Designing a Tablet-Based Software App for Mapping Bodily Symptoms: Usability Evaluation and Reproducibility Analysis
Bibliographic record
Abstract
BACKGROUND: Symptom drawings are widely used as a qualitative and quantitative method of assessing pain symptoms for both clinical and research purposes. As electronic drawings offer many advantages over classical pen-and-paper drawings, the last years have seen a shift toward tablet-based acquisition of symptom drawings. However, software that is used in clinical care requires special attention to usability aspects and design to provide easy access for physically impaired or elderly patients. OBJECTIVE: The aims of this project were to develop a new tablet-based software app specifically designed to collect patients' and doctors' drawings of pain and related bodily symptoms and test it for usability in 2 samples of chronic pain patients (Aim 1) and their treating doctors (Aim 2) as well as for test-retest reliability (Aim 3). METHODS: In 2 separate studies, symptom drawings from 103 chronic pain patients and their treating doctors were collected using 2 different versions of the app. Both patients and doctors evaluated usability aspects of the app through questionnaires. Results from study 1 were used to improve certain features of the app, which were then evaluated in study 2. Furthermore, a subgroup of 25 patients in study 2 created 2 consecutive symptom drawings for test-retest reproducibility analysis. Usability of both app versions was compared, and reproducibility was calculated for symptom extent, number of symptom clusters, and the whole symptom pattern. RESULTS: The changes we made to the app and the body outline led to significant improvements in patients' usability evaluation regarding the identification with the body outline (P=.007) and the evaluation of symptom depth (P=.02), and the overall difficultness of the drawing process (P=.003) improved significantly. Doctors' usability evaluation of the final app showed good usability with 75.63 (SD 19.51) points on the System Usability Scale, Attrakdiff 2 scores from 0.93 to 1.41, and ISONORM 9241/10 scores from -0.05 to 1.80. Test-retest analysis showed excellent reproducibility for pain extent (intraclass correlation coefficient, ICC=0.92) and good results for the number of symptom clusters (ICC=0.70) and a mean overlap of 0.47 (Jaccard index). CONCLUSIONS: We developed a tablet-based symptom drawing app and improved it based on usability assessment in a sample of chronic pain patients and their treating doctors. Increases in usability of the improved app comprised identification with the body outline, symptom depth evaluation, and difficultness of the drawing process. Test-retest reliability of symptom drawings by chronic pain patients showed fair to excellent reproducibility. Patients' usability evaluation is an important factor that should not be neglected when designing apps for mobile or eHealth apps.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | high |
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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".