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      Agile Requirements Engineering and Software Planning for a Digital Health Platform to Engage the Effects of Isolation Caused by Social Distancing: Case Study

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          Abstract

          Background

          Social distancing and shielding measures have been put in place to reduce social interaction and slow the transmission of the coronavirus disease (COVID-19). For older people, self-isolation presents particular challenges for mental health and social relationships. As time progresses, continued social distancing could have a compounding impact on these concerns.

          Objective

          This project aims to provide a tool for older people and their families and peers to improve their well-being and health during and after regulated social distancing. First, we will evaluate the tool’s feasibility, acceptability, and usability to encourage positive nutrition, enhance physical activity, and enable virtual interaction while social distancing. Second, we will be implementing the app to provide an online community to assist families and peer groups in maintaining contact with older people using goal setting. Anonymized data from the app will be aggregated with other real-world data sources to develop a machine learning algorithm to improve the identification of patients with COVID-19 and track for real time use by health systems.

          Methods

          Development of this project is occurring at the time of publication, and therefore, a case study design was selected to provide a systematic means of capturing software engineering in progress. The app development framework for software design was based on agile methods. The evaluation of the app’s feasibility, acceptability and usability shall be conducted using Public Health England's guidance on evaluating digital health products, Bandura’s model of health promotion, the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework and the Nonadoption, Abandonment and Challenges to the Scale-up, Spread and Suitability (NASSS) framework.

          Results

          Making use of a pre-existing software framework for health behavior change, a proof of concept was developed, and a multistage app development and deployment for the solution was created. Grant submissions to fund the project and study execution have been sought at the time of publication, and prediscovery iteration of the solution has begun. Ethical approval for a feasibility study design is being sought.

          Conclusions

          This case study lays the foundations for future app development to combat mental and societal issues arising from social distancing measures. The app will be tested and evaluated in future studies to allow continuous improvement of the app. This novel contribution will provide an evidence-based exemplar for future app development in the space of social isolation and loneliness.

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          Most cited references 7

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          Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months

          Key Points Question: Assuming social distancing measures are maintained, what are the forecasted gaps in available health service resources and number of deaths from the COVID-19 pandemic for each state in the United States? Findings: Using a statistical model, we predict excess demand will be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds at the peak of COVID-19. Peak ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674) ventilators. Peak demand will be in the second week of April. We estimate 81,114 (95% UI 38,242 to 162,106) deaths in the United States from COVID-19 over the next 4 months. Meaning: Even with social distancing measures enacted and sustained, the peak demand for hospital services due to the COVID-19 pandemic is likely going to exceed capacity substantially. Alongside the implementation and enforcement of social distancing measures, there is an urgent need to develop and implement plans to reduce non-COVID-19 demand for and temporarily increase capacity of health facilities. Abstract Importance: This study presents the first set of estimates of predicted health service utilization and deaths due to COVID-19 by day for the next 4 months for each state in the US. Objective: To determine the extent and timing of deaths and excess demand for hospital services due to COVID-19 in the US. Design, Setting, and Participants: This study used data on confirmed COVID-19 deaths by day from WHO websites and local and national governments; data on hospital capacity and utilization for US states; and observed COVID-19 utilization data from select locations to develop a statistical model forecasting deaths and hospital utilization against capacity by state for the US over the next 4 months. Exposure(s): COVID-19. Main outcome(s) and measure(s): Deaths, bed and ICU occupancy, and ventilator use. Results: Compared to licensed capacity and average annual occupancy rates, excess demand from COVID-19 at the peak of the pandemic in the second week of April is predicted to be 64,175 (95% UI 7,977 to 251,059) total beds and 17,380 (95% UI 2,432 to 57,955) ICU beds. At the peak of the pandemic, ventilator use is predicted to be 19,481 (95% UI 9,767 to 39,674). The date of peak excess demand by state varies from the second week of April through May. We estimate that there will a total of 81,114 (95% UI 38,242 to 162,106) deaths from COVID-19 over the next 4 months in the US. Deaths from COVID-19 are estimated to drop below 10 deaths per day between May 31 and June 6. Conclusions and Relevance: In addition to a large number of deaths from COVID-19, the epidemic in the US will place a load well beyond the current capacity of hospitals to manage, especially for ICU care. These estimates can help inform the development and implementation of strategies to mitigate this gap, including reducing non-COVID-19 demand for services and temporarily increasing system capacity. These are urgently needed given that peak volumes are estimated to be only three weeks away. The estimated excess demand on hospital systems is predicated on the enactment of social distancing measures in all states that have not done so already within the next week and maintenance of these measures throughout the epidemic, emphasizing the importance of implementing, enforcing, and maintaining these measures to mitigate hospital system overload and prevent deaths.
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            Covid-19: death rate is 0.66% and increases with age, study estimates

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              Social isolation, loneliness and their relationships with depressive symptoms: A population-based study

              Objectives To assess the relationship between various social isolation indicators and loneliness, and to examine the differential associations that social isolation indicators, loneliness have with depressive symptoms. Methods Baseline data for 1,919 adults (aged 21 years and above) from a representative health survey in the Central region of Singapore was used for this study. The association between social isolation indicators (marital status, living arrangement, social connectedness with relatives and friends) and loneliness (the three-item UCLA Loneliness) were assessed, and their differential associations with depressive symptoms (the Patient Health Questionnaire-9) were examined using multiple linear regression, controling for relevant covariates. Results There was significant overlap between loneliness and social isolation. Social connectedness with relatives and friends were mildly correlated with loneliness score (|r| = 0.14~0.16). Social isolation in terms of weak connectedness with relatives and with friends and loneliness were associated with depressive symptoms even after controling for age, gender, employment status and other covariates. The association of loneliness with depressive symptoms (β = 0.33) was independent of and stronger than that of any social isolation indicators (|β| = 0.00~0.07). Conclusions The results of the study establishes a significant and unique association of different social isolation indicators and loneliness with depressive symptoms in community-dwelling adults aged 21 and above.
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                Author and article information

                Contributors
                Journal
                JMIR Public Health Surveill
                JMIR Public Health Surveill
                JPH
                JMIR Public Health and Surveillance
                JMIR Publications (Toronto, Canada )
                2369-2960
                Apr-Jun 2020
                6 May 2020
                6 May 2020
                : 6
                : 2
                Affiliations
                [1 ] Digitally Enabled PrevenTative Health Research Group Department of Paediatrics University of Oxford Oxford United Kingdom
                [2 ] Department of Primary Care and Public Health Imperial College London London United Kingdom
                [3 ] Skein Ltd London United Kingdom
                [4 ] Institute of Biomedical Engineering University of Oxford Oxford United Kingdom
                Author notes
                Corresponding Author: Edward Meinert e.meinert14@ 123456imperial.ac.uk
                Article
                v6i2e19297
                10.2196/19297
                7205031
                32348293
                ©Edward Meinert, Madison Milne-Ives, Svitlana Surodina, Ching Lam. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 06.05.2020.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

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