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Record W4413849481 · doi:10.2196/64930

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

2025· article· en· W4413849481 on OpenAlexvenueno aff
Rebecca Lassell, Ada Metaxas, Katherine Wang, Sara Hantgan, Prabhat Gottipati, Sarah Zwerling, Triana Pena, Chava Pollak, Laura N. Gitlin, Sunit Jariwala

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintCognitionPsychologyGerontologyComputer scienceMedicineWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

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.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
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 head0.154
GPT teacher head0.475
Teacher spread0.321 · 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.

Study designOther design
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 routes1
Has abstractyes

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