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Record W4409946100 · doi:10.1177/19322968251335303

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

2025· article· en· W4409946100 on OpenAlexaffabout
R. Vela, Paula Voorheis, Jeremy Petch, Hertzel C. Gerstein, Diana Sherifali

Bibliographic record

VenueJournal of Diabetes Science and Technology · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Management and Education
Canadian institutionsInstitute for Work & HealthBridgepoint Active HealthcarePopulation Health Research InstituteHamilton Health SciencesUniversity of TorontoMcMaster University
Fundersnot available
KeywordsBlueprintCoachingHealth coachingDigital healthDesign thinkingService (business)Psychological interventionKnowledge managementProcess managementHealth carePsychologyComputer scienceMedical educationMedicineNursingEngineeringHuman–computer interactionPsychotherapist

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.066
GPT teacher head0.331
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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