Supporting whole person care: the power of longitudinal patient-provided real-world data
Read key takeaways from Evidation's webinar, "Using longitudinal RWD to boost activation along the patient journey," featuring speakers from Evidation, Veterans Affairs, and Myovant Sciences.
Using Longitudinal RWD to Boost Activation Along the Patient Journey
People now play a more active role in their own health—but too often don’t know about relevant care and resources.
American Life in Realtime: A Benchmark Registry of Health Data for Equitable Precision Health
Ritika R. Chaturvedi, Marco Angrisani, Wendy M. Troxel, Tania Gutsche, Eva Ortega, Monika Jain, Adrien Boch & Arie Kapteyn
ALiR, funded by a grant from the NIH, is a first-of-its-kind, publicly available benchmark registry and research infrastructure for person-generated health data collected from smartphones and wearables, made possible by Evidation.
Evidation and Duke Big Ideas Lab Partner to Boost Participation and Increase Equity in Digital Health Research
Evidation and Duke Big Ideas Lab announce a partnership with the goal of increasing diversity and representation in digital health studies
Creating Competitive Advantage: Privacy and Security by Design in mHealth and Digital Health Products
Alea Garbagnati, Esq. and Lauren Wu, Esq.
Evidation's Head of Privacy looks at the increased need for cybersecurity and privacy protection amidst the boom in digital health advancements.
TILES-2018, a Longitudinal Physiologic and Behavioral Data Set of Hospital Workers
Mundnich K, Booth B.M., L’Hommedieu M, Feng T, Girault B, L’Hommedieu J, Wildman M, Skaaden S, Nadaraian A, Villatte J.L, Falk T.H., Lerman K, Ferrara E, Naravanan S
Evidation investigated the use of off-the-shelf wearable and environmental sensors to understand individual-specific constructs of hospital workers over time in their day-to-day job settings.
The Case for Multimodal Data Capture: Deeper Insights and Increased Prediction Accuracy
In July 2021, Evidation participated in an IEEE EMBS workshop that explored how machine learning can convert data into measures for both disease detection and overall quality of life. The key takeaways are presented in this article.
Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors
Eric J. Daza, Logan Schneider
In this academic preprint, the model twin randomization (MoTR; "motor") method for analyzing possible causes and effects an individual's intensive longitudinal data is introduced.
Using Digital Health Technologies to Transform Community Health and Health Equity
Evidation responded to an RFI from the White House Office of Science and Technology Policy (OSTP) to provide input on how digital health tools can transform community health and health equity.
Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint
Ieuan Clay, Francesca Cormack, Szymon Fedor, Luca Foschini, Giovanni Gentile, Chris van Hoof, Priya Kumar, Florian Lipsmeier, Akane Sano, Benjamin Smarr, Benjamin Vandendriessche, Valeria De Luca
Evidation contributed to a summary of how multimodal technologies capable of measuring several modalities simultaneously provide value to stakeholders.
One Third of Medicare and Medicaid Beneficiaries Face Transportation Insecurity That Affects Access to Healthcare and Essential Medications
Evidation research reveals that beneficiaries missed doctors’ appointments or ran out of medicine because they could not access transportation.
Evidation, USC and Rand Awarded NIH R01 Research Project Grant to Develop a Representative Digital Health Dataset and Novel Data Science Methods
Evidation, USC, and RAND announce a partnership to create a nationally representative digital health dataset, dubbed American Life in Real-time (ALiR).
NYC Health and Evidation Collaborate with the Icahn School of Medicine at Mount Sinai to Launch Study on COVID-19’s Impact on Mental Health
Evidation and NYC Health announce a nationwide study that aims to understand the mental health impact of the COVID-19 pandemic.
Evidation Partners with Other Industry Leaders to Launch Resources to Drive Inclusion in Digital Health Measurement
Evidation and other members of the Digital Health Measurement Collaborative Community (DATAcc) announced a new set of open-access resources to guide inclusivity in digital health measurement products from development to deployment.
Next Generation Patient Engagement & Enriched Insights
Evidation has written a white paper that takes a closer look at the challenges of PGHD, its value to clinical decision makers, and how to contextualize it to optimize its value.
Translating PGHD into Measures that Matter
Evidation provides key highlights from its "Translating PGHD into Measures that Matter" webinar.
Evidation + HealthVerity: The Holistic Patient Journey: Fusing Real-World Data with Person-Generated Health Data
Evidation, Pfizer, and HealthVerity discuss how to fuse real-world data (RWD) with person-generated health data (PGHD).
Evolution or Revolution: Translating PGHD into Measures that Matter
Evidation and industry leaders address how to translate patient-generated health data (PGHD) into meaningful measures in this webinar.
AI: Leveraging Wearables and Other Patient-Generated Data in Research
Evidation co-founder and chief data scientist, Luca Foschini, discusses how patient-generated health data (PGHD) and artificial intelligence (AI) are advancing clinical studies for biotech and pharmaceutical companies.
Effect of Different Financial Incentive Structures on Promoting Physical Activity Among Adults
Bachireddy C, Joung A, John LK, Gino F, Tuckfield B, Foschini L, Milkman KL
Evidation research reveals that financial incentives for physical activity are most effective when offered at a constant rate.
Machine Intelligence in Healthcare—Perspectives on Trustworthiness, Explainability, Usability, and Transparency
Cutillo CM, Sharma KR, Foschini L, Kundu S, Mackintosh M, Mandl, KD
Evidation research reveals that machine intelligence approaches have the potential to improve health through the facilitation of more efficient and effective provision of care.
Design, Recruitment, and Baseline Characteristics of a Virtual 1-Year Mental Health Study on Behavioral Data and Health Outcomes: Observational Study
Kumar S, Tran JLA, Ramirez E, Lee W, Foschini L, Juusola J
Evidation's baseline analysis found that some passively tracked behavioral traits are associated with more severe forms of anxiety or depression.
Remote Digital Monitoring for Medical Product Development
Izmailova ES, Wagner JA, Ammour N, Amondikar N, Bell-Vlasov A, Berman S, Bloomfield D, Brady LS, Cai X, Calle RA, Campbell M, Cerreta F, Clay I, Foschini L, Furlong P, Goldel R, Goldsack JS, Groenen P, Folarin A, Heemskerk J, Honig P, Hotopf M, Kamphaus T, Karlin DR, Leptak C, Liu Q, Manji H, Mather RJ, Menetski JP, Narayan VA, Papadopoulos E, Patel B, Patrick-Lake B, Podichetty JT, Pratap A, Servais L, Stephenson D, Tenaerts P, Tromberg B, Usdin S, Vasudevan S, Zipunnikov V, Hoffmann SC
Evidation research concludes that digital monitoring data need to be integrated with the totality of the data in clinical trials to create a holistic picture of the health care of the future.
Comparison of Study Samples Recruited With Virtual Versus Traditional Recruitment Methods
Heidi Moseson, Shefali Kumar, Jessie L. Juusola
Evidation research reveals that virtual recruitment may enhance efficiency and enable more individuals to participate in clinical research.
Development and Application of a Patient Group Engagement Prioritization Tool for Use in Medical Product Development
Perry B, Dombeck C, Smalley JB, Levitan B, Leventhal D, Patrick-Lake B, Brennan L, McKenna K, Hallinan Z, Corneli A
Evidation research reveals that engagement activities can enhance the quality and efficiency of clinical trials by improving patient recruitment and retention, reduce costs, and help trials meet expectations of regulators and payers.
Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health
Manta C, Patrick-Lake B, Goldsack J.C.
Evidation synthesizes and defines a sequential framework of core principles for selecting and developing measurements in research and clinical care that are meaningful for patients.
Reproducibility in Machine Learning for Health Research: Still a Ways to Go
McDermott M.B.A, Wang S, Marinsek N, Ranganath R, Foschini L, and Ghassemi M
Evidation research reveals that machine learning for health compared poorly to other areas regarding reproducibility metrics.
Evaluation, Acceptance, and Qualification of Digital Measures: From Proof of Concept to Endpoint
Goldsack J.C, Dowling A.V, Samuelson D, Patrick-Lake B, and Clay I
Evidation research details the steps of bringing a successful proof of concept to scale, focusing on key decisions in the development of a new digital measure.
Digital Endpoints Must Meet Patient Needs
Patrick-Lake B, Manta C, and Goldsack J
Technological advances are speeding the development of digital measures and endpoints throughout drug development and across therapeutic areas and patient populations. While regulators encourage stakeholders to pursue patient-centric approaches, few actionable guidances or standards exist.
It Takes a Village: Development of Digital Measures for Computer Scientists
Goldsack J.C and Clay I
Evidation research reveals a range of challenges and considerations where computer scientists can have a particular impact on the development of a new digital measure.
Psychosocial Functioning Among Caregivers of Childhood Cancer Survivors Following Treatment Completion
Quast L.F, Lewis R.W, Lee J.L, Blount R.L, and Marshak J.G
Evidation research reveals that the majority of caregivers appear to be resilient and experience limited distress during the off therapy period.
Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices
Clay I, Angelopoulos C, Bailey A.L., Blocker A, Carini S, Carvajal R, Drummond D, McManus K.F., Oakley-Girvan I, Patel K.B., Szepietowski P, and Goldsack J.C.
Evidation research reveals that to achieve real impact on patient lives, sensor data needs to be integrated and contextualized with many other data types.
RWD-Cockpit: Application for Quality Assessment of Real-World Data
Babrak L, Smakaj E, Agac T, Asprion P, Grimberg F, Van der Werf D, van Ginkel E, Tosoni D, Clay I, Degan M, Brodbeck D, Natali E, Schkommodau E, MIho E
Evidation research reveals that multiple datasets can be preliminary evaluated regarding quality using the proposed metrics implemented in the RWD-Cockpit app.
Advancing Digital Health Applications: Priorities for Innovation in Real-World Evidence Generation
Stern A, Brönneke J, Debatin J, Hagen J, Matthies H, Patel S, Clay I, Eskofier B, Herr A, Hoeller K, Jakas A, Kramer D, Kyhlstedt M, Lofgren K, Mahendraratnam N, Muehlan H, Reif S, Riedermann L, Goldsack J
The Digital Medicine Society and the Health Innovation Hub of the German Federal Ministry of Health convened a set of roundtable discussions to bring together international experts in evidence generation for digital medicine products.
A Community Takes Action to Help Citizens Improve their Health by Harnessing Digital Technology
Evidation collaborates with Project TECH, a faith-based organization in South Carolina, to create a co-learning environment designed to help eliminate barriers to using digital technologies.
Making Equitable Health a Reality
Building an equitable and inclusive space in the healthcare ecosystem where everyone - research and health program participants, partners, and employees - can belong and feel valued is a foundational pillar of Evidation’s mission. National Minority Health Month provides an excellent opportunity for us to reflect on ways health and racial equity can be improved.
How Do We Make Patient Centricity a Reality?
For years, the subject of fostering direct connections with patients has been highlighted by notable figures across the healthcare and life sciences industries as a key feature to be explored in future strategies.
Predicting Changes in Depression Severity Using the PSYCHE-D (Prediction of Severity Change-Depression) Model Involving Person-Generated Health Data: Longitudinal Case-Control Observational Study
Ernesto Ramirez, Marta Ferreira, Ieuan Clay, Sanjeev P. Bhavnani, Anusha Narayan
Evidation research reveals that person-generated health data can be the basis of accurate and timely warnings that an individual's mental health may be deteriorating.
Digital Medicine Society, Evidation, and Industry Partners Launch Toolkit to Support Reimbursement for New Drugs Developed Using Digital Endpoints
Today, the Digital Medicine Society (DiMe), in partnership with Evidation, Anthem, Biogen, Eli Lilly, Janssen, Merck, Pfizer, and Savvy Co-op, announced the public launch of the 3Ps of Digital Endpoint Value (3Ps), a toolkit of resources created to facilitate the inclusion of digital endpoints as evidence for payers in reimbursement decisions for new drugs.