Adapting a Self-Guided eHealth Intervention Into a Tailored Therapist-Guided eHealth Intervention for Survivors of Colorectal Cancer
Bibliographic record
Abstract
Unlabelled: Therapist-guided eHealth interventions have been shown to engage users more effectively and achieve better outcomes than self-guided interventions when addressing psychological symptoms. Building on this evidence, this viewpoint aimed to describe the adaptation of iConquerFear, a self-guided eHealth intervention targeting fear of cancer recurrence, into a therapist-guided version (TG-iConquerFear) tailored specifically for survivors of colorectal cancer (CRC). The goal was to optimize patient outcomes while minimizing the need for extensive resources. The adaptation process followed the Information System research framework, which facilitated a systematic integration of knowledge and iterative testing. Drawing on insights from the original iConquerFear development, as well as feedback from end users, oncologists, and therapists, we began by identifying areas for improvement. These insights formed the foundation for the first design cycle. Initial internal testing revealed the need for several adjustments to enhance the intervention. While the core concept of iConquerFear remained unchanged, we made significant modifications to improve access by optimizing the platform for mobile devices, to support adherence by expanding the exercises, and to equip therapists with tools such as reflective questions and a monitoring control panel. External field testing with 5 survivors of CRC provided further validation. Participants reported a high level of acceptability, and their feedback guided additional minor points to consider incorporating in future versions. This study illustrates how a self-guided eHealth intervention can be successfully adapted into a therapist-guided format for fear of cancer recurrence, tailored to meet the needs of survivors of CRC. The described approach serves as a valuable framework for integrating therapist guidance into similar interventions, ensuring their relevance and effectiveness for targeted populations.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".