Bra strap orientations and designs to minimise bra strap discomfort and pressure during sport and exercise in women with large breasts
Bibliographic record
Abstract
Bra straps are a primary source of discomfort during sport and exercise, particularly for women with large breasts. This study aimed to investigate the effects of altering bra strap orientation and design on bra strap comfort, pressure and breast support in women with large breasts. This is a descriptive laboratory study. Bra strap discomfort (visual analogue scale, 0 to 10), pressure (custom-designed 10 mm 2 calibrated pressure sensor, 0.5 to 24 kPa range, 50 Hz, S2011, Novel GmbH, Munich, Germany, placed under the right bra strap at the crest of each participant’s shoulder), preference ranking and vertical breast displacement (VBD; Optotrak Certus® motion capture system, 200 Hz, Northern Digital, Ontario, Canada) data during dynamic treadmill running and static upright standing (pressure only) were collected for 23 active women with large breasts (D+ cup size) while they wore an encapsulation sports bra with six different bra strap conditions (two bra strap orientations: vertical and cross-back; three bra strap designs: standard width, wide and gel). Bra strap discomfort was significantly less ( p ≤ 0.001) in the vertical compared to the cross-back strap orientation, which was the most preferred orientation despite no significant difference in strap pressure. The wide strap design had the lowest discomfort scores, significantly lower strap pressure compared to the standard width and gel strap designs ( p < 0.001), and was equally the most preferred design with the gel straps. There was no significant difference in VBD among the six strap conditions. Bra straps that are vertically orientated and wide (approximately 4.5 cm in width) are preferable for women with large breasts during sport and exercise to minimise bra strap pressure and discomfort. The addition of gel pads under bra straps may also decrease discomfort and prevent straps slipping off the shoulders, although this notion warrants further investigation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".