In this study, patients will be prospectively enrolled for data collection to design prediction models that integrate claims data (inpatient, outpatient, and pharmacy), electronic health record data (on clinical, social, and behavioral indicators), and patient-generated activity data. Patients will be randomized to use either a smartphone or a wearable activity tracking device to capture patient-generated health data.

Many hospital readmissions could be prevented if higher risk patients were identified and effective interventions then targeted towards these individuals. However, most existing claims-based predictive models perform poorly and do not provide timely and actionable information. In this study, researchers will prospectively enroll patients for data collection to design prediction models that integrate claims data (inpatient, outpatient, and pharmacy), electronic health record data (on clinical, social, and behavioral indicators), and use wearable devices or smartphones to collect patient-generated data (physical activity and sleep patterns). Patients will be randomized to use either a smartphone or a wearable activity tracking device to capture patient-generated health data.

Inclusion Criteria:

1. Be 18 years or older
2. Be able to provide informed consent
3. Be admitted to the Hospital of the University of Pennsylvania or Penn Presbyterian Medical Center
4. Have a smartphone or tablet compatible with activity tracking devices
5. Plan to be discharged to home

Exclusion Criteria:

Have no medical condition which prohibits them from ambulating or plan for any medical procedure over the next 6 months that would prohibit them from ambulating.

The study population will be drawn from adults admitted to one of the University of Pennsylvania Health System hospitals. Patients will be invited to participate by a member of the research team prior to hospital discharge.

Individual participant data will not be made available.