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Objective The objective was to use the Activity Measure for Post-Acute Care "6-Clicks" scores at initial physical therapist and/or occupational therapist evaluation to assess (1) predictive ability for community versus institutional discharge, and (2) association with discharge destination (home/self-care [HOME], home health [HHA], skilled nursing facility [SNF], and inpatient rehabilitation facility [IRF]). Methods In this retrospective cohort study, initial "6-Clicks" Basic Mobility and/or Daily Activity t scores and discharge destination were obtained from electronic health records of 17,546 inpatient admissions receiving physical therapy/occupational therapy at an academic hospital between October 1, 2015 and August 31, 2018. For objective (1), postacute discharge destination was dichotomized to community (HOME and HHA) and institution (SNF and IRF). Receiver operator characteristic curves determined the most predictive Basic Mobility and Daily Activity scores for discharge destination. For objective (2), adjusted odds ratios (OR) from multinomial logistic regression assessed association between discharge destination (HOME, HHA, SNF, IRF) and cut-point scores for Basic Mobility (≤40.78 vs >40.78) and Daily Activity (≤40.22 vs >40.22), accounting for patient and clinical characteristics. Results Area under the curve for Basic Mobility was 0.80 (95% CI = 0.80–0.81) and Daily Activity was 0.81 (95% CI = 0.80–0.82). The best cut-point for Basic Mobility was 40.78 (raw score = 16; sensitivity = 0.71 and specificity = 0.74) and for Daily Activity was 40.22 (raw score = 19; sensitivity = 0.68 and specificity = 0.79). Basic Mobility and Daily Activity were significantly associated with discharge destination, with those above the cut-point resulting in increased odds of discharge HOME. The Basic Mobility scores ≤40.78 had higher odds of discharge to HHA (OR = 1.7 [95% CI = 1.5–1.9]), SNF (OR = 7.8 [95% CI = 6.8–8.9]), and IRF (OR = 7.5 [95% CI = 6.3–9.1]), and the Daily Activity scores ≤40.22 had higher odds of discharge to HHA (OR = 1.8 [95% CI = 1.7–2.0]), SNF (OR = 8.9 [95% CI = 7.9–10.0]), and IRF (OR = 11.4 [95% CI = 9.7–13.5]). Conclusion 6-Clicks at physical therapist/occupational therapist initial evaluation demonstrated good prediction for discharge decisions. Higher scores were associated with discharge to HOME; lower scores reflected discharge to settings with increased support levels. Impact Initial Basic Mobility and Daily Activity scores are valuable clinical tools in the determination of discharge destination. [ABSTRACT FROM AUTHOR]

Introduction Falls are a common cause of injuries in people over 65 years, usually resulting in life-threatening or life-altering sequalae for affected individuals. Falls risk assessment typically requires a face-to-face evaluation delivered by a trained professional in a clinical setting, thus limiting their application for many older people with falls. Therefore, there is a need for a remote monitoring system (RMS) that captures mobility data to assess comprehensively an older individual's true falls risk undertaking their everyday activities in their usual environment. Methods A pilot study was conducted with 16 participants over 65 years classified as high (n = 9) or low falls risk (n = 7) based on their fall's history. A RMS was developed, which combines inertial measurement unit (IMU) data from a wearable device and online real-time Machine Learning (ML) to generate a two-stage fall risk assessment. In stage one, a ML activity motion classifier identified raw IMU data from participants as either, walking, resting or transitional movements (turning or sitting to standing). In stage two, previously classified motion segments were analysed by a ML fall-risk classifier, providing an objective risk assessment based on gait characteristics during everyday motion. Falls incidence was recorded prospectively at one year follow-up. Results The motion classifier achieved an 94.8% accuracy in differentiating between resting, walking and transitional movements. The fall risk classifier initially trained with retrospective fall history data achieved an 87.5% accuracy with 88.9% sensitivity and specificity, which is comparable to other accurate classifiers in published literature. The fall risk classifier retrained with prospective one year follow-up data achieved a 73% accuracy which was superior to the 67% accuracy of the Timed Up and Go test for falls prediction. Conclusion Our study shows the potential of a machine-learning remote monitoring system using information from mobility data to classify an older person's falls' risk. [ABSTRACT FROM AUTHOR]