Quality of Blood Pressure Tracking Apps for the iPhone: Content Analysis and Evaluation of Adherence With Home Blood Pressure Measurement Best Practices (Preprint)
Bibliographic record
Abstract
BACKGROUND Blood pressure (BP) tracking apps may aid in hypertension (HTN) self-management, but app quality may be problematic. OBJECTIVE This study aimed to develop a content-dependent rating system for BP tracking apps and systematically evaluate BP tracking features, content-independent quality, functional characteristics, and educational comprehensiveness of English language iPhone apps developed with the primary purpose of tracking a consumer’s BP measurements. METHODS We created a 28-item checklist reflecting overall app quality and a simplified 2-item checklist to assess adherence with home BP monitoring best practices. Apps with educational information were evaluated for comprehensiveness on a 7-point scale and for consistency with evidence-based guidelines. Higher scores represent better quality and comprehensiveness. We searched the Canadian App Store on June 28, 2016, using the keywords hypertension and blood pressure. A total of 2 reviewers independently assessed apps according to the standardized template. We determined if paid apps, educational apps, or those rated ≥4 stars were of higher quality. RESULTS Of the 948 apps screened, 62 met the inclusion criteria. The mean overall quality score was 12.2 (SD 4.6, out of 28) and 6 apps (10%, 6/62) met the home BP monitoring best practice criteria. In all, 12 apps contained educational content (mean comprehensiveness 2.4, SD 1.6 out of 14), most commonly, background information on HTN. Apps with educational content (mean 15.1, SD 3.8 vs 11.8, SD 4.8; P=.03) or a ≥4 star rating (median 19, interquartile range [IQR] 15-20, vs 12, IQR 9-15; P=.02) had higher overall quality. CONCLUSIONS The BP tracking apps reviewed had variable quality and few met the home BP monitoring best practice criteria. When deciding to recommend a specific BP tracking app, we suggest clinicians should evaluate whether the app allows input of duplicate BP readings in the morning and evening for at least seven days and presents the mean BP value for user-specified dates. Greater attention to home BP measurement best practices is required during app development.
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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 | 0.035 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".