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Published in the Journal of the American Medical Informatics Association, this paper seeks to capture the effects of mood, demographics, and time-of- day on keyboard metadata.
Objective Ubiquitous technologies can be leveraged to construct ecologically relevant metrics that complement traditional psychological assessments. This study aims to determine the feasibility of smartphone-derived real-world keyboard metadata to serve as digital biomarkers of mood. Materials and Methods BiAffect, a real-world observation study based on a freely available iPhone app, allowed the unobtrusive collection of typing metadata through a custom virtual keyboard that replaces the default keyboard. User demographics and self-reports for depression severity (Patient Health Questionnaire-8) were also collected. Using >14 million keypresses from 250 users who reported demographic information and a subset of 147 users who additionally completed at least 1 Patient Health Questionnaire, we employed hierarchical growth curve mixed-effects models to capture the effects of mood, demographics, and time of day on keyboard metadata. Results We analyzed 86 541 typing sessions associated with a total of 543 Patient Health Questionnaires. Results showed that more severe depression relates to more variable typing speed (P < .001), shorter session duration (P < .001), and lower accuracy (P < .05). Additionally, typing speed and variability exhibit a diurnal pattern, being fastest and least variable at midday. Older users exhibit slower and more variable typing, as well as more pronounced slowing in the evening. The effects of aging and time of day did not impact the relationship of mood to typing variables and were recapitulated in the 250-user group. Conclusions Keystroke dynamics, unobtrusively collected in the real world, are significantly associated with mood despite diurnal patterns and effects of age, and thus could serve as a foundation for constructing digital biomarkers.
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The BiAffect team explores ethical, legal and regulatory issues inherent in the development and deployment of real-time monitoring systems for the diagnosis and management of psychiatric disorders in this article published in Focus: The Journal of Lifelong Learning in Psychiatry.
Abstract: Current management of psychiatric disorders relies heavily on retrospective, subjective reports provided by patients and their families. Consequently, psychiatric services are often provisioned inefficiently and with suboptimal outcomes. Recent advances in computing and sensor technologies have enabled the development of real-time monitoring systems for the diagnosis and management of psychiatric disorders. The state of these technologies is rapidly evolving, with passive monitoring and predictive modeling as two areas that have great potential to affect psychiatric care. Although outpatient psychiatry probably stands to benefit the most from the use of real-time monitoring technologies, there are also several ways in which inpatient psychiatry may also benefit. As the capabilities of these technologies increase and their use becomes more common, many ethical and legal issues will need to be considered. The role of governmental regulatory bodies and nongovernmental organizations in providing oversight of the implementation of these technologies is an active area of discussion. UIC Center on Depression and Resilience projects BiAffect and DiaBetty covered by Block Club Chicago9/24/2019  The technologies developed by the UIC Center on Depression and Resilience would allow patients to use their smartphones to track their neurological patterns.
Follow this link to read the rest of this article In a January 3 story about using smartphones to detect depression, Lindsey Tanner, a multimedia journalist covering national medical news for the Associated Press, interviewed Dr. Alex Leow, an app developer and associate professor of psychiatry and bioengineering at the University of Illinois’ Chicago campus about BiAffect and it’s place in the broader digital health ecosystem for a piece that illuminates the role of crowdsourcing, machine learning, and the role smartphones have to play as part of a broader story exploring several approaches to turning problematic device usage on its head and using digital phenotyping to tame technology.
hone Psychiatry story. Follow this link to read the corrected version of this article Read the full story by WSJ reporter Laine Higgins here
The latest wearable technology can reliably track heart beats and notify users of any irregularities. Up next? Reliably tracking your brain and mental health. A team of researchers at the Center on Depression and Resilience at the University of Illinois at Chicago is working on technology that could monitor users’ mood and cognition—important indicators of mental-health stress—by tracking their typing patterns with an iPhone app called BiAffect. Initial research has found it is possible to predict episodes of mania and depression among users with bipolar and major depressive disorder based on changes in their typing habits. For instance, a manic episode may be preceded by rising numbers of typos, faster typing, more frequent use of the “delete” key or tremors detected by the phone’s accelerometer, which measures the device’s tilting and orientation. During depressive episodes, users withdraw from their personal technology and tend to send short, infrequent messages. “It doesn’t track what you type, but how you type it,” says Dr. Alex Leow, an associate professor from the university’s College of Medicine and lead researcher on the project. Follow this link to read the rest of this article The most recent of five recent academic papers produced by the BiAffect team, this was published in the IEEE International Conference on Data Mining as a regular paper.
Abstract: Mood disorders are common and associated with significant morbidity and mortality. Early diagnosis has the potential to greatly alleviate the burden of mental illness and the ever increasing costs to families and society. Mobile devices provide us a promising opportunity to detect the users' mood in an unobtrusive manner. In this study, we use a custom keyboard which collects keystrokes' meta-data and accelerometer values. Based on the collected time series data in multiple modalities, we propose a deep personalized mood prediction approach, called {\pro}, by integrating convolutional and recurrent deep architectures as well as exploring each individual's circadian rhythm. Experimental results not only demonstrate the feasibility and effectiveness of using smart-phone meta-data to predict the presence and severity of mood disturbances in bipolar subjects, but also show the potential of personalized medical treatment for mood disorders. Among the latest of five recent academic papers put forth by the BiAffect team, this was published in the Journal of Medical Internet Research.
Abstract: Background: Mood disorders are common and associated with significant morbidity and mortality. Better tools are needed for their diagnosis and treatment. Deeper phenotypic understanding of these disorders is integral to the development of such tools. This study is the first effort to use passively collected mobile phone keyboard activity to build deep digital phenotypes of depression and mania. Objective: The objective of our study was to investigate the relationship between mobile phone keyboard activity and mood disturbance in subjects with bipolar disorders and to demonstrate the feasibility of using passively collected mobile phone keyboard metadata features to predict manic and depressive signs and symptoms as measured via clinician-administered rating scales. Methods: Using a within-subject design of 8 weeks, subjects were provided a mobile phone loaded with a customized keyboard that passively collected keystroke metadata. Subjects were administered the Hamilton Depression Rating Scale (HDRS) and Young Mania Rating Scale (YMRS) weekly. Linear mixed-effects models were created to predict HDRS and YMRS scores. The total number of keystrokes was 626,641, with a weekly average of 9791 (7861), and that of accelerometer readings was 6,660,890, with a weekly average 104,076 (68,912). Results: A statistically significant mixed-effects regression model for the prediction of HDRS-17 item scores was created: conditional R2=.63, P=.01. A mixed-effects regression model for YMRS scores showed the variance accounted for by random effect was zero, and so an ordinary least squares linear regression model was created: R2=.34, P=.001. Multiple significant variables were demonstrated for each measure. Conclusions: Mood states in bipolar disorder appear to correlate with specific changes in mobile phone usage. The creation of these models provides evidence for the feasibility of using passively collected keyboard metadata to detect and monitor mood disturbances. One of five academic papers put forward by the BiAffect team, this was published in the journal Bipolar Disorders.
Abstract: Smartphone innovations have opened new frontiers in the assessment of disease processes. Greater day‐to‐day instability in actively reported mood and passively recorded typing kinematics across 2 weeks predicted a poorer prospective course of depression and mania. This demonstrates the feasibility and utility of digital phenotyping in detecting individual differences in disease course. |
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