Investigating Mechanisms of Mindfulness and Positive Fantasizing as intervention Techniques for Reducing Perseverative Cognition in Remitted Depressed Individuals

Rasouli, Kaveh (2024) Investigating Mechanisms of Mindfulness and Positive Fantasizing as intervention Techniques for Reducing Perseverative Cognition in Remitted Depressed Individuals. Bachelor's Thesis, Artificial Intelligence.

Preview	Text thesiskavehS4770390.pdf Download (867kB) | Preview
	Text Toestemming Rasouli.pdf Restricted to Registered users only Download (137kB)

Abstract

Major depressive disorder (MDD) involves mood, cognitive, and psychomotor abnormalities, with a high recurrence rate— around 80% of remitted patients relapse. MDD patients often engage in rumination, or perseverative cognition (PC). Positive fantasizing and mindfulness are effective interventions for reducing PC and preventing relapse. Positive fantasizing uses positive imagery to counter dysfunctional attitudes, while mindfulness increases awareness of the present. This study explores how these interventions affect PC in remitted MDD (rMDD) and never-depressed (ND) individuals. Participants performed an app-based sustained attention to response task (SART), a go/no-go task measuring PC through self-reported thoughts and task performance. Research suggests that off-task patterns resembling PC are linked to poorer and more variable task performance. Results showed that both interventions reduced response times. Mindfulness improved go-trial accuracy and increased on-task thinking in ND controls but reduced it in rMDD individuals. Fantasizing improved accuracy at go trials in ND controls and increased on-task thinking in both groups. ND controls had a greater difference in response times between on-task and off-task conditions than rMDD individuals. Overall, mindfulness and fantasizing reduced PC but had different effects on task performance and focus, with mindfulness reducing task focus in rMDD individuals and positive fantasizing having a stronger impact on ND controls.

Item Type:	Thesis (Bachelor's Thesis)
Supervisor name:	Vugt, M.K. van and Besten, M.E.
Degree programme:	Artificial Intelligence
Thesis type:	Bachelor's Thesis
Language:	English
Date Deposited:	29 Aug 2024 08:18
Last Modified:	29 Aug 2024 08:18
URI:	https://fse.studenttheses.ub.rug.nl/id/eprint/34102

Actions (login required)

View Item