Will Women Interact with Technology to Understand Their Cardiovascular Risk and Potentially Increase Activity?
Bibliographic record
Abstract
Cardiovascular disease (CVD) continues to be one of the leading causes of death for women. New approaches need to be identified that will enable women to recognize modifiable risk factors and target their efforts toward prevention. The objectives of this study were to (1) determine if women would access Vivametrica™ to assess CVD risk, (2) identify whether women would increase their physical activity as measured by their daily step counts, and (3) elicit women's opinions about using the system, prospective observational study design. Thirty-six English-speaking women aged 45–64 years of age, without physical disability, were recruited. Participants attended two clinic visits and were asked to wear a sensor-based activity monitor (Garmin Vivosmart® HR Wrist Tracker) for 12 weeks. Twenty-six (72%) of participants accessed Vivametrica for the course of the study. The median number of steps at baseline and at study completion was 9329 (range 5406–18,228) and 10,181 (range 5398–21,401), respectively. There was no significant change in number of steps taken by the participants for the study period (Z = −1.086, p = 0.278). The women's responses to the three statements (related to using Vivametrica) are represented on bar graphs. Women's opinions were important to provide an understanding about how they realized the technology. Women did access Vivametrica. Women did not significantly increase their step count. However, these women were achieving beyond sedentary levels of activity (>5000 steps/day). Although the change in steps was not statistically significant, it represents a median increase in daily steps of 9%, which is clinically important.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".