Geographic environments, daily activities and stress in Luxembourg (the FragMent study): a protocol combining map-based questionnaires, geographically explicit ecological momentary assessment and vocal biomarkers of stress
Bibliographic record
Abstract
INTRODUCTION: Stress is nearly ubiquitous in everyday life; however, it imposes a tremendous burden worldwide by acting as a risk factor for most physical and mental diseases. The effects of geographic environments on stress are supported by multiple theories acknowledging that natural environments act as a stress buffer and provide deeper and quicker restorative effects than most urban settings. However, little is known about how the temporalities of exposure to complex urban environments (duration, frequency and sequences of exposures) experienced in various locations - as shaped by people's daily activities - affect daily and chronic stress levels. The potential modifying effect of activity patterns (ie, time, place, activity type and social company) on the environment-stress relationship also remains poorly understood. Moreover, most observational studies relied quasi-exclusively on self-reported stress measurements, which may not accurately reflect the individual physiological embodiment of stress. The FragMent study aims to assess the extent to which the spatial and temporal characteristics of exposures to environments in daily life, along with individuals' activity patterns, influence physiological and psychological stress. METHODS AND ANALYSIS: A sample of 2000 adults aged 18-65 and residing in the country of Luxembourg completed a traditional and a map-based questionnaire to collect data on their perceived built, natural and social environments, regular mobility, activity patterns and chronic stress at baseline. A subsample of 200 participants engaged in a 15-day geographically explicit ecological momentary assessment (GEMA) survey, combining a smartphone-enabled global positioning system (GPS) tracking and the repeated daily assessment of the participants' momentary stress, activities and environmental perceptions. Participants further complete multiple daily vocal tasks to collect data on vocal biomarkers of stress. Analytical methods will include machine learning models for stress prediction from vocal features, the use of geographic information systems (GIS) to quantify dynamic environmental exposures in space and time, and statistical models to disentangle the environment-stress relationships. ETHICS AND DISSEMINATION: Ethical approval (LISER REC/2021/024.FRAGMENT/4-5-9-10) was granted by the Research Ethics Committee of the Luxembourg Institute of Socio-Economic Research (LISER), Luxembourg. Results will be disseminated via conferences, peer-review journal papers and comic strips. All project outcomes will be made available at https://www.fragmentproject.eu/.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".