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How Is the Presence of Company Related to Thwarted Belongingness in Real Time? Taking a Closer Look at the Conceptualization of the Construct of the Interpersonal Theory of Suicide.

Nina HallenslebenHeide GlaesmerThomas ForkmannDajana RathMaria StraussAnette KerstingLena Spangenberg
Published in: International journal of environmental research and public health (2020)
(1) Background: The role of thwarted belongingness (TB) in predicting suicidal ideation, as originally assumed by the Interpersonal Theory of Suicide, is repeatedly challenged by empirical findings. This could be due to an inadequate conceptualization of the construct of TB that is assumed to be influenced by intrapersonal and interpersonal factors. (2) Methods: We examined the associations of TB with intrapersonal variables related to depression, and with interpersonal variables related to an individual's actual social environment. We analyzed data from an ecological momentary assessment study in psychiatric inpatients with depressive disorders. N = 73 participants rated momentary TB, depressive affect and status of company up to 10 times per day, over a period of six days, on smartphones. (3) Results: TB was lower when assessed while participants were in company compared to when they were alone, and the more desired the company was, the less TB was experienced. Individuals who had a partnership experienced less momentary TB. Furthermore, higher levels of momentary depressive affect, as well as more stable levels of depression, were related to higher levels of TB, and the relation between the presence of company and TB was weaker for more depressed persons. (4) Conclusions: Our findings can be seen as evidence that both intrapersonal and interpersonal factors relate to TB, and thus support the conceptualization of TB as proposed by the Interpersonal Theory of Suicide.
Keyphrases
  • mycobacterium tuberculosis
  • mental health
  • depressive symptoms
  • bipolar disorder
  • healthcare
  • machine learning
  • risk assessment
  • stress induced
  • big data