Fall Risk and Coping of Older Adults After Hospitalization: A Mixed Methods Study.

Coping is defined as cognitive and behavioral effort to manage specific external and/or internal demands, such as managing one's own fall risk. Little is known about the relationship between the risk of falling in older adults and their coping strategies. The purpose of this study is to examine the fall risk after hospitalization, the adequacy of self-perceived fall risk and coping strategies of older adults. In this mixed-methods study, the adequacy of perceived fall risk was determined using the de Morton Mobility Index and the ABC Scale in 98 geriatric patients recruited in a geriatric hospital. Semi-structured interviews were conducted with a subsample of 16 participants 6 months after discharge to identify coping strategies. The six interviewees who adequately assessed their fall risk reported active/positive coping. In contrast, participants who assessed their fall risk inadequately (10 out of 16) reported passive/negative coping. Older adults who inadequately assessed their fall risk need special accompaniment in geriatric wards to develop active/positive coping strategies.

App-Based Evaluation of Older People's Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study.

BACKGROUND: Falls and the risk of falling in older people pose a high risk for losing independence. As the risk of falling progresses over time, it is often not adequately diagnosed due to the long intervals between contacts with health care professionals. This leads to the risk of falling being not properly detected until the first fall. App-based software able to screen fall risks of older adults and to monitor the progress and presence of fall risk factors could detect a developing fall risk at an early stage prior to the first fall. As smartphones become more common in the elderly population, this approach is easily available and feasible. OBJECTIVE: The aim of the study is to evaluate the app Lindera Mobility Analysis (LIN). The reference standards determined the risk of falling and validated functional assessments of mobility. METHODS: The LIN app was utilized in home- and community-dwelling older adults aged 65 years or more. The Berg Balance Scale (BBS), the Tinetti Test (TIN), and the Timed Up & Go Test (TUG) were used as reference standards. In addition to descriptive statistics, data correlation and the comparison of the mean difference of analog measures (reference standards) and digital measures were tested. Spearman rank correlation analysis was performed and Bland-Altman (B-A) plots drawn. RESULTS: Data of 42 participants could be obtained (n=25, 59.5%, women). There was a significant correlation between the LIN app and the BBS (r=-0.587, P<.001), TUG (r=0.474, P=.002), and TIN (r=-0.464, P=.002). B-A plots showed only few data points outside the predefined limits of agreement (LOA) when combining functional tests and results of LIN. CONCLUSIONS: The digital app LIN has the potential to detect the risk of falling in older people. Further steps in establishing the validity of the LIN app should include its clinical applicability. TRIAL REGISTRATION: German Clinical Trials Register DRKS00025352; https://tinyurl.com/65awrd6a.

Correction: Hoffmann et al. Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks. Sensors 2021, 21, 1086.

There was an affiliation missing in the original article [...].

Categorizing fear of falling using the survey of activities and fear of falling in the elderly questionnaire in a cohort of hospitalized older adults: A cross-sectional design.

BACKGROUND: Fear of falling is commonly assessed using the Activities-specific Balance Confidence Scale which is an instrument to measure balance confidence, based on the assumption that fear of falling is due to the absence of balance confidence. The "Survey of Activities and Fear of Falling in the Elderly" measures the concept of fear of falling more directly on a scale of 0.0 and 3.0 points. However, there are no valid cut-off points that might help practitioners to interpret "Survey of Activities and Fear of Falling in the Elderly" scores. The aim of this study was to identify such cut-off points and distinguish between low, moderate and high fear of falling, in relation to balance confidence. METHOD: In this cross-sectional study different cut-off point schemes for classifying fear of falling scores as low, moderate or high were compared with F-values in ANOVA using the cut-off point scheme as an independent variable and the balance confidence scores as a dependent variable. The analysis was performed using data from a cohort of 98 hospitalized older adults. RESULTS: Using the Activities-specific Balance Confidence Scale as a reference tool, values of 0.6 and 1.4 were identified as optimal cut-off points for low, moderate and high fear of falling. CONCLUSIONS: This study was the first to systematically classify fear of falling using the "Survey of Activities and Fear of Falling in the Elderly". This classification can assist health practitioners to interpret fear of falling score and guide clinical decision making. Registration: The study is registered with the German Clinical Trials Register (DRKS00010773, date of registration 2016/05/07, date of recruitment 2016/11/07).

Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks.

Gait patterns are a result of the complex kinematics that enable human two-legged locomotion, and they can reveal a lot about a person's state and health. Analysing them is useful for researchers to get new insights into the course of diseases, and for physicians to track the progress after healing from injuries. When a person walks and is interfered with in any way, the resulting disturbance can show up and be found in the gait patterns. This paper describes an experimental setup for capturing gait patterns with a capacitive sensor floor, which can detect the time and position of foot contacts on the floor. With this setup, a dataset was recorded where 42 participants walked over a sensor floor in different modes, inter alia, normal pace, closed eyes, and dual-task. A recurrent neural network based on Long Short-Term Memory units was trained and evaluated for the classification task of recognising the walking mode solely from the floor sensor data. Furthermore, participants were asked to do the Unilateral Heel-Rise Test, and their gait was recorded before and after doing the test. Another neural network instance was trained to predict the number of repetitions participants were able to do on the test. As the results of the classification tasks turned out to be promising, the combination of this sensor floor and the recurrent neural network architecture seems like a good system for further investigation leading to applications in health and care.