Calorie-Counting Apps for Monitoring and Managing Calorie Intake in Adults Living With Weight-Related Chronic Diseases: Decade-Long Scoping Review (2013-2024) (Preprint)
Bibliographic record
Abstract
BACKGROUND Overweight and obesity, as defined by the World Health Organization, correspond to BMI values of 25-29.9 kg/m² for overweight and ≥30 kg/m² for obesity. Both conditions remain major public health challenges worldwide due to their strong link with type 2 diabetes, cardiovascular disease, and hypertension, which place a heavy clinical and economic burden on health care systems. In Canada, obesity rates are notably high, with vulnerable populations disproportionately affected due to socioeconomic barriers, limited access to preventive care, and higher comorbidity rates. Calorie-counting Mobile health (mHealth) apps support dietary self-monitoring and weight control; however, varied designs and evidence complicate assessment of feasibility and effectiveness. OBJECTIVE This study aimed to systematically evaluate the structure and content of 46 calorie-counting apps, identify factors related to their acceptability and feasibility among adults living with obesity or weight-related chronic diseases, and formulate evidence-based recommendations for app developers, clinicians, and researchers. METHODS We conducted a scoping review of papers on calorie-counting apps published between January 2013 and March 2024. We identified 771 records and applied the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) eligibility criteria. Data on app functions, features, user engagement, and acceptability and feasibility among adults with overweight or related chronic conditions were synthesized to generate practical recommendations for designing and clinically implementing calorie-counting apps. RESULTS A total of 68 studies met the inclusion criteria. Randomized controlled trials (23/68, 34%) and cohort studies (16/68, 24%) were the most common designs. Most studies targeted adults with overweight or obesity (53/68, 78%), while diabetes and hypertension were less frequently represented. In total, 46 distinct calorie-counting apps were identified, with MyFitnessPal and Lose It! being the most frequently studied. Nearly all apps (45/46, 98%) offered calorie logging, often through manual entry supported by food databases, and about half included goal-setting features. The most cited acceptability factors were personalization, automation, user-friendly design, and data sharing with health care professionals; barriers included technical issues, limited food databases, and manual entry. Adherence declined over time. For example, self-monitoring with MyFitnessPal decreased from 5.4 to 1.4 days per week from weeks 4 to 12, while use of Lose It! dropped to 4 days per week by the end of 12 weeks. Twelve recommendations were developed to enhance the feasibility and acceptability of calorie-counting apps for people living with weight-related chronic diseases. CONCLUSIONS Calorie-counting apps hold potential as tools for supporting individuals living with obesity and weight-related chronic diseases. To improve clinical usability, app developers should enhance engagement via personalization and automation, ensuring food database comprehensiveness, and minimizing tracking effort. Further research should validate effectiveness and strategies for sustaining adherence, thereby informing development of user-friendly mHealth interventions.
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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 | 0.015 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".