Evaluating the Acceptability of Using Virtual Reality to Promote Physical Activity Among Latino, Latina, and Latine Adults With Cardiometabolic Risk Factors and Obesity in Underresourced Settings: Protocol for a Qualitative Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Obesity represents a significant public health challenge in the United States particularly among Latino/a/x/e communities and those in under-resourced settings. Virtual Reality (VR) is a new and innovative technology that can promote physical activity and has the potential to overcome some structural barriers. However, there are few studies that explore the acceptability of using this new technology among high-risk groups in under-resourced settings. OBJECTIVE: We outline a community-informed protocol for conducting focus groups with Latino/a/x/e adults who have cardiometabolic risk factors and obesity, residing in under-resourced communities. The focus groups will assess the acceptability of a culturally aligned virtual reality (VR) program to promote physical activity. METHODS: Using a community-engaged approach informed by Community Health Workers (CHWs) and a Community Advisory Board (CAB), we delivered an immersive VR dance experience to Latino/a/x/e adult participants with cardiometabolic risk factors and obesity. Following the VR experience, we conducted semi-structured focus group interviews to assess acceptability, guided by the Theoretical Framework of Acceptability (TFA). Data collection included a baseline demographic survey and focus group discussions to evaluate participant experiences and programs' acceptability. RESULTS: As of April 2025, we completed seven focus groups with 44 participants across three age groups: 18-29, 30-49, and 50-70 years. CONCLUSIONS: The fully analyzed data from this study will offer insights into leveraging VR as an innovative tool for promoting physical activity in underserved populations, contributing to the broader literature on digital health equity and obesity prevention.
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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 | 0.041 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".