The Role of Practitioner- and User-Set Goals in Engagement and Psychological Distress Among Kooth Digital Health Users: Retrospective Analysis
Notice bibliographique
Résumé
BACKGROUND: Youth and young adult mental health concerns are rising globally, with digital mental health platforms offering a promising solution for accessible support. Among the various features these platforms provide, goal setting and achievement have been shown to positively influence behavior change and mental health outcomes. However, there is limited understanding of how user-set goals compare to those set collaboratively with a practitioner regarding their impact on user engagement and mental health outcomes in digital mental health platforms. OBJECTIVE: The purpose of this study was to examine the relationship between various goal-related variables (eg, the number of goals created and progress in user-set and practitioner-set goals) and user engagement as well as mental health (ie, psychological distress) on a free digital mental health platform. A secondary exploratory aim was to assess how different user-presenting issues were associated with platform engagement. METHODS: We leveraged secondary data from a free, web-based mental health platform for youth aged 10 to 25 years in the United Kingdom that offers goal-setting features, emotional journaling, peer support, asynchronous chat with practitioners, and various self-guided well-being activities. Data included in the analyses were from youth and young adults (mean age 15.84 years, SD 2.88; 522/691, 75.5% female) who engaged with the goal-setting feature and completed both pre- and postengagement psychological distress measures between January 2020 and December 2023. We examined the relationship between user-set goals and practitioner-set goals on user engagement and psychological distress via linear regressions. The impact of different user-presenting issues on engagement was also explored via linear regression. RESULTS: The number of practitioner-set goals created was positively associated with platform engagement (β=.16; P<.001), whereas the number of self-set goals and goal progress, whether self or practitioner set, were not. Progress on practitioner-set goals was significantly associated with reduced psychological distress (β=-.27; P<.001), while progress on self-set goals showed no significant association (P=.16). Physical health-related and school-related presenting issues were the strongest predictors of increased platform engagement (β=.21; P<.001 and β=.17; P<.001, respectively). CONCLUSIONS: These findings underscore the importance of collaborative goal setting in improving mental health outcomes for youth and young adults on digital mental health platforms. By highlighting the role of guided support and goal progression, this study enhances our understanding of how digital mental health platforms can better support young people's mental health and well-being. This paper also highlights how digital mental health platforms can serve as a valuable resource for addressing a wide range of mental health needs.
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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 | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».