Patient engagement strategies in digital health interventions for cancer survivors: A scoping review
Bibliographic record
Abstract
Individuals can face various mental and physical health challenges after a cancer diagnosis. Digital health platforms can address some of these challenges by providing self-management tools for improving lifestyle behaviors, while reducing the burden on healthcare systems and enhancing healthcare access to underserved populations. Involving individuals with a history of cancer, termed here as "cancer survivors", in the development and evaluation of digital health platforms can improve their effectiveness. This scoping review aimed to explore the state of patient engagement in research on digital health platforms for cancer survivors, including strategies for engagement, characteristics, and identifying gaps and barriers. A systematic search was conducted in OVID Medline, OVID EMBASE, and Scopus from inception until May 2023. The review followed Joanna Briggs Institute's guidance for scoping reviews. Eligible studies actively involved cancer survivors in the development or evaluation of digital health platforms. These studies focused on self-management digital health platforms delivering nutrition, physical activity, and/or mental health interventions. Reporting of patient engagement was evaluated according to the Guidance for Reporting Involvement of Patients and the Public 2 (GRIPP2). The search strategy captured 7 studies using various patient engagement approaches, with patient and public involvement being the most frequently used (43%, n = 3). Studies were conducted in 6 countries and most focused on the development or evaluation of web-based digital health platforms (71%, n = 5). Few studies reported all elements of GRIPP2's reporting checklist (29%, n = 2). We further identified barriers and areas of improvement for patient engagement in digital health research. Patient engagement improves digital health platforms, but few studies have meaningfully included patients, therefore reporting and evaluation of patient engagement is necessary to support its adoption in digital health research projects. In addition to exploring the gaps in patient engagement practices, this scoping review serves as a foundation for future research to advance patient-oriented digital health interventions for cancer survivors.
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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 | 0.032 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".