A Stress and Pain Self-management m-Health App for Adult Outpatients With Sickle Cell Disease

Our long-term goal is to reduce stress and improve sickle cell disease (SCD) pain control with less opioid use through an intervention with self-management relaxation/distraction exercises (RDE), named You Cope, We Support (YCWS). Americans living with SCD suffer recurrent episodes of acute and chronic pain, both of which are exacerbated by stress. Building on the successes of our prior formative studies, we now propose a well-designed, appropriately powered study to test efficacy of YCWS on outcomes (pain intensity, stress intensity, opioid use) in adults with SCD. We propose to recruit 170 adults for a randomized controlled trial of the short-term (8 weeks) and long-term (6 months) effects of YCWS on outcomes (pain, stress, and opioid use). Stratified on worst pain intensity (\<=5; \>5), we will randomly assign patients to groups: 85 to Control (Self-monitoring of outcomes + alerts/reminders), and 85 to Experimental (Self-monitoring of outcomes + alerts/reminders + use of YCWS \[RDE + Support\]). We will ask participants to report outcomes daily. During weeks 1-8, we will send system-generated alerts/reminders to both groups via phone call, text, or email to facilitate data entry (both groups) and intervention use support (experimental). If the patient does not enter data after 24 hours, study support staff will contact him/her for data entry trouble-shooting (both groups) and YCWS use (experimental). We will time stamp and track participants' online activities to understand the study context and conduct exit interviews on acceptability of the staff and system-generated support. We will analyze data using mixed-effects regression models (short-term, long-term) to account for repeated measurements over time and utilize machine learning to construct and evaluate models predictive of outcomes. Specific aims are: Aim 1: To determine the short-term effects of YCWS. Aim 2: To determine the long-term effects of YCWS. Aim 3 (exploratory): Using machine learning, to develop and evaluate models that predict patient outcomes based on their group assignment and on their personal (e.g., self-efficacy, sex, education, family income, computer experience, etc.,) and environmental characteristics (e.g., distance from care, quality of cell connection, etc.).