Pain Management in Cancer Patients: The Effectiveness of Digital Game-based Interventions: A Rapid Literature Review
Bibliographic record
Abstract
OBJECTIVES: Pain is a common side effect of cancer that negatively impacts biopsychosocial well-being and quality of life. There has been increasing interest in using digital game interventions for managing pain in cancer patients. The present study aimed to consolidate and summarize knowledge regarding the role of games in reducing pain among cancer patients and enhancing their overall quality of life. METHODS: We reviewed studies published between 2000 and April 8, 2023, from databases such as PubMed, Scopus, and Web of Science. The focus was on determining the impact of health games on pain management in cancer patients. RESULTS: An initial search identified 2,544 studies, which were narrowed down to 10 relevant articles after removing duplicates and assessing quality. These studies examined the use of mobile and computer games across various types of cancer, including both pediatric and adult cases. The findings indicate that digital games, particularly those utilizing virtual reality technologies, can diminish pain and anxiety while enhancing treatment outcomes. Overall, the application of these technologies has the potential to improve cancer treatment. CONCLUSIONS: Digital game interventions empower cancer patients by fostering effective communication and patient-centered approaches, which enhance perceptions, outcomes, and overall well-being. These games provide real-time feedback and facilitate interaction with healthcare professionals, which promotes self-management and boosts patient motivation and adherence to treatment protocols. As personalized educational platforms, they increase engagement through educational resources and symptom tracking, while also encouraging physical activity. Furthermore, they act as distraction tools during painful procedures, presenting new research opportunities in pain management and enhancing overall quality of life.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".