Usability Evaluation of an Online, Tailored Self-Management Intervention for Chronic Obstructive Pulmonary Disease Patients Incorporating Behavior Change Techniques
Bibliographic record
Abstract
BACKGROUND: An eHealth intervention using computer tailored technology including several behavior change techniques was developed to support the self-management of chronic obstructive pulmonary disease patients. OBJECTIVE: The goal of this study was to evaluate and improve the usability of the eHealth intervention. METHODS: We conducted a usability evaluation with 8 chronic obstructive pulmonary disease patients, with a mixed methods design. We improved the usability through iterative cycles of evaluation and adaptation. Participants were asked to think aloud during the evaluation sessions. Participants then completed a semi-structured interview. The sessions were observed and recorded. Descriptive statistics and content analysis were used to uncover usability issues. RESULTS: Areas for improvement were layout, navigation, and content. Most issues could be solved within 3 iterations of improvement. Overall, participants found the program easy to use. The length of the program urged us to further analyze the appreciation of behavior change techniques. Some were perceived as helpful and easy to use, while others evoked frustration. CONCLUSIONS: The usability study identified several issues for improvement, confirming the need for usability evaluation during the development of eHealth interventions. The uncovered strengths and limitations of behavior change techniques may lead to optimization of eHealth interventions, but further insight is needed.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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, 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".