Purpose:
Few studies measure individual’s visual function in the fall-risk assessment. Recently, a falls risk assessment tool - Physiological Profile Assessment (PPA) has been developed. It includes the measurement of vision, peripheral sensation, lower limb strength, coordination and balance. In this study, we adopted this tool to examine the falls risk profile in community-dwelling Chinese older adults in Hong Kong and investigated the predictive factors for their falls.
Methods:
435 community-dwelling elderly aged 60 to 95 years were recruited from community-dwelling centres using convenience sampling (75.5 ± 7.2 years). Demographic information and history of falls in the previous 3 months were collected. Fallers were identified as those who fell at least once. Fall risk for each subject was evaluated by the "short-form of PPA" which includes assessment of edge contrast sensitivity, proprioception, knee extensors, hand reaction time, and sway area on foam surface with eyes open. Individual’s performance for each assessment was computed in standardized (z) scores form, using the reference data from the large-scale normative database. Individual’s fall risk score was derived from the discriminant functional analysis through computing the weighted scores of these 5 measures into a single index. Higher fall risk score indicates higher risk of falls.
Results:
Only 7.8% of the participants reported at least one fall in the past 3 months, indicating a lower prevalence of falls compared with other populations’ studies (range from 10% to 30.6%). However, this low incidence of falls did not agree with the fall risk profiles measured in our elderly subjects whose reaction time, contrast sensitivity and balance function were underperformed compared with those in large-scale normative database of Caucasian population (mean z-scores of -1.75, -0.78 and -0.46 respectively). Due to the poor performances of these measures in PPA, the mean computed fall risk score for this Chinese elderly population was 1.90, indicating a moderate risk of falling. However, demographic variables and individual fall risk score could not be used to predict the incidence of falls in logistic regression (p>0.05).
Conclusions:
A disagreement was found between objectively-measured fall risk scores and subjectively-reported incidence of falls in this study. Possible reasons were unreliable recall memory, short observation period and other psychosocial factors.
Keywords: aging • clinical (human) or epidemiologic studies: risk factor assessment