Designing and Developing an eHealth Program for Patients With Persistent Physical Symptoms: Usability Study
Bibliographic record
Abstract
BACKGROUND: Patients with persistent physical symptoms presenting in primary care are often affected by multiple symptoms and reduced functioning. The medical and societal costs of these patients are high, and there is a need for new interventions tailored to both the patients and health care system. OBJECTIVE: This study aimed to examine the usability of an unguided, self-help treatment program, "My Symptoms," developed to assist patients and general practitioners in symptom management. METHODS: In all, 11 users (4 patients with persistent physical symptoms and 7 laypeople) participated in web-based thinking-aloud interviews involving the performance of predefined tasks in the program. Thematic analysis was used to categorize the severity of usability issues. General usability heuristics were cross-referenced with the usability issues. RESULTS: The analysis identified important usability issues related to functionality, navigation, and content. The study shows how therapeutic knowledge in some cases was lost in the translation of face-to-face therapy to a digital format. The user testing helped uncover how the functionality of the digital elements and general navigation of the program played a huge part in locating and accessing the needed treatment. Examples of redesign to mediate the therapeutic value in the digital format involving health care professionals, web developers, and users are provided. The study also highlights the differences of involving patients and laypeople in the interviews. CONCLUSIONS: Taking the experience of common symptoms as a point of departure, patients and laypeople contributed to finding usability issues on program functionality, navigation, and content to improve the program and make the treatment more accessible to users.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".