Increasing Colorectal Cancer Screening Among Black Men in Virginia: Development of an mHealth Intervention
Notice bibliographique
Résumé
BACKGROUND: In the United States, colorectal cancer (CRC) is the third leading cause of cancer death among Black men. Compared to men of all other races or ethnicities, Black men have the lowest rates of CRC screening participation, which contributes to later-stage diagnoses and greater mortality. Despite CRC screening being a critical component of early detection and increased survival, few interventions have been tailored for Black men. OBJECTIVE: This study aims to report on the multistep process used to translate formative research including prior experiences implementing a national CRC education program, community advisory, and preliminary survey results into a culturally tailored mobile health (mHealth) intervention. METHODS: A theoretically and empirically informed translational science public health intervention was developed using the Behavioral Design Thinking approach. Data to inform how content should be tailored were collected from the empirical literature and a community advisory board of Black men (n=7) and reinforced by the preliminary results of 98 survey respondents. RESULTS: A community advisory board identified changes for delivery that were private, self-paced, and easily accessible and content that addressed medical mistrust, access delays for referrals and appointments, lack of local information, misinformation, and the role of families. Empirical literature and survey results identified the need for local health clinic involvement as critical to screening uptake, leading to a partnership with local Federally Qualified Health Centers to connect participants directly to clinical care. Men surveyed (n=98) who live or work in the study area were an average of 59 (SD 7.9) years old and held high levels of mistrust of health care institutions. In the last 12 months, 25% (24/98) of them did not see a doctor and 16.3% (16/98) of them did not have a regular doctor. Regarding CRC, 27% (26/98) and 38% (37/98) of them had never had a colonoscopy or blood stool test, respectively. CONCLUSIONS: Working with a third-party developer, a prototype mHealth app that is downloadable, optimized for iPhone and Android users, and uses familiar sharing, video, and text messaging modalities was created. Guided by our results, we created 4 short videos (1:30-2 min) including a survivor vignette, animated videos about CRC and the type of screening tests, and a message from a community clinic partner. Men also receive tailored feedback and direct navigation to local Federally Qualified Health Center partners including via school-based family clinics. These content and delivery elements of the mHealth intervention were the direct result of the multipronged, theoretically informed approach to translate an existing but generalized CRC knowledge-based intervention into a digital, self-paced, tailored intervention with links to local community clinics. TRIAL REGISTRATION: ClinicalTrials.gov NCT05980182; https://clinicaltrials.gov/study/NCT05980182.
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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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».