The enormous burden associated with schizophrenia underscores the critical need for research that could lead to improvements in symptoms and quality of life of individuals with the disease. It is well understood that outcomes among these individuals are far more complex than usual research models can capture. It is generally unfeasible to collect continuous or temporally dense data due to issues such as cost and participant burden.
Mobile and digital technologies therefore represent a valuable method for gathering real-world patient data at multiple timepoints. Findings from 2 recent studies demonstrated the utility of this approach in schizophrenia research.
A 2017 study investigated the use of a wearable mobile health (mHealth) device to assess autonomic activity and other factors during the daily activities of 30 patients with schizophrenia compared with 25 controls.1 Over 6 days, the device was found to reliably measure autonomic activity via heart rate variability, which was observed to be lower in the patients vs controls. Parasympathetic deregulation was associated with illness severity, especially in terms of positive symptoms. Patients also showed lower levels of functioning and movement compared with control participants.
“The results are notable not only for their interesting physiological findings, but also how feasible the study was to run in a real world setting outside of the lab,” as noted in a commentary coauthored by John Torous, MD, staff psychiatrist, clinical informatics fellow, and co-director of the digital psychiatry program at Beth Israel Deaconess Medical Center of Harvard Medical School.2
Another recent study included 18 patients with schizophrenia whose symptoms and activity levels were assessed via a wearable sensor during daily activities over a 4-day period.3 The findings revealed a negative correlation between interview-based apathy and motor activity. In addition, functional magnetic resonance imaging performed during reward anticipation demonstrated a link between interview-based apathy and hypoactivation of the ventral striatum, and between the level of motor activity and hypoactivation of the inferior frontal gyrus.
“Spontaneous motor activity is an objective readout of apathy, which was specific and not present for diminished expression,” the authors concluded. Additionally, they concluded that divergent neural correlates between interview-based and objective measures of apathy are indicative of dissociable neural processes.
This research “may be considered examples of digital phenotyping, defined in 2016 as the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices,” wrote Dr Torous and his coauthor.2,4 These and other studies “support that those with psychotic illnesses are comfortable, able, and willing to use personal digital devices to monitor outcomes in their daily life.”
Smartphone ownership continues to increase, suggesting that capturing data with these devices will become even more feasible over time. It is also evident that novel data can be gathered easily and with little cost. “Beyond offering new tools to better quantify the lived experiences of those with schizophrenia, digital phenotyping also offers a new target for biologically focused research,” as stated in the commentary. Considering the limitations of many current models, the “continuous spectrum of new digital data streams may serve as novel dimensional intermediate phenotypes for genetic, molecular, and neuroimaging research.”
Psychiatry Advisor spoke with Dr Torous to glean the top takeaways, potential challenges, and next steps in digital phenotyping for schizophrenia research.
Psychiatry Advisor: Overall, what are some ways in which smartphones, wearable sensors, and similar devices could prove to be valuable tools for schizophrenia research?
John Torous, MD: There is already evidence that these devices can be useful in relapse prediction efforts as well as offering on-demand services to help with symptom management and developing skills.
Psychiatry Advisor: What are some potential challenges pertaining to this approach, and how might these be addressed?
Dr Torous: While there are many exciting reports of digital technology working well, there are far fewer cases of it working in a reproducible manner. We need to move beyond the initial excitement and now ask for reproducible results. We also need to demand transparent science and understand how digital monitoring and interventions are working and why. Our group has researched how smartphones may be useful for sleep monitoring and prediction in schizophrenia, and the apps and algorithms we use are publicly available.5
Psychiatry Advisor: What should be the focus of further studies on this topic?
Dr Torous: We are going to see many more serious research efforts designed to increase usability, engagement, validity, and clinical utility of these digital tools. The potential is clear, and now is the time to work toward it. Our group is now focusing on using smartphones to assess cognition in not only schizophrenia but several other diseases, as we think there is tremendous information we can learn here.
- Cella M, Okruszek Ł, Lawrence M, Zarlenga V, He Z, Wykes T. Using wearable technology to detect the autonomic signature of illness severity in schizophrenia [published online October 3, 2017].Schizophr Res. doi:10.1016/j.schres.2017.09.028
- Torous J, Keshavan M. A new window into psychosis: the rise digital phenotyping, smartphone assessment, and mobile monitoring [published online January 12, 2018].Schizophr Res. doi:10.1016/j.schres.2018.01.005
- Kluge A, Kirschner M, Hager OM, et al. Combining actigraphy, ecological momentary assessment and neuroimaging to study apathy in patients with schizophrenia [published online October 10, 2017].Schizophr Res. doi:10.1016/j.schres.2017.09.034
- Onnela JP, Rauch SL. Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health.Neuropsychopharmacology. 2016;41(7):1691-1696.
- Staples P, Torous J, Barnett I. A comparison of passive and active estimates of sleep in a cohort with schizophrenia. NPJ Schizophr. 2017;3:37.