Acceptability and Effectiveness of a Fully Web-Based Nutrition and Exercise Program for Individuals With Chronic Disease During COVID-19: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: In-person nutrition and exercise interventions improve physical function in chronic diseases, yet the acceptability and effectiveness of web-based delivery, especially with different levels of personnel support, require further investigation. OBJECTIVE: This study aims to evaluate a web-based nutrition and exercise intervention delivered entirely digitally from recruitment to trial completion. METHODS: A randomized controlled trial was conducted using the Heal-Me version 1 platform across 2 levels of personnel support (Light and Intensive). Eligible adults with a history of cancer, chronic lung disease, or liver or lung transplant; internet access; and prior participation in a rehabilitation program were enrolled in a fully web-based program to minimize barriers to exercise participation. Participants were randomly assigned (1:1:1) to 1 of 3 study groups. The control group received a detailed, self-directed digital nutrition and exercise guide. The Heal-Me Light group received the web-based intervention alongside dietitian and exercise specialist-led group classes. The Heal-Me Intensive group received web-based intervention, group classes, and one-to-one sessions with the dietitians and exercise specialists. All participants received a wearable activity tracker. The primary acceptability outcome was adherence to the intervention based on a priori targets. The primary effectiveness outcome was the change in Lower Extremity Functional Scale (LEFS) score. Secondary outcomes included physical function tests, which were performed and measured by videoconference. Questionnaires were used to assess well-being, quality of life, and food intake. Analyses adhered to the intention-to-treat principle. RESULTS: Of 216 participants, 202 (93.5%) completed the intervention (mean 61, SD 11 years; female: 130/202, 64.4%; cancer: 126/202, 62.4%). Adherence exceeded a priori targets, with 82% (105/128) attending >75% of the program elements including postintervention tests. Participants rated the program as "quite a bit" or "very" useful, with similar ratings between Heal-Me Light (56/64, 88%) and Heal-Me Intensive (51/58, 88%) groups (P=.69). No significant differences were found for changes in LEFS scores (control: mean 0.8, SD 7.7; Heal-Me: mean 0.3, SD 6.6; P=.53). Significant benefits were found in favor of the combined Heal-Me intervention groups versus controls for change in the 2-minute step test, World Health Organization-5 Well-Being Index, Short-Form-36 general, physical health role, energy or fatigue scales, and protein intake. While the change in physical function was similar between the 2 intervention arms, the more intensive one-to-one interaction (Heal-Me Intensive) led to greater improvements in perceived nutrition self-management. No serious adverse events occurred. CONCLUSIONS: The demonstrated satisfaction, adherence, and effectiveness highlight the high acceptability of a web-based, semisupervised nutrition and exercise intervention delivered entirely digitally in individuals with chronic disease. Future studies may benefit from having a baseline physical function inclusion threshold, the use of a more sensitive primary physical function measure, and a higher intensity digital exercise intervention in exercise-experienced participants. TRIAL REGISTRATION: Clinicaltrials.gov NCT04666558; https://clinicaltrials.gov/study/NCT04666558. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1016/j.cct.2022.106791.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".