A review and content analysis of national apps for COVID-19 management using Mobile Application Rating Scale (MARS)

Salehinejad, Simin; Kalhori, Sharareh R. Niakan; Hajesmaeel-Gohari, Sadrieh; Bahaadinbeigy, Kambiz; Fatehi, Farhad

doi:10.1080/17538157.2020.1837838

Cited by 45 publications

(39 citation statements)

References 21 publications

(21 reference statements)

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“…We do not review such apps. For a more thorough presentation of such systems, one may refer to Salehinejad et al (2021) and Skoll, Miller, and Saxon (2020)) . This paper is not a review of the limitations of digital or manual contact tracing strategies, although we discuss some of them.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, a rapid assessment of close contacts through digital contact tracing can break the chain of infection earlier, curbing the virus spread ( Ferretti et al, 2020 ). For a more thorough review of digital contact tracing and related apps against COVID-19, one may refer to Gasser, Ienca, Scheibner, Sleigh, and Vayena (2020) ; Salehinejad, Niakan Kalhori, Hajesmaeel Gohari, Bahaadinbeigy, and Fatehi (2021) , or Fagherazzi, Goetzinger, Rashid, Aguayo, and Huiart (2020) .…”

Section: Introductionmentioning

confidence: 99%

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Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

Grekousis

Liu

2021

Sustainable Cities and Society

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Digital contact tracing provides an expeditious and comprehensive way to collect and analyze data on people’s proximity, location, movement, and health status. However, this technique raises concerns about data privacy and its overall effectiveness. This paper contributes to this debate as it provides a systematic review of digital contact tracing studies between January 1, 2020, and March 31, 2021. Following the PRISMA protocol for systematic reviews and the CHEERS statement for quality assessment, 580 papers were initially screened, and 19 papers were included in a qualitative synthesis. We add to the current literature in three ways. First, we evaluate whether digital contact tracing can mitigate COVID-19 by either reducing the effective reproductive number or the infected cases. Second, we study whether digital is more effective than manual contact tracing. Third, we analyze how proximity/location awareness technologies affect data privacy and population participation. We also discuss proximity/location accuracy problems arising when these technologies are applied in different built environments (i.e., home, transport, mall, park). This review provides a strong rationale for using digital contact tracing under specific requirements. Outcomes may inform current digital contact tracing implementation efforts worldwide regarding the potential benefits, technical limitations, and trade-offs between effectiveness and privacy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

Grekousis

Liu

2021

Sustainable Cities and Society

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“…Mobile health solutions can potentially provide valuable services during the COVID-19 pandemic. These services can be in the form of education, resource allocation, surveillance, screening, treatment, diagnosis, prevention, management, and control ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

“…Applications for contact tracing, symptom monitoring and information provision are among the critical applications developed to manage COVID-19 pandemic ( 12 ). Though many applications have been developed for COVID-19, many do not have the right quality ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Design of a Mobile Application and Evaluation of Its Effects on Psychological Parameters of Covid-19 Inpatients: A Protocol for a Randomized Controlled Trial

et al. 2021

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Background: Panic of the disease and the associated concerns can lower the quality of life and physical performance. As long as the COVID-19 pandemic is ever on the rise, the psychological pandemic of the disease is on the rise, too. The high prevalence of COVID-19 has further increased physicians' work pressure. Patients' needs are not met adequately by physicians. It seems essential to use aids to monitor patients' needs and serve them properly. Thus, in the present research, suggestions are made on how to evaluate patients' physical and psychological conditions during the treatment via a mobile application.Methods and Analysis: The present research is a randomized, two parallel-group, controlled trial. One-hundred-twelve inpatients diagnosed with the coronavirus will be assigned randomly to the control and intervention groups. In the intervention group, a mobile application will be provided to educate patients, establish two-way interactions between patients and care providers and record patients' symptoms. Those in the control group will receive the usual care. The primary outcome is the change to the depression anxiety stress scales-21 (DASS-21) score from the baseline to 2 weeks after discharge from hospital. It will be measured at the baseline, at the time of discharge, and two weeks later.Ethics and Dissemination: The Ethics committee of Mashhad University of Medical Sciences' approval date was 2020-04-19 with IR.MUMS.REC.1399.118 reference code. Thus far, participants' recruitment has not been completed and is scheduled to end in March 2021. The results will be disseminated in a peer-reviewed journal.Trial Registration: IRCT20170922036314N4 (https://www.irct.ir/trial/47383).

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“…The Coronavirus disease 2019 (COVID-19) as a public health emergency of international concern, which emerged in late 2019, is thought to have originated in Wuhan, China (2). COVID-19 as a human pathogen, is spreading around the world rapidly (3). The main symptoms of Covid-19 include fever, cough, and shortness of breath, which in many cases appear to be similar to the influenza virus (4).…”

Section: Introductionmentioning

confidence: 99%

Data Mining Approach to Analyze COVID-19 Clinical Dataset

Yazdani¹,

Ravangard²,

Sharifian³

2021

PJMHS

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The new coronavirus has been spreading since the beginning of 2020 and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as data mining, enhanced intelligence, and other artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose and predict the COVID-19 epidemic. In this study, data mining models for early detection of Covid-19 in patients were developed using the epidemiological dataset of patients and individuals suspected of having Covid-19 in Iran. C4.5, support vector machine, Naive Bayes, logistic regression, Random Forest, and k-nearest neighbor algorithm were used directly on the dataset using Rapid miner to develop the models. By receiving clinical signs, this model diagnosis the risk of contracting the COVID-19 virus. Examination of the models in this study has shown that the support vector machine with 93.41% accuracy is more efficient in the diagnosis of patients with COVID-19 pandemic, which is the best model among other developed models. Keywords: COVID-19, Data mining, Machine Learning, Artificial Intelligence, Classification

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A review and content analysis of national apps for COVID-19 management using Mobile Application Rating Scale (MARS)

Cited by 45 publications

References 21 publications

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

Digital contact tracing, community uptake, and proximity awareness technology to fight COVID-19: a systematic review

Design of a Mobile Application and Evaluation of Its Effects on Psychological Parameters of Covid-19 Inpatients: A Protocol for a Randomized Controlled Trial

Data Mining Approach to Analyze COVID-19 Clinical Dataset

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