Developing an App for Real-Time Daily Life Observations in a Nursing Home Setting: Qualitative User-Centered Co-Design Approach
Bibliographic record
Abstract
Background: Assessing the daily lives of older adults, including their activities, social interactions, and well-being is essential, particularly in nursing homes, as it gains insights into their quality of life. Methods such as the Microsoft Excel-based Maastricht Electronic Daily Life Observation (MEDLO) tool are time-consuming and require extensive manual input, making them difficult to use. Objective: This study aimed to develop an app-based version of the MEDLO using a user-centered design (UCD) and co-design approach to enhance efficiency and usability. We looked to actively involve researchers and care professionals who have used the MEDLO before, throughout the development process. Methods: Participants included a diverse group of researchers and care professionals experienced in using the MEDLO tool. The UCD approach involved multiple iterative phases including semistructured interviews, user research sessions, and application development. Data were analyzed using a qualitative (thematic) approach of UCD and user research sessions. The app, which was preferred to the traditional Excel-based MEDLO, underwent multiple iterations. This method primed the continuous iterative development of the app, aimed for a minimum viable product (MVP). Results: This study included 14 participants, primarily female, from diverse professional backgrounds. Their feedback highlighted the need for efficiency improvements in tool preparation and data management. Key improvements included automated data handling, an intuitive tablet interface, and functionalities such as randomization and offline data syncing. Conclusions: The iterative development process led to an app that aligns with end-user needs, indicating potential for improved usability. Early and continuous user involvement was key in enhancing the application's usability, demonstrating the importance of user feedback in the development process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".