Engagement and Outcomes with Mobile Health Technology among Patients Hospitalized with Acute Venous Thromboembolism
Bibliographic record
Abstract
Mobile health (mHealth) technology can improve engagement and self-management, though few studies have assessed the factors associated with engagement of mHealth among hospitalized patients. We implemented a multifaceted transitions of care (TOC) intervention consisting of a novel patient-facing smartphone application (app), text message medication reminders, and access to a patient navigator for patients hospitalized with venous thromboembolism. Overall, application uptake (36%) and engagement were low. Patients who downloaded the app were young (50.5 vs 66.1 years, P < 0.01) and had a lower burden of disease (Charlson score 3.97 vs 5.65, P = 0.048). Similarly, patients who engaged with the app were young (48.5 vs 57.6 years, P = 0.049) and had a lower burden of disease (Charlson score 3.12 vs 7.14, P = 0.033). Our findings suggest that design and implementation of mHealth applications will be challenging for hospitalized populations characterized by old age, numerous comorbidities, and high acuity.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".