Efficacy of a Web-Based Executive Function Training Program to Induce Healthier Food Choices and Increase Valuation of Fruits and Vegetables in Adults: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Executive function (EF) is a set of explicit (top-down) cognitive abilities theorized to assist in conscious control of eating behavior. However, it is unclear whether EF training can improve valuation and consumption of nutrient-dense foods while concurrently altering attitudes toward and perceptions of nutrient-poor foods and their consumption. OBJECTIVE: The primary objective is to determine the efficacy of a web-based EF training program designed to concurrently improve attitudes toward and perceptions and consumption of nutrient-dense foods (fruits and vegetables) while worsening those of nutrient-poor foods (ie, candy and fried snacks). METHODS: . Potential participants are recruited through an open link to a prescreening questionnaire. Qualified participants are sent an electronic version of the informed consent form to sign. After consenting, they are invited to create an account on our semiautomated web-based platform and randomized to EF training via a suite of 4 web-based games that promote responding to fruits and vegetables, inhibit responses to nutrient-poor foods, and train attention toward the former and away from the latter or sham training that involves images of flowers and songbirds (control). Training occurs at least twice per week. EF is tested before and after 8 weeks of training. Dietary intake is measured before and after the 8-week intervention. Group and sex differences in frequency of participants violating inhibitory signals will be assessed using log-linear models. Linear mixed-effects models will test whether web-based EF training improves EF as measured via response inhibition and attention during computer-based gameplay. The independent and interactive associations between inhibitory and attentional learning and consumption of nutrient-poor foods will be tested using mixed model regression. If random assignment results in baseline imbalances across conditions on any variables, they will be used as covariates in the mixed models. We will use both an intention-to-treat analysis using (restricted) maximum likelihood estimation and a completer analysis to understand the results from those participants who completed the training at least twice per week. RESULTS: As of June 1, 2025, overall, 899 individuals have applied to participate in the study. Of 899 applicants, 763 (84.9%) qualified and 630 (70.1%) consented. There have been 386 withdrawals due to noncompliance with study requirements (n=342, 88.6%) or identification as a bot (n=35, 9.1%). CONCLUSIONS: The association between improved EF and healthy eating behaviors may be key to improving diet quality. If effective, this web-based EF training program will provide a platform that can be made widely available to aid individuals in food-related decision-making. The platform could be modified to target other food choices and other health behaviors, such as reducing sedentariness and increasing physical activity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05938894; https://clinicaltrials.gov/study/NCT05938894. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/66394.
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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 | 0,016 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,006 | 0,003 |
| Méta-épidémiologie (sens large) | 0,012 | 0,005 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,060 | 0,010 |
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 ».