AR Dancee: An Augmented Reality-Based Mobile Persuasive Intervention for Promoting Physical Activity Through Dancing
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".