Adherence to home-based videogame treatment for amblyopia in children and adults
Bibliographic record
Abstract
Clinical relevance: Home-based videogame treatments are increasingly popular for amblyopia treatment. However, at-home treatments tend to be done in short sessions and with frequent disruptions, which may reduce the effectiveness of binocular visual stimulation. These treatment adherence patterns need to be accounted for when considering dose-response relationships and treatment effectiveness.Background: Home-based videogame treatments are increasingly being used for various sensory conditions, including amblyopia (‘lazy eye’), but treatment adherence continues to limit success. To examine detailed behavioural patterns associated with home-based videogame treatment, we analysed in detail the videogame adherence data from the Binocular tReatment of Amblyopia with VideOgames (BRAVO) clinical trial (ACTRN12613001004752).Methods: Children (7-12 years), teenagers (13-17 years) and adults (≥ 18 years) with unilateral amblyopia were loaned iPod Touch devices with either an active treatment or placebo videogame and instructed to play for a total of 1-2 hours/day for six weeks at home. Objectively-recorded adherence data from device software were used to analyse adherence patterns such as session length, daily distribution of gameplay, use of the pause function, and differences between age groups. Objectively-recorded adherence was also compared to subjectively-reported adherence from paper-based diaries.Results: One hundred and five of the 115 randomised participants completed six weeks of videogame training. Average adherence was 65% (SD 37%) of the minimum hours prescribed. Game training was generally performed in short sessions (mean 21.5, SD 11.2 minutes), mostly in the evening, with frequent pauses (median every 4.1 minutes, IQR 6.1). Children played in significantly shorter sessions and paused more frequently than older age groups (p < 0.0001). Participants tended to over-report adherence in subjective diaries compared to objectively-recorded gameplay time.Conclusion: Adherence to home-based videogame treatment was characterised by short sessions interspersed with frequent pauses, suggesting regular disengagement. This complicates dose-response calculations and may interfere with the effectiveness of treatments like binocular treatments for amblyopia, which require sustained visual stimulation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".