Motivational Impacts and Sustainability Analysis of a Wearable-based Gamified Exercise and Fitness System
Bibliographic record
Abstract
Recent years have been hailed by many as the era of the wearables. Meanwhile, gamification in the area of health and fitness has rapidly emerged as a popular field of research. This paper reports the early results of a long-term (70-day) study of using wearable activity trackers and gamification to promote exercise and being more active. The research is being conducted to investigate the motivational effects of using sensor- based games to promote daily exercise, as well as how different methods of releasing the application and its updated features may affect user's enthusiasm and the game's life-cycle. From the data we have collected so far, we can see the gradual emergence of clear pattern based on our periodically updated application. The initial results seem to support the notion of using gradual addition of features or changes as means of sustaining the participants' interest and usage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".