AR Dancee: An Augmented Reality-Based Mobile Persuasive Intervention for Promoting Physical Activity Through Dancing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The importance of physical activity (PA) for overall health and well-being cannot be overstated, especially in today’s fast-paced and sedentary society. Engaging in enjoyable activities like dancing can significantly enhance PA levels and positively affect one’s mood. Advances in technology have the potential to increase individuals’ engagement in more physical activities. This work explores the effectiveness of augmented reality-driven persuasive intervention in enhancing users’ physical activity and mood. To achieve our goal, we developed AR Dancee, a mobile-driven intervention combining Augmented Reality, Machine Learning, and persuasive technology to encourage adults to increase their PA through dancing, ultimately improving their mood. A 15-day user study with 104 participants showed that the intervention effectively increased PA levels, with equal effectiveness across genders and a stronger impact on younger adults. The results also show that the intervention improved participants’ mood while reducing anxiety levels, demonstrating its potential for stress management. Overall, the contribution of this work to the HCI fields is threefold: (1) the design and development of an AR-driven persuasive mobile app, (2) providing design recommendations, and (3) pinpointing limitations and providing suggestions for future work.
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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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it