Chronic Musculoskeletal Pain
Conditions
Keywords
chronic musculoskeletal pain, clinical simulation, digital training, educational intervention, usability (SUS), concept shift, primary care professionals
Brief summary
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.
Detailed description
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.
Interventions
BEHAVIORALPainTrain-AI Behavioral Training
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.
Sponsors
Francesc Valenzuela Pascual
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
HEALTH_SERVICES_RESEARCH
Masking
NONE
Masking description
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Health professionals in Primary Care (medicine, physiotherapy, nursing, psychology, nutrition) and specialized setting; willingness to use the platform; institutional approval.
Exclusion criteria
* lack of institutional access; unavailability during pilot window
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Usability (SUS, 0-100) | 4-8 weeks after onboarding | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Concept Shift | 4-8 weeks. | 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. |
| Adherence | 4-8 weeks | 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. |
| Latency/friction | 4-8 weeks | 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. |
Countries
Spain
Contacts
CONTACTFran Valenzuela-Pascual, Dr
PRINCIPAL_INVESTIGATORFran Valenzuela-Pascual, Dr.
Institut de Recerca Biomèdica de Lleida (IRBLleida) / Universitat de Lleida (UdL)
Outcome results
None listed