BACKGROUND: fear of falling (FOF) is common in older people and associated with serious physical and psychosocial consequences. Identifying those at risk of FOF can help target interventions to both prevent falls and reduce FOF. OBJECTIVE: to identify factors associated with FOF. STUDY DESIGN: cross-sectional study in 1,088 community-dwelling older people aged ≥65 years. METHODS: data were collected on socio-demographic characteristics, self-perceived health, exercise, risk factors for falls, FOF (Short FES-I), and functional measures. Logistic regression models of increasing complexity identified factors associated with FOF. RESULTS: high FOF (Short FES-I ≥11) was reported by 19%. A simpler model (socio-demographic + falls risk factors) correctly classified as many observations (82%) as a more complex model (socio-demographic + falls risk factors + functional measures) with similar sensitivity and specificity values in both models. There were significantly raised odds of FOF in the simpler model with the following factors: unable to rise from a chair of knee height (OR: 7.39), lower household income (OR: 4.58), using a walking aid (OR: 4.32), difficulty in using public transport (OR: 4.02), poorer physical health (OR: 2.85), black/minority ethnic group (OR: 2.42), self-reported balance problems (OR: 2.17), lower educational level (OR: 2.01) and a higher BMI (OR: 1.06). CONCLUSIONS: a range of factors identify those with FOF. A simpler model performs as well as a more complex model containing functional assessments and could be used in primary care to identify those at risk of FOF, who could benefit from falls prevention interventions.
BACKGROUND: fear of falling (FOF) is common in older people and associated with serious physical and psychosocial consequences. Identifying those at risk of FOF can help target interventions to both prevent falls and reduce FOF. OBJECTIVE: to identify factors associated with FOF. STUDY DESIGN: cross-sectional study in 1,088 community-dwelling older people aged ≥65 years. METHODS: data were collected on socio-demographic characteristics, self-perceived health, exercise, risk factors for falls, FOF (Short FES-I), and functional measures. Logistic regression models of increasing complexity identified factors associated with FOF. RESULTS: high FOF (Short FES-I ≥11) was reported by 19%. A simpler model (socio-demographic + falls risk factors) correctly classified as many observations (82%) as a more complex model (socio-demographic + falls risk factors + functional measures) with similar sensitivity and specificity values in both models. There were significantly raised odds of FOF in the simpler model with the following factors: unable to rise from a chair of knee height (OR: 7.39), lower household income (OR: 4.58), using a walking aid (OR: 4.32), difficulty in using public transport (OR: 4.02), poorer physical health (OR: 2.85), black/minority ethnic group (OR: 2.42), self-reported balance problems (OR: 2.17), lower educational level (OR: 2.01) and a higher BMI (OR: 1.06). CONCLUSIONS: a range of factors identify those with FOF. A simpler model performs as well as a more complex model containing functional assessments and could be used in primary care to identify those at risk of FOF, who could benefit from falls prevention interventions.
Entities:
Keywords:
elderly; falling; fear of falling; older people; older persons; risk factors
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