A Systemic Review of Smart Technology Sport Bras for Examining Cardiovascular Function
Bibliographic record
Abstract
Introduction: Unremitting advancements in wearable technology provides female consumers a plethora of fabricated garments that claim to monitoring biological function. Aim: This systematic review examined current literature pertaining to smart technology sport bras fabricated to assess cardiovascular function. Material and Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilized, and the review was registered in the International prospective register of systemic reviews (PROSPERO) within the National Institute for Health and Care Research (NIHR). A total of 949 articles were screened after using key search terms using the Covidence systematic review software. Articles were sourced from PubMed, SPORTDiscus, or Medline via OVID databases. Results: A total of 19 articles were examined for eligibility and was reduced to a total of three articles to be included in the review. The results from this systematic review highlight the paucity of commercially available sport bras capable of accurately examining cardiovascular function during rest and exercise, while concomitantly providing the necessary support and comfort requirements for females. Of the commercially available sport bras, the Berlei sport bras was identified as the most accurate for recording cardiovascular stress during resting, walking, and running conditions. The three studies included in this review demonstrate strong potential for washable and flexible e-textile sensors with sufficient accuracy in pressure ranges that can be used in sport bras to monitor biological functioning. Conclusions: Future research should prioritize enhancing participant diversity in the examination of smart sport bras to ensure comprehensive inclusion of all body types.
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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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".