Inclusivity in Insomnia: Adolescents’ Perspectives on the Sleep Solved App: Qualitative Interview Study
Bibliographic record
Abstract
Abstract Background Adolescent sleep duration can substantially impact mood, behavior, and academic attainment. While hundreds of sleep-related apps are available to download, none have been cocreated with adolescents from underserved populations in the United Kingdom. Objective This study aimed to explore adolescents’ views, expectations, and experiences with a novel app to improve sleep, called Sleep Solved, to understand which features were perceived as positive and helpful, and to identify ways to further enhance its usefulness. Sleep Solved is part of a larger stepped behavior change study and was cocreated with adolescents from underserved groups to make the app accessible and engaging for this population. Methods A total of 63 participants aged 16‐18 years from across the United Kingdom completed semistructured interviews after trying the app. Interviews were analyzed using inductive thematic analysis, as outlined by Braun and Clarke, with a particular focus on the views of individuals from underserved ethnic and socioeconomic groups. Results Participants perceived Sleep Solved as a useful tool that provides helpful advice regarding changeable behaviors to improve sleep hygiene. Cocreated features of the app, such as the Sleep Stars gamified rewards system and the easy-read, science-based “sleep hacks,” were viewed positively by participants, who reported that they had a beneficial impact on their sleep and sleep schedule. Praise was given for the app’s ease of use and how the science of sleep was explained at an appropriate level, without being overwhelming. Compared to sleep advice on social media platforms, Sleep Solved was considered more reliable and trustworthy. Participants described better sleep hygiene, such as a regular sleep routine and a longer sleep duration, and increased feelings of improved mood and energy. Conclusions This study found that a cocreated sleep app, designed with input from adolescents in underserved UK populations, was perceived as accessible, reliable, and effective in supporting positive sleep behavior change. Although sleep duration was not objectively tested, participants, particularly those from low socioeconomic status backgrounds and diverse ethnicities, reported improved sleep routines and mood, highlighting the potential of co-designed digital tools to engage and benefit adolescent users.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".