Hoarding disorder studies are primarily based on persons who seek treatment and demonstrate good insight. The aim of the present study is to evaluate whether there are differences between community and treatment-seeking samples of individuals with hoarding disorder (HD).

Fourteen people with HD from the community and twenty treatment-seeking people with HD were assessed by a battery of instruments to evaluate HD features and other associated characteristics.

Compared to the treatment-seeking sample, the HD community sample was older, had poorer insight, and had a lower prevalence of comorbid obsessive-compulsive disorder (OCD). There were no differences in gender, education, presence of psychiatric comorbidities, quality of life, and hoarding behavior characteristics between the samples. The final logistic regression model with the Dimensional Obsessive-Compulsive Scale (DOCS) as the single predictor of treatment-seeking status was statistically significant, indicating that it was able to distinguish between the two samples. The model explained between 20.7% and 27.9% of the variance of subjects, and correctly classified 67.6% of cases.

Our results indicate that there appear to be few differences between the treatment-seeking and community samples of individuals with HD. The presence of comorbid OCD in treatment-seeking groups seems to be more frequent than in HD community samples.

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.

In the present study, we aimed to perform a systematic review evaluating the cognitive performance of patients with hoarding disorder (HD) compared with controls. We hypothesized that HD patients would present greater cognitive impairment than controls.

A systematic search of the literature using the electronic databases MEDLINE, SCOPUS, and LILACS was conducted on May 2020, with no date limit. The search terms were “hoarding disorder,” “cognition,” “neuropsychology,” “cognitive impairment,” and “cognitive deficit.” We included original studies assessing cognitive functioning in patients with HD.

We retrieved 197 studies initially. Of those, 22 studies were included in the present study. We evaluated 1757 patients who were 41 to 72 years old. All selected studies comprised case–control studies and presented fair quality. Contrary to our hypothesis, HD patients showed impairment only in categorization skills in comparison with controls, particularly at confidence to complete categorization tasks. Regarding attention, episodic memory, working memory, information-processing speed, planning, decision-making, inhibitory control, mental flexibility, language, and visuospatial ability, HD patients did not show impairment when compared with controls. There is a paucity of studies on social cognition in HD patients, although they may show deficits. The impact of emotion in cognition is also understudied in HD patients.

Except for categorization skills, the cognitive performance in HD patients does not seem to be impaired when compared with that in controls. Further work is needed to explore social cognition and the impact of emotion in cognitive performance in HD patients.

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 present study explored the influence of romantic love on the expression of several obsessive–compulsive disorder (OCD) characteristics, including symptom severity, symptom dimensions, age at onset, sensory phenomena (SP), and developmental course, as well as other related comorbid disorders. It was hypothesized that love-precipitated OCD would be associated with a set of distinct characteristics and exhibit greater rates of comorbid disorders.

The analyses were performed using a large sample (n = 981) of clinical patients with a primary diagnosis of OCD (Females = 67.3%, M age = 35.31).

Love-precipitated OCD was associated with greater severity of SP and later age at onset of obsessions. However, symptom severity, symptom dimension, developmental course, and psychiatric comorbidities were not associated with love-precipitated OCD.

It was concluded that romantic love does shape the expression of OCD, especially with regard to SP and onset age. These findings encourage further exploration to determine its clinical significance as a phenotype.

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.

The symptoms of obsessive−compulsive disorder (OCD) are highly heterogeneous and it is unclear what is the optimal way to conceptualize this heterogeneity. This study aimed to establish a comprehensive symptom structure model of OCD across the lifespan using factor and network analytic techniques.

A large multinational cohort of well-characterized children, adolescents, and adults diagnosed with OCD (N = 1366) participated in the study. All completed the Dimensional Yale-Brown Obsessive−Compulsive Scale, which contains an expanded checklist of 87 distinct OCD symptoms. Exploratory and confirmatory factor analysis were used to outline empirically supported symptom dimensions, and interconnections among the resulting dimensions were established using network analysis. Associations between dimensions and sociodemographic and clinical variables were explored using structural equation modeling (SEM).

Thirteen first-order symptom dimensions emerged that could be parsimoniously reduced to eight broad dimensions, which were valid across the lifespan: Disturbing Thoughts, Incompleteness, Contamination, Hoarding, Transformation, Body Focus, Superstition, and Loss/Separation. A general OCD factor could be included in the final factor model without a significant decline in model fit according to most fit indices. Network analysis showed that Incompleteness and Disturbing Thoughts were most central (i.e. had most unique interconnections with other dimensions). SEM showed that the eight broad dimensions were differentially related to sociodemographic and clinical variables.

Future research will need to establish if this expanded hierarchical and multidimensional model can help improve our understanding of the etiology, neurobiology and treatment of OCD.

Obsessive-compulsive disorder (OCD) is a prevalent and disabling condition with frequent chronic course. Staging models applied to psychiatric disorders seek to define their extent of progression at a particular time-point and differentiate early, milder clinical phenomena from those characterizing illness progression and chronicity. In OCD patients, a staging model has been recently proposed but not tested yet. This was the aim of the present study.

From an overall sample of 198 OCD patients, recruited across two psychiatric clinics in Northern Italy, 70 patients on stable treatment completed a follow-up assessment ranging from 12 to 24 months. At follow-up initiation, patients had been divided into four staging groups, according to the model proposed by Fontenelle and Yucel. At the end of the follow-up, patients were subdivided into three groups (no stage change, improved stage, or worsened stage) compared with statistical analyses.

At the end of the follow-up, 67.1% patients showed no stage changes, 24.3% a stage improvement, and 8.6% a stage progression. Worsened patients showed higher rates of comorbid disorders and higher rates of unfavorable employment characteristics compared to the other subgroups (P < .05). Patients with worsened stage showed higher prevalence of somatic obsessions (P < .05), while patients with improved stage showed higher rates of magical thinking and violence/harm obsessions compared to other groups (P < .05).

The present results provide epidemiologic and clinical correlates of the first application of a staging model in a sample of OCD patients, encouraging further studies to assess the utility of this approach in the field.

Although problematic pornography use (PPU) will soon be diagnosable through the International Classification for Diseases, 11th revision, its clinical profile remains contentious. The current study assessed whether PPU may be characterized by various symptoms sometimes observed among online recovery forums that currently lack empirical assessment, such as heightened cognitive-affective issues following pornography use and sexual dysfunction with partners as a result of escalating use.

Cross-sectional surveys were completed by male PPUs (N = 138, mean age = 31.75 years, standard deviation = 10.72) recruited via online recovery communities and Amazon Mechanical Turk. Multiple regression analysis was performed using the Problematic Pornography Use Scale as the dependent variable and variables of interest (Arizona Sexual Experiences Scales modified for partnered sex and pornography use, Brunel Mood Scale, Social Interaction Anxiety Scale, and the Tolerance subscale from the Problematic Pornography Consumption Scale) and potential confounders (eg, comorbid psychopathology) as independent variables.

Current levels of pornography use, indicators of tolerance and escalation, greater sexual functioning with pornography, and psychological distress were uniquely associated with PPU severity, while cognitive-affective issues after pornography use, impulsivity and compulsivity were not. Although sexual dysfunction did not predict PPU severity, nearly half the sample indicated sexual dysfunction with intimate partners.

The present findings suggest that PPU may be characterized by tolerance and escalation (as per substance addiction models), greater sexual responsivity toward pornography, and psychological distress. Meanwhile, the high rate of partnered sexual dysfunction observed suggests that PPU might be somewhat separable from other forms of compulsive sexual behavior.

To (1) confirm whether the Habit, Reward, and Fear Scale is able to generate a 3-factor solution in a population of obsessive-compulsive disorder and alcohol use disorder (AUD) patients; (2) compare these clinical groups in their habit, reward, and fear motivations; and (3) investigate whether homogenous subgroups can be identified to resolve heterogeneity within and across disorders based on the motivations driving ritualistic and drinking behaviors.

One hundred and thirty-four obsessive-compulsive disorder (n = 76) or AUD (n = 58) patients were assessed with a battery of scales including the Habit, Reward, and Fear Scale, the Yale-Brown Obsessive-Compulsive Scale, the Alcohol Dependence Scale, the Behavioral Inhibition/Activation System Scale, and the Urgency, (lack of ) Premeditation, (lack of ) Perseverance, Sensation Seeking, and Positive Urgency Impulsive Behavior Scale.

A 3-factor solution reflecting habit, reward, and fear subscores explained 56.6% of the total variance of the Habit, Reward, and Fear Scale. Although the habit and fear subscores were significantly higher in obsessive-compulsive disorder (OCD) and the reward subscores were significantly greater in AUD patients, a cluster analysis identified that the 3 clusters were each characterized by differing proportions of OCD and AUD patients.

While affective (reward- and fear-driven) and nonaffective (habitual) motivations for repetitive behaviors seem dissociable from each other, it is possible to identify subgroups in a transdiagnostic manner based on motivations that do not match perfectly motivations that usually described in OCD and AUD patients.

We assessed self-reported drives for alcohol use and their impact on clinical features of alcohol use disorder (AUD) patients. Our prediction was that, in contrast to “affectively” (reward or fear) driven drinking, “habitual” drinking would be associated with worse clinical features in relation to alcohol use and higher occurrence of associated psychiatric symptoms.

Fifty-eight Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) alcohol abuse patients were assessed with a comprehensive battery of reward- and fear-based behavioral tendencies. An 18-item self-report instrument (the Habit, Reward and Fear Scale; HRFS) was employed to quantify affective (fear or reward) and non-affective (habitual) motivations for alcohol use. To characterize clinical and demographic measures associated with habit, reward, and fear, we conducted a partial least squares analysis.

Habitual alcohol use was significantly associated with the severity of alcohol dependence reflected across a range of domains and with lower number of detoxifications across multiple settings. In contrast, reward-driven alcohol use was associated with a single domain of alcohol dependence, reward-related behavioral tendencies, and lower number of detoxifications.

These results seem to be consistent with a shift from goal-directed to habit-driven alcohol use with severity and progression of addiction, complementing preclinical work and informing biological models of addiction. Both reward-related and habit-driven alcohol use were associated with lower number of detoxifications, perhaps stemming from more benign course for the reward-related and lack of treatment engagement for the habit-related alcohol abuse group. Future work should further explore the role of habit in this and other addictive disorders, and in obsessive-compulsive related disorders.