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BackgroundThe World Health Organization has recognized the importance of assessing population-level mental health during the COVID-19 pandemic. During a global crisis such as the COVID-19 pandemic, a timely surveillance method is urgently needed to track the impact on public mental health. ObjectiveThis brief systematic review focused on the efficiency and quality of data collection of studies conducted during the COVID-19 pandemic. MethodsWe searched the PubMed database using the following search strings: ((COVID-19) OR (SARS-CoV-2)) AND ((Mental health) OR (psychological) OR (psychiatry)). We screened the titles, abstracts, and texts of the published papers to exclude irrelevant studies. We used the Newcastle-Ottawa Scale to evaluate the quality of each research paper. ResultsOur search yielded 37 relevant mental health surveys of the general public that were conducted during the COVID-19 pandemic, as of July 10, 2020. All these public mental health surveys were cross-sectional in design, and the journals efficiently made these articles available online in an average of 18.7 (range 1-64) days from the date they were received. The average duration of recruitment periods was 9.2 (range 2-35) days, and the average sample size was 5137 (range 100-56,679). However, 73% (27/37) of the selected studies had Newcastle-Ottawa Scale scores of

BackgroundA mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. ObjectiveThe primary aim of this study was to quantify user retention for the “Staff Hours” app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention. MethodsWe developed an app called “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. ResultsA total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700). ConclusionsOur findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

BackgroundReal-time global mental health surveillance is urgently needed for tracking the long-term impact of the COVID-19 pandemic. ObjectiveThis study aimed to use Google Trends data to investigate the impact of the pandemic on global mental health by analyzing three keywords indicative of mental distress: “insomnia,” “depression,” and “suicide.” MethodsWe examined increases in search queries for 19 countries. Significant increases were defined as the actual daily search value (from March 20 to April 19, 2020) being higher than the 95% CIs of the forecast from the 3-month baseline via ARIMA (autoregressive integrated moving average) modeling. We examined the correlation between increases in COVID-19–related deaths and the number of days with significant increases in search volumes for insomnia, depression, and suicide across multiple nations. ResultsThe countries with the greatest increases in searches for insomnia were Iran, Spain, the United States, and Italy; these countries exhibited a significant increase in insomnia searches on more than 10 of the 31 days observed. The number of COVID-19–related deaths was positively correlated to the number of days with an increase in searches for insomnia in the 19 countries (ρ=0.64, P=.003). By contrast, there was no significant correlation between the number of deaths and increases in searches for depression (ρ=–0.12, P=.63) or suicide (ρ=–0.07, P=.79). ConclusionsOur analysis suggests that insomnia could be a part of routine mental health screening during the COVID-19 pandemic.

BackgroundThere are numerous mobile apps for tracking work hours, but only a few of them record work hours automatically instead of relying on manual logging. No apps have been customized for medical staff, whose work schedules are highly complicated as they have both regular hours and on-call duties. ObjectiveThe specific aims of this study were to (1) identify the Staff Hours app users’ GPS-defined work hours, (2) examine the overtime work hours from the app-recorded total work hours and the participants’ self-reported scheduled work hours, and (3) compare these app-recorded total work hours among different occupations. MethodsWe developed an app, Staff Hours, to automatically calculate a user’s work hours via GPS background data. Users can enter their scheduled hours, including regular hours and on-call duties. The app automatically generates overtime reports by comparing the app-recorded total work hours with the user-defined scheduled hours. A total of 183 volunteers (60 females and 123 males; mean age 32.98 years, SD 6.74) were included in this study. Most of the participants (162/183, 88.5%) were medical staff, and their positions were resident physicians (n=89), visiting staff (n=38), medical students (n=10), registered nurses (n=25), and non–health care professionals (non-HCPs; n=21). ResultsThe total work hours (mean 55.69 hours, SD 21.34) of the 183 participants were significantly higher than their scheduled work hours (mean 50.67 hours, SD 21.44; P=.01). Medical staff had significantly longer total work hours (mean 57.01 hours, SD 21.20) than non-HCPs (mean 45.48 hours, SD 20.08; P=.02). Residents (mean 60.38 hours, SD 18.67) had significantly longer work hours than visiting staff (mean 51.42 hours, SD 20.33; P=.03) and non-HCPs (mean 45.48 hours, SD 20.08; P=.004). ConclusionsStaff Hours is the first automatic GPS location–based app designed for medical staff to track work hours and calculate overtime. For medical staff, this app could keep complete and accurate records of work hours in real time, reduce bias, and allow for better complying with labor regulations.

BackgroundModern smartphone use is pervasive and could be an accessible method of evaluating the circadian rhythm and social jet lag via a mobile app. ObjectiveThis study aimed to validate the app-recorded sleep time with daily self-reports by examining the consistency of total sleep time (TST), as well as the timing of sleep onset and wake time, and to validate the app-recorded circadian rhythm with the corresponding 30-day self-reported midpoint of sleep and the consistency of social jetlag. MethodsThe mobile app, Rhythm, recorded parameters and these parameters were hypothesized to be used to infer a relative long-term pattern of the circadian rhythm. In total, 28 volunteers downloaded the app, and 30 days of automatically recorded data along with self-reported sleep measures were collected. ResultsNo significant difference was noted between app-recorded and self-reported midpoint of sleep time and between app-recorded and self-reported social jetlag. The overall correlation coefficient of app-recorded and self-reported midpoint of sleep time was .87. ConclusionsThe circadian rhythm for 1 month, daily TST, and timing of sleep onset could be automatically calculated by the app and algorithm.

BackgroundAssessing human behaviors via smartphone for monitoring the pattern of daily behaviors has become a crucial issue in this century. Thus, a more accurate and structured methodology is needed for smartphone use research. ObjectiveThe study aimed to investigate the duration of data collection needed to establish a reliable pattern of use, how long a smartphone use cycle could perpetuate by assessing maximum time intervals between 2 smartphone periods, and to validate smartphone use and use/nonuse reciprocity parameters. MethodsUsing the Know Addiction database, we selected 33 participants and passively recorded their smartphone usage patterns for at least 8 weeks. We generated 4 parameters on the basis of smartphone use episodes, including total use frequency, total use duration, proactive use frequency, and proactive use duration. A total of 3 additional parameters (root mean square of successive differences, Control Index, and Similarity Index) were calculated to reflect impaired control and compulsive use. ResultsOur findings included (1) proactive use duration correlated with subjective smartphone addiction scores, (2) a 2-week period of data collection is required to infer a 2-month period of smartphone use, and (3) smartphone use cycles with a time gap of 4 weeks between them are highly likely independent cycles. ConclusionsThis study validated temporal stability for smartphone use patterns recorded by a mobile app. The results may provide researchers an opportunity to investigate human behaviors with more structured methods.

The COVID-19 pandemic forced universities worldwide to make the switch to online instruction, raising concerns about the quality of online courses and their impact on student satisfaction and engagement. This study aimed to explore Korean university students' satisfaction levels with online English-mediated instruction (EMI) courses during the pandemic and identify factors that influence class satisfaction. The purpose of this study was to provide insights into how EMI instructors could improve their online teaching practices during and after a pandemic. The hypothesis was that instructional strategies (IS), academic conscientiousness (AC), and academic integration (AI) could mediate the link between engagement and satisfaction. The study used a survey design to collect data from 219 Korean university students who took online EMI courses during the spring 2020 semester. The survey collected demographic information as well as students' perceptions of valuable IS, AC, AI, and satisfaction. Data analysis included independent samples t-test, correlation analysis, Structural Equation Modelling (SEM), and multiple regression analysis. The results showed IS use and satisfaction differed among disciplines. Specifically, there were significant differences in satisfaction levels between Arts, STEM, Business, Social Sciences, and Literature and Languages majors. Additionally, there were significant relationships between demographics, AC, AI, IS use, and satisfaction. SEM was used to provide a general view of factors mediating the link between engagement and satisfaction. The results revealed that AC, AI, and IS use mediated the link between engagement and satisfaction. Multiple regression analysis showed that students were more satisfied with instructors who demonstrated care and warmth using social networking sites to communicate. Overall, this study provides valuable insights into student satisfaction with online EMI courses during the COVID-19 pandemic and for the future of online EMI teaching-learning. The findings suggest that online EMI instructors should consider using social networking sites to communicate with students in order to increase satisfaction levels. Additionally, instructors should be aware that different disciplines may require different instructional strategies to maximize student engagement and satisfaction.

It is unknown which posttranscriptional regulatory mechanisms are required for oncogenic competence. Here, we show that the LIN28 family of RNA-binding proteins (RBPs), which facilitate posttranscriptional RNA metabolism within ribonucleoprotein networks, is essential for the initiation of diverse oncotypes of hepatocellular carcinoma (HCC). In HCC models driven by [NRAS.sup.G12V]/Tp53, CTNNB1/YAP/TpS3, or AKT/Tp53, mice without Lin28a and Lin28b were markedly impaired in cancer initiation. We biochemically defined an oncofetal regulon of 15 factors connected to LIN28 through direct mRNA and protein interactions. Interestingly, all were RBPs and only 1 of 15 was a Let-7 target. Polysome profiling and reporter assays showed that LIN28B directly increased the translation of 8 of these 15 RBPs. As expected, overexpression of LIN28B and IGFBP1-3 was able to genetically rescue cancer initiation. Using this platform to probe components downstream of LIN28, we found that 8 target RBPs were able to restore [NRAS.sup.G12V]/Tp53 cancer formation in Lin28a/Lin28b-deficient mice. Furthermore, these LIN28B targets promote cancer initiation through an increase in protein synthesis. LIN28B, central to an RNP regulon that increases translation of RBPs, is important for tumor initiation in the liver., Introduction In chronically inflamed tissues subject to field cancerization, tumor initiation occurs repeatedly in different parts of the organ over long time periods. For cancer mutations to drive transformation, cells [...]

Flipped classroom is one of the important teaching modes among many novel teaching methods in recent years, students watch the video in the pre-class. However, if students cannot focus on the pre-class video learning or have problems with the learning content, the learning effect will be less than expected. Therefore, this research proposes a flipped classroom combined with the WPAQ learning model extended by the WSQ learning model to carry out programming courses, allowing students to perform flipped classroom learning with peers online before class and jointly complete the tasks of flipped classroom learning lists, and share with them a summary of learning content to enhance learning effectiveness. The research results show that the use of flipped classrooms combined with the WPAQ learning model of the learning system can help students improve learning effectiveness, learning motivation, self-efficacy, reflective ability, programming learning attitudes, and reduce students' cognitive load.

The amounts of the intracellular glycosylation, O-GlcNAc modification, are increased in essentially all tumors when compared to healthy tissue, and lowering O-GlcNAcylation levels results in reduced tumorigenesis and increased cancer cell death. Therefore, the pharmacological reduction of O-GlcNAc may represent a therapeutic vulnerability. The most direct approach to this goal is the inhibition of O-GlcNAc transferase (OGT), the enzyme that directly adds the modification to proteins. However, despite some recent success, this enzyme has proven difficult to inhibit. An alternative strategy involves starving OGT of its sugar substrate UDP-GlcNAc by targeting enzymes of the hexosamine biosynthetic pathway (HBP). Here, we explore the potential of the rate-determining enzyme of this pathway, glutamine fructose-6-phosphate amidotransferase (GFAT). We first show that CRISPR-mediated knockout of GFAT results in inhibition of cancer cell growth in vitro and a xenograft model that correlates with O-GlcNAcylation levels. We then demonstrate that pharmacological inhibition of GFAT sensitizes a small panel of cancer cells to undergo apoptosis in response to diamide-induced oxidative stress. Finally, we find that GFAT expression and O-GlcNAc levels are increased in a spontaneous mouse model of liver cancer. Together these experiments support the further development of inhibitors of the HBP as an indirect approach to lowering O-GlcNAcylation levels in cancer.