Effect of Minimal Individual or Group Enhancement in an eHealth Program on Mental Health, Health Behavior, and Work Ability in Employees With Obesity: Randomized Controlled Trial
Notice bibliographique
Résumé
Background: Mental health problems and adverse health behaviors are enriched in individuals with obesity and need to be considered in weight loss interventions. Regarding weight loss, hybrid interventions combining digital and in-person elements have proven superior to eHealth-only interventions. However, it remains unclear whether minimal group or individual enhancement could bring additional benefits to the mental health and health behavior domains in individuals with obesity. Objective: This study aimed to explore whether minimal group or individual enhancements could offer additional benefits to an eHealth intervention in relation to mental health, perceived work ability, and health behavior in a sample of occupational health patients with obesity. In addition, the study sought to examine the overall effects of the health behavior-focused intervention across these domains. Methods: This study was a randomized controlled trial with a 12-month intervention (March 2021-2022), with selected variables followed for another 12 months without additional support. Recruited from occupational health care, 111 working-age adults with BMI 30-40 kg/m2 were randomized to one of the 3 treatment arms, such as eHealth, eHealth+Group, or eHealth+Individual. All treatment arms received a web-administered, coach-assisted eHealth program based on acceptance and commitment therapy, and, in addition, the eHealth+Group and eHealth+Individual arms received 3 remotely facilitated group or individual meetings. The overall intervention effects were evaluated quasi-experimentally by combining the 3 treatment arms into a single eHealth arm. Participants were assessed for depression (the Beck Depression Index 21 [BDI-21]), burnout (Bergen Burnout Inventory 18 [BBI-15]), perceived work ability, eating behavior (ecSatter 2.0, Three Factor Eating Questionnaire [TFEQ], and Binge Eating Scale [BES]), and physical activity (Baecke Habitual Physical Activity Questionnaire, BHPAQ). Results: We observed inconsistent fluctuations between the treatment arms in depression and burnout scores, indicating a lack of meaningful intervention effects despite statistical significance. Therefore, none of the treatment arms showed superiority over another. Across all participants, depression showed an estimated mean decrease of 2.5 BDI-21 points, with older participants experiencing a greater reduction in depressive symptoms. Furthermore, binge eating tendency decreased by 4.9 BES points during the 12-month intervention. We also observed increases in eating competence, controlled restraint, and physical activity. However, the 24-month measurements showed an adverse effect on eating competence, especially on attitudes toward eating and food, during the follow-up period without further support. Conclusions: Minimal enhancement through either group or individual video-conference meetings did not provide additional benefits in the mental health or eating habit domains compared with the eHealth intervention alone. Nevertheless, our results indicate that eHealth interventions for weight loss have the potential to reduce depression symptoms and binge-eating tendencies, while also improving eating competence and physical activity across the study population. Continued support may be necessary to sustain positive changes.
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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 | 0,003 | 0,004 |
| 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 | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».