Spiro Play: a Suite of Breathing Games for Spirometry by kids & experts
Bibliographic record
Abstract
CHI PLAY '20, November 2–4, 2020, Virtual Event, Canada \nWe have built and implemented a set of metaphors for breathing games by involving children and experts. These games are made to facilitate prevention of asthma exacerbation via regular monitoring of children with asthma through spirometry at home. To instruct and trigger children to execute the (unsupervised) spirometry correctly, we have created interactive metaphors that respond in real-time to the child’s inhalation and exhalation. Eleven metaphors have been developed in detail. Three metaphors have been fully implemented based on current guidelines for spirometry and were tested with 30 asthmatic children. Each includes multi-target incentives, responding to three different target values (inhalation, peak expiration, and complete exhalation). We postulate that the metaphors should use separate goals for these targets, have independent responses, and allow to also go beyond expected values for each of these targets. From the selected metaphors, most children preferred a dragon breathing fire and a soccer player kicking a ball into a goal as a metaphor; least liked were blowing seeds of a dandelion and applying lotion to a dog to grow its hair. Based on this project we discuss the potential and benefits of a suite-of-games approach: multiple games that each can be selected and adapted depending on personal capabilities and interests.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".