The present study developed a simplified decision-tree algorithm for fall prediction with easily measurable predictors using data from a longitudinal cohort study: 2520 community-dwelling older adults aged 65 years or older participated. Fall history, age, sex, fear of falling, prescribed medication, knee osteoarthritis, lower limb pain, gait speed, and timed up and go test were assessed in the baseline survey as fall predictors. Moreover, recent falls were assessed in the follow-up survey. We created a fall-prediction algorithm using decision-tree analysis (C5.0) that included 14 nodes with six predictors, and the model could stratify the probabilities of fall incidence ranging from 30.4% to 71.9%. Additionally, the decision-tree model outperformed a logistic regression model with respect to the area under the curve (0.70 vs. 0.64), accuracy (0.65 vs. 0.62), sensitivity (0.62 vs. 0.50), positive predictive value (0.66 vs. 0.65), and negative predictive value (0.64 vs. 0.59). Our decision-tree model consists of common and easily measurable fall predictors, and its white-box algorithm can explain the reasons for risk stratification; therefore, it can be implemented in clinical practices. Our findings provide useful information for the early screening of fall risk and the promotion of timely strategies for fall prevention in community and clinical settings.

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Background The coronavirus disease 2019 (COVID-19) pandemic has led to social isolation measures, forcing many people to stay indoors, stop daily outdoor activities, and limit face-to-face social interactions with friends, colleagues, and family. This study aimed to identify if non-face-to-face interaction affects depressive symptoms and frailty in older adults. Methods We included 3834 older adults (age: 71.1 ± 6.9 [mean ± standard deviation] years; range: 60–96 years; 2153 women) from the National Center for Geriatrics and Gerontology–Study of Geriatric Syndromes. Interaction status was assessed using a self-reported questionnaire. Participants were categorized into: “both interactions” (both face-to-face and non-face-to-face interactions), “face-to-face only” (only face-to-face interactions), “non-face-to-face only” (only non-face-to-face interactions), “no interactions” (neither face-to-face nor non-face-to-face interactions) groups. Depressive symptoms and frailty were measured using the 15-item Geriatric Depression Scale and Kihon Checklist, respectively. Results Potential confounding factors-adjusted odds ratios for both, face-to-face only and non-face-to-face only groups for developing depressive symptoms were 0.39 (95%CI, 0.26–0.57; p<0.001), 0.56 (95%CI, 0.38–0.84; p=0.004), and 0.51 (95%CI, 0.27–0.96; p=0.038), respectively, and those for development of frailty were 0.44 (95%CI, 0.30–0.65; p<0.001), 0.59 (95%CI, 0.39–0.87; p=0.008), and 0.63 (95%CI, 0.34–1.15; p=0.128), respectively. Conclusions Our findings indicate that non-face-to-face interactions are also important in preventing the deterioration of mental health, which is a concern during the COVID-19 pandemic. However, non-face-to-face interactions alone may not be sufficient to maintain physical health, and it is important to maintain opportunities for face-to-face interaction among older adults, particularly during the COVID-19 pandemic.

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