Tailoring implementation strategies for the healthy actions and lifestyles to Avoid Dementia or Hispanos y el ALTo a la Demencia Program: Lessons learned from a survey study
Bibliographic record
Abstract
BACKGROUND: Healthy Actions and Lifestyles to Avoid Dementia Program (HALT-AD) or Hispanos y el ALTo a la Demencia is a recently-developed online educational platform to help individuals identify and modify their own dementia modifiable risk factors (MRF). In light of known challenges in recruiting and retaining diverse participants in research studies, there is a need to identify data-informed strategies that will contribute to effective outreach and tailored implementation of HALT-AD among its intended users of Hispanic and non-Hispanic midlife and older adults in the US. OBJECTIVES: To identify factors (i.e, demographic, medical, psychosocial and environmental) that may facilitate or impede effective program enrollment and participation. DESIGN: Cross-sectional study SETTING: Data from an online and self-administered survey conducted between January and April 2023 PARTICIPANTS: Residents of California, predominately San Diego, who were 50 to 85 years old, with no dementia or Alzheimer's disease, proficient in English or Spanish and with enough technical ability to complete the survey electronically (n=157; 43% Hispanic). INTERVENTION (IF ANY): none MEASUREMENTS: RedCap was used to capture answers to closed and open-ended survey questions. Mixed-methods analysis was used: For quantitative data, descriptive statistics, comparisons by group (Hispanic/non-Hispanic), and exploratory factor analysis were conducted in SPSS. Thematic analysis with open coding in Excel was used for qualitative responses. RESULTS: Independent of ethnicity, participants' most preferred method of reach for recruitment was through a conversation with their doctor or with a family member or friend. Their least preferred method was receiving a Facebook advertisement especially among non-Hispanics. Interest in program participation did not differ by sociodemographic characteristics or self-rated satisfaction with individualized MRFs. Instead, having higher confidence in one's ability to commit to behavior change was significantly associated with higher interest in program participation. While a common theme to motivate both groups to participate was the potential to decrease dementia risk, non-Hispanics were motivated by the premise of supporting research and having a positive user experience. For program implementation, Hispanics were more likely to be interested in participating if live sessions, either online or in-person, were provided to offer support with making lifestyle changes as adjunct to completing online courses independently. In both groups, participation may be further facilitated by offering wearable devices which provide participants with feedback on lifestyle change progress. CONCLUSIONS: A "one-size-fits-all" approach to recruitment and implementation of HALT-AD may not be effective in enrolling and retaining participants in future studies or for clinical use. Instead, a tailored approach that accounts for personal and ethnically-dependent preferences may be more beneficial.
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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 | 0.038 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".