Improving Access to Behavioral Strategies to Improve Mental Well-being With an Entertaining Breakfast Show App: Feasibility Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Although mental ill-health is more prevalent among people from lower socioeconomic groups, digital mental well-being innovations are often developed for people from higher socioeconomic groups, who already have resources to maintain good mental and physical health. To decrease health inequalities and ensure that available solutions are appealing and accessible to people with fewer resources, new approaches should be explored. We developed the app Wakey!, which focused on creating engaging mental health content that is accessible, particularly among lower socioeconomic groups in the United Kingdom. OBJECTIVE: The aim of this study is to assess engagement with the app, investigate initial effectiveness data for 6 well-being outcomes, and explore participants' subjective experiences of using Wakey! METHODS: The app Wakey! was publicly launched on January 20, 2020, and was free to download from Apple Store and Google Play. The app provided its users with entertaining and educational content related to mental well-being. Concurrently, a single-arm mixed methods feasibility trial was carried out from January to April 2020 among people who had downloaded the app and created an account. The primary outcome was engagement, which was collected passively from data logs. Secondary outcome measures were 6 well-being outcomes collected from self-report questionnaires. Individual interviews with 19 app users were carried out in April 2020. RESULTS: In total, 5413 people fit the inclusion criteria and were included in the final sample-65.62% (3520/5364) women, 61.07% (3286/5381) aged between 25 and 44 years, 61.61% (2902/4710) in employment, 8.92% (420/4710) belonging to the lower socioeconomic group, and 8.09% (438/5413) were engaged users. There was no evidence of a difference in engagement regarding sociodemographic and socioeconomic characteristics. There was evidence that users with a higher average daily sleep score, who joined the study more recently, who had higher baseline self-report of sleep quality, and who found episodes more entertaining were more likely to be engaged users. Among 230 users who provided follow-up data, there was evidence of improvements on four of the six well-being outcomes: life satisfaction (P<.001), feeling that life is worthwhile (P=.01), ease of getting up in the morning (P<.001), and self-efficacy (P=.04). The app and its content were well received by those who were interviewed, and several people perceived a positive change in their mental well-being. CONCLUSIONS: This study shows that the app Wakey! could potentially be engaging across different socioeconomic groups, and there is an indication that it could positively impact the mental well-being of those engaged with the app. However, this study was a pragmatic trial with a limited sample, and the selection bias was present in the qualitative and quantitative study. Further work is needed to make any generalizable conclusions. TRIAL REGISTRATION: ClinicalTrials.gov NCT04287296; https://clinicaltrials.gov/ct2/show/NCT04287296.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".