This commentary intends to provide constructive input into the “Ten myths about work addiction” by Griiffiths et al. (2018). I place the information into an appetitive motivation theoretical lens of addiction as well as outline the kernels of truth associated with each myth. Advancement of an understanding of the underlying mechanisms of addiction demands consideration that any number of appetitive-associated behaviors might become disrupted – including those at the workplace.
The most popular recreational pastime in the U.S. is television viewing. Some researchers have claimed that television may be addictive. We provide a review of the definition, etiology, prevention and treatment of the apparent phenomenon of television addiction.
We provide a description of television (TV) addiction, including its negative consequences, assessment and potential etiology, considering neurobiological, cognitive and social/cultural factors. Next, we provide information on its prevention and treatment.
Discussion and conclusions
We suggest that television addiction may function similarly to substance abuse disorders but a great deal more research is needed.
Authors:Steve Sussman, Pallav Pokhrel, Ping Sun, Louise A. Rohrbach and Donna Spruijt-Metz
Background and Aims
Recent work has studied addictions using a matrix measure, which taps multiple addictions through single responses for each type. This is the first longitudinal study using a matrix measure.
We investigated the use of this approach among former alternative high school youth (average age = 19.8 years at baseline; longitudinal n = 538) at risk for addictions. Lifetime and last 30-day prevalence of one or more of 11 addictions reviewed in other work was the primary focus (i.e., cigarettes, alcohol, hard drugs, shopping, gambling, Internet, love, sex, eating, work, and exercise). These were examined at two time-points one year apart. Latent class and latent transition analyses (LCA and LTA) were conducted in Mplus.
Prevalence rates were stable across the two time-points. As in the cross-sectional baseline analysis, the 2-class model (addiction class, non-addiction class) fit the data better at follow-up than models with more classes. Item-response or conditional probabilities for each addiction type did not differ between time-points. As a result, the LTA model utilized constrained the conditional probabilities to be equal across the two time-points. In the addiction class, larger conditional probabilities (i.e., 0.40–0.49) were found for love, sex, exercise, and work addictions; medium conditional probabilities (i.e., 0.17–0.27) were found for cigarette, alcohol, other drugs, eating, Internet and shopping addiction; and a small conditional probability (0.06) was found for gambling.
Discussion and Conclusions
Persons in an addiction class tend to remain in this addiction class over a one-year period.
Authors:Steve Sussman PhD, FAAHB, FAPA, Thalida Em Arpawong, Ping Sun, Jennifer Tsai, Louise A. Rohrbach and Donna Spruijt-Metz
Background and Aims
Recent work has studied multiple addictions using a matrix measure, which taps multiple addictions through single responses for each type.
The present study investigated use of a matrix measure approach among former alternative high school youth (average age = 19.8 years) at risk for addictions. Lifetime and last 30-day prevalence of one or more of 11 addictions reviewed in other work (Sussman, Lisha & Griffiths, 2011) was the primary focus (i.e., cigarettes, alcohol, other/hard drugs, eating, gambling, Internet, shopping, love, sex, exercise, and work). Also, the co-occurrence of two or more of these 11 addictive behaviors was investigated. Finally, the latent class structure of these addictions, and their associations with other measures, was examined.
We found that ever and last 30-day prevalence of one or more of these addictions was 79.2% and 61.5%, respectively. Ever and last 30-day co-occurrence of two or more of these addictions was 61.5% and 37.7%, respectively. Latent Class Analysis suggested two groups: a generally Non-addicted Group (67.2% of the sample) and a “Work Hard, Play Hard”-addicted Group that was particularly invested in addiction to love, sex, exercise, the Internet, and work. Supplementary analyses suggested that the single-response type self-reports may be measuring the addictions they intend to measure.
Discussion and Conclusions
We suggest implications of these results for future studies and the development of prevention and treatment programs, though much more validation research is needed on the use of this type of measure.