1 / 20

The Effect of Negative Mood on Constructs Related to Compulsions

The Effect of Negative Mood on Constructs Related to Compulsions. By Gary Britton & Graham Davey. The Toothbrush Effect. Constructs & Theories of Compulsions. Mood-as-input hypothesis (MacDonald & Davey, 2005) Inflated Responsibility (Salkovskis, 1985)

chen
Download Presentation

The Effect of Negative Mood on Constructs Related to Compulsions

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The Effect of Negative Mood on Constructs Related to Compulsions By Gary Britton & Graham Davey

  2. The Toothbrush Effect

  3. Constructs & Theories of Compulsions • Mood-as-input hypothesis (MacDonald & Davey, 2005) • Inflated Responsibility (Salkovskis, 1985) • Elevated Evidence Requirements (Wahl, Salkovskis & Cotter, 2007) • Not Just Right Experiences (Coles, Frost, Heimberg & Rheaume, 2003) • Intolerance of Uncertainty (Dugas et al., 1998)

  4. Mood-as-input Hypothesis Positive Mood Negative mood ‘AS MANY AS CAN’ STOP RULE ‘FEEL LIKE CONTINUING’ STOP RULE ‘AS MANY AS CAN’ STOP RULE ‘FEEL LIKE CONTINUING’ STOP RULE PERSEVERATION AT A TASK/COMPULSIVE BEHAVIOR PERSEVERATION AT A TASK/COMPULSIVE BEHAVIOR Clinical Interest

  5. Purpose of the Research • The Toothbrush Effect • Risk Factors v Causes • How Do Explanatory Constructs for Compulsions Interact? • Questionnaires & Experiments

  6. Questionnaire Study • Purpose: to explore relationships between possible causal factors involved in OCD and their relationship with different sub-components of OCD • Sample (n = 191; male = 41, female = 150; age = M: 34.26, SD: 13.01) • Non-clinical, student, opportunity sample

  7. Measures • Compulsion Measures: (MOCI, CBOCI, OBQ). • Construct Measures: Responsibility (RAS), Intolerance of Uncertainty (IUS), Not Just Right Experiences (NJRE-QR), Elevated Evidence Requirements, Stop Rules for Checking (AMAC/FLC) • Mood Measures: Depression (BDI), Trait Anxiety (STAI Y2), Trait Mood (PANAS)

  8. Regression Analysis • Focused on compulsions sub-scale of CBOCI as outcome variable • All measures entered into one model using forced entry

  9. Regression Analysis – Results 1 • 4 significant predictors in model: • Negative mood (= .24, p < .001). • AMAC (= .36, p < .001). • Not just right experiences (= .18, p < .05). • Elevated evidence requirements (= -.12, p < .05). • (A negative relationship was expected due to scale used in the EER questionnaire. It represents the negative relationship between low evidence requirements and compulsion scores).

  10. Regression Analysis – Results 2 • All 4 variables remained significant in further exploratory, hierarchical regression analyses • No other predictor variables were significant • Nonsignificant predictor variables were responsibility and intolerance of uncertainty

  11. Schematic Model

  12. Experimental Study • Experimental Manipulation of Predictor Variables • Where do Responsibility & Intolerance of Uncertainty fit in?

  13. Manipulating Mood • Negative Mood & Mood-as-input Predictions • Negative Mood Induces Higher Performance Standards (Scott & Cervone, 2002) • Negative Mood Promotes a Systematic Information-Processing Style (Tiedens & Linton, 2001)

  14. Method • Student sample: (males: 7; females: 52; age: M = 21.03, SD = 5.61). • 2 groups (negative mood group [n = 29] and positive mood group [n = 30]). • Participants were told the experiment was about “music and music comprehension” to help disguise the mood induction.

  15. Mood Induction • Participants were induced into a negative or positive mood through listening to music on headphones shown in previous studies to alter mood in the intended direction (negative mood music: Gyorgy Ligeti, Lux Aeterna; positive mood music; Delibes, Mazurka from Coppelia).

  16. Measures • Participants were asked to fill in a questionnaire (they were told this questionnaire was part of a different experiment) containing questions measuring mood and VAS questions measuring: • responsibility • stop rules • elevated evidence requirements • not just right experiences • intolerance of uncertainty. • Participants were then given a fictitious music comprehension questionnaire as well as full version questionnaires measuring compulsions (CBOCI),responsibility (RAS), intolerance of uncertainty (IUS), not just right experiences (NJRE-QR), elevated evidence requirements, stop rules, and mood

  17. Results – Mood Inductions • Negative group (m = 30.67) significantly sadder than positive group (m = 9.57) (p <.001). • Positive group (m = 73.90) significantly happier than negative group (m = 56.79) (p <.001). • Negative group (m = 37.59) significantly more anxious than positive group (m = 18.20) (p <.01)

  18. Results – Dependent Variables • Negative Group (m=35.8 v 25.7) score significantly higher on responsibility (p<.05) • Negative Group (m=60.3 v 46.8) score significantly higher on AMAC (p<.05) • Positive Group (m=66.6 v 48.4) score significantly higher on FL (p<.05) • Negative Group (m=41.3 v 32.2) score significantly higher on Intolerance of Uncertainty (p<.05, one-tailed) • No effect of Mood on NJRE or Elevated Evidence Requirements (both ps >.1)

  19. Adjusted Model

  20. Future Studies • Experimental Manipulations • Responsibility • Stop Rules • Intolerance of Uncertainty • Construct Overlap • Factor Analysis • Overarching Theoretical Processes • Systematic v Heuristic Processing of Information

More Related