Incorporating a Static Versus Supportive Mobile Phone App Into a Partial Meal Replacement Program With Face-to-Face Support: Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Mobile phone apps may be acceptable to users and could improve retention and adherence over more traditional methods, but there is mixed literature supporting their efficacy. In the weight management space, very little is known about how a mobile phone app integrating features beyond text messaging (short message service) can affect behavior, particularly when combined with face-to-face support. OBJECTIVE: The objective of this study was to examine the effectiveness of a mobile phone app when combined with a partial meal replacement program including face-to-face support. This paper compares a static versus supportive app over a 6-month randomized trial for effects on weight loss, weight-related biomarkers, and psychological outcomes. METHODS: Overweight and obese adults (71.2% female, 104/146; mean 48.11, SD 11.75 years) were recruited to participate in the weight loss study, and they were randomized on a 1:1 basis using a computer algorithm. The supportive app (n=75) provided information, food intake recording, rewards, prompts for regular interaction through reminders, and the opportunity to review personal compliance with the dietary program. The static app (n=71) included only recipes and weight loss information. Both groups recieved equal amounts of face-to-face support in addition to app. RESULTS: The overall reduction in app usage over 24 weeks was lower for the supportive app in comparison with the static app; approximately 39.0% (57/146) of the users were still using the app at week 24. Despite the promising results for app usage, there were no differences in weight loss between groups (F1,128.12=0.83, P=.36). However, it should be noted that almost 60% (49/84) of all participants lost 5% or more of body weight during the trial. No weight-related biomarkers were significantly different between groups. Both groups experienced an increase in positive mood, but this was significantly higher for those who received the static app (F1,118.12=4.93, P=.03). CONCLUSIONS: Although the supportive app was well received by users, we found little evidence of the added benefit of this versus the static app in combination with face-to-face support in a community-delivered weight loss program. Future versions of the app may incorporate more unique behavioral techniques beyond those provided by the consultant to improve the potency of the app. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12613000547741; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=364187 (Archived by WebCite http://www.webcitation.org/6yivwfMI9).
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 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 ».