Ignite Creation Date:
2025-12-25 @ 3:47 AM
Ignite Modification Date:
2025-12-26 @ 2:34 AM
Study NCT ID:
NCT06856902
Status:
RECRUITING
Last Update Posted:
2025-05-14
First Post:
2025-01-07
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
BEhavioral and Adherence Model for Improving Quality, Health Outcomes and Cost-Effectiveness of healthcaRe
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D009369', 'term': 'Neoplasms'}, {'id': 'D035583', 'term': 'Rare Diseases'}, {'id': 'D000074822', 'term': 'Treatment Adherence and Compliance'}], 'ancestors': [{'id': 'D020969', 'term': 'Disease Attributes'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}, {'id': 'D015438', 'term': 'Health Behavior'}, {'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'TRIPLE', 'whoMasked': ['PARTICIPANT', 'CARE_PROVIDER', 'INVESTIGATOR'], 'maskingDescription': 'After the randomization, both the investigator and the health care provider will know which group the patient is in, though some patients will understand which group they are in - this will not be announced to the patient so it is correct to write "Single-blinded (patients)".'}, 'primaryPurpose': 'SUPPORTIVE_CARE', 'interventionModel': 'SINGLE_GROUP', 'interventionModelDescription': 'Overall, this study has a Mixed Methods approach as both quantitative and qualitative data will be collected and merged to answer the SQs. A Randomised Controlled Trial (RCT) is chosen as study design for the quantitative data collection as the design is well suited for drawing conclusions from data about the effects of interventions (SQ 5). In addition, the remaining quantitative data needed to answer the SQs can be collected through this design. The qualitative data collection will be done by conducting individual and focus group interviews with HCPs and patients. The purpose of the qualitative data collection is to validate that the B-COMPASS groupings and identified needs of support are correct and to complement the quantitative data collected from the pilot studies. The qualitative data will provide insights into the perceptions, experiences, and preferences of the stakeholders involved in the intervention, as well as the contextual factors that may influence the B-COMPASS.'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 7410}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2025-03-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-03', 'completionDateStruct': {'date': '2025-12-31', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-05-08', 'studyFirstSubmitDate': '2025-01-07', 'studyFirstSubmitQcDate': '2025-02-26', 'lastUpdatePostDateStruct': {'date': '2025-05-14', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2025-03-04', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-12-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Treatment Adherence Perception Questionnaire', 'timeFrame': 'At first data collection and then aging at second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.', 'description': 'Treatment Adherence Perception Questionnaire (TAP-Q) that contains 8 yes/no questions, 6 Likert scale questions with 7 response options, and 4 Likert scale questions with 4 response options. The higher response on the scale = the better outcome.'}, {'measure': 'Quality of Life (EQ-5D)', 'timeFrame': 'At second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.', 'description': 'Quality of life questionnaire that contains 5 multiple choice questions and 2 questions with a 0 to 100 scale. The higher response on the scale = the better outcome.'}, {'measure': 'Patients experience', 'timeFrame': 'At second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.', 'description': 'Project developed questionnaire that contains 5 questions about patients experience with the received engagement with HCP/Research Lead that is not part of standard care. Likert scale with 7 response options, 1 = Fully disagree, 7 = Fully agree.'}, {'measure': 'Usefulness', 'timeFrame': 'From first data collection to second data collection which is 2 weeks - 6 months after first data collection. The exact time vary between pilot sites and disease area.', 'description': 'Project developed questionnaire that contains 5 questions about the usefulness of B-COMPASS for HCPs /Research Lead. Likert scale with 7 response options, 1 = Fully disagree, 7 = Fully agree.'}], 'secondaryOutcomes': [{'measure': 'BEAMER questionnaire', 'timeFrame': 'At first data collection and then aging at second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.', 'description': 'Project developed validated questionnaire that contains 22 questions about subjective health experience and subjective awareness of health condition. Likert scale with 7 response options, 1 = Fully disagree, 7 = Fully agree (11 questions). Likert scale with 6 response options, 1 = Fully disagree, 6 = Fully agree (4 questions). Likert scale with 5 response options, 1 = Fully disagree, 5 = Fully agree (5 questions). Score on a 10-step ladder where step 1 = The day at the past 4 weeks I felt at my worst, step 10 = The day in the past 4 weeks I felt at my best (2 questions).'}, {'measure': 'Demographics', 'timeFrame': 'At first data collection and then aging at second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.', 'description': 'Project developed questionnaire that contains 7 demographic questions. 1 numerical question, 5 multiple choice question, and 1 question with a scale from 0-10.'}, {'measure': 'Subjective feedback', 'timeFrame': 'From first data collection to second data collection which is 2 weeks - 6 months after first data collection. The exact time vary between pilot sites and disease area. The interviews will be conducted immediately after the second data collection', 'description': 'Semi-structured interviews will be conducted with patients and HCPs to get a deeper understanding of their experiences with the B-COMPASS and its resulting engagement.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Digital health', 'adherence to treatment', 'healthcare'], 'conditions': ['Cardiovascular Diseases', 'Endocrinology', 'Inmunology', 'Neurology', 'Oncology', 'Rare Diseases']}, 'descriptionModule': {'briefSummary': 'Lack of adherence to treatment is a widespread issue worldwide, which leads to higher healthcare utilisation rates and even premature death. While the level of adherence may differ based on the specific condition and treatment, studies estimate that approximately 50% of medications are not taken according to the prescribed instructions. In addition, adherence rates tend to decrease even further when the treatment requires a behavioural change. Literature reviews about factors that affect people\'s adherence show that it is challenging to predict whom can be considered to have adherent and non-adherent behaviours. In addition, the studies highlight that it is challenging to support a person to be adherent. Based on this knowledge the BEAMER project was established (Behavioural and Adherence Model for improving quality, health outcomes and cost-Effectiveness of healthcaRe). The overall goal of the project is to improve the quality of life of individuals, enhance healthcare accessibility and sustainability, thereby transforming the way healthcare stakeholders engage with patients to understand their condition and adherence levels throughout their healthcare journey. To address the overall goal, the BEAMER project has developed a disease agnostic model named "B-COMPASS: BEAMER-COmputational Model for Patient Adherence and Support Solutions". The aim of the B-COMPASS is to identify patients\' needs and preferences which enables the creation of patient-specific supports, with the intention of improving their adherence to treatment within the heterogeneity of the different disease-areas and healthcare contexts. Based on the validated BEAMER questionnaire, the B-COMPASS predicts relative adherence and offers an elicitation process of patient needs and preferences to enable targeted supports to improve patient adherence. This results in an allocation of patients to different groups based on their needs and preferences. Overall, the B-COMPASS provides patient insights that will enable more effective design of patient support, most likely resulting in better patient experience, improved adherence and lower healthcare and societal costs.\n\nSo far, several activities from a technical and user perspective have already been conducted in the project to refine the B-COMPASS. This has been done by applying an iterative mixed method approach were both stakeholders (regulator, pharma, academic/research and small and medium-sized enterprises) and end users (patients, health providers and health systems) have been involved. Despite the finetuning of the B-COMPASS, the effectiveness of the B-COMPASS hinges on empirical investigations into the structural elements that impact patient behaviour and the identification of predictive factors that can assist healthcare providers\' (HCP) and Research Leads in designing more effective treatment plans (the term HCPs/Research Lead include both the individuals and the institutions where care is delivered). Therefore, validation studies will be conducted to assess the B-COMPASS\'s performance in six therapeutic areas (cardiovascular, endocrinology, immunology, neurology, oncology and rare diseases) with patients recruited in at least Italy (FISM), Portugal (APDP and MEDIDA) Norway (AHUS), Spain (FHUNJ and FIIBAP), The Netherlands (WDO), and Germany (UDUS). The collected data will be used to evaluate the B-COMPASS\'s capacity to attend to a variety of needs and challenges for adherence.', 'detailedDescription': 'The aim of the study is to validate the B-COMPASS in real-life settings. Overall, the purpose is to provide evidence 1) to validate the B-COMPASS (primary purpose), and 2) to demonstrate the effectiveness and implementability of the B-COMPASS (secondary purpose). The project will carry out the final iterative steps of refining the model, guided by the validation studies that have been conducted. This process will be based on evaluating the performance of the generic model across six selected therapeutic areas (cardiovascular, endocrinology, immunology, neurology, oncology and rare diseases). Study participants (patients and HCPs) will be recruited (at least) from Italy, Portugal, Norway, Spain, The Netherlands, and Germany, and the validation will evaluate the capacity to attend to a variety of needs and challenges for adherence to treatment.\n\nOverall, the study has 8 Scientific Questions (SQs), where SQ 1-4 address the validity of the B-COMPASS (primary purpose) and SQ 5-8 address the effectiveness and implementability of the B-COMPASS (secondary purpose). The SQs are:\n\nSQ 1 How accurately does the B-COMPASS predict the relative adherence to treatment for patients? SQ 2 How valid are the B-COMPASS groupings? SQ 3 How accurately are the patient support needs identified by the B-COMPASS? SQ 4 How reliable is the B-COMPASS over time? SQ 5 To what extent does the use of the B-COMPASS affect patient adherence to treatment? SQ 6 How do the patients perceive the received engagement with HCPs/Research Leads based on the B-COMPASS? SQ 7 How do HCPs/Research Leads perceive the B-COMPASS? SQ 8 How does the B-COMPASS impact the cost-effectiveness of healthcare utilisation?'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['CHILD', 'ADULT', 'OLDER_ADULT'], 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Having the diagnosis of the pilot sites target groups described above as per clinical assessment or validated diagnosis criteria\n* Having the age of the pilot sites target groups described above\n* Having accepted to participate in the study and provided written informed consent\n* Having the availability to participate on all study activities\n\nExclusion Criteria:\n\n* Individuals that do not fulfil ALL the inclusion criteria will be excluded to participate.'}, 'identificationModule': {'nctId': 'NCT06856902', 'acronym': 'BEAMER', 'briefTitle': 'BEhavioral and Adherence Model for Improving Quality, Health Outcomes and Cost-Effectiveness of healthcaRe', 'organization': {'class': 'OTHER', 'fullName': 'Technical University of Madrid'}, 'officialTitle': 'BEhavioral and Adherence Model for Improving Quality, Health Outcomes and Cost-Effectiveness of healthcaRe', 'orgStudyIdInfo': {'id': 'BBAAMFIQHOACOH-GF-20241108'}, 'secondaryIdInfos': [{'id': '101034369', 'type': 'OTHER_GRANT', 'domain': "European Union's Horizon 2020 Research and Innovation Programme, IMI"}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'NO_INTERVENTION', 'label': 'Control arm', 'description': "At the first data collection, patients will complete the BEAMER questionnaire and adherence-related measures will be collected. Based on patients' answers, the B-COMPASS will assign them into groups, list their support needs, and predict their relative adherence, forming their B-COMPASS description. Patients will then be randomised into either 1) an intervention arm, or 2) a control arm using stratified randomisation via the Adherence Intelligence Visualisation Platform (AIVP), ensuring balance in B-COMPASS descriptions, gender, and age. Control patients will receive standard care and HCPs/Research Leads will not be informed of their B-COMPASS description. Where possible, at pilot sites, HCPs/Research Leads will also be randomised to ensure that HCPs/Research Leads in the control groups have as limited knowledge of the B-COMPASS/patient description as possible."}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Intervention arm', 'description': "At the first data collection, patients will complete the BEAMER questionnaire and adherence-related measures will be collected. Based on patients' answers, the B-COMPASS will assign them into groups, list their support needs, and predict their relative adherence, forming their B-COMPASS description. Patients will then be randomised into either 1) an intervention arm, or 2) a control arm using stratified randomisation via the Adherence Intelligence Visualisation Platform (AIVP), ensuring balance in B-COMPASS descriptions, gender, and age. The patients in the intervention arm will receive B-COMPASS enhanced engagement in addition to standard care. The enhanced engagement is implemented as educational material to the HCP who is engaging with the patient. The content of the educational material will be based on the patient's B-COMPASS patient description. The engagement will either be in person or via phone call depending on the patient visiting schedule of each recruited patient.", 'interventionNames': ['Behavioral: B-COMPASS implementation']}], 'interventions': [{'name': 'B-COMPASS implementation', 'type': 'BEHAVIORAL', 'description': "The patients in the intervention arm will receive B-COMPASS enhanced engagement in addition to standard care. The enhanced engagement is implemented as educational material to the HCP who is engaging with the patient. The content of the educational material will be based on the patient's B-COMPASS patient description. The engagement will either be in person or via phone call depending on the patient visiting schedule of each recruited patient.", 'armGroupLabels': ['Intervention arm']}, {'name': 'B-COMPASS implementation', 'type': 'BEHAVIORAL', 'description': "At the first data collection, patients will complete the BEAMER questionnaire and adherence-related measures will be collected. Based on patients' answers, the B-COMPASS will assign them into groups, list their support needs, and predict their relative adherence, forming their B-COMPASS description. Patients will then be randomised into either 1) an intervention arm, or 2) a control arm using stratified randomisation via the Adherence Intelligence Visualisation Platform (AIVP), ensuring balance in B-COMPASS descriptions, gender, and age. The patients in the intervention arm will receive B-COMPASS enhanced engagement in addition to standard care. The enhanced engagement is implemented as educational material to the HCP who is engaging with the patient. The content of the educational material will be based on the patient's B-COMPASS patient description. The engagement will either be in person or via phone call depending on the patient visiting schedule of each recruited patient.", 'armGroupLabels': ['Intervention arm']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Porto', 'status': 'RECRUITING', 'country': 'Portugal', 'contacts': [{'name': 'Rita Amaral', 'role': 'CONTACT', 'email': 'rita.s.amaral@gmail.com', 'phone': '000000000'}], 'facility': 'MEDCIDS - Departamento de Medicina da Comunidade Informação e Decisão em Saúde', 'geoPoint': {'lat': 41.1485, 'lon': -8.61097}}], 'centralContacts': [{'name': 'Giuseppe Fico, Professor', 'role': 'CONTACT', 'email': 'giuseppe.fico@upm.es', 'phone': '(+34) 91 067 2636'}, {'name': 'Beatriz Merino, PhD', 'role': 'CONTACT', 'email': 'beatriz.merino@upm.es'}], 'overallOfficials': [{'name': 'Giuseppe Fico, Professor', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Universidad Politecnica de Madrid'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Technical University of Madrid', 'class': 'OTHER'}, 'collaborators': [{'name': 'University of Oslo', 'class': 'OTHER'}, {'name': 'PREDICTBY RESEARCH AND CONSULTING S.L.', 'class': 'UNKNOWN'}, {'name': 'Pfizer', 'class': 'INDUSTRY'}, {'name': 'Merck KGaA, Darmstadt, Germany', 'class': 'INDUSTRY'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Postdoctoral researcher', 'investigatorFullName': 'Beatriz Merino', 'investigatorAffiliation': 'Technical University of Madrid'}}}}