AW-AD-6: PREVALENCE OF VALIDATED BLOOD PRESSURE MEASURING DEVICES BEING SOLD BY AMAZON: 12-MONTH PROSPECTIVE ANALYSIS ACROSS 10 COUNTRIES
Bibliographic record
Abstract
Objective: The online purchase of automated blood pressure (BP) devices is a multibillion-dollar industry, but most BP devices available for online purchase have not passed adequate clinical validation testing. This study aimed to determine the extent to which BP devices available in best-selling lists of the e-commerce business Amazon were properly validated for accuracy, as well as their cost and ratings. Design and method: The 100 best-selling automated (upper arm and wrist) cuff BP devices sold by Amazon in 10 countries located in Europe, Asia-Pacific, North and South America were recorded at seven time points during a 12-month period of observation. Results: 81% of the 100 best-selling BP devices had not undergone adequate clinical validation (interquartile range [IQR] 74 to 90, averaged across all countries and time points) and this percentage was highly consistent within each country across the measurement period. The highest percentages of properly validated upper-arm BP devices being sold were 35% in Germany and 31% in Canada, whereas the lowest percentages were 3% in India and 5% in Australia. Non-validated upper-arm cuff BP devices were cheaper than clinically validated devices (median, IQR: $32.0 USD (26.0 to 43.9) versus $67.2 USD, (42.7 to 89.7)). Non-validated upper-arm cuff BP devices received fewer total numbers of consumer ratings than clinically validated devices (median, IQR: 210, 44 to 770 versus 651, 95 to 2962) despite near identical consumer ratings out of five stars (median, IQR: 4.5, 4.2 to 4.6 versus 4.5, 4.3 to 4.7). Similar patterns were observed for wrist-cuff BP devices. None of the wrist-cuff devices being sold in the US were clinically validated. Conclusions: Four out of five automated BP devices within the 100 best-selling lists of Amazon had not passed adequate clinical validation testing for BP measurement accuracy and precision. People should not buy a BP device online unless they can be certain it has passed adequate clinical validation testing.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".