The Impact of Social Media Use for News on Academic Performance in Underrepresented Undergraduate College Students.

Researchers have demonstrated that the relationship between social media use (SMU) and academic performance is mixed. This study extends those findings by examining how SMU for news predicts grade point average (GPA) for Hispanic, Black/African American, and White college students while controlling for gender. Students (N = 378) completed surveys where they reported how many times per week they used various social media platforms for news, whether or not they used those platforms to consume certain types of news, and demographic information. For Hispanic students, results indicated that using YouTube for entertainment news predicted lower GPAs, whereas using YouTube for news predicted higher GPAs. Black/African American students' use of Facebook for news predicted lower GPAs. SMU for news for White students did not predict GPA. Findings indicate that race/ethnicity should be considered when examining the relationship between SMU and academic performance and that minority students' GPAs are affected by their use of social media for news.

A Novel Method for the Extraction of Fetal ECG Signals from Wearable Devices.

The role of fetal surveillance for the prediction and timely assessment of fetal distress is widely established. Fetal ECG (fECG) monitoring via wearable devices is a feasible solution for performing continuous monitoring of fetal wellbeing and it has seen a net increase in popularity in recent years. In this paper, we propose a novel adaptation of the Smart AdaptiVe Ecg Recognition (SAVER) algorithm for the detection of fECG in long-duration recordings acquired in clinical as well as unconventional settings. The methodology was trained and tested on 50 recordings of duration 1 hour ( 59.33 ±5.54 min) obtained using the Monica AN24 fetal monitor. We validated the performance against the automatic extraction performed by the Monica DK software. Our results show superior reliability of the proposed methodology in extracting fECG and associated estimates of fetal heart rate (fHR). Clinical relevance- The proposed methodology provides an efficient and reliable approach for the extraction of fECG signals acquired via wearable technologies, enabling continuous monitoring of fECG in and outside clinical settings.