Motivating patients to exercise
Bibliographic record
Abstract
OBJECTIVE: Even with the 2008 physical activity guidelines for Americans and the strong epidemiological evidence, physicians are not routinely emphasizing the importance of exercise. We try to explore an innovative way to communicate the benefits of physical activity in a term familiar to patients. METHODS AND RESULTS: A cohort of 470, 163 adults from a medical screening program in Taiwan were recruited between 1994 and 2008. Their vital status was followed up by matching with the National Death File. Individuals were classified as 'inactive', 'low active', or 'fully active', with 'fully active' meeting the current exercise recommendation of 150 min per week or more. Cox proportional model was used to calculate the hazard ratio. More than one-half of the cohort was inactive (54%), with one-quarter fully active (24%). One in seven was hypertensive (14%), defined as SBP at least 140 mmHg. Among the hypertensive individuals, mortality risks were increased by 37% for the inactive. Inactive individuals had higher all-cause mortality than active ones across all blood pressure (BP) levels. At 110-119 mmHg, the inactive had a risk as high as the risk at 155 mmHg, an increased mortality risk equivalent to a risk of BP increase of 41.2 mmHg. CONCLUSION: The mortality risk of being inactive was equivalent to an increase of around 40 mmHg in SBP or 20 mmHg in DBP, a number relevant to hypertensive patients. Appreciating this relationship may convince the inactive to start exercising, a behavior as important as controlling BP.
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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".