Behavioral Nudges to Enhance Fidelity in Telehealth Sessions (BENEFITS): Protocol for Developing and Pilot Testing a Telehealth Tool to Improve Cognitive Behavioral Therapy Implementation
Notice bibliographique
Résumé
BACKGROUND: The rapid expansion of telehealth provides a unique opportunity to integrate behavioral economics (BE) strategies into telehealth platforms to improve clinician fidelity to cognitive behavioral therapy (CBT)-either by enhancing clinicians' motivation to use CBT or by helping clinicians who are already motivated to act consistently on their intentions. OBJECTIVE: We will develop and evaluate "Tele-BE," a novel telehealth platform designed to nudge and incentivize clinicians to use core structural components of CBT. We focus on these structural components because they align with practices most likely to benefit from BE strategies, are delivered across diagnoses, and represent CBT competencies independently linked to improved patient outcomes. METHODS: We will refine the Tele-BE prototype in collaboration with clinicians and supervisors, who are the target end users (aim 1). Working closely with our web development team, we will field test and iteratively refine Tele-BE using rapid-cycle prototyping to optimize user experience and fine-tune the BE strategies (aim 2). The revised platform will then be evaluated in a 12-week open trial involving 30 community mental health clinicians, who will be randomized to either Tele-BE or telehealth as usual. Each clinician will deliver treatment to 2 patients, resulting in a total of 60 patient participants. All sessions will be recorded and coded to assess CBT fidelity. Clinicians and patients will complete questionnaires at weeks 1, 5, 9, and 12, with qualitative interviews conducted at the end of the trial. Primary outcomes will focus on fidelity to CBT structural components, measured via coding of recorded sessions. Secondary outcomes will include target implementation mechanisms-intentions and their determinants (attitudes, norms, and self-efficacy)-assessed using mixed methods, as well as overall CBT fidelity (aim 3). Additionally, trial data will be used to evaluate the acceptability and feasibility of Tele-BE from both patient and clinician perspectives, along with any potential ethical concerns associated with its use (aim 4). RESULTS: The study received National Institute of Mental Health funding in June 2024. Recruitment for aim 1 began in October 2024. As of March 2025, 6 participants had been enrolled in the initial development stage. Recruitment is ongoing, and we anticipate completing aim 1 by May 2025, after which we will prepare for aim 2 activities. We aim to complete all study data collection by the end of 2026. In accordance with our grant award, deidentified data from aims 3 and 4 will be submitted to the National Institute of Mental Health Data Archive for participants who consent to data sharing. CONCLUSIONS: Findings will provide insight into the utility of a BE-informed telehealth platform for increasing clinicians' use of core structural CBT components, thereby improving overall CBT fidelity and patient outcomes. Results will also inform the design of future confirmatory trials. TRIAL REGISTRATION: ClinicalTrials.gov NCT06601062; https://clinicaltrials.gov/ct2/show/NCT06601062. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/76035.
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 | 0,053 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,048 | 0,010 |
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 ».