Remote Monitoring Technology to Improve Blood Pressure in a Rural Population
Bibliographic record
Abstract
Hypertension increases the risk of developing cardiovascular disease and the risk is greater in rural communities with reduced access to health services. Exercise has been shown to improve blood pressure (BP), but facilities may be lacking in rural areas. PURPOSE: The purpose of this study was to examine the effects of exercise prescription and remote monitoring of BP and activity on systolic and diastolic BP. METHODS: 40 participants were given a BlackBerry Smartphone with Healthanywhere monitoring software, a Bluetooth enabled BP monitor and a pedometer for home monitoring of thrice-weekly BP and daily steps. The intervention was 24 weeks with comparison of BP and steps from weeks 1-12 and 13-24. Personalized exercise prescription was based on the results of a submaximal STEP™ test. RESULTS: Daily activity increased from an average of 7580±418 steps/day during the first 12 weeks to 8053±415 steps/day during the latter half of the intervention (p=0.042). Systolic BP was reduced from 127±2 to 123±2mmHg (p<0.001) and diastolic BP from 81±1 to 80±1mmHg (p=0.013). These positive changes occurred despite the fact that the percent of pedometer readings submitted was reduced from 85.2±2.6% to 77.6±4.5% (p=0.016) and BP measurements submitted decreased from 93.7±1.6% to 85.3% (p=0.006) from the first to the second half of the intervention. CONCLUSION: These findings support the use of remote monitoring technology in conjunction with exercise prescription to effectively reduce cardiovascular risk. Further, positive outcomes may persist with reduced frequency of health monitoring.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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".