Cuffless blood pressure in 2025: from promise to practice: a narrative review
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cuffless blood pressure (BP) technologies have moved from concept to everyday wearables, spurred by consumer adoption and recent regulatory milestones. Yet clinicians face mixed signals on accuracy, calibration stability, and clinical use. This review synthesizes current evidence and emerging standards to clarify where cuffless data can add value. RECENT FINDINGS: Most devices estimate BP from surrogate signals and require periodic calibration, making outputs reliable for trends around the last calibration but less so for absolute values. New cuffless-specific validation frameworks mandate dynamic testing (position/hydrostatics, activity/exercise, awake-sleep, pharmacologic response, and prerecalibration drift). Professional bodies currently advise against diagnosis or drug titration using cuffless readings unless a device passes such protocols. Real-world studies show feasibility and patient preference but only moderate agreement with cuffs and limited outcomes data. Special populations (chronic kidney disease/dialysis, pregnancy, pediatrics/frail, diverse skin tones) introduce additional accuracy and equity concerns. SUMMARY: Cuffless wearables may be used as adjuncts to surface patterns and support engagement; diagnosis and treatment decisions must be confirmed with validated upper-arm measurements. A pragmatic "good-enough" checklist includes stable calibration over weeks, accuracy across dynamic states and subgroups, and transparent multisite validation. Priorities include harmonized standards, inclusive datasets, and trials linking cuffless monitoring to clinical outcomes.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".