Web App for Emotional Management During the COVID-19 Pandemic: Platform Development and Retrospective Analysis of Its Use Throughout Two Waves of the Outbreak in Spain (Preprint)

2022 ◽  
Author(s):  
Sara Guila Fidel-Kinori ◽  
Gerard Carot-Sans ◽  
Andres Cuartero ◽  
Damià Valero-Bover ◽  
Rosa Romà-Monfà ◽  
...  

BACKGROUND Quarantines and nationwide lockdowns dictated for containing the spread of the COVID-19 pandemic may lead to distress and increase the frequency of anxiety and depression symptoms among the general population. During the national lockdown of the first wave of the COVID-19 outbreak in Spain, we developed and launched a Web App to promote emotional self-care in the general population and facilitate contact with healthcare professionals. OBJECTIVE To describe the Web App and analyse its utilization pattern throughout two successive waves of the COVID-19 outbreak in Spain. METHODS The Web App targeted all individuals aged 18 years or more and was designed by adapting the contents of a mobile App for adjuvant treatment of post-traumatic stress disorder (i.e., the PTSD Coach App) to the general population and the pandemic/lockdown scenario. We retrospectively assessed the utilization pattern of the Web App using data systematically retrieved from Google Analytics. Data were grouped into three time periods, defined using a join point analysis of COVID-19 incidence in our area: first wave, between-wave period, and second wave. RESULTS The resulting Web App, named gesioemocional.cat, maintains the navigation structure of the PTSD Coach App, with three main modules: tools for emotional self-care, a self-assessment test, and professional resources for on-demand contact. The self-assessment test combines the Patient Health Questionnaire-2 (PHQ-2) and the 7-item Generalized Anxiety Disorder Scale (GAD-7) and offers professional contact in the advent of a high level of depression and anxiety; contact is prioritized according to a screening questionnaire administered at the time of obtaining individual consent to be contacted. The tools for emotional self-care can be accessed either on-demand or symptom-driven. The utilization analysis showed a high number of weekly accesses during the first wave. In this period, press releases regarding critical events of the pandemic progression and government decisions on containment measures were followed by a utilization peak, irrespective of the sense (i.e., positive or negative) of the information. Positive information pieces (e.g., relaxation of containment measures due to a reduction of COVID-19 cases) resulted in a sharp increase in utilization immediately after information release, followed by a successive decline in utilization. The second wave was characterized by a lower and less responsive utilization of the Web App. CONCLUSIONS mHealth tools may help the general population coping with stressful conditions associated with the pandemic scenario. Future studies shall investigate the effectiveness of these tools among the general population―including individuals without diagnosed mental illnesses―and strategies to reach as many people as possible. CLINICALTRIAL Not applicable

2021 ◽  
Author(s):  
Sara Guila Fidel Kinori ◽  
Gerard Carot-Sans ◽  
Andrés Cuartero ◽  
Damià Valero-Bover ◽  
Jordi Piera-Jiménez ◽  
...  

BACKGROUND Quarantines and nationwide lockdowns dictated for containing the spread of the COVID-19 pandemic may lead to distress and increase the frequency of anxiety and depression symptoms among the general population. During the national lockdown of the first wave of the COVID-19 epidemic in Spain, we developed and launched a Web App aimed at promoting emotional self-care in the general population and facilitate contact with healthcare professionals. OBJECTIVE To describe the Web App development and analyze its utilization pattern throughout two successive waves of the COVID-19 epidemic in Spain. METHODS The Web App targeted all individuals aged 18 years or more and was designed by adapting the contents of a mobile App for adjuvant treatment of post-traumatic stress disorder (i.e., the PTSD Coach App) to the general population and the pandemic/lockdown scenario. We retrospectively assessed the utilization pattern of the Web App using data systematically retrieved from Google Analytics. Data were grouped into three time periods, defined using a joinpoint analysis of COVID-19 incidence in our area: first wave, between-wave period, and second wave. RESULTS The resulting Web App, named gesioemocional.cat, maintains the navigation structure of the PTSD Coach App, with three main modules: tools for emotional self-care, a self-assessment test, and professional resources for on-demand contact. The self-assessment test combines the Patient Health Questionnaire-2 (PHQ-2) and the 7-item Generalized Anxiety Disorder Scale (GAD-7) and offers professional contact in the advent of high level of depression and anxiety; contact is prioritized according to a screening questionnaire administered at the time of obtaining individual consent to be contacted. The tools for emotional self-care can be accessed either on-demand or symptom-driven. The utilization analysis showed a high number of weekly accesses during the first wave. In this period, press releases regarding critical events of the pandemic progression and government decisions on containment measures were followed by a utilization peak, irrespective of the sense (i.e., positive or negative) of the information. Positive information pieces (e.g., relaxation of containment measures due to a reduction of COVID-19 cases) resulted in a utilization peak immediately after the press release, followed by a successive decline in utilization. The second wave was characterized by a lower and less responsive utilization of the Web App. CONCLUSIONS mHealth tools may help the general population coping with stressful conditions associated with the pandemic scenario. Future studies shall investigate the extent of the potential benefits of these tools in the general (i.e., without diagnosed mental illnesses) population and the strategies to reach as many people as possible. CLINICALTRIAL Not applicable


2021 ◽  
Author(s):  
Sara Guila Fidel-Kinori ◽  
Gerard Carot-Sans ◽  
Andrés Cuartero-Barbanoj ◽  
Damià Valero ◽  
Jordi Piera-Jiménez ◽  
...  

Background: Quarantines and nationwide lockdowns dictated for containing the spread of the COVID-19 pandemic may lead to distress and increase the frequency of anxiety and depression symptoms among the general population. During the national lockdown of the first wave of the COVID-19 outbreak in Spain, we developed and launched a Web App (Gestioemocional.cat) to promote emotional self-care in the general population and facilitate contact with healthcare professionals. Methods: Gestioemocional.cat targeted all individuals aged 18 years or more and was designed by adapting the contents of a mobile App for adjuvant treatment of post-traumatic stress disorder (i.e., the PTSD Coach App) to the general population and the pandemic/lockdown scenario. We retrospectively assessed the utilization pattern of the Web App using data systematically retrieved from Google Analytics. Data were grouped into three time periods, defined using a join point analysis of COVID-19 incidence in our area: first wave, between-wave period, and second wave. Results: The resulting Web App, maintains the navigation structure of the PTSD Coach App, with three main modules: tools for emotional self-care, a self-assessment test, and professional resources for on-demand contact. The self-assessment test combines the Patient Health Questionnaire-2 (PHQ-2) and the 7-item Generalized Anxiety Disorder Scale (GAD-7) and offers professional contact in the advent of a high level of depression and anxiety; contact is prioritized according to a screening questionnaire administered at the time of obtaining individual consent to be contacted. The tools for emotional self-care can be accessed either on-demand or symptom-driven. The utilization analysis showed a high number of weekly accesses during the first wave. In this period, press releases regarding critical events of the pandemic progression and government decisions on containment measures were followed by a utilization peak, irrespective of the sense (i.e., positive or negative) of the information. Positive information pieces (e.g., relaxation of containment measures due to a reduction of COVID-19 cases) resulted in a sharp increase in utilization immediately after information release, followed by a successive decline in utilization. The second wave was characterized by a lower and less responsive utilization of the Web App. Conclusions: mHealth tools may help the general population coping with stressful conditions associated with the pandemic scenario. Future studies shall investigate the effectiveness of these tools among the general population―including individuals without diagnosed mental illnesses―and strategies to reach as many people as possible.


Author(s):  
Vesa Jormanainen ◽  
Leena Soininen

In Finland, it is possible to quickly produce medical symptom self-assessment tools within the existing infrastructure. The Finnish Omaolo Covid-19 web-based symptom self-assessment tool (symptom checker) was launched on March 16, 2020 after a 6-day development period. By using the web-based Omaolo Covid-19 symptom checker during the second wave of the epidemic, some 1.72 million questionnaires were recorded, out of which 1.55 million from symptomatic persons. Some 15% of the responses (245,500) were directed to seek emergency medical care based on the online screening by respondent response profiles.


2020 ◽  
Author(s):  
Arfan Ahmed ◽  
Nashva ALi ◽  
Sarah Aziz ◽  
Alaa A Abd-Alrazaq ◽  
Asmaa Hassan ◽  
...  

BACKGROUND Anxiety and depression rates are at an all-time high along with other mental health disorders. Smartphone-based mental health chatbots or conversational agents can aid psychiatrists and replace some of the costly human based interaction and represent a unique opportunity to expand the availability and quality of mental health services and treatment. Regular up-to-date reviews will allow medics and individuals to recommend or use anxiety and depression related smartphone based chatbots with greater confidence. OBJECTIVE Assess the quality and characteristics of chatbots for anxiety and depression available on Android and iOS systems. METHODS A search was performed in the App Store and Google Play Store following the Preferred Reporting Items for Systematic reviews and Meta-Analysis (PRISMA) protocol to identify existing chatbots for anxiety and depression. Eligibility of the chatbots was assessed by two individuals based on predefined eligibility criteria. Meta-data of the included chatbots and their characteristics were extracted from their description and upon installation by 2 reviewers. Finally, chatbots quality information was assessed by following the mHONcode principles. RESULTS Although around 1000 anxiety and depression related chatbots exist, only a few (n=11) contained actual chatbots that could provide the user a real substitute for a human-human based interaction, even with today's Artificial Intelligence advancements, only one of these chatbots had voice as an input/output modality. Of the selected apps that contained chatbots all were clearly built with a therapeutic human substitute goal in mind. The majority had high user ratings and downloads highlighting the popularity of such chatbots and their promising future within the realm of anxiety and depression. CONCLUSIONS Anxiety and depression chatbot apps have the potential to increase the capacity of mental health self-care providing much needed assistance to professionals. In the current covid-19 pandemic, chatbots can also serve as a conversational companion with the potential of combating loneliness, especially in lockdowns where there is a lack of social interaction. Due to the ubiquitous nature of chatbots users can access them on-demand at the touch of a screen on ones’ smartphone. Self-care interventions are known to be effective and exist in various forms and some can be made available as chatbot features, such as assessment, mood tracking, medicine tracking, or simply providing conversation in times of loneliness.


Author(s):  
Tatsuya Yoshihara ◽  
Kazuya Ito ◽  
Masayoshi Zaitsu ◽  
Eunhee Chung ◽  
Izumi Aoyagi ◽  
...  

Coronavirus disease 2019 (COVID-19) has become a serious public health problem worldwide. In general, healthcare workers are considered to be at higher risk of COVID-19 infection. However, the prevalence of COVID-19 among healthcare workers in Japan is not well characterized. In this study, we aimed to examine the seroprevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antibodies among 2160 healthcare workers in hospitals and clinics that are not designated to treat COVID-19 patients in Japan. The prevalence of SARS-CoV-2 immunoglobulin G was 1.2% in August and October 2020 (during and after the second wave of the pandemic in Japan), which is relatively higher than that in the general population in Japan (0.03–0.91%). Because of the higher risk of COVID-19 infection, healthcare workers should be the top priority for further social support and vaccination against SARS-CoV-2.


2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Wayne M. Getz ◽  
Richard Salter ◽  
Ludovica Luisa Vissat ◽  
Nir Horvitz

Abstract Background No 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. Methods Our 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. Results We 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. Conclusion Our 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.


2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Martín Martínez ◽  
Elkin O. Luis ◽  
Edwin Yair Oliveros ◽  
Pablo Fernández-Berrocal ◽  
Ainize Sarrionandia ◽  
...  

Abstract Background In a context where there is no treatment for the current COVID-19 virus, the combination of self-care behaviours together with confinement, are strategies to decrease the risk of contagion and remain healthy. However, there are no self-care measures to screen self-care activities in general population and which, could be briefly in a lockdown situation. This research aims to build and validate a psychometric tool to screen self-care activities in general population. Methods Firstly, an exploratory factor analysis was performed in a sample of 226 participants to discover the underlying factorial structure and to reduce the number of items in the original tool into a significant pool of items related to self-care. Later a confirmatory factor analyses were performed in a new sample of 261 participants to test for the fit and goodness of factor solutions. Internal validity, reliability, and convergent validity between its score with perceived stress and psychological well-being measures were examined on this sample. Results The exploratory analyses suggested a four-factor solution, corresponding to health consciousness, nutrition and physical activity, sleep, and intra-personal and inter-personal coping skills (14 items). Then, the four-factor structure was confirmed as the best model fit for self-care activities. The tool demonstrated good reliability, predictive validity of individuals’ perception of coping with COVID-19 lockdown, and convergent validity with well-being and perceived stress. Conclusions This screening tool could be helpful to address future evaluations and interventions to promote healthy behaviours. Likewise, this tool can be targeted to specific population self-care’s needs during a scalable situation.


2018 ◽  
Vol 42 (4) ◽  
pp. 380 ◽  
Author(s):  
Jiqiong You ◽  
Yuejen Zhao ◽  
Paul Lawton ◽  
Steven Guthridge ◽  
Stephen P. McDonald ◽  
...  

Objective The aim of the present study was to evaluate the potential effects of different health intervention strategies on demand for renal replacement therapy (RRT) services in the Northern Territory (NT). Methods A Markov chain simulation model was developed to estimate demand for haemodialysis (HD) and kidney transplantation (Tx) over the next 10 years, based on RRT registry data between 2002 and 2013. Four policy-relevant scenarios were evaluated: (1) increased Tx; (2) increased self-care dialysis; (3) reduced incidence of end-stage kidney disease (ESKD); and (4) reduced mortality. Results There were 957 new cases of ESKD during the study period, with most patients being Indigenous people (85%). The median age was 50 years at onset and 57 years at death, 12 and 13 years younger respectively than Australian medians. The prevalence of RRT increased 5.6% annually, 20% higher than the national rate (4.7%). If current trends continue (baseline scenario), the demand for facility-based HD (FHD) would approach 100 000 treatments (95% confidence interval 75 000–121 000) in 2023, a 5% annual increase. Increasing Tx (0.3%), increasing self-care (5%) and reducing incidence (5%) each attenuate demand for FHD to ~70 000 annually by 2023. Conclusions The present study demonstrates the effects of changing service patterns to increase Tx, self-care and prevention, all of which will substantially attenuate the growth in FHD requirements in the NT. What is known about the topic? The burden of ESKD is projected to increase in the NT, with demand for FHD doubling every 15 years. Little is known about the potential effect of changes in health policy and clinical practice on demand. What does this paper add? This study assessed the usefulness of a stochastic Markov model to evaluate the effects of potential policy changes on FHD demand. What are the implications for practitioners? The scenarios simulated by the stochastic Markov models suggest that changes in current ESKD management practices would have a large effect on future demand for FHD.


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