Sleep, Sensors, and the Smartwatch: Co-Design Interaction Modalities for On-Wrist Health Apps
Bibliographic record
Abstract
Smartwatches have emerged as a key platform for health monitoring and digital interventions, yet their small screen size poses a unique challenge for user interaction, and designing intuitive, engaging, and context-aware interactions for health apps remains a challenge. This paper presents findings from a co-design workshop with 45 participants exploring possible input and output modalities for smartwatch-based serious sleep game apps. Participants mapped interaction modalities (e.g., movement, voice, ambient light) to app features and daily time periods, proposing sensor-driven designs that span active, reflective, and passive use cases. Features like "Fighting Monsters" were linked to daytime movement-based inputs, while heart rate, light, and microphone sensors were proposed for passive interactions during sleep. A heatmap analysis revealed distinct temporal patterns in interaction preferences, offering insights for timing-aware, multimodal design. Our findings inform the development of on-wrist health apps that leverage smartwatch capabilities to support health behavior change through context-sensitive engagement, contributing design strategies that balance usability, engagement, and the clinical potential of wearable health technologies.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".