Utilization of a Directly Supervised Telehealth-Based Exercise Training Program in Patients With Nonalcoholic Steatohepatitis: Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Most patients with nonalcoholic fatty liver disease (NAFLD) are physically inactive despite the well-known benefits of physical activity. Telehealth offers promise as a novel way to deliver an exercise training program and increase physical activity. However, the feasibility, safety, and efficacy of telehealth-based exercise programs is unknown in patients with NAFLD. OBJECTIVE: The aim of this study was to determine the feasibility of a directly supervised exercise training program delivered exclusively with telehealth to patients with nonalcoholic steatohepatitis (NASH), the progressive form of NAFLD. METHODS: In response to COVID-19 research restrictions, we adapted an existing clinical trial and delivered 20 weeks of moderate-intensity aerobic training 5 days a week under real-time direct supervision using an audio-visual telehealth platform. Aerobic training was completed by walking outdoors or using a home treadmill. Fitness activity trackers with heart rate monitors ensured exercise was completed at the prescribed intensity with real-time feedback from an exercise physiologist. RESULTS: Three female patients with biopsy-proven NASH were enrolled with a mean age of 52 (SD 14) years. The mean body mass index was 31.9 (SD 5.1) kg/m2. All patients had metabolic syndrome. All patients completed over 80% of exercise sessions (mean 84% [SD 3%]) and no adverse events occurred. Body weight (mean -5.1% [SD 3.7%]), body fat (mean -4.4% [SD 2.3%]), and waist circumference (mean -1.3 in. [SD 1.6 in.]) all improved with exercise. The mean relative reduction in magnetic resonance imaging-proton density fat fraction (MRI-PDFF) was 35.1% (SD 8.8%). Mean reductions in hemoglobin A1c and Homeostatic Model Assessment for Insulin Resistance were also observed (-0.5% [SD 0.2%] and -4.0 [SD 1.2], respectively). The mean peak oxygen consumption (VO2peak) improved by 9.9 (SD 6.6) mL/kg/min. CONCLUSIONS: This proof-of-concept study found that supervised exercise training delivered via telehealth is feasible and safe in patients with NASH. Telehealth-based exercise training also appears to be highly efficacious in patients with NASH, but this will need to be confirmed by future large-scale trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT03518294; https://clinicaltrials.gov/ct2/show/NCT03518294.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".