Impact of a Health Coach–Led, Text-Based Digital Behavior Change Intervention on Weight Loss and Psychological Well-Being in Patients Receiving a Procedureless Intragastric Balloon Program: Prospective Single-Arm Study
Notice bibliographique
Résumé
BACKGROUND: Digital health interventions show promise for weight management. However, few text-based behavior change interventions have been designed to support patients receiving intragastric balloons, and none have simultaneously evaluated weight loss, psychological well-being, and behavior change despite the crucial interplay of these factors in weight management. OBJECTIVE: This study aims to assess whether a health coach-led, asynchronous, text-based digital behavior change coaching intervention (DBCCI) delivered to participants receiving an intragastric balloon and its aftercare program was feasible and acceptable to participants and supported improved outcomes, including weight loss, psychological well-being, and lifestyle behavior change conducive to weight loss maintenance. METHODS: receiving a procedureless intragastric balloon (PIGB) at 5 bariatric clinics in the United Kingdom and the Netherlands. Participants received the DBCCI and the clinic-led PIGB aftercare program (remotely delivered) for 6 months after PIGB placement and then no intervention for an additional 6 months. The DBCCI was an evidence-based, personalized intervention wherein health coaches supported participants via exchanged asynchronous in-app text-based messages. Over the 12-month study, we assessed percentage of total body weight loss and psychological well-being via self-administered validated questionnaires (Warwick-Edinburgh Mental Wellbeing Scale, Generalized Anxiety Disorder Scale, Impact of Weight on Quality of Life-Lite-Clinical Trials Version, Loss of Control Over Eating Scale-Brief, Weight Efficacy Lifestyle Questionnaire-Short Form, and Barriers to Being Active Quiz). Participant engagement with and acceptability of the intervention were assessed via self-reported surveys. RESULTS: ) were included in the analysis. Mean total body weight loss was 13.5% (SEM 2.3%) at the end of the DBCCI and 11.22% (SEM 2.3%) at the 12-month follow-up (P<.001). Improvements were observed for all psychological well-being measures throughout the 12 months except for the Generalized Anxiety Disorder Scale (improvement at month 1) and Barriers to Being Active Quiz (improvements at months 3 and 6). Surveys showed high levels of engagement with and acceptability of the DBCCI. CONCLUSIONS: This study provides evidence that the health coach-led, asynchronous, text-based DBCCI was engaging and acceptable to participants with overweight and obesity. The DBCCI, delivered alongside the PIGB and its aftercare program, supported improved weight loss outcomes and psychological well-being versus baseline and was associated with lifestyle behavior changes known to help achieve and maintain long-term weight loss and improved health outcomes. Follow-up findings suggest a potential need for longer-term, more intense coaching to focus on weight loss maintenance and support ongoing self-coaching. This could be achieved by leveraging generative artificial intelligence to provide ongoing automated behavior change coaching support to augment human-led care. TRIAL REGISTRATION: ClinicalTrials.gov NCT05884606; https://clinicaltrials.gov/study/NCT05884606.
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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 | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| 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 ».