JMIR Publications

ABSTRACT

Background:

Alcohol use disorder (AUD) is associated with severe chronic medical conditions and premature mortality. Expanding the reach or access to effective evidence-based treatments to help persons with AUD is a public health objective. Mobile phone or smartphone technology has the potential to increase the dissemination of clinical and behavioral interventions (mHealth) that increase the initiation and maintenance of sobriety among individuals with AUD. Studies about how this group uses their mobile phone and their attitudes toward technology may have meaningful implications for participant engagement with these interventions.

Objective:

This exploratory study examined the potential relationships between demographic characteristics (race, gender, age, marital status, income), substance use characteristics (frequency of alcohol and cannabis use), and clinical variables (anxiety and depression symptoms) with indicators of mobile phone use behavioral, and attitudes toward technology.

Methods:

A sample of 71 adults with AUD (mean age =42.9, SD =10.9) engaged in an alcohol partial hospitalization program completed four subscales from the Media Technology Usage and Attitudes (MTUA) assessment: Smartphone Usage measures various mobile phone behaviors/activities, Positive Attitudes and Negative Attitudes measure attitudes toward technology, and the Technological Anxiety/Dependence measure assesses level of anxiety when individuals are separated from their phone and dependence on this device. Participants also provided demographic information and completed the Epidemiologic Studies Depression Scale (CES-D) and the Generalized Anxiety Disorder (GAD). Lastly, Participants reported their frequency of alcohol use over the past three months using the Drug Use Frequency Scale.

Results:

Results for the factors showed a significant main effect for age and Smartphone Usage (P=.003; ηp2=.14) and Positive Attitudes (P=.01; ηp2=.07). Marital status (P=.03; ηp2=.13) and income (P=.032; ηp2=.14) were associated only with the Technological Anxiety and Dependence subscale. Moreover, a significant trend was found for alcohol use and Anxiety/Dependence subscale (P=.06; R2 =.02). Lastly, CES-D scores (P=.03; R2 =.08) and GAD symptoms (P=.004; R2 =.13) were significant predictors only of Anxiety/Dependence subscale.

Conclusions:

Findings indicate differences in mobile phone use patterns and attitudes toward technology across demographic, substance use, and clinical measures among patients with AUD. These results may help inform the development of future mHealth interventions among this population.