Exercise preferences and perceptions of women during the menopausal transition
Bibliographic record
Abstract
High-intensity resistance and impact training (HiRIT) may be more effective than low- to-moderate-intensity resistance training for improving bone health. However, it is unclear if women want to participate in HiRIT interventions. We developed an electronic survey and distributed it to Canadian women aged 40-60 years via social media. The primary objective was to understand how women in peri-menopause, early post-menopause, and late post-menopause perceive resistance training and their interest in participating in a HiRIT protocol designed to reduce bone loss and menopausal symptoms. Our second objective was to determine whether current exercise preferences, motivators, and barriers differed across the menopause continuum. The survey included questions to determine menopausal status, exercise perceptions and preferences, and demographics. 1648 women responded to the advertisements, 1007 started the survey, 996 consented, 975 were eligible, and 739 completed the survey. Of the completed respondents, 628 respondents were in peri-menopause (46.7%), early post-menopause (31.5%), or late post-menopause (21.8%). 86.5% of women expressed interest in participating in a resistance training protocol to improve bone health, and 71.8% were interested in participating in a HiRIT study targeting bone health and menopause symptoms. Key motivators of exercise were improving or maintaining general health (17.4%) and strength (15.2%). The main barriers were lack of time (23.6%) and cost (14.3%), with no differences between menopausal status. Peri- and post-menopausal women are interested in resistance training, aiming to improve bone health and reduce menopausal symptoms. This information will be used to develop a HiRIT program for women during the menopause transition.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".