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.

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.

The extent to which obsessive–compulsive and related disorders (OCRDs) are impulsive, compulsive, or both requires further investigation. We investigated the existence of different clusters in an online nonclinical sample and in which groups DSM-5 OCRDs and other related psychopathological symptoms are best placed.

Seven hundred and seventy-four adult participants completed online questionnaires including the Cambridge–Chicago Compulsivity Trait Scale (CHI-T), the Barratt Impulsiveness Scale (BIS-15), and a series of DSM-5 OCRDs symptom severity and other psychopathological measures. We used K-means cluster analysis using CHI-T and BIS responses to test three and four factor solutions. Next, we investigated whether different OCRDs symptoms predicted cluster membership using a multinomial regression model.

The best solution identified one “healthy” and three “clinical” clusters (ie, one predominantly “compulsive” group, one predominantly “impulsive” group, and one “mixed”—“compulsive and impulsive group”). A multinomial regression model found obsessive–compulsive, body dysmorphic, and schizotypal symptoms to be associated with the “mixed” and the “compulsive” clusters, and hoarding and emotional symptoms to be related, on a trend level, to the “impulsive” cluster. Additional analysis showed cognitive-perceptual schizotypal symptoms to be associated with the “mixed” but not the “compulsive” group.

Our findings suggest that obsessive–compulsive disorder; body dysmorphic disorder and schizotypal symptoms can be mapped across the “compulsive” and “mixed” clusters of the compulsive–impulsive spectrum. Although there was a trend toward hoarding being associated with the “impulsive” group, trichotillomania, and skin picking disorder symptoms did not clearly fit to the demarcated clusters.

To examine associations between hours worked and diet quality, frequency of eating out and consuming takeaways.

Data were taken from the National Diet and Nutrition Survey (2008–2014). Associations between hours worked in paid employment and diet quality, assessed using the Diet Quality Index (DQI) and selected foods and nutrients, were tested using linear regression models. Associations between hours worked and frequency of eating out and consuming takeaways were tested using ordinal logistic regression models. All models were adjusted for sex, age, equivalised household income, household composition and household food role.

UK.

Adults (n 2154) aged 19–64 years in employment.

Mean (95 % CI) hours worked per week was 36·1 (35·6, 36·6) and mean DQI score was 41·9 (41·2, 42·5) %. Hours worked was not associated with DQI score, frequency of eating out or consuming takeaways. Hours worked was positively associated with consuming red meat, processed meat and alcohol intake. Adults working more hours had lower intake of fibre but higher total fat and saturated fat intakes if they lived in households with children.

Working hours may not be the main factor driving poor-quality diets among this sample of UK adults in employment. Focusing on consumption of foods prepared outside the household may not be the most efficient way to improve diet quality as effort is needed at all levels. Although it is unclear what is driving the differences in nutrient intakes according to household composition, they are important to consider when developing interventions to improve healthy eating.