Factors Influencing Nutritional Intake and Interests in Educational Content of Athletes and Sport Professionals Toward the Development of a Clinician-Supported Mobile App to Combat Relative Energy Deficiency in Sport: Formative Research and a Description of App Functions
Notice bibliographique
Résumé
BACKGROUND: Relative energy deficiency in sport (RED-S) as a consequence of athlete malnutrition remains a prominent issue. However, it remains underrecognized, in part due to the perceived outward health of athletes. The Eat2Win app was designed to combat RED-S and athlete malnutrition by providing education, behavior modification, and direct communication with expert sports dietitians to athletes and sport professionals (professionals who work with athletes, eg, sport coaches and athletic trainers). OBJECTIVE: The purpose of this formative research was to gain critical insight on motivators and barriers to optimal nutritional intake from both the athletes' and sport professionals' perspectives. Additionally, since these 2 groups represent the primary end users of an app aimed at improving athlete nutrition and reducing the risk of RED-S, a secondary objective was to gain insight on the preferences and perceptions of app-based educational content and functionality. METHODS: An electronic survey was developed by an interdisciplinary team of experts. Survey questions were established based upon prevailing literature, professional dietetic field experience, and app design considerations to obtain respondent knowledge on key sports nutrition topics along with motivations and barriers to meal choices. Additionally, the survey included questions about the development of an integrative, clinician-support app aimed at addressing RED-S. These questions included preferences for educational content, modes of in-app information, and communication delivery for the target population (app end users: athletes and sport professionals). The survey was distributed through Research Electronic Data Capture (REDCap) to athletes and sport professionals using targeted email, social media, and community engagement campaigns. The electronic survey was available from May 4 to August 2, 2022. RESULTS: Survey respondents (n=1352) included athletes and professionals who work with athletes from a variety of settings, like high school, collegiate, professional, and club sports. Respondents reported high interest in 8 core sports nutrition topics. The preferred modes of information and communication delivery were visual formats (eg, videos and infographics) and in-app alerts (eg, direct messaging and meal reminders). Only athlete respondents were asked about motivators and barriers that influence meal choices. "Health" and "sports performance" were the highest scoring motivators, while the highest scoring barriers were "cost of food," "easy access to unhealthy food," and "time to cook or prepare food." Notably, survey respondents provided positive feedback and interest using a novel function of the app: real-time meal feedback through food photography. CONCLUSIONS: The Eat2Win app is designed to combat RED-S and athlete malnutrition. Results from this study provide critical information on end-user opinions and preferences and will be used to further develop the Eat2Win app. Future research will aim to determine whether the Eat2Win app can prevent RED-S and the risk of athlete malnutrition to improve both health and performance.
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 | 0,014 | 0,056 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».