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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".