Both impulsivity and compulsivity have been identified as risk factors for problematic use of the internet (PUI). Yet little is known about the relationship between impulsivity, compulsivity and individual PUI symptoms, limiting a more precise understanding of mechanisms underlying PUI.

The current study is the first to use network analysis to (a) examine the unique association among impulsivity, compulsivity and PUI symptoms, and (b) identify the most influential drivers in relation to the PUI symptom community.

We estimated a Gaussian graphical model consisting of five facets of impulsivity, compulsivity and individual PUI symptoms among 370 Australian adults (51.1% female, mean age = 29.8, s.d. = 11.1). Network structure and bridge expected influence were examined to elucidate differential associations among impulsivity, compulsivity and PUI symptoms, as well as identify influential nodes bridging impulsivity, compulsivity and PUI symptoms.

Results revealed that four facets of impulsivity (i.e. negative urgency, positive urgency, lack of premeditation and lack of perseverance) and compulsivity were related to different PUI symptoms. Further, compulsivity and negative urgency were the most influential nodes in relation to the PUI symptom community due to their highest bridge expected influence.

The current findings delineate distinct relationships across impulsivity, compulsivity and PUI, which offer insights into potential mechanistic pathways and targets for future interventions in this space. To realise this potential, future studies are needed to replicate the identified network structure in different populations and determine the directionality of the relationships among impulsivity, compulsivity and PUI symptoms.

“Subsyndromal” obsessive-compulsive disorder symptoms (OCDSs) are common and cause impaired psychosocial functioning. OCDSs are better captured by dimensional models of psychopathology, as opposed to categorical diagnoses. However, such dimensional approaches require a deep understanding of the underlying neurocognitive drivers and impulsive and compulsive traits (ie, neurocognitive phenotypes) across symptoms. This study investigated inhibitory control and self-monitoring across impulsivity, compulsivity, and their interaction in individuals (n = 40) experiencing mild–moderate OCDSs.

EEG recording concurrent with the stop-signal task was used to elicit event-related potentials (ERPs) indexing inhibitory control (ie, N2 and P3) and self-monitoring (ie, error-related negativity and correct-related negativity (CRN): negativity following erroneous or correct responses, respectively).

During unsuccessful stopping, individuals high in both impulsivity and compulsivity displayed enhanced N2 amplitude, indicative of conflict between the urge to respond and need to stop (F(3, 33) = 1.48, P < .05, 95% Cl [−0.01, 0.001]). Individuals high in compulsivity and low in impulsivity showed reduced P3 amplitude, consistent with impairments in monitoring failed inhibitory control (F(3, 24) = 2.033, P < .05, 95% CI [−0.002, 0.045]). Following successful stopping, high compulsivity (independent of impulsivity) was associated with lower CRN amplitude, reflecting hypo-monitoring of correct responses (F(4, 32) = 4.76, P < .05, 95% CI [0.01, 0.02]), and with greater OCDS severity (F(3, 36) = 3.32, P < .05, 95% CI [0.03, 0.19]).

The current findings provide evidence for differential, ERP-indexed inhibitory control and self-monitoring profiles across impulsive and compulsive phenotypes in OCDSs.

Poor mental health is a state of psychological distress that is influenced by lifestyle factors such as sleep, diet, and physical activity. Compulsivity is a transdiagnostic phenotype cutting across a range of mental illnesses including obsessive–compulsive disorder, substance-related and addictive disorders, and is also influenced by lifestyle. Yet, how lifestyle relates to compulsivity is presently unknown, but important to understand to gain insights into individual differences in mental health. We assessed (a) the relationships between compulsivity and diet quality, sleep quality, and physical activity, and (b) whether psychological distress statistically contributes to these relationships.

We collected harmonized data on compulsivity, psychological distress, and lifestyle from two independent samples (Australian n = 880 and US n = 829). We used mediation analyses to investigate bidirectional relationships between compulsivity and lifestyle factors, and the role of psychological distress.

Higher compulsivity was significantly related to poorer diet and sleep. Psychological distress statistically mediated the relationship between poorer sleep quality and higher compulsivity, and partially statistically mediated the relationship between poorer diet and higher compulsivity.

Lifestyle interventions in compulsivity may target psychological distress in the first instance, followed by sleep and diet quality. As psychological distress links aspects of lifestyle and compulsivity, focusing on mitigating and managing distress may offer a useful therapeutic approach to improve physical and mental health. Future research may focus on the specific sleep and diet patterns which may alter compulsivity over time to inform lifestyle targets for prevention and treatment of functionally impairing compulsive behaviors.

Compulsivity can be seen across various mental health conditions and refers to a tendency toward repetitive habitual acts that are persistent and functionally impairing. Compulsivity involves dysfunctional reward-related circuitry and is thought to be significantly heritable. Despite this, its measurement from a transdiagnostic perspective has received only scant research attention. Here we examine both the psychometric properties of a recently developed compulsivity scale, as well as its relationship with compulsive symptoms, familial risk, and reward-related attentional capture.

Two-hundred and sixty individuals participated in the study (mean age = 36.0 [SD = 10.8] years; 60.0% male) and completed the Cambridge-Chicago Compulsivity Trait Scale (CHI-T), along with measures of psychiatric symptoms and family history thereof. Participants also completed a task designed to measure reward-related attentional capture (n = 177).

CHI-T total scores had a normal distribution and acceptable Cronbach’s alpha (0.84). CHI-T total scores correlated significantly and positively (all p < 0.05, Bonferroni corrected) with Problematic Usage of the Internet, disordered gambling, obsessive-compulsive symptoms, alcohol misuse, and disordered eating. The scale was correlated significantly with history of addiction and obsessive-compulsive related disorders in first-degree relatives of participants and greater reward-related attentional capture.

These findings suggest that the CHI-T is suitable for use in online studies and constitutes a transdiagnostic marker for a range of compulsive symptoms, their familial loading, and related cognitive markers. Future work should more extensively investigate the scale in normative and clinical cohorts, and the role of value-modulated attentional capture across compulsive disorders.

Impulsivity and compulsivity have been implicated as important transdiagnostic dimensional phenotypes with potential relevance to addiction. We aimed to develop a model that conceptualizes these constructs as overlapping dimensional phenotypes and test whether different components of this model explain the co-occurrence of addictive and related behaviors.

A large sample of adults (N = 487) was recruited through Amazon’s Mechanical Turk and completed self-report questionnaires measuring impulsivity, intolerance of uncertainty, obsessive beliefs, and the severity of 6 addictive and related behaviors. Hierarchical clustering was used to organize addictive behaviors into homogenous groups reflecting their co-occurrence. Structural equation modeling was used to evaluate fit of the hypothesized bifactor model of impulsivity and compulsivity and determine the proportion of variance explained in the co-occurrence of addictive and related behaviors by each component of the model.

Addictive and related behaviors clustered into 2 distinct groups: Impulse-Control Problems, consisting of harmful alcohol use, pathological gambling, and compulsive buying, and Obsessive-Compulsive-Related Problems, consisting of obsessive-compulsive symptoms, binge eating, and internet addiction. The hypothesized bifactor model of impulsivity and compulsivity provided the best empirical fit, with 3 uncorrelated factors corresponding to a general Disinhibition dimension, and specific Impulsivity and Compulsivity dimensions. These dimensional phenotypes uniquely and additively explained 39.9% and 68.7% of the total variance in Impulse-Control Problems and Obsessive-Compulsive-Related Problems.

A model of impulsivity and compulsivity that represents these constructs as overlapping dimensional phenotypes has important implications for understanding addictive and related behaviors in terms of shared etiology, comorbidity, and potential transdiagnostic treatments.

We aimed to determine whether individuals with obsessive-compulsive disorder (OCD) and demographically matched healthy individuals can be clustered into distinct clinical subtypes based on dimensional measures of their self-reported compulsivity (OBQ–44 and IUS–12) and impulsivity (UPPS–P).

Participants (n=217) were 103 patients with a clinical diagnosis of OCD; 79 individuals from the community who were “OCD-likely” according to self-report (Obsessive-Compulsive Inventory–Revised scores equal or greater than 21); and 35 healthy controls. All data were collected between 2013 and 2015 using self-report measures that assessed different aspects of compulsivity and impulsivity. Principal component analysis revealed two components broadly representing an individual's level of compulsivity and impulsivity. Unsupervised clustering grouped participants into four subgroups, each representing one part of an orthogonal compulsive-impulsive phenotype.

Clustering converged to yield four subgroups: one group low on both compulsivity and impulsivity, comprised mostly of healthy controls and demonstrating the lowest OCD symptom severity; two groups showing roughly equal clinical severity, but with opposing drivers (i.e., high compulsivity and low impulsivity, and vice versa); and a final group high on both compulsivity and impulsivity and recording the highest clinical severity. Notably, the largest cluster of individuals with OCD was characterized by high impulsivity and low compulsivity. Our results suggest that both impulsivity and compulsivity mediate obsessive-compulsive symptomatology.

Individuals with OCD can be clustered into distinct subtypes based on measures of compulsivity and impulsivity, with the latter being found to be one of the more defining characteristics of the disorder. These dimensions may serve as viable and novel treatment targets.