PATTERNS OF SYMPTOM ONSET AND REMISSION IN EPISODES OF HOPELESSNESS DEPRESSION

Authors


  • All work was performed at Temple University and University of Wisconsin-Madison.

Correspondence to: Brian M. Iacoviello, Psychiatry Department, Mount Sinai School of Medicine, 1 Gustave L Levy Pl., Box 1230, New York, NY 10029. E-mail: brian.iacoviello@mssm.edu

Abstract

Background

Hopelessness depression (HD) is a subtype of depression postulated by the Hopelessness Theory of Depression to present as a constellation of symptoms occurring when an individual with a specific cognitive vulnerability (negative inferential style) experiences negative life events. In the current study, the course of HD episodes was evaluated prospectively and analyzed to explore patterns of symptom onset and remission.

Methods

In 169 HD episodes reported by 65 participants, survival analyses were conducted on the time to onset or remission for 29 individual symptoms. Survival analyses yielded probability density graphs for risk of onset and risk of offset that indicated whether the symptom tended to appear or remit early, late, or unpredictably during the episode.

Results

The symptom of hopelessness often appeared earliest in HD episodes, followed by self-blame, brooding/worry, decreased self-esteem, dependency, and decreased appetite. Hopelessness, decreased self-esteem, self-blame, brooding/worry, dependency, and increased appetite were typically the latest symptoms to remit.

Conclusions

The current study provided evidence for patterns of symptom onset and remission in HD episodes. Hopelessness and other symptoms predicted to appear according to the Hopelessness Theory were generally the earliest to appear, latest to remit, and appeared to form the core syndrome of these HD episodes. Identifying patterns of symptom onset and remission may provide a tool for subtyping depression episodes. Clinically, these results point to the utility of attending to patterns of symptom onset and remission in patients presenting with HD episodes, particularly for treatment planning and monitoring.

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