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March 28, 2026Digital Health0 citationsOpen Access

Conjoint analysis of social inequality indicators in diet and physical activity app (non-)users: Representative online surveys in Austria, Germany and Italy

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LKLaura M KönigLVLucia VolpiTKTheresa JS Koch

Key Points

  • This research aims to understand the relationship between socio-demographic factors and the use of nutrition and physical activity mobile apps.
  • Conducted online surveys with 1974 participants across Austria, Germany, and Italy.
  • Analyzed socio-demographic characteristics related to social inequality using the PROGRESS-Plus framework.
  • Employed latent class analysis to identify distinct groups of mHealth app (non-)users.
  • All socio-demographic factors, except residency and migration status, showed associations with mHealth app use.
  • Four classes of app (non-)users identified: 'young and diverse citizens', 'economically strong employees', 'established retirees', and 'low-income workers'.
  • 'Young and diverse citizens' and 'economically strong employees' were more likely to use mHealth apps than the other groups.

Abstract

Background Mobile interventions for health promotion (mHealth) are promising behaviour change tools. Yet they are infrequently used, and research suggests that use may be unevenly distributed in the population, potentially widening existing health inequalities. Objective This study tested for individual and joint associations between socio-demographic characteristics and nutrition and physical activity app use. Methodology Nationally representative samples for Austria, Germany and Italy were recruited with N = 1974 participants in total. In an online survey, participants reported on nutrition and physical activity app use as well as a range of relevant socio-demographic characteristics associated with social inequality according to PROGRESS-Plus (age, gender, education, income, employment status, rural vs. urban residency, Austrian/German/Italian citizenship, migration history, minority status, and sexual orientation). Results Except for residency and migration status, all socio-demographic characteristics were associated with mHealth app (non-)use if analysed independently. A latent class analysis revealed four distinct classes of mHealth app (non-)users. ‘Young and diverse citizens’ (characterised by young age and lowest proportion of heterosexuals) and ‘economically strong employees’ (characterised by highest levels of education and income) were more likely to use mHealth apps compared to ‘established retirees’ (characterised by largest share of retired individuals) and ‘low-income workers’ (characterised by lowest levels of education and income). Conclusion Age, education, income and employment are crucial inequality indicators for mHealth app use. These results confirm the existence of a digital health divide in Europe that urgently needs addressing to promote digital health for all. Trial registration https://osf.io/s2wya (Austria and Germany), https://osf.io/tpu2m (Italy).

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Cite This Study

König et al. (2026) studied this question.

synapsesocial.com/papers/69c772158bbfbc51511e2595https://doi.org/10.1177/20552076261429621
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