Bibliographic record
Abstract
Wearable devices such as smartwatches and fitness bands are increasingly being touted for use in healthcare. The suggestion that they could enhance treatment while reducing costs has resonated with governments in the USA, the UK, and beyond. This exploratory article examines the regulatory challenges that arise as wearables transition from consumer to health contexts. The amount of data wearables generate poses a challenge to device manufacturers and data processors-whose terms and conditions and security measures have drawn numerous data protection, privacy, and surveillance concerns. This article presents findings from empirical research into contemporary use of wearables in the UK, based on a Freedom of Information request submitted to 37 National Health Service Hospital Trusts. It casts doubt on whether individual consent to data processing is appropriate for a healthcare context characterized by unequal power dynamics between patients, health professionals, and corporate interests. The assumption that consent will suffice forms the basis of existing regulations, including the EU General Data Protection Regulation 2018 and the UK Data Protection Act 2018. Alternative regulatory models, including open data and data sovereignty, should be considered if public healthcare systems are to utilize wearables without damaging patient trust and confidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.106 | 0.057 |
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".