Background: In clinical practice, therapists often rely on clinical outcome measures to quantify a patient's impairment and function. Predicting a patient's discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient's assistive needs and discharge care plan. The objective of this study was to develop predictive models for four standardized clinical outcome measures (Functional Independence Measure, Ten-Meter Walk Test, Six-Minute Walk Test, Berg Balance Scale) during inpatient rehabilitation. Methods: Fifty stroke survivors admitted to a United States inpatient rehabilitation hospital participated in this study. Predictors chosen for the clinical discharge scores included demographics, stroke characteristics, and scores of clinical tests at admission. We used the Pearson product-moment and Spearman's rank correlation coefficients to calculate correlations among clinical outcome measures and predictors, a cross-validated Lasso regression to develop predictive equations for discharge scores of each clinical outcome measure, and a Random Forest based permutation analysis to compare the relative importance of the predictors. Results: The predictive equations explained 70-77% of the variance in discharge scores and resulted in a normalized error of 13-15% for predicting the outcomes of new patients. The most important predictors were clinical test scores at admission. Additional variables that affected the discharge score of at least one clinical outcome were time from stroke onset to rehabilitation admission, age, sex, body mass index, race, and diagnosis of dysphasia or speech impairment. Conclusions: The models presented in this study could help clinicians and researchers to predict the discharge scores of clinical outcomes for individuals enrolled in an inpatient stroke rehabilitation program that adheres to U.S. Medicare standards.

Background Falls are a leading cause of accidental deaths and injuries worldwide. The risk of falling is especially high for individuals suffering from balance impairments. Retrospective surveys and studies of simulated falling in lab conditions are frequently used and are informative, but prospective information about real-life falls remains sparse. Such data are essential to address fall risks and develop fall detection and alert systems. Here we present the results of a prospective study investigating a proof-of-concept, smartphone-based, online system for fall detection and notification. Methods The system uses the smartphone’s accelerometer and gyroscope to monitor the participants’ motion, and falls are detected using a regularized logistic regression. Data on falls and near-fall events (i.e., stumbles) is stored in a cloud server and fall-related variables are logged onto a web portal developed for data exploration, including the event time and weather, fall probability, and the faller’s location and activity before the fall. Results In total, 23 individuals with an elevated risk of falling carried the phones for 2070 days in which the model classified 14,904,000 events. The system detected 27 of the 37 falls that occurred (sensitivity = 73.0 %) and resulted in one false alarm every 46 days (specificity > 99.9 %, precision = 37.5 %). 42.2 % of the events falsely classified as falls were validated as stumbles. Conclusions The system’s performance shows the potential of using smartphones for fall detection and notification in real-life. Apart from functioning as a practical fall monitoring instrument, this system may serve as a valuable research tool, enable future studies to scale their ability to capture fall-related data, and help researchers and clinicians to investigate real-falls.

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