Predictive Models on Psychosocial Profile, Fatigue and Sleep Quality

March 3, 2026 updated by: University of Castilla-La Mancha

Development of Artificial Intelligence-Based Predictive Models to Analyze the Impact of Psychosocial Profile, Fatigue, and Sleep Quality on Disease Severity in Patients With Fibromyalgia

The primary goal of this research project is to develop different prediction models in fibromyalgia disease through the application of machine learning techniques and to assess the explainability of the results. As specific objective the research project intends to evaluate the influence of psychosocial variables, fatigue, and sleep quality on the prediction of disease severity in patients with fibromyalgia using an artificial intelligence-based model.

Study Overview

Status

Status

Indicates the current recruitment status or the expanded access status.
Not yet recruiting

Conditions

Conditions

The disease, disorder, syndrome, illness, or injury that is being studied. On ClinicalTrials.gov, conditions may also include other health-related issues, such as lifespan, quality of life, and health risks.

Detailed Description

Fibromyalgia (FM) is a condition characterized by chronic musculoskeletal pain, the pathophysiology of which remains unclear. In addition, this disorder is frequently associated with sleep disturbances, pronounced fatigue, morning stiffness, poor quality of life, cognitive alterations (primarily memory-related problems), and psychological disturbances, including depression, anxiety, and stress.

FM is significantly more prevalent in women, with an estimated female-to-male ratio of approximately 3:1. Its prevalence in the adult population is estimated at 2-3%, increasing with age and peaking between 45 and 65 years. The socioeconomic and healthcare burden of this condition is substantial, as FM is associated with high rates of medical consultations, unemployment, work disability, and the need for disability-related financial support.

FM has been linked to higher levels of negative affect, defined as a general state of distress encompassing aversive emotions such as sadness, fear, anger, and guilt. Patients with FM commonly exhibit elevated levels of anxiety, depression, pain catastrophizing, and stress, which are associated with worsening of symptoms, including cognitive impairments.

In recent years, machine learning, data mining, and artificial intelligence (AI) techniques have been successfully applied to the development of computer-aided diagnosis (CAD) systems for the identification of complex health conditions, achieving good levels of accuracy and efficiency by recognizing potentially meaningful patterns in health-related data. Accordingly, these technologies provide powerful tools for multivariable data analysis, enabling model-based predictions and offering a clear advantage in risk assessment across a wide range of diseases. In addition, these approaches not only support the development of clinical predictive models but also enhance clinicians' ability to interpret and apply their results in practice.

Explainable machine learning models allow clinical experts to make data-driven decisions and to deliver personalized treatments while maintaining a high standard of care. These models fall within the field of explainable artificial intelligence (XAI), which aims to develop interpretable models that preserve high predictive accuracy while improving the transparency, understanding, and trustworthiness of model outputs.

A previous publication by our research group demonstrated, using machine learning models, a strong association between mental health factors-particularly anxiety and depression-and fibromyalgia severity, with a greater influence than pain intensity itself. Other prevalent symptoms of fibromyalgia, such as fatigue and sleep quality disturbances, are also considered by patients to be major contributors to reduced quality of life, often perceived as more impactful than pain.

Accordingly, the present study aims to develop predictive models of fibromyalgia severity using machine learning and explainable artificial intelligence techniques, incorporating fatigue and sleep quality as novel variables in the analysis.

Understanding the individual and combined relevance of these psychosocial components in determining patients' clinical status may contribute to improved disease management and more personalized treatment strategies, ultimately enhancing the quality of care.

Study Type

Study Type

Describes the nature of a clinical study. Study types include interventional studies (also called clinical trials), observational studies (including patient registries), and expanded access.
Observational

Enrollment (Estimated)

Enrollment

The number of participants in a clinical study. The "anticipated" enrollment is the target number of participants that the researchers need for the study.
150

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact

The name and contact information for the person who can answer enrollment questions for a clinical study. Each location where the study is being conducted may also have a specific contact, who may be better able to answer those questions.

Study Contact Backup

Study Locations

  • Spain
    • Seville
      • Seville, Seville, Spain, 41008
        • Rafael Velasco Velasco
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Eligibility Criteria

The key requirements that people who want to participate in a clinical study must meet or the characteristics they must have. Eligibility criteria consist of both inclusion criteria (which are required for a person to participate in the study) and exclusion criteria (which prevent a person from participating). Types of eligibility criteria include whether a study accepts healthy volunteers, has age or age group requirements, or is limited by sex.

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

The study population will consist of adult volunteers diagnosed with fibromyalgia who meet the eligibility criteria and provide written informed consent to participate in the study. Participants will be recruited from a primary care center within the Spanish public healthcare system.

Description

Inclusion Criteria:

  • Age between 18 and 65 years.
  • Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.
  • Understanding of spoken and written Spanish.

Exclusion Criteria:

  • Diagnosed psychiatric pathology.
  • Rheumatic pathology not medically controlled.
  • Neurological pathologies that make evaluations difficult.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

What is the study measuring?

Primary Outcome Measures

Primary Outcome Measures

In a clinical study's protocol, the planned outcome measure that is the most important for evaluating the effect of an intervention/treatment. Most clinical studies have one primary outcome measure, but some have more than one.
Outcome Measure
Measure Description
Time Frame
Disease severity
Time Frame: Baseline
For this purpose, the Spanish validated version of the Fibromyalgia Impact Questionnaire (FIQ) will be used. The questionnaire comprises several components assessing physical, psychological, and social status, as well as overall well-being. It consists of 10 items scored on 0-10 scales, yielding a total score ranging from 0 to 100, with higher scores indicating greater disease impact and poorer quality of life.
Baseline

Secondary Outcome Measures

Secondary Outcome Measures

In a clinical study's protocol, a planned outcome measure that is not as important as the primary outcome measure for evaluating the effect of an intervention but is still of interest. Most clinical studies have more than one secondary outcome measure.
Outcome Measure
Measure Description
Time Frame
Sleep quality
Time Frame: Baseline
Sleep quality will be assessed using the Spanish validated version of the Medical Outcomes Study Sleep Scale (MOS-SS). This instrument consists of 12 items evaluating six sleep domains: sleep disturbance (ability to initiate and maintain sleep), sleep adequacy (sufficiency of sleep to feel rested), sleep quantity (number of hours of sleep), somnolence (daytime sleepiness), snoring, and shortness of breath or headache. A sleep problems index can be derived on a 0-100 scale, with higher scores indicating greater sleep-related problems.
Baseline
Fatigue
Time Frame: Baseline
Fatigue severity will be assessed using the Spanish adaptation of the Multidimensional Fatigue Inventory (MFI). This is a self-administered questionnaire consisting of 20 items. It comprises five subscales that analytically assess general fatigue, physical fatigue, reduced activity, reduced motivation, and mental fatigue. The total score ranges from 0 to 100, with higher scores indicating greater fatigue severity.
Baseline
Pain intensity
Time Frame: Baseline
It will be measured with a visual analog scale (VAS) of 100 millimeters in length. The subject has to indicate the level of pain he feels, being 0 the absence of pain and 100 the maximum imaginable.
Baseline
Pain catastrophizing
Time Frame: Baseline
The Spanish validated version of the Pain Catastrophizing Scale (PCS) will be administered. The PCS consists of 13 items assessing catastrophic cognitive-emotional processes related to pain and encompasses the three main dimensions of catastrophizing: rumination (persistent concern and inability to inhibit pain-related thoughts), magnification (exaggeration of the unpleasantness of pain and expectations of negative consequences), and helplessness (perceived inability to cope with painful situations). Items are rated on a 5-point Likert scale ranging from 0 (never) to 4 (always). The total score is calculated by summing the responses to all 13 items, with higher scores indicating greater levels of pain catastrophizing.
Baseline
Depression
Time Frame: Baseline
Depressive symptoms will be assessed using the Spanish adaptation of the Beck Depression Inventory-II (BDI-II). This instrument consists of 21 items designed to measure affective and cognitive status, as well as the signs and symptoms of depression experienced over the preceding two weeks. Total scores range from 0 to 63, where higher scores indicate greater severity.
Baseline
Anxiety
Time Frame: Baseline
Anxiety will be assessed using the Spanish validated version of the State Anxiety Scale (STAI-S) of the State-Trait Anxiety Inventory (STAI). It consists of 20 items scored on a Likert-type scale from 0 to 3. The score ranges from 0 to 60 points, with higher scores indicating worse anxiety levels.
Baseline

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Sponsor

The organization or person who initiates the study and who has authority and control over the study.
University of Castilla-La Mancha

Collaborators

Collaborators

An organization other than the sponsor that provides support for a clinical study. This support may include activities related to funding, design, implementation, data analysis, or reporting.
Tampere University
Andaluz Health Service

Investigators

Investigators

A researcher involved in a clinical study. Related terms include site principal investigator, site sub-investigator, study chair, study director, and study principal investigator.
  • Principal Investigator: Rafael Velasco Velasco, Mr., University of Castilla-La Mancha

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

Study Start

The actual date on which the first participant was enrolled in a clinical study. The "anticipated" study start date is the date that the researchers think will be the study start date.
February 24, 2026

Primary Completion (Estimated)

Primary Completion

The date on which the last participant in a clinical study was examined or received an intervention to collect final data for the primary outcome measure. Whether the clinical study ended according to the protocol or was terminated does not affect this date. For clinical studies with more than one primary outcome measure with different completion dates, this term refers to the date on which data collection is completed for all the primary outcome measures. The "anticipated" primary completion date is the date that the researchers think will be the primary completion date for the study.
November 30, 2026

Study Completion (Estimated)

Study Completion

The date on which the last participant in a clinical study was examined or received an intervention/treatment to collect final data for the primary outcome measures, secondary outcome measures, and adverse events (that is, the last participant's last visit). The "anticipated" study completion date is the date that the researchers think will be the study completion date.
November 30, 2026

Study Registration Dates

First Submitted

First Submitted

The date on which the study sponsor or investigator first submitted a study record to ClinicalTrials.gov. There is typically a delay of a few days between the first submitted date and the record's availability on ClinicalTrials.gov (the first posted date).
February 13, 2026

First Submitted That Met QC Criteria

First Submitted That Met QC Criteria

The date on which the study sponsor or investigator first submits a study record that is consistent with National Library of Medicine (NLM) quality control (QC) review criteria. The sponsor or investigator may need to revise and submit a study record one or more times before NLM's QC review criteria are met. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
February 13, 2026

First Posted (Actual)

First Posted

The date on which the study record was first available on ClinicalTrials.gov after National Library of Medicine (NLM) quality control (QC) review has concluded. There is typically a delay of a few days between the date the study sponsor or investigator submitted the study record and the first posted date.
February 20, 2026

Study Record Updates

Last Update Posted (Actual)

Last Update Posted

The most recent date on which changes to a study record were made available on ClinicalTrials.gov. There may be a delay between when the changes were submitted to ClinicalTrials.gov by the study's sponsor or investigator (the last update submitted date) and the last update posted date.
March 5, 2026

Last Update Submitted That Met QC Criteria

Last Update Submitted That Met QC Criteria

The most recent date on which the study sponsor or investigator submitted changes to a study record that are consistent with National Library of Medicine (NLM) quality control (QC) review criteria. It is the responsibility of the sponsor or investigator to ensure that the study record is consistent with the NLM QC review criteria.
March 3, 2026

Last Verified

Last Verified

The most recent date on which the study sponsor or investigator confirmed the information about a clinical study on ClinicalTrials.gov as accurate and current. If a study with a recruitment status of recruiting; not yet recruiting; or active, not recruiting has not been confirmed within the past 2 years, the study's recruitment status is shown as unknown.
March 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

Other Study ID Numbers

Identifiers or ID numbers other than the NCT number that are assigned to a clinical study by the study's sponsor, funders, or others. These numbers may include unique identifiers from other trial registries and National Institutes of Health grant numbers.
  • FibroIA

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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