„Die Hoffnung, informiert zu sein“. Effekte der Corona-Warn-App

Urban, Monika

doi:10.1007/s11553-021-00854-9

Cited by 5 publications

(2 citation statements)

References 12 publications

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“… 13 A review and analysis of apps for Covid-19 in English using the Mobile Application Rating Scale (MARS) showed that all included mHealth apps scored acceptable (values > 3.0). 14 While evidence about some of the Covid-19 apps in Germany have been published, 13 , 15 , 16 no evidence evaluating all apps with the same tools exists yet.…”

Section: Introductionmentioning

confidence: 99%

Apps for Covid-19 in Germany: assessment using the German Mobile App Rating Scale

et al. 2022

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Objective The purpose of this study is to provide an overview of apps to help control the spread of Covid-19 in Germany and rate them according to standardized instruments. Materials and Methods The Apple App Store and Google Play Store were systematically searched to identify apps dealing with Covid-19 in Germany. The German Mobile App Rating Scale (MARS-G) was used to independently assess app quality by two trained reviewers. Results Overall, the quality of the six rated apps was good with a mean score of 4.15 (3.88 – 4.34). The best-rated apps were NINA app (4.34) and Corona Health App (4.29). The best-rated sections were Functionality (4.40), Aesthetic (4.25), and Information (4.25). In contrast, the worst-rated section was Engagement (3.63). Even though some of the apps were used by more people than others, there was no correlation between the MARS-G rating and app store rating. In addition, the MARS-G proved to be effective even with rating apps, which have different goals and methods to achieve them. Conclusions To our knowledge, this is the first study that identified and evaluated German Covid-19 mHealth apps available in the German app stores. The review shows that despite the excellent quality in aspects like information and functionality, there is still a gap in the engagement section. To motivate more people to use the Covid-19 apps, new ideas are needed, besides more information and education about the functionality of the apps, to gain trust in app developers and raise the number of downloads. LAY SUMMARY The Coronavirus disease 2019 (Covid-19) pandemic has put healthcare systems around the globe and many mobile health apps were developed to help control the spread. This study gives overview of the Covid-19 apps in Germany and rates them with a standardized instrument. Six apps were included in the analysis: Corona-Warn-App, Corona Health App, luca app, NINA app, SafeVac app, and STIKO app. The overall quality of the six rated apps was good. The review showed that despite the excellent quality in aspects like information and functionality, there is still a gap in the engagement section. To motivate more people to use the Covid-19 apps, new ideas are needed, besides more information and education about the functionality of the apps, to gain trust in app developers and raise the number of downloads. The findings from the study highlight the feasibility of quickly developing mHealth apps as a tool when responding to rapidly emerging health threads like a pandemic. It is important to pay attention to an app design that engages the user to use the app to ensure high utilization. Especially for communicable health treads, where contact tracing is important, high utilization is key.

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Section: Introductionmentioning

confidence: 99%

Apps for Covid-19 in Germany: assessment using the German Mobile App Rating Scale

et al. 2022

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“…That is, rather than solely analyzing self-reports of a given social network, we simulate 12 time points of sparse transfer data (in which transfers were accurately documented), and analyze these data in addition to the standard self-report data. These sparse networks of “ground-truth” data might come from field observations (e.g., “scan sampling” or “spot-checks”; Borgerhoff Mulder et al, 1985), diary methods (Paolisso & Hames, 2010), experimental games (Ross & Redhead, 2021), video recordings (DeTroy et al, 2021), GPS tracking (Davis et al, 2018), proximity detection (e.g., through cell phone data; Urban, 2021), or a variety of other methods. Even with sparse, incomplete “ground-truth” data, network reconstruction can be improved, and covariate effects on network structure and reporting parameters disambiguated.…”

Section: Model Validationmentioning

confidence: 99%

Reliable network inference from unreliable data: A tutorial on latent network modeling using STRAND.

Redhead¹,

McElreath²,

Ross³

2024

Psychological Methods

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Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. However, standard self-report measures-for example, as collected through the popular "name-generator" method-do not provide an impartial representation of such ties, be they transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic-variable across members of any given group. Past research has highlighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we provide a latent network model that allows researchers to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact fundamental network properties. These impacts are not adequately remedied using the most frequently deployed approaches for network reconstruction in the social sciences (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population.

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Digitale Verhaltenspolitik

Straßheim

2023

Handbuch Digitalisierung in Staat Und Verwaltung

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„Die Hoffnung, informiert zu sein“. Effekte der Corona-Warn-App

Cited by 5 publications

References 12 publications

Apps for Covid-19 in Germany: assessment using the German Mobile App Rating Scale

Apps for Covid-19 in Germany: assessment using the German Mobile App Rating Scale

Reliable network inference from unreliable data: A tutorial on latent network modeling using STRAND.

Digitale Verhaltenspolitik

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