Exergaming in Youth and Young Adults: A Narrative Overview
Bibliographic record
Abstract
Because of rapid evolution in exergaming technology and content, the literature on the benefits of exergaming needs ongoing review. Updated syntheses incorporating high-quality critical assessments of included articles can provide cutting-edge evidence to drive research and practice. The objectives were to summarize evidence from systematic reviews and meta-analyses on the association between exergaming and (1) physical activity (PA), sedentary behavior and energy expenditure (EE); and (2) body composition, body mass index (BMI), and other weight-related outcomes among persons younger than 30 years; and to summarize recommendations in the articles retained. The Elton B. Stephens Co. (ESBSCO) database for reviews was searched from January 1995 to July 2019. Data on study characteristics, findings, and recommendations for future research, game design, and intervention development were extracted from articles that met the inclusion criteria, quality scores were attributed to each article, and a narrative overview of the evidence was undertaken. Twenty-eight reviews, with 5-100 articles per review, were identified. Seventeen assessed the evidence on the association between exergaming and PA, EE, and/or sedentary behavior, and 11 examined the association with body composition, BMI, or other weight-related outcomes. There was substantial heterogeneity across reviews in objectives, definitions, and methods. A positive relationship between exergaming and EE is well documented, but whether exergaming increases PA or changes body composition is not established. The reviews retained also provide evidence that exergaming is a healthier alternative to sedentary behavior and that it can be an exciting enjoyable pastime for youth, which adds variety in PA options for health and dietary interventions. Exergaming is likely more physically health promoting than traditional videogames because of higher EE and possibly improved physical fitness and body composition. Longitudinal studies are needed to assess if exergaming reduces sedentary time, has other health benefits, or is a sustainable behavior. We recommend that exergaming interventions be designed using behavior change theory, and that future reviews use standard review criteria and include recommendations for research, game design, and intervention development.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".