Barriers to Exercise in Patients With Metabolic Dysfunction-Associated Steatotic Liver Disease: A Patient Survey
Bibliographic record
Abstract
Background: Although adequate physical activity is an essential component of treatment for metabolic dysfunction-associated steatotic liver disease (MASLD), the majority of people with MASLD do not engage in regular exercise and lead sedentary lifestyles. We aimed to identify perceived barriers to exercise and to examine awareness about the role of exercise in the treatment of MASLD. Methods: Individuals aged 18 years and above were recruited from a hepatology outpatient clinic. MASLD severity was assessed using controlled attenuation parameter (CAP) and transient elastography (TE) determined liver stiffness measurement (LSM) for the severity of hepatic steatosis and fibrosis, respectively. An online questionnaire was administered to record self-reported exercise patterns, barriers to exercise, and knowledge regarding effectiveness of different types of exercise for MASLD. Results: Eighty-one participants (57% female) with a mean age of 55.3 ± 13.4 years and a mean body mass index (BMI) of 33.8 ± 6.4 answered the questionnaire. The mean CAP score was 335.7 ± 47.8 dB/m, and the median LSM was 12.45 kPa. While most patients (83%) considered MASLD to be a serious health concern, 73% did not achieve the recommended exercise levels of ≥ 150 min of moderate-intensity physical activity per week, and 54% were unsure about the role of exercise in the treatment of MASLD. Commonly reported barriers to exercise included physical and mental health issues (57%), lack of time (43%), lack of enjoyment in exercising (31%), fatigue caused by exercise (24%), and others (25%). Conclusions: Most participants with MASLD were unaware of the role of exercise as a potential treatment option and were not achieving recommended exercise levels. Inadequate time, physical and mental health problems, lack of enjoyment in exercise, and fatigue were major barriers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".