Digital Traces of Behaviour Within Addiction: Response to Griffiths (2017)

Association between smartphone overdependence and generalized anxiety disorder among Korean adolescents

BACKGROUND

This study aimed to investigate the association between smartphone overdependence and generalized anxiety disorder among South Korean adolescents.

METHODS

Participants were selected from the Korean Youth Health Behavior Survey 2020. The primary dependent variable was the generalized anxiety disorder that was measured based on seven item instrument (GAD-7). The main exposure of interest was the smartphone overdependence using the integrated scale developed by the National Information Society Agency in Korea. Weighted chi-square test and multiple logistic regression were used to assess the association between smartphone overdependence and generalized anxiety disorder.

RESULTS

The final participants comprised of 54,948 middle and high school students. 13,775 students (25.1 %) were classified as the smartphone overdependence group. Of those who reported overly dependent on smartphones, 2803 students (20.3 %) had generalized anxiety disorder. The risk for generalized anxiety disorder were 2.15 folds (95 % CI 2.01-2.30) higher among the overdependence group in compared to their counterparts. Specifically, the risk for generalized anxiety disorder increased when smartphone has negatively affected relationships with friends and colleagues (OR: 2.35, 95 % CI 2.08-2.64). The sensitivity of smartphone overdependence scale was verified and the risk for generalized anxiety disorder increased in magnitude with the severity of smartphone overdependence.

CONCLUSION

The findings of the study showed significant association between smartphone overdependence and generalized anxiety disorder among South Korean adolescents. The negative changes in social relationships due to excessive smartphone use and the severity of overdependence accounted for the risk for generalized anxiety disorder.

Do evaluation and self-identification relate to self-reported and actual social media use?

Background and aims

Social media use can sometimes become excessive and damaging. To deal with this issue, scholars and practitioners have called for the development of measures that predict social media use. The current studies test the utility of evaluation and self-identification measures for predicting social media use.

Method

Study 1 examined the relation between evaluation (n = 58) and self-identification (n = 56) measures on the one hand and several self-report measures of social media use on the other hand. Study 2 examined whether the main results of Study 1 could be replicated and whether evaluation (n = 68) and self-identification (n = 48) also relate to actual social media use. We probed evaluation and self-identification using implicit and explicit measures.

Results

Explicit evaluation and self-identification measures significantly correlated with several of the self-report measures of social media. Explicit evaluation also significantly correlated with several indices of actual social media use. Implicit measures did not relate to social media use.

Discussion and conclusions

The current results suggest that researchers and practitioners could benefit from using explicit evaluation and self-identification measures when predicting social media use, especially an evaluation measure since this measure also seems to relate to actual social media use. Study 2 was one of the first to test the ecological validity of social media use measures. Although implicit measures could provide benefits for predicting social media use, the current studies did not show evidence for their predictive utility.

Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach

More than three billion users are currently on one of Meta's online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized (via psychological targeting). In this context, it is debated if social media platforms can elicit addictive behaviors. In the present work, we demonstrate in N = 1,094 users that it is possible to predict from digital footprints of the Facebook users their self-reported addictive tendencies toward social media (R > 0.30) by applying machine-learning strategies. More specifically, we analyzed the predictive power of a set of models based on different sets of features extracted from digital traces, namely posting activity, language use, and page Likes. To maximize the predictive power of the models, we used an ensemble of linear and non-linear prediction algorithms. This work showed also sufficient accuracy rates (AUC above 0.70) in distinguishing between disordered and non-disordered social media users. In sum, individual differences in tendencies toward "social networks use disorder" can be inferred from digital traces left on the social media platform Facebook. Please note that the present work is limited by its cross-sectional design.

Risks and Opportunities for Youth in the Digital Era

Abstract. Due to continued groundbreaking digital advancements, Internet use has increased significantly. This has led to a heated debate in relation to weighing the many advantages of the technology against its potentially deleterious effects. To address such questions, experts converge on the need for greater knowledge around the way individual differences, partly shaped by an individual’s unique experiences of engaging with the medium, and partly by other real-life experiences, lead to different developmental trajectories. Consequently, the goals of the present review are to (i) broadly describe differences in digital media applications, users, and usage; (ii) introduce the Cyber-Developmental Framework (CDF), as an overarching framework for understanding individual differences in adaptive and maladaptive digital media use among youth; (iii) delineate the cyber-component of this framework in relation to users’ experience of the digital context, their activity within it, as well as their digital self-presence, which may have an impact on their digital media use; and (iv) summarize priorities and future directions through the lens of this CDF. Within this context, this review particularly emphasizes the effect of digital media use on youth’s psychological well-being. It is argued that the trajectory youth will follow in their use of the Internet is a function of the interplay between their characteristics, their proximate and distal contexts, and the particular features of the digital application(s) that the individual is engaged in. The review points to the need to conduct research focusing on better understanding the developmental and digital-context-related influences on youth’s trajectories of Internet use.

Passive objective measures in the assessment of problematic smartphone use: A systematic review

Research focussing on problematic smartphone use has predominantly employed psychometric tests which cannot capture the automatic processes and behaviours associated with problematic use. The present review aimed to identify passive objective measures that have been used or developed to assess problematic smartphone use. A systematic search was conducted using Web of Science, Scopus, PsychInfo and PubMed databases to identify passive objective measures that have been employed to assess problematic smartphone use, resulting in 18 studies meeting the inclusion criteria. Objective data that were monitored predominantly focussed on general screen usage time and checking patterns. Findings demonstrate that passive monitoring can enable smartphone usage patterns to be inferred within a relatively short timeframe and provide ecologically valid data on smartphone behaviour. Challenges and recommendations of employing passive objective measures in smartphone-based research are discussed.

Predictors of Problematic Smartphone Use: An Examination of the Integrative Pathways Model and the Role of Age, Gender, Impulsiveness, Excessive Reassurance Seeking, Extraversion, and Depression

Background: The progression of mobile phone technology has led to the development of multi-functional smartphones providing access to features such as social media, e-mail, and videos alongside the basic functions of a mobile phone. Increasing amounts of research has explored the potential addictive nature of smartphones to develop a theoretical framework that describes personality factors related to problematic use. The present study examined the Integrative Pathways Model and the effect of age, gender, impulsiveness, excessive reassurance seeking, extraversion, and depression on problematic smartphone use. Method: A total of 147 smartphone users (mean age = 30.96, SD = 12.97, 69.4% female) completed an online survey comprising of measures of problematic smartphone use, excessive reassurance seeking, extraversion, depression, and impulsiveness. Results: Age, impulsiveness, excessive reassurance seeking, and depression were all significantly related to problematic smartphone use, however extraversion was not significantly related. Furthermore, age and impulsiveness were significant independent predictors of problematic smartphone use. No gender differences were found. Conclusions: The findings presented several factors that predict problematic smartphone use, implications and suggestions for future research are discussed.

Determining Typical Smartphone Usage: What Data Do We Need?

Problematic smartphone use is an emerging issue in behavioral addiction research. At the same time, measuring smartphone use with mobile apps has become increasingly common. However, understanding how much data are necessary requires careful consideration if the field is to move forward. Here, we examine how much time should be spent measuring mobile phone operation to reliably infer general patterns of usage and repetitive checking behaviors. In a second analysis, we consider whether a self-report measure of problematic smartphone use is associated with real-time patterns of use. Results suggest that smartphone usage collected for a minimum of 5 days will reflect typical weekly usage (in hours), but habitual checking behaviors (uses lasting <15 seconds) can be reliably inferred within 2 days. These measurements did not reliably correlate with a self-reported measure. We conclude that patterns of smartphone use are repetitive and our results suggest that checking behavior is a particularly consistent and efficient measure when quantifying typical and problematic smartphone usage.