Public attitudes and predictors of public awareness of personal digital health data sharing for research: A cross-sectional study in Japan (Preprint)
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".