Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity

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.

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This study is active but is not currently recruiting participants.

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Key information

Conditions

Obesity; Overweight Body Weight Nutrition Disorders Nutritional and Metabolic Diseases Obesity Overnutrition Overweight Pathological Conditions, Signs and Symptoms Signs and Symptoms

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

No intervention (observational study)

Other

no intervention

Primary outcomes

Machine learning model to estimate vo2max

Time frame: 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

Sponsors and collaborators

Lead sponsor

Sykehuset i Vestfold HF

Other

Registry information

Acronym: FitML-O

Important dates

Study start: 2025
Primary completion: 2027
Study completion: 2027
First posted: Jun 8, 2025
Registry last updated: Jun 8, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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