Using a Transdisciplinary Approach in Learning Communities for Designing Wearable Stress Management for Vulnerable Populations: Development and Usability Study
Bibliographic record
Abstract
Background: Software solutions for wearable-based stress monitoring offer significant potential in health care, particularly for vulnerable populations such as individuals with dementia or persistent physical symptoms. Despite technological advances, designing user-centered, ethically grounded, and contextually relevant software remains challenging. Vulnerable populations often have specific cognitive, physical, and emotional needs that require customization, yet these are rarely prioritized in mainstream development. Our so-called Sensors2Care project addressed these challenges by co-developing stress-monitoring prototypes in collaboration with stakeholders from health care, law, and technology within a transdisciplinary setting. Objective: This article has two aims: first, to describe how the Sensors2Care project operationalized the transdisciplinary approach (TDA) within a learning community (LC) to guide the development of stress-monitoring software; second, to share stakeholder needs and design requirements for wearable technologies in complex health care contexts, derived from this process. Methods: The Sensors2Care project applied a TDA embedded in an LC. This approach combined participatory design research with mixed methods across 3 iterative components: requirements gathering, prototype development, and early-stage evaluation. Research activities included scoping reviews, semistructured interviews, focus groups, legal analyses, and field testing. In the LC, students and researchers from health professions, computer science, and law collaborated with patients, (in)formal caregivers, and industry partners in a transdisciplinary consortium. User stories served as both a methodological tool and design outcome, helping to capture stakeholder needs and align input from technical, health, and legal domains. Feedback was collected continuously and used to refine requirements and prototypes throughout the development process. Results: User stories revealed 7 key themes relevant to developing and using wearable-based stress monitoring, including strategic use, notifications, user input, data insight, data access and sharing, hardware design, and support. Stakeholders emphasized the need for customization, durability, and comfort, aligned with the cognitive and physical needs of the target populations. Prototype evaluations indicated the practical relevance of these features and revealed a need for training and insight into long-term usability. Beyond their role in capturing content-driven input, user stories also supported transdisciplinary collaboration by aligning legal, health, technical, and experiential perspectives. This was facilitated by the LC structure, which enabled sustained engagement between students, researchers, and societal stakeholders and illustrated the feasibility of implementing TDA in a university context. Conclusions: This project illustrates how TDA, when embedded in an LC, supports the co-development of ethically grounded, contextually relevant, and practically applicable stress-monitoring software for vulnerable populations. The iterative design process enabled early integration of legal, health, and technical considerations, while user stories supported structured collaboration across domains. Although the project resulted in concrete prototypes and clustered design requirements, further research is needed to assess long-term use and real-world implementation across health care contexts. Embedding TDA in LCs may strengthen future professionals' ability to address complex health care challenges collaboratively.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".