Personality biomarkers of pathological gambling: A machine learning study

Diligent for better or worse: Conscientiousness is associated with higher likelihood of suicidal behavior and more severe suicidal intent in later life

BACKGROUND

Contradictory findings link trait conscientiousness in mid- and late life to increased healthspan and lifespan, as well as to death by suicide. It remains unclear whether conscientiousness is associated with higher odds of attempting suicide or with more severe suicidal behavior among attempters, and whether its relationship to suicide risk varies with aging-related stressors, such as declining health.

METHODS

In this cross-sectional study comprising 313 depressed adults aged ≥40 years and participating in the Longitudinal Research Program in Late-Life Suicide (Pittsburgh, USA), we employed logistic and linear regression to test whether conscientiousness was associated with the presence of recent suicidal behavior (≤2 years) and with intent severity in recent attempters (n = 84). We further tested whether the above relationships varied based on mental, cognitive, and physical health status, measured as depression severity, cognitive functioning, and the presence/absence of severe physical illness.

RESULTS

Participants were 62.1 years old on average (SD = 7.6), 85% White, and 53% female. Recent attempters had a mean age of 61.8 years at their most recent attempt (SD = 8.5), had lower cognitive functioning and were more likely severely physically ill than comparisons. Conscientiousness was positively associated with a higher likelihood of recent suicidal behavior overall (adjusted OR = 1.44, 95% CI = 1.09, 1.90, p = .010), but not in case of co-occurring severe physical illness (interaction OR = 0.54, 95% CI = 0.30, 0.97, p = .039). Conscientiousness was also positively associated with suicidal intent at the most recent attempt (adjusted β = 1.60, SE = 0.62, p = .012), explaining 7% of its variance, although this association lost significance after adjusting for other personality dimensions.

CONCLUSIONS

Highly conscientious middle-aged and older adults may be at increased risk of resolute suicidal behavior, although conscientiousness may not confer additional suicide risk among those severely physically ill.

Using artificial intelligence algorithms to predict self-reported problem gambling with account-based player data in an online casino setting

In recent years researchers have emphasized the importance of artificial intelligence (AI) algorithms as a tool to detect problem gambling online. AI algorithms require a training dataset to learn the patterns of a prespecified group. Problem gambling screens are one method for the collection of the necessary input data to train AI algorithms. The present study's main aim was to identify the most significant behavioral patterns which predict self-reported problem gambling. In order to fulfil the aim, the study analyzed data from a sample of real-world online casino players and matched their self-report (subjective) responses concerning problem gambling with the participants' actual (objective) gambling behavior. More specifically, the authors were given access to the raw data of 1,287 players from a European online gambling casino who answered questions on the Problem Gambling Severity Index (PGSI) between September 2021 and February 2022. Random forest and gradient boost machine algorithms were trained to predict self-reported problem gambling based on the independent variables (e.g., wagering, depositing, gambling frequency). The random forest model predicted self-reported problem gambling better than gradient boost. Moreover, problem gamblers showed a distinct pattern with respect to their gambling based on the player tracking data. More specifically, problem gamblers lost more money per gambling day, lost more money per gambling session, and deposited money more frequently per gambling session. Problem gamblers also tended to deplete their gambling accounts more frequently compared to non-problem gamblers. A subgroup of problem gamblers identified as being at greater harm (based on their response to PGSI items) showed even higher values with respect to the aforementioned gambling behaviors. The study showed that self-reported problem gambling can be predicted by AI algorithms with high accuracy based on player tracking data.

The Association between the Five-factor Model of Personality and Problem Gambling: a Meta-analysis

This meta-analysis examined the associations between five-factor personality model traits and problem gambling. To be eligible for inclusion in the meta-analysis, studies had to provide effect size data that quantified the magnitude of the association between all five personality traits and problem gambling. Studies also had to use psychometrically sound measures. The meta-analysis included 20 separate samples from 19 studies and 32,222 total participants. The results showed that problem gambling was significantly correlated with the five-factor model of personality. The strongest personality correlate of problem gambling was neuroticism r = .31, p = < 0.001, 95% CI [0.17, 0.44], followed by conscientiousness r = - .28, p = < 0.001, 95% CI [-0.38,-0.17] ), agreeableness r = - .22, p = < 0.001, 95% CI [-0.34, - 0.10], openness r = - .17, p = < 0.001, 95% CI [-0.22,-0.12], and extraversion r = - .11, p = .024, 95% CI [-0.20,-0.01]. These results suggest problem gamblers tend to share a common personality profile - one that could provide clues as to the most effective ways to prevent and to treat problem gambling.

Personality construct as a biomarker in fibromyalgia: A narrative review from an autonomic rehabilitation perspective

BACKGROUND

The heterogeneity of symptoms and ineffective treatment raise questions about the current diagnostic criteria of fibromyalgia (FM). Misdiagnosis of FM often leads to less than efficacious treatment and poor quality of life.

OBJECTIVE

This article reviews relevant evidence-based literature on personality traits in FM patients with an autonomic dysfunction perspective based on a hierarchical model to explain the utility of considering the personality trait in FM diagnosis.

METHODS

A narrative review of articles concerning chronic pain, FM, and personality traits with respect to autonomic dysfunction in FM was conducted after extensive relevant literature searches.

RESULTS

Reports discussing the predisposing factors, including coping styles, anger, suicide risk, a lack of physical activity and social support, in maintaining persistent pain in FM exist. Relationships between pain duration and severity and personality traits like neuroticism and extraversion have been reported. Coexisting clinical manifestations of FM like sleep disorders, anxiety, and intestinal irritability indicate autonomic dysfunction.

CONCLUSIONS

This article lays out a constructive framework for individualized and personalized medicine for the effective rehabilitation of FM patients. The quest to find a definitive diagnosis of FM should include personality biomarkers that might translate into personalized medicine. An individualistic approach may bank upon artificial intelligence algorithms for both diagnostic as well as prognostic purposes in FM.

A Multi-Method Investigation of Normative and Pathological Personality Across the Spectrum of Gambling Involvement

Pathological Gambling (PG) has been linked to both specific personality traits and personality disorders (PDs). However, previous studies have used a wide variety of research designs that preclude clear conclusions about the personality features that distinguish adults with PG from other groups. The current investigation seeks to advance this research by using a sample including adults who do not gamble, who gamble socially, and who exhibit PG, using self-report, informant-report, and interview-rated measures of personality traits and disorders. A total of 245 adults completed measures of gambling behaviour and problems, as well as normative and pathological personality over two assessment visits. A multivariate ANCOVA was conducted to investigate differences between groups. Analyses supported numerous group differences including differences between all groups on the Neuroticism facet of Impulsivity, and between non-gambling/socially gambling and PG groups on the Conscientiousness facet of Self-Discipline. Adults with PG exhibited more symptoms of Borderline, Paranoid, Schizotypal, Avoidant, and Dependent PDs than adults who gamble socially or not at all. The current investigation provides a comprehensive survey of personality across a wide range of gambling involvement, using a multi-method approach. Our findings help to clarify the most pertinent personality risk factors for PG.

The Relationship Between Gambling Problems and the Five-Factor Model of Personality: A Systematic Review and Meta-Analysis

Objectives: The aim of the present meta-analysis was to synthesize results from the association between problem gambling (PG) and dimensions of the five factor model of personality and to identify potential moderators (gambling diagnosis: yes/no, comorbidity: yes/no and trait assessment: four or fewer items vs. five items or more) of these associations in meta-regressions. Methods: Searches were conducted in six databases; Medline, Web of Science, PsychInfo, Google Scholar, OpenGrey, and Cochrane Library (conducted on February, 22, 2021). Included studies: (1) reported a relationship between PG and at least one of the personality traits in the five-factor model, (2) contained information of zero-order correlations or sufficient data for such calculations, and (3) were original articles published in any European language. Case-studies, qualitative studies, and reviews were excluded. All articles were independently screened by two authors. Final agreement was reached through discussion or by consulting a third author. Risk of bias of the included studies was assessed by the Newcastle-Ottawa Scale. Data were synthesized using a random effects model. Results: In total 28 studies, comprising 20,587 participants, were included. The correlations between PG and the traits were as follows: Neuroticism: 0.273 (95% CI = 0.182, 0.358), conscientiousness -0.296 (95% CI = -0.400, -0.185), agreeableness -0.163 (95% CI = -0.223, -0.101), openness -0.219 (95% CI = -0.308, -0.127), and extroversion -0.083 (95% CI = -0.120, -0.046). For all meta-analyses the between study heterogeneity was significant. Presence of gambling diagnosis was the only moderator that significantly explained between-study variance showing a more negative correlation to extroversion when participants had a gambling diagnosis compared to when this was not the case. Discussion: The results indicated some publication bias. Correcting for this by a trim-and-fill procedure showed however that the findings were consistent. Clinicians and researchers should be aware of the associations between personality traits and PG. Previous studies have for example showed neuroticism to be related to treatment relapse, low scores on conscientiousness to predict treatment drop-out and agreeableness to reduce risk of treatment drop-out. Systematic Review Registration: PROSPERO (CRD42021237225).

Cue-induced effects on decision-making distinguish subjects with gambling disorder from healthy controls

While an increased impact of cues on decision-making has been associated with substance dependence, it is yet unclear whether this is also a phenotype of non-substance-related addictive disorders, such as gambling disorder (GD). To better understand the basic mechanisms of impaired decision-making in addiction, we investigated whether cue-induced changes in decision-making could distinguish GD from healthy control (HC) subjects. We expected that cue-induced changes in gamble acceptance and specifically in loss aversion would distinguish GD from HC subjects. Thirty GD subjects and 30 matched HC subjects completed a mixed gambles task where gambling and other emotional cues were shown in the background. We used machine learning to carve out the importance of cue dependency of decision-making and of loss aversion for distinguishing GD from HC subjects. Cross-validated classification yielded an area under the receiver operating curve (AUC-ROC) of 68.9% (p = .002). Applying the classifier to an independent sample yielded an AUC-ROC of 65.0% (p = .047). As expected, the classifier used cue-induced changes in gamble acceptance to distinguish GD from HC. Especially, increased gambling during the presentation of gambling cues characterized GD subjects. However, cue-induced changes in loss aversion were irrelevant for distinguishing GD from HC subjects. To our knowledge, this is the first study to investigate the classificatory power of addiction-relevant behavioral task parameters when distinguishing GD from HC subjects. The results indicate that cue-induced changes in decision-making are a characteristic feature of addictive disorders, independent of a substance of abuse.

An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning

Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance.

Gambling disorder in adolescents: what do we know about this social problem and its consequences?

Gambling disorder (GD) is a psychiatric condition and it is characterized by a maladaptive pattern of gambling behavior that persists despite negative consequences in major areas of life functioning. In Italy, CNR (National Research Council) underlined how over 17 million, 42.8% of the population aged 15-64 have a gambling behavior. Among them, there are over one million students, aged 15-19, equal to 44.2% of Italian students; the number of minors in Italy with GD in 2017 was 580,000, equal to 33.6%. Various psychosocial treatment models have been adapted for GD; on the other hand no drug has received regulatory approval in any jurisdiction as a specific psychopharmacological treatment for GD. Family therapy interventions for treatment of substance abuse problems have been adapted for adolescents GD. Given the increasing overall prevalence of adolescent gambling, it is imperative that Pediatricians appreciate that gambling problems can also afflict adolescents. In conclusion underage gambling appears to be associated positively with alcohol, tobacco and other substance use, as well as with other individual behaviors, therefore we need that collaborative efforts between scientific societies, government and stake holders can influence the uptake of research findings necessary to implement social policies and design effective public health intervention options. Educational-based problem gambling prevention programs are important avenues in targeting at-risk behaviors among adolescents to prevent an escalation of problematic behaviors into adulthood.

The predictive capacity of DSM-5 symptom severity and impulsivity on response to cognitive-behavioral therapy for gambling disorder: A 2-year longitudinal study

BACKGROUND

DSM-5 proposed a new operational system by using the number of fulfilled criteria as an indicator of gambling disorder severity. This method has proven to be controversial among researchers and clinicians alike, due to the lack of studies indicating whether severity, as measured by these criteria, is clinically relevant in terms of treatment outcome. Additionally, numerous studies have highlighted the associations between gambling disorder and impulsivity, though few have examined the impact of impulsivity on long-term treatment outcomes.

METHODS

In this study, we aimed to assess the predictive value of DSM-5 severity levels on response to cognitive-behavioral therapy (CBT) in a sample of male adults seeking treatment for gambling disorder (n = 398). Furthermore, we explored longitudinal predictors of CBT treatment response at a follow-up, considering UPPS-P impulsivity traits.

RESULTS

Our study failed to identify differences in treatment outcomes between patients categorized by DSM-5 severity levels. Higher baseline scores in negative urgency predicted relapse during CBT treatment, and higher levels of sensation seeking were predictive of drop-out from short-term treatment, as well as of drop-out at 24-months.

CONCLUSIONS

These noteworthy findings raise questions regarding the clinical utility of DSM-5 severity categories and lend support to the implementation of dimensional approaches for gambling disorder.