Building a Decentralized Biobanking App for Research Transparency and Patient Engagement: Participatory Design Study
Notice bibliographique
Résumé
BACKGROUND: Patient-derived biospecimens are invaluable tools in biomedical research. Currently, there are no mechanisms for patients to follow along and learn about the uses of their donated samples. Incorporating patients as stakeholders and meaningfully engaging them in biomedical research first requires transparency of research activities. OBJECTIVE: In this paper, we describe the use of participatory design methods to build a decentralized biobanking "de-bi" mobile app where patients could learn about biobanking, track their specimens, and engage with ongoing research via patient-friendly interfaces overlaying institutional biobank databases, initially developed for a breast cancer use case. METHODS: This research occurred in 2 phases. In phase 1, we designed app screens from which patients could learn about ongoing research involving their samples. We embedded these screens in a survey (n=94) to gauge patients' interests regarding types of feedback and engagement opportunities; survey responses were probed during 6 comprehensive follow-up interviews. We then held an immersive participatory design workshop where participants (approximately 50) provided general feedback about our approach, with an embedded codesign workshop where a subset (n=15) provided targeted feedback on screen designs. For phase 2, we refined user interfaces and developed a functional app prototype in consultation with institutional stakeholders to ensure regulatory compliance, workflow compatibility, and composability with local data architectures. We presented the app at a second workshop, where participants (n=25, across 9 groups) shared thoughts on the app's usability and design. In this phase, we conducted cognitive walkthroughs (n=13) to gain in-depth feedback on in-app task navigation. RESULTS: Most of the survey participants (61/81, 75%) were interested in learning the outcomes of research on their specimens, and 49% (41/83) were interested in connecting with others with the same diagnosis. Participants (47/60, 78%) expressed strong interest in receiving patient-friendly summaries of scientific information from scientists using their biospecimens. The first design workshop identified confusion in terminology and data presentation (eg, 9/15, 60% of co-designers were unclear on the biospecimens "in use"), though many appreciated the ability to view their personal biospecimens (7/15, 47%), and most were excited about connecting with others (12/15, 80%). In the second workshop, all groups found the app's information valuable. Moreover, 44% (5/9) noted they did not like the onboarding process, which was echoed in cognitive walkthroughs. Walkthroughs further confirmed interest in biospecimen tracking, and 23% (3/13) had confusion about not finding any of their biospecimens in the app. These findings guided refinements in onboarding, design, and user experience. CONCLUSIONS: Designing a patient-facing app that displays information about biobanked specimens can facilitate greater transparency and engagement in biomedical research. Co-designing the app with patient stakeholders confirmed interest in learning about biospecimens and related research, improved presentation of data, and ensured usability of the app in preparation for a pilot study.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,005 |
| 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».