OBJECTIVE: To construct a risk model in order to identify elderly individuals at risk of frequent falling. DESIGN: Prospective community-based cohort study over 12 months. SETTING: Baseline measures were performed at a local community centre. SUBJECTS: Two hundred and sixty-three community-dwelling elderly people (mean age 72 years). MEASUREMENTS: A variety of variables were evaluated, including medical, psychological, sensory, physical and postural control measurements. Fall incidence was monitored retrospectively and during one-year follow-up. RESULTS: Logistic regression analysis showed that polypharmacia was the most prominent medical fall predictor with an odds ratio (OR) of 1.29 (P= 0.005), poor visual acuity the best sensory predictor (OR = 0.84; P= 0.009) and general fear of falling the most crucial psychological predictor (OR = 3.25; P< 0.001). Increased postural sway in near-tandem stance with eyes open was selected as the best balance predictor for falls (OR = 5.60; P= 0.010), followed by delayed anteroposterior movement velocity during rhythmic weight shifts (OR = 0.42; P= 0.004). The best physical predictor was a low score on the Physical Performance Test (OR =4.16; P< 0.001), followed by decreased maximal handgrip strength (OR = 0.87; P< 0.001) and increased timed chair-stands (OR = 1.13; P= 0.003). Step-by-step regression analysis revealed a risk model for the prediction of future falls, as a combination of the Physical Performance Test and maximal handgrip strength. CONCLUSION: This study confirms the multicausality of falls, since medical, psychological, sensory, postural control as well as physical variables provides a predictive value. The composed fall risk model was mainly physically oriented.
OBJECTIVE: To construct a risk model in order to identify elderly individuals at risk of frequent falling. DESIGN: Prospective community-based cohort study over 12 months. SETTING: Baseline measures were performed at a local community centre. SUBJECTS: Two hundred and sixty-three community-dwelling elderly people (mean age 72 years). MEASUREMENTS: A variety of variables were evaluated, including medical, psychological, sensory, physical and postural control measurements. Fall incidence was monitored retrospectively and during one-year follow-up. RESULTS: Logistic regression analysis showed that polypharmacia was the most prominent medical fall predictor with an odds ratio (OR) of 1.29 (P= 0.005), poor visual acuity the best sensory predictor (OR = 0.84; P= 0.009) and general fear of falling the most crucial psychological predictor (OR = 3.25; P< 0.001). Increased postural sway in near-tandem stance with eyes open was selected as the best balance predictor for falls (OR = 5.60; P= 0.010), followed by delayed anteroposterior movement velocity during rhythmic weight shifts (OR = 0.42; P= 0.004). The best physical predictor was a low score on the Physical Performance Test (OR =4.16; P< 0.001), followed by decreased maximal handgrip strength (OR = 0.87; P< 0.001) and increased timed chair-stands (OR = 1.13; P= 0.003). Step-by-step regression analysis revealed a risk model for the prediction of future falls, as a combination of the Physical Performance Test and maximal handgrip strength. CONCLUSION: This study confirms the multicausality of falls, since medical, psychological, sensory, postural control as well as physical variables provides a predictive value. The composed fall risk model was mainly physically oriented.
Authors: Anna E Mattlage; Sara A Redlin; Michael A Rippee; Michael G Abraham; Marilyn M Rymer; Sandra A Billinger Journal: J Neurol Phys Ther Date: 2015-07 Impact factor: 3.649
Authors: Jennifer S Gewandter; William Dale; Allison Magnuson; Chintan Pandya; Charles E Heckler; Tatyana Lemelman; Breton Roussel; Rafa Ifthikhar; James Dolan; Katia Noyes; Supriya G Mohile Journal: J Geriatr Oncol Date: 2015-09-11 Impact factor: 3.599