Ethical Considerations When Creating Evidence from Real World Digital Health Data
Nebeker, C.; Leavy, V.; Roitmann, E.; Steinhubl, S.
Show abstract
BackgroundPersonal health data (PHD) are collected using digital self-tracking technologies and present opportunities to increase self-knowledge and, also biometric surveillance. PHD become "big" data and are used in health-related research studies. We surveyed consumers regarding expectations regarding consent and sharing of PHD for biomedical research. MethodsData sharing preferences were assessed via an 11-item survey. The survey link was emailed to 89539 English-speaking Withings product users. Responses were accepted for 5 weeks. Descriptive statistics were calculated using Excel and qualitative data were analyzed to provide additional context. ResultsNearly 1640 people or 5.7% of invitees responded representing 62 countries with 80% identifying as Caucasian, 75% male with 78% being college educated. The majority were agreeable to having their data shared with researchers to advance knowledge and improve health care. Participants responding to open ended items (N=247) appeared unaware that the company had access to their personal health data. ConclusionsWhile the majority of respondents were in favor of data sharing, individuals expressed concerns about the ability to de-identify data and associated risks of re-identification as well as an interest in having some control over the use of "their" data. Given consumer misconception about data ownership, access and use, efforts to increase transparency when interacting with individual digital health data must be prioritized. Moreover, the basic ethical principle of "respect for persons" demonstrated via the informed consent process will be critical in advancing the adoption of digital technologies that create real-world evidence and advance opportunities for N-of-1 self-study.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The challenges of replication: a worked example of methods reproducibility using routinely collected healthcare data 93%
- Methods for analytical validation of novel digital clinical measures: A simulation study 91%
- The Pandemic Journaling Project: A new dataset of first-person accounts of the COVID-19 pandemic 91%
Similar papers in this journal
- Data-driven discovery of changes in clinical code usage over time: a case-study on changes in cardiovascular disease recording in two English electronic health records databases (2001-2015) 92%
- Adoption and continued use of mobile contact tracing technology: Multilevel explanations from a three-wave panel survey and linked data 92%
- Examining Australian's beliefs, misconceptions, and sources of information for COVID-19: A national online survey 91%
Similar papers in this journal
- Cracking the Code: A Scoping Review to Unite Disciplines in Tackling Legal Issues in Health Artificial Intelligence 92%
- Connecting Artificial Intelligence and Primary Care Challenges: Findings from a Multi-Stakeholder Collaborative Consultation 91%
- Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey 91%
Similar papers in this journal
- Development and Evaluation of MADDIE: Method to Acquire Delivery Date Information from Electronic Health Records 93%
- Digital applications to support self-management of multimorbidity: A scoping review 92%
- Synthetic Data Generation in Healthcare: A Scoping Review of reviews on domains, motivations, and future applications 92%
Similar papers in this journal
- Defining Destigmatizing Design Guidelines for Use in Sexual Health-Related Digital Technologies: A Delphi Study 93%
- Benefits and Challenges of Using Virtual Primary Care During the COVID-19 Pandemic: From Key Lessons to a Framework for Implementation 92%
- Use of Generative AI for Health Among Urban Youth in Pakistan: A Mixed-Methods Study 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.