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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".