Effects of Active Video Games in Patients With Cancer: Systematic Review
Bibliographic record
Abstract
BACKGROUND: Physical activity (PA) is now considered an adjuvant therapy in cancer treatment; nevertheless, multiple barriers could reduce PA engagement during treatment. Active video games (AVGs) lead to the achievement of mild- to moderate-intensity PA and represent a promising tool for regular movement and exercise. OBJECTIVE: This paper aims to review the current literature and provide updated content on the physiological and psychological effects of AVG-based interventions in patients with cancer undergoing treatment. METHODS: Four electronic databases were investigated. Studies reporting on AVG interventions delivered to patients undergoing treatment were included. A total of 21 articles (17 interventions) were identified for data extraction and quality assessment. RESULTS: A total of 362 patients with cancer participated in the studies (number of participants 3-70). The majority underwent treatment for breast, lung, prostate, hematologic, or oral or laryngeal cancer. The types and stages of cancer varied in all studies. Participants ranged in age from 3 to 93 years. Four studies included patients with pediatric cancer. The duration of interventions ranged from 2 to 16 weeks, with a minimum of 2 sessions per week and a maximum of 1 daily session. Sessions were supervised in 10 studies, and 7 included home-based interventions. AVG interventions improved endurance, quality of life, cancer-related fatigue, and self-efficacy. Effects were mixed on strength, physical function, and depression. AVGs did not affect activity level, body composition, or anxiety. Compared with standard physiotherapy, physiological effects were lower or similar, and psychological effects were higher or similar. CONCLUSIONS: Overall, our results suggest that AVGs can be recommended to patients undergoing cancer treatment, given the physiological and psychological benefits. When AVGs are proposed, supervision of the sessions should be considered as it can limit dropouts. In the future, it is important to develop AVGs that combine endurance and muscle strengthening, with the possibility of achieving moderate to high exercise intensity, depending on the physical abilities of the patients, as indicated in the World Health Organization's recommendations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".