Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review

Shandhi, Md. Mobashir Hasan; Goldsack, Jennifer C.; Ryan, Kyle; Bennion, Alexandra; Kotla, Aditya; Feng, Alina; Jiang, Yihang; Wang, Will Ke; Hurst, Tina L.; Pateña, John; Carini, Simona; Chung, Jeanne; Dunn, Jessilyn

doi:10.2196/29875

Cited by 12 publications

(10 citation statements)

References 38 publications

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“…Ensuring they are developed and deployed within the right environment, with diverse teams and appropriate safeguards is critical if value is to be realized [90][91][92][93]. Research into ethical and privacy matters related to digital health is expanding but still remains underexplored [94]. Engagement has also been a challenge across all digital measures in mental health to date [95], but when technology is created with patient feedback [96] and data are used to advance patient care, engagement can actually be quite high [97].…”

Section: "From Efficacy To Effectiveness" Use Casesmentioning

confidence: 99%

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Clay,

Peerenboom,

Connors

et al. 2023

Digit Biomark

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Background: Digital measures offer an unparalleled opportunity to create a more holistic picture of how people who are patients behave in their real-world environments, thereby establishing a better connection between patients, caregivers, and the clinical evidence used to drive drug development and disease management. Reaching this vision will require achieving a new level of co-creation between the stakeholders who design, develop, use, and make decisions using evidence from digital measures. Summary: In September 2022, the second in a series of meetings hosted by the Swiss Federal Institute of Technology in Zürich, the Foundation for the National Institutes of Health Biomarkers Consortium, and sponsored by Wellcome Trust, entitled “Reverse Engineering of Digital Measures,” was held in Zurich, Switzerland, with a broad range of stakeholders sharing their experience across four case studies to examine how patient centricity is essential in shaping development and validation of digital evidence generation tools. Key Messages: In this paper, we discuss progress and the remaining barriers to widespread use of digital measures for evidence generation in clinical development and care delivery. We also present key discussion points and takeaways in order to continue discourse and provide a basis for dissemination and outreach to the wider community and other stakeholders. The work presented here shows us a blueprint for how and why the patient voice can be thoughtfully integrated into digital measure development and that continued multistakeholder engagement is critical for further progress.

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Section: "From Efficacy To Effectiveness" Use Casesmentioning

confidence: 99%

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Clay,

Peerenboom,

Connors

et al. 2023

Digit Biomark

View full text Add to dashboard Cite

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“…There are several specific technical hurdles that get in the way of effective data integration, including the plethora of formats [ 29 , 30 ] and the corresponding lack of consensus around common metadata standards [ 14 , 26 ], although progress is being made [ 31 ]. Perhaps more worrisome is the fact that permissioning and consent often exclude integration and exploration [ 32 ], and massive gaps in digital health research remain regarding data rights and governance [ 33 , 34 ].…”

Section: What Are the Key Issues?mentioning

confidence: 99%

“…Moreover, understanding the legal and ethical consequences of sharing and integrating sensor data is at a nascent stage [ 45 ]. Recent work has shown that “the three most underrepresented areas of research into digital clinical measures were ethics, security, and data rights and governance” [ 33 ].…”

Section: What Are the Key Issues?mentioning

confidence: 99%

Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices

Clay¹,

Angelopoulos²,

Bailey³

et al. 2021

J Med Internet Res

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Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.

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“…Ultimately, the same technology could be used both in and outside the hospital, diminishing the difference between in-patient and out-patient care even further. Clinical research should keep up with these developments, and place the focus of research beyond simply assessing the validity of sensor measurements [66]. This includes the aim to:…”

Section: Future Perspectives and Opportunities For Further Researchmentioning

confidence: 99%

“…Since big data storage will be needed to achieve this aim, health care institutions will also have to carefully consider 9. How to organise storage, standards, security, ethics, data rights, and governance [66].…”

Section: Future Perspectives and Opportunities For Further Researchmentioning

confidence: 99%

Modern methods in vital signs monitoring

Goor¹

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Vital signs provide an indication of the patient's health and stability. Technological innovation, which has been boosted by the COVID-19 pandemic, creates the possibility to measure vital parameters continuously and remotely, without restricting the patient's mobility. Consequently, deterioration might be seen earlier than with current intermittent measurements. In this thesis we highlight several aspects of these modern methods of measurement. In the first part we show that a circadian rhythm can be seen in the continuously measured data, and explore how continuous data can best be summarized. Both aspects are important for the interpretation of the data and for use in prediction models. The second part shows that there is currently still a lot of variation in how doctors and nurses interpret continuous data. In addition to training, the aforementioned prediction models could help to achieve a more standardized assessment. Furthermore, we show that the use of continuous monitoring does not result in less patient visits by nurses, even though they can assess vital signs remotely. In the last part, we describe a randomized trial of a common intervention during the pandemic: hospital care at home assisted by remote monitoring. We could not demonstrate a clear gain in number of days at home in this study. Notably, only a small proportion of the patients were eligible and wanted to participate. In the last study we therefore focus on developing a similar intervention for internal medicine patients with an infection. We explore for which group of patients this could be a valuable intervention, and what care is needed to make it successful. In the discussion, reflections on the studies in this dissertation and suggestions for future research and interventions are discussed. Important points that emerge regarding continuous monitoring are: 1). Aspects of data that a predictive algorithm should take into account. 2). Ways to improve the interpretation by healthcare providers. 3). How continuous monitoring can be integrated into daily practice at the ward. Regarding hospital care at home using remote monitoring, the following topics are discussed: 1). The selection of the right patient population. 2). Providing the right intervention for the right patient population. 3). Considerations involving effectiveness and costs. Important research questions that will need to be answered in the future are discussed in the last part. Ultimately, modern vital signs measurement methods can help bring the patient even further to the center of care.

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Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review

Cited by 12 publications

References 38 publications

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Reverse Engineering of Digital Measures: Inviting Patients to the Conversation

Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices

Modern methods in vital signs monitoring

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