Investigating Serious Games That Incorporate Medication Use for Patients: Systematic Literature Review
Bibliographic record
Abstract
BACKGROUND: The United States spends more than US $100 billion annually on the impact of medication misuse. Serious games are effective and innovative digital tools for educating patients about positive health behaviors. There are limited systematic reviews that examine the prevalence of serious games that incorporate medication use. OBJECTIVE: This systematic review aimed to identify (1) serious games intended to educate patients about medication adherence, education, and safety; (2) types of theoretical frameworks used to develop serious games for medication use; and (3) sampling frames for evaluating serious games on medication use. METHODS: PubMed, Scopus, and Web of Science databases were searched for literature about medication-based serious games for patients. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed for article selection. RESULTS: Using PRISMA guidelines, 953 publications and 749 unique titles were identified from PubMed, Scopus, and Web of Science. A total of 16 studies featuring 12 unique serious games were included with components of medication adherence, education, and safety, published from 2003 to 2019. Of the 12 games included, eight serious games were tested in adolescents, three games were tested in young adults, and one game was tested in adults. Most studies (n=11) used small sample sizes to test the usability of serious games. Theoretical frameworks identified in the 12 serious games included information, motivation, and behavior theory; social cognitive theory; precede-proceed model; middle-range theory of chronic illness; adult learning theory; experiential learning theory; and the theory of reasoned action. Existing reviews explore serious games focused on the management of specific disease states, such as HIV, diabetes, and asthma, and on the positive impact of serious game education in each respective disease state. Although other reviews target broad topics such as health care gamification and serious games to educate health care workers, no reviews focus solely on medication use. Serious games were mainly focused on improving adherence, whereas medication safety was not widely explored. Little is known about the efficacy and usability of medication-focused serious games often because of small and nonrepresentative sample sizes, which limit the generalizability of existing studies. CONCLUSIONS: Limited studies exist on serious games for health that incorporate medication use. The findings from these studies focus on developing and testing serious games that teach patients about medication use and safety. Many of these studies do not apply a theoretical framework in the design and assessment of these games. In the future, serious game effectiveness could be improved by increasing study sample size and diversity of study participants, so that the results are generalizable to broader populations. Serious games should describe the extent of theoretical framework incorporated into game design and evaluate success by testing the player's retention of learning objectives.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".