A Coach-Supported mHealth Lifestyle Intervention to Reduce Dementia Risk in Persons With Low Socioeconomic Status or a Migration Background: Qualitative Co-Design Study
Bibliographic record
Abstract
BACKGROUND: The prevalence and incidence of dementia are higher in migrants and those with low socioeconomic status (SES). Mobile health (mHealth) interventions offer a potentially scalable way to reduce dementia risk via risk factor modification. OBJECTIVE: We co-designed the MIND-PRO app-an mHealth intervention targeting dementia risk factors through self-managed lifestyle changes and remote coaching-specifically designed for Dutch individuals with low SES and those with Turkish or South Asian Surinamese migration backgrounds. We focused on these migrant populations as they are the largest in the Netherlands and have the highest risk of developing dementia. METHODS: In this qualitative study, we explored the needs and preferences of our target populations aged 50-75 years old at increased dementia risk by conducting semistructured interviews and focus groups. Participant feedback was used to iteratively refine and adapt a prototype intervention based on insights from prior mHealth trials. RESULTS: We interviewed 23 participants (median age 59, IQR 55-63 y; n=15, 65% female) and conducted two focus groups with 7 Turkish women and 13 Dutch participants with low SES. The target populations emphasized personalization features such as goal setting, self-tracking, educational material, and remote coaching. Participants highlighted the importance of social interaction and autonomy in achieving sustainable lifestyle changes. Tailoring coaching and lifestyle advice to cultural practices was deemed beneficial. CONCLUSIONS: Optimal mHealth interventions targeting dementia risk factors in migrants and individuals with low SES should be personalized and interactive, respect autonomy, and integrate cultural needs and preferences.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".