Reductions of Anxiety Symptoms, State Anxiety, and Anxious Arousal in Youth Playing the Videogame MindLight Compared to Online Cognitive Behavioral Therapy
Bibliographic record
Abstract
Objective: Anxiety disorders are the most prevalent form of psychopathology among youth. Because demand for treatment far exceeds availability, there is a need for alternative approaches that are accessible, engaging, and incorporate practice to reach as many youth as possible. MindLight is a novel videogame intervention that combines evidence-based anxiety reduction techniques with neurofeedback mechanics that has been shown to reduce anxiety symptoms in youth. This study examined the effectiveness of MindLight compared with online cognitive behavioral therapy (CBT) to replicate and extend those findings by testing the reduction of reactivity to anxiety-eliciting laboratory stressors. Materials and Methods: A randomized controlled trial was conducted with laboratory assessments at pre-intervention, post-intervention, and 3-month follow-up. Participants were 117 anxious youth (66.7% female, 33.3% male; age range: 8.05–15.93 years) who were randomized into MindLight (n = 56) and CBT (n = 60) conditions. Both interventions were completed in five 1-hour sessions within a 3-week period. At each time point, anxiety symptoms were assessed through self-report, and state anxiety and anxious arousal were measured during laboratory stress tasks. Results: All measures of anxiety significantly decreased over time in both conditions (P < 0.05). Moreover, youth in the MindLight condition showed greater pre-to-post reductions in anxiety symptoms compared with youth in the CBT condition (P < 0.05). Conclusion: Findings demonstrate that the effects of MindLight and online CBT are not only associated with reductions in anxiety symptoms, but also impact how youth react to laboratory stressors in the moment. ClinicalTrials.gov Identifier: NCT02326545
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".