Co-Designed Mobile-Based Cognitive Training for Older Chinese Americans: Protocol for a Pilot Randomized Controlled Trial Assessing Feasibility and Acceptability
Bibliographic record
Abstract
BACKGROUND: Older Chinese Americans are at high risk of dementia, yet they often do not access culturally relevant services/programs to reduce their risks due to issues such as language barriers and transportation. BrainHQ is a mobile-based, effective cognitive training program that can potentially address these barriers and delay cognitive decline in older Chinese Americans. OBJECTIVE: We aim to evaluate the feasibility and acceptability of a mobile-based cognitive training intervention co-designed by older Chinese Americans and their adult children. METHODS: We applied an experience-based co-design approach that leverages existing cognitive training features and older Chinese Americans' prior knowledge, lived experiences, and social norms around dementia to co-develop a cognitive training intervention. We conducted an experience-based co-design workshop with Older Chinese Americans (n=10), and their adult children (n=4) to optimize the cultural and linguistic relevance of the cognitive training intervention. Participants used a journey map to brainstorm challenges they may experience when participating in the intervention. Then, the participants created prototypes of intervention components to address these challenges. Finally, we incorporated these prototypes into the co-designed intervention protocol. A total of 30 participants will be recruited into the intervention study and will be randomly assigned to the intervention or waitlist control group (2:1 ratio). The intervention group will complete the mobile-based cognitive training for between 10 and 15 minutes daily for 12 weeks. The primary outcomes are feasibility and acceptability. Global cognition, mental health, physical functioning, and quality of life will be assessed at baseline, 8, and 12 weeks. RESULTS: This pilot trial received institutional review board approval (Pro00109934l) in November 2024. We enrolled the first participant in December 2024 and aim to complete enrollment by May 2025. We expect to complete all data collection by September 2025. We will analyze the data and report study findings by February 2026. CONCLUSIONS: This study leverages partnerships with academic, industry, and community stakeholders and provides the groundwork for a large-scale randomized controlled trial to test the efficacy of a mobile-based cognitive training intervention for older Chinese Americans. The co-design workshop served as a feasible, innovative approach to engage with the participants and improve the study design. These findings will enhance the culturally tailored delivery of cognitive training to older Chinese Americans and provide insights for broader implementation, improving their engagement in dementia research. TRIAL REGISTRATION: ClinicalTrials.gov NCT05355870; https://clinicaltrials.gov/study/NCT05355870. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/69303.
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 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 | 0.043 | 0.033 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.008 |
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".