Smartphone Apps and Wearables for Health Parameters in Young Adulthood: Cross-Sectional Study
Notice bibliographique
Résumé
Background: Fostering innovative and more effective interventions to support active aging strategies from youth is crucial to help this population adopt healthier lifestyles using technologies they are already familiar with. Mobile health (mHealth), particularly apps and wearables, represents a promising approach due to its versatility, ease of use, and ability to monitor multiple health variables simultaneously. Moreover, these devices offer opportunities for personalization and support in health behavior change, making them valuable tools for shaping healthy habits from a young age. Objective: This study aims to (1) investigate whether young adults (18-26 years old) use apps or wearables to monitor or improve their health variables (ie, physical activity, diet, and mental health); (2) examine how they use them; (3) identify the most commonly used apps and wearables and the most frequently monitored health variables across these domains; and (4) evaluate the importance of different characteristics and functions of apps and wearables for health purposes. Methods: This cross-sectional study used a public involvement framework to enhance the research quality and was conducted through an anonymous web survey disseminated across Italy over a 3-month period. The survey consisted of 5 sections: (1) demographics, (2) mobile apps and wearable devices for physical activity and sports, (3) mobile apps and wearable devices for diet, (4) mobile apps and wearable devices for mental health, and (5) preferences regarding mobile apps and wearable devices. Participants were eligible if they were young adults who reported using at least one app or wearable device to monitor at least one health variable (eg, steps, training, sleep, calorie intake). No additional eligibility criteria were applied. Results: A total of 693 questionnaires were analyzed for aims 1 and 4, with the sample showing an equal gender distribution (females: 363/693, 52.4%). For aims 2 and 3, a total of 317 questionnaires were included. Participants using an app or wearable for physical activity accounted for 320 (46.2%), while 60 (8.7%) and 156 (22.5%) reported use for diet and mental health, respectively. Moreover, the frequency of use was predominantly on a daily basis, particularly for wearables. The app and wearable characteristics identified as most important were user-friendliness, free access to content, loading speed, and icon clarity. Conclusions: Findings suggest that Italian young adults, particularly women, predominantly use wearables over apps to track health data, with both being checked on a daily basis. Physical activity is the most frequently monitored domain, likely due to its ease of tracking, while diet and mental health receive less attention. Overall, these tools are used more for monitoring than for actively improving health-related variables. The most valued characteristics identified by young adults include ease of use, free access to all content, and fast loading speed. These insights should guide the design and refinement of digital health interventions targeting this population.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».