The QardioArm App in the Assessment of Blood Pressure and Heart Rate: Reliability and Validity Study
Bibliographic record
Abstract
BACKGROUND: Self-measurement of blood pressure is a priority strategy for managing blood pressure. OBJECTIVE: The aim of this study was to evaluate the reliability and validity of blood pressure and heart rate following the European Society of Hypertension's international validation protocol, as measured with the QardioArm, a fully automatic, noninvasive wireless blood pressure monitor and mobile app. METHODS: A total of 100 healthy volunteers older than 25 years from the general population of Ciudad Real, Spain, participated in a test-retest validation study with two measurement sessions separated by 5 to 7 days. In each measurement session, seven systolic blood pressure, diastolic blood pressure, and heart rate assessments were taken, alternating between the two devices. The test device was the QardioArm and the previously validated criterion device was the Omron M3. Sessions took place at a single study site with an evaluation room that was maintained at an appropriate temperature and kept free from noises and distractions. RESULTS: The QardioArm displayed very consistent readings both within and across sessions (intraclass correlation coefficients=0.80-0.95, standard errors of measurement=2.5-5.4). The QardioArm measurements corresponded closely to those from the criterion device (r>.96) and mean values for the two devices were nearly identical. The QardioArm easily passed all validation standards set by the European Society of Hypertension International Protocol. CONCLUSIONS: The QardioArm mobile app has validity and it can be used free of major measurement error.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".