DESCRIPTION (provided by applicant): BACKGROUND AND AIMS: Suicide prevention is a top VHA priority. Suicide prevention in every health system is hampered by difficulties with predicting the risk of suicidal behavior, due to low base rates leading to very low positive predictive values. Very recently, machine learning regression tree methods have succeeded in better identifying a group at particularly high risk of suicide post-discharge from military hospitals. This advance is greatly needed, since the first months to year post-discharge has been repeatedly shown to be one of the very highest-risk periods for suicide that is known. Nevertheless, suicide and suicidal behavior risk prediction post-discharge (and at any other time) is still extremely challenging. For instance, this study's Principal Investigator has found that, in the relatively recent past, a large majority (73%) of VHA patients with depression denied suicidal ideation even when asked within 7 days of their suicide death. A clear need exists to develop measures of suicidal behavior risk that are not heavily dependent on patient self-report. Recently, our Co-Investigators conducted nonlinear dynamic analysis of movement data from non-Veteran inpatients and identified a signal that was correlated more strongly to suicidal ideation than any other characteristic tested. RESEARCH DESIGN: A prospective cohort study of 115-300 Veterans will be conducted to determine if the previously-identified specific actigraphy-based measurements highly associated with suicidal ideation in non- Veterans will predict suicidal ideation, suicidal behavior, and/or rehospitalizatin in Veterans. METHODS: An analysis of 115-300 Veterans admitted to the Bedford, Massachusetts VAMC acute psychiatry unit will be conducted. The primary analysis will focus upon 75-200 Veterans with current suicidal ideation or recent suicidal behavior (SI/SB) who do not have a primary psychotic disorder, Alzheimer's, or Parkinson's disease, and who are not undergoing alcohol detoxification. A separate analysis will be conducted of 40-100 patients undergoing alcohol detoxification, half with SI/SB and half without SI/SB. Participants will wear a small, unobtrusive, wristwatch-like actigraph on their nondominant wrist, and complete self-rated and clinician- rated assessments of suicidal ideation, as well as self-rated assessments of the severity of other psychiatric symptoms. A Resiliency Index (RI) will be calculated using nonlinear dynamic analysis of the amplitude of movements over time frames from 6 minutes - 2 hours. These time frames are the periods for which a clear structure to the movement data is evident, with patients with suicidal ideation showing less variation in amplitude than patients without suicidal ideation. If medications given for alcohol detoxification are determined to not interfere with the RI, then a secondary analysis will examine the entire sample of 115-300 Veterans. One Aim will focus upon det...