An open label feasibility study of a nutrition and exercise app-based solution in cirrhosis
Bibliographic record
Abstract
Background: Nutrition and exercise are the mainstay of therapy for the prevention and treatment of frailty in cirrhosis. This pilot study assessed feasibility of the online delivery of an app-based semi-supervised nutrition and exercise intervention in this population. Methods: The 11-week pilot recruited adults with cirrhosis who owned internet-connected devices. Patients were encouraged to participate in exercise sessions 3× per week including a combination of online group exercise (weekly) and home-based follow-along exercise (biweekly). They also participated in group nutrition classes (five sessions) and one-to-one exercise and nutrition check-ins delivered through the app. Primary outcome measures pertained to program feasibility: recruitment, retention, adherence, and satisfaction. Exploratory measures included physical performance (liver frailty index [LFI], 6-minute walk test [6MWT]), health behaviour domains, and quality of life. Results: Twenty three patients completed baseline measures. Of these, 18 (72%) completed end of study measures (mean MELD-Na, 9.2; female, 44.4%). Over 70% of participants fulfilled 75% or more of the feasibility criteria. Satisfaction with the program was high (mean, 89%). Exercise program modifications were required for 17 patients to accommodate health events or abilities. Exploratory evaluation showed improvement in the LFI and the 6MWT by -0.58-units (95% CI: -0.91 to -0.25) and 46.0 m (95% CI: 22.7-69.3) respectively without changes in quality of life or health behaviour domains. Conclusions: Outcomes demonstrate feasibility of the app-based delivery of programming with promising exploratory impact on efficacy for physical performance. Findings can guide the design of a large-scale app-based randomized controlled trials in cirrhosis.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".