Improving Access to Behavioral Strategies to Improve Mental Well-being With an Entertaining Breakfast Show App: Feasibility Evaluation Study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».