Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions.

We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011–2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016–2018, LS2: 2018–2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample.

Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10–30% of respondents with the highest predicted risk included 44.9–92.5% of 12-month SAs.

An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

The Trial Innovation Network (TIN) is a collaborative initiative within the National Center for Advancing Translational Science (NCATS) Clinical and Translational Science Awards (CTSA) Program. To improve and innovate the conduct of clinical trials, it is exploring the uses of gamification to better engage the trial workforce and improve the efficiencies of trial activities. The gamification structures described in this article are part of a TIN website gamification toolkit, available online to the clinical trial scientific community.

The game designers used existing electronic trial platforms to gamify the tasks required to meet trial start-up timelines to create friendly competitions. Key indicators and familiar metrics were mapped to scoreboards. Webinars were organized to share and applaud trial and game performance.

Game scores were significantly associated with an increase in achieving start-up milestones in activation, institutional review board (IRB) submission, and IRB approval times, indicating the probability of completing site activation faster by using games. Overall game enjoyment and feelings that the game did not apply too much pressure appeared to be an important moderator of performance in one trial but had little effect on performance in a second.

This retrospective examination of available data from gaming experiences may be a first-of-kind use in clinical trials. There are signals that gaming may accelerate performance and increase enjoyment during the start-up phase of a trial. Isolating the effect of gamification on trial outcomes will depend on a larger sampling from future trials, using well-defined, hypothesis-driven statistical analysis plans.