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AzTrials

Pain, ChronicpAInNCT07081737

Person-Centred AI Support in Interdisciplinary Rehabilitation for Chronic Pain

A study testing Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS) and Interdisciplinary treatment (IDT) in people with Pain, Chronic and Chronic Pain, Widespread, run by Bjorn Ang at 1 site in Sweden.

Not yet recruiting

Opening soon. The study is approved but enrolment has not started yet. Sites may already take enquiries.

Sites and contacts
Official record
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Trial summary

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. Read more about this study

Who is it for?
Ages 18 to 67 years
What is being tested?
Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS) and Interdisciplinary treatment (IDT)
Could I receive a placebo?
No, everyone receives an active treatment
How many people
400 participants
Where
1 siteSweden
Study dates
1 May 2026to 31 Mar 2029
Phase
Interventional
Are there results?
Not yet: the study is under way

Key requirements

Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.

  • Ages 18 to 67 years
  • All sexes
  • No healthy volunteers
  • Adult, Older adult

Who may be able to join

  • 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

Who cannot join

  • 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

Trial locations and contacts

1 site in 1 country. Contact the study team or a site near you to ask about taking part.

Tony Bohman, Ass. Professor contact

+46702996263

tbo@du.se

Marika Hagelberg, MSc contact

+4623778418

mhb@du.se

Sweden1 site

  • Dalarna University

    Falun, Dalarna County, 79188

    Tony Bohman, Ass. Professor, +46702996263, tbo@du.se

    Björn Äng, Professor, +46705518850, bja@du.se

Other trials in Sweden

About this study

Official title: The Future of Pain Management: Can Person-centred and Precision AI-Based Decision Support Enhance Interdisciplinary Rehabilitation for Chronic Pain?

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.

Read the detailed description

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.

Being tested
Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS), Interdisciplinary treatment (IDT)
Keywords
chronic pain, prediction, Artificial Intelligence, Interdisciplinary pain rehabilitation, Machine learning, Clinical Decision Support System (CDSS)
Medical subject headings
Chronic Pain, Agnosia

What participants receive

What participants receive. In a randomised study each person is assigned to one group by chance.

  • Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS)Other

    Interdisciplinary treatment (IDT) combined with Clinical Decision Support System (CDSS)

  • Interdisciplinary treatment (IDT)Other

    Interdisciplinary treatment (IDT)

Study groups in NCT07081737
Study groupRoleWhat they receive
Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS)ExperimentalParticipants 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.
Receives: Other: Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS)
Interdisciplinary treatment (IDT)Active comparatorParticipants 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.
Receives: Other: Interdisciplinary treatment (IDT)

What is being measured

The primary outcome is the main question the study is designed to answer.

  1. Change from Baseline in Patient-Prioritized Health-Related Well-being Composite at 12 Months

    A person-centred composite score based on eight validated domains: pain intensity (NRS; 0-10), sleep problems (ISI; 0-28), physical health (SF-36 PF; 0-100), mental health (SF-36 MH; 0-100), depression and anxiety (HADS-A/D; 0-21), work ability (WAI single item; 0-10), and pain interference (single item; 0-10). At baseline, participants prioritize these domains together with the interdisciplinary treatment (IDT) team. The Clinical Decision Support System (CDSS) stores these weights. Each domain is normalized to 0-100 and combined into a weighted composite. Higher scores indicate better health-related well-being. Although composed of several scales, the outcome is reported as a single, aggregated primary measure.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

Secondary outcomes (13)

  1. Change from Baseline in Pain Intensity (NRS) at 12 Months

    Measured using the Numeric Rating Scale (NRS, 0-10), where 0 = no pain and 10 = worst imaginable pain. Participants report average pain over the last seven days. A reduction in score indicates clinical improvement.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  2. Change from Baseline in Sleep Problems Measured by the Insomnia Severity Index (ISI) at 12 Months

    The ISI assesses perceived sleep difficulties through seven items. Total scores range from 0 to 28, with higher values indicating more severe insomnia. A lower score reflects improved sleep quality.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  3. Change from Baseline in Physical Health Functioning (SF-36 PF) at 12 Months

    Measured using the SF-36 Physical Functioning subscale (10 items). Scores range from 0 to 100, with higher scores representing better physical functioning.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  4. Change from Baseline in Mental Health (SF-36 MH) at 12 Months

    Measured using the SF-36 Mental Health subscale (5 items). Scores range from 0 to 100; higher values indicate better mental well-being.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  5. Change from Baseline in Emotional Distress Measured by the Hospital Anxiety and Depression Scale (HADS) at 12 Months

    The HADS includes two 7-item subscales for anxiety and depression. Each subscale ranges from 0 to 21; higher scores indicate greater emotional distress.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  6. Change from Baseline in Work Ability Measured by the Work Ability Index (WAI) Single Item at 12 Months

    Assessed via the single-item WAI: "Current work ability compared to lifetime best", scored from 0 (completely unable to work) to 10 (work ability at its best).

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  7. Change from Baseline in Pain Interference in Daily Life at 12 Months

    Measured using validated items adapted from the Multidimensional Pain Inventory or equivalent, reflecting the extent to which pain disrupts everyday activities. Scores are standardized from 0 (no interference) to 100 (maximum interference).

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  8. Change from Baseline in Physical Activity Measured by the Exercise Vital Sign (EVS) at 12 Months

    The EVS includes two questions: (1) days per week of moderate-to-strenuous activity, and (2) average minutes per day. The product gives total minutes/week. Validated against objective activity measures.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  9. Treatment Expectations at Baseline

    A single-item self-report question regarding the individual's expectation of treatment benefit, rated on a Likert scale from "no improvement expected" to "complete recovery expected".

    Measured: At enrollment (baseline) only.

  10. Change from Baseline in Sick Leave Status at 12 Months

    Self-reported current sick leave status (full-time, part-time, or not on leave). Changes over time reflect return-to-work or ongoing sick leave status.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  11. Change from Baseline in Self-Reported Medication Use at 12 Months

    Captured through a validated web-based tool. Participants report medication name (text), dose (mg), frequency (time/day), and form (e.g. tablet). Variables are analyzed separately using descriptive statistics and longitudinal methods to assess changes in medication use over time.

    Measured: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).

  12. Change in Cost-Utility at 24 Months (EQ-5D-5L)

    Measured in Quality-Adjusted Life Years (QALYs) derived from the EuroQol 5-Dimension 5-Level (EQ-5D-5L) instrument using standard value sets. The Incremental Cost-Effectiveness Ratio (ICER) will be calculated as the ratio of the difference in costs to the difference in health outcomes (QALYs gained), comparing the intervention to the control. ICER provides a summary measure of the additional cost required to gain one additional unit of health benefit.

    EQ-5D index scores typically range from below zero (e.g., -0.594 in the UK value set) to 1.000, or from 0.296 to 1.000 using the Swedish experience-based value set, where higher values indicate better health-related quality of life.

    Measured: (1) Up to 18 weeks after baseline, (2) 12 months, and (3) 24 months after treatment completion.

  13. Change in Cost-Utility at 24 Months (RAND SF-36)

    Measured using the RAND 36-Item Health Survey (RAND SF-36), which assesses physical and mental health across eight domains. Each domain score ranges from 0 to 100, with higher scores indicating better health status. A cost-utility analysis will also be performed using utility estimates derived from RAND SF-36, and an ICER will be calculated accordingly. ICER will be interpreted as the additional cost per unit of utility gained, based on mapped utility values. (Note: Mapping algorithms may be applied to derive utility weights from RAND SF-36, subject to validation.)

    Measured: (1) Up to 18 weeks after baseline, (2) 12 months, and (3) 24 months after treatment completion.

Timeline

From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.

  1. First posted23 Jul 2025
  2. Study start1 May 2026 (estimated)
  3. Primary completion31 Mar 2029 (estimated)
  4. Study completion31 Mar 2029 (estimated)

Resources

More details

For doctors and researchers: how the study is designed, who runs it, and its registry IDs.

Study design
Study type
Interventional
Phase
Phase N/A
Primary purpose
Treatment
Allocation
Randomised
Intervention model
Parallel groups
Model description
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.
Masking
Double blind
Who is masked
Investigator, Outcomes assessor
Enrolment
400 participants (estimated)
IDs, dates and publications
ClinicalTrials.gov ID
NCT07081737
Sponsor's protocol ID
2023-04532-01
Other IDs
2023
Acronym
pAIn
Status last verified
Jul 2025
First submitted
23 Jan 2024
Last updated
23 Jul 2025
Results posted
No
Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
Individual data sharing
Yes: De-identified individual participant data (IPD) may be shared upon reasonable request, pending legal and ethical approval. The research team is currently reviewing the feasibility of data sharing under Swedish regulations (e.g., GDPR).

Publications

  • Elf M, Nordmark S, Lyhagen J, Lindberg I, Finch T, Aberg AC. The Swedish version of the Normalization Process Theory Measure S-NoMAD: translation, adaptation, and pilot testing. Implement Sci. 2018 Dec 4;13(1):146. doi: 10.1186/s13012-018-0835-5.

    PubMed 30509289

  • Edwards RR, Schreiber KL, Dworkin RH, Turk DC, Baron R, Freeman R, Jensen TS, Latremoliere A, Markman JD, Rice ASC, Rowbotham M, Staud R, Tate S, Woolf CJ, Andrews NA, Carr DB, Colloca L, Cosma-Roman D, Cowan P, Diatchenko L, Farrar J, Gewandter JS, Gilron I, Kerns RD, Marchand S, Niebler G, Patel KV, Simon LS, Tockarshewsky T, Vanhove GF, Vardeh D, Walco GA, Wasan AD, Wesselmann U. Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations. J Pain. 2023 Feb;24(2):204-225. doi: 10.1016/j.jpain.2022.08.010. Epub 2022 Oct 2.

    PubMed 36198371

  • May CR, Mair F, Finch T, MacFarlane A, Dowrick C, Treweek S, Rapley T, Ballini L, Ong BN, Rogers A, Murray E, Elwyn G, Legare F, Gunn J, Montori VM. Development of a theory of implementation and integration: Normalization Process Theory. Implement Sci. 2009 May 21;4:29. doi: 10.1186/1748-5908-4-29.

    PubMed 19460163

  • Moore GF, Audrey S, Barker M, Bond L, Bonell C, Hardeman W, Moore L, O'Cathain A, Tinati T, Wight D, Baird J. Process evaluation of complex interventions: Medical Research Council guidance. BMJ. 2015 Mar 19;350:h1258. doi: 10.1136/bmj.h1258.

    PubMed 25791983

  • Eldridge SM, Lancaster GA, Campbell MJ, Thabane L, Hopewell S, Coleman CL, Bond CM. Defining Feasibility and Pilot Studies in Preparation for Randomised Controlled Trials: Development of a Conceptual Framework. PLoS One. 2016 Mar 15;11(3):e0150205. doi: 10.1371/journal.pone.0150205. eCollection 2016.

    PubMed 26978655

  • Gronkvist R, Vixner L, Ang B, Grimby-Ekman A. Measurement Error, Minimal Detectable Change, and Minimal Clinically Important Difference of the Short Form-36 Health Survey, Hospital Anxiety and Depression Scale, and Pain Numeric Rating Scale in Patients With Chronic Pain. J Pain. 2024 Sep;25(9):104559. doi: 10.1016/j.jpain.2024.104559. Epub 2024 May 10.

    PubMed 38734041

  • Sjoberg V, Westergren J, Monnier A, Lo Martire R, Hagstromer M, Ang BO, Vixner L. Wrist-Worn Activity Trackers in Laboratory and Free-Living Settings for Patients With Chronic Pain: Criterion Validity Study. JMIR Mhealth Uhealth. 2021 Jan 12;9(1):e24806. doi: 10.2196/24806.

    PubMed 33433391

  • Sjoberg V, Monnier A, Tseli E, LoMartire R, Hagstromer M, Bjork M, Ang B, Vixner L. Feasibility and acceptability of design and conduct of a registry-based randomised clinical trial evaluating eVIS as a digital support for physical activity in interdisciplinary pain rehabilitation programs: A randomised pilot study. Digit Health. 2024 Nov 25;10:20552076241299648. doi: 10.1177/20552076241299648. eCollection 2024 Jan-Dec.

    PubMed 39600393

  • Westergren J, Sjoberg V, Vixner L, Nyberg RG, Moulaee Conradsson D, Monnier A, LoMartire R, Enthoven P, Ang BO. Acute exercise as active inference in chronic musculoskeletal pain, effects on gait kinematics and muscular activity in patients and healthy participants: a study protocol for a randomised controlled laboratory trial. BMJ Open. 2023 May 31;13(5):e069747. doi: 10.1136/bmjopen-2022-069747.

    PubMed 37258077

  • LoMartire R, Ang BO, Gerdle B, Vixner L. Psychometric properties of Short Form-36 Health Survey, EuroQol 5-dimensions, and Hospital Anxiety and Depression Scale in patients with chronic pain. Pain. 2020 Jan;161(1):83-95. doi: 10.1097/j.pain.0000000000001700.

    PubMed 31568237

  • Skivington K, Matthews L, Simpson SA, Craig P, Baird J, Blazeby JM, Boyd KA, Craig N, French DP, McIntosh E, Petticrew M, Rycroft-Malone J, White M, Moore L. A new framework for developing and evaluating complex interventions: update of Medical Research Council guidance. BMJ. 2021 Sep 30;374:n2061. doi: 10.1136/bmj.n2061.

    PubMed 34593508

  • Strom P, Kartasalo K, Olsson H, Solorzano L, Delahunt B, Berney DM, Bostwick DG, Evans AJ, Grignon DJ, Humphrey PA, Iczkowski KA, Kench JG, Kristiansen G, van der Kwast TH, Leite KRM, McKenney JK, Oxley J, Pan CC, Samaratunga H, Srigley JR, Takahashi H, Tsuzuki T, Varma M, Zhou M, Lindberg J, Lindskog C, Ruusuvuori P, Wahlby C, Gronberg H, Rantalainen M, Egevad L, Eklund M. Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study. Lancet Oncol. 2020 Feb;21(2):222-232. doi: 10.1016/S1470-2045(19)30738-7. Epub 2020 Jan 8.

    PubMed 31926806

  • Moazemi S, Vahdati S, Li J, Kalkhoff S, Castano LJV, Dewitz B, Bibo R, Sabouniaghdam P, Tootooni MS, Bundschuh RA, Lichtenberg A, Aubin H, Schmid F. Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: A systematic review. Front Med (Lausanne). 2023 Mar 31;10:1109411. doi: 10.3389/fmed.2023.1109411. eCollection 2023.

    PubMed 37064042

  • Granviken F, Vasseljen O, Bach K, Jaiswal A, Meisingset I. Decision Support for Managing Common Musculoskeletal Pain Disorders: Development of a Case-Based Reasoning Application. JMIR Form Res. 2024 May 10;8:e44805. doi: 10.2196/44805.

    PubMed 38728686

  • Tseli E, LoMartire R, Vixner L, Grooten WJA, Gerdle B, Ang BO. What Is the Effectiveness of Different Duration Interdisciplinary Treatment Programs in Patients with Chronic Pain? A Large-Scale Longitudinal Register Study. J Clin Med. 2020 Aug 29;9(9):2788. doi: 10.3390/jcm9092788.

    PubMed 32872448

Questions about this trial

What is this clinical trial studying?

A study testing Interdisciplinary treatment (IDT) + Clinical Decision Support System (CDSS) and Interdisciplinary treatment (IDT) in people with Pain, Chronic and Chronic Pain, Widespread, run by Bjorn Ang at 1 site in Sweden.

The full registered description is in the About this study section and the official record.

Who may be eligible to take part?

The sponsor lists 7 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.

Is this clinical trial currently recruiting?

The record says: opening soon, as of the sponsor's last update on 23 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.

Where is the study taking place?

The record lists 1 site in Sweden. Whether a site is taking part right now should be confirmed with that site. See every site.

What are the possible benefits and risks?

They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.

Are results available for this study?

This study is still under way. Results are not available in the source record yet.

How can a doctor refer a patient or contact the research team?

Contact Tony Bohman, Ass. Professor (+46702996263, tbo@du.se), or a site near the patient. AzTrials does not enrol participants or take referrals.

What does the primary outcome measure?

Change from Baseline in Patient-Prioritized Health-Related Well-being Composite at 12 Months (time frame: (1) Baseline, (2) up to 18 weeks after baseline*, and (3) 12-month follow-up. (*Most programs last 6-8 weeks, but some clinics extend to 18 weeks (fewer sessions/week) due to existing routine practice).). It is what the study was designed to measure, not a finding.

Where can I read the original study information?

On the ClinicalTrials.gov record for NCT07081737, which also lists 39 linked publications.

Does a listing mean the treatment is approved or proven to work?

No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.

How often is this information updated?

AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jul 2025.

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