The Effect of Nutritional Mobile Apps on Populations With Cancer: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Limited access to nutrition support among populations with cancer is a major barrier to sustainable and quality cancer care. Increasing use of mobile health in health care has raised concerns about its validity and health impacts. OBJECTIVE: This systematic review aimed to determine the effectiveness of commercial or cancer-specific nutritional mobile apps among people living with cancer. METHODS: A systematic search of the CENTRAL, Embase, PubMed (MEDLINE), and Scopus databases was carried out in May 2024. All types of intervention studies were included, except observational studies, gray literature, and reference lists of key systematic reviews. Studies were eligible for inclusion if they involved (1) patients with or survivors of cancer and (2) nutrition-related mobile apps. Studies were excluded if the nutrition intervention was not delivered via mobile app or the app intervention was accompanied by dietary counseling. The review process was conducted based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The Risk of Bias 2 and Risk of Bias in Nonrandomized Studies tools were used to assess the study quality. The Cochrane Review Manager (version 5.4) software was used to synthesize the results of the bias assessment. RESULTS: A total of 13 interventions were included, comprising 783 adults or teenagers with cancer. Most studies focused on breast cancer (6/13, 46%), overweight (6/13, 46%), and survivors (9/13, 69%). Data on anthropometry and body composition (7/13, 54%; 387 participants), nutritional status (3/13, 23%; 249 participants), dietary intake (7/13, 54%; 352 participants), and quality of life (6/13, 46%; 384 participants) were gathered. Experimental groups were more likely to report significant improvements in body weight or composition, dietary compliance, nutritional status, and quality of life than control groups. CONCLUSIONS: Although mobile app platforms are used to deliver nutrition interventions, the evidence for long-term efficacy, particularly in populations with cancer, remains elusive. More robust randomized controlled trials with larger sample sizes, as well as more homogeneous population characteristics and outcome measures, are warranted. TRIAL REGISTRATION: PROSPERO CRD42023330575; https://tinyurl.com/55v56yaj.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".