Obesity; OverweightFitML-ONCT07011108
Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity
An observational study of Obesity; Overweight, run by Sykehuset i Vestfold HF.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Obesity; Overweight
- How many people
- 1,700 participants
- Where
- No sites listed yet
- Study dates
- 30 May 2025to 24 Dec 2027
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with obesity from Norway
Who may be able to join
- 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
Who cannot join
- not obese
About this study
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.
- Conditions
- Obesity; Overweight
- Being tested
- No intervention (observational study)
- Keywords
- Obesity; overweight; Cardiorespiratory fitness; VO2max; Machine learning; ML, web-application;
- Medical subject headings
- Obesity, Overweight
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No intervention (observational study)Other
no intervention
What is being measured
The primary outcome is the main question the study is designed to answer.
Machine learning model to estimate vo2max
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
Measured: The project will be completed in 2027
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Jun 2025
- Study start30 May 2025
- Primary completion10 Dec 2027 (estimated)
- Study completion24 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Obesity; OverweightStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 1,700 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Sykehuset i Vestfold HF
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Jarle Berge, Sykehuset i Vestfold HF
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07011108
- Sponsor's protocol ID
- FitML-O
- Acronym
- FitML-O
- Status last verified
- May 2025
- First submitted
- 30 May 2025
- Last updated
- 8 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: IPD will not be shared due to confidentiality concerns, lack of participant consent for data sharing, or institutional policy restrictions.
Publications
Berge J, Nunavath V, Asbjornsen RA, Borgeraas H, Hertel JK, Linge AD, Groote I, Stensrud T, Gjevestad E, Mathisen L, Caan MW, Paulson M, Stoa EM, Johansen JM; AI and VO2max Group; Singstad BJ. Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity: Protocol for a Retrospective and Prospective Multicenter Cohort Study. JMIR Res Protoc. 2026 Mar 2;15:e85069. doi: 10.2196/85069.
PubMed 41773679
Study at a glance
From the registry record, last updated by the sponsor on 8 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity
- ClinicalTrials.gov ID
- NCT07011108
- Acronym / sponsor ID
- FitML-O / FitML-O
- Conditions
- Obesity; Overweight
- Keywords
- Obesity; overweight; Cardiorespiratory fitness; VO2max; Machine learning; ML, web-application;
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 1,700 participants (estimated)
- Lead sponsor
- Sykehuset i Vestfold HFUniversity, hospital or other
- Study start
- 30 May 2025 (actual)
- Primary completion
- 10 Dec 2027 (estimated)
- Study completion
- 24 Dec 2027 (estimated)
- First posted
- 8 Jun 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with obesity from Norway
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (1 listed)
- not obese
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No intervention (observational study)no intervention | Other | – |
Primary outcome
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
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jun 2025.
- Derived from this study: PubMed 41773679Berge J, Nunavath V, Asbjornsen RA, Borgeraas H, Hertel JK, Linge AD, Groote I, Stensrud T, Gjevestad E, Mathisen L, Caan MW, Paulson M, Stoa EM, Johansen JM; AI and VO2max Group; Singstad BJ. Machine Learning for Estimating Cardiorespiratory Fitness in Patients With Obesity: Protocol for a Retrospective and Prospective Multicenter Cohort Study. JMIR Res Protoc. 2026 Mar 2;15:e85069. doi: 10.2196/85069.
- Individual participant dataSharing planned: No. IPD will not be shared due to confidentiality concerns, lack of participant consent for data sharing, or institutional policy restrictions.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
Questions about this trial
What is this clinical trial studying?
An observational study of Obesity; Overweight, run by Sykehuset i Vestfold HF.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 8 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Machine learning model to estimate vo2max (time frame: The project will be completed in 2027). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07011108, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Jun 2025.