Designing Digital Mental Health Tools to Support the Needs of Black Adults in the United States: Qualitative Analysis
Notice bibliographique
Résumé
Background: Depression and anxiety are associated with excess morbidity and mortality, constituting a major health care challenge. The prevalence of these conditions is increasing. In the United States, the health-related burden of depression and anxiety may disproportionately affect Black adults, who face unique stressors impacting their mental health and barriers to accessing treatment, including but not limited to systemic racism, discrimination, underdiagnosis of common mental health concerns (ie, depression, anxiety), limited access to culturally sensitive care, and mental health stigma within and outside Black communities. Objective: This study aimed to explore the mental health experiences of nontreatment-seeking Black adults, and how these experiences relate to their needs and preferences for the design of digital mental health (DMH) tools through user-centered design methods. Methods: This study included 25 nontreatment-seeking Black adults (aged 18-61 years) with experiences of depression or anxiety to share their perspectives on how DMH tools can meet their needs. Participants were recruited either through social media advertisements or depression and anxiety questionnaires. All participants engaged in an asynchronous online discussion group in which they discussed their past and current mental health experiences, distinct challenges faced by Black Americans, and perceptions of DMH tools, as well as how such tools can be tailored to meet their mental health needs. Participants also completed a technology probe in which they used an automated mental health self-management text messaging tool (Small Steps SMS; Audacious Software) for 18 days. They shared their perceptions of the tool and ideas for specific design improvements in the discussion group. A subset (n=6) completed follow-up interviews to elaborate on their online discussion group posts. Results: All participants reported significant mental health concerns and difficulty managing related symptoms. A majority of participants (22/25, 88%) expressed that racism and mental health stigma severely impacted their mental health and limited opportunities to discuss their experiences within and outside Black communities. They were interested in the use of DMH tools for mental health self-management and nearly all participants (23/25, 92%) endorsed text messaging as a convenient way to introduce techniques for coping with symptoms of depression and anxiety; however, some participants strongly advocated for additional design features that they believed would improve the program, including the integration of content that centers the experiences of Black individuals, creating nonjudgmental spaces for discussing mental health experiences, and linking formal mental health treatment resources for those who want them. Conclusions: These findings suggest that our participants hold generally favorable views toward DMH tools, which can provide psychoeducation, self-management support tailored to the needs of Black adults, and a safe environment to address mental health concerns. Furthermore, it is critical to consider the role of racial discrimination and mental health stigma when designing inclusive and culturally sensitive DMH tools.
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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 | 0,016 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».