Dataset Information

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

ABSTRACT: BACKGROUND:Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened. OBJECTIVE:This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak. METHODS:The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group. RESULTS:The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application. CONCLUSIONS:We developed an expert-opinion-based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.

SUBMITTER: Heo J

PROVIDER: S-EPMC7652594 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

Heo JoonNyung J Sung MinDong M Yoon Sangchul S Jang Jinkyu J Lee Wonwoo W Han Deokjae D Kim Hyung-Jun HJ Kim Han-Kyeol HK Han Ji Hyuk JH Seog Woong W Ha Beomman B Park Yu Rang YR

Journal of medical Internet research 20201106 11

<h4>Background</h4>Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened.<h4>Objective</h4>This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medi ...[more]

PMID: 33079692

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Project description:BackgroundThe COVID-19 pandemic has forced drastic changes to daily life, from the implementation of stay-at-home orders to mandating facial coverings and limiting in-person gatherings. While the relaxation of these control measures has varied geographically, it is widely agreed that contact tracing efforts will play a major role in the successful reopening of businesses and schools. As the volume of positive cases has increased in the United States, it has become clear that there is room for digital health interventions to assist in contact tracing.ObjectiveThe goal of this study was to evaluate the use of a mobile-friendly app designed to supplement manual COVID-19 contact tracing efforts on a university campus. Here, we present the results of a development and validation study centered around the use of the MyCOVIDKey app on the Vanderbilt University campus during the summer of 2020.MethodsWe performed a 6-week pilot study in the Stevenson Center Science and Engineering Complex on Vanderbilt University's campus in Nashville, TN. Graduate students, postdoctoral fellows, faculty, and staff >18 years who worked in Stevenson Center and had access to a mobile phone were eligible to register for a MyCOVIDKey account. All users were encouraged to complete regular self-assessments of COVID-19 risk and to key in to sites by scanning a location-specific barcode.ResultsBetween June 17, 2020, and July 29, 2020, 45 unique participants created MyCOVIDKey accounts. These users performed 227 self-assessments and 1410 key-ins. Self-assessments were performed by 89% (n=40) of users, 71% (n=32) of users keyed in, and 48 unique locations (of 71 possible locations) were visited. Overall, 89% (202/227) of assessments were determined to be low risk (ie, asymptomatic with no known exposures), and these assessments yielded a CLEAR status. The remaining self-assessments received a status of NOT CLEAR, indicating either risk of exposure or symptoms suggestive of COVID-19 (7.5% [n=17] and 3.5% [n=8] of self-assessments indicated moderate and high risk, respectively). These 25 instances came from 8 unique users, and in 19 of these instances, the at-risk user keyed in to a location on campus.ConclusionsDigital contact tracing tools may be useful in assisting organizations to identify persons at risk of COVID-19 through contact tracing, or in locating places that may need to be cleaned or disinfected after being visited by an index case. Incentives to continue the use of such tools can improve uptake, and their continued usage increases utility to both organizational and public health efforts. Parameters of digital tools, including MyCOVIDKey, should ideally be optimized to supplement existing contact tracing efforts. These tools represent a critical addition to manual contact tracing efforts during reopening and sustained regular activity.

Project description:BackgroundThe COVID-19 pandemic has significantly impacted mental health and well-being. Mobile mental health apps can be scalable and useful tools in large-scale disaster responses and are particularly promising for reaching vulnerable populations. COVID Coach is a free, evidence-informed mobile app designed specifically to provide tools and resources for addressing COVID-19-related stress.ObjectiveThe purpose of this study was to characterize the overall usage of COVID Coach, explore retention and return usage, and assess whether the app was reaching individuals who may benefit from mental health resources.MethodsAnonymous usage data collected from COVID Coach between May 1, 2020, through October 31, 2020, were extracted and analyzed for this study. The sample included 49,287 unique user codes and 3,368,931 in-app events.ResultsUsage of interactive tools for coping and stress management comprised the majority of key app events (n=325,691, 70.4%), and the majority of app users tried a tool for managing stress (n=28,009, 58.8%). COVID Coach was utilized for ≤3 days by 80.9% (n=34,611) of the sample whose first day of app use occurred within the 6-month observation window. Usage of the key content in COVID Coach predicted returning to the app for a second day. Among those who tried at least one coping tool on their first day of app use, 57.2% (n=11,444) returned for a second visit; whereas only 46.3% (n=10,546) of those who did not try a tool returned (P<.001). Symptoms of anxiety, depression, and posttraumatic stress disorder (PTSD) were prevalent among app users. For example, among app users who completed an anxiety assessment on their first day of app use (n=4870, 11.4% of users), 55.1% (n=2680) reported levels of anxiety that were moderate to severe, and 29.9% (n=1455) of scores fell into the severe symptom range. On average, those with moderate levels of depression on their first day of app use returned to the app for a greater number of days (mean 3.72 days) than those with minimal symptoms (mean 3.08 days; t1=3.01, P=.003). Individuals with significant PTSD symptoms on their first day of app use utilized the app for a significantly greater number of days (mean 3.79 days) than those with fewer symptoms (mean 3.13 days; t1=2.29, P=.02).ConclusionsAs the mental health impacts of the pandemic continue to be widespread and increasing, digital health resources, such as apps like COVID Coach, are a scalable way to provide evidence-informed tools and resources. Future research is needed to better understand for whom and under what conditions the app is most helpful and how to increase and sustain engagement.

Project description:BackgroundContact tracing apps are considered useful means to monitor SARS-CoV-2 infections during the off-peak stages of the COVID-19 pandemic. Their effectiveness is, however, dependent on the uptake of such COVID-19 apps.ObjectiveWe examined the role of individuals' general health status in their willingness to use a COVID-19 tracing app as well as the roles of socioeconomic characteristics and COVID-19 proximity.MethodsWe drew data from the WageIndicator Foundation Living and Working in Coronavirus Times survey. The survey collected data on labor market status as well as the potential confounders of the relationship between general health and COVID-19 tracing app usage, such as sociodemographics and regular smartphone usage data. The survey also contained information that allowed us to examine the role of COVID-19 proximity, such as whether an individual has contracted SARS-CoV-2, whether an individual has family members and colleagues with COVID-19, and whether an individual exhibits COVID-19 pandemic-induced depressive and anxiety symptoms. We selected data that were collected in Spain, Italy, Germany, and the Netherlands from individuals aged between 18 and 70 years (N=4504). Logistic regressions were used to measure individuals' willingness to use a COVID-19 tracing app.ResultsWe found that the influence that socioeconomic factors have on COVID-19 tracing app usage varied dramatically between the four countries, although individuals experiencing forms of not being employed (ie, recent job loss and inactivity) consistently had a lower willingness to use a contact tracing app (effect size: 24.6%) compared to that of employees (effect size: 33.4%; P<.001). Among the selected COVID-19 proximity indicators, having a close family member with SARS-CoV-2 infection was associated with higher contact tracing app usage (effect size: 36.3% vs 27.1%; P<.001). After accounting for these proximity factors and the country-based variations therein, we found that having a poorer general health status was significantly associated with a much higher likelihood of contact tracing app usage; compared to a self-reported "very good" health status (estimated probability of contact tracing app use: 29.6%), the "good" (estimated probability: +4.6%; 95% CI 1.2%-8.1%) and "fair or bad" (estimated probability: +6.3%; 95% CI 2.3%-10.3%) health statuses were associated with a markedly higher willingness to use a COVID-19 tracing app.ConclusionsCurrent public health policies aim to promote the use of smartphone-based contact tracing apps during the off-peak periods of the COVID-19 pandemic. Campaigns that emphasize the health benefits of COVID-19 tracing apps may contribute the most to the uptake of such apps. Public health campaigns that rely on digital platforms would also benefit from seriously considering the country-specific distribution of privacy concerns.

Project description:BackgroundAt the onset of the COVID-19 pandemic general practitioners complained about feeling uninformed and lacking a sufficient flow of information from the local health authorities. Secure instant messaging describes a digital, chat-based form of communication enabling ambulatory care providers to connect in real-time and share information across medial sectors. KomPan, a proof-of-concept study, established a secure instant messaging structure in two model regions in Germany to improve communication between general practitioners and local health authorities via an additional communication pathway. This paper presents results of a qualitative user survey.MethodsWe recruited general practitioners (n = 43) and staff of local health authorities (n=10) in two Hessian model regions for using the secure instant messaging (SIM) app of the Famedly GmbH, starting in December 2020 (2nd COVID-19 wave). We asked participants to share their usage experiences after a usage time of multiple months. In guided telephone interviews, we primarily asked how communication between the two user groups had changed while using secure instant messaging. The interviews were transcribed verbatim and analysed using thematic analysis.ResultsWe conducted ten interviews with general practitioners and two with staff of the participating local health authorities. Using our SIM-based platform promoted professional exchange between general practitioners. In contrast, the app had little impact on communication between local health authorities and general practitioners. Opposing expectations and usage patterns of the two user groups, among other reasons, probably led to a reduced direct trans-sectoral communication via secure instant messaging.ConclusionEstablishing local chat groups for general practitioners was welcomed, especially during the pandemic situation, to improve professional exchange while experiencing challenging working conditions. To use secure instant messaging effectively for trans-sectoral communication a more comprehensive approach seems to be needed, such as digitalisation of institutional communication structures and improved networks of local healthcare providers.

Project description:BackgroundAnxiety symptoms during public health crises are associated with adverse psychiatric outcomes and impaired health decision-making. The interaction between real-time social media use patterns and clinical anxiety during infectious disease outbreaks is underexplored.ObjectiveWe aimed to evaluate the usage pattern of 2 types of social media apps (communication and social networking) among patients in outpatient psychiatric treatment during the COVID-19 surge and lockdown in Madrid, Spain and their short-term anxiety symptoms (7-item General Anxiety Disorder scale) at clinical follow-up.MethodsThe individual-level shifts in median social media usage behavior from February 1 through May 3, 2020 were summarized using repeated measures analysis of variance that accounted for the fixed effects of the lockdown (prelockdown versus postlockdown), group (clinical anxiety group versus nonclinical anxiety group), the interaction of lockdown and group, and random effects of users. A machine learning-based approach that combined a hidden Markov model and logistic regression was applied to predict clinical anxiety (n=44) and nonclinical anxiety (n=51), based on longitudinal time-series data that comprised communication and social networking app usage (in seconds) as well as anxiety-associated clinical survey variables, including the presence of an essential worker in the household, worries about life instability, changes in social interaction frequency during the lockdown, cohabitation status, and health status.ResultsIndividual-level analysis of daily social media usage showed that the increase in communication app usage from prelockdown to lockdown period was significantly smaller in the clinical anxiety group than that in the nonclinical anxiety group (F1,72=3.84, P=.05). The machine learning model achieved a mean accuracy of 62.30% (SD 16%) and area under the receiver operating curve 0.70 (SD 0.19) in 10-fold cross-validation in identifying the clinical anxiety group.ConclusionsPatients who reported severe anxiety symptoms were less active in communication apps after the mandated lockdown and more engaged in social networking apps in the overall period, which suggested that there was a different pattern of digital social behavior for adapting to the crisis. Predictive modeling using digital biomarkers-passive-sensing of shifts in category-based social media app usage during the lockdown-can identify individuals at risk for psychiatric sequelae.

Dataset Information

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

Publications

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.

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