Background Although a growing body of literature highlights the potential benefit of smartphone-based mobile apps to aid in self-management and treatment of bipolar disorder, it is unclear whether such evidence-based apps are readily available and accessible to a user of the app store. Results Using our systematic framework for the evaluation of mental health apps, we analyzed the accessibility, privacy, clinical foundation, features, and interoperability of the top-returned 100 apps for bipolar disorder. Only 56% of the apps mentioned bipolar disorder specifically in their title, description, or content. Only one app’s efficacy was supported in a peer-reviewed study, and 32 apps lacked privacy policies. The most common features provided were mood tracking, journaling, and psychoeducation. Conclusions Our analysis reveals substantial limitations in the current digital environment for individuals seeking an evidence-based, clinically usable app for bipolar disorder. Although there have been academic advances in development of digital interventions for bipolar disorder, this work has yet to be translated to the publicly available app marketplace. This unmet need of digital mood management underscores the need for a comprehensive evaluation system of mental health apps, which we have endeavored to provide through our framework and accompanying database (apps.digitalpsych.org).

Background - Postoperative atrial fibrillation (PoAF) remains a significant risk factor for increased morbidity and mortality after cardiac surgery. The ability to accurately identify patients at risk through clinical risk factors is limited. There is growing evidence that polygenic risk contributes significantly to PoAF, and incorporating measures of genetic risk could enhance prediction. Methods - A retrospective cohort study of 1,047 patients of white European ancestry who underwent either coronary artery bypass grafting (CABG) or valve surgery at a tertiary academic center, and were free from a history or persistent preoperative AF. The primary outcome was defined as PoAF based on postoperative ECG reports, medical record documentation, and changes in medication. The exposure was a polygenic risk score (PRS) comprising 2,746 SNPs previously associated with AF risk. The prediction of PoAF risk was assessed using measures of model discrimination, calibration, and net reclassification improvement (NRI). Results - A total of 259 patients (24.7%) developed PoAF. The PRS was significantly associated with a higher risk for PoAF (OR = 1.63 per standard deviation increase in PRS; 95% CI, 1.41-1.90). Addition of PRS to patient- and procedure-related predictors of PoAF significantly increased the C statistic from 0.742 to 0.782 (change in C statistic, 0.040; 95% CI, 0.021-0.060) while maintaining good calibration. The addition of the PRS to patient- and procedure-related predictors of postoperative AF improved model fit (likelihood ratio test p = 2.8 x 10 -15 ) and significantly improved measures of reclassification (NRI, 0.158; 95% CI, 0.066- 0.274). Conclusions - The PRS for PoAF was associated with improved discrimination, calibration, and risk reclassification compared to conventional clinical predictors suggesting that a PoAF PRS may enhance risk prediction of PoAF in patients undergoing CABG or valve surgery.

Smartphone applications related to coronavirus disease 2019 (COVID-19) continue to emerge and evolve, but despite a wide variety of different app functions, there has yet to be a comprehensive study of what the most prevalent publicly available apps provide, and there exists no standardized evaluation system for end users to determine the safety and efficacy of an app before they download it. Furthermore, limited oversight means that the rapidly growing space creates challenges for end users trying to find a relevant app. We adapted the M-Health Index and Navigation Database (MIND) from apps.digitalpsych.org that previously has been used to evaluate mental health applications to guide the assessment of COVID apps. Using this framework, we conducted a thorough analysis of the top-100 returned coronavirus apps on two separate dates a month apart to understand the clinical utility and features of COVID-19 apps and how these change in a short period of time. We ultimately identified a significant turnover rate, as well as privacy concerns around lack of privacy policies and disclosure of personal information. Our research offers insight into the current status of COVID-19 apps and provides a comprehensive and adaptable framework to help individuals assess the growing number of such digital tools in the wake of the pandemic.