COPD Exacerbation Modelling Using Unobtrusive Sensors - the TOLIFE Clinical Study A

NCT ID: NCT06172712

Last Updated: 2024-05-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

150 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-05-08

Study Completion Date

2025-07-31

Brief Summary

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This work is a multicentric prospective cohort study designed to improve chronic obstructive pulmonary disease (COPD) treatment and management. The study involves 150 patients diagnosed with COPD who are at risk of exacerbations. These patients are recruited from three tertiary hospitals in Spain, Germany, and Italy. The study will last 18 months, with a 12-month follow-up duration for each patient. The primary objective of this study is to develop and test Artificial Intelligence (AI)-based models that can predict moderate-to-severe COPD exacerbations early on. This will be done by analyzing daily-life data collected from unobtrusive sensors that monitor patients' psycho-physiological and environmental signals. By accurately predicting exacerbations, the study aims to support clinicians in providing more precise, optimized, and personalized treatment to COPD patients. A secondary objective is to train and test AI-based models to estimate the 12-month dynamics of health-related quality of life (HRQoL) in COPD patients. This will involve analyzing data related to the patients' functional exercise capacity, dyspnea (difficulty breathing), and health-related quality of life, as measured by the Clinical COPD Questionnaire (CCQ) score and the COPD Assessment Test (CAT) score.

Detailed Description

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Conditions

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Chronic Obstructive Pulmonary Disease (COPD)

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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Variations in daily life activity signals detected by unobtrusive sensors

Patients will be equipped with unobtrusive devices for a duration of 12 months. Throughout this period, they will attend scheduled visits every three months following the baseline visit. These assessments will focus on determining the frequency of exacerbations, evaluating exercise capacity, measuring the severity of dyspnea, and assessing health-related quality of life. The data gathered from the sensors embedded in these unobtrusive devices will be instrumental in developing AI-based models. These models aim to accurately predict COPD exacerbations and effectively estimate the progression of the previously mentioned health outcome.

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Diagnosis of COPD (FEV1/FVC \< 0.7) for at least a year.
* Participants with moderate-to-very severe COPD (FEV1 ≤ 80% predicted)
* Documented history of ≥ 1 moderate (treated with SABDs and oral corticosteroids ± antibiotics) or severe COPD exacerbation\* within 12 months before enrolment.
* Adults aged 40 or over.
* Able to walk 4 meters independently with or without walking aids.
* Anticipated availability for repeated study visits over 12 months.
* Willingness to use smart sensors.
* Able to read and write in the first language in the respective location.
* Capable of giving signed informed consent, which includes compliance with the requirements and restrictions listed in the informed consent form (ICF) and the study protocol.

Exclusion Criteria

* Occurrence of any of the following within three months before informed consent: myocardial infarction, hospitalization for unstable angina, stroke, coronary artery bypass graft (CABG), percutaneous coronary intervention (PCI), implantation of a cardiac resynchronization therapy device (CRTD).
* Uncontrolled congestive heart disease (NYHA class \>3).
* Having undergone major lung surgery (e.g., lung transplant)
* Primary respiratory diseases other than COPD.
* Participant who has experienced a major respiratory infection or exacerbation within 2 weeks before screening
* Lung volume reduction within six months before screening.
* Active treatment for cancer or other malignant diseases that in the opinion of the investigator, have an impact on the patient's quality of life such as to prevent adherence to the study.
* Acute psychosis or major psychiatric disorders or continued substance abuse.
* Severe disease that limits survival to 1 year.
* Patients with severe cognitive impairment (MMSE \< 18).
* Substantial limitations in mobility due to factors other than COPD.
* Inability to follow the study procedures (e.g., due to language problems, psychological disorders) or unable to read, understand and fill in a questionnaire.
Minimum Eligible Age

40 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University of Pisa

OTHER

Sponsor Role collaborator

LungenClinic Grosshansdorf

OTHER

Sponsor Role collaborator

Hospital del Mar Research Institute

UNKNOWN

Sponsor Role collaborator

Fondazione C.N.R./Regione Toscana "G. Monasterio", Pisa, Italy

OTHER_GOV

Sponsor Role collaborator

Universidad Politecnica de Madrid

OTHER

Sponsor Role collaborator

TIMELEX

UNKNOWN

Sponsor Role collaborator

Avvale

UNKNOWN

Sponsor Role collaborator

Barcelona Institute for Global Health

OTHER

Sponsor Role lead

Responsible Party

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Judith Garcia-Aymerich

Research Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Judith Garcia-Aymerich

Role: PRINCIPAL_INVESTIGATOR

Barcelona Institute for Global Health

Locations

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Pulmonary Research Institute

Großhansdorf, Schleswig-Holstein, Germany

Site Status RECRUITING

Azienda Ospedaliero Universitaria Pisana

Pisa, , Italy

Site Status NOT_YET_RECRUITING

Hospital del Mar Research Institute

Barcelona, , Spain

Site Status RECRUITING

Countries

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Germany Italy Spain

Central Contacts

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Judith Garcia-Aymerich, MD; PhD

Role: CONTACT

+342147380

Facility Contacts

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Henrik Watz

Role: primary

+4941028881111

Alessandro Celi

Role: primary

+39050995236

Joaquim Gea

Role: primary

+34618175694

Other Identifiers

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101057103CSA

Identifier Type: -

Identifier Source: org_study_id

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