Pharmaceutical Perspective: How Digital Biomarkers and Contextual Data Will Enable Therapeutic Environments

Rodarte, Carlos

doi:10.1159/000479951

Cited by 17 publications

(11 citation statements)

References 16 publications

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“…Researchers have begun to leverage activity trackers, health and fitness apps, and other digital technologies in clinical and health outcome studies to collect behavioral data and analyze it alongside more conventional types of health outcomes and clinical data [ 2 ]. These joint data sets can then be used to identify various behavioral characteristics and patterns associated with different health conditions and can help provide a better understanding of disease status, onset, and progression, thus potentially leading to improved screening and monitoring techniques, therapeutic innovations, and disease management [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study

Kumar¹,

Tran²,

Ramírez³

et al. 2020

JMIR Ment Health

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Background Depression and anxiety greatly impact daily behaviors, such as sleep and activity levels. With the increasing use of activity tracking wearables among the general population, there has been a growing interest in how data collected from these devices can be used to further understand the severity and progression of mental health conditions. Objective This virtual 1-year observational study was designed with the objective of creating a longitudinal data set combining self-reported health outcomes, health care utilization, and quality of life data with activity tracker and app-based behavioral data for individuals with depression and anxiety. We provide an overview of the study design, report on baseline health and behavioral characteristics of the study population, and provide initial insights into how behavioral characteristics differ between groups of individuals with varying levels of disease severity. Methods Individuals who were existing members of an online health community (Achievement, Evidation Health Inc) and were 18 years or older who had self-reported a diagnosis of depression or anxiety were eligible to enroll in this virtual 1-year study. Participants agreed to connect wearable activity trackers that captured data related to physical activity and sleep behavior. Mental health outcomes such as the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder Questionnaire (GAD-7), mental health hospitalizations, and medication use were captured with surveys completed at baseline and months 3, 6, 9, and 12. In this analysis, we report on baseline characteristics of the sample, including mental health disease severity and health care utilization. Additionally, we explore the relationship between passively collected behavioral data and baseline mental health status and health care utilization. Results Of the 1304 participants enrolled in the study, 1277 individuals completed the baseline survey and 1068 individuals had sufficient activity tracker data. Mean age was 33 (SD 9) years, and the majority of the study population was female (77.2%, 994/1288) and identified as Caucasian (88.3%, 1137/1288). At baseline, 94.8% (1211/1277) of study participants reported experiencing depression or anxiety symptoms in the last year. This baseline analysis found that some passively tracked behavioral traits are associated with more severe forms of anxiety or depression. Individuals with depressive symptoms were less active than those with minimal depressive symptoms. Severe forms of depression were also significantly associated with inconsistent sleep patterns and more disordered sleep. Conclusions These initial findings suggest that longitudinal behavioral and health outcomes data may be useful for developing digital measures of health for mental health symptom severity and progression.

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Section: Introductionmentioning

confidence: 99%

Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study

Kumar¹,

Tran²,

Ramírez³

et al. 2020

JMIR Ment Health

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“…The range of sensors and biomarkers they are capable of tracking is ever increasing, and, coupled with potential changes in healthcare reimbursement models, represents an important juncture and opportunity for the industry to consider. [18]…”

Section: Introductionmentioning

confidence: 99%

Harnessing the Digital Exhaust: Incorporating Wellness into the Pharma Model

Wright

Jones

2018

Digit Biomark

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The increasing availability of devices capable of tracking biomarkers presents major opportunities in contemporary healthcare. Herein we advocate a new role for the pharmaceutical industry to capitalize on these opportunities and, in doing so, incorporate wellness and patient engagement programs into their standard business models. Medical-grade decision-making using diagnostic, prognostic, and monitoring biomarkers will require coordinated approaches between the pharmaceutical and technology industries and the careful design of longitudinal clinical studies to validate their efficacy. These studies will also require data capture, archiving, curating, and sharing on a previously unprecedented scale, and raise additional concerns with regard to data security and ownership. Concurrently, systems-based approaches to the capture and interpretation of a new class of digital biomarkers are emerging, and they hold promise for heightened levels of patient engagement and remote sensing. Collectively, if these new opportunities are approached within the context of the patient-provider ecosystem, major repositioning of the pharmaceutical industry may be possible in the near future.

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“…As such, the era of digital monitoring biomarkers (DMBs) is dawning, with the capacity to revolutionize aspects of the healthcare ecosystem if implemented appropriately [ 24 – 26 ]. Continual advances in technology development and wireless network capacity globally suggest that technical barriers to adoption will be minimal, representing an unparalleled opportunity for the managed healthcare industry to transform itself [ 27 , 28 ]. Herein we dissect the various DMB component systems, and, by incorporating contextual elements, offer a standardization framework which will inspire its rapid evolution.…”

Section: Introduction and Current Statementioning

confidence: 99%

Can composite digital monitoring biomarkers come of age? A framework for utilization

Kovalchick

Sirkar

Regele

et al. 2017

J. Clin. Trans. Sci.

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Introduction The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision making dashboard. Methods A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare. Results The de novo framework implies consideration of physiological, behavioral and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed loop systems is illustrated. Conclusions The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.

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Pharmaceutical Perspective: How Digital Biomarkers and Contextual Data Will Enable Therapeutic Environments

Cited by 17 publications

References 16 publications

Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study

Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study

Harnessing the Digital Exhaust: Incorporating Wellness into the Pharma Model

Can composite digital monitoring biomarkers come of age? A framework for utilization

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