A comparison design study of feedback modalities to support deep breathing whilst performing work tasks
Bibliographic record
Abstract
BACKGROUND: Deep breathing exercises are known to help decrease stress. Wearable and ambient computing can help initiate and support deep breathing exercises. Most studies have focused on a single sensory modality for providing feedback on the quality of breathing and other physiological data. OBJECTIVE: Our research compares different feedback modalities on an individual's experience and ability to perform breath-based techniques at work. METHODS: We designed three different interactive prototypes that used light, vibration and sound feedback modalities. We tested each prototype with 19 participants whilst they were performing typical work tasks in a naturalistic setting, followed by semi-structured interviews. RESULTS: We found that sound was the most successful feedback for the majority of participants, followed by vibration and ambient light. We developed an analytic tool, the Extended Cycle of Awareness, to facilitate understanding of the patterns of awareness and the flow of experience generated by participant interaction with prototype systems that provide feedback on the quality of breathing. Participants followed one of three different types of patterns: (1) ignoring the feedback; (2) not understanding the feedback and being overwhelmed by it; (3) successfully using the feedback to initiate deep breathing and reflect on the change in the quality of breathing. CONCLUSIONS: We offer a set of design recommendations for crafting interactive systems to support deep breathing at work, including personalization, designing for the cyclical process of attention and awareness, and designing for reflective practice.
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 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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".