Long-Term Feasibility and Outcomes of a Digital Health Program to Improve Liver Fat and Cardiometabolic Markers in Individuals With Nonalcoholic Fatty Liver Disease: Prospective Single-Arm Feasibility Study
Bibliographic record
Abstract
Background: A 12-week digital health program for nonalcoholic fatty liver disease (NAFLD) previously showed feasibility in engagement, program retention, and clinical outcomes. This study investigates whether improvements in cardiometabolic risk factors achieved during a 12-week active program were sustained over a subsequent 6-month follow-up period. Objective: The primary objective of this analysis was to evaluate whether the clinical improvements achieved after a 12-week program were maintained over the subsequent 6-month period, which did not include coaching or new intervention materials. In addition, the study aimed to assess participants' retention and engagement with the maintenance program. Methods: In a 9-month, single-arm study using the Sidekick app (Sidekick Health), individuals with NAFLD and BMI >30 or metabolic syndrome or type 2 diabetes were included. The initial 12 weeks focused on providing education about diet, physical activity, stress management, and sleep, followed by 6 months without coaching or new intervention materials. The measured outcomes encompassed demographics, body composition, liver fat assessed using magnetic resonance imaging-proton density fat fraction (MRI-PDFF), and blood markers. Results: Of the 34 participants who completed the first 12 weeks, 28 (82%) completed the 9-month study measurements. The median age was 63.0 years (IQR 53.5-71.0) and 57.1% (16/28) were women. At 9 months, compared to baseline, the mean weight loss was 4.0 kg (SD 5.0; P<.001). Liver fat decreased by 2.5% (SD 4.5; P<.001), with an 18.4% relative reduction. Systolic blood pressure decreased by 8.3 mm Hg (SD 13.4, P<.001) and diastolic by 2.5 mm Hg (SD 6.0; P=.02). Waist circumference decreased by 4.7 cm (SD 7.1; P<.001) and median glycated hemoglobin A1c (HbA1c) decreased by 19.5 mmol/mol (P<.001). Conclusions: Sustained improvements in liver fat and metabolic markers suggest that Sidekick Health's digital program is a promising strategy for managing NAFLD without requiring continuous coaching.
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 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".