Development of a Digital Tool for People With a Long-Term Condition Using Stroke as a Case Example: Participatory Design Approach
Bibliographic record
Abstract
BACKGROUND: In patient care, demand is growing for digital health tools to enable remote services and enhance patient involvement. People with chronic conditions often have multiple health problems, and long-term follow-up is recommended to meet their needs and enable access to appropriate support. A digital tool for previsit preparation could enhance time efficiency and guide the conversation during the visit toward the patient's priorities. OBJECTIVE: This study aims to develop a digital previsit tool and explore potential end user's perceptions, using a participatory approach with stroke as a case example. METHODS: The digital tool was developed and prototyped according to service design principles, informed by qualitative participant data and feedback from an expert panel. All features were processed in workshops with a team that included a patient partner. The resulting tool presented questions about health problems and health information. Study participants were people with stroke recruited from an outpatient clinic and patient organizations in Sweden. Development and data collection were conducted in parallel. For conceptualization, the initial prototype was based on the Post-Stroke Checklist and research. Needs and relevance were explored in focus groups, and we used a web survey and individual interviews to explore perceived utility, ease of use, and acceptance. Data were thematically analyzed following the Framework Method. RESULTS: The development process included 22 participants (9 women) with a median age of 59 (range 42-83) years and a median of 51 (range 4-228) months since stroke. Participants were satisfied or very satisfied with using the tool and recommended its use in clinical practice. Three main themes were constructed based on focus group data (n=12) and interviews (n=10). First, valuable accessible information illuminated the need for information to confirm experiences, facilitate responses, and invite engagement in their care. Amendments to the information in turn reconfigured their expectations. Second, utility and complexity in answering confirmed that the questions were relevant and comprehensible. Some participants perceived the answer options as limiting and suggested additional space for free text. Third, capturing needs and value of the tool highlighted the tool's potential to identify health problems and the importance of encouraging further dialog. The resulting digital tool, Strokehälsa [Strokehealth] version 1.0, is now incorporated into a national health platform. CONCLUSIONS: The participatory approach to tool development yielded a previsit digital tool that the study group perceived as useful. The holistic development process used here, which integrated health information, validated questions, and digital functionality, offers an example that could be applicable in the context of other long-term conditions. Beyond its potential to identify care needs, the tool offers information that confirms experiences and supports answering the questions in the tool. The tool is freely shared for adaptation in different contexts. TRIAL REGISTRATION: researchweb 236341; https://www.researchweb.org/is/vgr/project/236341.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".