Empowering self-care: leveraging user insights to co-design AI-powered self-care technology for Parkinson's disease
Bibliographic record
Abstract
This paper presents the co-design process of a self-care technology for people with Parkinson's disease (PwPs). The aim was to use patient insights to inform the design of a technology to support self-care practices. To this end, an 8-week usage diary method was used to capture the real-life experiences of PwPs using digital health technology for self-care in Parkinson's disease. The data collection process involved the use of two methods: weekly diary entries and semi-structured interviews. The qualitative data were analyzed using reflexive thematic analysis. The findings revealed that technology needs to be flexible, adaptive, and responsive to the evolving care needs of PwPs by incorporating interactive goal-setting, personalized self-tracking tools to promote active awareness, tailored care tips, and resources to support self-care practices at home. The study underscores how contextualized use of a digital health technology shapes self-care practices and highlights the need to develop solutions that are both technically reliable and integrated into the social and emotional realities of PwPs. The results of the usage diary study help identify potential ways to leverage artificial intelligence to advance self-care 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".