Search Results

You are looking at 1 - 4 of 4 items for

  • Author or Editor: Gerhard Gmel x
  • Refine by Access: All Content x
Clear All Modify Search

Background and aims

Cybersex use (CU) is highly prevalent in Switzerland’s population, particularly among young men. CU may have negative consequences if it gets out of control. This study estimated prevalence of CU, frequency of CU (FCU), and problematic CU (PCU) and their correlates.

Methods

A non-selective sample of young Swiss men (N = 5,332, mean age = 25.45) completed a questionnaire assessing FCU and PCU, sociodemographics (age, linguistic region, and education), sexuality (being in a relationship, number of sexual partners, and sexual orientation), dysfunctional coping (denial, self-distraction, behavioral disengagement, and self-blame), and personality traits (aggression/hostility, sociability, anxiety/neuroticism, and sensation seeking). Associations were tested using hurdle and negative binomial regression models.

Results

At least monthly CU was reported by 78.6% of participants. CU was associated positively with post-secondary schooling (vs. primary schooling), German-speaking (vs. French-speaking), homosexuality, bisexuality (vs. heterosexuality), more than one sexual partner (vs. one), dysfunctional coping (except denial), and all personality traits except sociability, but negatively with being in a relationship (vs. not), age, and sociability. FCU was associated positively with homosexuality, bisexuality, no or more than one sexual partner, dysfunctional coping (except denial), and all personality traits except sociability, but negatively with age, being in a relationship, and sociability. PCU was associated positively with bisexuality, four or more sexual partners, dysfunctional coping, and all personality traits except sociability, but negatively with German-speaking and sociability.

Discussion and conclusions

CU should be viewed in light of its associations with sociodemographic, sexual, and psychological factors. Healthcare professionals should consider these aspects to adapt their interventions to patients’ needs.

Open access

Abstract

Background and aims

Social determinants are closely related to addiction, both as a cause and a consequence of substance use and other addictive behaviors. The present paper examines prosocialness (i.e. the tendency to help, empathize, and care for others) among a population of young males. We compared prosocialness across different types of addiction and examined whether prosocialness varied according to the presence of multiple addictions.

Methods

A sample of 5,675 young males, aged 19–29 years old (Mean = 21.4; Median = 21), completed a questionnaire that included screening tools to identify addictive behaviors with regards to alcohol, nicotine, cannabis, gambling, and gaming. The questionnaire also included a scale to measure prosocialness.

Results

Compared to a no-addiction control group, the subgroups of young men suffering from behavioral addictions (i.e., gambling and gaming) reported the lowest levels of prosocialness. Respondents with an alcohol addiction also showed lower prosocialness compared to no-addiction controls. By contrast, no significant differences in prosocialness were found between respondents with nicotine disorder or cannabis disorder and the no-addiction controls. Furthermore, the number of addictions had no clear, observable effects on prosocialness. Significant differences were found between the no-addiction control group and the groups reporting one or more addictions, but not between the separate groups reporting one, two, and three or more addictions.

Discussion and conclusions

A better understanding of the social dimension affecting young males with addiction, particularly gambling and gaming addictions, may be useful for their prevention and treatment.

Open access

Background and aims

Behavioral addictions (BAs) and substance use disorders (SUDs) tend to co-occur; both are associated with mental health problems (MHPs). This study aimed to estimate the proportion of variance in the severity of MHPs explained by BAs and SUDs, individually and shared between addictions.

Methods

A sample of 5,516 young Swiss men (mean = 25.47 years old; SD = 1.26) completed a self-reporting questionnaire assessing alcohol, cannabis, and tobacco use disorders, illicit drug use other than cannabis, six BAs (Internet, gaming, smartphone, Internet sex, gambling, and work) and four MHPs (major depression, attention-deficit hyperactivity disorder, social anxiety disorder, and borderline personality disorder). Commonality analysis was used to decompose the variance in the severity of MHPs explained (R 2) by BAs and SUDs into independent commonality coefficients. These were calculated for unique BA and SUD contributions and for all types of shared contributions.

Results

BAs and SUDs explained between a fifth and a quarter of the variance in severity of MHPs, but individual addictions explained only about half of this explained variance uniquely; the other half was shared between addictions. A greater proportion of variance was explained uniquely or shared within BAs compared to SUDs, especially for social anxiety disorder.

Conclusions

The interactions of a broad range of addictions should be considered when investigating their associations with MHPs. BAs explain a larger part of the variance in MHPs than do SUDs and therefore play an important role in their interaction with MHPs.

Open access
Journal of Behavioral Addictions
Authors: Michelle Dey, Joseph Studer, Michael Patrick Schaub, Gerhard Gmel, David Daniel Ebert, Jenny Yi-Chen Lee, and Severin Haug

Background and aims

This study aimed to examine associations between risk factors suggested in the pathway model proposed by Billieux et al., demographic and substance use variables, and problematic smartphone use (PSU).

Methods

The analytical sample consisted of 5,096 Swiss men (mean age = 25.5 years, SD = 1.26). Multiple linear regression analyses were conducted with PSU as dependent and the following as independent variables: (a) Billieux’s pathway model variables (depression, social anxiety, ADHD, aggression–hostility, and sensation seeking); (b) substance use variables [alcohol: at-risk risky single-occasion drinking (RSOD); at-risk volume drinking; tobacco use: daily smoking; illicit drug use: more than weekly cannabis use; having used at least one other illicit drug besides cannabis over the preceding 12 months]; and (c) sociodemographic variables (age, language region, and education).

Results

All pathway-model variables except sensation seeking were significant predictors of PSU, especially symptoms of social anxiety (β = 0.196) and ADHD (β = 0.184). At-risk RSOD was positively (β = 0.071) associated with PSU, whereas both frequent cannabis use (β = −0.060) and daily cigarette smoking (β = −0.035) were negatively associated with PSU. Higher-achieved educational levels and being from the German-speaking part of Switzerland predicted PSU.

Discussion and conclusions

The findings of this study can be used to develop tailored interventional programs that address the co-occurrence of certain risky behaviors (e.g., at-risk RSOD and PSU) and target individuals who might be particularly prone to PSU. Such interventions would need to ensure that addressing one problem (e.g., decreasing PSU) does not lead to some other compensatory behavior (e.g., frequent cigarette smoking).

Open access