Investigating the effective factors of using mHealth apps for monitoring COVID-19 symptoms and contact tracing: A survey among Iranian citizens

Rahimi, Rezvan; Khoundabi, Batoul; fathian, Akram

doi:10.1016/j.ijmedinf.2021.104571

Cited by 20 publications

(11 citation statements)

References 55 publications

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“…Moreover, there were a few other unexpected results. The clusters were predictive of the intention to use and the adoption of the CM app in wave 2, which is in line with papers that have put forward these psychosocial perceptions as being explanatory of the adoption of CTAs [ 5 , 13 - 16 ]. However, when the intention to use and the adoption of the CM app from wave 1 were added to the model, the clusters had less predictive power.…”

Section: Discussionsupporting

confidence: 88%

“…Previous research during the COVID-19 crisis has already shown that there are several determinants for the adoption of CTAs [1,5,[9][10][11][12][13][14][15][16]. From those studies, 6 psychosocial perceptions have emerged that influence the adoption of CTAs: trust in the government, beliefs about personal data, social norms, perceived personal and societal benefits, risk perceptions, and self-efficacy.…”

Section: Introductionmentioning

confidence: 99%

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The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

Hengst¹,

Lechner²,

Laan³

et al. 2023

JMIR Form Res

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Background During the COVID-19 pandemic, there was limited adoption of contact-tracing apps (CTAs). Adoption was particularly low among vulnerable people (eg, people with a low socioeconomic position or of older age), while this part of the population tends to have lesser access to information and communication technology and is more vulnerable to the COVID-19 virus. Objective This study aims to understand the cause of this lagged adoption of CTAs in order to facilitate adoption and find indications to make public health apps more accessible and reduce health disparities. Methods Because several psychosocial variables were found to be predictive of CTA adoption, data from the Dutch CTA CoronaMelder (CM) were analyzed using cluster analysis. We examined whether subgroups could be formed based on 6 psychosocial perceptions (ie, trust in the government, beliefs about personal data, social norms, perceived personal and societal benefits, risk perceptions, and self-efficacy) of (non)users concerning CM in order to examine how these clusters differ from each other and what factors are predictive of the intention to use a CTA and the adoption of a CTA. The intention to use and the adoption of CM were examined based on longitudinal data consisting of 2 time frames in October/November 2020 (N=1900) and December 2020 (N=1594). The clusters were described by demographics, intention, and adoption accordingly. Moreover, we examined whether the clusters and the variables that were found to influence the adoption of CTAs, such as health literacy, were predictive of the intention to use and the adoption of the CM app. Results The final 5-cluster solution based on the data of wave 1 contained significantly different clusters. In wave 1, respondents in the clusters with positive perceptions (ie, beneficial psychosocial variables for adoption of a CTA) about the CM app were older (P<.001), had a higher education level (P<.001), and had higher intention (P<.001) and adoption (P<.001) rates than those in the clusters with negative perceptions. In wave 2, the intention to use and adoption were predicted by the clusters. The intention to use CM in wave 2 was also predicted using the adoption measured in wave 1 (P<.001, β=–2.904). Adoption in wave 2 was predicted by age (P=.022, exp(B)=1.171), the intention to use in wave 1 (P<.001, exp(B)=1.770), and adoption in wave 1 (P<.001, exp(B)=0.043). Conclusions The 5 clusters, as well as age and previous behavior, were predictive of the intention to use and the adoption of the CM app. Through the distinguishable clusters, insight was gained into the profiles of CM (non)intenders and (non)adopters. Trial Registration OSF Registries osf.io/cq742; https://osf.io/cq742

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

Hengst¹,

Lechner²,

Laan³

et al. 2023

JMIR Form Res

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“…The slowest time of finding secondary cases from the main case and unidentified contacts can also hamper the effectiveness of breaking the chain of transmission of COVID-19 (Keeling et al, 2020). Other obstacles, such as the risk of personal data leakage for users of health applications used for monitoring covid-19 tracing (Rahimi et al, 2021). Perceptions that arise for some people regarding the use of tracing applications in the UK such as confused in using them, assumptions and personal; little information about how the application functions; and Perception of a better tracing system, causing confusion to use this application (Williams et al, 2021 Based on the results of Bayesian analysis it was found that a total of 7770 contacts were identified, 1150 close contacts were symptomatic of the results.…”

Section: Contact Tracing Managementmentioning

confidence: 99%

“…so it is estimated that 36% of individuals with coronavirus infection show symptoms. Rahimi, Khoundabi et al / 2021 This study aims to analyze the factors that influence the use of mobile health applications for "contact tracing" and "symptom monitoring" of COVID-19.…”

Section: Contact Tracing Managementmentioning

confidence: 99%

Implementation of Tracing System for Covid-19: A Literature Review

Hanardi¹,

Rochmawati²

2022

J. Sci. n.a

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COVID19 virus spreads very fast and can result in death. COVID19 incident has also impacted all sectors of life. Governments in many countries made various efforts to control the rate of transmission including developing and implementing contact tracing for COVID19. Contact tracing or tracing systems is a priority effort to contain the spread of the coronavirus and reduce mortality due to covid19. This paper aims to analyze the implementation covid-19 tracing system as an effort to restrain the covid-19 outbreak. The following bibliographic databases were searched to identify potentially relevant documents: SCOPUS, EBSCOHost, EMERALD, ScienceDirect, and PubMed. The search strategy was developed by the first author and further refined through team discussions. The final search results are exported to EndNote, and duplicates are removed automatically and manually. Then for the report format, follow PRISMA. 12 articles were included in the review. We found several things in analyzing the implementation of contact tracing such as the impact of implementing contact tracing, contact tracing management, and obstacles to implementing contact tracing in controlling covid19 outbreak. Contact tracing or tracing systems have a positive impact on reducing and controlling the transmission and spread of the COVID19 disease.

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“…Por ejemplo, Aydin et al (2015) encontró que los usuarios de atención médica utilizan cada vez más aplicaciones con este propósito. Rahimi et al, (2021) determinó que el género, la edad, el nivel de educación, la actitud frente a la tecnología y el miedo a contraer COVID-19 influyeron en la intención de usar este tipo de Apps. Con respecto a aplicaciones de actividad física, el uso es mayor en mujeres entre 25 y 50 años que residen en zonas donde la mayor parte de la población tiene un nivel socioeconómico bajo (Pontin et al, 2021).…”

Section: Introductionunclassified

¿Qué influye en el uso de Apps? Un estudio en el contexto de la pandemia COVID-19, en Loja-Ecuador

Encalada

Chamba-González

Soto-Alvarado

et al. 2021

rte

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La digitalización de la economía ha tomado un rol preponderante en el desarrollo económico, acentuado por la pandemia COVID-19 al restringir las interacciones físicas de la población. En este contexto, las aplicaciones móviles se han identificado como un modo de acceso dominante para la conectividad individual, transformando el estilo de vida de la población y el funcionamiento de la economía. Este estudio explora el uso de las aplicaciones móviles en Loja – Ecuador durante el último año y examina los factores que influyeron en su uso. Esto se logró aplicando una encuesta estructurada en línea y un modelo econométrico de respuesta ordinal. Los principales hallazgos sugieren que aspectos sociodemográficos, como la generación a la que pertenecen y el nivel de educación, y factores relacionados con el uso y descarga de aplicaciones (Apps), como el uso de Facebook y Zoom, influyen significativamente en el uso de aplicaciones móviles. El estudio presenta, además, información que pueda orientar un mejor uso de las aplicaciones móviles en el marco de la economía creativa.

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Investigating the effective factors of using mHealth apps for monitoring COVID-19 symptoms and contact tracing: A survey among Iranian citizens

Cited by 20 publications

References 55 publications

The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

The Adoption of a COVID-19 Contact-Tracing App: Cluster Analysis

Implementation of Tracing System for Covid-19: A Literature Review

¿Qué influye en el uso de Apps? Un estudio en el contexto de la pandemia COVID-19, en Loja-Ecuador

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