Clinical System for Mood Disorder Care in Córdoba, Colombia: Participatory Design and Scenario-Based Usability Evaluation Study
Bibliographic record
Abstract
BACKGROUND: Mood disorders are among the leading causes of disability worldwide and present a growing public health concern. In Córdoba, Colombia, suicide rates have risen significantly in recent years, exposing structural gaps in mental health care delivery. Digital health solutions and telehealth interventions can expand access to early detection, referral, and monitoring of patients in underserved regions. However, their effectiveness depends on rigorous and diverse evaluations to ensure adoption and sustainability. OBJECTIVE: This study evaluated the usability of a clinical telehealth system for mood disorder care developed through participatory design, with emphasis on user-centered functionality and workload analysis. METHODS: The system was designed through 2 iterative development cycles, followed by a scenario-based usability evaluation. A functional Domain Ontology was constructed to prioritize 8 core functionalities, including telecounseling, a georeferenced institutional directory, hotline services, patient self-report tools, educational content, forums, and a population dashboard. Thirty participants representing patients, caregivers, clinical staff, and administrative personnel were recruited through convenience sampling. Usability was assessed through cognitive walk-throughs, the NASA (National Aeronautics and Space Administration) Task Load Index, and the Post-Study System Usability Questionnaire. RESULTS: A total of 34 usability sessions and 223 task-level workload assessments were conducted across 2 evaluation cycles. The system demonstrated high usability, with overall Post-Study System Usability Questionnaire scores of 2.2 in cycle 1 and 2.3 in cycle 2. Interfaces prioritized for patients and clinical staff achieved better evaluations (average 1.9-2.0) than administrative interfaces (average 3.0). Workload analysis indicated improvements between cycles, particularly for patient-centered tasks, with mental workload as the most significant source of cognitive demand. Twenty-three critical issues (9 system errors and 14 design flaws) were identified and corrected between cycles, leading to measurable usability gains. CONCLUSIONS: The participatory and scenario-based approach facilitated early identification of usability challenges and supported iterative refinement of the system. Results suggest that the system is usable, acceptable, and effective in reducing workload for key user groups, particularly patients and clinicians. The findings reinforce the value of participatory methodologies in digital mental health and highlight the need to prioritize patient-facing interfaces. Future research should extend evaluations to mobile platforms and larger populations to support scalability and integration into regional mental health services.
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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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".