Gamifying Movement: A Comprehensive Survey of Physical Activity Interventions
Bibliographic record
Abstract
Gamification has emerged as a powerful strategy for enhancing physical activity (PA) engagement by integrating motivational techniques and game elements into interventions. This research presents a comprehensive survey of gamified PA interventions by systematically analyzing the frequency of specific gamification elements and motivational techniques across various journal articles. A total of six major academic databases were examined to identify trends and research gaps in gamified PA applications. The study introduces the EAM Score Algorithm, which quantifies the presence and pairing of key game elements (e.g., leaderboard, challenges, progress bars) with motivational techniques (e.g., goal-setting, feedback, social support) to assess their impact on engagement, adherence, and motivation. To validate the EAM Score Algorithm, the study also analyzes 20 physical activity applications, including some of the most frequently studied PA apps. By comparing the frequency of gamification elements and motivational techniques in these apps with those in research articles, the study assesses the alignment between theoretical trends and real-world implementations. This approach helps identify gaps between academic research and practical application, providing valuable insights for researchers, health professionals, and developers working on gamified PA interventions. Findings indicate that engagement techniques (48.3%) are the most frequently discussed in the literature, highlighting their importance in gamified PA applications. Adherence (24.5%) is also a key focus but appears slightly less frequently. The overall EAM Score reflects a moderate emphasis on engagement, adherence, and motivation, indicating the need for a more balanced approach in future interventions. Using the EAM Algorithm, this research applies the framework in a real-world scenario by selecting the three most commonly studied apps. This implementation aims to evaluate the effectiveness of engagement, adherence, and motivation within these applications, providing insights into how gamification elements and motivational techniques influence user behavior. 52% of behavioral change techniques (BCTs) were identified in journal articles, making them the most frequently discussed motivational strategies compared to other techniques. The findings suggest that Health4Life offers the most well-balanced combination of engagement (68.35%), adherence (20.25%), and motivation (11.39%). Meanwhile, PuzzleWalk could strengthen its adherence strategies (12.50%) to enhance user adherence, while OnTheMove! may benefit from further improvements in its motivational components (11.48%) to sustain long-term engagement.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".