Youth Codesign of a Mobile Phone App to Facilitate Self-Monitoring and Management of Mood Symptoms in Young People With Major Depression, Suicidal Ideation, and Self-Harm
Notice bibliographique
Résumé
BACKGROUND: Effective treatment of depression in young people is critical, given its prevalence, impacts, and link to suicide. Clinical practice guidelines point to the need for regular monitoring of depression symptom severity and the emergence of suicidal ideation to track treatment progress and guide intervention delivery. Yet, this is seldom integrated in clinical practice. OBJECTIVE: The objective of this study was to address the gap between guidelines about monitoring and real-world practice by codesigning an app with young people that allows for self-monitoring of mood and communication of this monitoring with a clinician. METHODS: We engaged young people aged 18 to 25 years who had experienced depression, suicidal ideation including those who self-harm, as well as clinicians in a codesign process. We used a human-centered codesign design studio methodology where young people designed the features of the app first individually and then as a group. This resulted in a minimal viable product design, represented through low-fidelity hand-drawn wireframes. Clinicians were engaged throughout the process via focus groups. RESULTS: The app incorporated a mood monitoring feature with innovative design aspects that allowed customization, and was named a "well-being tracker" in response to the need for a positive approach to this function. Brief personalized interventions designed to support young people in the intervals between face-to-face appointments were embedded in the app and were immediately available via pop-ups generated by a back-end algorithm within the well-being tracker. Issues regarding the safe incorporation of alerts generated by the app into face-to-face clinical services were raised by clinicians (ie, responding in a timely manner) and will need to be addressed during the full implementation of the app into clinical services. CONCLUSIONS: The potential to improve outcomes for young people via technology-based enhancement to interventions is enormous. Enhancing communication between young people and their clinicians about symptoms and treatment progress and increasing access to timely and evidence-based interventions are desirable outcomes. To achieve positive outcomes for young people using technology- (app) based interventions, it is critical to understand and incorporate, in a meaningful way, the expectations and motivations of both young people and clinicians.
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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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 ».