Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity
Active, not recruiting
- Conditions
- Obesity; Overweight
- Registration Number
- NCT07011108
- Lead Sponsor
- Sykehuset i Vestfold HF
- Brief Summary
The primary aim of this study is to develop an obesity-specific machine learning (ML) model capable of accurately estimating VO2max, a key indicator of cardiovascular fitness.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ACTIVE_NOT_RECRUITING
- Sex
- All
- Target Recruitment
- 1700
Inclusion Criteria
- diagnosed with severe obesity
- body mass index (BMI) ≥40.0 kg •m-2, or 35.0-39.9 kg •m-2 with at least one obesity-related comorbidity
Exclusion Criteria
- not obese
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Machine learning model to estimate vo2max The project will be completed in 2027 The primary aim of this study is to develop an obesity-specific ML model capable of accurately estimating VO2max, a key indicator of cardiovascular fitness
- Secondary Outcome Measures
Name Time Method
Related Research Topics
Explore scientific publications, clinical data analysis, treatment approaches, and expert-compiled information related to the mechanisms and outcomes of this trial. Click any topic for comprehensive research insights.
What molecular features correlate with VO2max prediction in obesity-specific machine learning models?
How does ML-based VO2max estimation compare to traditional cardiopulmonary exercise testing in overweight populations?
What biomarkers are associated with improved cardiorespiratory fitness outcomes in NCT07011108 study?
Are there adverse events linked to ML-driven fitness assessments in patients with metabolic syndrome?
What drug classes or interventions synergize with machine learning approaches to enhance obesity-related cardiovascular health?