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Most fall risk and normal values are collected from the community-dwelling population, which is not representative of nursing home residents. The purpose of this study was twofold: 1) to determine the relationship of clinical impairment and activity limitation variables to the number of falls in nursing home residents; and 2) to determine the amount of variability that can be explained for the number of falls from these predictor variables. Seventeen active nursing home residents (83.7 ± 11.7 years) consented to participate. Mini-Mental Status Examination (MMSE), lower extremity handheld dynamometry, ankle plantar flexion (PF)/dorsiflexion (DF) active range of motion (AROM), hand grip strength, gait speed (GS), Timed Up and Go (TUG), and 5 Times Sit-to-Stand (5TSTS) were recorded in a single visit. Regression analysis was performed to identify the better clinical outcome tool to determine falls. This was followed by a stepwise multiple regression model to predict the criterion variable-number of falls. Of the clinical impairment measures collected, significant correlations with past falls include the following: right DF AROM (-0.436; p = 0.040) and right DF strength (-0.504; p = 0.023). Of the activity limitation measures collected, significant correlations with past falls include the following: 5TSTS (0.585; p = 0.007); TUG time (0.475; p = 0.027); and GS (0.457; p = 0.032). The stepwise multiple regression model explained 59% of the variance using right DF AROM, right DF strength, 5TSTS, and TUG time. These measures are benchmarks for the community dwelling population. The present study indicates that these measures might also be useful in determining fall risk screening for ambulatory nursing home residents.

Assessing functional mobility is an important aspect of determining fall risk in the growing population of assisted living. Little is known about the sit to walk (STW) transition, especially what characteristics correlate with the duration it takes to perform a STW. Methods In this cross sectional, exploratory study, 45 assisted living residents were evaluated for baseline measures: history of falls, assistive device (AD), lower extremity strength, and balance (Tinetti POMA). The residents were then timed performing a STW, and, from these durations, were stratified into fast and slow groups. Statistical analysis was performed to determine relationships between the two duration groups and the baseline measures. Results Participants who had experienced a fall were significantly more likely to use an assistive device (p = 0.001). The use of an AD had a moderate negative correlation with composite strength (r =-0.428, p = 0.003). Duration of STW had a weak negative correlation with composite strength (r =-0.299, p = 0.046) and a moderate correlation with use of AD (r = 0.419, p = 0.004). Those with a faster duration of STW had significantly better balance (p = 0.027). Conclusion These correlations support the need for healthcare professionals to address the use of adaptive equipment and physical training when evaluating functional mobility in assisted living residents. Providing appropriate equipment and improving overall strength in these individuals can help reduce their risk of falls. Individuals in assisted living facilities can be assessed similar to other geriatric populations, with special consideration on functional tasks and measures.

Purpose: Health care professionals use smartphones in the clinic with mobile device applications (apps) to measure data such as joint ROM. The purpose of this study was to examine goniometer apps and to compare their measurements to an electronic goniometer gold standard to identify the most precise apps. Method: 7 different apps were identified for Apple and Android devices. Each of these apps provided measurements concurrently with an electronic goniometer reference at 6 different predetermined angles. Descriptive statistics, including mean, standard deviation (SD) from the gold standard electronic goniometer, standard error of mean (SEM), mean absolute difference (MAD), and 95% confidence intervals were calculated. An intraclass correlation coefficient (ICC) was calculated to observe for reliability. Bland-Altman plots were configured to show directional preference of two measurement techniques. A Pearson correlation coefficient (r) was used to determine validity to describe the strength and direction of the relationship for Goniometer Pro (Apple) or 360 Protractor (Android) and the gold standard. Results: The most accurate Apple app was Goniometer Pro and Android app was 360 Protractor. The ICCs for reliability for both the Apple app and the Android app were 0.99 (95% CI 0.98, 1.00). The Pearson correlation coefficient for validity was significant at r=1.00 (95% CI 0.99,1.00) Conclusion: Both Goniometer Pro on the Apple device and 360 Protractor on the Android device were the most accurate with high reliability and validity.