Optimizing smartphone intervention features to improve chronic disease management: A rapid review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
While there are an increasing number of mobile health applications to facilitate self-management in patients with chronic disease, little is known about which application features are responsible for impact. The objective was to uncover application features associated with increased usability or improved patient outcomes. A rapid review was conducted in MEDLINE for recent studies on smartphone applications. Eligible studies examined applications for adult chronic disease populations, with self-management content, and assessed specific features. The features studied and their impacts on usability and patient outcomes were extracted. From 3661 records, 19 studies were eligible. Numerous application features related to interface (e.g. reduced number of screens, limited manual data entry) and content (e.g. simplicity, self-tracking features) were linked to improved usability. Only three studies examined patient outcomes. Specific features were shown to have a higher impact. Implementing them can improve chronic disease management and reduce app development efforts.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 it