Importance-There are concerns about the current quality of undergraduate medical education (UME) and its effect on students' well-being.Objective-This systematic review was designed to identify best practices for UME learning environment interventions that are associated with improved emotional well-being of students.Data Sources-Learning environment interventions were identified by searching the biomedical electronic databases Ovid MEDLINE, EMBASE, the Cochrane Library, and the ERIC database from the database inception dates to October 2016. Studies examined any intervention designed to promote medical students' emotional well-being in the setting of a US academic medical school, with an outcome defined as students' reports of well-being as assessed by surveys, semistructured interviews, or other quantitative methods.Data Extraction and Synthesis-Two investigators independently reviewed abstracts and full-text articles. Data were extracted into tables to summarize results. Study quality was assessed by the Medical Education Research Study Quality Instrument (MERQSI), which has a possible range of 5-18; higher scores indicate higher design and methods quality, and a score of ≥ 14 indicates a high-quality study.Findings-Twenty-eight articles including at least 8224 participants met eligibility criteria. Study designs included single-group cross-sectional or post-test only (n=10), single-group pre-/ post-test (n=2), nonrandomized two-group (n=13), and randomized clinical trial (n=3); 93% were

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PTSD due to acute cardiovascular events may be uniquely defined by enduring perceptions of somatic threat. We tested whether PTSD at 1 month post-acute coronary syndrome (ACS) indeed required both high peritraumatic threat during the ACS and ongoing cardiac threat perceptions. We assessed peritraumatic threat during emergency department (ED) enrollment of 284 patients with a provisional ACS diagnosis, and cardiac threat perceptions and PTSD symptoms 1 month post-discharge. In a multiple regression model with adjustment for important covariates, ED threat perceptions were associated with higher 1-month PTSD symptoms only among those with high levels of ongoing cardiac threat.

Compared to the traditional one-size-fits-all, nomothetic model that generalizes population-evidence for individuals, the proposed N-of-1 model can better capture the individual difference in their stressbehavior pathways. In this paper, we demonstrate it is feasible to perform personalized exercise behavior prediction, mainly made possible by mobile health technology and machine learning analytics.