Dataset Information

Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers.

ABSTRACT: BACKGROUND:Hospital workers have been the most frequently and severely affected professional group during the COVID-19 pandemic, and have a big impact on transmission. In this context, innovative tools are required to measure the symptoms compatible with COVID-19, the spread of infection, and testing capabilities within hospitals in real time. OBJECTIVE:We aimed to develop and test an effective and user-friendly tool to identify and track symptoms compatible with COVID-19 in hospital workers. METHODS:We developed and pilot tested Hospital Epidemics Tracker (HEpiTracker), a newly designed app to track the spread of COVID-19 among hospital workers. Hospital staff in 9 hospital centers across 5 Spanish regions (Andalusia, Balearics, Catalonia, Galicia, and Madrid) were invited to download the app on their phones and to register their daily body temperature, COVID-19-compatible symptoms, and general health score, as well as any polymerase chain reaction and serological test results. RESULTS:A total of 477 hospital staff participated in the study between April 8 and June 2, 2020. Of note, both health-related (n=329) and non-health-related (n=148) professionals participated in the study; over two-thirds of participants (68.8%) were health workers (43.4% physicians and 25.4% nurses), while the proportion of non-health-related workers by center ranged from 40% to 85%. Most participants were female (n=323, 67.5%), with a mean age of 45.4 years (SD 10.6). Regarding smoking habits, 13.0% and 34.2% of participants were current or former smokers, respectively. The daily reporting of symptoms was highly variable across participating hospitals; although we observed a decline in adherence after an initial participation peak in some hospitals, other sites were characterized by low participation rates throughout the study period. CONCLUSIONS:HEpiTracker is an already available tool to monitor COVID-19 and other infectious diseases in hospital workers. This tool has already been tested in real conditions. HEpiTracker is available in Spanish, Portuguese, and English. It has the potential to become a customized asset to be used in future COVID-19 pandemic waves and other environments. TRIAL REGISTRATION:ClinicalTrials.gov NCT04326400; https://clinicaltrials.gov/ct2/show/NCT04326400.

SUBMITTER: Soriano JB

PROVIDER: S-EPMC7511223 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers.

Soriano Joan B JB Fernández Esteve E de Astorza Álvaro Á Pérez de Llano Luis A LA Fernández-Villar Alberto A Carnicer-Pont Dolors D Alcázar-Navarrete Bernardino B García Arturo A Morales Aurelio A Lobo María M Maroto Marcos M Ferreras Eloy E Soriano Cecilia C Del Rio-Bermudez Carlos C Vega-Piris Lorena L Basagaña Xavier X Muncunill Josep J Cosio Borja G BG Lumbreras Sara S Catalina Carlos C Alzaga José María JM Gómez Quilón David D Valdivia Carlos Alberto CA de Lara Celia C Ancochea Julio J

JMIR public health and surveillance 20200921 3

<h4>Background</h4>Hospital workers have been the most frequently and severely affected professional group during the COVID-19 pandemic, and have a big impact on transmission. In this context, innovative tools are required to measure the symptoms compatible with COVID-19, the spread of infection, and testing capabilities within hospitals in real time.<h4>Objective</h4>We aimed to develop and test an effective and user-friendly tool to identify and track symptoms compatible with COVID-19 in hospi ...[more]

PMID: 32845852

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Project description:BackgroundCOVID-19 has significantly altered health care delivery, requiring clinicians and hospitals to adapt to rapidly changing hospital policies and social distancing guidelines. At our large academic medical center, clinicians reported that existing information on distribution channels, including emails and hospital intranet posts, was inadequate to keep everyone abreast with these changes. To address these challenges, we adapted a mobile app developed in-house to communicate critical changes in hospital policies and enable direct telephonic communication between clinical team members and hospitalized patients, to support social distancing guidelines and remote rounding.ObjectiveThis study aimed to describe the unique benefits and challenges of adapting an app developed in-house to facilitate communication and remote rounding during COVID-19.MethodsWe adapted moblMD, a mobile app available on the iOS and Android platforms. In conjunction with our Hospital Incident Command System, resident advisory council, and health system innovation center, we identified critical, time-sensitive policies for app usage. A shared collaborative document was used to align app-based communication with more traditional communication channels. To minimize synchronization efforts, we particularly focused on high-yield policies, and the time of last review and the corresponding reviewer were noted for each protocol. To facilitate social distancing and remote patient rounding, the app was also populated with a searchable directory of numbers to patient bedside phones and hospital locations. We monitored anonymized user activity from February 1 to July 31, 2020.ResultsOn its first release, 1104 clinicians downloaded moblMD during the observation period, of which 46% (n=508) of downloads occurred within 72 hours of initial release. COVID-19 policies in the app were reviewed most commonly during the first week (801 views). Users made sustained use of hospital phone dialing features, including weekly peaks of 2242 phone number dials, 1874 directory searches, and 277 patient room phone number searches through the last 2 weeks of the observation period. Furthermore, clinicians submitted 56 content- and phone number-related suggestions through moblMD.ConclusionsWe rapidly developed and deployed a communication-focused mobile app early during COVID-19, which has demonstrated initial and sustained value among clinicians in communicating with in-patients and each other during social distancing. Our internal innovation benefited from our team's familiarity with institutional structures, short feedback loops, limited security and privacy implications, and a path toward sustainability provided by our innovation center. Challenges in content management were overcome through synchronization efforts and timestamping review. As COVID-19 continues to alter health care delivery, user activity metrics suggest that our solution will remain important in our efforts to continue providing safe and up-to-date clinical care.

Project description:BackgroundNo versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap.MethodsOur web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters.ResultsWe illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world's fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed.ConclusionOur web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.

Project description:BackgroundThe rapid advancement in media technology has radically changed the way we learn and interact with one another. Games, with their engaging and interactive approach, hold promise in the delivery of knowledge and building of skills. This has potential in child and adolescent mental health work, where the lack of insight and motivation for therapy are major barriers to treatment. However, research on the use of serious games in mental health interventions for children and adolescents is still in its infancy.ObjectiveThis study adds to the research on serious games in mental health interventions through the development and evaluation of RegnaTales, a series of 6 mobile apps designed to help children and adolescents manage anger. We examined the usability and playability of RegnaTales, as well as children's aggression levels before and after the game play.MethodsA total of 72 children aged between 6 and 12 years were recruited for the study. Thirty-five participants had a clinical diagnosis of disruptive behavior disorders (DBD), whereas 37 were typically developing (TD) children. Each child played 1 of the 6 RegnaTales apps for approximately 50 min before completing the Playability and Usability Questionnaire. The Reactive-Proactive Aggression Questionnaire was completed before and after the game play.ResultsThe overall results showed high levels of enjoyment and playability. TD children and children with DBD had similar experienced fun and perceived playability scores on all 6 mobile apps. All 6 mobile apps garnered comparable experienced fun and perceived playability scores. Furthermore, 42% (5/12) to 67% (8/12) of the children indicated that they would like to play the games again. Importantly, children felt that they acquired skills in anger management, were motivated to use them in their daily lives, and felt confident that the skills would help them better manage their anger. Children reported significantly lower reactive aggression after playing the mobile apps Rage Raver (P=.001), Abaddon (P=.008), and RegnaTools (P=.03). These apps focused on the psychoeducation of the link between thoughts and emotions, as well as equipping the participants with various emotion regulation strategies such as relaxation and cognitive restructuring.ConclusionsThis study presents evidence to support RegnaTales as a feasible serious game. The preliminary findings associated with reduction in reactive aggression, coupled with future research to further establish its efficacy, could warrant RegnaTales as a potential intervention for anger issues among clinical and community populations.

Project description:BackgroundInternalizing disorders are the most common psychiatric problems observed among youth in Canada. Sadly, youth with internalizing disorders often avoid seeking clinical help and rarely receive adequate treatment. Current methods of assessing internalizing disorders usually rely on subjective symptom ratings, but internalizing symptoms are frequently underreported, which creates a barrier to the accurate assessment of these symptoms in youth. Therefore, novel assessment tools that use objective data need to be developed to meet the highest standards of reliability, feasibility, scalability, and affordability. Mobile sensing technologies, which unobtrusively record aspects of youth behaviors in their daily lives with the potential to make inferences about their mental health states, offer a possible method of addressing this assessment barrier.ObjectiveThis study aims to explore whether passively collected smartphone sensor data can be used to predict internalizing symptoms among youth in Canada.MethodsIn this study, the youth participants (N=122) completed self-report assessments of symptoms of anxiety, depression, and attention-deficit hyperactivity disorder. Next, the participants installed an app, which passively collected data about their mobility, screen time, sleep, and social interactions over 2 weeks. Then, we tested whether these passive sensor data could be used to predict internalizing symptoms among these youth participants.ResultsMore severe depressive symptoms correlated with more time spent stationary (r=0.293; P=.003), less mobility (r=0.271; P=.006), higher light intensity during the night (r=0.227; P=.02), and fewer outgoing calls (r=-0.244; P=.03). In contrast, more severe anxiety symptoms correlated with less time spent stationary (r=-0.249; P=.01) and greater mobility (r=0.234; P=.02). In addition, youths with higher anxiety scores spent more time on the screen (r=0.203; P=.049). Finally, adding passively collected smartphone sensor data to the prediction models of internalizing symptoms significantly improved their fit.ConclusionsPassively collected smartphone sensor data provide a useful way to monitor internalizing symptoms among youth. Although the results replicated findings from adult populations, to ensure clinical utility, they still need to be replicated in larger samples of youth. The work also highlights intervention opportunities via mobile technology to reduce the burden of internalizing symptoms early on.

Project description:BackgroundResearch that investigates standalone effects of a mobile phone-based cognitive behavioral therapy without any human contact for reducing both psychological distress and risky drinking has been advancing; however, the number of studies is still limited. A mobile phone app called Self Record that facilitates cognitive restructuring through self-monitoring of daily thoughts and activities was developed in Japan.ObjectiveThis study conducted a nonrandomized controlled pilot trial of the Self Record app to investigate standalone effects of the intervention on psychological distress and alcohol consumption among Japanese workers. Additionally, we examined moderating effects of negative mood regulation expectancies, which are beliefs about one's ability to control one's negative mood.MethodsA quasi-experimental design with a 1-month follow-up was conducted online in Japan from February 2016 to March 2016. A research marketing company recruited participants. The selection criteria were being a Japanese full-time worker (age 20-59 years), experiencing mild to moderate psychological distress, and having some interest in self-record apps. Assignment to group was based on participants' willingness to use the app in the study. All participants completed outcome measures of negative mood regulation expectancies, positive well-being, general distress, depression, anxiety, and typical/most weekly alcohol consumption.ResultsFrom the recruitment, 15.65% (1083/6921) of participants met the inclusion criteria. Of these, 51.43% (557/1083) enrolled in the study: 54.9% (306/557) in the intervention group and 45.1% (251/557) in the control group. At the 1-month follow-up, 15.3% (85/557) of participants had dropped out. Intention-to-treat analyses revealed that participants in the intervention group reported increased typical drinking (?2=.009) and heavy drinking (?2=.001). Adherence to using the app was low; 64.8% (199/306) of participants in the intervention group discontinued using the app on the first day. Additionally, 65.7% (366/557) of the total sample did not correctly answer the validity checks in the outcome measures (eg, "Please select 'mildly agree' for this item"). Therefore, per-protocol analyses were conducted after removing these participants. Results showed that continuing app users (42/127) in the intervention group reported increases in anxiety (?2=.006), typical drinking (?2=.005), and heavy drinking (?2=.007) compared to those in the control group (85/127). Negative mood regulation expectancies moderated the effects of the intervention for general distress (beta=.39).ConclusionsResults were contrary to our hypotheses. Self-recording methods of standalone mobile phone interventions may heighten individuals' awareness of their pathological thought and drinking behavior, but may be insufficient to decrease them unless combined with a more intense or face-to-face intervention. Limitations include high attrition in this study; measures to improve the response rate are discussed.

Dataset Information

Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers.

Publications

Hospital Epidemics Tracker (HEpiTracker): Description and pilot study of a mobile app to track COVID-19 in hospital workers.

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