Using Social Media to Engage and Enroll Underrepresented Populations: Longitudinal Digital Health Research
Notice bibliographique
Résumé
BACKGROUND: Emerging digital health research poses roadblocks to the inclusion of historically marginalized populations in research. Exclusion of underresourced communities in digital health research is a result of multiple factors (eg, limited technology access, decreased digital literacy, language barriers, and historical mistrust of research and research institutions). Alternative methods of access and engagement may aid in achieving long-term sustainability of diversified participation in digital health research, ensuring that developed technologies and research outcomes are effective and equitable. OBJECTIVE: This study aims to (1) characterize socioeconomic and demographic differences in individuals who enrolled and engaged with different remote, digital, and traditional recruitment methods in a digital health pregnancy study and (2) determine whether social media outreach is an efficient way of recruiting and retaining specific underrepresented populations (URPs) in digital health research. METHODS: The Better Understanding the Metamorphosis of Pregnancy (BUMP) study was used as a case example. This is a prospective, observational, cohort study using digital health technology to increase understanding of pregnancy among 524 women, aged 18-40 years, in the United States. The study used different recruitment strategies: patient portal for genetic testing results, paid/unpaid social media ads, and a community health organization providing care to pregnant women (Moses/Weitzman Health System). RESULTS: Social media as a recruitment tool to engage URPs in a digital health study was overall effective, with a 23.6% (140/594) enrollment rate of those completing study interest forms across 25 weeks. Community-based partnerships were less successful, however, resulting in 53.3% (57/107) engagement with recruitment material and only 8.8% (5/57) ultimately enrolling in the study. Paid social media ads provided access to and enrollment of a diverse potential participant pool of race- or ethnicity-based URPs in comparison to other digital recruitment channels. Of those that engaged with study materials, paid recruitment had the highest percentage of non-White (non-Hispanic) respondents (85/321, 26.5%), in comparison to unpaid ads (Facebook and Reddit; 37/167, 22.2%). Of the enrolled participants, paid ads also had the highest percentage of non-White (non-Hispanic) participants (14/70, 20%), compared to unpaid ads (8/52, 15.4%) and genetic testing service subscribers (72/384, 18.8%). Recruitment completed via paid ads (Instagram) had the highest study retention rate (52/70, 74.3%) across outreach methods, whereas recruitment via community-based partnerships had the lowest (2/5, 40%). Retention of non-White (non-Hispanic) participants was low across recruitment methods: paid (8/52, 15.4%), unpaid (3/35, 14.3%), and genetic testing service subscribers (50/281, 17.8%). CONCLUSIONS: Social media recruitment (paid/unpaid) provides access to URPs and facilitates sustained retention similar to other methods, but with varying strengths and weaknesses. URPs showed lower retention rates than their White counterparts across outreach methods. Community-based recruitment showed lower engagement, enrollment, and retention. These findings highlight social media's potential for URP engagement and enrollment, illuminate potential roadblocks of traditional methods, and underscore the need for tailored research to improve URP enrollment and retention.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,029 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».