Acceptability, Engagement, and Exploratory Outcomes of an Emotional Well-being App: Mixed Methods Preliminary Evaluation and Descriptive Analysis
Notice bibliographique
Résumé
Background There is growing evidence suggesting that the emotional well-being of the public has been negatively affected in the past year. Consequently, demand for well-being support has increased. Although there is substantial empirical support for mental health apps that target diagnosed conditions, there is less research on emotional well-being apps. Among existing well-being apps, few studies have been conducted on apps that are based on lived experience and those that seek to enhance users’ understanding of their emotional patterns. Thus, the acceptability of these novel apps requires further evaluation before upscaling. Objective This evaluation aims to describe the acceptability, engagement, and preliminary outcomes of using an app (Paradym) designed to promote emotional well-being and positive mental health. Methods This is a pre-post, mixed-methods, single-arm evaluation that is aggregated with digital analytics data. We anonymously collected real-world data on the demographics and well-being of the participants as well as the usability and acceptance of the app using validated questionnaires and open-ended questions. Participants tested the app for a minimum of 2 weeks before completing the follow-up measures. Google Analytics was used to record the level of app engagement. Chi-square and 2-tailed t tests were conducted to analyze quantitative data, and a thematic analysis approach was adopted for qualitative data. Results A total of 115 participants completed baseline questionnaires, of which 79.1% (91/115) users downloaded the app. The sample was diverse in terms of ethnicity, including 43.4% (50/115) people who self-identified as belonging to minority ethnic groups. Most of the participants were female (78/115, 67.8%) and between the ages of 18 and 25 years (39/115, 33.9%). A total of 34 app users who completed questionnaires at baseline and follow-up provided valuable feedback to inform the future directions of Paradym. Favorable themes emerged describing the app’s content, functionality, and underlying principles. Although usability feedback varied across items, a considerable number of participants (22/34, 64%) found that the app was easy to use. Google Analytics revealed that at least 79% (27/34) of people used the app daily. On the basis of preliminary observations, app users experience increased mental well-being. Post hoc analyses indicated that the reduction in depression scores (t33=−2.16) and the increase in the well-being measures (t33=2.87) were statistically significant. No adverse events were reported during the follow-up period. Conclusions The findings of this evaluation are encouraging and document positive preliminary evidence for the Paradym app.
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 | 0,101 | 0,117 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,004 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».