Abstract 203: Adoptions of Smartphones, Tablets, and Mobile Applications Among Patients with Hypertension
Bibliographic record
Abstract
optimize management, monitoring, and therapy compliance in patients with hypertension (HTN). It could potentially prevent unnecessary emergency room visits and hospital admissions. Methods: We surveyed patients with HTN in cardiology and primary care clinics regarding their use of mobile technology. Results: 148 patients were included (79 female; age 16-64: 47%, >65: 53%, 70 male; 18-64:41%, >65: 59%). Associated diagnosis were coronary artery disease (21%), myocardial infarction (13%), arrhythmias (36%), heart failure (6%), or other forms of heart disease (3%). 83% own a cell phone, 29% are smartphones. 78% own a personal computer, laptop, iPod, or tablet. 35% report using APPS, of which 26% use health-related applications. 2% used APPS for blood pressure monitoring or management. 17% used APPS for diet or calorie counters, 13% for exercise, fitness or heart monitoring. 63% use APPS on a smartphone, 12% on iPod Touch, 62% on Tablet. Among all HTN patients surveyed, 64% report looking up medical information online on a computer, 53% more than once per month. When asked about willingness to pay, 44% were not willing to pay for a smartphone or tablet that would help monitor their condition. 61% were not willing to pay if an ER visit and its copayment could be prevented. 26% were not willing to use these devices if they were free or covered by insurance. Conclusions: A majority of HTN patients surveyed had access to technology with 22% using smartphones with APPS. However, there was limited use of health-specific APPS, especially those related to HTN. Limiting factors may include lack of outcomes-based research, structured programs that incorporate APPS and devices, or lack of familiarity with APPS and technological devices. A comprehensive strategy to develop, market, and demonstrate benefits of this rapidly growing technology is urgently needed.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".