Usability Testing of a Smartphone for Accessing a Web-based e-Diary for Self-monitoring of Pain and Symptoms in Sickle Cell Disease
Bibliographic record
Abstract
We examined the usability of smartphones for accessing a web-based e-Diary for self-monitoring symptoms in children and adolescents with sickle cell disease (SCD). One group of participants (n = 10; mean age, 13.1 ± 2.4 y; 5 M; 5 F) responded to questions using precompleted paper-based measures. A second group (n = 21; mean age, 13.4 ± 2.4 y; 10 M; 11 F) responded based on pain and symptoms they experienced over the previous 12 hours. The e-Diary was completed with at least 80% accuracy when compared to paper-based measures. Symptoms experienced over the previous 12 hours included feeling tired (33.3%), headache (28.6%), coughing (23.8%), lack of energy/fatigue (19.0%), yellowing of the eyes (19.0%), pallor (19.0%), irritability (19.0%), stiffness in joints (19.0%), general weakness (14.3%), and pain (14.3%), rating on average as 2.0 ± 1.7 (on 0 to 10 scale). Overall, sleep was good (8.1 ± 1.4 on the 0 to 10 scale). In conclusion, children with SCD were able to use smartphones to access a web-based e-Diary for reporting pain and symptoms. Smartphones may improve self-reporting of symptoms and communication between patients and their health care providers, who may consequently be able to improve pain and symptom management in children and adolescents with SCD in a timely manner.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".