Public attitudes and predictors of public awareness of personal digital health data sharing for research: A cross-sectional study in Japan (Preprint)
Notice bibliographique
Résumé
Background: As digital technology advances, health-related data can be scientifically analyzed to predict illnesses. The analysis of international data collected during health examinations and health status monitoring, along with data collected during medical care delivery, can contribute to precision medicine and the public good. Understanding citizens' attitudes and predictors of digital health data sharing is critical in promoting data-driven research. Objective: This study aims to determine the public acceptability of data sharing and the attitudes and influencing factors toward data sharing. Methods: A cross-sectional web-based survey was conducted in Japan from November 11-18, 2023. We analyzed 1000 valid responses. Five factors were investigated as predictors of participants' attitudes toward sharing digital health data for social benefit: (1) individual sociodemographic characteristics, (2) types of health data shared, (3) motivation for sharing data, (4) data sharing concerns, and (5) reasonable access and control over the data. The association of these factors with the respondents' willingness to share was analyzed. We summarized demographic characteristics based on gender, age group, affiliated educational institution, and education history and degree. Continuous variables are expressed as mean (SE). Logistic regression was used to analyze the association between attitudes and acceptability of sharing digital health data and the predicting factors, such as participants' preferences regarding data access and control, underlying concerns, motivations for data sharing, demographic characteristics, and eHealth literacy. Results: The mean age of the participants and the SD was 52.8 (19.8) years. We identified the factors influencing respondents' willingness to share a wide range of personal digital health data in Japan, including data in medical records, biobank samples, and digitized social communication. Approximately 70% of the participants were willing to share their digital health data. The motives associated with positive willingness to share digital health data were helping future patients (odds ratio [OR] 2.5860, 95% CI 1.8849-3.5481; P<.001), receiving their own results (OR 2.2261, 95% CI 1.6243-3.0509; P<.001), and receiving financial benefits (OR 1.8059, 95% CI 1.2630-2.5822; P=.001). Concerns associated with negative willingness to share data were data being used for unethical projects (OR 0.5104, 95% CI 0.5104-0.722; P<.001) and agreeing to contract terms that they did not fully understand (OR 0.7114, 95% CI 0.5228-0.9681; P=.04). Compared with men, women were less willing to share data (OR 0.722, 95% CI 0.539-0.967; P=.03). Furthermore, the higher one's eHealth literacy, the more positive their willingness to share digital health data (OR 1.0680, 95% CI 1.0450-1.0920; P<.001). Conclusions: This study found differences in the types of data people are willing to share. Therefore, the significance of sharing data should be fully communicated to people to motivate them to share their data and contribute to their overall health.
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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».