Pharmageek
journée d'étude sur les libertés numériques en temps de covid-19. technologie, données de santé et droit, organisé par l'oppee, université de bordeaux ...
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Background: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results.
Objective: The aim of this study was to evaluate patients’ compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort.
Methods: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants’ biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study.
Results: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r=0.23; P=.01). In addition, age (r=0.30; P=.001), BMI (r=0.19; P=.03), and the severity of symptoms (r=0.19; P=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R2=0.1; β=1.6; P=.01).
Conclusions: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables.
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Member States of the World Health Organization have agreed to a global process to draft and negotiate a convention, agreement or other international instrument under the Constitution of the World Health Organization to strengthen pandemic prevention, preparedness and response.
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Author summary Capturing food exposure of school children is a challenging task due to recall bias. In this manuscript, we describe a machine-learning-based data-collection tool that can automatically record school children’s exposure to food items, food advertisements and food outlets in their homes, schools and neighborhoods. Our data-collection tool consists of a wearable camera to capture continuous footage of children’s environments during a typical school day, and a series of machine learning models that can extract food-related images from the recorded footage and classify them into images that contain food items consumed by the child wearing the camera, or consumed by others, images that contain food advertisements, and images that contain food outlets. We report on a user-centered design study that assessed the acceptability of using wearable cameras to capture food exposure among school children in two urban Arab centers, namely Greater Beirut in Lebanon and Greater Tunis in Tunisia. We then describe how we trained our various machine learning models to capture food exposure among school children and categorize such food exposure into a predefined typology. Finally, we also report on the results of deploying our data-collection tool in a real-world case study in Tunisia.
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