To Tailor or Not to Tailor: An Investigation of Narrative Tailoring for Health Communication.

One possible way of enhancing the effectiveness of health narratives is by using tailoring. However, evidence of the effectiveness of narrative tailoring is mixed. Some studies have found tailoring to be effective, while others have found no difference between tailored and non-tailored stories. One explanation for these mixed results is that much of the previous research in this area has focused on purely demographic factors. This study aimed to determine whether or not adding theoretically derived tailoring dimensions provides benefits above and beyond demographic tailoring. Participants (N = 812, aged 18-26) were assigned to either a facts only control condition, a non-tailored narrative, a demographically tailored narrative, or a demographically and theoretically tailored narrative. Across all conditions, the stimuli focused on the benefits of the HPV vaccine. Results found that the narrative conditions outperformed the control, but there was no significant difference between tailoring conditions on vaccination expectations, narrative transportation, identification, or perceived personalization. Further analysis showed that perceived personalization and narrative transportation predicted vaccination expectations across all narrative conditions.

Measuring Problematic Internet Use, Internet Gaming Disorder, and Social Media Addiction in Young Adults: Cross-sectional Survey Study.

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.