Designing with Dynamics: Reflections on Co-design Workshops Between People Living with Dementia and Their Care Partners

Research Background and Issues

• What problems or challenges did the authors identify?
  The paper explores the challenges of designing technologies for dementia patients and informal caregiving partners. These challenges include: how to effectively capture the complex and diverse relational dynamics within caregiving partnerships; how to design technologies that adapt to these dynamics; and the role and influence of researchers in the design process.

• Why is this issue important?
  The growing number of dementia patients inevitably leads to an increase in caregiving partnerships. These relationships often face complex dynamics, including power imbalances, unequal responsibilities, and communication barriers. Designing appropriate technologies to support these relationships can not only improve the caregiving experience but also enhance the autonomy of dementia patients and the well-being of caregiving partners.

• Research Motivation and Related Work:
  Human-Computer Interaction (HCI) research often employs personas to guide design, incorporating assumptions about the relational dynamics between caregiving partners and dementia patients. However, current designs frequently focus on a single type of relational dynamic, neglecting the diversity of actual user needs. This singular perspective risks rendering technological designs ineffective and reinforcing stereotypes.

Solutions

• What methods or solutions did the authors propose?
  The authors conducted a series of co-design workshops to explore the potential for designing technologies that enhance the relationships between dementia patients and their caregiving partners. Through two iterative workshops, the first phase involved collecting caregiving partners' diaries, and the second phase engaged in co-design activities aimed at designing social robots that address diverse relational dynamics.

• What are the innovative aspects of this solution?

  1. Dynamic-Oriented Design: The workshops focused on the diverse dynamics within caregiving partnerships, such as guilt-driven design, caregiver-dominated design, conflict-driven design, and collaborative design.
  2. Reflexive Analysis: The authors deeply reflected on the complexity of these dynamics and the influence of researchers themselves on these dynamics during the design process.
  3. Iterative Approach: The two rounds of workshops were progressively optimized to reduce cognitive load and accommodate the participation capabilities of dementia patients.

• What are the implementation steps and key techniques used?

  1. Data Collection: The first phase involved diary studies to capture caregiving partners' daily reflections and interaction dynamics in real-world settings.
  2. Workshop Design: The second phase included activity cards, scenario cards, storyboard development, and discussion presentations.
  3. Redesign: In the second workshop, the authors simplified the activity process to reduce cognitive load and used AI-generated images to visualize scenarios and facilitate discussions.
  4. Analysis Method: Reflexive thematic analysis was employed to deeply explore the impact of relational dynamics on technology design.

Research Outcomes

• What specific outcomes were achieved?

  1. Dynamic Classification:
     • Guilt-Driven Dynamics: One party often blames themselves for relational issues, leading to design solutions focused on supporting personal reflection or emotional management.
     • Caregiver-Dominated Dynamics: The caregiving partner dominates design decisions, with technological solutions aimed at reducing the burden on dementia patients.
     • Conflict-Driven Dynamics: Disagreements arise between parties on problem definitions, making it challenging to unify design goals.
     • Collaborative Dynamics: Both parties actively participate in design, exploring collaborative technologies to address relational challenges.
  2. Impact Identification: The study revealed the potential influence of researchers on participants' relational dynamics through activity design, guiding language, and scenario presentation during the workshops.

• What advantages does it have compared to existing solutions?

  1. Understanding Diversity: Emphasizes the variability in relational dynamics rather than relying on stereotypical, single-dynamic designs.
  2. Enhanced Participation: Personalized design activities improved dementia patients' participation, reducing the likelihood of their voices being overlooked in discussions.
  3. Reflexive Practice: Provides in-depth analysis of how researchers influence participants' relational dynamics.

• What were the experimental or evaluation results?
  Across two rounds of workshops, researchers observed how the relational dynamics between dementia patients and caregiving partners influenced technology design. While the initial workshops highlighted the complexity of engaging dementia patients in activities, subsequent iterative optimizations improved the quality of design and the comprehensiveness of dynamic expression.

• Limitations and Future Directions:

  1. Small Sample Size: The study analyzed only eight participant pairs, necessitating more diverse data to validate the dynamic classifications and applicability of technological designs.
  2. Online vs. Offline Impact: The study did not explore differences in participant behavior and dynamic expression between online and offline workshops.
  3. Long-Term Impact and Dynamic Fluidity: The study did not address the evolution of relational dynamics as dementia progresses.

Conclusion and Recommendations

This paper highlights the importance of understanding relational dynamics when designing technologies for dementia patients and caregiving partners, as well as the influence of researchers during the design process. By classifying relational dynamics, reflecting on design methods, and optimizing workshop structures, the authors provide valuable guidance for future HCI research. Future studies could expand participant samples and research methods to further explore dynamic evolution and the long-term impact of technological interventions.