Limitations
This study also has limitations. First, the sample included patients with a mean age of about 46 years from medicine services at one health system who were being discharged to home and less than 10% of those approached agreed to participate, which limits generalizability. Future studies should evaluate a broader sample of patients. Second, the analyses evaluated hospital readmissions within 30 days. Future studies should evaluate prediction of rehospitalization at longer periods of time after discharge. Third, data on hospital readmissions was obtained from Penn Medicine and a database on hospitalizations in the State of Pennsylvania. We did not have access to readmissions that occurred outside of Pennsylvania. Fourth, while participant characteristics between study arms were similar, the readmission rate was lower in the wearable arm. This imbalance may have been due to random chance and the small sample size. However, it does not impact the main findings that RPM data with machine learning improved prediction. Fifth, RPM data was limited to measures of physical activity and sleep. We did not have data on heart rate or other biometrics which can be captured by some wearable devices. Nonetheless, we did find improvements in prediction with these RPM measures and future studies could evaluate if additional biometric data can further improvement.