Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment

Vandendriessche, Benjamin; Godfrey, Alan; Izmailova, Elena S.

doi:10.1016/j.maturitas.2021.06.009

Cited by 5 publications

(4 citation statements)

References 64 publications

(80 reference statements)

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“…Although technology access and patient training and willingness are hurdles, perhaps the largest one is the lack of data infrastructure and expertise. 12 Because of my professional expertise, I was playing two roles: that of a patient, and that of the patient's own dedicated clinical scientist with a digital mindset. I could review sensor data with awareness of published data, 13,14 and screen for anomalies, processing, interpreting, and bringing only the most salient information into the limited time I had with my physician.…”

Section: Discussionmentioning

confidence: 99%

When Work Hits Home: The Cancer-Treatment Journey of a Clinical Scientist Driving Digital Medicine

Izmailova¹,

Ellis²

2022

JCO Clinical Cancer Informatics

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The following represents disclosure information provided by authors of this manuscript. All relationships are considered compensated unless otherwise noted. Relationships are self-held unless noted. I = Immediate Family Member, Inst = My Institution. Relationships may not relate to the subject matter of this manuscript. For more information about ASCO's conflict of interest policy, please refer to www.asco.org/rwc or ascopubs. org/cci/author-center. Open Payments is a public database containing information reported by companies about payments made to US-licensed physicians (Open Payments).

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Section: Discussionmentioning

confidence: 99%

When Work Hits Home: The Cancer-Treatment Journey of a Clinical Scientist Driving Digital Medicine

Izmailova¹,

Ellis²

2022

JCO Clinical Cancer Informatics

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“…Successful deployment of DHT‐derived measures requires in‐depth knowledge of DHTs, a platform enabling data collection and integration, data analytics, and statistical analyses tailored to the specific nature of a measure of interest. This infrastructure takes place often in conjunction with data processing algorithms and development which traditionally reside outside of BioPharma R&D 6 , 10 (Table 1 ). Similar partnerships are already standard practice for biomarker companion diagnostic assays.…”

Section: Industry Perspectivementioning

confidence: 99%

Digital technologies: Innovations that transform the face of drug development

Izmailova¹,

Abuasal

Hassan

et al. 2023

Clinical Translational Sci

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Recently, digital health technologies (DHTs) and digital biomarkers have gained a lot of traction in clinical investigations, motivating sponsors, investigators, and regulators to discuss and implement integrated approaches for deploying DHTs. These new tools present new and unique challenges for optimal technology integration in clinical trial processes, including operational, ethical, and regulatory issues. In this paper, we gathered different perspectives to discuss challenges and perspectives from three different stakeholders: industry, US regulators, and a public‐private partnership consortium. The complexities of DHT implementation, which include regulatory definitions, defining the scope of validation experiments, and the need for partnerships between BioPharma and the technology sectors, are highlighted. Most of these challenges are related to translation of DHT‐derived measures into endpoints that are meaningful to clinicians and patients, participant safety, training, and retention and privacy of data. The example of the Wearable Assessments in the Clinic and Home in PD (WATCH‐PD) study is discussed as an example that demonstrated the advantages of pre‐competitive collaborations, which include early regulatory feedback, data sharing, and multistakeholder alignment. Future advances in DHTs are expected to spur device‐agnostic measured development and incorporate patient reported outcomes in drug development. More efforts are needed to define validation experiments for a defined context of use, incentivize data sharing and development of data standards. Multistakeholder collaborations via precompetitive consortia will help facilitate broad acceptance of DHT‐enabled measures in drug development.

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“…Novel machine learning applications are pioneering the conversion of these multimodal data into measures for health-related quality of life (QOL)-relevant symptoms like fatigue [1], stress [2], and depression [3,4]. These insights have the potential to enable improved care delivery [5] and a deeper understanding of patients' lived experiences and better, more personalized medicines. However, important barriers remain to realize these benefits, both in technical and social aspects of real-world adoption.…”

Section: Introductionmentioning

confidence: 99%

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint

Clay¹,

Cormack²,

Fedor³

et al. 2022

J Med Internet Res

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The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities.

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Multimodal biometric monitoring technologies drive the development of clinical assessments in the home environment

Cited by 5 publications

References 64 publications

When Work Hits Home: The Cancer-Treatment Journey of a Clinical Scientist Driving Digital Medicine

When Work Hits Home: The Cancer-Treatment Journey of a Clinical Scientist Driving Digital Medicine

Digital technologies: Innovations that transform the face of drug development

Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint

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