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Introduction: Following behavioral recommendations is key to successful containment of the COVID-19 pandemic. Therefore, it is important to identify causes and patterns of non-compliance in the population to further optimize risk and health communication.
Methods: A total of 157 participants [80% female; mean age = 27.82 years (SD = 11.01)] were surveyed regarding their intention to comply with behavioral recommendations issued by the German government. Latent class analysis examined patterns of compliance, and subsequent multinomial logistic regression models tested sociodemographic (age, gender, country of origin, level of education, region, and number of persons per household) and psychosocial (knowledge about preventive behaviors, risk perception, stigmatizing attitudes) predictors.
Results: Three latent classes were identified: high compliance (25%) with all recommendations; public compliance (51%), with high compliance regarding public but not personal behaviors; and low compliance (24%) with most recommendations. Compared to high compliance, low compliance was associated with male gender [relative risk ratio (RRR) = 0.08 (0.01; 0.85)], younger age [RRR = 0.72 (0.57; 0.93)], and lower public stigma [RRR = 0.21 (0.05; 0.88)]. Low compliers were also younger than public compliers [RRR = 0.76 (0.59; 0.98)].
Discussion: With 25% of the sample reporting full compliance, and 51% differing in terms of public and personal compliance, these findings challenge the sustainability of strict regulatory measures. Moreover, young males were most likely to express low compliance, stressing the need for selective health promotion efforts. Finally, the positive association between public stigma and compliance points to potential othering effects of stigma during a pandemic, but further longitudinal research is required to examine its impact on health and social processes throughout the pandemic.
Introduction
Privacy concerns are an important barrier to adoption and continued use of digital technologies, particularly in the health sector. With the introduction of mobile health applications (mHealth apps), the construct of app information privacy concerns has received increased attention. However, few validated measures exist to capture said concerns in population samples, although they can help to improve public health efforts.
Methods
Using a cross-sectional survey of German adults (mean age = 35.62; 63.5% female), this study examined psychometric properties of the app information privacy concerns scale (AIPC). Analyses comprised confirmatory factor analysis, factorial validity (exploratory factor analysis), internal consistency, convergent validity (i.e., correlations with privacy victimhood, and app privacy concerns), and discriminant validity (i.e., daily app use, adoption intentions, and attitudes toward COVID-19 contact tracing app use).
Results
The analysis did not support the proposed three-factor structure of the AIPC (i.e., anxiety, personal attitude, and requirements). Instead, a four-factor model was preferable that differentiated requirements regarding disclosure policies, and personal control. In addition, factors mirroring anxiety and personal attitude were extracted, but shared a significant overlap. However, these factors showed good reliability, convergent and discriminant validity.
Discussion
The findings underline the role of app information privacy concerns as a significant barrier to mHealth app use. In this context, anxiety and personal attitudes seemed particularly relevant, which has implications for health communication. Moreover, the observed differentiation of external (disclosure) and internal (control) requirements aligns with health behavior change models and thus is a promising area for future research.