Designing and Developing a Mobile Application for Monitoring & Visualizing Blood Pressure Data
Bibliographic record
Abstract
High blood pressure is one of the major causes of cardiovascular diseases, renal failure, and even sudden death. To avoid developing these health issues, it is important to consistently monitor blood pressure levels. Monitoring blood pressure levels regularly allows people to observe changes in their blood pressure measurements and contact their healthcare providers for guidance if needed. The aim of this paper is designing and developing a mobile application that can assist individuals to better understand the changes in their blood pressure levels by introducing a novel approach to visualizing blood pressure data and managing missing data. Employing a user-centered design approach, we designed and developed an app. To complete this study, we conducted a literature review to identify key design requirements for such apps and then, designed our low-fidelity prototype based on these design requirements. Next, we consulted with two experts to obtain their feedback on the content, presentation, and usability of our initial low-fidelity prototype. Based on these experts' feedback we designed our mid-fidelity and high-fidelity prototype. Eventually, we conducted a user evaluation study to evaluate our high-fidelity prototype. The results demonstrated this app provides a clear visual representation of blood pressure measurements over time.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".