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Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity

Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT07011108
Acronym
FitML-O
Enrollment
1700
Registered
2025-06-08
Start date
2025-05-30
Completion date
2027-12-24
Last updated
2025-06-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Obesity; Overweight

Keywords

Obesity; overweight; Cardiorespiratory fitness; VO2max; Machine learning; ML, web-application;

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.

Interventions

OTHERNo intervention (observational study)

no intervention

Sponsors

Sykehuset i Vestfold HF
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
OTHER

Eligibility

Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
Machine learning model to estimate vo2maxThe project will be completed in 2027The primary aim of this study is to develop an obesity-specific ML model capable of accurately estimating VO2max, a key indicator of cardiovascular fitness

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026