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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 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”
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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. Read the full story by The Guardian reporter Zoë Corbyn here
The BiAffect study, a research effort focused on keystroke behaviour to predict bipolar episodes run by researchers at the University of Illinois at Chicago, has an open science component that allows the public to download an app and take part so that differences between healthy adults and those with bipolar disorder can be better understood. It has about 2,000 participants. Follow this link to read the rest of this article A review on the feasibility and validity of building the digital behaviorome via sensor data for neuropsychiatric research published in the journal Current Opinion in Systems Biology.
This review article summarizes how emerging connected technologies (e.g., smartphones and wearables) may provide novel avenues to understand an individual's behavior through the lens of systems biology. First, we surveyed recent research efforts that leveraged the multimodal high temporal resolution data derived from connected devices to build digital phenotypes and/or discover digital biomarkers of the behaviorome. We write this review with a particular emphasis on the detection, diagnosis, and symptom monitoring of neuropsychiatric disorders, as these pathologies may manifest primarily as disruptions to the behaviorome. We then discussed new opportunities and challenges these state-of-art research efforts bring as they intersect with other areas of natural and social sciences. Ultimately, we suggest how incorporating systems biology and connected technologies data can lead to a better understanding of complex neuropsychiatric disorders. Read the full story by science writer Teresa Carey in Freethink
For starters, I forgot the alphabet. It was one of those weeks where I missed every deadline already — unable to focus on work or anything else. I was instructed to quickly touch alternating letters and numbers in order: 1A, 2B, 3C, 4D, 5E, 6... What is the next letter? The stopwatch ticked on, and I imagined my multitasking score dropping. (Oh well, multitasking isn't a thing anyway, right?) I'm a volunteer for a study that uses the BiAffect app to screen my virtual mental health. Created by a University of Illinois team, BiAffect is a phone app that monitors mood and how it impacts cognition. The study is built around the theory that personal devices, like fitness trackers, phones or smartwatches, act as a digital proxy for human behavior. This emerging scientific field is called digital phenotyping... Follow this link to read the rest of this article |
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