TABLE OF CONTENTS 1. 1.1.TABLE OF CONTENTS 4.3. Data analysis 4.3.1. Data Quality Assessment 4.3.2. Signal comparison -Cross correlation function 4.3.3. Parameters comparison -Bland Altman Plot 4.3.4. Event detection comparison -Event Difference Plots 4.4. Results 4.4.1. Signal comparison -Cross correlation function 4.4.2. Parameter comparison -Bland Altman Plot 4.4.3. Event level -Event difference Plots 4.5. Discussion 4.5.1. Validity assessment protocol 4.5.2. Application to E4 wearable 4.5.3. Further research 4.6. Conclusion Acknowledgements Appendix A Detailed Power Analysis 5. Design decisions for a real time, alcohol craving study using physio-and psychological measures 1.1.TABLE OF CONTENTS 7. Discussion 7.1. HR and Craving 7.1.1. Null mapping -Dual Process 7.1.2. Many-to-one mapping -Other contextual or social variables 7.1.3. One-to-one -Lack of craving consciousness 7.2. EDA and Craving 7.3. Methodological considerations 7.3.1. Population choice 7.3.2. Wearable choice 7.4. Clinical implications 7.5. Future research 7.6. Conclusion Summary Nederlandse Samenvatting References Acknowledgements 1.3. Relation between physiology and cravingThe use of physiological parameters to determine the state of the mind is not new, where Cacioppo and Tassinary already established first principles of psychophysiology in 1990. These principles can still be considered as authoritative. They cite the first studies that were performed around the end of 1800. In psychophysiology, the nervous system is regarded as having a physiological exit connected to other elements with multiple functional outputs, including verbal, behavioral and contextual data. Cacioppo et al. (2016) explain that there are multiple ways to view the relation between physiological and psychological events (see Figure 1). Psychophysiological relations can be as straightforward and simple as one-on-one EMA design and assessment of the intensity, burden and data quantityCurrently, little research is available to make informed decisions about EMA designs and wearable use, taking into account the intensity, burden, data quantity and data quality in daily life studies (Eisele et al., 2020). This is crucial to the study into the relations between physiology and craving, since a possible limitation of measuring in the "wild" is the incompleteness of both wearable and EMA data. Because the completeness of the data relies on the compliance of respondents and the functioning of the technology, missing values are likely to occur (Shiffman, 2009). If the wearable biosensor is not perceived as usable and comfortable, this could be a reason to stop wearing the sensor. Furthermore, because people diagnosed with AUD are known to value anonymity (Hufford & Shiffman, 2002), wearing a biosensor for an alcohol craving study might make them feel stigmatized. Additionally, a possible drawback of using a repeated intensive assessment like EMA is that the high burden might discourage participation. This can result in a sampling bias where only participants with certain personalit...