Objective: This study aimed to compare the effects of two aerobic exercise programs of different intensities on energy expenditure.Methods: This was a single‐center randomized controlled trial of patients with severe obesity allocated to a 24‐week moderate‐intensity continuous training (MICT) program or a combined MICT with high‐intensity interval training (HIIT/MICT) program. The primary outcome was energy expenditure during exercise (EEDE). Secondary outcomes included resting metabolic rate, cardiorespiratory fitness, and body composition.Results: A total of 82 (56% females) patients were screened, and 71 (55% females) patients were allocated to HIIT/MICT (n = 37) or MICT (n = 34). Per‐protocol analysis showed that EEDE increased by 10% (95% CI: 3%‐17%) in the HIIT/MICT group (n = 16) and 7.5% (95% CI: 4%‐10%) in the MICT group (n = 24), with no differences between groups. In the 8‐ to 16‐ week per‐protocol analysis, the HIIT/MICT group had a significantly larger increase in EEDE compared with the MICT group. Resting metabolic rate remained unchanged in both groups. HIIT/MICT and MICT were associated with significant weight loss of 5 kg and 2 kg, respectively.Conclusions: Patients completing a 24‐week combined HIIT/MICT program did not achieve a higher EEDE compared with those who completed a 24‐week MICT program. The HIIT/MICT group experienced, on average, a 3‐kg‐larger weight loss than the MICT group.

Background: Aerobic exercise is an important part of obesity treatment and may improve health-related quality of life (HRQOL). The objective of this study was to compare the effect of two different exercise programs on health-related quality of life in patients with severe obesity. Methods: This was a single-center, open-label, randomized, parallel-group study comparing the effects of a 24-week moderate-intensity continuous training (MICT) program and a combined high-intensity interval training program with MICT (HIIT/MICT). The primary objective (specified secondary outcome) was to assess HRQOL by using the general health dimension of the Short Form Health Survey (SF-36). The secondary objectives were to assess other dimensional SF-36 scores, the impact of weight on the physical and psychosocial aspects of quality of life (IWQOL-Lite), and the burden of obesity-specific weight symptoms (WRSM). Results: 73 patients were enrolled and reported patient reported outcome measures, with 71 patients (55% females) allocated to either MICT (n = 34) or HIIT/MICT (n = 37). In the intention-to-treat analysis, general health scores increased between baseline and 24-week follow-up in both the HIIT/MICT group and the MICT group, with a mean change of 13 (95% CI 6–21) points and 11 (95% CI 5–17) points, respectively, with no difference between the groups. The effect sizes of these changes were moderate. The vitality and social functioning scores of SF-36, and the physical function and self-esteem scores of IWQOL-Lite increased moderately in both groups, with no difference between groups. The tiredness, back pain, and physical stamina scores based on WRSM showed moderate to strong changes in both the groups. Conclusions: Patients who had completed a combined HIIT/MICT program did not experience larger improvements in general health compared with those completing a clean 24-week MICT program. Exercise may confer general health benefits independent of intensity. Trials registration: Regional Committees for Medical and Health Research Ethics south east, Norway, October 23, 2013 (identifier: 2013/1849) and ClinicalTrials.gov December 8, 2014 (identifier: NCT02311738).

To assess the association between cardiorespiratory fitness (CRF) and weight changes in treatment seeking patients with severe obesity who underwent a 1-year intensive lifestyle intervention (ILI) program. Retrospective cohort study conducted at a tertiary care outpatient rehabilitation center from November 1, 2013 through January 1, 2017. CRF was measured as maximal oxygen consumption during a maximal oxygen uptake (VO2max) test on a treadmill or bicycle at baseline and after 3 months. A total of 180 patients had a baseline mean (SD) BMI 41.1 (4.8) kg/m2 and CRF of 79.4 (14.9) mL·kg-0.75·min− 1. Patients with a baseline CRF above median achieved a greater 3-month and 1-year weight loss compared with patients with CRF below median; mean (95% CI) 2.5 kg (1.3, 3.8) and 4.0 kg (0.8, 7.2), respectively. In addition, patients with 3-month changes of CRF above median had 4.0 kg (0.9, 7.1) greater weight loss at 1-year follow-up than those below median. Among patients with severe obesity who underwent a 1-year ILI program, higher baseline CRF was associated with significantly larger weight loss after 3 months and 1 year. In addition, those with higher initial 3-month CRF changes had greater weight loss at 1 year. Retrospectively registered in Regional Committees for Medical and Health Research Ethics (REC) south east September 22, 2016 (2016/1414) and clinicaltials.gov August 13, 2018 (identifier: NCT03593798 ).

Background: Cardiorespiratory fitness (CRF) is a key predictor of cardiovascular and other health-related diseases in individuals with obesity. CRF is most accurately assessed through maximal exercise testing with advanced gas-analysis equipment (maximum volume of oxygen [VO _2max ]); however, this approach is time-consuming, costly, and requires specialized expertise. Therefore, submaximal tests and self-reported physical activity levels have been used to develop predictive algorithms to estimate CRF, yet they often performed poorly in individuals with low CRF levels, such as patients with obesity, because they are predominantly developed using data from healthy populations. Studies using machine learning (ML) models based on VO _2max data from patients with obesity appear to be lacking in the literature. ML models based on routinely collected clinical measures may offer a more practical and potentially accurate way to estimate CRF, reducing time, costs, and clinical burden. Objective: The primary aim of this study is to use multicenter, longitudinal, real-world clinical data from a uniquely characterized population with obesity to develop and validate a clinically relevant ML model for estimating CRF and to compare its performance with the gold standard of VO _2max testing. Methods: A retrospective data set combining assessments of VO _2max tests and clinical parameters from adult patients with severe obesity BMI (≥40.0 kg/m ² or 35.0‐39.9 kg/m ² with at least 1 obesity-related comorbidity) from Vestfold Hospital Trust, Muritunet Rehabilitation Institution, and Norwegian School of Sport Sciences will be the foundation for developing the ML model. The clinically relevant ML model for estimating CRF will be presented as a web application, allowing easy access and interaction. The model’s estimations will be compared against direct VO _2max measurements obtained from medical equipment across institutions as part of a prospective validation. Ethical approval has been obtained for the use of 2 databases in the initial model development; approval for the remaining data and prospective phase is pending. Results: Vestfold Hospital Trust, the Norwegian School of Sport Medicine, and Muritunet Rehabilitation Institution conducted more than 2623 VO _₂max tests and collected clinical parameters from 1279 adults with severe obesity during 2013-2025, both before, during, and after lifestyle interventions. The first scientific publication of the clinically relevant ML model is expected to be published in 2026. The results of the overall project are expected to be completed in 2028. The project was awarded salary funding in two rounds from Vestfold Hospital (October and December 2023) and salary funding from the Research Council of Norway (October 2024). In addition, the project received allocated supervision hours from InnoMed Norway (April 2024). Conclusions: This project aims to develop a clinically relevant ML model, which serves as a cost-effective tool for CRF estimation in individuals with obesity, improving accessibility to this important health marker. To our knowledge, this is the first initiative in Norway to estimate CRF in individuals with obesity using ML, based on a unique clinical database. The project carries substantial societal value and holds national and international relevance for health care practice and patient outcomes.

BACKGROUND Cardiorespiratory fitness (VO2max) is a key predictor of cardiovascular and other health-related diseases and is often impaired in individuals with obesity due to functional and structural limitations. Improving cardiorespiratory fitness enhances overall health and reduces mortality, making it an important indicator for preventing and treating obesity. OBJECTIVE The primary aim of this study is to develop an obesity-specific machine learning (ML) model that can accurately estimate VO2max, a key indicator of cardiovascular fitness, and make it accessible through a web-based application. METHODS A ML model will be trained to estimate VO2max in individuals with obesity using retrospective data combining assessments of VO2max tests and clinical parameters from adult patients with severe obesity body mass index (BMI ≥40.0 kg/m2, or 35.0-39.9 kg/m2 with at least one obesity-related comorbidity) from Vestfold Hospital Trust, Muritunet Rehabilitation Institution and Norwegian School of Sport Sciences. The ML-based VO2max estimation model will be presented as a web application, allowing easy access and interaction. The model’s estimations will be compared against direct VO2max measurements obtained from medical equipment across institutions as part of a prospective validation. Ethical approval has been obtained for the use of two databases in the initial model development; approval for the remaining data and prospective phase is pending. RESULTS Vestfold Hospital Trust, the Norwegian School of Sport Medicine, and Muritunet Rehabilitation have from 2013 to 2025 conducted > 2623 VO2max test and clinical parameters from 1,279 adults with severe obesity, both before, during and after lifestyle interventions. This comprehensive data set serves as the foundation for developing the ML model, which is presented through a user-friendly web application. A web application has been developed and will be tested with patients and healthcare personnel. The first scientific publication of the ML model is expected to be published in 2026. The results of the overall project are expected to be completed in 2028. CONCLUSIONS This project aims to develop a machine learning model, which serves as a cost-effective tool for VO2max estimation in individuals with obesity, improving accessibility to this important health marker. This is the first known attempt in Norway to estimate VO2max in an obese population using machine learning, based on a unique clinical database. The project holds significant societal value with potential national and international relevance for healthcare and patient outcomes. CLINICALTRIAL ClinicalTrials.gov (Identifier: NCT07011108)