Applying the Human-Centered Innovation Biodesign Framework to the Development and Piloting of a Program to Mitigate Risk for Cognitive Decline Among Historically Underrepresented Individuals: Case Study
Bibliographic record
Abstract
BACKGROUND: Physical inactivity is a modifiable risk factor for dementia. Past physical activity interventions often overlook the voices of the end user in the design process, particularly minoritized groups living with dementia or memory challenges. To develop physical activity interventions, we use the principles of human-centered design. OBJECTIVE: We applied human-centered design using the innovation biodesign framework to develop a physical activity intervention, Nurturing Aging Through Uplifting Activities in a Restorative Environment (NATURE) program for minoritized individuals as a use case. METHODS: The innovation biodesign framework has three domains: (1) problem space, (2) invention, and (3) solution space. Each domain includes several activities. The problem space involves a needs assessment, needs screening, evidence-based literature review, review of existing models of programs, and iterative feedback from partners, leading to an invention. The solution space encompasses the implementation and validation of the invention and outcomes. We applied this framework in 3 steps: (1) identifying the problem: we used data points from multiple sources to identify needs and mapped them onto the problem space. These sources included reviews of the literature to identify existing interventions, findings from other nature programs to surmise gaps, and focus groups to iteratively identify unmet needs. (2) Designing the invention: we developed NATURE with Hispanic or Latino people with memory challenges and identified their preferred outcomes. (3) Mapping the pilot study. We added the study protocol and planned outcomes to the solution space. RESULTS: In step 1, three evidence-based programs guided the development of NATURE to address physical inactivity and related risks of decreased well-being and dementia. We received 50 referrals for focus group participants, 22 were eligible and completed consent, and 21 (n=6 Hispanic or Latino people with memory challenges and care partners, n=8 outdoor professionals, and n=7 health care providers) participants completed the focus groups. We received feedback from participants on local nature activities, program frequency, duration, and delivery mode, a referral pathway, and outcomes using 5 focus groups and 2 interviews. In step 2, the 12-week NATURE program was developed to promote an active lifestyle and well-being, using nature activities that a person enjoys. NATURE accounts for a person's preferences, needs, and daily situation and includes 4-6 sessions with 2 phone check-ins. Preferred outcomes were well-being, sleep, and social connections. In step 3, we mapped the plan to pilot NATURE using activity tracker technology to measure sleep, heart rate, and activity (well-being), and validated questionnaires. CONCLUSIONS: The framework provided a systematic approach for mapping the development of NATURE to address the needs of Hispanic or Latino people with memory challenges, using human-centered design principles. Application of the framework can be a helpful tool to map the development of other interventions for minoritized populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT06403345; https://clinicaltrials.gov/study/NCT06403345.
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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 | 0.060 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| 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".