About this Research Topic
This Research Topic is part of the Smart Mobile Data Collection in the Context of Neuroscience series: Smart Mobile Data Collection in the Context of Neuroscience, Volume I
For a growing number of questions in neuroscience, the field of mobile technology can be a beneficial alley. For example, the collection of data in real-life situations can be effectively achieved by the use of mobile technology. The goal of such in-situ measurements can be to learn more about a medical phenomenon and to be potentially able to eventually develop new therapies. The recent past has revealed many new research methods and strategies in this context, showing more and more that mobile devices can constitute a shift in the way how patient data are collected. Most importantly, mobile technology offers possibilities that cannot be provided by many other traditional technologies. For example, several disorders come along with symptoms that vary, sometimes even on a moment-to-moment basis, making it difficult without a permanent technical low-threshold companion to be reliably measured and captured. Mobile devices can close this gap and are therefore increasingly used in the field of neuroscience. However, although mobile technology can help in many ways, researchers are still facing challenges and obstacles in their daily research business. This ranges from technical development questions to questions about how established data sources can be meaningfully analyzed. Beyond such issues, researchers have to understand and know the different strategies that have emerged during the last years. Digital Phenotyping or Routine Outcome Monitoring are only two of these strategies that have garnered attention recently. Consequently, more efforts are needed to make those strategies, data evaluations and technical developments better understandable and more comparable. Therefore, the second edition of this Research Topic on smart mobile data collection welcomes original research works on the following aspects, topics, and issues:
- What are the drawbacks and limitations of data sets that are collected when using mobile devices?
- What does data quality mean in this context?
- Can we derive guidelines to establish standardized mobile data collection procedures?
- Can we actually derive new insights in the context of the moment-to-moment variability of patients?
- Can we derive adjustment factors to mobile collected data?
- Is this kind of data only new wine into old wineskins or does mobile data collection constitute a disruptive innovation in the context of neuroscience and chronic disorders?
- What role does the ecological momentary assessment (EMA; also known as ambulatory assessment & experience sampling) play in this context?
- What role does Digital Phenotyping play in this context?
- What are the differences between the available collection strategies and concepts: Patient-Reported Outcome, Routine Outcome Monitoring, Digital Phenotyping, Ecological Momentary Assessment, Mobile Sensing, Mobile Crowdsensing, Mobile Crowdsourcing?
- What role do data security and data privacy play in this context?
- How do we cope with ethical aspects?
- What are the general risks of this new kind of data collection?
- Should we consider threats that arise with mobile technology in general?
- How do we cope with information and selection bias?
- What are the benefits to use methods of artificial intelligence in this context?
- What are the benefits of multi-modal data fusion in this context?
For a growing number of questions in neuroscience, the field of mobile technology can be a beneficial alley. For example, the collection of data in real-life situations can be effectively achieved by the use of mobile technology. The goal of such in-situ measurements can be to learn more about a medical phenomenon and to be potentially able to eventually develop new therapies. The recent past has revealed many new research methods and strategies in this context, showing more and more that mobile devices can constitute a shift in the way how patient data are collected. Most importantly, mobile technology offers possibilities that cannot be provided by many other traditional technologies. For example, several disorders come along with symptoms that vary, sometimes even on a moment-to-moment basis, making it difficult without a permanent technical low-threshold companion to be reliably measured and captured. Mobile devices can close this gap and are therefore increasingly used in the field of neuroscience. However, although mobile technology can help in many ways, researchers are still facing challenges and obstacles in their daily research business. This ranges from technical development questions to questions about how established data sources can be meaningfully analyzed. Beyond such issues, researchers have to understand and know the different strategies that have emerged during the last years. Digital Phenotyping or Routine Outcome Monitoring are only two of these strategies that have garnered attention recently. Consequently, more efforts are needed to make those strategies, data evaluations and technical developments better understandable and more comparable. Therefore, the second edition of this Research Topic on smart mobile data collection welcomes original research works on the following aspects, topics, and issues:
- What are the drawbacks and limitations of data sets that are collected when using mobile devices?
- What does data quality mean in this context?
- Can we derive guidelines to establish standardized mobile data collection procedures?
- Can we actually derive new insights in the context of the moment-to-moment variability of patients?
- Can we derive adjustment factors to mobile collected data?
- Is this kind of data only new wine into old wineskins or does mobile data collection constitute a disruptive innovation in the context of neuroscience and chronic disorders?
- What role does the ecological momentary assessment (EMA; also known as ambulatory assessment & experience sampling) play in this context?
- What role does Digital Phenotyping play in this context?
- What are the differences between the available collection strategies and concepts: Patient-Reported Outcome, Routine Outcome Monitoring, Digital Phenotyping, Ecological Momentary Assessment, Mobile Sensing, Mobile Crowdsensing, Mobile Crowdsourcing?
- What role do data security and data privacy play in this context?
- How do we cope with ethical aspects?
- What are the general risks of this new kind of data collection?
- Should we consider threats that arise with mobile technology in general?
- How do we cope with information and selection bias?
- What are the benefits to use methods of artificial intelligence in this context?
- What are the benefits of multi-modal data fusion in this context?
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