Game-Based eHealth Interventions for the Reduction of Fatigue in People With Chronic Diseases: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Fatigue is a common and debilitating side effect of chronic diseases, significantly impacting patients' quality of life. While physical exercise and psychological treatments have been shown to reduce fatigue, patients often struggle with adherence to these interventions in clinical practice. Game-based eHealth interventions are believed to address adherence issues by making the intervention more accessible and engaging. OBJECTIVE: This study aims to compile empirical evidence on game-based eHealth interventions for fatigue in individuals with chronic diseases and to evaluate their effectiveness in alleviating fatigue. METHODS: A comprehensive literature search was performed across Embase, MEDLINE ALL, PsycINFO, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and Google Scholar in August 2021. Study characteristics and outcomes from the included studies were extracted, and a random-effects meta-analysis was conducted. Sensitivity and subgroup analyses were performed to identify sources of heterogeneity. RESULTS: Of 1742 studies identified, 17 were included in the meta-analysis. These studies covered 5 different chronic diseases: multiple sclerosis (n=10), cancer (n=3), renal disease (n=2), stroke (n=1), and Parkinson disease (n=1). All but 1 study used exergaming interventions. The meta-analysis revealed a significant moderate effect size in reducing fatigue favoring the experimental interventions (standardized mean difference [SMD] -0.65, 95% CI -1.09 to -0.21, P=.003) compared with control conditions consisting of conventional care and no care. However, heterogeneity was high (I2=85.87%). Subgroup analyses were conducted for the 2 most prevalent diseases. The effect size for the multiple sclerosis subgroup showed a trend in favor of eHealth interventions (SMD -0.47, 95% CI -0.95 to 0.01, P=.05, I2=63.10%), but was not significant for the cancer group (SMD 0.61, 95% CI -0.36 to 1.58, P=.22). Balance exercises appeared particularly effective in reducing fatigue (SMD -1.19, 95% CI -1.95 to -0.42, P=.002). CONCLUSIONS: Game-based eHealth interventions appear effective in reducing fatigue in individuals with chronic diseases. Further research is needed to reinforce these findings and explore their impact on specific diseases. Additionally, there is a lack of investigation into interventions beyond exergaming within the field of game-based learning.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".