Co-Design of a Depression Self-Management Tool for Adolescent and Young Adult Cancer Survivors: User-Centered Design Approach
Notice bibliographique
Résumé
BACKGROUND: Adolescent and young adult (AYA) cancer survivors are more likely to experience elevated depressive symptoms than older survivors and healthy age-matched peers. Despite the elevated risk of depressive symptoms in AYA cancer survivors and the existence of evidence-based interventions to address depression, it is unclear whether AYA cancer survivors can access support services. Digital tools are a potential solution to overcoming barriers to AYA cancer survivors' unmet needs for psychosocial support, but they have not been tailored to the needs and preferences of this unique population. OBJECTIVE: This study engaged AYA cancer survivors and their providers in the concept generation and ideation step of the user-centered design process through online co-design workshops. The goal was to generate concepts and ideas for a digital depression self-management tool tailored to AYA cancer survivors. METHODS: We conducted 5 co-design workshops-4 with AYA cancer survivors and 1 with providers who serve them. Participants were asked to provide feedback on an existing digital mindfulness course using an "I like, I wish, I wonder" framework. Then, participants were asked "How might we..." questions focused on brainstorming ideas for how the digital tool might work. Participants brainstormed responses independently and then worked as a group to categorize and expand on their ideas. Co-design workshops were autotranscribed using Webex (Cisco) software. Transcripts underwent thematic analysis with additional context provided by the products created during the workshop. RESULTS: Eight AYA cancer survivors (aged 15-37 years) and 4 providers (2 oncologists and 2 social workers) participated in co-design workshops. We identified 6 themes: barriers to engagement, desired content, preferences for content delivery, preferences for interface, features, and aspects to avoid. Each theme had 2-7 subthemes that we relied upon when making design decisions for the prototype. CONCLUSIONS: Co-design workshops provided critical insights that informed the prototype development of a digital depression self-management tool tailored to AYA cancer survivors. Key takeaways that were integrated into prototype design include (1) using stories from other AYA cancer survivors to demonstrate concepts; (2) delivering content in brief lessons; and (3) using encouraging notifications, organizational tools, and reward systems to keep AYA cancer survivors engaged with the tool. Some of the themes identified in this study (eg, desired content and features) are consistent with known strategies for promoting user engagement and co-design work in other cancer survivors. However, this study extended previous research by identifying uniquely relevant strategies for tailoring to AYA cancer survivors, such as delivering content in brief sessions to overcome the time constraints AYA cancer survivors experience, providing opportunities for private expression, and maintaining an encouraging tone throughout the tool. These data were used to inform the prototype development of a digital depression self-management tool tailored to AYA cancer survivors.
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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 | 0,018 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».