The Smartphone as
Mobile Research Laboratory
Embedded Files
PhoneStudy Archive: Research App 2014–2025
PROJECT
The smartphone has become a daily companion for many of us: in a wide variety of situations, we leave digital traces that reflect our everyday experiences and behaviors. We, as an interdisciplinary team at LMU Munich, have made these everyday data usable for research.
From 2014 to 2025 we developed the PhoneStudy research app to bring science into everyday life. To do this, we researched mobile sensing methods – that is, smartphone tracking – according to the highest ethical and data protection standards and deployed the app in numerous empirical studies on personality and well-being. In doing so, we contributed to establishing the smartphone as a mobile, privacy-friendly research laboratory in the field.
We concluded the development of the PhoneStudy app in 2025; however, we remain committed to research on mobile sensing methods: We continue to research human experiences and behavior in everyday life and address a diverse set of psychological research questions (see Publications). Based on the knowledge foundation gained through PhoneStudy, a new, independent app for mobile sensing research has now been developed: PULSE. Learn more about it here.
STUDY PROJECTS
Our in-house developed PhoneStudy app has been used in numerous studies of our own and in collaborative projects:
The Smartphone Sensing Panel Study (SSPS) was a collaborative project between LMU Munich and the Leibniz Institute for Psychology (ZPID). It was conducted in 2020 with over 800 participants, specifically using a quota sample representative of the German population. Over a period of three to six months, their smartphone usage habits were analyzed to better understand individual differences in experiences and behaviors in everyday life.
Learn more about the study design in the study protocol. An overview of projects working with the dataset can be found in the associated OSF repository.
The Moody Life Study is a LMU research project that investigated how everyday behaviors are related to our affective well-being. Specifically, we examined two key aspects of daily life: sleep patterns and music listening. We were interested in understanding how sleep habits and quality are related to mood, and whether music listening behavior provides insights into emotional states throughout the day.
Over 14 days, participants used our PhoneStudy app to log their everyday smartphone behavior, wore an activity tracking band to monitor sleep, and completed daily well-being reports. Whenever participants opened a music app, they received a prompt to report their current mood allowing us to explore the immediate relationship between music listening and emotional states in real time.
The project Dynamic Interactions of Personality Traits and Social Relationships (DIPS) explored how personality traits (such as extraversion and need for affiliation) and social context shape relationship dynamics in everyday life. Funded by the German Research Foundation (DFG) grants to Prof. Dr. Cornelia Wrzus (Heidelberg University) and Prof. Dr. David Richter (German Institute for Economic Research), the study combined smartphone sensing and experience sampling data with survey measures to capture daily social interactions in real-world settings.
Materials and data have been published to support further research on personality and social relationships.
In the Coping with Corona (CoCo) study, a DFG-funded collaborative project of University of Münster (Prof. Dr. Mitja Back), University of Osnabrück (Prof. Dr. Maarten van Zalk), and LMU Munich (Prof. Dr. Markus Bühner), we investigated well-being and coping during times of crisis. While the project originally focused on the COVID-19 pandemic, since the start of the war in Ukraine we have also examined its impact on daily experiences and mental health. Using smartphone sensing and experience sampling data collected from a student sample across three measurement waves, we examined relationships between personality traits, social interactions, and well-being. By doing so, we aimed to capture how individuals authentically handle crisis situations in real-world settings.
More information about the study design and materials can be found in the associated OSF repository.
TEAM
The PhoneStudy app was developed starting in 2014 through a collaboration between the Chair of Psychological Methods and Assessment (Prof. Dr. Markus Bühner) and the Chair of Media Informatics (Prof. Dr. Heinrich Hussmann †) at LMU Munich, with contributions from numerous postdoctoral researchers, doctoral students, and student assistants. Many members of the core development team (see photos) continue to conduct research in mobile sensing today at LMU Munich and at other universities in Germany and Switzerland.
PUBLICATIONS
AMBULATORY ASSESSMENT: MEASUREMENT IN THE WILD
Preprocessing Pipelines for Mobile-Sensing Data
Schoedel, R., Sust, L., Sterner, P., Goretzko, D. (2026). From Digital Data to Psychological Insights: Making Sense of Mobile-Sensing Data through Integrative Preprocessing Pipelines. Psychometrika. Published online, 1-28. DOI 10.1017/psy.2026.10083
Selection Bias in Mobile Sensing Research
Schoedel, R., Reiter, T., Krämer, M. D., Roos, Y., Bühner, M., Richter, D., Mehl, M. R., & Wrzus, C. (2026). Person-related selection bias in mobile sensing research: Robust findings from two panel studies. Journal of Personality and Social Psychology, 130(3), 597–625. DOI 10.1037/pspp0000585
Preprocessing of Keyboard Usage Data
Bemmann, F., Koch, T. K., Bergmann, M., Stachl, C., Buschek, D., Schoedel, R., & Mayer, S. (2025). Contextualizing Smartphone-Typed Language With User Input Intention. In Proceedings of the 2025 Mensch und Computer 2025, 520-526. DOI 10.1145/3743049.3748537
Combining Mobile Sensing and Experience Sampling Data
Reiter, T., Sakel, S., Scharbert, J., Horst, J., van Zalk, M., Back, M., Buehner, M., & Schoedel, R. (2025). Side Effects of Experience Sampling Protocols: A Systematic Analysis of How They Affect Data Quality, Data Quantity & Bias in Study Results. Advances in Methods and Practices in Psychological Science, 8(3), 1-17. DOI 10.1177/25152459251347274
Assessing Smartphone Use During Social Interactions
Reiter, T., Sakel, S., Scharbert, J., Ter Horst, J., Back, M., Van Zalk, M., Buehner, M., & Schoedel, R. (2024). Investigating Phubbing in Everyday Life: Challenges & Lessons for Future Research. Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’24), 1-8. Association for Computing Machinery. DOI 10.1145/3613905.3651009
Using Mobile Sensing Data to Investigate Nonresponse in Experience Sampling Studies
Reiter, T., & Schoedel, R. (2024). Never miss a beep: Using mobile sensing to investigate (non-) compliance in experience sampling studies. Behavior Research Methods, 56, 4038–4060. DOI 10.3758/s13428-023-02252-9
Comparing Different Ambulatory Assessment Methods
Roos, Y., Krämer, M. D., Richter, D., Schoedel, R., & Wrzus, C. (2023). Does Your Smartphone “Know” Your Social Life? A Methodological Comparison of Day Reconstruction, Experience Sampling, and Mobile Sensing. Advances in Methods and Practices in Psychological Science, 6(3). DOI 10.1177/25152459231178738
Categorisation of Apps
Schoedel, R., Oldemeier, M., Bonauer, L., & Sust, L. (2022). Systematic Categorisation of 3,091 Smartphone Applications From a Large-Scale Smartphone Sensing Dataset. Journal of Open Data Psychology, 10(7), 1-10. DOI 10.5334/jopd.59
WELLBEING IN THE EVERYDAY DIGITAL LIFE
Smartphone Use Before Bedtime & Sleep Quality
große Deters, F., Reiter, T., & Schoedel, R. (2026). From Swipe to Sleep: An Experience Sampling Study Using Smartphone Sensing to Examine Evening Smartphone Usage and Sleep Outcomes. Computers in Human Behavior Reports, 101114. DOI 10.1016/j.chbr.2026.101114
Smartphone Usage Patterns & Wellbeing
große Deters, F., & Schoedel, R. (2024). Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being. Computers in Human Behavior, 150, 107977. DOI 10.1016/j.chb.2023.107977
Day-Night Activity Patterns
Schoedel, R., Pargent, F., Au, Q., Völkel, S. T., Schuwerk, T., Bühner, M., and Stachl, C. (2020). To Challenge the Morning Lark and the Night Owl: Using Smartphone Sensing Data to Investigate Day–Night Behaviour Patterns. European Journal of Personality, 34(5), 733-752. DOI 10.1002/per.2258
PERSONALITY AND INDIVIDUAL DIFFERENCES IN THE DIGITAL AGE
Music Listening
Sust, L., & Schoedel, R. (2026). Investigating Everyday Music Choice on Smartphones: The Role of Mood States and Personality Traits. Personality Science, 7. DOI 10.1177/27000710261456849
Sust, L., Bergmann, M., Bühner, M., & Schoedel, R. (2026). Deep Beats, Deep Thoughts? Predicting General Cognitive Ability from Natural Music-Listening Behavior. Journal of Intelligence, 14(2), 29. DOI 10.3390/jintelligence14020029
Sust, L., Stachl, C., Kudchadker, G., Bühner, M., & Schoedel, R. (2023). Personality Computing With Naturalistic Music Listening Behavior: Comparing Audio and Lyrics Preferences. Collabra, 9(1), 75214. DOI 10.1525/collabra.75214
Sensing Situations: The Objective and the Psychological
Schoedel, R., Kunz, F., Bergmann, M., Bemmann, F., Bühner, M., & Sust, L. (2023). Snapshots of daily life: Situations investigated through the lens of smartphone sensing. Journal of Personality and Social Psychology, 125(6), 1442–1471. DOI 10.1037/pspp0000469
Social Interactions in Everyday Life
Wrzus, C., Roos, Y., Krämer, M. D., Schoedel, R., Back, M. D., & Richter, D. (2025). Affiliation motive and social interactions in people’s daily life: A temporal processes approach using ecological momentary assessment and mobile sensing. Journal of Personality and Social Psychology, 129(2), 341–362. DOI 10.1037/pspp0000555
Krämer, M. D., Roos, Y., Schoedel, R., Wrzus, C., & Richter, D. (2024). Social dynamics and affect: Investigating within-person associations in daily life using experience sampling and mobile sensing. Emotion, 24(3), 878-893. DOI 10.1037/emo0001309
Harari, G. M., Müller, S. R., Stachl, C., Wang, R., Wang, W., Bühner, M., … & Gosling, S. D. (2020). Sensing sociability: Individual differences in young adults’ conversation, calling, texting, and app use behaviors in daily life. Journal of Personality and Social Psychology, 119(1), 204. DOI 10.1037/pspp0000245
Schuwerk, T., Kaltefleiter, L. J., Au, J. Q., Hoesl, A., & Stachl, C. (2019). Enter the wild: Autistic traits and their relationship to mentalizing and social interaction in everyday life. Journal of Autism and Developmental Disorders, 49, 4193-4208. DOI 10.1007/s10803-019-04134-6
Smartphone Usage Patterns
Stachl, C., Au, Q., Schoedel, R., Gosling, S. D., Harari, G. M., Buschek, D., ... Bühner, M. (2020). Predicting personality from patterns of behavior collected with smartphones. Proceedings of the National Academy of Sciences, 117(30), 17680-17687. DOI 10.1073/pnas.1920484117
Schoedel, R., Au, Q., Völkel, S., Lehmann, F., Becker, D., Bühner, M., Bischl, B., Hussmann, H. & Stachl, C. (2018). Digital Footprints of Sensation Seeking: A Traditional Concept in the Big Data Era. Zeitschrift für Psychologie, 226(4), 232–245. DOI 10.1027/2151-2604/a000342
Stachl, C., Hilbert, S., Au, J.-Q., Buschek, D., De Luca, A., Bischl, B., Hussmann, H., and Bühner, M. (2017) Personality Traits Predict Smartphone Usage. European Journal of Personality, 31 (6), 701–722. DOI: 10.1002/per.2113
Page updated
Google Sites
Report abuse