Investigating In-Situ Personal Health Data Queries on Smartwatches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Smartwatches enable not only the continuous collection of but also ubiquitous access to personal health data. However, exploring this data in-situ on a smartwatch is often reserved for singular and generic metrics, without the capacity for further insight. To address our limited knowledge surrounding smartwatch data exploration needs, we collect and characterize desired personal health data queries from smartwatch users. We conducted a week-long study (N = 18), providing participants with an application for recording responses that contain their query and current activity related information, throughout their daily lives. From the responses, we curated a dataset of 205 natural language queries. Upon analysis, we highlight a new preemptive and proactive data insight category, an activity-based lens for data exploration, and see the desired use of a smartwatch for data exploration throughout daily life. To aid in future research and the development of smartwatch health applications, we contribute the dataset and discuss implications of our findings.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.010 |
| 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 it