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Stress influences health not only directly, but also indirectly through changes in health-related behaviours, such as diet. Research has shown that stress influences individuals’ eating behaviour in different ways: Some increase, some decrease food intake, while others show no change. Identifying individuals at risk for stress-induced eating is essential for the development of tailored strategies for the prevention and treatment of overweight and obesity. The individual-difference model of stress-induced eating suggests that individual differences in the dietary response to stress are determined by differences in learning history, attitudes, or biology. Even though many studies have tried to identify person-characteristics that explain individual differences in the dietary response to stress, evidence remains inconclusive. Considering that eating is a repeated-occurrence health behaviour which is performed multiple times a day, Ecological Momentary Assessment (EMA) seems particularly promising to study the complex relationship between stress and food intake when and where it naturally occurs. Despite its potential, the number of studies applying EMA to assess the stress and eating relationship is limited. Furthermore, previous EMA studies show two limitations: (1) Actual food intake is not assessed and (2) inappropriate data analysis approaches are applied to semicontinuous outcomes. Therefore, the first aim of the present dissertation was to address the lack of an EMA tool that allows the assessment of stress and actual food intake by developing and evaluating the APPetite-mobile-app. Feasibility and usability of the APPetite-mobile-app as well as validity of the incorporated food record were empirically examined (Paper 1). Given the lack of an appropriate data analysis procedure, the second aim of the present dissertation was the introduction of a sophisticated statistical approach for semicontinuous data (Paper 2): Multilevel two-part modelling allows studying the influence of stress on the occurrence (i.e., whether individuals eat) as well as the amount of food intake (i.e., how much individuals eat) while accounting for the potential dependency between the two. Lastly, the novel EMA tool and the advanced data analysis procedure were integrated in order to gain novel insights into individual differences in the dietary response to stress and thereby identify individuals at risk for stress-induced eating in daily life (Paper 3). Results of Paper 1 showed good feasibility and acceptable usability of the APPetite-mobile-app as well as validity of the incorporated food record. Findings of Paper 2 highlight that multilevel two-part models offer novel and distinct insights in terms of the occurrence and the amount of food intake and are therefore not only methodologically but also conceptually promising. Paper 3 provides first evidence that the dietary response to stress might not be as stable as yet assumed. Time-varying factors might moderate the relationship between stress and actual food intake. Therefore, an expansion of the individual-difference model is proposed which accounts for time-varying factors. Further EMA studies are needed to verify the expanded model and identify time-varying factors which influence the dietary response to stress. Beyond that, improvements in the dietary assessment are required in order to allow prolonged EMA periods as well as larger samples. The present dissertation contributes to the research on the stress and eating relationship as it overcomes limitations of previous EMA studies and yields novel insights into the relationship between stress and actual food intake in daily life. Not only identifying individuals at risk for stress-induced eating, but also the identification of situations with an increased risk for stress-induced eating appears to be important for the development of targeted strategies for the prevention and treatment of overweight and obesity.