Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
This pilot will assess feasibility and usability (SUS) and explore concept shift (reduction of neurophysiology misconceptions) after exposure to a behavioral educational intervention (PainTrain AI), a digital clinical simulation "fenced" by evidence (RAG) for safe, non diagnostic training. The study will be conducted in Primary Care and in a specialized comparative setting, without patient data collection. Primary endpoint: SUS score. Secondary endpoints: concept shift, adherence to micromodules, and interaction latency as friction metric. The platform does not generate clinical advice; it retrieves validated teaching content.
This feasibility pilot will evaluate the use of PainTrain AI, a behavioral educational intervention designed to improve clinical reasoning about chronic pain through simulated dialogues with virtual standardized patients. The platform uses a Retrieval Augmented Generation (RAG) safety architecture that restricts all generated responses to validated pedagogical content and does not provide clinical advice. No patient information is collected.
The study includes health professionals from Primary Care and a specialized comparative setting. Participants will complete a baseline assessment and then interact with the PainTrain AI simulation for a defined training period. Usability will be assessed using the System Usability Scale (SUS). Concept shift will be measured as the reduction in predefined neurophysiology misconceptions scored through a standardized rubric. Additional metrics include adherence to micromodules and interaction latency as indicators of technological friction.
This pilot aims to determine feasibility, acceptability, and preliminary signals of educational effectiveness to inform a future implementation trial in Primary Care. The intervention is educational only, non diagnostic, and GDPR compliant.
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| PainTrain-AI Behavioral Training | Experimental | Participants in this arm will receive the behavioral educational intervention PainTrain-AI, a digital clinical simulation that uses a retrieval-augmented generation (RAG) safety architecture to deliver evidence-based teaching content. The platform provides simulated dialogues with virtual standardized patients to train biopsychosocial reasoning about chronic pain. All participants receive the same intervention; there is no control group. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| PainTrain-AI Behavioral Training | Behavioral | PainTrain-AI is a behavioral educational intervention delivered through a digital clinical simulation platform. The system uses a retrieval-augmented generation (RAG) safety architecture to ensure that all responses are generated only from validated pedagogical content. Participants engage in simulated dialogues with virtual standardized patients to practice biopsychosocial reasoning about chronic pain. The intervention is non-diagnostic, does not provide clinical advice, and does not involve patient data. All participants receive the same intervention. |
| Measure | Description | Time Frame |
|---|---|---|
| Usability (SUS, 0-100) | Usability will be assessed using the System Usability Scale (SUS), a validated 10-item questionnaire that yields a score from 0 to 100. Higher scores indicate better usability. Participants will complete the SUS after interacting with the PainTrain-AI educational simulation for the predefined training period. The primary feasibility criterion is achieving a mean SUS score ≥75, indicating good usability. | 4-8 weeks after onboarding |
| Measure | Description | Time Frame |
|---|---|---|
| Concept Shift | Concept-shift will be assessed as the change in the proportion of predefined misconceptions about pain neurophysiology identified in a standardized scoring rubric. Participants will complete a brief conceptual test before and after exposure to the PainTrain-AI educational simulation. Concept-shift is calculated as the reduction in incorrect or outdated statements related to chronic pain mechanisms. Higher improvement reflects better conceptual understanding. No patient data are involved. |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Fran Valenzuela-Pascual, Dr | Contact | +34 973 702 459 | fran.valenzuela@udl.cat |
| Name | Affiliation | Role |
|---|---|---|
| Fran Valenzuela-Pascual, Dr. | Institut de Recerca Biomèdica de Lleida (IRBLleida) / Universitat de Lleida (UdL) | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Universitat de Lleida | Lleida | Lleida | 25198 | Spain |
No IPD will be shared because the study does not involve patient data or clinical individual-level information. Only anonymized usage datasets and aggregated concept-shift scores may be shared according to a FAIR plan (anonymized logs + codebooks), deposited in public repositories after embargo per grant policy.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
This is an open-label feasibility study. No masking is applied. All participants receive the same behavioral educational intervention, and the study does not involve patient data or clinical outcomes that require blinded assessment.
Not provided
|
| 4-8 weeks. |
| Adherence | Adherence will be measured as the proportion of scheduled training modules or simulation sessions that each participant completes within the study period. Adherence is calculated as the percentage of completed sessions relative to the total number of modules assigned. Higher values indicate greater engagement with the PainTrain-AI educational intervention. No patient data are involved. | 4-8 weeks |
| Latency/friction | Latency will be measured as the average system response time (in seconds) during user interactions with the PainTrain-AI digital simulation. Latency is calculated automatically from platform logs as the time elapsed between a user input and the system's generated response. Higher latency indicates greater technological friction. This outcome evaluates technical feasibility and user experience. No patient data are involved. | 4-8 weeks |