SciKit Digital Health (SKDH) is a compilation of algorithm implementations based on previous work. The goal of SKDH is to provide commonly used algorithms in mobility research from wearable inertial sensors under a common framework, with sensible defaults, and an easily extensible framework that allows for customization based on the end-users needs. To this end, SKDH provides modules for data ingestion, pre-processing, and processing in gait, sit-to-stand, activity, and sleep, making going from raw data to digital health biomarkers fast and easy. Join authors Yiorgos Christakis, MSc and Lukas Adamowicz, MSc, I Quantitative Scientists at Pfizer, as they present their publication, ” SciKit Digital Health: Python Package for Streamlined Wearable Inertial Sensor Data Processing at DiMe’s October Journal Club. Register now for this #AskMeAnything and join the discussion on sensor data processing!
Advances in digital technology and increasing competitive space have led to a steady exploration and increase in the number of trials that are focused on a bring-your-own-device (BYOD) strategy. The BYOD approach not only is a cost and resource-effective measure of data collection, but it also saves the need to source and provision devices. While this provides a very user-friendly avenue to conduct trials, the absence of formal guidance and various concerns associated with BYOD trials has led to the present body of work. This workstream has explored in depth the different segments that need to be considered while designing a BYOD trial. Join DiMe’s Journal Club for a discussion with the authors on September 15th at 11 am ET. Register now!
Have you ever been asked what tangible benefits digital biomarkers bring to clinical studies? Inspired by the book and film Moneyball, the authors developed a novel method to quantify the values of patient selection and endpoint technologies using Monte Carlo simulation. Their work illustrates the model and potential application using Parkinson’s Disease example. This presentation will discuss:
1. The project rationale and Moneyball metaphor
2. SV95C and its clinical study impact
3. Monte Carlo approach and the model
4. The results and the way forward
Learn more about the key tools in the DATAcc Toolkits for Inclusivity – what they are, how they can be used and the impact they can have on your work in the development or deployment of digital health measurement products – in a conversation with four of the experts involved in creating them.
We’ll spend the first half discussing the Toolkit for Inclusive Development and the second half the Toolkit for Inclusive Deployment.
The June DiMe Journal Club featured “Regulatory Acceptance of Patient-Reported Outcome (PRO) Data from Bring-Your-Own-Device (BYOD) Solutions to Support Medical Product Labeling Claims.”
This presentation will outline the current landscape for BYOD adoption and why there is still hesitancy in utilizing this method to capture PROs in clinical trials. It will touch upon the only publicized example of a PRO endpoint captured electronically using BYOD as a primary safety outcome in a phase 3 pivotal trial. It will conclude with the proposal to set-up a database where sponsors can transparently contribute details about the PRO endpoints they have captured using BYOD, followed by a live Q&A.
Listen in to the discussion with Florence Mowlem, PhD Director, eCOA at Medable
Connected sensor technologies represent a potential solution to capturing adherence data accurately, objectively, and continually throughout a clinical trial. However, best practices for measuring and reporting adherence to digital health technologies are unclear
The DiMe Research Committee’s latest publication describes the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Their work identified nine key recommendations for investigators planning on capturing and reporting adherence data using digital tools. Join us on May 12 at 11 am ET for a fantastic presentation on the Research Committee’s work and recommendations.
Patient experience and expectations have changed after the pandemic, as telehealth is becoming the new norm replacing in-person visits. At our April #AskeMeAnything Journal Club, our colleagues from the Abigail Wexner Research Institute at Nationwide Children’s Hospital will share 2 of their recent studies focusing on telehealth satisfaction survey development, implementation, and validation.
(1) The goal of this study was to assess patient satisfaction and assess the quality of services. The team developed a telehealth patient satisfaction survey (TPSS) with a multi-stakeholder group. In this first study, we shared survey components and explained how we developed the survey. We also proposed a conceptual research model to explain patient behavior towards telehealth satisfaction.
(2) In the second paper, the team validated their model, the telehealth patient satisfaction model, (TPSM) testing the factors (admission process, perceived quality of service) influencing patient satisfaction.
To accelerate innovation in evidence generation to support broad acceptance of digital health applications, DiMe and the health innovation hub (hih) of the German Federal Ministry of Health is releasing a manuscript end of February to define global priorities in evidence generation related to digital health applications that will speed the use of high-quality digital medicine products in routine care. Over the last year, an international group of researchers and experts in the use of real-world data and real-world evidence generation for digital medicine products joined a set of roundtable discussions and identified innovative approaches to digital medical product evaluation.
Join the co-authors of this manuscript in an open discussion and learn about the methodological challenges and opportunities for this emerging field of research, alongside examples of novel approaches to health evaluation research that can be readily applied to the evaluation of DiGA in Germany and novel digital health applications more broadly globally.
The Digital Medicine Society recently launched a multi-stakeholder Sensor Data Integration Tour of Duty to address these challenges and more, provide a clear direction on how sensor data can fulfill its potential to enhance patient lives.
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, permissions, 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.
Listen in as the co-authors of this paper on sensor data integration in an open discussion and learn more about the new cross-industry collaboration with the aim to articulate value, define needs, and advance a framework for best practices.
The use of digital health technologies in clinical studies introduces unique design complexities, as well as exciting opportunities, to measure what matters to patients. Using Parkinson’s as a case example, the 3DT (Digital Drug Development Tools) team of the Critical Path consortium shares recommendations that integrate the patient voice at every step of the way in order to maximize engagement, optimize recruitment, and increase retention and protocol adherence. The objective of this presentation is to overview a set of guidelines, recommendations, and considerations for integration of DHT, regardless of the type of device, in PD clinical studies in order to improve the overall study design and execution, with the engagement of patients as a key component of this process.
Listen in to the discussion with the panelists:
– Johan Hellsten, PhD, Senior specialist in Patient Insights (R&D), Lundbeck
– Martijn Müller, PhD, Senior Scientific Director, Critical Path for Parkinson’s, Critical Path Institute
– Cindy Zadikoff, MD, Medical Director & CPP Industry Co-Director, AbbVie
– Mark Frasier, MD, Chief Science Officer, Research Programs, Michael J. Fox Foundation
A patient’s health state can be characterized by a multitude of signals from many different data modalities. This high-dimensional, personalized data stream aggregated over patients’ lives has spurred interest in developing new clinical AI models. One of the rate-limiting factors in developing AI models that generalize to real-world scenarios is the very attribute that makes the data exciting—their high-dimensional nature.
At DiMe’s #AskMeAnything Journal, author Visar Berisha, PhD led a discussion on how “the curse of dimensionality” can doom models to failure, even when they seem to work well during development. We explored the key highlights of his Nature publication “Digital medicine and the curse of dimensionality“, Visar also provided some suggestions on how to develop clinical AI models that are more likely to fare well during prospective validation.
The collaborators on The Playbook have published findings of a systematic review that identifies the need for more research funding related to digital clinical measures.
Among all its findings, one that stands out is that in the last two years, out of 295 research studies published on digital clinical measures, there was only one academic publication reporting cybersecurity research, one study examining data rights and governance, and zero publications reporting research into the ethical implications of remote patient monitoring tools.
Join the co-authors of this manuscript in a discussion about the current state of funding for academic research and help shape an integrated and coordinated effort across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust.
As digital measures become more complex, in terms of 1) the technologies, methods and data used to derive them, and 2) in terms of the aspects of health that they address, computer scientists, electrical engineers and other people coming from a data or computing background are increasingly important members of this village.
What can we do to bridge the gap from data-orientated background to clinical application of their skills? Can a unified lexicon aid communication throughout this process?
Ieuan Clay and Jen Goldsack presented their publication, “It takes a village: development of digital measures for computer scientists” and opened up a discussion on the range of challenges and considerations where computer scientists can have a particular impact on the development of a new digital measure.
Why should you report your modeling plan or statistical analysis plan before seeing any data? Why should we all ditch the term ‘statistical significance’ but keep statistical evidence? And how? A fantastic discussion with Eric Daza, Lead Statistician for Digital Health Outcomes at Evidation Health, as he dives into key themes from his recent pieces: Artifice or intelligence? and Ditch ‘statistical significance’.
Our conversation explored two proposed research changes for our field: 1) Splitting all gathered data into a small number of random subsamples to test reproducibility/replicability of results; 2) For exploratory analyses, continue to report CI’s and p-values—but explicitly state as possible uncertainty to expect using new data.
Do we fully understand the sensitivity of gait speed as a potential endpoint for clinical trials studies? Matthew D. Czech, Isik Karahanoglu, Xuemei Cai, Charmaine Demanuele, and their colleagues aimed to find out through their recent study, “Age and environment-related differences in gait in healthy adults using wearables” in NPJ. Their work shows that a single lumbar-worn sensor can be used for monitoring gait under free-living conditions and capture meaningful information about real-world functions that might not be possible in controlled settings. Their work also shows that despite higher variability, at-home gait speed was able to capture age-related group differences in healthy volunteers, which were not observed during in-lab gait assessments. Furthermore, they present the statistical methodology for deriving the number of monitoring days required to reliably estimate at-home gait speed that can be used to optimize clinical study design.
Science is catalyzed by new technology, but the process of technology adoption and implementation typically requires more time and ingenuity than often appreciated. Join us for DiMe’s #AskMeAnything Journal Club with Dr. David Shaywitz as he reviews the life cycle of technology innovations (Perez model), and then introduces five new books he recently reviewed in the Wall Street Journal that attempt to contextualize where we are in the artificial intelligence (AI) journey.
A physician-scientist by training, Dr. Shaywitz has focused his career on biomedical innovation as an operator and investor. In January 2020, he founded Astounding HealthTech, advising senior executives on digital health and connected fitness. He is a lecturer in the Department of Biomedical Informatics at Harvard Medical School, and an adjunct scholar at the American Enterprise Institute in Washington, D.C. He lives in the Boston Area with his wife, three daughters, and a clumber spaniel named Roscoe.
Listen in to this #AskMeAnything Journal event and join the conversation with Dr. Shaywitz and your fellow DiMe colleagues. You can check out his review of several AI books here!
The EVIDENCE (EValuatIng connecteD sENsor teChnologiEs) checklist promotes high-quality reporting in studies where the primary objective is an evaluation of a digital measurement product or its constituent parts. The checklist is a product of DiMe’s most recent Tour of Duty and will be published on May 18.
The EVIDENCE checklist is applicable to five types of evaluations: (1) proof of concept; (2) verification, (3) analytical validation, and (4) clinical validation as defined by the V3 framework; and (5) utility and usability assessments. Using EVIDENCE, those preparing, reading, or reviewing studies evaluating digital measurement products will be better equipped to distinguish necessary reporting requirements to drive high-quality research. With broad adoption, the EVIDENCE checklist will serve as a much-needed guide to raise the bar for quality reporting in published literature evaluating digital measurement products.
Did you miss it? You can still check out the following:
With the adoption of digital phenotyping in clinical research and patient care, a common vision for the future of these technologies remains unclear. Listen in to this Journal Club recording with author Anzar Abbas, PhD who opened up a discussion on his work, “Digital Measurement of Mental Health: Challenges, Promises, and Future Directions.” The discussion explored how to classify emerging tools for digital measurement of mental health and discuss the promises and challenges they face.
Anzar Abbas is a neuroscientist focused on developing technology to improve the measurement of health, increase access to care, and inform clinical decision-making using data-driven insights. He is one of the co-creators of OpenDBM, an open-source library of methods in digital phenotyping.
“Personalized therapies in the Future of Health: Winning with digital medicine products.” Deloitte Insights. Davis B., Ahmed A., Elsner N., Miranda W. March 2021.
Narayan VA,et al RADAR-CNS Consortium. “Using Smartphones and Wearable Devices to Monitor Behavioral Changes During COVID-19.” J Med Internet Res 2020;22
Manta, Christine, Bray Patrick-Lake, and Jennifer C. Goldsack. “Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health.” Digital Biomarkers 4.3 (2020).
Izmailova, E., Godfrey, A. A., Vandendriessche, B., Bakker, J. P., Fitzer‐Attas, C., Gujar, N., Hobbs, M., … & Zipunnikov, V. Clinical and Translational Science. (2020).
Gerke, S., Stern, A.D. & Minssen, T. “Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries.” npj Digit. Med. 3, 94 (2020).
Pratap, Abhishek, et al. NPJ digital medicine 3.1 (2020): 1-10.
Bent, B., Goldstein, B.A., Kibbe, W.A. et al. Investigating sources of inaccuracy in wearable optical heart rate sensors. npj Digit. Med. 3, 18 (2020).
Mahadevan, N., Patel, S. et al. Development of digital biomarkers for resting tremor and bradykinesia using a wrist-worn wearable device. npj Digit. Med. 3, 5 (2020).
Mueller, Arne, et al. “Continuous digital monitoring of walking speed in frail elderly patients: noninterventional validation study and longitudinal clinical trial.” JMIR mHealth and uHealth 7.11 (2019): e15191.
Bakker, J. P., Goldsack, J. C., Clarke, M., Coravos, A., Geoghegan, C., Godfrey, A., … & Ramirez, E. (2019).
A systematic review of feasibility studies promoting the use of mobile technologies in clinical research. npj Digital Medicine, 2(1), 47.