Digital Cognitive Behavioral- and Mindfulness-Based Stress-Management Interventions for Survivors of Breast Cancer: Development Study
Bibliographic record
Abstract
BACKGROUND: Psychosocial stress-management interventions can reduce stress and distress and improve the quality of life for survivors of cancer. As these in-person interventions are not always offered or accessible, evidence-informed digital stress-management interventions may have the potential to improve outreach of psychosocial support for survivors of cancer. Few such digital interventions exist so far, few if any have been developed specifically for survivors of breast cancer, and few if any have attempted to explore more than 1 distinct type of intervention framework. OBJECTIVE: This study aimed to develop 2 digital psychosocial stress-management interventions for survivors of breast cancer; 1 cognitive behavioral therapy-based intervention (CBI), and 1 mindfulness-based intervention (MBI). METHODS: The development of the CBI and MBI interventions originated from the existing StressProffen program, a digital stress-management intervention program for survivors of cancer, based on a primarily cognitive behavioral therapeutic concept. Development processes entailed a multidisciplinary design approach and were iteratively conducted in close collaboration between key stakeholders, including experts within psychosocial oncology, cancer epidemiology, stress-management, and eHealth as well as survivors of breast cancer and health care providers. Core psychosocial oncology stress-management and cancer epidemiology experts first conducted a series of workshops to identify cognitive behavioral and mindfulness specific StressProffen content, overlapping psychoeducational content, and areas where development and incorporation of new material were needed. Following the program content adaptation and development phase, phases related to user testing of new content and technical, privacy, security, and ethical aspects and adjustments ensued. Intervention content for the distinct CBI and MBI interventions was refined in iterative user-centered design processes and adjusted to electronic format through stakeholder-centered iterations. RESULTS: For the CBI version, the mindfulness-based content of the original StressProffen was removed, and for the MBI version, cognitive behavioral content was removed. Varying degrees of new content were created for both versions, using a similar layout as for the original StressProffen program. New content and new exercises in particular were tested by survivors of breast cancer and a project-related editorial team, resulting in subsequent user centered adjustments, including ensuring auditory versions and adequate explanations before less intuitive sections. Other improvements included implementing a standard closing sentence to round off every exercise, and allowing participants to choose the length of some of the mindfulness exercises. A legal disclaimer and a description of data collection, user rights and study contact information were included to meet ethical, privacy, and security requirements. CONCLUSIONS: This study shows how theory specific (ie, CBI and MBI) digital stress-management interventions for survivors of breast cancer can be developed through extensive collaborations between key stakeholders, including scientists, health care providers, and survivors of breast cancer. Offering a variety of evidence-informed stress-management approaches may potentially increase interest for outreach and impact of psychosocial interventions for survivors of cancer. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/47195.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".