Calorie-Counting Apps for Monitoring and Managing Calorie Intake in Adults Living With Weight-Related Chronic Diseases: Decade-Long Scoping Review (2013-2024) (Preprint)
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,062 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,010 |
| Bibliométrie | 0,011 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».