical Activity as Predictors of Prospective Falls in Community-Dwelling Older Adults by Frailty Level: Application of Wearable Technology",
abstract = "Background: Few studies of the association between prospective falls and sensor-based measures of motor performance and physical activity (PA) have evaluated subgroups of frailty status separately. Objective: To evaluate wearable sensor-based measures of gait, balance, and PA that are predictive of future falls in community-dwelling older adults. Methods: The Arizona Frailty Cohort Study in Tucson, Arizona, followed community-dwelling adults aged 65 years and over (without baseline cognitive deficit, severe movement disorders, or recent stroke) for falls over 6 months. Baseline measures included Fried frailty criteria: in-home and sensor-based gait (normal and fast walk), balance (bipedal eyes open and eyes closed), and spontaneous daily PA over 48 h, measured using validated wearable technologies. Results: Of the 119 participants (36{\%} non-frail, 48{\%} pre-frail, and 16{\%} frail), 48 reported one or more fall (47{\%} of non-frail, 33{\%} of pre-frail, and 47{\%} of frail). Although balance deficit and PA were independent fall predictors in pre-frail and frail groups, they were not sensitive to predict prospective falls in the non-frail group. Even though gait performance deteriorated as frailty increased, gait was not a predictor of prospective falls when participants were stratified based on frailty status. In pre-frail and frail participants combined, center of mass sway [odds ratio (OR) = 5.9, 95{\%} confidence interval (CI) 2.6-13.7], PA mean walking bout duration (OR = 1.1, 95{\%} CI 1.0-1.2), PA mean standing bout duration (OR = 0.94, 95{\%} CI 0.91-0.99), and a fall in previous 6 months (OR = 7.3, 95{\%} CI 1.5-36.4) were independent predictors of prospective falls (area under the curve: 0.882). Conclusion: This study suggests that independent predictors of falls are dependent on frailty status. Among sensor-derived parameters, balance deficit, longer typical walking episodes, and shorter typical standing episodes were the most sensitive predictors of prospective falls in the combined pre-frail and frail sample. Gait deficit was not a sensitive fall predictor in the context of frailty status.",
keywords = "Balance, Falls, Frailty, Gait, Monitoring, Physical activity, Physical function, Wearable sensors",
author = "Mohler, {Martha J} and Wendel, {Christopher S} and Taylor-Piliae, {Ruth E} and Nima Toosizadeh and Bijan Najafi",
year = "2016",
month = "4",
day = "30",
doi = "10.1159/000445889",
language = "English (US)",
journal = "Gerontology",
issn = "0304-324X",
publisher = "S. Karger AG",

}

TY - JOUR

T1 - Motor Performance and Physical Activity as Predictors of Prospective Falls in Community-Dwelling Older Adults by Frailty Level

T2 - Application of Wearable Technology

AU - Mohler, Martha J

AU - Wendel, Christopher S

AU - Taylor-Piliae, Ruth E

AU - Toosizadeh, Nima

AU - Najafi, Bijan

PY - 2016/4/30

Y1 - 2016/4/30

N2 - Background: Few studies of the association between prospective falls and sensor-based measures of motor performance and physical activity (PA) have evaluated subgroups of frailty status separately. Objective: To evaluate wearable sensor-based measures of gait, balance, and PA that are predictive of future falls in community-dwelling older adults. Methods: The Arizona Frailty Cohort Study in Tucson, Arizona, followed community-dwelling adults aged 65 years and over (without baseline cognitive deficit, severe movement disorders, or recent stroke) for falls over 6 months. Baseline measures included Fried frailty criteria: in-home and sensor-based gait (normal and fast walk), balance (bipedal eyes open and eyes closed), and spontaneous daily PA over 48 h, measured using validated wearable technologies. Results: Of the 119 participants (36% non-frail, 48% pre-frail, and 16% frail), 48 reported one or more fall (47% of non-frail, 33% of pre-frail, and 47% of frail). Although balance deficit and PA were independent fall predictors in pre-frail and frail groups, they were not sensitive to predict prospective falls in the non-frail group. Even though gait performance deteriorated as frailty increased, gait was not a predictor of prospective falls when participants were stratified based on frailty status. In pre-frail and frail participants combined, center of mass sway [odds ratio (OR) = 5.9, 95% confidence interval (CI) 2.6-13.7], PA mean walking bout duration (OR = 1.1, 95% CI 1.0-1.2), PA mean standing bout duration (OR = 0.94, 95% CI 0.91-0.99), and a fall in previous 6 months (OR = 7.3, 95% CI 1.5-36.4) were independent predictors of prospective falls (area under the curve: 0.882). Conclusion: This study suggests that independent predictors of falls are dependent on frailty status. Among sensor-derived parameters, balance deficit, longer typical walking episodes, and shorter typical standing episodes were the most sensitive predictors of prospective falls in the combined pre-frail and frail sample. Gait deficit was not a sensitive fall predictor in the context of frailty status.

AB - Background: Few studies of the association between prospective falls and sensor-based measures of motor performance and physical activity (PA) have evaluated subgroups of frailty status separately. Objective: To evaluate wearable sensor-based measures of gait, balance, and PA that are predictive of future falls in community-dwelling older adults. Methods: The Arizona Frailty Cohort Study in Tucson, Arizona, followed community-dwelling adults aged 65 years and over (without baseline cognitive deficit, severe movement disorders, or recent stroke) for falls over 6 months. Baseline measures included Fried frailty criteria: in-home and sensor-based gait (normal and fast walk), balance (bipedal eyes open and eyes closed), and spontaneous daily PA over 48 h, measured using validated wearable technologies. Results: Of the 119 participants (36% non-frail, 48% pre-frail, and 16% frail), 48 reported one or more fall (47% of non-frail, 33% of pre-frail, and 47% of frail). Although balance deficit and PA were independent fall predictors in pre-frail and frail groups, they were not sensitive to predict prospective falls in the non-frail group. Even though gait performance deteriorated as frailty increased, gait was not a predictor of prospective falls when participants were stratified based on frailty status. In pre-frail and frail participants combined, center of mass sway [odds ratio (OR) = 5.9, 95% confidence interval (CI) 2.6-13.7], PA mean walking bout duration (OR = 1.1, 95% CI 1.0-1.2), PA mean standing bout duration (OR = 0.94, 95% CI 0.91-0.99), and a fall in previous 6 months (OR = 7.3, 95% CI 1.5-36.4) were independent predictors of prospective falls (area under the curve: 0.882). Conclusion: This study suggests that independent predictors of falls are dependent on frailty status. Among sensor-derived parameters, balance deficit, longer typical walking episodes, and shorter typical standing episodes were the most sensitive predictors of prospective falls in the combined pre-frail and frail sample. Gait deficit was not a sensitive fall predictor in the context of frailty status.

KW - Balance

KW - Falls

KW - Frailty

KW - Gait

KW - Monitoring

KW - Physical activity

KW - Physical function

KW - Wearable sensors

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DO - 10.1159/000445889

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