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.
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.
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/.
by Vahid Sadeghi, Alireza Mehridehnavi, Maryam Behdad, Alireza Vard, Mina Omrani, Mohsen Sharifi, Yasaman Sanahmadi, Niloufar Teyfouri
A considerable amount of undesirable factors in the wireless capsule endoscopy (WCE) procedure hinder the proper visualization of the small bowel and take gastroenterologists more time to review. Objective quantitative assessment of different bowel preparation paradigms and saving the physician reviewing time motivated us to present an automatic low-cost statistical model for automatically segmenting of clean and contaminated regions in the WCE images. In the model construction phase, only 20 manually pixel-labeled images have been used from the normal and reduced mucosal view classes of the Kvasir capsule endoscopy dataset. In addition to calculating prior probability, two different probabilistic tri-variate Gaussian distribution models (GDMs) with unique mean vectors and covariance matrices have been fitted to the concatenated RGB color pixel intensity values of clean and contaminated regions separately. Applying the Bayes rule, the membership probability of every pixel of the input test image to each of the two classes is evaluated. The robustness has been evaluated using 5 trials; in each round, from the total number of 2000 randomly selected images, 20 and 1980 images have been used for model construction and evaluation modes, respectively. Our experimental results indicate that accuracy, precision, specificity, sensitivity, area under the receiver operating characteristic curve (AUROC), dice similarity coefficient (DSC), and intersection over union (IOU) are 0.89 ± 0.07, 0.91 ± 0.07, 0.73 ± 0.20, 0.90 ± 0.12, 0.92 ± 0.06, 0.92 ± 0.05 and 0.86 ± 0.09, respectively. The presented scheme is easy to deploy for objectively assessing small bowel cleansing score, comparing different bowel preparation paradigms, and decreasing the inspection time. The results from the SEE-AI project dataset and CECleanliness database proved that the proposed scheme has good adaptability.