About this Research Topic
Traditional mental health assessments occur cross-sectionally and rarely capture information intensively across time. Recently, methods which repeatedly ask persons to complete momentary self-report surveys (i.e., ecological momentary assessments) have been developed to address this. However, such methods that do assess persons longitudinally typically necessitate substantial burden. Recent advances in technology have led to an unprecedented ability to gather information about a person without active burden. Passively available data are multifaceted and can come from a variety of sources, including smartphone and wearable sensors, electronic health records, social media data, and internet search data (among others). With participant consent, these data can be collected with minimal burden and can assess a wide corpus of behavioral, social, and physiological factors longitudinally with minimal burden and within free-living conditions.
As an example, consider smartphone sensor data which can longitudinally capture social contact (calls, texts), movement type and intensity (via accelerometer data), and characteristics of one’s location and physical environment (via GPS). These data could be used to understand mental health measures by quantitatively estimating their association with mental health outcomes (e.g., a self-report instrument or a psychiatric interview), to develop predictive models of psychopathology (e.g., can we use this sensor data to predict changes in major depressive disorder symptoms across time?) and/or to test theoretical models (e.g., does a lack of social contact with others today relate to the severity of depressive changes tomorrow?).
Therefore, the current Research Topic aims to examine use of quantitative methods that integrate passively available data (of any type) with mental health assessment and objectives. Our goal is to demonstrate how passive sources of data, when paired with quantitative methods, can lead to an enhanced prediction and understanding of mental health. We welcome manuscript contributions with the aim of exemplifying the use of data collected with minimal active burden to better understand and/or predict mental health.
Data sources of interest include, but are not limited to, the following:
• Smartphone and wearable sensors;
• Electronic health records;
• Social media data;
• Internet search data
Prof. Dr. Burkhardt Funk is a shareholder in HelloBetter. The other Topic Editors declare no competing interests in relation to the Research Topic theme.
As an example, consider smartphone sensor data which can longitudinally capture social contact (calls, texts), movement type and intensity (via accelerometer data), and characteristics of one’s location and physical environment (via GPS). These data could be used to understand mental health measures by quantitatively estimating their association with mental health outcomes (e.g., a self-report instrument or a psychiatric interview), to develop predictive models of psychopathology (e.g., can we use this sensor data to predict changes in major depressive disorder symptoms across time?) and/or to test theoretical models (e.g., does a lack of social contact with others today relate to the severity of depressive changes tomorrow?).
Therefore, the current Research Topic aims to examine use of quantitative methods that integrate passively available data (of any type) with mental health assessment and objectives. Our goal is to demonstrate how passive sources of data, when paired with quantitative methods, can lead to an enhanced prediction and understanding of mental health. We welcome manuscript contributions with the aim of exemplifying the use of data collected with minimal active burden to better understand and/or predict mental health.
Data sources of interest include, but are not limited to, the following:
• Smartphone and wearable sensors;
• Electronic health records;
• Social media data;
• Internet search data
Prof. Dr. Burkhardt Funk is a shareholder in HelloBetter. The other Topic Editors declare no competing interests in relation to the Research Topic theme.
Keywords: passive sensing, digital phenotyping, digital biomarkers, computational psychiatry, sensor data
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
