Statistical analysis
death, Comorbidity
REGRESSION
All randomly assigned patients were included in the intention-to-treat analysis. For each patient and on each day of the study (patient-day level), we obtained RPM activity data. Data could be missing for any day if the patient did not use the device, did not sync it to upload data, or the device did not capture it (e.g. smartphones did not track sleep data). We also excluded outliers (top and bottom 1 percentile) in activity data and coded them as missing. To account for missing values in the RPM data, we included a dichotomous missing-value indicator variable for each RPM data variableWe used a discrete-time survival analysis and evaluated hospital readmission or death within 30 days of discharge at the patient-day level as the unit of observation. The sample was split into training and testing folds for each arm that were mutually exclusive at the patient level in a 3:2 ratio. The training set used fivefold cross-validation and then final model results are from predictions on the test set.We fit a standard model that comprised data available up to the time of hospital discharge including demographic information (age, gender, race/ethnicity, education, marital status, annual household income, and body mass index), time (calendar month and year), hospital length of stay, vitals near discharge (temperature, heart rate, systolic and diastolic blood pressures, oxygen saturation, respirations per minute), and Charlson Comorbidity Index. We fit an enhanced model with RPM data that included the same variables as the standard model as well as the following: daily measures of step counts, distance, calories burned (active and total), minutes of activity (soft and moderate intensity), minutes of sleep (total, light, and deep), minutes awake, and number of times awakened. The enhanced model with RPM also including missing-value and lagged indicators for each RPM measure, as previously described. For all models, we compared traditional parametric regression (logit and lasso) to nonparametric machine learning approaches (random forest, gradient boosting, and ensemble machine learning). The ensemble machine learning model used a combination of logit, lasso, random forest, and gradient boosting. Hyperparameter specification is described in Supplementary Table Model performance was assessed using the area under the receiver operator characteristic curve (AUC)