Data representations for in-situ exploration of health and fitness data
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
Wearable devices that collect and generate masses of health related data, such as number of steps taken in a day and heart-rate have seen widespread adoption among general consumers. The wearers of such devices need to interpret the data being generated to ensure they meet their physical activity goals. Little is currently known about how users of such devices explore such data and the corresponding visual representations, in-situ, i.e. during the course of their physical activity. Through a series of interview sessions with users of health and fitness data, i.e., quantified-selfers, we gained an understanding of how they benefit from in-situ data exploration. Our findings reveal the wide number of in-situ tasks, data types, and requirements for designing data representations that support immediate reflection on data being collected. We further solicited the aid of professional designers to sketch visual representations for carrying out the necessary in-situ tasks identified by our users. From these exploratory studies, we derive broader implications for the design of data representations supporting in-situ exploration.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.002 | 0.001 |
| 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 it