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Record W2763083873 · doi:10.2196/biomedeng.8179

Heart Rate Monitoring Apps: Information for Engineers and Researchers About the New European Medical Devices Regulation 2017/745

2017· article· en· W2763083873 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJMIR Biomedical Engineering · 2017
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsnot available
Fundersnot available
KeywordsParliamentDirectiveMedical deviceMember statesMedical researchBusinessPolitical scienceMedicineEuropean unionComputer scienceLawInternational trade

Abstract

fetched live from OpenAlex

Background: After years in the making, on April 5, 2017, the European Parliament and Council finally adopted Regulation (EU) 2017/745, the new Medical Devices Regulation (MDR), repealing the existing Medical Device Directive (MDD) 93/42/EEC. Though long anticipated, this shift in policy will have strong and lasting effects in the medical devices industry. Objective: This paper focuses specifically on the classification of software as a potential medical device under MDD and MDR and examines whether or not the regulatory framework for health apps has changed substantially and what, if any, impact is to expected. A particular emphasis will be on the issue of classification uncertainty raised by borderline cases such as heart rate monitoring and well-being apps. The paper primarily targets researchers and engineers unfamiliar with regulatory requirements for medical devices and aims to provide a concise, yet accurate, overview of the European regulatory framework. This is of particular relevance as with the exponential growth of fitness and health-related apps, the lines between toys, lifestyle products, and medical devices have increasingly blurred. Methods: The recently published European Medical Device Regulation is analyzed and compared to the preceding MDD. Results: The previous regulatory framework already provided for the possibility of apps to fall under the definition of medical devices, in which case classification rules for active medical devices applied. However, while applicability of the new regulatory framework still hinges on whether the intended purpose is medical or not, the threshold for classifying as a medical device has been considerably lowered due to a broader interpretation of what constitutes a medical purpose. Conclusions: The adoption of the new European regulation on medical devices entails the risk that manufacturers previously unaffected by the medical devices regulatory framework may now unwillingly and unwittingly find themselves in the arena of medical device manufacturing.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.822
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.074
GPT teacher head0.449
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it