Peer Review #2 of "Motivational strategies and approaches for single and multi-player exergames: a social perspective (v0.1)"
Bibliographic record
Abstract
Background.Exergames have attracted the interest of academics, practitioners, and designers, in domains as diverse as health, human-computer interaction, psychology, and information technology.This is primarily because exergames can make the exercise experience more enjoyable and entertaining, and in turn, can increase exercise levels.Despite the many benefits of exergames, they suffer from retention problems.Thus, the objective of this article is to review theories and game elements that have been empirically examined or employed in an attempt to make exergames more motivating so people engage in sustained physical activity (duration of physical activity) in a repeating pattern over time (frequency of physical activity). Methodology.A literature search and narrative review were conducted.Results.Five major theories and elements were prevalent in the exergaming literature: (1) selfdetermination theory, (2) gamification, (3) competition and cooperation, (4) situational interest, and (5) social interaction.These theories and elements are important for encouraging long-term play and show promise for designing exergames to promote sustained engagement and motivate physical activity.We discuss their strengths and weaknesses throughout the paper. Conclusions.The long-term effectiveness of exergame interventions is unclear mainly because of the limited number of long-term studies completed to date.Better metrics are also needed to evaluate this effectiveness.We also identified particular attention to social factors and group dynamics, such as multi-player exergames and more effective player matchmaking strategies for increasing social connectedness, as a key area of future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.143 | 0.082 |
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".