Patient Involvement as Experts in the Development and Assessment of a Smartphone App as a Patient Education Tool for the Management of Thalassemia and Iron Overload Syndromes
Bibliographic record
Abstract
Our aim was to develop and assess the feasibility of an education tool to improve health outcomes of patients with thalassemia. Thirty-five patients attending a Canadian thalassemia clinic were enrolled. Acting in an expert role, they participated in a Delphi method to reach consensus as to what tools and information should be incorporated in the development of a self management Smartphone app. One- and 6-month usability and health impact feedback surveys were built-in. Sixty percent of responders were 18-34 years old, over 50.0% had a college degree. The Delphi method successfully generated a comprehensive list of features important to patients. The app has been downloaded 147 times globally. Between March 2015 and January 2016, 19 responses for the 1-month survey were collected and the trends described. Responders reported improved medication adherence. The personal adherence pledge feature supports gamification of health apps to individualize goals of therapy. The impact of tracking iron levels was highly favorable. The Delphi method was an effective way to introduce a patient education and empowerment tool to the thalassemia population. The long-term impact requires data maturation. Use of validated methodology is essential to ensure ehealth interventions are positively contributing to patient education and disease outcomes.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".