College Freshmen Students’ Perspectives on Weight Gain Prevention in the Digital Age: Web-Based Survey
Notice bibliographique
Résumé
BACKGROUND: College freshmen are highly vulnerable to experiencing weight gain, and this phenomenon is associated with an increased risk of chronic diseases and mortality in older adulthood. Technology offers an attractive and scalable way to deliver behavioral weight gain prevention interventions for this population. Weight gain prevention programs that harness the appeal and widespread reach of Web-based technologies (electronic health or eHealth) are increasingly being evaluated in college students. Yet, few of these interventions are informed by college students' perspectives on weight gain prevention and related lifestyle behaviors. OBJECTIVE: The objective of this study was to assess college freshmen students' concern about weight gain and associated topics, as well as their interest in and delivery medium preferences for eHealth programs focused on these topics. METHODS: Web-based surveys that addressed college freshmen students' (convenience sample of N=50) perspectives on weight gain prevention were administered at the beginning and end of the fall 2015 semester as part of a longitudinal investigation of health-related issues and experiences in first semester college freshmen. Data on weight gain prevention-related concerns and corresponding interest in eHealth programs targeting topics of potential concern, as well as preferred program delivery medium and current technology use were gathered and analyzed using descriptive statistics. RESULTS: A considerable proportion of the freshmen sample expressed concern about weight gain (74%, 37/50) and both traditional (healthy diet: 86%, 43/50; physical activity: 64%, 32/50) and less frequently addressed (stress: 82%, 41/50; sleep: 74%, 37/50; anxiety and depression: 60%, 30/50) associated topics within the context of behavioral weight gain prevention. The proportion of students who reported interest in eHealth promotion programs targeting these topics was also generally high (ranging from 52% [26/50] for stress management to 70% [35/50] for eating a healthy diet and staying physically active). Email was the most frequently used electronic platform, with 96% (48/50) of students reporting current use of it. Email was also the most frequently cited preferred eHealth delivery platform, with 86% (43/50) of students selecting it. Facebook was preferred by the second greatest proportion of students (40%, 20/50). CONCLUSIONS: Most college freshmen have concerns about an array of weight gain prevention topics and are generally open to the possibility of receiving eHealth interventions designed to address their concerns, preferably via email compared with popular social media platforms. These preliminary findings offer a foundation to build upon when it comes to future descriptive investigations focused on behavioral weight gain prevention among college freshmen in the digital age.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».