Smartphone addiction and well-being in adolescents: testing the mediating role of self-regulation and attention
Bibliographic record
Abstract
Background: Smartphone addiction can have negative consequences such as anxiety, depression, insomnia, and a loss of social connectivity. Understanding smartphone addiction is still in its early stages, but self-regulation and attention deficit hyperactivity disorder (ADHD) symptoms are two established risk factors. Exploring these risk factors and their impact on individuals’ well-being may help prevent smartphone addiction. Objective: This study aims to (1) explore the relationship between smartphone addiction and psychological and social well-being (e.g., friendship validation and caring, and friendship and intimate exchange) among adolescents. (2) Examine whether self-regulation mediates the relationship between smartphone addiction and psychological well-being and social well-being. (3) Examine whether attention mediates the relationship between smartphone addiction and psychological well-being and social well-being. Methods: This was a cross-sectional study conducted in middle school in Victoria, British Columbia, Canada. Students (Grade 6-8) completed an online survey that measured smartphone addiction, attention, self-regulation, and psychological and social well-being. A bivariate correlational analysis was used to examine the relationship between smartphone addiction, self-regulation, attention psychological well-being, and social well-being. Multiple mediation analyses were used to perform the mediation between smartphone addiction, attention, self-regulation, and psychological and social well-being. Results: The bivariate correlation showed significant negative associations between smartphone addiction and attention, self-regulation, psychological well-being, and friendship validation and caring. Smartphone addiction did not have a significant relationship with friendship intimate exchange. The mediation analysis showed that attention was a significant mediator between smartphone addiction and psychological well-being (indirect effect= -.102; 95% CI -.142, -.066) and between smartphone addiction and friendship validation and caring (indirect effect= -.056; 95% CI -.093, -.024; direct effect= -.071; 95% CI -.155, .013). Attention did not significantly mediate the relationship between smartphone addiction and the friendship intimate exchange aspect of social well-being (indirect effect= -.005; 95% CI -.026, .016). Self-regulation showed a significant partial mediation between smartphone addiction and psychological well-being (indirect effect= -.016; 95% CI -.034, -.002). Self-regulation did not significantly mediate the relationship between smartphone addiction and friendship validation and caring (indirect effect=-.014; 95% CI -.034, .001) and friendship intimate exchange (indirect effect=-.001; 95% CI -.007, .007). Conclusion: The results indicated that the negative relationship between smartphone addiction and psychological well-being can be partially explained by adolescents’ attention and self-regulation abilities. The negative relationship between smartphone addiction and social well-being (validation and caring) can be partially explained by adolescents’ attention. However, both aspects of social well-being (validation and caring and intimate exchange) were not impacted by self-regulation. This study identified potential mediators that may be used for future interventions to prevent smartphone addiction and promote wellbeing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".