Path Tortuosity in Everyday Movements of Elderly Persons Increases Fall Prediction Beyond Knowledge of Fall History, Medication Use, and Standardized Gait and Balance Assessments

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• 作者：William D. Kearns ; PhD^a ; ^{Kearns@usf.edu} ; James L. Fozard ; PhD^b ; Marion Becker ; RN ; PhD^c ; Jan M. Jasiewicz ; PhD^d ; Jeffrey D. Craighead ; PhD^d ; Lori Holtsclaw ; BA^b ; Charles Dion ; MA^e
• 关键词：Falls ; telemedicine ; standardized gait and balance ; psychoactive medications ; assisted living facilities ; movement variability ; elderly ; telesurveillance
• 刊名：Journal of the American Medical Directors Association
• 出版年：2012
• 出版时间：September, 2012
• 年：2012
• 卷：13
• 期：7
• 页码：665.e7-665.e13
• 全文大小：415 K

文摘

Objectives

We hypothesized that variability in voluntary movement paths of assisted living facility (ALF) residents would be greater in the week preceding a fall compared with residents who did not fall.

Design

Prospective, observational study using telesurveillance technology.

Setting

Two ALFs.

Participants

The sample consisted of 69 older ALF residents (53 female) aged 76.9 (SD ¡À 11.9 years).

Measurement

Daytime movement in ALF common use areas was automatically tracked using a commercially available ultra-wideband radio real-time location sensor network with a spatial resolution of approximately 20 cm. Movement path variability (tortuosity) was gauged using fractal dimension (fractal D). A logistic regression was performed predicting movement related falls from fractal D, presence of a fall in the prior year, psychoactive medication use, and movement path length. Fallers and non-fallers were also compared on activities of daily living requiring supervision or assistance, performance on standardized static and dynamic balance, and stride velocity assessments gathered at the start of a 1-year fall observation period. Fall risk due to cognitive deficit was assessed by the Mini Mental Status Examination (MMSE), and by clinical dementia diagnoses from participant¡¯s activities of daily living health record.

Results

Logistic regression analysis revealed odds of falling increased 2.548 (P?= .021) for every 0.1 increase in fractal D, and having a fall in the prior year increased odds of falling by 7.36 (P?= .006). There was a trend for longer movement paths to reduce the odds of falling (OR .976 P?= .08) but it was not significant. Number of psychoactive medications did not contribute significantly to fall prediction in the model. Fallers had more variable stride-to-stride velocities and required more activities of daily living assistance.

Conclusions

High fractal D levels can be detected using commercially available telesurveillance technologies and offers a new tool for health services administrators seeking to reduce falls at their facilities.