Suicide is the second leading cause of death among adolescents and young adults. To better predict and prevent suicidal thoughts and behaviors (STBs), identification of proximal, transdiagnostic risk factors that would serve as viable treatment targets is critically needed. Sleep disturbances represent one such risk factor, but understanding of why sleep and STBs are associated is lacking. To better understand the link between sleep and STBs, data are needed on the full 24-h rest-activity cycle, including activity occurring during waking hours. Alterations in sleep and daytime activity are transdiagnostic features of mental disorders, and are both regulated by a complex system of biological and behavioral rhythms. However, these variables have rarely been studied simultaneously or in the context of STBs. In this project, the putative link between the 24-h rest- activity cycle and STBs will be examined using recent advances in technology (i.e., wearable sensors, smartphones) to monitor these processes as they unfold in real-time. A sample of suicidal adolescents (N=100) will be recruited during hospitalization and followed over the high-risk four-week post-hospitalization period. There are three primary research aims: (1) Examine phenotypes of rest-activity associated with suicidal ideation/attempt over the study period, and examine the unique variance contributed by rest-activity variables predicting STBs compared to other well-known factors (e.g., depression), (2) Examine day-to-day associations between rest-activity and STBs to establish strength of associations and proximal directionality, and (3) Explore idiographic (i.e., individual-level) results to examine qualitative differences between participants, a method recently used to identify personalized treatment targets. Results from this project will provide granular insight into the dynamics between putative transdiagnostic risk factors and STBs, and will shed light on the viability of these factors as proximal treatment targets for the candidate?s planned R01. The accompanying training plan is designed to ensure success of the current project and to support growth of the candidate?s independent program of research focused on understanding, predicting, and intervening upon short-term suicide risk by building expertise in four areas: (1) Designing, implementing and managing real-time monitoring studies among high-risk adolescents, (2) Acquiring statistical skills needed to process and analyze intensive longitudinal data, (3) Expanding content-area expertise in biological and behavioral rhythms regulating the rest- activity cycle and related processes, and (4) Preparing for a future R01 by gaining knowledge in development of technology-delivered, personalized interventions for adolescents. This K23 will take place at Harvard University under the mentorship of: mentor Dr. Matthew Nock, a world-leading adolescent suicide researcher, co-mentor Dr. Evan Kleiman, who has technical expertise in collecting and analyzing real-time monitoring data; consultant Dr. Rosalind Picard, an expert in using wearable sensors to study and intervene on psychopathology, and consultant Dr. Frank Scheer, who studies chronobiology and sleep.
Suicide is the second leading cause of death among adolescents and young adults; however, little research has examined proximal, transdiagnostic risk factors that may inform short-term prediction and prevention efforts. The current project uses real-time monitoring (i.e., active and passive monitoring using smartphone and wearable sensors) to examine the associations between sleep and daytime activity (i.e., the rest-activity cycle) and suicidal thoughts and behaviors among at-risk adolescents. This study will provide novel, fine-grained measurement of the associations between the 24-h cycle of rest-activity and suicidal thoughts and behavior as they unfold in real-time, which will also shed light on the viability of these factors as proximal treatment targets for the subsequent development of real-time interventions for suicide.
