BEhavioral and Adherence Model for Improving Quality, Health Outcomes and Cost-Effectiveness of healthcaRe

NCT06856902 | Recruiting

• 2 other identifiers: BBAAMFIQHOACOH-GF-20241108101034369
• Study Type: interventional
• Target: 3,100
• Locations: 8 countries
• Sites: 10

Brief Summary

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. So 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.

Conditions

Inmunology; Cardiovascular Diseases; Endocrinology; Neurology; Oncology; Rare Diseases

Keywords

Digital health; adherence to treatment; healthcare

Outcome Measures

Primary Outcomes (4)

• Accuracy of the B-COMPASS to predict adherence

  Predictive accuracy of B-COMPASS of adherence to treatment will be validated against TAP-Q in Visit 1 and other specific adherence measurements for all participants. The B-COMPASS is an adherence prediction model based on the BEAMER questionnaire, which is a 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).

  At first data collection

• Validity of B-COMPASS groupings

  B-COMPASS groupings will be validated through the qualitative thematic analysis of interviews and focus groups with patients and HCPs in the intervention groups.

  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.

• Accuracy of identification of patients' support needs

  The accuracy of the support needs identified by the B-COMPASS will also be analysed through qualitative analysis of focus groups and interviews with the intervention groups.

  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.

• Reliability of B-COMPASS prediction over time

  The over-time reliability of the B-COMPASS will be measured by comparing its groupings and adherence prediction results at the first and second visits for the control group.

  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.

Secondary Outcomes (4)

• Extent to which the B-COMPASS affects patient adherence to treatment

  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.

• Patients' perceptions of the received engagement with HCPs based on B-COMPASS

  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.

• Perception of HCPs of the B-COMPASS

  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.

• Cost-effectiveness impact of B-COMPASS on healthcare utilisation

  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.

Study Arms (2)

Control arm

NO INTERVENTION

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.

Intervention arm

ACTIVE COMPARATOR

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.

Behavioral: B-COMPASS implementation

Interventions

B-COMPASS implementation BEHAVIORAL

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.

Intervention arm

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Having the diagnosis of the pilot sites target groups described above as per clinical assessment or validated diagnosis criteria
• Having the age of the pilot sites target groups described above
• Having accepted to participate in the study and provided written informed consent
• Having the availability to participate on all study activities

Sponsors & Collaborators

• University of Oslo: collaborator
• Technical University of Madrid: lead
• PREDICTBY RESEARCH AND CONSULTING S.L.: collaborator
• Pfizer: collaborator
• Merck KGaA, Darmstadt, Germany: collaborator
• Empirica: collaborator
• Centre for Research and Technology Hellas (CERTH): collaborator
• Innovation Sprint: collaborator
• Fundacio d'Investigacio en Atencio Primaria Jordi Gol i Gurina: collaborator
• Janssen Pharmaceutica N.V., Belgium: collaborator
• Novo Nordisk A/S: collaborator
• Servier Affaires Médicales: collaborator
• Takeda Pharmaceuticals International, Inc.: collaborator
• Tilburg University: collaborator

Study Sites (10)

UDUS - Heinrich-Heine-University Duesseldorf

Düsseldorf, Germany

RECRUITING

FISM - Italian Multiple Sclerosis Foundation

Genova, Italy

RECRUITING

WDO - World Duchenne Organization

Veenendaal, Netherlands

RECRUITING

AHUS - Akershus University Hospital

Lørenskog, Akershus, 1478, Norway

RECRUITING

APDP Diabetes Portugal

Lisbon, 1250-203, Portugal

RECRUITING

MEDCIDS - Departamento de Medicina da Comunidade Informação e Decisão em Saúde

Porto, Portugal

RECRUITING

UMCM - University Medical Center Maribor

Maribor, Slovenia

RECRUITING

FHUNJ - Fundación para la Investigación Biomédica del Hospital Infantil Universitario Niño Jesús

Madrid, Spain

RECRUITING

FIIBAP - Fundación para la Investigación e Innovación Biosanitaria de Atención Primaria

Madrid, Spain

RECRUITING

KCRI - Kilimanjaro Clinical Research Institute

Moshi, Tanzania

NOT YET RECRUITING

MeSH Terms

Conditions

Cardiovascular Diseases; Neoplasms; Rare Diseases; Treatment Adherence and Compliance

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Health Behavior; Behavior

Study Officials

• Giuseppe Fico, Professor

  Universidad Politecnica de Madrid

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Giuseppe Fico, Professor

CONTACT

(+34) 91 067 2636; giuseppe.fico@upm.es

Beatriz Merino, PhD

CONTACT

beatriz.merino@upm.es

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Masking Details: Considering the nature of the intervention, it is not possible to blind participants or health care providers.
• Purpose: SUPPORTIVE CARE
• Intervention Model: SINGLE GROUP
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Postdoctoral researcher

Model Details: 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.

Study Record Dates

• First Submitted: January 7, 2025
• First Posted: March 4, 2025
• Study Start: March 1, 2025
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): September 30, 2026
• Last Updated: April 1, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

TA (1); IT (1); ES (2); NL (1); DE (1); NO (1); SL (1); PT (2)