Generating a prediction Model of Impulsive control Behaviours: I. Putative risk factors, mediators and moderators of risky behaviours in a general population sample

Generating a Prediction Model of Impulse Control Behaviours: I. Putative risk factors, mediators and moderators of risky behaviours in a general population sample Mahdis vagharimehr Abstract Background Taking risks in life is inevitable. It's how human beings can grow and find their own limitations and potentials. So, risks are important and necessary to take, regardless of your age. However, what those risks lead us to is important. Are they leading us to meaningful experiences and growth, or are they maladaptive? Behaviours like riding in cars with drunk drivers, carrying weapons, engaging in unprotected sexual activities leading to unwanted pregnancies or STDs, abusing alcohol or drugs, or engaging in unhealthy dietary behaviours can be called maladaptive forms of risk. The psychological factors that place individuals at risk of maladaptive and distressing impulse control disorders (ICD) like hypersexuality, compulsive eating, and gambling disorder, are uncertain. Aim The purpose of this study is to understand why some individuals are more likely to keep engaging in behaviours when the consequences are maladaptive for them and or others. The study explores how risky behaviours may share transdiagnostic psychological traits with Impulse Control Disorders (ICD), including impulsivity, risk-taking, and anxiety, but do not meet the criteria for ICD diagnosis. By identifying these factors, the study hopes to contribute to a better understanding of the underlying mechanisms that lead to maladaptive forms of risk-taking. Design This is a non-randomized, cross-sectional exploratory survey study. Participants One hundred forty-seven participant answer sheets were used for analysis in this study. The calculation was based on a mediation analysis that assumed a small effect size for the direct path (τ′ = 0.14) and small-medium indirect effect sizes for both mediator paths (α = 0.26, β = 0.26, as described in Fritz and MacKinnon (2007)). Recruitment: A convenience sample of adults aged 18+ years was recruited from an online crowd-sourcing community (https://www.prolific.co/) and social media. Participants were paid an hourly rate of £8.00 pro rata (rounded up to the nearest 15-minute interval) or a 15 euro-valued Amazon voucher, respectively. Results The mediation analysis examined the relationships between DERS, motor impulsivity, and distress (RISC_Distress) and gratification (RISC_Pleasure) outcomes. For RISC_Distress, DERS had a significant direct effect (b = 0.264, p < .001), and motor impulsivity acted as a substantial mediator (indirect effect: b = 0.075, p < .011). The total effect of DERS on RISC_Distress was also significant (b = 0.340, p < .001). For RISC_Pleasure, the direct effect of DERS was non-significant, but motor impulsivity was a significant mediator (indirect effect: b = 0.089, p < .007). The total effect of DERS on RISC_Pleasure was significant (b = 0.195, p < .004). These findings highlight the role of motor impulsivity as a mediator between DERS and both distress and gratification behaviours. Conclusions The mediation analysis revealed significant contributions of both DERS and Motor Impulsivity in understanding distress and gratification behaviors. Emotional dysregulation directly influenced distress behaviour, with Motor Impulsivity mediating this relationship. Although the direct effect of emotional dysregulation on gratification behavior was non-significant, Motor Impulsivity acted as a significant mediator. Collectively, DERS and Motor Impulsivity accounted for a substantial portion of the variance in both distress and gratification behaviours. These findings emphasize the interplay between emotional dysregulation, impulsivity, and adaptive behaviours, highlighting their importance in interventions targeting distress and promoting well-being.