Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic
- Introduction of the Corona Health app platform and its technical details;
- Presentation of statistics about data collected with Corona Health from 27 July 2020 to 11 May 2021.
2. Background and Related Work
3. Technical Details
3.1. User Perspective
3.2. App Architecture and Modules
3.3. Database Schema
3.4. Data Exchange
3.5. Mobile Sensing
3.6. Content Pipeline
3.7. Medical Device Regulation
4. Collected Data
- Study on Mental health for adults (18 years +): People’s health is very important to us. Hence, we would like to ask you something: How are you feeling these days? While physical illness is prominent in news and the public, we are interested in the mood and possible stress factors that influence it. Moving freely, daily work routine, eating out, visiting grandparents, or meeting friends are important ingredients for us to feel good. Consequently, we would like to learn more about how you maintain social contacts, as these are important factors related to your well-being. In order to limit the effort for you as much as possible, we would like to automatically record your social contact via your smartphone (e.g., number and duration of calls, text messages). We would also like to automatically record your location at the time of the survey (GPS signal) in order to take regional differences into account. The data as well as your answers are stored completely anonymously, to profile you as a person is not possible. This first survey is a little more extensive and takes about 20 min—the follow-up surveys are only half of the length. By taking the time and participating in this survey you are a great help!
- Study on Mental health for adolescents (12 to 17 years): This scientific study investigates the burden on adolescents worldwide aged 12–17 years during the coronavirus-pandemic. Understanding your struggles may help us physicians and scientists to develop strategies to better cope with the pandemic. Would you like to take part and help us? The first survey takes 15 min. You may then take part in weekly follow-up surveys (5 min). The study is completely anonymous. The study has been approved by an ethics committee and by a data protection official. No tracking is done by the app and only the data that you agree to will be transmitted anonymously. If you are unsure, whether you should take part, please talk to your parents about it. THANK YOU!
- Study on Physical health for adults (18 years +): Many things in our daily life have changed since the Corona crisis. This also includes more basic things such as our diet or our free time for example, how we do sports. These are factors that also have an influence on the development and course of diseases such as cardiovascular diseases. In the following questionnaire, we would thus like to learn more about how the Corona crisis affects your habits with a focus on factors that are related to the cardiovascular system. Filling in the questionnaire only takes about 15 min. All data is collected anonymously. It is therefore not possible to relate any of the collected data with an individual person. Thank you for your support!
- Study on Recognizing stress for adults (18 years and up): Recognizing stress is important for your mental and physical health. That is why we invite you to fill out a short stress questionnaire once a week. People react very differently to stress. Therefore, we are also interested in factors that influence the experience of stress such as your age, your family status, your gender, and your smartphone usage (e.g., frequency of using communication services and social networks). The smartphone usage is recorded automatically to minimize the effort for you. Likewise, your whereabouts at the time of the survey are recorded automatically (GPS signal) in order to take regional differences in the stress experience into account. The data as well as your answers are stored completely anonymously. No conclusions can be drawn about the content of your social interactions or your person. Thank you very much for your effort and time in answering the questions about your perceived stress. You are a great help!
- Study on University Medicine Network: Compass project on the acceptance of pandemic apps (18 years and older): The variety of pandemic apps, apps that have been developed, for example, in hackathons to control COVID-19, shows the great potential that many experts see in them. But for apps to be effective in the pandemic, they must be used by many people. This applies not only to the Corona warning app, but also in particular to apps for assessing individual risk, for example in the case of certain pre-existing conditions. It is therefore necessary that such apps enjoy trust among the general population and that data can be analyzed together for medical research with the consent of the users. In COMPASS, scientists from a wide range of disciplines from university hospitals are joining forces with partners from science and industry in an interdisciplinary project to jointly develop a coordination and technology platform for pandemic apps. Help us with this survey so that pandemic apps can be developed even better and with broad acceptance and transparency in the future.
- Our API can be flexibly used to manage various study types.
- Our apps can be flexibly tailored to the needs of researchers from the respective healthcare domain. Experience gained from previous projects, how such apps shall operate when applied large scale in real-life proved very valuable for the design procedure.
- Our proven and accepted content pipeline is able to flexibly add and change content between healthcare experts and computer scientists (see Section 3.6).
- We were able to find regulatory experts that are experienced in the MDR in the context of software engineering projects.
- Our general user journey of the app is accepted by participants.
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
- Zhang, Y.; Ma, Z.F. Impact of the COVID-19 Pandemic on Mental Health and Quality of Life among Local Residents in Liaoning Province, China: A Cross-Sectional Study. Int. J. Environ. Res. Public Health 2020, 17, 2381. [Google Scholar] [CrossRef][Green Version]
- Wang, C.; Pan, R.; Wan, X.; Tan, Y.; Xu, L.; Ho, C.S.; Ho, R.C. Immediate Psychological Responses and Associated Factors during the Initial Stage of the 2019 Coronavirus Disease (COVID-19) Epidemic among the General Population in China. Int. J. Environ. Res. Public Health 2020, 17, 1729. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Gualano, M.R.; Lo Moro, G.; Voglino, G.; Bert, F.; Siliquini, R. Effects of Covid-19 Lockdown on Mental Health and Sleep Disturbances in Italy. Int. J. Environ. Res. Public Health 2020, 17, 4779. [Google Scholar] [CrossRef] [PubMed]
- Xiong, J.; Lipsitz, O.; Nasri, F.; Lui, L.M.W.; Gill, H.; Phan, L.; Chen-Li, D.; Iacobucci, M.; Ho, R.; Majeed, A.; et al. Impact of COVID-19 Pandemic on Mental Health in the General Population: A Systematic Review. J. Affect. Disord. 2020, 277, 55–64. [Google Scholar] [CrossRef] [PubMed]
- Gloster, A.T.; Lamnisos, D.; Lubenko, J.; Presti, G.; Squatrito, V.; Constantinou, M.; Nicolaou, C.; Papacostas, S.; Aydın, G.; Chong, Y.Y.; et al. Impact of COVID-19 Pandemic on Mental Health: An International Study. PLoS ONE 2020, 15, e0244809. [Google Scholar] [CrossRef] [PubMed]
- Limcaoco, R.S.G.; Mateos, E.M.; Fernández, J.M.; Roncero, C. Anxiety, Worry and Perceived Stress in the World Due to the COVID-19 Pandemic, March 2020. Preliminary Results. medRxiv 2020. [Google Scholar] [CrossRef][Green Version]
- Pieh, C.; Budimir, S.; Probst, T. The Effect of Age, Gender, Income, Work, and Physical Activity on Mental Health during Coronavirus Disease (COVID-19) Lockdown in Austria. J. Psychosom. Res. 2020, 136, 110186. [Google Scholar] [CrossRef] [PubMed]
- Pierce, M.; McManus, S.; Hope, H.; Hotopf, M.; Ford, T.; Hatch, S.L.; John, A.; Kontopantelis, E.; Webb, R.T.; Wessely, S.; et al. Mental Health Responses to the COVID-19 Pandemic: A Latent Class Trajectory Analysis Using Longitudinal UK Data. Lancet Psychiatry 2021. [Google Scholar] [CrossRef]
- Pieh, C.; Budimir, S.; Delgadillo, J.; Barkham, M.; Fontaine, J.R.J.; Probst, T. Mental Health During COVID-19 Lockdown in the United Kingdom. Psychosom. Med. 2021, 83, 328–337. [Google Scholar] [CrossRef]
- Dale, R.; Budimir, S.; Probst, T.; Stippl, P.; Pieh, C. Mental Health during the COVID-19 Lockdown over the Christmas Period in Austria and the Effects of Sociodemographic and Lifestyle Factors. Int. J. Environ. Res. Public Health 2021, 18, 3679. [Google Scholar] [CrossRef]
- Budimir, S.; Pieh, C.; Dale, R.; Probst, T. Severe Mental Health Symptoms during COVID-19: A Comparison of the United Kingdom and Austria. Healthcare 2021, 9, 191. [Google Scholar] [CrossRef]
- Ferreira, D.; Kostakos, V.; Dey, A.K. AWARE: Mobile Context Instrumentation Framework. Front. ICT 2015, 2. [Google Scholar] [CrossRef][Green Version]
- Xiong, H.; Huang, Y.; Barnes, L.E.; Gerber, M.S. Sensus: A Cross-Platform, General-Purpose System for Mobile Crowdsensing in Human-Subject Studies. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2016), Heidelberg, Germany, 12–16 September 2016; pp. 415–426. [Google Scholar] [CrossRef][Green Version]
- Schobel, J.; Probst, T.; Reichert, M.; Schickler, M.; Pryss, R. Enabling sophisticated lifecycle support for mobile healthcare data collection applications. IEEE Access 2019, 7, 61204–61217. [Google Scholar] [CrossRef]
- Schobel, J.; Pryss, R.; Schickler, M.; Ruf-Leuschner, M.; Elbert, T.; Reichert, M. End-user programming of mobile services: empowering domain experts to implement mobile data collection applications. In Proceedings of the 2016 IEEE International Conference on Mobile Services (MS), San Francisco, CA, USA, 27 June–2 July 2016; pp. 1–8. [Google Scholar]
- Kumar, D.; Jeuris, S.; Bardram, J.E.; Dragoni, N. Mobile and Wearable Sensing Frameworks for mHealth Studies and Applications: A Systematic Review. ACM Trans. Comput. Healthc. 2020, 2, 8:1–8:28. [Google Scholar] [CrossRef]
- Hamel, M.B.; Cortez, N.G.; Cohen, I.G.; Kesselheim, A.S. FDA regulation of mobile health technologies. N. Engl. J. Med. 2014, 371, 372. [Google Scholar]
- Jogova, M.; Shaw, J.; Jamieson, T. The regulatory challenge of mobile health: Lessons for Canada. Healthc. Policy 2019, 14, 19. [Google Scholar] [CrossRef] [PubMed]
- Vogel, C.; Pryss, R.; Schobel, J.; Schlee, W.; Beierle, F. Developing Apps for Researching the COVID-19 Pandemic with the TrackYourHealth Platform. In Proceedings of the 2021 IEEE/ACM 8th International Conference on Mobile Software Engineering and Systems (MobileSoft), Pittsburgh, PA, USA, 17–19 May 2021; pp. 65–68. [Google Scholar] [CrossRef]
- Pryss, R.; Schlee, W.; Langguth, B.; Reichert, M. Mobile crowdsensing services for tinnitus assessment and patient feedback. In Proceedings of the 2017 IEEE International Conference on AI & Mobile Services (AIMS), Honolulu, HI, USA, 25–30 June 2017, 25–30 June 2017; pp. 22–29. [Google Scholar]
- Pryss, R.; John, D.; Schlee, W.; Schlotz, W.; Schobel, J.; Kraft, R.; Spiliopoulou, M.; Langguth, B.; Reichert, M.; O’Rourke, T.; et al. Exploring the time trend of stress levels while using the Crowdsensing Mobile health platform, TrackYourStress, and the influence of perceived stress reactivity: Ecological momentary assessment pilot study. JMIR mHealth uHealth 2019, 7, e13978. [Google Scholar] [CrossRef]
- Unnikrishnan, V.; Shah, Y.; Schleicher, M.; Strandzheva, M.; Dimitrov, P.; Velikova, D.; Pryss, R.; Schobel, J.; Schlee, W.; Spiliopoulou, M. Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations-Where to Learn from? In International Conference on Discovery Science; Springer: Berlin/Heidelberg, Germany, 2020; pp. 659–673. [Google Scholar]
- Stone, A.A.; Shiffman, S. Ecological Momentary Assessment (EMA) in Behavioral Medicine. Ann. Behav. Med. 1994, 16, 199–202. [Google Scholar] [CrossRef]
- Larson, R.; Csikszentmihalyi, M. The Experience Sampling Method. In Flow and the Foundations of Positive Psychology: The Collected Works of Mihaly Csikszentmihalyi; Reis, H., Ed.; Volume Flow and the Foundations of Positive Psychology; Springer: Dordrecht, The Netherlands, 2014; pp. 41–56. [Google Scholar] [CrossRef]
- Van Berkel, N.; Ferreira, D.; Kostakos, V. The Experience Sampling Method on Mobile Devices. ACM Comput. Surv. 2017, 50, 93:1–93:40. [Google Scholar] [CrossRef]
- Pryss, R.; Probst, T.; Schlee, W.; Schobel, J.; Langguth, B.; Neff, P.; Spiliopoulou, M.; Reichert, M. Prospective Crowdsensing versus Retrospective Ratings of Tinnitus Variability and Tinnitus–Stress Associations Based on the TrackYourTinnitus Mobile Platform. Int. J. Data Sci. Anal. 2019, 8, 327–338. [Google Scholar] [CrossRef][Green Version]
- Beierle, F.; Tran, V.T.; Allemand, M.; Neff, P.; Schlee, W.; Probst, T.; Pryss, R.; Zimmermann, J. Context Data Categories and Privacy Model for Mobile Data Collection Apps. Procedia Comput. Sci. 2018, 134, 18–25. [Google Scholar] [CrossRef]
- Burke, J.; Estrin, D.; Hansen, M.; Parker, A.; Ramanathan, N.; Reddy, S.; Srivastava, M.B. Participatory Sensing. In Workshop on World-Sensor-Web (WSW’06): Mobile Device Centric Sensor Networks and Applications; ACM: New York, NY, USA, 2006; pp. 117–134. [Google Scholar]
- Lane, N.D.; Miluzzo, E.; Lu, H.; Peebles, D.; Choudhury, T.; Campbell, A.T. A Survey of Mobile Phone Sensing. IEEE Commun. Mag. 2010, 48, 140–150. [Google Scholar] [CrossRef]
- Ganti, R.K.; Ye, F.; Lei, H. Mobile Crowdsensing: Current State and Future Challenges. IEEE Commun. Mag. 2011, 49, 32–39. [Google Scholar] [CrossRef]
- Kraft, R.; Schlee, W.; Stach, M.; Reichert, M.; Langguth, B.; Baumeister, H.; Probst, T.; Hannemann, R.; Pryss, R. Combining mobile crowdsensing and ecological momentary assessments in the healthcare domain. Front. Neurosci. 2020, 14, 164. [Google Scholar] [CrossRef] [PubMed]
- Boubiche, D.E.; Imran, M.; Maqsood, A.; Shoaib, M. Mobile Crowd Sensing—Taxonomy, Applications, Challenges, and Solutions. Comput. Hum. Behav. 2019, 101, 352–370. [Google Scholar] [CrossRef]
- Pryss, R. Mobile crowdsensing in healthcare scenarios: Taxonomy, conceptual pillars, smart mobile crowdsensing services. In Digital Phenotyping and Mobile Sensing; Springer: Cham, Switzerland, 2019; pp. 221–234. [Google Scholar]
- Baumeister, H.; Montag, C. (Eds.) Digital Phenotyping and Mobile Sensing: New Developments in Psychoinformatics; Studies in Neuroscience, Psychology and Behavioral Economics; Springer: Cham, Switzerland, 2019. [Google Scholar] [CrossRef]
- Insel, T.R. Digital Phenotyping: Technology for a New Science of Behavior. JAMA 2017, 318, 1215–1216. [Google Scholar] [CrossRef] [PubMed]
- Jain, S.H.; Powers, B.W.; Hawkins, J.B.; Brownstein, J.S. The Digital Phenotype. Nat. Biotechnol. 2015, 33, 462–463. [Google Scholar] [CrossRef] [PubMed]
- Montag, C.; Sindermann, C.; Baumeister, H. Digital Phenotyping in Psychological and Medical Sciences: A Reflection about Necessary Prerequisites to Reduce Harm and Increase Benefits. Curr. Opin. Psychol. 2020, 36, 19–24. [Google Scholar] [CrossRef] [PubMed]
- Onnela, J.P. Opportunities and Challenges in the Collection and Analysis of Digital Phenotyping Data. Neuropsychopharmacology 2021, 46, 45–54. [Google Scholar] [CrossRef]
- Beierle, F. Integrating Psychoinformatics with Ubiquitous Social Networking: Advanced Mobile-Sensing Concepts and Applications; Springer: Cham, Switzerland, 2021. [Google Scholar] [CrossRef]
- Montag, C.; Baumeister, H.; Kannen, C.; Sariyska, R.; Meßner, E.M.; Brand, M. Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology. J 2019, 2, 102–115. [Google Scholar] [CrossRef][Green Version]
- Beierle, F.; Matz, S.C.; Allemand, M. Mobile Sensing in Personality Science. In Mobile Sensing in Psychology: Methods and Applications; Mehl, M.R., Wrzus, C., Eid, M., Harari, G., Priemer, U.E., Eds.; Guilford Press: New York, NY, USA, 2021; in press. [Google Scholar]
- Rooksby, J.; Morrison, A.; Murray-Rust, D. Student Perspectives on Digital Phenotyping: The Acceptability of Using Smartphone Data to Assess Mental Health. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow, UK, 4–9 May 2019; pp. 425:1–425:14. [Google Scholar] [CrossRef][Green Version]
- Zulueta, J.; Piscitello, A.; Rasic, M.; Easter, R.; Babu, P.; Langenecker, S.A.; McInnis, M.; Ajilore, O.; Nelson, P.C.; Ryan, K.; et al. Predicting Mood Disturbance Severity with Mobile Phone Keystroke Metadata: A BiAffect Digital Phenotyping Study. J. Med. Internet Res. 2018, 20, e241. [Google Scholar] [CrossRef] [PubMed]
- Beierle, F.; Tran, V.T.; Allemand, M.; Neff, P.; Schlee, W.; Probst, T.; Zimmermann, J.; Pryss, R. What Data Are Smartphone Users Willing to Share with Researchers? J. Ambient. Intell. Humaniz. Comput. 2020, 11, 2277–2289. [Google Scholar] [CrossRef]
- Borra, S. COVID-19 Apps: Privacy and Security Concerns. In Intelligent Systems and Methods to Combat Covid-19; Joshi, A., Dey, N., Santosh, K.C., Eds.; SpringerBriefs in Applied Sciences and Technology; Springer: Singapore, 2020; pp. 11–17. [Google Scholar] [CrossRef]
- Ahmed, N.; Michelin, R.A.; Xue, W.; Ruj, S.; Malaney, R.; Kanhere, S.S.; Seneviratne, A.; Hu, W.; Janicke, H.; Jha, S.K. A Survey of COVID-19 Contact Tracing Apps. IEEE Access 2020, 8, 134577–134601. [Google Scholar] [CrossRef]
- Pryss, R.; Schobel, J.; Reichert, M. Requirements for a Flexible and Generic API Enabling Mobile Crowdsensing mHealth Applications. In Proceedings of the 2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems (RESACS), Banff, AB, Canada, 20–20 August 2018; pp. 24–31. [Google Scholar] [CrossRef][Green Version]
- Pryss, R.; Schlee, W.; Hoppenstedt, B.; Reichert, M.; Spiliopoulou, M.; Langguth, B.; Breitmayer, M.; Probst, T. Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study. J. Med. Internet Res. 2020, 22, e15547. [Google Scholar] [CrossRef]
- Canzian, L.; Musolesi, M. Trajectories of Depression: Unobtrusive Monitoring of Depressive States by Means of Smartphone Mobility Traces Analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan, 7–11 September 2015; pp. 1293–1304. [Google Scholar] [CrossRef]
- Heller, A.S.; Shi, T.C.; Ezie, C.E.C.; Reneau, T.R.; Baez, L.M.; Gibbons, C.J.; Hartley, C.A. Association between Real-World Experiential Diversity and Positive Affect Relates to Hippocampal–Striatal Functional Connectivity. Nat. Neurosci. 2020, 23, 800–804. [Google Scholar] [CrossRef]
- Beierle, F.; Probst, T.; Allemand, M.; Zimmermann, J.; Pryss, R.; Neff, P.; Schlee, W.; Stieger, S.; Budimir, S. Frequency and Duration of Daily Smartphone Usage in Relation to Personality Traits. Digit. Psychol. 2020, 1, 20–28. [Google Scholar] [CrossRef]
- Gründahl, M.; Deckert, J.; Hein, G. Three questions to consider before applying ecological momentary interventions (EMI) in psychiatry. Front. Psychiatry 2020, 11, 333. [Google Scholar] [CrossRef][Green Version]
- Holfelder, M.; Mulansky, L.; Schlee, W.; Baumeister, H.; Schobel, J.; Greger, H.; Hoff, A.; Pryss, R. Medical Device Regulation Efforts for mHealth Apps – An Experience Report of Corona Check and Corona Health. arXiv 2021, arXiv:2104.13635. [Google Scholar]
- Seifert, A.; Hofer, M.; Allemand, M. Mobile Data Collection: Smart, but Not (Yet) Smart Enough. Front. Neurosci. 2018, 12. [Google Scholar] [CrossRef] [PubMed]
- Hargittai, E. Potential Biases in Big Data: Omitted Voices on Social Media. Soc. Sci. Comput. Rev. 2020, 38, 10–24. [Google Scholar] [CrossRef]
- Wetzel, B.; Pryss, R.; Baumeister, H.; Edler, J.S.; Gonçalves, A.S.O.; Cohrdes, C. “How Come You Don’t Call Me?” Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 6212. [Google Scholar] [CrossRef] [PubMed]
|beginTime||Beginning of observed period|
|endTime||End of observed period|
|collectedAt||Timestamp of observation time|
|apps||List of apps and their usage statistics (see Table 2)|
|top5Apps||Top 5 most used apps and their usage statistics (see Table 2)|
|sleepTimes||List of tuples with beginning and end timestamps of time windows in which the device was sleeping for at least one hour (no active screen)|
|screenTime||Contains two lists with daily cummulated time values. List 1: use time of all apps (screen on with app in visible foreground). List 2: time in which the screen was active (including no app being used or woken up from a notification).|
|packageName||Name of the package|
|completeUseTime||Sum of daily use time|
|completeFGServiceUseTime||Sum of ForegroundService use time (app is running without being visible on the screen)|
|dailyValues||List of containers for the following values and timestamps. One Container per day.|
|useTime||Time the app was in the foreground (visible on the screen)|
|firstUseTime||Timestamp of first visible use|
|lastUseTime||Timestamp of the last visible use|
|FGServiceUseTime||Sum of ForegroundService use time (app is running without being visible)|
|firstFGServiceUseTime||Timestamp of first ForeGroundService use|
|lastFGServiceUseTime||Timestamp of (start of) last ForeGroundService use|
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Beierle, F.; Schobel, J.; Vogel, C.; Allgaier, J.; Mulansky, L.; Haug, F.; Haug, J.; Schlee, W.; Holfelder, M.; Stach, M.; Schickler, M.; Baumeister, H.; Cohrdes, C.; Deckert, J.; Deserno, L.; Edler, J.-S.; Eichner, F.A.; Greger, H.; Hein, G.; Heuschmann, P.; John, D.; Kestler, H.A.; Krefting, D.; Langguth, B.; Meybohm, P.; Probst, T.; Reichert, M.; Romanos, M.; Störk, S.; Terhorst, Y.; Weiß, M.; Pryss, R. Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 7395. https://doi.org/10.3390/ijerph18147395
Beierle F, Schobel J, Vogel C, Allgaier J, Mulansky L, Haug F, Haug J, Schlee W, Holfelder M, Stach M, Schickler M, Baumeister H, Cohrdes C, Deckert J, Deserno L, Edler J-S, Eichner FA, Greger H, Hein G, Heuschmann P, John D, Kestler HA, Krefting D, Langguth B, Meybohm P, Probst T, Reichert M, Romanos M, Störk S, Terhorst Y, Weiß M, Pryss R. Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2021; 18(14):7395. https://doi.org/10.3390/ijerph18147395Chicago/Turabian Style
Beierle, Felix, Johannes Schobel, Carsten Vogel, Johannes Allgaier, Lena Mulansky, Fabian Haug, Julian Haug, Winfried Schlee, Marc Holfelder, Michael Stach, Marc Schickler, Harald Baumeister, Caroline Cohrdes, Jürgen Deckert, Lorenz Deserno, Johanna-Sophie Edler, Felizitas A. Eichner, Helmut Greger, Grit Hein, Peter Heuschmann, Dennis John, Hans A. Kestler, Dagmar Krefting, Berthold Langguth, Patrick Meybohm, Thomas Probst, Manfred Reichert, Marcel Romanos, Stefan Störk, Yannik Terhorst, Martin Weiß, and Rüdiger Pryss. 2021. "Corona Health—A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic" International Journal of Environmental Research and Public Health 18, no. 14: 7395. https://doi.org/10.3390/ijerph18147395