The Phased Implementation of a National Telehealth Weight Management Program for Veterans: Mixed-Methods Program Evaluation
Notice bibliographique
Résumé
BACKGROUND: The burden of obesity is high among US veterans, yet many face barriers to engaging in in-person, facility-based treatment programs. To improve access to weight-management services, the Veterans Health Administration (VHA) developed TeleMOVE, a home-based, 82-day curriculum that utilizes in-home messaging devices to promote weight loss in VHA patients facing barriers to accessing facility-based services. OBJECTIVE: The primary aim was to establish preliminary evidence for the program by comparing outcomes for TeleMOVE with standard, facility-based MOVE weight-management services (group, individual modalities) over the evaluation period based on the number of patients enrolled per site and the program's clinical effectiveness, as demonstrated by average weight lost per patient. The secondary aim was to understand factors influencing TeleMOVE implementation variability across demonstration sites to develop recommendations to improve national program dissemination. METHODS: We employed a formative mixed-methods design to evaluate the phased implementation of TeleMOVE at 9 demonstration sites and compare patient- and site-level measures of program uptake. Data were collected between October 1, 2009 and September 30, 2011. Patient-level program outcomes were extracted from VHA patient care databases to evaluate program enrollment rates and clinical outcomes. To assess preliminary clinical effectiveness, weight loss outcomes for veterans who enrolled in TeleMOVE were compared with outcomes for veterans enrolled in standard MOVE! at each demonstration site, as well as with national averages during the first 2 years of program implementation. For the secondary aim, we invited program stakeholders to participate in 2 rounds of semistructured interviews about aspects of TeleMOVE implementation processes, site-level contextual factors, and program delivery. Twenty-eight stakeholders participated in audio-recorded interviews. RESULTS: Although stakeholders at 3 sites declined to be interviewed, objective program uptake was high at 2 sites, delayed-high at 2 sites, and low at 5 sites. At 6 months post enrollment, the mean weight loss was comparable for TeleMOVE (n=417) and MOVE! (n=1543) participants at -5.2 lb (SD 14.4) and -5.1 lb (SD 12.2), respectively (P=.91). All sites reported high program complexity because TeleMOVE required more staff time per participant than MOVE! due to logistical and technical assistance issues related to the devices. High-uptake sites overcame implementation challenges by leveraging communication networks with stakeholders, adapting the program to patient needs whenever possible, setting programmatic goals and monitoring feedback of results, and taking time to reflect and evaluate on delivery to foster incremental delivery improvements, whereas low-uptake sites reported less leadership support and effective communication among stakeholders. CONCLUSIONS: This implementation evaluation of a clinical telehealth program demonstrated the value of partnership-based research in which researchers not only provided operational leaders with feedback regarding the effectiveness of a new program but also relevant feedback into contextual factors related to program implementation to enable adaptations for national deployment efforts.
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 | 0,003 | 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) | 0,000 | 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 ».