Research

Main research topics

Assessment of and intervention on eating behavior and related health behavior such as...

• eating disorder symptoms (i.e., binge eating, restriction)

• food related approach avoidance tendencies

• eating behaviors/styles (i.e., emotional eating, stress eating, restrictive eating), 

• intention behavior gaps,

• food cue reactivity and food craving, etc.

• physical activity

...in healthy, overweight and eating disordered individuals. We investigate these topics with a range of different methods including smartphone-based ambulatory assessments and interventions, computerized behavioral tasks (i.e., approach avoidance task, idiosyncratic emotional food task, food decision task), tracking of physical activity, and psychophysiological measures including EEG and fMRI.

Current Projects (selection)

• Ecological momentary assessment in eating disorders: An important part of our methodological spectrum is ambulatory assessment (or ecological momentary assessment; EMA). With this method, data are collected via smartphones in daily routine and, thus, eating behavior can be studied under real-life conditions. For our EMA studies, we created and use our customized smartphone app PsyDiary complemented by platforms and apps from other research groups. Recently, we extended our scope in this domain to intervention paradigms employing ecological momentary interventions (EMI) and just-in-time adaptive interventions. We are currently investigating triggers of binge eating and fasting across eating disorders, factors leading to higher intention behavior gaps and the effectiveness of interventions on eating behavior using EMA.

• Just-in-time adaptive interventions (JITAIs): Our customized SmartEater app provides intelligent mobile logging of stress and eating behavior as a basis for intervention and follow-up care in clinics treating eating disorders and obesity. In SmartEater, users repeatedly enter data on experienced craving for foods and stress. SmartEater then ‘learns’ from the user through sophisticated machine learning algorithms: Temporal pattern analysis of individual time-series allows prediction of stress and craving bouts into the near future. Such predictions allow the app to respond to upcoming eating 'crises’ e.g. overeating/binge eating and launch situation-appropriate tips. SmartEater is routed in psychological models of eating behavior, but allows for idiographic prediction models tailored to individual triggers of problematic eating behaviors like binge eating. In current studies, we aim to improve the validity of these models and the effectiveness of situation-appropriate interventions. 

• Ecological momentary assessment in physical activity: In addition to eating behaviour, we are also interested in physical activity and how to support it using modern technology. In a current project, implemented together with sports-medicine, we utilise a customised smartphone app as well as wearables to investigate individual windows of opportunity to support healthy physical activity. Our goal is to find out when to intervene to increase the extent of physical activities as well as satisfaction and motivation in addition to the use of implementation intentions. We also plan to develop JITAIs based on the EMA research. 

• Approach avoidance tasks/interventions in eating and beyond: The approach-avoidance task allows us to measure automatic and rapid behavioral responses to a wide range of stimuli. We have used the approach-avoidance task to investigate many topics in the eating domain, including generalized and specific food cravings, hunger and food deprivation, responses to rotten food, orthorexia nervosa (with Jana Strahler), food avoidance in anorexia nervosa, body image in eating-related psychopathology, and emotional eating behavior. Additionally, we have investigated the role of approach-avoidance biases in spider fear (with Mike Rinck) and alcohol consumption (with Reinout Wiers). We have also used the approach-avoidance task on the smartphone to modify chocolate approach biases in individuals who wanted to improve their diet. In a current study, we intend to intervene on approach-avoidance biases and improve eating behavior in accordance with individual diet goals. Recently, we have developed a new approach bias retraining paradigm and begun testing it in an online setting. 

• Development and improvement of the approach-avoidance paradigm: We have done considerable work exploring different experimental designs and task setups to learn how approach bias can best be measured. For this goal, we have compared the reliability and validity of relevant-feature and irrelevant-feature versions of the AAT, as well as the performance of different input devices such as touchscreens, computer mice, and joysticks (with Charlotte Wittekind). We have been collaborating with Arne Bathke to develop methods of assessing and improving the reliability and validity of our experimental data, yielding the R-package 'AATtools'. 

Our Funders

Our group has recently completed the NewEat research project. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme ('ERC-StG-2014 639445 NewEat). Our group has also received support from the Austrian Research Foundation.