Board Games for Health: A Systematic Literature Review and Meta-Analysis
Bibliographic record
Abstract
Nondigital board games are being used to engage players and impact outcomes in health and medicine across diverse populations and contexts. This systematic review and meta-analysis describes and summarizes their impact based on randomized and nonrandomized controlled trials. An electronic search resulted in a review of n = 21 eligible studies. Sample sizes ranged from n = 17 to n = 3110 (n = 6554 total participants). A majority of the board game interventions focused on education to increase health-related knowledge and behaviors (76%, n = 16). Outcomes evaluated included self-efficacy, attitudes/beliefs, biological health indicators, social functioning, anxiety, and executive functioning, in addition to knowledge and behaviors. Using the Cochrane Collaboration tool for assessing bias, most studies (52%, n = 11) had an unclear risk of bias (33% [n = 7] had a high risk and 14% [n = 3] had a low risk). Statistical tests of publication bias were not significant. A random-effects meta-analysis showed a large average effect of board games on health-related knowledge (d* = 0.82, 95% confidence interval; CI [0.15-1.48]), a small-to-moderate effect on behaviors (d* = 0.33, 95% CI [0.16-0.51]), and a small-to-moderate effect on biological health indicators (d* = 0.37, 95% CI [0.21-0.52]). The findings contribute to the literature on games and gamified approaches in healthcare. Future research efforts should aim for more consistent high scientific standards in their evaluation protocols and reporting methodologies to provide a stronger evidence base.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".