Interactive Conversational Agents to Improve Dietary Behaviors for Health Promotion: Mixed Systematic Review
Notice bibliographique
Résumé
Background: Chronic diseases are the leading global cause of death, largely driven by Western lifestyles characterized by poor diets and physical inactivity. Digital interventions offer promising tools to support health behavior change. Interactive conversational agents (CAs) provide real-time, personalized meal planning and dietary advice. Their interactive nature and adaptability make them valuable for promoting healthy dietary behaviors in the context of diet-related chronic diseases. However, evidence of their effectiveness remains limited. Systematic evaluations of their impact, features, and user acceptability are needed to clarify their role in public health strategies for improving dietary behaviors and preventing chronic diseases. Objective: This review aimed to evaluate the effectiveness of CAs in improving dietary behaviors, to describe their features, functions, conversational capabilities, and impact on nutritional knowledge, usability, acceptability, user experience, and engagement. Methods: Five electronic databases were searched: MEDLINE, CINAHL, Embase, Web of Science, and PsycINFO. We only included sources that focused on the use of CAs to change dietary behavior. Eligible studies were published since 2013 in English, French, or Spanish. Two independent reviewers screened studies, with a third resolving disagreements. The quality of studies was appraised using the Mixed Methods Appraisal Tool (McGill University). Quantitative and qualitative findings were synthesized narratively. Results: In total, 2200 references were identified, and after screening and eligibility assessment, 11 references (10 studies with approximately 20-480 participants) were included. Among the included studies, improvements in fruit and vegetable intake were reported in 2 studies (P=.04 and P=.005). One study found significant increases in adherence to the Mediterranean diet at 6 weeks with gains maintained at 12 weeks. Two additional studies reported enhanced nutritional knowledge (eg, nutrition label use). Effects on protein, whole grains, sugar, sodium, and caffeine intake were mixed or nonsignificant. Some studies reported increased physical activity (+109.8 min/wk) and reduced alcohol use for stress management. One randomized controlled trial showed modest but significant weight loss and decrease in waist circumference (-2.1 cm, 95% CI -3.5 to 0.7; P=.003). Engagement varied between studies. Usability and user experience were generally positive; goal setting, feedback, and tailored recommendations were linked to higher satisfaction. Reported challenges included unnatural conversation style, simplistic content, and limited perceived usefulness. Conclusions: CAs show promising potential to improve dietary behaviors, with evidence of gains in fruit and vegetable intake, Mediterranean diet adherence, nutritional knowledge, physical activity, and modest weight loss. Overall usability was favorable, but variability in outcomes, high attrition, and limited impact on social support highlight areas for refinement. Future research should use larger samples, longer follow-up, standardized outcomes, and strategies to enhance sustained engagement and inclusivity. Systematic evaluations and refined designs are essential to establish the role of CAs as scalable, evidence-based tools in chronic disease prevention.
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 | 0,060 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,008 |
| Bibliométrie | 0,010 | 0,012 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».