Rigorous and rapid evidence assessment in digital health with the evidence DEFINED framework.

Dozens of frameworks have been proposed to assess evidence for digital health interventions (DHIs), but existing frameworks may not facilitate DHI evidence reviews that meet the needs of stakeholder organizations including payers, health systems, trade organizations, and others. These organizations may benefit from a DHI assessment framework that is both rigorous and rapid. Here we propose a framework to assess Evidence in Digital health for EFfectiveness of INterventions with Evaluative Depth (Evidence DEFINED). Designed for real-world use, the Evidence DEFINED Quick Start Guide may help streamline DHI assessment. A checklist is provided summarizing high-priority evidence considerations in digital health. Evidence-to-recommendation guidelines are proposed, specifying degrees of adoption that may be appropriate for a range of evidence quality levels. Evidence DEFINED differs from prior frameworks in its inclusion of unique elements designed for rigor and speed. Rigor is increased by addressing three gaps in prior frameworks. First, prior frameworks are not adapted adequately to address evidence considerations that are unique to digital health. Second, prior frameworks do not specify evidence quality criteria requiring increased vigilance for DHIs in the current regulatory context. Third, extant frameworks rarely leverage established, robust methodologies that were developed for non-digital interventions. Speed is achieved in the Evidence DEFINED Framework through screening optimization and deprioritization of steps that may have limited value. The primary goals of Evidence DEFINED are to a) facilitate standardized, rapid, rigorous DHI evidence assessment in organizations and b) guide digital health solutions providers who wish to generate evidence that drives DHI adoption.

Social motivation in schizophrenia: How research on basic reward processes informs and limits our understanding.

Limited quantity and quality of interpersonal exchanges and relationships predict worse symptomatic and hospitalization outcomes and limit functional recovery in people with schizophrenia. While deficits in social skills and social cognition contribute to much of the impairment in social functioning in schizophrenia, our focus on the current review is social motivation-the drive to connect with others and form meaningful, lasting relationships. We pay particular attention to how recent research on reward informs, and limits, our understanding of the construct. Recent findings that parse out key components of human motivation, especially the temporal nature of reward and effort, are informative for understanding some aspects of social motivation. This approach, however, fails to fully integrate the critical influence of uncertainty and punishment (e.g., avoidance, threat) in social motivation. In the current review, we argue for the importance of experimental paradigms and real-time measurement to capture the interaction between social approach and avoidance in characterizing social affiliation in schizophrenia. We end with suggestions for how researchers might move the field forward by emphasizing the ecological validity of social motivation paradigms, including dynamic, momentary assessment of social reward and punishment using mobile technology and other innovative tools.