Background: Digital technology use is nearly ubiquitous among young adults; this use provides both benefits and risks. The risks of technology use include maladaptive technology use or technology addiction. Several conceptualizations of these addictions have emerged, each with its 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 outcomes such as problematic alcohol use, sleep disorders, and mental illness. These maladaptive technology conditions have been most commonly studied in isolation from each other. Objective: The aim of this study is 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 aged 18-25 years. The survey assessed PIU, IGD, and SMA. Survey measures also included assessments of problematic alcohol use, sleep, depression, and anxiety. We evaluated the frequency of 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 the positive predictive value and negative predictive value of the screening tools using the most prevalent maladaptive technology type. Results: Our 6000 participants had an average age of 21.7 (SD 2.5) years. Of these 6000 participants, 3062 (51.03%) were female, 3431 (57.18%) were Caucasian, 1686 (28.1%) were in a 4-year college program, and 2319 (38.65%) worked full time. The mean PIU score was 3.5 (SD 3.1), and 53.58% (3215/6000) of participants met the criteria for PIU. The mean IGD score was 2.7 (SD 2.6), and 24.33% (1460/6000) of participants met the criteria for IGD. The mean SMA score was 7.5 (SD 5.7), and 3.42% (205/6000) met the criteria for SMA. Across all 3 maladaptive technology use diagnoses, there were varied associations with demographic variables and similar overlap with health outcomes. The sensitivity of PIU screening to detect IGD was 82% and to detect SMA was 93%, whereas 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 adolescents and young adults for PIU, IGD, and SMA to determine prevalence and overlap, demographic associations with each, and associations between these technology-related conditions and health outcomes. There was overlap across PIU, IGD, and SMA in some associated demographic variables and health outcomes. However, the patterns in the associated variables demonstrated unique qualities of each of these conditions.

Despite the effectiveness of vaccines, parents are increasingly opting for non-medical exemption. For health advice on such topics, many parents are now more likely to look to online communities than rely on medical expertise. There is scant literature outlining how parents contribute to online discussion of vaccination. The purpose of this study was to analyze parent vaccination opinions expressed online. Using a codebook adapted from previous research, content analysis was conducted to evaluate comments on parenting blog posts related to vaccination. Variables included stance, argument rhetoric, and accuracy of information. Comments recognized as aggressive or accusatory were recorded into an “attack” category. Descriptive statistics were used to assess trends in comment content. Nine blogs were included, from which 244 comments were analyzed. The most common argument rhetoric was providing, requesting, or evaluating source credibility for both pro- and anti–vaccine comments (24% and 36%). 25% of comments were considered inaccurate health information; 27% fell into the “attack” category, of which 60% were pro-vaccination. The high percentage of “attack” comments and inaccurate information is evidence that blog comments may not be reliable for information-seeking parents. Clinicians should use this data to understand anti–vaccination arguments in efforts to improve vaccine communication.

The state of Washington passed Initiative Measure No. 502 in 2012, which legalized recreational marijuana use for persons older than 21 years.^1,2 Legalization of marijuana has raised concerns about access to marijuana by youths younger than 21 years, and increases in the underage consumption of marijuana.² Adolescents are a crucial at-risk population for marijuana use; rates of use among adolescents are approximately 15% for use within the last 30 days and 30% for lifetime use.³ Consequences of marijuana use include academic difficulties such as school dropout, psychiatric impairment including memory loss, and progression of use to other drugs.^4,5