ABSTRACT
This study reports the physical activity (PA) levels among medical and nursing students at a university in Bahrain during the COVID-19 pandemic. Through self-selection sampling of an online survey, participants' data on general demographics, PA levels before and during the COVID-19 pandemic and reasons for PA changes were collected. From the 110 valid responses, 70 participants (63%) experienced a decrease in PA during the COVID-19 lockdown. Fear of contracting COVID-19 and lack of motivation were two significant reasons for reduced PA levels (p < 0.001) compared to those who did not experience a decrease in PA. Other factors significantly associated with reduced PA levels include living alone (p < 0.018) or with roommates (p < 0.006) compared to living with family. Having more time available was associated with positive changes to PA levels (p < 0.001). Significant differences in MET-min/week were seen between students who experienced increased PA (median of 1605 MET-min/week) compared to those who experienced a decrease (424 MET-min/week) or no change (1070 MET-min/week) in PA levels (p < 0.001). In conclusion, low PA levels are prevalent within medical and nursing students in Bahrain (51% reported < 600 MET-min/week), with ~2 in 3 students reporting a decrease in PA levels during the COVID-19 pandemic. Support programs and strategies to increase engagement in PA within this population are warranted.
Subject(s)
COVID-19 , Students, Nursing , Humans , COVID-19/epidemiology , Pandemics , Communicable Disease Control , Exercise/physiologyABSTRACT
The wildfire-like spread of COVID-19, caused by severe acute respiratory syndrome-associated coronavirus-2, has resulted in a pandemic that has put unprecedented stress on the world's healthcare systems and caused varying severities of socio-economic damage. As there are no specific treatments to combat the virus, current approaches to overcome the crisis have mainly revolved around vaccination efforts, preventing human-to-human transmission through enforcement of lockdowns and repurposing of drugs. To efficiently facilitate the measures implemented by governments, rapid and accurate diagnosis of the disease is vital. Reverse-transcription polymerase chain reaction and computed tomography have been the standard procedures to diagnose and evaluate COVID-19. However, disadvantages, including the necessity of specialized equipment and trained personnel, the high financial cost of operation and the emergence of false negatives, have hindered their application in high-demand and resource-limited sites. Nanoparticle-based methods of diagnosis have been previously reported to provide precise results within short periods of time. Such methods have been studied in previous outbreaks of coronaviruses, including severe acute respiratory syndrome-associated coronavirus and middle east respiratory syndrome coronavirus. Given the need for rapid diagnostic techniques, this review discusses nanoparticle use in detecting the aforementioned coronaviruses and the recent severe acute respiratory syndrome-associated coronavirus-2 to highlight approaches that could potentially be used during the COVID-19 pandemic.
ABSTRACT
The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.
ABSTRACT
The SARS-CoV-2 virus, which causes the COVID-19 pandemic, has had an unprecedented impact on healthcare requiring multidisciplinary innovation and novel thinking to minimize impact and improve outcomes. Wide-ranging disciplines have collaborated including diverse clinicians (radiology, microbiology, and critical care), who are working increasingly closely with data-science. This has been leveraged through the democratization of data-science with the increasing availability of easy to access open datasets, tutorials, programming languages, and hardware which makes it significantly easier to create mathematical models. To address the COVID-19 pandemic, such data-science has enabled modeling of the impact of the virus on the population and individuals for diagnostic, prognostic, and epidemiological ends. This has led to two large systematic reviews on this topic that have highlighted the two different ways in which this feat has been attempted: one using classical statistics and the other using more novel machine learning techniques. In this review, we debate the relative strengths and weaknesses of each method toward the specific task of predicting COVID-19 outcomes.
ABSTRACT
BACKGROUND: The artificial neural network (ANN) is an increasingly important tool in the context of solving complex medical classification problems. However, one of the principal challenges in leveraging artificial intelligence technology in the health care setting has been the relative inability to translate models into clinician workflow. OBJECTIVE: Here we demonstrate the development of a COVID-19 outcome prediction app that utilizes an ANN and assesses its usability in the clinical setting. METHODS: Usability assessment was conducted using the app, followed by a semistructured end-user interview. Usability was specified by effectiveness, efficiency, and satisfaction measures. These data were reported with descriptive statistics. The end-user interview data were analyzed using the thematic framework method, which allowed for the development of themes from the interview narratives. In total, 31 National Health Service physicians at a West London teaching hospital, including foundation physicians, senior house officers, registrars, and consultants, were included in this study. RESULTS: All participants were able to complete the assessment, with a mean time to complete separate patient vignettes of 59.35 (SD 10.35) seconds. The mean system usability scale score was 91.94 (SD 8.54), which corresponds to a qualitative rating of "excellent." The clinicians found the app intuitive and easy to use, with the majority describing its predictions as a useful adjunct to their clinical practice. The main concern was related to the use of the app in isolation rather than in conjunction with other clinical parameters. However, most clinicians speculated that the app could positively reinforce or validate their clinical decision-making. CONCLUSIONS: Translating artificial intelligence technologies into the clinical setting remains an important but challenging task. We demonstrate the effectiveness, efficiency, and system usability of a web-based app designed to predict the outcomes of patients with COVID-19 from an ANN.