Illustrating User Needs for eHealth With Experience Map: Interview Study With Chronic Kidney Disease Patients
Bibliographic record
Abstract
BACKGROUND: Chronic kidney disease (CKD) is a common condition worldwide and home dialysis (HD) provides economic, quality of life, and clinical advantages compared to other dialysis modalities. Human-centered design aims to support the development of eHealth solutions with high usability and user experience. However, research on the eHealth needs of patients using HD is scarce. OBJECTIVE: This study aimed to support the design of eHealth for patients with CKD, particularly for patients using HD, by developing a kidney disease experience map that illustrates user needs, concerns, and barriers. The research questions were (1) what experiences do patients, particularly older adults, have in their everyday lives with CKD? (2) what user needs do patients with CKD have for HD eHealth? (3) how can these needs be illustrated using the experience map technique? The study focused on patients aged >60 years, as they are at a higher risk of chronic conditions. The study was conducted as part of the eHealth in HD project, coordinated by Hospital District of Helsinki and Uusimaa, Finland. METHODS: In total, 18 patients in different care modalities participated in retrospective interviews conducted between October 2020 and April 2021. The interviews included a preliminary task with patient journey illustrations and questions about their experiences and everyday lives with CKD. The data analysis was conducted using a thematic analysis approach and the process included several phases. RESULTS: On the basis of the thematic analysis, 5 categories were identified: healthy habits, concerns about and barriers to eHealth use, digital communication, patients' emotions, and everyday life with CKD. These were illustrated in the first version of the kidney disease experience map. The patients had different healthy habits regarding social life, sports, and other activities. They had challenges with poorly functioning eHealth software and experienced other factors, such as a lack of interest and lack of skills for eHealth use. Technical devices do not always meet the emotional or physical needs of their users. This caused feelings of frustration, worry, and fear in patients, yet also fostered situational awareness and hope. CONCLUSIONS: The experience map is a promising method for illustrating user needs and communicating the patient's voice for eHealth development. eHealth offers possibilities to support patient's everyday life with chronic disease. The patient's situation and capacity to use eHealth solutions vary with their everyday challenges, opportunities, and their current stage of treatment. The kidney disease experience map will be used and further developed in the ongoing research project "Better Health at Home-Optimized Human-Centered Care of Predialysis and Home Dialysis Patients" (2022 to 2026).
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".