Predictors of Unrelieved Symptoms in All of Us Research Program Participants With Chronic Conditions.

CONTEXT: Over half of American adults are diagnosed with a chronic condition, with an increasing prevalence being diagnosed with multiple chronic conditions. These adults are at higher risk for having unrelieved, co-occurring symptoms, known as symptom clusters. OBJECTIVES: To identify symptom phenotypes of patients diagnosed with four common chronic conditions, specifically, cancer, chronic obstructive pulmonary disease, heart failure, and/or type 2 diabetes mellitus, and to understand factors that predict membership in symptomatic phenotypes. METHODS: We conducted a retrospective, cross-sectional analysis using participant responses (N=14,127) to All of Us Research Program, a National Institutes of Health biomedical database, survey questions. We performed hierarchical clustering to generate symptom phenotypes of fatigue, emotional distress, and pain and used multinomial regression to determine if demographic, healthcare access and utilization, and health-related variables predict symptom phenotype. RESULTS: Four phenotypes, one asymptomatic or mildly symptomatic and three highly symptomatic (characterized by severe symptoms, severe pain, and severe emotional distress), were identified. The percentage of participants belonging to the severe symptoms phenotype increased with the number of chronic conditions. Most notably, foregoing or delaying medical care and rating mental health as poor or fair increased the odds of belonging to a highly symptomatic phenotype. CONCLUSION: We found meaningful relationships between demographic, healthcare access and utilization, and health-related factors and symptom phenotypes. With the increasing trends of American adults with one or more chronic conditions and a demand to individualize care in the precision health era, it is critical to understand the factors that lead to unrelieved symptoms.

A Sensor-Controlled Digital Game for Heart Failure Self-Care Based on Behavioral Change Frameworks.

Self-care behaviors are critical to manage the adverse impact of heart failure disease. However, engaging in self-care behaviors such as physical activity or daily weight-monitoring can be difficult due to lack of knowledge or motivation. Digital games can serve as an alternative to traditional patient education to provide information and motivate engagement in critical self-care behaviors. In this paper, we describe a sensor-controlled digital game (SCDG) in which game play is driven by the player's real life self-care behaviors. We also present the design and development of the next iteration of the SCDG based on playtesting results and behavioral theoretical frameworks.