key: cord-1025497-edfovifw
authors: Kaspar, Kai
title: Motivations for Social Distancing and App Use as Complementary Measures to Combat the COVID-19 Pandemic: Quantitative Survey Study
date: 2020-08-27
journal: J Med Internet Res
DOI: 10.2196/21613
sha: f0e41564b73983c0d9609aacfeb3fcb383311ed0
doc_id: 1025497
cord_uid: edfovifw

BACKGROUND: The current COVID-19 pandemic is showing negative effects on human health as well as on social and economic life. It is a critical and challenging task to revive public life while minimizing the risk of infection. Reducing interactions between people by social distancing is an effective and prevalent measure to reduce the risk of infection and spread of the virus within a community. Current developments in several countries show that this measure can be technologically accompanied by mobile apps; meanwhile, privacy concerns are being intensively discussed. OBJECTIVE: The aim of this study was to examine central cognitive variables that may constitute people’s motivations for social distancing, using an app, and providing health-related data requested by two apps that differ in their direct utility for the individual user. The results may increase our understanding of people’s concerns and convictions, which can then be specifically addressed by public-oriented communication strategies and appropriate political decisions. METHODS: This study refers to the protection motivation theory, which is adaptable to both health-related and technology-related motivations. The concept of social trust was added. The quantitative survey included answers from 406 German-speaking participants who provided assessments of data security issues, trust components, and the processes of threat and coping appraisal related to the prevention of SARS-CoV-2 infection by social distancing. With respect to apps, one central focus was on the difference between a contact tracing app and a data donation app. RESULTS: Multiple regression analyses showed that the present model could explain 55% of the interindividual variance in the participants’ motivation for social distancing, 46% for using a contact tracing app, 42% for providing their own infection status to a contact tracing app, and 34% for using a data donation app. Several cognitive components of threat and coping appraisal were related to motivation measurements. Trust in other people’s social distancing behavior and general trust in official app providers also played important roles; however, the participants’ age and gender did not. Motivations for using and accepting a contact tracing app were higher than those for using and accepting a data donation app. CONCLUSIONS: This study revealed some important cognitive factors that constitute people’s motivation for social distancing and using apps to combat the COVID-19 pandemic. Concrete implications for future research, public-oriented communication strategies, and appropriate political decisions were identified and are discussed.

Statistical assumptions were checked for all regression models to assess the robustness of the results [1] . Across models, no outliers were found by means of Cook's distance (max. distance = 0.13) using a threshold value of 1 [2] and by means of leverage points (max. value = 0.16) using a threshold value of 0.2 [3] . Also, no multicollinearity was present (max. VIF = 1.91), given a threshold value of 10 [4] . The linearity and normality assumptions were visually inspected and generally met, but the Shapiro-Wilk test showed a significant result in two out of the four regression models. However, the present sample size was sufficiently large so that the regression models can be considered robust to violations of the normality assumption [5, 6] . The Durbin-Watson test showed independence among error terms, given a target value of 2 [7] . Although OLS estimates are unbiased in the presence of heteroscedasticity, significance tests may lead to biased results under such circumstances.

Scatterplots were visually inspected and indicated noticeable deviation from homoscedasticity only for the regression model assessing participants' motivation for social distancing. Also, the Breusch-Pagan test showed a significant result in this case. Hence, the robust HC3 estimator was used for significance testing in this model [8] , while the standard OLS estimator was preferred for the other models due to its superiority given homoscedasticity [9] . In general, the different accounts only slightly differed regarding the observed P values (see Table MA2 ). Table MA2 indicate significant relations that changed to non-significant relations (.05 < P < .10), or vice versa (P < .05), when applying an ordinal regression analysis to the data instead of the preferred linear regression analysis. All analyses were computed with SPSS 25. 

The assumptions of the linear regression model

Residuals and Influence in Regression

Robust Statistics

Linear regression and the normality assumption

The importance of the normality assumption in large public health data sets

Use of the Durbin-Watson statistic in inappropriate situations

Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation

Using heteroscedasticity consistent standard errors in the linear regression model

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