Ethical considerations for the use of consumer wearables in health research
Bibliographic record
Abstract
Background: The UN's High Commissioner's request for a moratorium on the use and adoption of specific Artificial Intelligence (AI) systems that pose serious risk to human rights, this commentary explores the current environment and future implications of using third-party wearable technologies in research for participants' data privacy and data security. While wearables have been identified as tools for improving users' physical and mental health and wellbeing by providing users with more personalized data and tailored interventions, the use of this technology does not come without concern. Objective: Primarily, as researchers, we are concerned with enmeshment of corporate and research interests and what this can mean for participant data. Methods: By drawing on specific sections of the UN Report 'The right to privacy in the digital age', we discuss the conflicts between corporate and research agendas and point out the current and future implications of the involvement of third-party companies for participant data privacy, data security and data usage. Finally, we offer suggestions for researchers and third-party wearable developers for conducting ethical and transparent research with wearable tech. Conclusion: We propose that this commentary be used as a foothold for further discussions about the ethical implications of using third-party wearable tech in research.
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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 | 0.182 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.075 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.061 | 0.040 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".