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).
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).
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).
Authors:Severin Haug, Raquel Paz Castro, Min Kwon, Andreas Filler, Tobias Kowatsch, and Michael P. Schaub
Background and Aims
Smartphone addiction, its association with smartphone use, and its predictors have not yet been studied in a European sample. This study investigated indicators of smartphone use, smartphone addiction, and their associations with demographic and health behaviour-related variables in young people.
A convenience sample of 1,519 students from 127 Swiss vocational school classes participated in a survey assessing demographic and health-related characteristics as well as indicators of smartphone use and addiction. Smartphone addiction was assessed using a short version of the Smartphone Addiction Scale for Adolescents (SAS-SV). Logistic regression analyses were conducted to investigate demographic and health-related predictors of smartphone addiction.
Smartphone addiction occurred in 256 (16.9%) of the 1,519 students. Longer duration of smartphone use on a typical day, a shorter time period until first smartphone use in the morning, and reporting that social networking was the most personally relevant smartphone function were associated with smartphone addiction. Smartphone addiction was more prevalent in younger adolescents (15–16 years) compared with young adults (19 years and older), students with both parents born outside Switzerland, persons reporting lower physical activity, and those reporting higher stress. Alcohol and tobacco consumption were unrelated to smartphone addiction.
Different indicators of smartphone use are associated with smartphone addiction and subgroups of young people have a higher prevalence of smartphone addiction.
The study provides the first insights into smartphone use, smartphone addiction, and predictors of smartphone addiction in young people from a European country, which should be extended in further studies.
Authors:Mareike Augsburger, Andreas Wenger, Severin Haug, Sophia Achab, Yasser Khazaal, Joël Billieux, and Michael P. Schaub
Background and aims
Buying-shopping disorder and its transferability to the online sector is controversial. This study investigates in-store and online shopping patterns by comparing data-based modeling to a diagnostic cut-off approach. Further aims were to test model equivalence for gender and identify socio-demographic risk factors.
In a representative survey, the Bergen Shopping Addiction Scale (BSAS) was applied, using both an online and in-store version. Latent class analyses were followed by multinomial logistic regression analyses to investigate socio-demographic variables. Measurement invariance across genders was tested with multi-group comparisons.
With N = 1,012, 3-class solutions provided the best model fit for both in-store and online shopping. Most individuals (76, 86%) were grouped in non-addicted classes, followed by risky (21, 11%) and addicted classes (both 3%). Twenty-eight percent of individuals in the online addicted shopping class remained unidentified using the cut-off. For online shopping, only lower age and education differentiated classes significantly.
Results indicate a close link between online and in-store shopping, albeit with distinguishing features. The cut-off yielded findings discrepant from class probabilities. That buying-shopping disorder mainly affects younger women of lower educational level must be questioned, given the limited associations identified.
It is important not only to consider different settings of pathological shopping, but also to focus on groups that may not have appeared at risk in previous investigations (e.g., men, older age). The BSAS cut-off warrants further research.