Apps to Support Self-Management for People With Hypertension: Content Analysis
Bibliographic record
Abstract
Background: Home blood pressure monitoring (HBPM) is one component of effective supported self-management, which may potentially be mediated by mobile apps. Objective: The aim of this study was to identify the self-management features (HBPM and broader support strategies) offered by currently available apps and to determine the features associated with download frequency and user ratings. Methods: We searched Google Play store, Apple App store, National Health Services Apps Library and myhealthapps.net (first search on February 1, 2018; updated August 18, 2018). We included high blood pressure apps available in the United Kingdom and extracted their features, number of downloads, and the average users’ rating from the app stores. We mapped the features to the holistic Practical Reviews In Self-Management Support (PRISMS) taxonomy of self-management support. We employed a regression analysis to determine if any features were associated with download frequency or user rating. Results: We included 151 apps. The 3 most common features were as follows: monitoring blood pressure (BP) and charting logs; lifestyle (exercise or dietary) advice; and providing information about hypertension. The other 11 components of the PRISMS taxonomy were rarely featured. There was little evidence to support associations between specific features and the download statistics and rating scores, with only 2 uncommon features achieving borderline significant associations. The presence of social support features, such as a forum, was weakly but significantly (R2=.04, P=.02) correlated with the number of downloads. Apps designed specifically for particular BP monitors/smart watches were weakly associated with a higher rating score (R2=.05, P<.001). Apps with more ratings were associated with more downloads (R2=.91, P<.001). Conclusions: The functionality of currently available apps is limited to logging BP, offering lifestyle advice, and providing information about hypertension. Future app development should consider broadening the remit to produce a system that can respond flexibly to the diversity of support that enables people to self-manage their hypertension.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".