Is the Blood Pressure-Enabled Smartwatch Ready to Drive Precision Medicine? Supporting Findings From a Validation Study
Bibliographic record
Abstract
Background: The popular wrist-worn wearables recording a variety of health metrics such as blood pressure (BP) in real time could play a potential role to advance precision medicine, but these devices are often insufficiently validated for their performance to enhance confidence in its use across diverse populations. The accuracy of BP-enabled smartwatch is assessed among the multi-ethnic Malaysians, and findings is discussed in comparison with conventional automated upper-arm BP device. Methods: Validation procedures followed the guidelines by the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization (AAMI/ESH/ISO) Universal Standard (ISO 81060-2:2018). Quota sampling was employed to obtain eligible patients with normal and abnormal BP as per guideline. The measurements of BP were taken at wrist using HUAWEI WATCH D (test BP); and the readings were assessed against reference BP by the mercury sphygmomanometer. Agreement statistics and linear regression analyses were performed. Results: BP measurements (234 data pairs) from 78 patients that fulfilled AAMI/ESH/ISO protocol were analyzed. The BP readings taken by the HUAWEI WATCH D were comparable to reference BP by sphygmomanometer based on 1) Criterion 1: systolic blood pressure (SBP) = -0.034 (SD 5.24) and diastolic blood pressure (DBP) = -0.65 (SD 4.66) mm Hg; and 2) Criterion 2: SBPs = -0.034 (SD 4.18) and DBPs = -0.65 (SD 3.94) mm Hg. Factors of sociodemographic characteristics, anthropometric measurements, cardiovascular comorbidities, and wrist hair density were not significantly associated with the mean BP differences. Conclusions: HUAWEI WATCH D fulfilled criteria 1 and 2 of the AAMI/ESH/ISO Universal Standard (ISO 81060-2:2018) guidelines. It can be recommended for clinical use across a wider population. The rich data from real-time BP measurements in concurrent with other health-related parameters recorded by the smartwatch wearable offer opportunities to drive precision medicine in tackling therapeutic inertia by personalizing BP control regimen.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".