A latent class analysis of adolescents' technology and interactive social media use: Associations with academics and substance use

Identifying protective and risk behavior patterns of online communication in young people

INTRODUCTION

Research has investigated the association between time spent online and mental well-being, however the nuances between specific online behaviors and well-being have been less explored. This research examines how specific online behaviors (i.e., how young people are engaging online and with whom), are associated with one another, and how these patterns of behaviors are related to well-being.

METHODS

We used the November 2020 and March 2021 Understanding Society COVID-19 Panel data. The sample consisted of 1432 adolescents aged 10-15 years, who participated in November 2020. Latent class analysis was used to explore patterns of online behaviors. We also investigated how sociodemographic characteristics differed across the classes, along with physical, social, and mental well-being as distal outcomes both cross-sectionally and longitudinally.

RESULTS

We identified four classes: "Avid users," "Scholars," "Midways," and the "Passengers." The avid users had the highest frequency of posting online content regularly, likewise the scholars also posted online content regularly, however the scholars were differentiated by their higher frequency of schoolwork and news intake online. The midways had more complex activity characterized by talking to friends often and having a social media account, but posted online content less frequently. The passengers were the least active online as they posted pictures and videos less (76% said "never") and only 63% had a social media account. The avid users had the lowest well-being cross-sectionally and longitudinally, and the midways had lower social well-being and appearance dissatisfaction.

CONCLUSIONS

Online behaviors such as regularly posting or talking to internet-only friends could be related to lower well-being. Policymakers should consider both improving regulations online and building an evidence base to enable caregivers from all backgrounds to support young people.

Capturing the bigger picture: A gestalt of general and alcohol-specific social media usage during the transition to college as a predictor of first-year alcohol use and consequences

Previous research has shown a reliable association between social media (SM) use and drinking among college students. However, most studies have investigated SM behaviors (e.g., time spent on a platform, posting frequency) in isolation and on a single site. While some have studied multiple SM behaviors across platforms using person-centered approaches (e.g., latent profile analysis [LPA]), these studies have failed to take alcohol-related SM behaviors into account. This longitudinal study addressed this gap in the literature by using LPA to identify subpopulations of SM users during the college transition (N = 319; 62.1% female) using general (frequency of checking, time spent on, and frequency of posting to Instagram/Facebook/Snapchat; Finstagram ownership) and alcohol-related SM behaviors (posting alcohol, partying, and marijuana content). LPA results revealed three SM user profiles at baseline: low general use with low alcohol-related posting (LGU + LAP), high general use with low alcohol-related posting (HGU + LAP), and high general use with high alcohol-related posting (HGU + HAP). Prospective analyses revealed that HGU + HAP membership was associated with greater descriptive peer drinking norms, alcohol use, and consequences relative to HGU + LAP and LGU + LAP membership. Results suggest that there are distinct patterns of general and alcohol-related SM use during the college transition associated with risky drinking that can inform interventions combating SM-related alcohol risks. These findings illustrate the importance of investigating SM use holistically and suggests studying alcohol-related SM behaviors may reveal differences in individuals' alcohol risk that general SM behaviors might not capture.

[Measuring adolescents' use of social media: A systematic review]

OBJECTIVE

The purpose of this review was to provide an overview of the methods used to measure social network site use among adolescents. Such a review is important given the number and diversity of sites and the prevalence of their use among adolescents.

METHODS

A systematic review of the literature was conducted in line with PRISMA guidelines to arrive at an analysis of 58 peer-reviewed studies indexed in PubMed, PsychInfo, or Scopus. To be included in the review, articles had to be peer-reviewed, available in full text, and published in French or English. For inclusion, studies were required to pertain to adolescents aged between 11 and 18 years; to focus on social network site use and indicate how their use was estimated. Studies that simultaneously examined social network sites and general internet use or video games and only provided a combined analysis were excluded. The keywords for the search were: social media use, social media usage, social media misuse, measure, teen, and adolescents.

RESULTS

In the literature, different methods were used to operationalize the use of social network sites: quantitative methods with the measurement of frequency and duration of use of social network sites, and qualitative methods to measure the different activities adolescents engage in on such sites. In this review, 28 articles investigated the use of social media through a measurement of frequency and 22 articles through a measurement of the duration of use. Most of the studies focused on social network sites in general, without specifying which sites in particular were considered by the respondents when answering the question. However, some articles provided cross measures of quantitative and qualitative measurements resulting in the examination of both the frequency of use and the time spent on specific activities on social network sites.

CONCLUSIONS

The present review highlights the great diversity in the methods used to measure the use of social network sites among adolescents. The reliance on frequency-based measurements without taking into account the specificities of the activities or sites involved limits the investigation of their impact on adolescent functioning and behaviors. Given the observed discrepancies in the literature concerning the measurement of social network site use among adolescents and their associated biases, the development of specific instruments is needed to advance in this important field of research.

Latent classes associated with the intention to use a symptom checker for self-triage

It is currently unknown which attitude-based profiles are associated with symptom checker use for self-triage. We sought to identify, among university students, attitude-based latent classes (population profiles) and the association between latent classes with the future use of symptom checkers for self-triage. Informed by the Technology Acceptance Model and a larger mixed methods study, a cross-sectional survey was developed and administered to students (aged between 18 and 34 years of age) at a University in Ontario. Latent class analysis (LCA) was used to identify attitude-based profiles that exist among the sample while general linear modeling was applied to identify the association between latent classes and future symptom checker use for self-triage. Of the 1,547 students who opened the survey link, 1,365 did not use a symptom checker in the past year and were thus identified as "non-users". After removing missing data (remaining sample = n = 1,305), LCA revealed five attitude-based profiles: tech acceptors, tech rejectors, skeptics, tech seekers, and unsure acceptors. Tech acceptors and tech rejectors were the most and least prevalent classes, respectively. As compared to tech rejectors, tech seekers and unsure acceptors were the latent classes with the highest and lowest odds of future symptom checker use, respectively. After controlling for confounders, the effect of latent classes on symptom checker use remains significant (p-value < .0001) with the odds of future use in tech acceptors being 5.6 times higher than the odds of future symptom checker use in tech rejectors [CI: (3.458, 9.078); p-value < .0001]. Attitudes towards AI and symptom checker functionality result in different population profiles that have different odds of using symptom checkers for self-triage. Identifying a person's or group's membership to a population profile could help in developing and delivering tailored interventions aimed at maximizing use of validated symptom checkers.