P2477Assessing self-measurement, activity, and weight change behaviors of connected scale users who reduced their pulse wave velocity over 4 months
Bibliographic record
Abstract
Introduction: Pulse wave velocity (PWV) is widely known as a marker of cardiovascular risk. As a preventive measure against cardiovascular events it is desirable to reduce the PWV. While there are many cross-sectional analyses describing the association between cardiovascular risk and PWV, little research has evaluated longitudinal changes and the behaviors associated with a PWV reduction. PWV measurements have until recently been limited mostly to research and medical centers because measurements required the use of expensive and complex devices. The new connected scales that permit self-measurement of the PWV makes it possible to analyze the PWV of large cohorts with a high measurement frequency. Purpose: The study aims to leverage the availability of high-frequency PWV self-measurements of a large number of connected scale users to determine the behaviors of users who reduce their pulse wave velocity over the course of 4 months. Methods: The study was conducted on anonymous data from a pool of 99,327 users of connected scales that measure both weight and PWV. 59,497 of these users also track their daily steps with wearable activity trackers. The data was collected from August to December 2016 worldwide with more than 90% of users from US, Canada, Europe, Japan, and China. To identify users that had reduced their PWV we calculated the percent change in PWV between August and December averages and set a threshold of -10%. We used a Welch's t-test for evaluating whether there was a difference in the levels of self-measurement and activity between users who reduced their PWV and other users. To evaluate the difference in weight change from August to December between users who reduced their PWV and other users we performed a linear regression for each group and compared the slopes with a Student t-test.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".