Digital biomarkers of mood disorders and symptom change

Jacobson, Nicholas C.; Weingarden, Hilary; Wilhelm, Sabine

doi:10.1038/s41746-019-0078-0

Cited by 100 publications

(102 citation statements)

References 8 publications

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“…This level of accuracy in diagnostic classification is greater than published inter-reliability rates for second raters using the Structured Clinical Interview for the DSM (SCID). And results showed that actigraphy data predicted the majority of variation in patients’ depression severity over an ~2-week period [ 66 ].…”

Section: The State Of the Science Of Digital Health Data-driven Appromentioning

confidence: 99%

Digital health data-driven approaches to understand human behavior

Marsch¹

2020

Neuropsychopharmacol.

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Advances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals’ behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.

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Section: The State Of the Science Of Digital Health Data-driven Appromentioning

confidence: 99%

Digital health data-driven approaches to understand human behavior

Marsch¹

2020

Neuropsychopharmacol.

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“…Also these digital biomarkers are more in line with the phenomenological understanding of behavioural therapies. The quantification of behavioural and consciousness involving processes can assist squaring the circle of qualia-related outcome [6,7].…”

Section: Doi: 101159/000506672mentioning

confidence: 99%

Quantified Complementary and Alternative Medicine: Convergence of Digital Health Technologies and Complementary and Alternative Medicine

Steckhan

Arnrich

2020

Complement Med Res

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“…Digital phenotyping is an especially attractive clinical tool for substance use treatment during the COVID-19 pandemic, as it not only operates remotely but is also convenient for patients (no active input required), is not administratively burdensome, and may be an effective way to address historic challenges in detecting problematic substance use behaviors and delivering timely clinical interventions. Digital phenotyping has shown initial success in relapse prediction for schizophrenia [ 12 ], bipolar disorder [ 13 , 14 ], and mood disorder detection [ 14 ]. We describe three broad applications of digital phenotyping for addiction treatment, highlighting their potential clinical use, state of evidence, and next steps for implementation.…”

Section: Introductionmentioning

confidence: 99%

Digital Phenotyping to Enhance Substance Use Treatment During the COVID-19 Pandemic

Hsu¹,

Ahern²,

Suzuki³

2020

JMIR Ment Health

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Due to the COVID-19 pandemic, many clinical addiction treatment programs have been required to transition to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social support. Digital phenotyping, which leverages the unique functionality of smartphone sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous amount of collected behavioral data. Activity-based analysis in real time can potentially be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when a person has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite the initial promise of this approach, privacy, security, and barriers to access are important issues to address.

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Digital biomarkers of mood disorders and symptom change

Cited by 100 publications

References 8 publications

Digital health data-driven approaches to understand human behavior

Digital health data-driven approaches to understand human behavior

Quantified Complementary and Alternative Medicine: Convergence of Digital Health Technologies and Complementary and Alternative Medicine

Digital Phenotyping to Enhance Substance Use Treatment During the COVID-19 Pandemic

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