BiAffect welcomes retired US Army Major General Gregg Martin, who will be joining the team as an advisor. A bipolar expert by lived experience, General Martin will be lending his tremendous insight to help guide BiAffect research team as we translate our research findings. His amazing journey is detailed in his recently published book, Bipolar General, available now. Listen to General Martin in conversation with Dr. Leow here.
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Read the full review article by Theresa Nguyen and colleagues in the journal Brain Sciences here
Abstract Can digital technologies provide a passive unobtrusive means to observe and study cognition outside of the laboratory? Previously, cognitive assessments and monitoring were conducted in a laboratory or clinical setting, allowing for a cross-sectional glimpse of cognitive states. In the last decade, researchers have been utilizing technological advances and devices to explore ways of assessing cognition in the real world. We propose that the virtual keyboard of smartphones, an increasingly ubiquitous digital device, can provide the ideal conduit for passive data collection to study cognition. Passive data collection occurs without the active engagement of a participant and allows for near-continuous, objective data collection. Most importantly, this data collection can occur in the real world, capturing authentic datapoints. This method of data collection and its analyses provide a more comprehensive and potentially more suitable insight into cognitive states, as intra-individual cognitive fluctuations over time have shown to be an early manifestation of cognitive decline. We review different ways passive data, centered around keystroke dynamics, collected from smartphones, have been used to assess and evaluate cognition. We also discuss gaps in the literature where future directions of utilizing passive data can continue to provide inferences into cognition and elaborate on the importance of digital data privacy and consent. Read the full journal article by Emma Ning and colleagues in the Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems here
Abstract We examine the feasibility of using accelerometer data exclusively collected during typing on a custom smartphone keyboard to study whether typing dynamics are associated with daily variations in mood and cognition. As part of an ongoing digital mental health study involving mood disorders, we collected data from a well-characterized clinical sample (N = 85) and classified accelerometer data per typing session into orientation (upright vs. not) and motion (active vs. not). The mood disorder group showed lower cognitive performance despite mild symptoms (depression/mania). There were also diurnal pattern differences with respect to cognitive performance: individuals with higher cognitive performance typed faster and were less sensitive to time of day. They also exhibited more well-defined diurnal patterns in smartphone keyboard usage: they engaged with the keyboard more during the day and tapered their usage more at night compared to those with lower cognitive performance, suggesting a healthier usage of their phone. Mindy Ross and colleagues have developed a new method of processing accelerometer data collected from research participants typing on their smartphones that can enable the unobtrusive detection of changes in the severity of these participants' depression without the need for clinical input, and published their results in a special issue of the journal Sensors.
Read the full journal article in the special issue “Smartphone Based Biosensing” of the journal Sensors here Abstract The treatment of mood disorders, which can become a lifelong process, varies widely in efficacy between individuals. Most options to monitor mood rely on subjective self-reports and clinical visits, which can be burdensome and may not portray an accurate representation of what the individual is experiencing. A passive method to monitor mood could be a useful tool for those with these disorders. Some previously proposed models utilized sensors from smartphones and wearables, such as the accelerometer. This study examined a novel approach of processing accelerometer data collected from smartphones only while participants of the open-science branch of the BiAffect study were typing. The data were modeled by von Mises-Fisher distributions and weighted networks to identify clusters relating to different typing positions unique for each participant. Longitudinal features were derived from the clustered data and used in machine learning models to predict clinically relevant changes in depression from clinical and typing measures. Model accuracy was approximately 95%, with 97% area under the ROC curve (AUC). The accelerometer features outperformed the vast majority of clinical and typing features, which suggested that this new approach to analyzing accelerometer data could contribute towards unobtrusive detection of changes in depression severity without the need for clinical input.Smartphone Based Biosensing” A paper published by the BiAffect team — Casey C. Bennett, Mindy K. Ross, EuGene Baek, Dohyeon Kim & Alex D. Leow — in Nature npj Digital Medicine examining whether smartphone accelerometer data (alone or in concert with typing dynamics) may serve as a proxy for traditional clinical data.
Read the full journal article in npj Digital Medicine here Abstract Being able to track and predict fluctuations in symptoms of mental health disorders such as bipolar disorder outside the clinic walls is critical for expanding access to care for the global population. To that end, we analyze a dataset of 291 individuals from a smartphone app targeted at bipolar disorder, which contains rich details about their smartphone interactions (including typing dynamics and accelerometer motion) collected everyday over several months, along with more traditional clinical features. The aim is to evaluate whether smartphone accelerometer data could serve as a proxy for traditional clinical data, either by itself or in combination with typing dynamics. Results show that accelerometer data improves the predictive performance of machine learning models by nearly 5% over those previously reported in the literature based only on clinical data and typing dynamics. This suggests it is possible to elicit essentially the same “information” about bipolar symptomology using different data sources, in a variety of settings. Dr. Michelle Chen, whose study utilized BiAffect to examine the associations between smartphone keystroke dynamics and cognitive functioning among persons with multiple sclerosis, and colleagues published the results of their study in the journal Digital Health.
Read the full journal article in Digital Health here Methods Sixteen persons with MS with no self-reported upper extremity or typing difficulties and 10 healthy controls (HCs) completed six weeks of remote monitoring of their keystroke dynamics (i.e., how they typed on their smartphone keyboards). They also completed a comprehensive neuropsychological assessment and symptom ratings about fatigue, depression, and anxiety at baseline. Results A total of 1,335,787 keystrokes were collected, which were part of 30,968 typing sessions. The MS group typed slower (P < .001) and more variably (P = .032) than the HC group. Faster typing speed was associated with better performance on measures of processing speed (P = .016), attention (P = .022), and executive functioning (cognitive flexibility: P = .029; behavioral inhibition: P = .002; verbal fluency: P = .039), as well as less severe impact from fatigue (P < .001) and less severe anxiety symptoms (P = .007). Those with better cognitive functioning and less severe symptoms showed a stronger correlation between the use of backspace and autocorrection events (P < .001) Conclusion Typing speed may be sensitive to cognitive functions subserved by the frontal–subcortical brain circuits. Individuals with better cognitive functioning and less severe symptoms may be better at monitoring their typing errors. Keystroke dynamics have the potential to be used as an unobtrusive remote monitoring method for real-life cognitive functioning among persons with MS, which may improve the detection of relapses, evaluate treatment efficacy, and track disability progression. BiAffect PI Dr. Alex Leow is quoted in a U.S. News & World Report story about the distinct changes that unfold in new fathers' brains about the birth of their child.
Read the full story by HealthDay reporter Cara Murez here Dr. Alex Leow, a professor of psychiatry and bioengineering at the University of Illinois Chicago, said science is now appreciating that people have many differences and that it's important to study different groups. There is more awareness of sex differences in the brains of men and women, in both structure and function, Leow said. Leow noted that the default mode network is a concept that has been popular among brain scientists for about 10 to 15 years. The idea is that the brain is active even if it's just thinking through the day's tasks. "A lot of the things the brain's thinking about when we are not doing a task is really contemplating ourselves. It turns out that's exactly a fundamental function of this default mode network," Leow said. Where a person is in terms of their life span can also make a difference. A person's focus may be different in their 20s than when they have kids, when their children are in their teen years or when they are approaching retirement, Leow said. "And I can see that when parents are now expecting a newborn baby, that reflection is totally different. It's a very different kind of reflection," Leow said. "And I think in that sense, it makes a lot of sense for the primary part of the brain to be affected being the default mode network." Other studies have followed mothers for a longer period of time to see if changes after pregnancy reverted back to baseline or were permanent. That would be an interesting follow-up question to pursue after this study, Leow said. University of Georgia researcher Brian Bauer and his former mentor, BiAffect PI Dr. Alex Leow, have developed a platform called the Continued Service Network that will enable vets to help each other, that was recently awarded $250,000 by Mission Daybreak, a part of the U.S. Department of Veteran Affairs’ 10-year strategy to end veteran suicide through a comprehensive, public health approach.
Read the full story by UGA reporter Katie Cowart here To solve existential brain challenges spanning neurology, mental health, education, workforce development, and neuroscience—we need a fresh approach to technologies and investing. We need a new investment opportunity: Brain Capital.
BiAffect is listed as an innovative patient reported #outcomes tool in this commentary article in the periodical Psychiatric Times. Published in the journal Physica A: Statistical Mechanics and its Applications, this paper describes the reliable and accurate power-law estimation approach developed by its authors for analyzing the limited and noisy data generated from virtual keyboard typing dynamics.
Abstract Ubiquitous use of smartphones has significantly shaped interpersonal communications in modern life, in particular interactions via text messaging. To study the underlying dynamics of these smartphone-based human communications, we used a unique smartphone typing dataset that was passively and unobtrusively collected in-the-wild from 296 users via a custom-made iPhone keyboard. To reliably and accurately characterize the underlying distribution of the inter-event time between two consecutive keypresses, we (i) developed a statistical approach that integrates existing methods for estimating power-law distribution, and (ii) showed that power-law is a plausible candidate to represent human typing dynamics. We designed synthetic-data simulations in multiple scenarios where the synthetic data may or may not imitate human typing characteristics. Using numerical simulations, we showed that our approach, in all scenarios, improves the accuracy and stability of power-law estimates upon the common methods. We further demonstrated that when the synthetic data follow human typing characteristics, common methods lead to significant misestimations of power-law exponent as they fail to take into account the key characteristics of the observed data. More broadly, our approach applies beyond the power-law estimation for human typing dynamics data. |