Conduct problems, hyperactivity, and screen time among community youth: Can mindfulness help?
Bibliographic record
Abstract
Introduction While technology continues to evolve and the prevalence of screen-based activities is rising, limited studies have investigated the effect of various types of screen time on youth behavioural problems. Further, the influence of mindfulness intervention programs on behavioural problems beyond hyperactivity is largely understudied. Objectives This study aims to address a research gap by examining the associations between four types of screen time and hyperactivity and conduct problems among community youth during the pandemic. The current study also aimed to investigate the efficacy of a mindfulness-based intervention in reducing hyperactivity and conduct problems. Methods Community youth aged 12-25 from Ontario, Canada, were recruited between April 2021 and April 2022 (n=117, mean age=16.82, male=22%, non-White=21%). The Mindfulness Ambassador Program, a structured, 12-week, evidence-based intervention program, was offered live, online and led by two MAP-certified facilitators. We conducted linear regression analyses using pre-intervention data to examine the unique association between the four types of screen time and behavioural problems (hyperactivity and conduct problems). The efficacy of the MAP on adolescent hyperactivity and conduct problems was examined considering the three survey time points (pre-, post-, and follow-up) using a series of linear regression models utilizing the Generalized Least Squares (GLS) Maximum Likelihood (ML), unstructured model. Results The average score for conduct problems was classified within the normal range, while the average score for hyperactivity was considered borderline at baseline. More than 5 hours of playing video games were significantly associated with increased conduct problems [β= -1.75, 95% CI=-0.20 – 3.30, p=0.03]. Accounting for age, sex, baseline mental health status, and screen time, the mindfulness intervention program significantly contributed to decreased hyperactivity at post-intervention compared to the baseline [β=-0.49, 95% CI=-0.91 to -0.08, p=0.02]. It was maintained at follow-up [β=-0.64, 95% CI=-1.26 to -0.03, p=0.04]. Conclusions Our findings suggest an adverse impact of excessive video gaming on behavioural problems among community youth and confirm that the trend remains the same. Considering the simplicity, brevity, non-invasive nature and other mental health benefits of the mindfulness intervention, we argue that the results are promising and worthy of further study and larger-scale implementation. Clinicians, parents, and educators should work collaboratively to provide developmentally appropriate strategies to moderate screen time spent on video games among youth. Disclosure of Interest None Declared
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".