Ignite Creation Date:
2026-03-26 @ 3:19 PM
Ignite Modification Date:
2026-04-06 @ 9:01 AM
Study NCT ID:
NCT07424534
Status:
NOT_YET_RECRUITING
Last Update Posted:
2026-03-05
First Post:
2026-02-13
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Predictive Models on Psychosocial Profile, Fatigue and Sleep Quality
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}, 'conditionBrowseModule': {'meshes': [{'id': 'D005356', 'term': 'Fibromyalgia'}], 'ancestors': [{'id': 'D009135', 'term': 'Muscular Diseases'}, {'id': 'D009140', 'term': 'Musculoskeletal Diseases'}, {'id': 'D012216', 'term': 'Rheumatic Diseases'}, {'id': 'D009468', 'term': 'Neuromuscular Diseases'}, {'id': 'D009422', 'term': 'Nervous System Diseases'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 150}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2026-02-24', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-03', 'completionDateStruct': {'date': '2026-11-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2026-03-03', 'studyFirstSubmitDate': '2026-02-13', 'studyFirstSubmitQcDate': '2026-02-13', 'lastUpdatePostDateStruct': {'date': '2026-03-05', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-02-20', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-11-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Disease severity', 'timeFrame': 'Baseline', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Sleep quality', 'timeFrame': 'Baseline', 'description': '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.'}, {'measure': 'Fatigue', 'timeFrame': 'Baseline', 'description': '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.'}, {'measure': 'Pain intensity', 'timeFrame': 'Baseline', 'description': '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.'}, {'measure': 'Depression', 'timeFrame': 'Baseline', 'description': '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.'}, {'measure': 'Pain catastrophizing', 'timeFrame': 'Baseline', 'description': '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.'}, {'measure': 'Anxiety', 'timeFrame': 'Baseline', 'description': '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.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Fibromyalgia']}, 'descriptionModule': {'briefSummary': '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.', 'detailedDescription': "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.\n\nFM 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.\n\nFM 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.\n\nIn 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.\n\nExplainable 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.\n\nA 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.\n\nAccordingly, 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.\n\nUnderstanding 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '65 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age between 18 and 65 years.\n* Fullfilled the 2010 American Collegue of Rheumathology criteria for fibromyalgia.\n* Understanding of spoken and written Spanish.\n\nExclusion Criteria:\n\n* Diagnosed psychiatric pathology.\n* Rheumatic pathology not medically controlled.\n* Neurological pathologies that make evaluations difficult.'}, 'identificationModule': {'nctId': 'NCT07424534', 'briefTitle': 'Predictive Models on Psychosocial Profile, Fatigue and Sleep Quality', 'organization': {'class': 'OTHER', 'fullName': 'University of Castilla-La Mancha'}, 'officialTitle': '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', 'orgStudyIdInfo': {'id': 'FibroIA'}}, 'contactsLocationsModule': {'locations': [{'zip': '41008', 'city': 'Seville', 'state': 'Seville', 'country': 'Spain', 'contacts': [{'name': 'Rafael Velasco Velasco, Mr.', 'role': 'CONTACT', 'email': 'Rafael.Velasco@uclm.es', 'phone': '+34954786735'}], 'facility': 'Rafael Velasco Velasco', 'geoPoint': {'lat': 37.38283, 'lon': -5.97317}}], 'centralContacts': [{'name': 'Rafael Velasco Velasco, Mr.', 'role': 'CONTACT', 'email': 'Rafael.Velasco@uclm.es', 'phone': '+34954786735'}, {'name': 'Rubén Arroyo Fernández, Dr.', 'role': 'CONTACT', 'email': 'Ruben.Arroyo@uclm.es', 'phone': '+34925803600', 'phoneExt': '86589'}], 'overallOfficials': [{'name': 'Rafael Velasco Velasco, Mr.', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'University of Castilla-La Mancha'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'University of Castilla-La Mancha', 'class': 'OTHER'}, 'collaborators': [{'name': 'Tampere University', 'class': 'OTHER'}, {'name': 'Andaluz Health Service', 'class': 'OTHER_GOV'}], 'responsibleParty': {'type': 'SPONSOR'}}}}