Validation of the english version of the Multidimensional Mentalizing Questionnaire (MMQ).

BACKGROUND: Mentalizing refers to the ability to understand one's own and others' mental states. Mentalizing is considered a key component of social cognition and healthy personality development. A multinational assessment tools able to appraise the multidimensional and multifaceted aspects of this complex construct are needed. OBJECTIVE: The present study had two aims: (a) validate an English version of the Multidimensional Mentalizing Questionnaire (MMQ, 33 items) which was designed to assess mentalizing based on an integrated and multilevel model of mentalizing; (b) explore the correlational relationships between the six dimensions of the MMQ and a set of sociodemographic, psycho-cognitive, mental health, and socio-functional variables. METHODS: Overall, 1823 individuals (age: 19-76 years old [M = 45; SD = 16]; sex: male = 48.51%, female = 50.57%, non-binary = 0.9%) participated in an online survey. While the participants came from 77 different countries, most of them were residents in UK and USA (95%). Data analytics include confirmatory factorial analysis and Pearson correlations. RESULTS: The CFA results validated the factorial structure of a 28-items MMQ-English version, with acceptable goodness of fit indices. Regarding the psychometric properties, the MMQ-English version showed good internal reliability and significant positive correlation with another scale designed to assess an analogue construct showing a fair convergent validity. The findings indicated that males, individuals with lower levels of education, lower socio-economic status, depressed, and with a higher score of loneliness are significantly more likely to report poor mentalizing compared with females, individuals with higher education level, greater SES, happier, and with lower scores of loneliness. CONCLUSION: The present study validated the English version of the MMQ.

How much online pornography is too much? A comparison of two theoretically distinct assessment scales.

BACKGROUND: Online pornography use, an ever more common activity, has raised myriad psychosocial and clinical concerns. While there is a need to screen for and measure its problematic dimension, there is a debate about the adequacy of existing assessment tools. OBJECTIVE: The study compares two instruments for measuring pathological online pornography use (POPU) that are based on different theoretical frameworks-one in line with DSM-5 criteria and the six-component addiction model and one in line with ICD-11 criteria. METHODS: An international sample of 1,823 adults (Mean age = 31.66, SD = 6.74) answered an online questionnaire that included the Short Version of the Problematic Pornography Consumption Scale (PPCS-6) and the Assessment of Criteria for Specific Internet-Use Disorders (ACSID-11). Factorial, correlational, and network analyses were conducted on the data. RESULTS: Both tools adequately screened for online "addictive" behavior, but the ACSID-11 was superior in assessing the degree of clinical risk. CONCLUSION: Depending on the specific aim of the assessment (screening vs. clinical diagnostics), both online pornography measurement tools may be useful.

Predictors of compulsive cyberporn use: A machine learning analysis.

Introduction: Compulsive cyberporn use (CCU) has previously been reported among people who use cyberporn. However, most of the previous studies included convenience samples of students or samples of the general adult population. Research examining the factors that predict or are associated with CCU are still scarce.In this study, we aimed to (a) assess compulsive cyberporn consumption in a broad sample of people who had used cyberporn and (b) determine, among a diverse range of predictor variables, which are most important in CCU scores, as assessed with the eight-item Compulsive Internet Use Scale adapted for cyberporn. Materials and Methods: Overall, 1584 adult English speakers (age: 18-75 years, M = 33.18; sex: 63.1 % male, 35.2 % female, 1.7 % nonbinary) who used cyberporn during the last 6 months responded to an online questionnaire that assessed sociodemographic, sexual, psychological, and psychosocial variables. Their responses were subjected to correlation analysis, analysis of variance, and machine learning analysis. Results: Among the participants, 21.96% (in the higher quartile) presented CCU symptoms in accordance with their CCU scores. The five most important predictors of CCU scores were related to the users' strength of craving for pornography experiences, suppression of negative emotions porn use motive, frequency of cyberporn use over the past year, acceptance of rape myths, and anxious attachment style. Conclusions: From a large and diverse pool of variables, we determined the most important predictors of CCU scores. The findings contribute to a better understanding of problematic pornography use and could enrich compulsive cyberporn treatment and prevention.

Online dating: predictors of problematic tinder use.

BACKGROUND: Geolocation apps have radically transformed dating practices around the world, with profound sociocultural implications. Few studies, however, have explored their addictive potential or factors that are associated with their misuse. OBJECTIVE: The present study aimed to assess the level of problematic Tinder use (PTU) in an adult sample, using a machine learning algorithm to determine, among 29 relevant variables, the most important predictors of PTU. METHODS: 1,387 users of Tinder (18-74 years-old; male = 50.3%; female = 49.1%) completed an online questionnaire, and a machine learning tool was used to analyze their responses. RESULTS: On 5-point scale, participants' mean PTU score was 1.91 (SD = 0.70), indicating a relatively low overall level of problematic app use. Among the most important predictors of Problematic use were the use of Tinder for enhancement (reduce boredom and increase positive emotions), coping with psychological problems, and increasing social connectedness. The number of "matches" (when two users show mutual interest), the number of online contacts on Tinder, and the number of resulting offline dates were also among the top predictors of PTU. Depressive mood and loneliness were among the middle-ranked predictors of PTU. CONCLUSION: In accordance with the Interaction of Person-Affect-Cognition-Execution model of problematic internet use, the results suggest that PTU relates to how individual experience on the app interacts with dispositional and situational characteristics. However, variables that seemed to relate to PTU, including lack of self-esteem, negative mood states and loneliness, are not problems that online dating services as currently designed can be expected to resolve. This argues for increased digital services to identify and address potential problems helping drive the popularity of dating apps.

Impact of gaming disorder on first episode psychosis patients' evolution: Protocol for a multicentered prospective study.

AIMS: The objective of this study is to underline the impact of Gaming Disorder on the clinical evolution of patients with First Episode Psychosis. The specific aims of the study are to determine the prevalence of gaming disorder among those patients and assess the consequences of gaming on their clinical trajectory. METHODS: This is a prospective multicenter cohort study that will enrol 800 patients diagnosed with a first episode psychosis, with a follow-up period of up to 3 years. Using a systematic screening procedure for gaming disorder, the clinical staff will assess patients gaming habits at admission and every 6 months thereafter. Information from patients' medical records will also be extracted using the same timeframe. RESULTS: The patients' characteristics at admission and during follow-up will be presented in the form of descriptive statistics and compared between different subgroups of patients using uni- and multivariate logistic regression models. Repeated measures ANCOVA will also be performed to analyse the impact of gaming disorders on patients' clinical path as assessed by the Positive and Negative Syndrome Scale and the Clinical Global Impression scale, considering covariates such as psychiatric diagnosis, pharmacological treatment, age, sex/gender, and duration of untreated psychosis. CONCLUSION: These findings will guide the development of prevention, detection, and treatment strategies for the comorbidity between gaming disorder and first episode psychosis, ultimately improving the patients' recovery.

A 13-item Health of the Nation Outcome Scale (HoNOS-13): validation by item response theory (IRT) in patients with substance use disorder.

BACKGROUND: The Health of the Nation Outcome Scale (HoNOS) is a widely used 12-item tool to assess mental health and social functioning. The French version has an added 13th item measuring adherence to psychotropic medication. The aim of the current study is to uncover the unknown pattern of the new item 13 and to compare the unidimensional and multidimensional fit of the new HoNOS-13 using Item Response Theory (IRT). This research question was studied among inpatients with substance use disorder (SUD). METHODS: Six hundred and nine valid questionnaires of HoNOS-13 were analyzed using unidimensional (one-factor) and multidimensional (two-factor) IRT modeling. RESULTS: The multidimensional model suggesting a first factor capturing psychiatric/impairment-related issues and a second factor reflecting social-related issues yielded better goodness-of-fit values compared to the unidimensional solution. This resulted in an improvement of all slope parameters which in turn translates to better discriminative power. Significant improvement in item location parameters were observed as well. The new item 13 had a good discriminative power (1.17) and covered a wide range of the latent trait (- 0.14 to 2.64). CONCLUSIONS: We were able to validate the 13-item questionnaire including medication compliance and suggest that the HoNOS-13 can be recommended as a clinical evaluation tool to assess the problems and treatment needs for inpatients with SUD. Interestingly, the majority of item response categories are endorsed by respondents who are below and above the average levels of HoNOS. This indicates that the scale is able to discriminate between participants both at the low and at the high ends of the latent trait continuum. More importantly, the new item 13 has a good discriminative power and covers a broad range of the latent trait below and above the mean. It therefore has the desired profile of a good item and is a useful measure for the assessment of mental health and social functioning. Trial registration ClinicalTrials.gov, Identifier: NCT03551301. Registered: 11.06.2018. Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03551301 .

Effectiveness of a web-based self-help tool to reduce problem gambling: A randomized controlled trial.

Background and Aims: Problem gambling constitutes a public health concern associated with psychopathological comorbidity, substance use, and financial difficulties. Most individuals with gambling problems avoid counseling services due to perceived stigma and their preference for self-reliance. Treatment accessibility could be improved through web-based interventions. Methods: We recruited 360 individuals with gambling problems and randomized them to a web-based intervention (n = 185) or an active control group consisting of a self-help manual for problem gambling (n = 175). The primary outcome was the number of days of gambling in the last 30 days. Secondary outcomes included money spent in the last 30 days, time gambling in the last 7 days, gambling-related problems, consumption of alcohol and cigarettes, and psychopathological comorbidity measured at posttreatment and 6-month follow-up. Results: The primary outcome decreased significantly for both groups, with no significant difference between the groups. There were significant group × time interactions according to the Gambling Symptom Assessment Scale (F = 8.83, p <0 .001), the Problem Gambling Severity Index (F = 3.54, p = 0.030), for cigarettes smoked in the last 7 days (F = 26.68, p < 0.001), the Patient Health Questionnaire-9 (F = 19.41, p <0 .001), and the Generalized Anxiety Disorder-7 (F = 41.09, p <0 .001) favoring the intervention group. We experienced an overall high dropout rate (76%). Conclusions: Win Back Control seems to be an effective low-threshold treatment option for individuals with gambling problems that might otherwise be unapproachable for outpatient treatment services. Nevertheless, the high dropout rate should be considered when interpreting the study results, as they may have introduced a degree of variability.

Predicting smoking cessation, reduction and relapse six months after using the Stop-Tabac app for smartphones: a machine learning analysis.

BACKGROUND: An analysis of predictors of smoking behaviour among users of smoking cessation apps can provide useful information beyond what is already known about predictors in other contexts. Therefore, the aim of the present study was to identify the best predictors of smoking cessation, smoking reduction and relapse six months after starting to use the smartphone app Stop-Tabac. METHOD: Secondary analysis of 5293 daily smokers from Switzerland and France who participated in a randomised trial testing the effectiveness of this app in 2020, with follow-up at one and six months. Machine learning algorithms were used to analyse the data. The analyses for smoking cessation included only the 1407 participants who responded after six months; the analysis for smoking reduction included only the 673 smokers at 6-month follow-up; and the analysis for relapse at 6 months included only the 502 individuals who had quit smoking after one month. RESULTS: Smoking cessation after 6 months was predicted by the following factors (in this order): tobacco dependence, motivation to quit smoking, frequency of app use and its perceived usefulness, and nicotine medication use. Among those who were still smoking at follow-up, reduction in cigarettes/day was predicted by tobacco dependence, nicotine medication use, frequency of app use and its perceived usefulness, and e-cigarette use. Among those who had quit smoking after one month, relapse after six months was predicted by intention to quit, frequency of app use, perceived usefulness of the app, level of dependence and nicotine medication use. CONCLUSION: Using machine learning algorithms, we identified independent predictors of smoking cessation, smoking reduction and relapse. Studies on the predictors of smoking behavior among users of smoking cessation apps may provide useful insights for the future development of these apps and future experimental studies. CLINICAL TRIAL REGISTRATION: ISRCTN Registry: ISRCTN11318024, 17 May 2018. http://www.isrctn.com/ISRCTN11318024 .

Finding Intimacy Online: A Machine Learning Analysis of Predictors of Success.

While an extensive scientific literature now exists on the use of online dating services, there are very few studies on user satisfaction with dating apps and with the resulting offline dates. This study aimed to assess the level of satisfaction with Tinder use (STU) and the level of satisfaction with Tinder offline dates (STOD) in a sample of adult users of the app. The study also aimed to examine, among 28 variables, those that are the most important in predicting STU and STOD. Overall, 1,387 Tinder users completed an online questionnaire. A machine learning model was used to rank order predictors from most to least important. On a 4-point scale, participants' mean STU score was 2.39, and, on a 5-point scale, mean STOD score was 3.05. The results indicate that satisfaction with dating apps and with resulting offline dates is strongly predicted by participants' age and by their motives for using Tinder (enhancement, emotional coping, socialization, finding "true love," or casual sexual partners), whereas the variables negatively associated with satisfaction were those related to psychopathology. Interestingly, 65.3 percent of app users were married or "in a relationship," and only 50.3 percent of app users were using it to meet someone offline. Generally, participants who engage with the app to cope with personal difficulties seem more likely to report higher levels of dissatisfaction, suggesting that dating apps are a poor coping mechanism and highlighting the need to address underlying problems or pathologies that may be driving their use.

Predicting the Users' Level of Engagement with a Smartphone Application for Smoking Cessation: Randomized Trial and Machine Learning Analysis.

INTRODUCTION: Studies of the users' engagement with smoking cessation application (apps) can help understand how these apps are used by smokers, in order to improve their reach and efficacy. OBJECTIVE: The present study aimed at identifying the best predictors of the users' level of engagement with a smartphone app for smoking cessation and at examining the relationships between predictors and outcomes related to the users' level of engagement with the app. METHODS: A secondary analysis of data from a randomized trial testing the efficacy of the Stop-Tabac smartphone app was used. The experimental group used the "full" app and the control group used a "dressed down" app. The study included a baseline and 1-month and 6-month follow-up questionnaires. A total of 5,293 participants answered at least the baseline questionnaires; however, in the current study, only the 1,861 participants who answered at least the baseline and the 1-month follow-up questionnaire were included. Predictors were measured at baseline and after 1 month and outcomes after 6 months. Data were analyzed using machine learning algorithms. RESULTS: The best predictors of the outcomes were, in decreasing order of importance, intention to stop smoking, dependence level, perceived helpfulness of the app, having quit smoking after 1 month, self-reported usage of the app after 1 month, belonging to the experimental group (vs. control group), age, and years of smoking. Most of these predictors were also significantly associated with the participants' level of engagement with the app. CONCLUSIONS: This information can be used to further target the app to specific groups of users, to develop strategies to enroll more smokers, and to better adapt the app's content to the users' needs.

Predictors of Cyberchondria During the COVID-19 Pandemic: Cross-sectional Study Using Supervised Machine Learning.

BACKGROUND: Cyberchondria is characterized by repeated and compulsive online searches for health information, resulting in increased health anxiety and distress. It has been conceptualized as a multidimensional construct fueled by both anxiety and compulsivity-related factors and described as a "transdiagnostic compulsive behavioral syndrome," which is associated with health anxiety, problematic internet use, and obsessive-compulsive symptoms. Cyberchondria is not included in the International Classification of Diseases 11th Revision or the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and its defining features, etiological mechanisms, and assessment continue to be debated. OBJECTIVE: This study aims to investigate changes in the severity of cyberchondria during the COVID-19 pandemic and identify the predictors of cyberchondria at this time. METHODS: Data collection started on May 4, 2020, and ended on June 10, 2020, which corresponds to the first wave of the COVID-19 pandemic in Europe. At the time the study took place, French-speaking countries in Europe (France, Switzerland, Belgium, and Luxembourg) all implemented lockdown or semilockdown measures. The survey consisted of a questionnaire collecting demographic information (sex, age, education level, and country of residence) and information about socioeconomic circumstances during the first lockdown (eg, economic situation, housing, and employment status) and was followed by several instruments assessing various psychological and health-related constructs. Inclusion criteria for the study were being at least 18 years of age and having a good understanding of French. Self-report data were collected from 725 participants aged 18-77 (mean 33.29, SD 12.88) years, with females constituting the majority (416/725, 57.4%). RESULTS: The results showed that the COVID-19 pandemic affected various facets of cyberchondria: cyberchondria-related distress and compulsion increased (distress z=-3.651, P<.001; compulsion z=-5.697, P<.001), whereas the reassurance facet of cyberchondria decreased (z=-6.680, P<.001). In addition, COVID-19-related fears and health anxiety emerged as the strongest predictors of cyberchondria-related distress and interference with functioning during the pandemic. CONCLUSIONS: These findings provide evidence of the impact of the COVID-19 pandemic on cyberchondria and identify factors that should be considered in efforts to prevent and manage cyberchondria at times of public health crises. In addition, they are consistent with a theoretical model of cyberchondria during the COVID-19 pandemic proposed in 2020. These findings have implications for the conceptualization and future assessment of cyberchondria.

Validation of the Polish Problematic Tinder Use Scale and Its Relationship with Safe Sex Behaviors.

INTRODUCTION: Online dating is a common phenomenon. The manageability and access of the application allows people to quickly reach many potential partners, which can increase risky sexual behaviors. The Problematic Tinder Use Scale (PTUS) was developed and validated in a Polish population by analyzing the reliability, validity, and factor structure of the responses given by Polish-speaking participants. METHODS: Two samples of adult Tinder users were recruited online. The first study aimed to perform the reliability coefficient Cronbach's, interrater analysis, exploratory, and confirmatory factor analysis. The second sample was recruited to investigate the factor structure by combining it with the Safe Sex Behavior Questionnaire (SSBQ). The sociodemographic data, such as hours of use and number of dates, were also investigated. RESULTS: The Polish participants' responses to the PTUS (sample 1: N = 271, sample 2: N = 162) revealed the one-factor structure of the tool. The reliability of the measurement was α = 0.80. The construct validity was confirmed. The results showed a significant, negative, and weak correlation between the PTUS and SSBQ scores and their subscales: risky sexual behaviors (r = -0.18), condom use (r = -0.22), and avoidance of body fluids (r = -0.17). Moreover, the number of partners met in the real world had a statistically significant, moderate relationship with the PTUS scores. CONCLUSIONS: The PTUS measurement is valid and reliable for the Polish population. The findings highlight the need for harm prevention strategies related to potentially addictive Tinder use, as well as the possible risky sexual behaviors associated with dating app use.

Willingness to Pay for a Dating App: Psychological Correlates.

The smartphone dating app, Tinder, has become hugely popular in recent years. Although most people use a free version of the app, some pay for an augmented version to improve their experience. However, there is little evidence of the association between the willingness to pay for a dating app such as Tinder and users' psychological characteristics. This study thus aims to compare Tinder paying versus non-paying users in terms of their pattern of use, excessive use of Tinder, motives for using Tinder, impulsivity traits, depressive mood, and sociodemographic variables, as well as to examine which variables best predict group membership. A total of 1159 Tinder users participated in an online survey. Group comparisons indicated that payers were more frequently male, reported greater motives for using Tinder than non-payers, and differed in their pattern of use compared with non-payers. Impulsivity traits did not significantly differ between the two groups. Being male and reporting greater motives for Tinder use significantly predicted being a payer. These findings provide insights into the processes that stimulate users' greater consumption of online dating apps, such as reinforcement mechanisms and reward sensitivity.

Smartphone apps for mental health and wellbeing: A usage survey and machine learning analysis of psychological and behavioral predictors.

Objective: Despite the availability of thousands of mental health applications, the extent to which they are used and the factors associated with their use remain largely unknown. The present study aims to (a) assess in a representative US-based population sample the use of smartphone apps for mental health and wellbeing (SAMHW), (b) determine the variables predicting the use of SAMHW, and (c) explore how a set of variables related to mental health, smartphone use, and smartphone "addiction" may be associated with the use of SAMHW. Methods: Data was collected via online questionnaire from 1989 adults. The data gathered included information on smartphone use behavior, mental health, and the use of SAMHW. Latent class analysis was used to categorize participants. Machine learning and logistic regression analyses were used to determine the most important predictors of SAMHW use and associations between predictors and outcome variables. Results: While two-thirds of participants had a statistically high probability for using SAMHW, nearly twice more had high probability for using them to improve wellbeing compared to using them to address mental health problems (43% vs. 18%). In both groups, these participants were more likely to be female and in the younger adult age bracket than male and in the adult or older adult age bracket. According to the machine learning model, the most important predictors for using the relevant smartphone apps were variables associated with smartphone problematic use, COVID-19 impact, and mental health problems. Conclusion: Findings from the present study confirm that the use of SAMHW is growing, particularly among younger adult and female individuals who are negatively impacted by problematic smartphone use, COVID-19, and mental health problems. These individuals tend to bypass traditional care via psychotherapy or psychopharmacology, relying instead on smartphones to address mental health conditions or improve wellbeing. Advising users of these apps to also seek professional help and promoting efforts to prove the efficacy and safety of SAMHW would seem necessary.