JMIR Publications

ABSTRACT

Background:

Digital technology use is nearly ubiquitous among young adults, this use provides both benefits and risks. Risks of technology use include maladaptive technology use or technology addiction. Several conceptualizations of these addictions have emerged, each with their own assessment tools. These conditions include Problematic Internet Use (PIU), Internet Gaming Disorder (IGD) and Social Media Addiction (SMA). These conditions have been associated with health behaviors and conditions such as problematic alcohol use, sleep disorders and mental illness. These maladaptive technology conditions have most commonly been studied in isolation from each other.

Objective:

The purpose of this study was to examine PIU, IGD and SMA together to better inform future research approaches and provider screening practices for young adults.

Methods:

This cross-sectional survey study was conducted using Qualtrics panel-based recruitment and survey hosting. We recruited US young adults ages 18-25 years. The survey assessed PIU, IGD and SMA. Survey measures also included assessments for problematic alcohol use, sleep, depression and anxiety. We evaluated the frequency and overlap in positive screening scores among PIU, IGD and SMA, and modeled each condition using multivariate logistic regression. Finally, we calculated sensitivity and specificity, as well as positive predictive value and negative predictive value of the screening tools using the most prevalent maladaptive technology type.

Results:

Our 6000 participants had average age of 21.7 (SD=2.35). They were 51% female and 57.3% Caucasian; 28.1% were in a 4-year college and 38.8% worked full time. The mean PRIUSS score was 3.5 (SD=3.1), a total of 3215 (55%) of participants met criteria for PIU. The mean IGD score was 2.7 (SD=2.6), a total of 1460 (25%) met criteria for IGD. The mean SMA score was 7.5 (SD=5.7), and a total of 205 (3.5%) met criteria for SMA. Across all three maladaptive technology use diagnoses, there were varied associations with demographic variables and similar overlap with health conditions. The sensitivity of PIU screening to detect IGD was 82%, and to detect SMA was 93%, while the specificity and positive predictive value were much lower (37-54% specificity, 6-37% positive predictive value).

Conclusions:

This cross-sectional survey screened a large national sample of AYAs for PIU, IGD and SMA to determine prevalence and overlap, demographic associations with each, and associations between these technology-related conditions and health behaviors and conditions. There was overlap across PIU, IGD and SMA in some associated demographic variables as well as health conditions. However, the patterns in associated variables demonstrated unique qualities of each of these conditions.