Co-design of a Smartphone App for People Living With Dementia by Applying Agile, Iterative Co-design Principles: Development and Usability Study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The benefits of involving those with lived experience in the design and development of health technology are well recognized, and the reporting of co-design best practices has increased over the past decade. However, it is important to recognize that the methods and protocols behind patient and public involvement and co-design vary depending on the patient population accessed. This is especially important when considering individuals living with cognitive impairments, such as dementia, who are likely to have needs and experiences unique to their cognitive capabilities. We worked alongside individuals living with dementia and their care partners to co-design a mobile health app. This app aimed to address a gap in our knowledge of how cognition fluctuates over short, microlongitudinal timescales. The app requires users to interact with built-in memory tests multiple times per day, meaning that co-designing a platform that is easy to use, accessible, and appealing is particularly important. Here, we discuss our use of Agile methodology to enable those living with dementia and their care partners to be actively involved in the co-design of a mobile health app. OBJECTIVE: The aim of this study is to explore the benefits of co-design in the development of smartphone apps. Here, we share our co-design methodology and reflections on how this benefited the completed product. METHODS: Our app was developed using Agile methodology, which allowed for patient and care partner input to be incorporated iteratively throughout the design and development process. Our co-design approach comprised 3 core elements, aligned with the values of patient co-design and adapted to meaningfully involve those living with cognitive impairments: end-user representation at research and software development meetings via a patient proxy; equal decision-making power for all stakeholders based on their expertise; and continuous user consultation, user-testing, and feedback. RESULTS: This co-design approach resulted in multiple patient and care partner-led software alterations, which, without consultation, would not have been anticipated by the research team. This included 13 software design alterations, renaming of the product, and removal of a cognitive test deemed to be too challenging for the target demographic. CONCLUSIONS: We found patient and care partner input to be critical throughout the development process for early identification of design and usability issues and for identifying solutions not previously considered by our research team. As issues addressed in early co-design workshops did not reoccur subsequently, we believe this process made our product more user-friendly and acceptable, and we will formally test this assumption through future pilot-testing.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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