Suicidal ideation and behavior are growing public health problems in the United States. Unfortunately, our current ability to predict suicide is only slightly above chance, which may be attributable to an overreliance on distal/cross-sectional risk factors that are weak proximal predictors of suicide risk. Modeling the complex process by which atypical sleep impacts daily functioning in conjunction with established proximal risk factors can aid in identifying contexts and time periods of greatest suicidal risk, modeled at the individual level. The proposed study builds upon our team?s extensive expertise in sleep/wake cycles, psychophysiology, deep phenotyping, and multi-method, multivariate, ecologically valid models of suicide vulnerability in high-risk psychiatric populations. We will examine how a holistic model of atypical sleep relates to known trait (baseline neurocognitive performance; e.g., greater impulsive tendencies, higher loss sensitivity, reduced ability to regulate emotions) and state (time-varying, occurring hours to days before SI/SB; e.g., momentary fluctuations in emotional reactivity, impulsivity; greater emotional lability; greater isolative tendencies), risk factors for suicide, and examine how these factors together proximally influence suicidal ideation and confer risk for future suicidal behavior. We will recruit 200 psychiatric inpatients at high risk for suicide and conduct a baseline assessment of sleep/wake functioning and trait risk factors and use laboratory-based tasks coupled with psychophysiology (i.e., event- related potentials, heart rate variability, and electrodermal activity) to phenotype risk processes linked to arousal and cognitive systems. This baseline assessment will be followed by four weeks of EMA and digital phenotyping coupled with SAFTE-derived actigraphy to characterize key state risk factors. We will conduct follow-up assessments at 1-, 3-, and 6-months post hospital discharge to determine how our proximal model of risk prospectively predicts SI and SB. The proposed study aims to characterize proximal risk for suicide using intensive longitudinal methods and to identify ?windows? of greatest risk for suicide, which may vary from person to person, that serve as markers for intensive intervention. Finally, we will leverage this extensive dataset to develop a model of the sleep-suicide relationship emphasizing the contribution of trait and state factors. The results of this study have the potential to greatly enhance our understanding of the phenomenology of suicide risk as it exists in the real world, with the potential to improve our ability to predict, prevent, and intervene using both traditional and technology-enhanced psychotherapies.
Suicidal ideation and suicidal behavior are increasingly serious public health concerns in the United States. We propose the development of a sleep-based, integrated, multi-method model of proximal suicide risk emphasizing near-term and proximal measures of risk and derived from a combination of intensive ecological momentary assessment, digital phenotyping, laboratory-based behavioral assessments, peripheral psychophysiological biomarkers, and cross-sectional measures including clinical interviews and self-report measures.