Leveraging Co-Design, Design Thinking, and Service Blueprinting to Create Digital Health Behavior Change Innovations: Insights From a Co-Design Workshop With Type 2 Diabetes Remission Health Coaches
Bibliographic record
Abstract
INTRODUCTION: Digital health innovations are increasingly being designed to support chronic disease management. Digital health innovations may be particularly valuable for supporting health coaching interventions for type 2 diabetes (T2D) remission. To design more effective digitally enabled health coaching for T2D remission, design methods that utilize co-design, design thinking, and service blueprinting may be advantageous. METHODS: A one-day collaborative design thinking workshop in Toronto, Canada involved health coaches from pan-Canadian T2D remission research sites. Health coaches reflected on their experiences and identified digital innovation opportunities. Workshop activities included empathizing with each other, defining clear opportunities, and ideating solutions. Data were collected through audio recordings, field notes, and activity outputs, and then analyzed using qualitative content analysis. Researchers synthesized the data into a service blueprint, which outlined specific needs for delivering future digitally enabled T2D remission programming. RESULTS: Health coaches emphasized the importance of personalized goal setting, deep relationship building, and responsive behavioral recommendations in effective T2D remission coaching. Coaches envisioned digital tools as fundamental for improving information accessibility, streamlining workflows, and delivering tailored support throughout the T2D remission journey. The developed service blueprint pinpointed key opportunities where digital technology could enhance the coaching process over time, offering actionable solutions to address patient, coach, provider, and system needs. CONCLUSION: This study demonstrates the transformative potential of using co-design, design thinking, and service blueprinting to create more meaningful digital health self-management interventions. Future research should validate the developed service blueprint in real-world settings and explore the impact of digitally enabled health coaching on long-term T2D remission outcomes.
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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 | 0.041 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.016 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".