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Updated: May 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Fall risk assessment among older adults with mild Alzheimer disease
John J Ryan1, Constance McCloy, Peter Rundquist
1Health First, Melbourne, FL, USA. jjryan@cfl.rr.com
Purpose:
Older adults with Alzheimer disease (AD) fall more than twice as often as those without dementia, yet few studies have assessed fall risk in this population. The purpose of the study was to determine whether a fall assessment, the Physical Performance Test 7-item (PPT 7-item), could accurately identify subjects with history of falls in a group of community-dwelling elders with mild AD. An additional purpose was to determine whether the PPT 7-item, a cognitive screen, and/or nonperformance data could predict falling in this population.
Methods:
Forty-three community-dwelling elders diagnosed with mild AD completed the fall risk assessment. In addition, the following data were collected: Mini-Mental State Examination (MMSE) score, age, gender, education, gait aid use, number of falls in the past 6 months, and history of fall-related injury.
Results:
There was a significant difference in the PPT 7-item total score between subjects with history of falls and subjects without history of falls (z = -2.04, P = .042), with items related to turning (z = -2.56, P = .01) and walking (z = -2.89, P = .004) accounting for most of the difference. However, only gait aid usage predicted falling (45.8% of the variance).
Conclusion:
While the PPT 7-item was able to detect differences in mobility between subjects with history of falls and subjects without history of falls in subjects with mild AD, total PPT 7-item score did not predict falling. Gait aid usage was more strongly related to falling in these subjects. Early detection of fall risk in individuals with mild AD is important to prevent injuries and moderate costs of care.
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