Leveraging Implementation Science at the Early-Stage Development of a Novel Telehealth-Delivered Fear of Exercise Program to Understand Intervention Feasibility and Implementation Potential: Feasibility Behavioral Intervention Study
Notice bibliographique
Résumé
BACKGROUND: To increase real-world adoption of effective telehealth-delivered behavioral health interventions among midlife and older adults with cardiovascular disease, incorporating implementation science (IS) methods at earlier stages of intervention development may be needed. OBJECTIVE: This study aims to describe how IS can be incorporated into the design and interpretation of a study assessing the feasibility and implementation potential of a technology-delivered behavioral health intervention. METHODS: We assessed the feasibility and implementation potential of a 2-session, remotely delivered, home-based behavioral intervention composed of psychoeducation, interoceptive exposure through low-to-moderate intensity walking, interoceptive counseling, and homework (Reducing Exercise Sensitivity with Exposure Training; RESET) among patients with recent acute coronary syndrome (ACS) and some fear of exercise. To assess intervention feasibility, we measured patient protocol adherence, intervention delivery fidelity, and completion of intervention outcome assessments using direct observations, fidelity checklists, surveys, and device-measured physical activity. To assess implementation potential, we measured implementation outcomes (feasibility, acceptability, and appropriateness) using 4-item measures, each rated from the patient perspective on a 1 to 5 Likert scale (1=completely disagree and 5=completely agree; criteria: ≥4=agree or completely agree), and patient-perceived implementation determinants and design feedback using survey and interview data. Interview data underwent thematic analysis to identify implementation determinant themes, which were then categorized into Consolidated Framework for Implementation Research (CFIR) domains and constructs. RESULTS: Of 31 patients approached during recruitment, 3 (10%) were eligible, enrolled, and completed the study (mean age 46.3, SD 14.0 y; 2/3, 67% male; 1/3, 33% Black; and 1/3, 33% Asian). The intervention was delivered with fidelity for all participants, and all participants completed the entire intervention protocol and outcome assessments. On average, participants agreed that the RESET intervention was feasible and acceptable, while appropriateness ratings did not meet implementation criteria (feasibility: mean 4.2, SD 0.4; acceptability: mean 4.3, SD 0.7; and appropriateness: mean 3.7, SD 0.4). Key patient-perceived implementation determinants were related to constructs in the innovation (design, adaptability, and complexity), inner setting (available resources [physical space, funding, materials, and equipment] and access to knowledge and information), and innovation recipient characteristics (motivation, capability, opportunity, and need) domains of the CFIR, with key barriers related to innovation design. Design feedback indicated that the areas requiring the most revisions were the interoceptive exposure design and the virtual delivery modality, and reasons why included low dose and poor usability. CONCLUSIONS: The RESET intervention was feasible but not implementable in a small sample of patients with ACS. Our theory-informed, mixed methods approach aided our understanding of what, how, and why RESET was not perceived as implementable; this information will guide intervention refinement. This study demonstrated how integrating IS methods early in intervention development can guide decisions regarding readiness to advance interventions along the translational research pipeline.
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 | 0,066 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».