Mindful Eating Mobile Health Apps: Review and Appraisal
Notice bibliographique
Résumé
BACKGROUND: Mindful eating is an emerging area of research for managing unhealthy eating and weight-related behaviors such as binge eating and emotional eating. Although there are numerous commercial mindful eating apps available, their quality, effectiveness, and whether they are accurately based on mindfulness-based eating awareness are unknown. OBJECTIVE: This review aimed to appraise the quality of the mindful eating apps and to appraise the quality of content on mindful eating apps. METHODS: A review of mindful eating apps available on Apple iTunes was undertaken from March to April 2018. Relevant apps meeting the inclusion criteria were subjectively appraised for general app quality using the Mobile App Rating Scale (MARS) guidelines and for the quality of content on mindful eating. A total of 22 apps met the inclusion criteria and were appraised. RESULTS: Many of the reviewed apps were assessed as functional and had moderate scores in aesthetics based on the criteria in the MARS assessment. However, some received lower scores in the domains of information and engagement. The majority of the apps did not teach users how to eat mindfully using all five senses. Hence, they were scored as incomplete in accurately providing mindfulness-based eating awareness. Instead, most apps were either eating timers, hunger rating apps, or diaries. Areas of potential improvement were in comprehensiveness and diversity of media, in the quantity and quality of information, and in the inclusion of privacy and security policies. To truly teach mindful eating, the apps need to provide guided examples involving the five senses beyond simply timing eating or writing in a diary. They also need to include eating meditations to assist people with their disordered eating such as binge eating, fullness, satiety, and craving meditations that may help them with coping when experiencing difficulties. They should also have engaging and entertaining features delivered through diverse media to ensure sustained use and interest by consumers. CONCLUSIONS: Future mindful eating apps could be improved by accurate adherence to mindful eating. Further improvement could be achieved by ameliorating the domains of information, engagement, and aesthetics and having adequate privacy policies.
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 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 | 0,000 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».