Exploring Physical Activity During the Discharge Transition Phase in People With Acquired Brain Injury-An Observational Study.

Objective: To explore physical activity trajectories during the discharge transition phase after in-hospital rehabilitation after acquired brain injury (ABI). Design: A cross-sectional observational study. Setting: Transition from an in-hospital rehabilitation center to community-based living. Participants: Independently walking patients with ABI (n=10) who were ready for discharge. Interventions: Not applicable. Main Outcome Measures: Two weeks of physically active time continuously monitored with an accelerometer and classified by a machine learning algorithm summed as daily average and total active time for each participant and classified into standing, walking, running, bike riding, stair climbing, ambulation, and sedentary time. Physical activity trajectories showing the total daily active time for all participants were inspected before and after discharge, and the average active time per participant was plotted against self-reported scores of potentially explanatory factors. Results: Average total physically active time was 5:49 hours (range 4:26-7:13 hours). Average daily physically active time for participants appeared to be related to functional independence measure sub scores, fatigue, and pre-morbid physical activity level. Individual physical activity trajectories showed a decreased walking activity after discharge, which increased again after 1-2 days. Conclusions: Daily total physically active time among participants was higher than expected. Factors expectedly related to physical activity trajectories in the discharge transition phase were explored and showed some relation to functional scores.

Monitoring Physical Behavior in Rehabilitation Using a Machine Learning-Based Algorithm for Thigh-Mounted Accelerometers: Development and Validation Study.

BACKGROUND: Physical activity is emerging as an outcome measure. Accelerometers have become an important tool in monitoring physical behavior, and newer analytical approaches of recognition methods increase the degree of details. Many studies have achieved high performance in the classification of physical behaviors through the use of multiple wearable sensors; however, multiple wearables can be impractical and lower compliance. OBJECTIVE: The aim of this study was to develop and validate an algorithm for classifying several daily physical behaviors using a single thigh-mounted accelerometer and a supervised machine-learning scheme. METHODS: We collected training data by adding the behavior classes-running, cycling, stair climbing, wheelchair ambulation, and vehicle driving-to an existing algorithm with the classes of sitting, lying, standing, walking, and transitioning. After combining the training data, we used a random forest learning scheme for model development. We validated the algorithm through a simulated free-living procedure using chest-mounted cameras for establishing the ground truth. Furthermore, we adjusted our algorithm and compared the performance with an existing algorithm based on vector thresholds. RESULTS: We developed an algorithm to classify 11 physical behaviors relevant for rehabilitation. In the simulated free-living validation, the performance of the algorithm decreased to 57% as an average for the 11 classes (F-measure). After merging classes into sedentary behavior, standing, walking, running, and cycling, the result revealed high performance in comparison to both the ground truth and the existing algorithm. CONCLUSIONS: Using a single thigh-mounted accelerometer, we obtained high classification levels within specific behaviors. The behaviors classified with high levels of performance mostly occur in populations with higher levels of functioning. Further development should aim at describing behaviors within populations with lower levels of functioning.