Person-Centred AI Support in Interdisciplinary Rehabilitation for Chronic Pain
NCT ID: NCT07081737
Last Updated: 2025-07-23
Study Results
Results pending
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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Recruitment Status
NOT_YET_RECRUITING
Clinical Phase
NA
Total Enrollment
400 participants
Study Classification
INTERVENTIONAL
Study Start Date
2026-05-01
Study Completion Date
2029-03-31
Brief Summary
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This cluster randomized controlled trial evaluates whether a person-centred, AI-supported Clinical Decision Support System (CDSS) can improve outcomes and cost-effectiveness in interdisciplinary rehabilitation for people with complex chronic pain. The CDSS is designed to assist clinicians in making personalized treatment decisions within standard interdisciplinary treatment (IDT). It has been developed using machine learning models trained on real-world data from over 100,000 patients in the Swedish Quality Registry for Pain Rehabilitation (SQRP), linked to several national registers, including the National Patient Register, the Prescribed Drug Register, the Social Insurance Agency database (MiDAS), and the Cause of Death Register. This enables individualized predictions of treatment outcomes, work ability, and healthcare utilization.
The trial includes 400 adult patients with chronic pain, enrolled at 20 IDT clinics randomized to either CDSS-supported or standard IDT. The study has three phases: feasibility, effectiveness, and implementation. The primary outcome is a patient-prioritized composite single-index of health-related well-being, based on domains such as pain, sleep, physical and mental health, emotional distress, and work ability. Patients prioritize these domains together with their clinical team, enabling a person-centred assessment. Secondary outcomes include HRQoL (EQ-5D, SF-36), emotional distress (HADS), and work ability (WAI), measured at baseline, post-treatment, 6- and 12-month follow-up.
A parallel mixed-methods process evaluation will examine implementation outcomes such as usability, clinician adherence, and workflow integration, using logs, surveys (e.g., S-NoMAD), and interviews. Normalization Process Theory guides the analysis. Cost-utility will be assessed using QALYs and ICERs from a societal perspective, with long-term projections using simulation models. Results will be reported in peer-reviewed publications.
The trial includes 400 adult patients with chronic pain, enrolled at 20 IDT clinics randomized to either CDSS-supported or standard IDT. The study has three phases: feasibility, effectiveness, and implementation. The primary outcome is a patient-prioritized composite single-index of health-related well-being, based on domains such as pain, sleep, physical and mental health, emotional distress, and work ability. Patients prioritize these domains together with their clinical team, enabling a person-centred assessment. Secondary outcomes include HRQoL (EQ-5D, SF-36), emotional distress (HADS), and work ability (WAI), measured at baseline, post-treatment, 6- and 12-month follow-up.
A parallel mixed-methods process evaluation will examine implementation outcomes such as usability, clinician adherence, and workflow integration, using logs, surveys (e.g., S-NoMAD), and interviews. Normalization Process Theory guides the analysis. Cost-utility will be assessed using QALYs and ICERs from a societal perspective, with long-term projections using simulation models. Results will be reported in peer-reviewed publications.
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Detailed Description
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This project consists of three integrated phases aimed at evaluating a machine learning-based Clinical Decision Support System (CDSS) to improve interdisciplinary rehabilitation for individuals with complex chronic pain. The evaluation encompasses feasibility, clinical effectiveness, cost-utility, and implementation in routine care. The results will be reported in multiple peer-reviewed scientific publications.
Phase 1: Development, validation, and feasibility By the end of 2025, the CDSS-developed in an ongoing project-will be ready for clinical testing. It is based on predictive models trained on registry-linked data from over 100,000 patients in the Swedish Quality Registry for Pain Rehabilitation (SQRP), linked to several national registers, including the National Patient Register, the Prescribed Drug Register, the Social Insurance Agency database (MiDAS), and the Cause of Death Register. The system provides personalized forecasts for treatment outcomes, long-term work ability, and healthcare use. A pilot cluster-RCT will be conducted at 10 clinics (5 patients per site) to evaluate feasibility outcomes such as recruitment, retention, usability, data completeness, and workflow fit. These will be assessed using structured surveys, usage data, and interviews. Outcome measures will be collected at baseline, immediately after the intervention (i.e., up to 18 weeks after baseline), and at 12-month follow-up. While a typical interdisciplinary rehabilitation program lasts 6-8 weeks, some clinics may extend the intervention up to 18 weeks (with less treatment occasions per week) due to their ordinary and existing treatment procedures at that specific clinic. Published results indicate however no significant differences in treatment outcomes based on such extended program duration (Tseli et al., 2020). No major changes to the CDSS algorithm or interface are planned during the trial.
Phase 2: Clinical effectiveness and health economic evaluation The full evaluation will be conducted through a non-registry-based cluster randomized controlled trial involving 400 patients across 20 interdisciplinary rehabilitation clinics. Outcomes will be analyzed using linear mixed-effects models adjusted for time, group, clustering, and covariates. The primary endpoint is at 12-month follow-up. Secondary outcomes will be assessed at baseline, up to 18 weeks after baseline (i.e., immediately post intervention), and at 6- and 12-month follow-up. Health economic analyses will include within-trial cost-utility evaluation (QALYs from EQ-5D and SF-36) and longer-term modelling using Markov or microsimulation methods. Both direct (healthcare) and indirect (productivity loss) costs will be included. Sensitivity analyses will address uncertainty and robustness.
Phase 3: Implementation research A mixed-methods process evaluation will examine real-world adoption, scalability, and sustainability. Data will include system logs (e.g., reach, fidelity), survey responses (S-NoMAD), and interviews with clinicians and decision-makers. Analysis is guided by Normalization Process Theory, focusing on coherence (understanding), cognitive participation (engagement), collective action (integration), and reflexive monitoring (clinical utility). This structure enables a rigorous, practice-oriented evaluation of AI support in pain rehabilitation, integrating clinical, economic, and implementation perspectives to guide responsible and scalable integration into healthcare.
Phase 1: Development, validation, and feasibility By the end of 2025, the CDSS-developed in an ongoing project-will be ready for clinical testing. It is based on predictive models trained on registry-linked data from over 100,000 patients in the Swedish Quality Registry for Pain Rehabilitation (SQRP), linked to several national registers, including the National Patient Register, the Prescribed Drug Register, the Social Insurance Agency database (MiDAS), and the Cause of Death Register. The system provides personalized forecasts for treatment outcomes, long-term work ability, and healthcare use. A pilot cluster-RCT will be conducted at 10 clinics (5 patients per site) to evaluate feasibility outcomes such as recruitment, retention, usability, data completeness, and workflow fit. These will be assessed using structured surveys, usage data, and interviews. Outcome measures will be collected at baseline, immediately after the intervention (i.e., up to 18 weeks after baseline), and at 12-month follow-up. While a typical interdisciplinary rehabilitation program lasts 6-8 weeks, some clinics may extend the intervention up to 18 weeks (with less treatment occasions per week) due to their ordinary and existing treatment procedures at that specific clinic. Published results indicate however no significant differences in treatment outcomes based on such extended program duration (Tseli et al., 2020). No major changes to the CDSS algorithm or interface are planned during the trial.
Phase 2: Clinical effectiveness and health economic evaluation The full evaluation will be conducted through a non-registry-based cluster randomized controlled trial involving 400 patients across 20 interdisciplinary rehabilitation clinics. Outcomes will be analyzed using linear mixed-effects models adjusted for time, group, clustering, and covariates. The primary endpoint is at 12-month follow-up. Secondary outcomes will be assessed at baseline, up to 18 weeks after baseline (i.e., immediately post intervention), and at 6- and 12-month follow-up. Health economic analyses will include within-trial cost-utility evaluation (QALYs from EQ-5D and SF-36) and longer-term modelling using Markov or microsimulation methods. Both direct (healthcare) and indirect (productivity loss) costs will be included. Sensitivity analyses will address uncertainty and robustness.
Phase 3: Implementation research A mixed-methods process evaluation will examine real-world adoption, scalability, and sustainability. Data will include system logs (e.g., reach, fidelity), survey responses (S-NoMAD), and interviews with clinicians and decision-makers. Analysis is guided by Normalization Process Theory, focusing on coherence (understanding), cognitive participation (engagement), collective action (integration), and reflexive monitoring (clinical utility). This structure enables a rigorous, practice-oriented evaluation of AI support in pain rehabilitation, integrating clinical, economic, and implementation perspectives to guide responsible and scalable integration into healthcare.
Conditions
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Pain, Chronic
Chronic Pain, Widespread
Pain Management
Study Design
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Allocation Method
RANDOMIZED
Intervention Model
PARALLEL
A two-armed, multi-site cluster randomized controlled trial (2026-2029) will be conducted across 20 interdisciplinary rehabilitation clinics. Clinics are randomized to standard interdisciplinary treatment (IDT) with or without a Clinical Decision Support System (CDSS). The design follows the UK Medical Research Council (MRC) framework for complex interventions and includes a pilot RCT, a full-scale effectiveness and cost-utility trial, and a concurrent process evaluation. Patients are recruited via routine care. Outcomes are assessed at baseline, post-IDT, and 12-month follow-up. Primary outcome: a patient-prioritized composite of health-related well-being. Secondary outcomes: SF-36, EQ-5D, HADS, WAI, and register-based data on sickness absence and medication (followed for 5 years). Cost-utility (QALYs, ICERs) and implementation (using Normalization Process Theory) are evaluated.
Primary Study Purpose
TREATMENT
Blinding Strategy
DOUBLE
Investigators
Outcome Assessors
Study Groups
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Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS)
Participants in this arm receive standard interdisciplinary treatment (IDT) for complex chronic pain, supported by a Clinical Decision Support System (CDSS). The CDSS provides individualized prognostic and predictive outputs using advanced AI-clustered models trained on linked national registry data. Clinicians access the CDSS through a secure interface integrated into clinical workflows, offering data-driven support for person-centred treatment planning and goal setting. The intervention is designed to enhance decision-making, treatment precision, and long-term outcomes such as work ability, well-being, and quality of life. The CDSS is used by the care team prior to and during the rehabilitation program.
Group Type
EXPERIMENTAL
Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS)
Intervention Type
OTHER
Interdisciplinary treatment (IDT) combined with Clinical Decision Support System (CDSS)
Interdisciplinary treatment (IDT)
Participants in this control-arm receive standard interdisciplinary treatment (IDT) for complex chronic pain. IDT is delivered by a coordinated team of healthcare professionals-typically including physicians, psychologists, physiotherapists, and occupational therapists-and is based on evidence-informed rehabilitation protocols. The program emphasizes biopsychosocial assessment, goal setting, and individually tailored interventions aimed at improving function, coping, and quality of life. No use of the Clinical Decision Support System (CDSS) is included in this arm.
Group Type
ACTIVE_COMPARATOR
Interdisciplinary treatment (IDT)
Intervention Type
OTHER
Interdisciplinary treatment (IDT)
Interventions
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Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS)
Interdisciplinary treatment (IDT) combined with Clinical Decision Support System (CDSS)
Intervention Type
OTHER
Interdisciplinary treatment (IDT)
Interdisciplinary treatment (IDT)
Intervention Type
OTHER
Eligibility Criteria
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Inclusion Criteria
* Aged 18 to 67 years
* Diagnosed with chronic non-malignant pain persisting longer than 3 months
* Pain condition includes, but is not limited to: fibromyalgia, widespread pain, back pain, neck pain, or shoulder pain
* Eligible for and referred to interdisciplinary rehabilitation (IDT) at a participating clinic
* Willing and able to participate in digital assessment and follow-up procedures
* Able to communicate and complete study materials in Swedish
* Provides written informed consent
* Diagnosed with chronic non-malignant pain persisting longer than 3 months
* Pain condition includes, but is not limited to: fibromyalgia, widespread pain, back pain, neck pain, or shoulder pain
* Eligible for and referred to interdisciplinary rehabilitation (IDT) at a participating clinic
* Willing and able to participate in digital assessment and follow-up procedures
* Able to communicate and complete study materials in Swedish
* Provides written informed consent
Exclusion Criteria
* Pain caused by malignancy or cancer-related treatment
* Pain caused by systemic diseases such as rheumatoid arthritis, lupus, or other autoimmune or inflammatory conditions
* Severe psychiatric conditions interfering with study participation (e.g., untreated psychosis or severe depression requiring immediate psychiatric care)
* Documented cognitive impairment limiting ability to understand study participation or complete self-reported measures
* Currently enrolled in another interventional clinical trial that may confound the outcomes of this study
* Not expected to remain in the clinic's follow-up system for the duration of the study
* Pain caused by systemic diseases such as rheumatoid arthritis, lupus, or other autoimmune or inflammatory conditions
* Severe psychiatric conditions interfering with study participation (e.g., untreated psychosis or severe depression requiring immediate psychiatric care)
* Documented cognitive impairment limiting ability to understand study participation or complete self-reported measures
* Currently enrolled in another interventional clinical trial that may confound the outcomes of this study
* Not expected to remain in the clinic's follow-up system for the duration of the study
Minimum Eligible Age
18 Years
Maximum Eligible Age
67 Years
Eligible Sex
ALL
Accepts Healthy Volunteers
No
Sponsors
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Bjorn Ang
OTHER
Sponsor Role
lead
Göteborg University
OTHER
Sponsor Role
collaborator
Forte
INDUSTRY
Sponsor Role
collaborator
Dalarna County Council, Sweden
OTHER
Sponsor Role
collaborator
Karolinska Institutet
OTHER
Sponsor Role
collaborator
The Swedish Research Council
OTHER_GOV
Sponsor Role
collaborator
Responsible Party
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Bjorn Ang
Professor
Responsibility Role
SPONSOR_INVESTIGATOR
Principal Investigators
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Björn O Äng, Professor
Role: PRINCIPAL_INVESTIGATOR
Dalarna University
Locations
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Dalarna University
Falun, Dalarna County, Sweden
Site Status
Countries
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Sweden
Central Contacts
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Facility Contacts
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References
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Related Links
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https://www.du.se/en/research/research-project/?projectid=110
Research project/group
Other Identifiers
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2023-04532-01
Identifier Type: -
Identifier Source: org_study_id
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Providing Standardized Consented PROMs (Patient Reported Outcome Measures) for Improving Pain Treatment
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COMPLETED
Musculoskeletal Nociceptive Pain in Participants With Neuromuscular Disorders
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COMPLETED
Living Well with Chronic Pain
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ACTIVE_NOT_RECRUITING
NA
Pain Burden in Children and Adolescents With Cerebral Palsy
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COMPLETED
Stanford Chronic Pain Self-Management Programme in Danish Chronic Pain Patients
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COMPLETED
NA
DEterminants of Decisional Autonomy In Chronic Pain Patients and Assessment of Treatment Effectiveness
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RECRUITING
Pre-habilitation Interventions to Empower Patients With Chronic Pain
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RECRUITING
NA
eVISualisation of Physical Activity and Pain (eVIS) for Patients With Chronic Pain
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RECRUITING
NA
Nationwide Evaluation of Multimodal Rehabilitation in Patients With Chronic Musculoskeletal Pain
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UNKNOWN
Activity and Life-role Targeted Pain Rehabilitation in Primary Health Care
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TERMINATED
NA
Insomnia in Patients Participating in Interdisciplinary Pain Rehabilitation
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ENROLLING_BY_INVITATION
Chronic Pain Self-management for Older Adults With Cognitive Impairment: A Randomized Pilot Trial
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COMPLETED
NA
Attentional Bias Modification Training for People With Chronic Musculoskeletal Pain
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UNKNOWN
NA
Effect of Research Participation Versus Usual Clinical Treatment on Pain in Patients With Musculoskeletal Disorders
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COMPLETED
Qualitative Study for Pain Measurement Using Innovative Health Technology
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COMPLETED
Experimental Pain in Parkinsons
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RECRUITING
NA
Descriptive Study of Pain, Anxiety and Depression
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COMPLETED