How and why do Australians obtain blood pressure devices for use at home? A mixed-methods study
Bibliographic record
Abstract
ABSTRACT Background Only 10-20% of blood pressure (BP) devices available are validated. Little is known about how and why patients choose BP devices for home BP measurement (HBPM), which was the aim of this study. Methods Mixed-methods study (online survey (n=241), phone interviews among a purposive subsample (n=27)) among adults who perform HBPM in Australia (June-Dec 2023). Survey questions determined how BP devices were obtained, device make/model and factors influencing device selection. Interviews further explored these topics. Device validation status was determined using the STRIDE BP and Medaval websites. Results Participants were middle aged (58±16 years, 52% women) and 91% purchased a device for HBPM (n=189; 9% borrowed a device), with 69% (n=130) purchased from pharmacies (53% validated) and 21% (n=39) purchased online (51% validated). Accuracy was said to be the most important consideration when choosing a device for most participants (n=129, 77%). Interview participants described using brand recognition, online reviews and cost to select an ‘accurate’ device; avoiding cheaper devices and preferring brands used in healthcare settings. Participants did not consider validation status and did not receive advice on device accuracy at point-of-sale. Conclusion This study highlights real world experiences of adults when obtaining HBPM devices that can be used to inform strategies to direct adults to validated devices. Strategies such as increasing signage at the point-of-sale and training healthcare practitioners to identify and direct consumers to validated devices may be effective in increasing uptake. Regulatory bodies should mandate the sale of validated devices in healthcare settings to increase availability. GRAPHICAL ABSTRACT
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".