Designing pervasive technology for physical activity self-management in arthritis patients
Bibliographic record
Abstract
Arthritis is a chronic condition which impairs mobility and reduces the quality of life. A physically active lifestyle is crucial for the successful management of the disease. Pervasive technology such as activity trackers can make patients more aware of their physical activity (PA), and help clinicians in getting an objective view of their patients' lifestyle. We developed a web application called FitViz which gathers data from an arthritis patient's Fitbit device and allows her clinician to use this data in setting personalized goals for the patient. We conducted a pilot study with 10 knee Osteoarthritis patients and 10 Rheumatoid Arthritis patients to test the feasibility of the application. 11 participants were interviewed to share their experiences after using FitViz for a month. The use of pervasive technology --- Fitbit and FitViz --- increased PA awareness, and helped in realistic goal-setting. Participants expressed different emotions --- including mistrust in technology --- concerning goal achievement. We use these findings to draw design implications for future pervasive technologies for arthritis patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".