Assessing Associations Between Environmental, Sleep, and Physical Activity Factors and Metabolic Syndrome Risk: Protocol for the FEASible Study
Bibliographic record
Abstract
BACKGROUND: FEASible is a cross-sectional observational study that explores women's daily living patterns through wearable devices and home environment sensors to validate the use of physical activity and indoor air quality data as indicators of risk for metabolic syndrome (MetS) and cardiovascular disease. OBJECTIVE: Leveraging transdisciplinary expertise, we implement a low-cost, dense sampling approach among 800 adult women, 60% Hispanic or Latina, with a subgroup of 225 participants opting for neuroimaging to assess MetS-related brain vulnerability. METHODS: Participants residing in Central Texas use a smartwatch and a custom-built air quality sensor to monitor their activities and environment for two weeks. Other variables, such as social determinants of health, medical history, and lifestyle, are reported through surveys. During their initial visit, we gather blood pressure measurements, body composition, and lipid profile information. RESULTS: As of August 2025, a total of 805 participants have completed the eligibility survey, of whom 204 have completed 2 weeks of sensor data collection, showing a diverse participant pool comprising 60% (210/348) Hispanic or Latinas, 39% (135/348) non-Hispanic or Latinas, and 1% (3/348) unknown. Participants are aged 18-40 years, with an average age of 27 years (SD 6 years). The study was funded in May 2023 (National Heart, Lung, and Blood Institute; grant R01HL168374), and data collection began in October 2023, with a projected completion date of May 2028. Ongoing analyses and publication of early cohorts are expected to occur throughout 2026 and 2027, with final analyses and dissemination of results anticipated by the end of the funding period in 2028. CONCLUSIONS: We have established a feasible pipeline to collect data that will help yield valuable insights into MetS risk factors among Latina women, including brain scan magnetic resonance imaging data and environmental exposure measurements. This paper aims to provide a rationale for procedures and case examples from the first year of data collection leading to health risk modeling, thus informing future interventions on MetS, heart disease, and all-cause mortality among Latina women. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/82034.
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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 | 0.024 | 0.018 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.018 |
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".