Elsevier

Journal of Psychiatric Research

Volume 85, February 2017, Pages 1-14
Journal of Psychiatric Research

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

https://doi.org/10.1016/j.jpsychires.2016.10.019Get rights and content

Abstract

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders.

Section snippets

Biomarkers in psychiatric clinical research

Psychiatric disorders are common (Kessler et al., 2005) and a leading risk factor for poor quality of life worldwide (Whiteford et al., 2013). Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Central to moving the mental health field forward is better understanding of complex mechanisms underlying psychiatric disorders and further optimizing treatment strategies to each patient's needs, referred to recently as precision medicine

mHealth has improved real-time measurement of clinically relevant data

Most mHealth research in psychiatry has focused on using short message service (SMS) text messaging or web-enabled applications to measure self-reported symptoms or functioning; measuring and linking contextual factors (e.g., time, location) to self-reported symptoms or behaviors; or delivering interventions via mobile devices, typically based on user self-report or environmental factors. The advent of mobile phones and personal digital assistants brought new opportunities to gather

The appeal of mHealth for remote biomarker acquisition to advance psychiatric research

Careful measurement of biomarker data alongside other indicators and correlates of psychopathology and substance use aligns with strategic priorities of public health agencies. For example, the National Institute of Mental Health (NIMH) Research Domain Criteria (RDoC) initiative prioritizes consideration of the causes of psychopathology and how they interact with environmental factors across development or change in response to treatment (Cuthbert and Insel, 2013). Per the RDoC initiative,

Method

The authors conducted an extensive literature search through January 2016 using several electronic databases, including PubMed, PsycINFO, and Google Scholar. The following keywords (and permutations thereof) were used in our search: mobile health, mhealth, psychiatry, psychology, stress, mental health, mental illness, psychiatric disorders, substance use disorders, addiction, alcohol, tobacco, technology, biomarkers, biochemical, sensors, biosensors, physiology, mobile phones, and smartphones.

Mobile biosensing of stress and anxiety

There are several examples from the literature demonstrating the use of mobile biosensors to measure autonomic nervous system (ANS) functioning, psychological stress, and anxiety. Although the role of stress and ANS clearly extends beyond psychiatric disorders, these factors are strongly implicated in a range of mental health problems. Plarre et al. (2011) used the Bluetooth-enabled AutoSense wearable sensor suite (Ertin et al., 2011, Hovsepian et al., 2015) to measure electrocardiogram (ECG)

Future applications of mHealth biomarker collection in psychiatric research

To date, mHealth methods have been used to study a small fraction of psychiatric phenomena and biomarkers. One potential avenue to build on this progress would be to integrate remote biomarker collection strategies like those described above into research on a broader range of disorders and constructs and in large samples. To illustrate, acute threat (a construct in the NIMH RDoC Negative Valence Systems domain) may be operationalized using physiological measures of startle, heart rate, skin

Conclusions

The future of mHealth tools and methods in psychiatric research is bright. Strides in this field will emerge through synergistic, multidisciplinary collaborations among engineers, computer scientists, informaticians, clinical scientists, health care professionals, regulatory experts, user experience teams, and end-users themselves. Research and service funding institutions increasingly recognize the value of mHealth approaches and have prioritized training and multidisciplinary research in this

Role of funding sources

The funding sources did not play a role in the study conceptualization or design; collection, analysis, or interpretation of data; writing of the manuscript; or the decision to submit the article for publication.

Contributors

Drs. Adams and McClure developed the initial concept for this manuscript, conducted the literature review, and wrote the manuscript. Drs. Gray, Danielson, Ruggiero, and Treiber contributed to the conceptualization of the study, offered substantive feedback on iterative versions of the manuscript, and assisted with editing the paper. All authors approved have approved the final manuscript.

Disclosures

We do not have any conflicts to disclose other than grants from the National Institutes of Health, Substance Abuse and Mental Health Services Administration, the Veterans Administration, and the Duke Endowment that supported the authors’ time and efforts in preparation of this manuscript.

Acknowledgements

This work was supported primarily by National Institutes of Health (ZWA, grant number, K23DA038257; EAM, grant number K01DA036739; KMG, grant number U01DA031779; CKD, grant numbers K24DA039783, R01DA031285, and T32MH018869; KJR, grant numbers I21HX001729, R34MH096907, and R01MH107641; and FAT, grant numbers R01DK098777, R01HL114957, and R21HL118447; and NIH grant number UL1RR029882). Additional funding came from the Substance Abuse and Mental Health Services Administration (CKD), Veterans

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