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Valachis A, Lindman H. Lessons learned from an unsuccessful decentralized clinical trial in Oncology. NPJ Digit Med 2024; 7:211. [PMID: 39138304 PMCID: PMC11322600 DOI: 10.1038/s41746-024-01214-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 08/03/2024] [Indexed: 08/15/2024] Open
Affiliation(s)
- Antonis Valachis
- Department of Oncology, Faculty of Medicine and Health, Örebro University Hospital, Örebro University, Örebro, Sweden.
| | - Henrik Lindman
- Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology; Clinical Oncology, Faculty of Medicine, Uppsala University Hospital, Uppsala, Sweden
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2
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Tackney MS, Steele A, Newman J, Fritzsche MC, Lucivero F, Khadjesari Z, Lynch J, Abbott RA, Barber VS, Carpenter JR, Copsey B, Davies EH, Dixon WG, Fox L, González J, Griffiths J, Hinchliffe CHL, Kolanko MA, McGagh D, Rodriguez A, Roussos G, So KBE, Stanton L, Toshner M, Varian F, Williamson PR, Yimer BB, Villar SS. Digital endpoints in clinical trials: emerging themes from a multi-stakeholder Knowledge Exchange event. Trials 2024; 25:521. [PMID: 39095915 PMCID: PMC11297702 DOI: 10.1186/s13063-024-08356-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND Digital technologies, such as wearable devices and smartphone applications (apps), can enable the decentralisation of clinical trials by measuring endpoints in people's chosen locations rather than in traditional clinical settings. Digital endpoints can allow high-frequency and sensitive measurements of health outcomes compared to visit-based endpoints which provide an episodic snapshot of a person's health. However, there are underexplored challenges in this emerging space that require interdisciplinary and cross-sector collaboration. A multi-stakeholder Knowledge Exchange event was organised to facilitate conversations across silos within this research ecosystem. METHODS A survey was sent to an initial list of stakeholders to identify potential discussion topics. Additional stakeholders were identified through iterative discussions on perspectives that needed representation. Co-design meetings with attendees were held to discuss the scope, format and ethos of the event. The event itself featured a cross-disciplinary selection of talks, a panel discussion, small-group discussions facilitated via a rolling seating plan and audience participation via Slido. A transcript was generated from the day, which, together with the output from Slido, provided a record of the day's discussions. Finally, meetings were held following the event to identify the key challenges for digital endpoints which emerged and reflections and recommendations for dissemination. RESULTS Several challenges for digital endpoints were identified in the following areas: patient adherence and acceptability; algorithms and software for devices; design, analysis and conduct of clinical trials with digital endpoints; the environmental impact of digital endpoints; and the need for ongoing ethical support. Learnings taken for next generation events include the need to include additional stakeholder perspectives, such as those of funders and regulators, and the need for additional resources and facilitation to allow patient and public contributors to engage meaningfully during the event. CONCLUSIONS The event emphasised the importance of consortium building and highlighted the critical role that collaborative, multi-disciplinary, and cross-sector efforts play in driving innovation in research design and strategic partnership building moving forward. This necessitates enhanced recognition by funders to support multi-stakeholder projects with patient involvement, standardised terminology, and the utilisation of open-source software.
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Affiliation(s)
- Mia S Tackney
- MRC-Biostatistics Unit, University of Cambridge, Cambridge, UK.
| | - Amber Steele
- Strategic Funding Partnerships Hub (SFPH), Cambridge University Hospitals, Cambridge, UK
| | - Joseph Newman
- Department of Medicine, University of Cambridge and Royal Papworth Hospital, Cambridge, UK
| | - Marie-Christine Fritzsche
- Institute of History and Ethics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- School of Social Sciences and Technology, Technical University of Munich, Munich, Germany
| | - Federica Lucivero
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zarnie Khadjesari
- School of Health Sciences, University of East Anglia, Norwich, England
| | | | | | - Vicki S Barber
- Oxford Clinical Trials Research Unit (OCTRU), University of Oxford, Oxford, UK
| | - James R Carpenter
- MRC Clinical Trials Unit at University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - Bethan Copsey
- Leeds Clinical Trials Research Unit, University of Leeds, Leeds, UK
| | - Elin H Davies
- Aparito, a wholly owned subsidiary company of Eli Lilly and Company, Wrexham, Wales, UK
| | - William G Dixon
- Centre for Epidemiology Versus Arthritis, Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
| | - Lisa Fox
- Clinical Trials and Statistics Unit (ICR-CTSU), The Institute of Cancer Research, London, UK
| | | | - Jessica Griffiths
- Clinical Trials and Statistics Unit (ICR-CTSU), The Institute of Cancer Research, London, UK
| | - Chloe H L Hinchliffe
- Translational and Clinical Research Institute, Newcastle University, Newcastle, UK
| | - Magdalena A Kolanko
- UK Dementia Research Institute Care Research and Technology Centre, London, UK
- Imperial College London, London, UK
| | - Dylan McGagh
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | - George Roussos
- School of Computing and Mathematical Sciences, Birkbeck College, University of London, London, UK
| | - Karen B E So
- Alexion Rare Oncology, AstraZeneca, Cambridge, UK
| | - Louise Stanton
- Southampton Clinical Trials Unit, University of Southampton, Southampton, UK
| | - Mark Toshner
- Royal Papworth Hospital and Department of Medicine, Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | | | | | - Belay B Yimer
- Centre for Epidemiology, University of Manchester, Manchester, UK
| | - Sofía S Villar
- MRC-Biostatistics Unit, University of Cambridge, Cambridge, UK
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3
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Leyens L, Batchelor J, De Beuckelaer E, Langel K, Hartog B. Unlocking the full potential of digital endpoints for decision making: a novel modular evidence concept enabling re-use and advancing collaboration. Expert Rev Pharmacoecon Outcomes Res 2024; 24:731-741. [PMID: 38747565 DOI: 10.1080/14737167.2024.2334347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 03/20/2024] [Indexed: 06/17/2024]
Abstract
INTRODUCTION Over the last decade increasing examples indicate opportunities to measure patient functioning and its relevance for clinical and regulatory decision making via endpoints collected through digital health technologies. More recently, we have seen such measures support primary study endpoints and enable smaller trials. The field is advancing fast: validation requirements have been proposed in the literature and regulators are releasing new guidances to review these endpoints. Pharmaceutical companies are embracing collaborations to develop them and working with academia and patient organizations in their development. However, the road to validation and regulatory acceptance is lengthy. The full value of digital endpoints cannot be unlocked until better collaboration and modular evidence frameworks are developed enabling re-use of evidence and repurposing of digital endpoints. AREAS COVERED This paper proposes a solution by presenting a novel modular evidence framework -the Digital Evidence Ecosystem and Protocols (DEEP)- enabling repurposing of measurement solutions, re-use of evidence, application of standards and also facilitates collaboration with health technology assessment bodies. EXPERT OPINION The integration of digital endpoints in healthcare, essential for personalized and remote care, requires harmonization and transparency. The proposed novel stack model offers a modular approach, fostering collaboration and expediting the adoption in patient care.
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Affiliation(s)
- Lada Leyens
- Regulatory Science, DEEP Measures Oy, Helsinki, Finland
- Product Development Regulatory, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | | | | | - Kai Langel
- Regulatory, Janssen Cilag S.A, Madrid, Spain
| | - Bert Hartog
- Clinical Operations and Innovation, Janssen-Cilag B.V, DS Breda, The Netherlands
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4
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Robin J, Xu M, Kaufman LD, Simpson W, McCaughey S, Tatton N, Wolfus C, Ward M. Development of a Speech-based Composite Score for Remotely Quantifying Language Changes in Frontotemporal Dementia. Cogn Behav Neurol 2023; 36:237-248. [PMID: 37878468 PMCID: PMC10683975 DOI: 10.1097/wnn.0000000000000356] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 04/07/2023] [Indexed: 10/27/2023]
Abstract
BACKGROUND Changes to speech and language are common symptoms across different subtypes of frontotemporal dementia (FTD). These changes affect the ability to communicate, impacting everyday functions. Accurately assessing these changes may help clinicians to track disease progression and detect response to treatment. OBJECTIVE To determine which aspects of speech show significant change over time and to develop a novel composite score for tracking speech and language decline in individuals with FTD. METHOD We recruited individuals with FTD to complete remote digital speech assessments based on a picture description task. Speech samples were analyzed to derive acoustic and linguistic measures of speech and language, which were tested for longitudinal change over the course of the study and were used to compute a novel composite score. RESULTS Thirty-six (16 F, 20 M; M age = 61.3 years) individuals were enrolled in the study, with 27 completing a follow-up assessment 12 months later. We identified eight variables reflecting different aspects of language that showed longitudinal decline in the FTD clinical syndrome subtypes and developed a novel composite score based on these variables. The resulting composite score demonstrated a significant effect of change over time, high test-retest reliability, and a correlation with standard scores on various other speech tasks. CONCLUSION Remote digital speech assessments have the potential to characterize speech and language abilities in individuals with FTD, reducing the burden of clinical assessments while providing a novel measure of speech and language abilities that is sensitive to disease and relevant to everyday function.
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Affiliation(s)
- Jessica Robin
- Winterlight Labs, Incorporated, Toronto, Ontario, Canada
| | - Mengdan Xu
- Winterlight Labs, Incorporated, Toronto, Ontario, Canada
| | | | - William Simpson
- Winterlight Labs, Incorporated, Toronto, Ontario, Canada
- Department of Psychiatry and Behavioural Neuroscience, McMaster University, Hamilton, Ontario, Canada
| | | | | | | | - Michael Ward
- Alector, Incorporated, San Francisco, California
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5
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Romijnders R, Salis F, Hansen C, Küderle A, Paraschiv-Ionescu A, Cereatti A, Alcock L, Aminian K, Becker C, Bertuletti S, Bonci T, Brown P, Buckley E, Cantu A, Carsin AE, Caruso M, Caulfield B, Chiari L, D'Ascanio I, Del Din S, Eskofier B, Fernstad SJ, Fröhlich MS, Garcia Aymerich J, Gazit E, Hausdorff JM, Hiden H, Hume E, Keogh A, Kirk C, Kluge F, Koch S, Mazzà C, Megaritis D, Micó-Amigo E, Müller A, Palmerini L, Rochester L, Schwickert L, Scott K, Sharrack B, Singleton D, Soltani A, Ullrich M, Vereijken B, Vogiatzis I, Yarnall A, Schmidt G, Maetzler W. Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases. Front Neurol 2023; 14:1247532. [PMID: 37909030 PMCID: PMC10615212 DOI: 10.3389/fneur.2023.1247532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 09/18/2023] [Indexed: 11/02/2023] Open
Abstract
Introduction The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings. Methods Here, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data. Results and discussion The results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of -0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, -0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases.
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Affiliation(s)
- Robbin Romijnders
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Francesca Salis
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Clint Hansen
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
| | - Arne Küderle
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Anisoara Paraschiv-Ionescu
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Lisa Alcock
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Kamiar Aminian
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Clemens Becker
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Stefano Bertuletti
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Tecla Bonci
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Philip Brown
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Ellen Buckley
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Alma Cantu
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anne-Elie Carsin
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Marco Caruso
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Lorenzo Chiari
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRISDV), University of Bologna, Bologna, Italy
| | - Ilaria D'Ascanio
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
| | - Silvia Del Din
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Björn Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | | | | | - Judith Garcia Aymerich
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Eran Gazit
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition and Mobility, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Department of Physical Therapy, Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Hugo Hiden
- School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Emily Hume
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Alison Keogh
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Cameron Kirk
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Novartis Institute of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Sarah Koch
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Faculty of Health and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Claudia Mazzà
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Dimitrios Megaritis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Encarna Micó-Amigo
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Arne Müller
- Novartis Institute of Biomedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Luca Palmerini
- Department of Electrical, Electronic and Information Engineering “Guglielmo Marconi”, University of Bologna, Bologna, Italy
- Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRISDV), University of Bologna, Bologna, Italy
| | - Lynn Rochester
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Lars Schwickert
- Gesellschaft für Medizinische Forschung, Robert-Bosch Foundation GmbH, Stuttgart, Germany
| | - Kirsty Scott
- INSIGNEO Institute for In Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Basil Sharrack
- Department of Neuroscience and Sheffield NIHR Translational Neuroscience BRC, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom
| | - David Singleton
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland
- School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | - Abolfazl Soltani
- Laboratory of Movement Analysis and Measurement, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
- Digital Health Department, CSEM SA, Neuchâtel, Switzerland
| | - Martin Ullrich
- Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ioannis Vogiatzis
- Department of Sport, Exercise and Rehabilitation, Northumbria University, Newcastle upon Tyne, United Kingdom
| | - Alison Yarnall
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom
| | - Gerhard Schmidt
- Digital Signal Processing and System Theory, Electrical and Information Engineering, Faculty of Engineering, Kiel University, Kiel, Germany
| | - Walter Maetzler
- Arbeitsgruppe Neurogeriatrie, Department of Neurology, Universitätsklinikum Schleswig-Holstein, Kiel, Germany
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Betcheva L, Kim JY, Erhun F, Oraiopoulos N, Getz K. Applying Systems Thinking to Inform Decentralized Clinical Trial Planning and Deployment. Ther Innov Regul Sci 2023:10.1007/s43441-023-00540-2. [PMID: 37389795 PMCID: PMC10400692 DOI: 10.1007/s43441-023-00540-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 05/23/2023] [Indexed: 07/01/2023]
Abstract
Recently, there has been a growing interest in understanding how decentralized clinical trial (DCT) solutions can mitigate existing challenges in clinical development, particularly participant burden and access, and the collection, management, and quality of clinical data. This paper examines DCT deployments, emphasizing how they are integrated and how they may impact clinical trial oversight, management, and execution. We propose a conceptual framework that employs systems thinking to evaluate the impact on key stakeholders through a reiterative assessment of pain points. We conclude that decentralized solutions should be customized to meet patient needs and preferences and the unique requirements of each clinical trial. We discuss how DCT elements introduce new demands and pressures within the existing system and reflect on enablers that can overcome DCT implementation challenges. As stakeholders look for ways to make clinical research more relevant and accessible to a larger and more diverse patient population, further robust and granular research is needed to quantify the impact of DCTs empirically.
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Affiliation(s)
- Lidia Betcheva
- Judge Business School, University of Cambridge, Cambridge, CB2 1AG, UK.
| | - Jennifer Y Kim
- Tufts Center for the Study of Drug Development, Tufts University, Boston, MA, 02111, USA
| | - Feryal Erhun
- Judge Business School, University of Cambridge, Cambridge, CB2 1AG, UK
| | | | - Kenneth Getz
- Tufts Center for the Study of Drug Development, Tufts University, Boston, MA, 02111, USA
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7
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Gehr S, Balasubramaniam NK, Russmann C. Use of mobile diagnostics and digital clinical trials in cardiology. Nat Med 2023; 29:781-784. [PMID: 37002368 DOI: 10.1038/s41591-023-02263-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Affiliation(s)
- Sinje Gehr
- Charité Universitätsmedizin Berlin, Berlin, Germany
- Health Campus Goettingen, University of Applied Sciences and Arts, Goettingen, Lower Saxony, Germany
| | | | - Christoph Russmann
- Health Campus Goettingen, University of Applied Sciences and Arts, Goettingen, Lower Saxony, Germany.
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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8
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Washington V, Franklin JB, Huang ES, Mega JL, Abernethy AP. Diversity, Equity, and Inclusion in Clinical Research: A Path Toward Precision Health for Everyone. Clin Pharmacol Ther 2023; 113:575-584. [PMID: 36423203 DOI: 10.1002/cpt.2804] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022]
Abstract
Healthcare disparities are a persistent societal problem. One of the contributing factors to this status quo is the lack of diversity and representativeness of research efforts, which result in nongeneralizable evidence that, in turn, provides suboptimal means to enable the best possible outcomes at the individual level. There are several strategies that research teams can adopt to improve the diversity, equity, and inclusion (DEI) of their efforts; these strategies span the totality of the research path, from initial design to the shepherding of clinical data through a potential regulatory process. These strategies include more intentionality and DEI-based goal-setting, more diverse research and leadership teams, better community engagement to set study goals and approaches, better tailored outreach interventions, decentralization of study procedures and incorporation of innovative technology for more flexible data collection, and self-surveillance to identify and prevent biases. Within their remit of overlooking research efforts, regulatory authorities, as stakeholders, also have the potential for a positive effect on the DEI of emerging clinical evidence. All these are implementable tools and mechanisms that can make study participation more approachable to diverse communities, and ultimately generate evidence that is more generalizable and a conduit for better outcomes. The research community has an imperative to make DEI principles key foundational aspects in study conduct in order to pursue better personalized medicine for diverse patient populations.
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Affiliation(s)
| | | | - Erich S Huang
- Verily Life Sciences, South San Francisco, California, USA
| | - Jessica L Mega
- Verily Life Sciences, South San Francisco, California, USA
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9
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Garjani A, Liu BJY, Allen CM, Gunzler DD, Gerry SW, Planchon SM, das Nair R, Chataway J, Tallantyre EC, Ontaneda D, Evangelou N. Decentralised clinical trials in multiple sclerosis research. Mult Scler 2023; 29:317-325. [PMID: 35735014 PMCID: PMC9972228 DOI: 10.1177/13524585221100401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Randomised controlled trials (RCTs) play an important role in multiple sclerosis (MS) research, ensuring that new interventions are safe and efficacious before their introduction into clinical practice. Trials have been evolving to improve the robustness of their designs and the efficiency of their conduct. Advances in digital and mobile technologies in recent years have facilitated this process and the first RCTs with decentralised elements became possible. Decentralised clinical trials (DCTs) are conducted remotely, enabling participation of a more heterogeneous population who can participate in research activities from different locations and at their convenience. DCTs also rely on digital and mobile technologies which allows for more flexible and frequent assessments. While hospitals quickly adapted to e-health and telehealth assessments during the COVID-19 pandemic, the conduct of conventional RCTs was profoundly disrupted. In this paper, we review the existing evidence and gaps in knowledge in the design and conduct of DCTs in MS.
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Affiliation(s)
- Afagh Garjani
- Mental Health and Clinical Neurosciences
Academic Unit, School of Medicine, University of Nottingham, Nottingham,
UK/Academic Neurology, Nottingham University Hospitals NHS Trust,
Nottingham, UK
| | | | - Christopher Martin Allen
- Mental Health and Clinical Neurosciences
Academic Unit, School of Medicine, University of Nottingham, Nottingham,
UK/Academic Neurology, Nottingham University Hospitals NHS Trust,
Nottingham, UK
| | | | - Stephen William Gerry
- Centre for Statistics in Medicine, Nuffield
Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences,
University of Oxford, Oxford, UK
| | | | - Roshan das Nair
- Mental Health and Clinical Neurosciences
Academic Unit, School of Medicine, University of Nottingham, Nottingham,
UK/Institute of Mental Health, Nottinghamshire Healthcare NHS Foundation
Trust, Nottingham, UK
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre,
Department of Neuroinflammation, UCL Queen Square Institute of Neurology,
Faculty of Brain Sciences, University College London, London, UK/National
Institute for Health Research, University College London Hospitals
Biomedical Research Centre, London, UK/MRC CTU at UCL, Institute of Clinical
Trials and Methodology, University College London, London, UK
| | - Emma C Tallantyre
- Helen Durham Neuro-Inflammatory Unit,
University Hospital of Wales, Cardiff, UK/Division of Psychological Medicine
and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Daniel Ontaneda
- Mellen Center for Multiple Sclerosis,
Cleveland Clinic, Cleveland, OH, USA
| | - Nikos Evangelou
- N Evangelou Academic Neurology, Nottingham
University Hospitals NHS Trust, C Floor, South Block, Queen’s Medical Centre,
Nottingham NG7 2UH, UK. ;
@nikosevangelou3
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Palmerini L, Reggi L, Bonci T, Del Din S, Micó-Amigo ME, Salis F, Bertuletti S, Caruso M, Cereatti A, Gazit E, Paraschiv-Ionescu A, Soltani A, Kluge F, Küderle A, Ullrich M, Kirk C, Hiden H, D’Ascanio I, Hansen C, Rochester L, Mazzà C, Chiari L. Mobility recorded by wearable devices and gold standards: the Mobilise-D procedure for data standardization. Sci Data 2023; 10:38. [PMID: 36658136 PMCID: PMC9852581 DOI: 10.1038/s41597-023-01930-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 01/03/2023] [Indexed: 01/21/2023] Open
Abstract
Wearable devices are used in movement analysis and physical activity research to extract clinically relevant information about an individual's mobility. Still, heterogeneity in protocols, sensor characteristics, data formats, and gold standards represent a barrier for data sharing, reproducibility, and external validation. In this study, we aim at providing an example of how movement data (from the real-world and the laboratory) recorded from different wearables and gold standard technologies can be organized, integrated, and stored. We leveraged on our experience from a large multi-centric study (Mobilise-D) to provide guidelines that can prove useful to access, understand, and re-use the data that will be made available from the study. These guidelines highlight the encountered challenges and the adopted solutions with the final aim of supporting standardization and integration of data in other studies and, in turn, to increase and facilitate comparison of data recorded in the scientific community. We also provide samples of standardized data, so that both the structure of the data and the procedure can be easily understood and reproduced.
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Affiliation(s)
- Luca Palmerini
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Luca Reggi
- grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
| | - Tecla Bonci
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Silvia Del Din
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - M. Encarna Micó-Amigo
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Francesca Salis
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Stefano Bertuletti
- grid.11450.310000 0001 2097 9138University of Sassari, Department of Biomedical Sciences, Sassari, Italy
| | - Marco Caruso
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy ,grid.4800.c0000 0004 1937 0343Politecnico di Torino, PolitoBIOMed Lab – Biomedical Engineering Lab, Torino, Italy
| | - Andrea Cereatti
- grid.4800.c0000 0004 1937 0343Politecnico di Torino, Department of Electronics and Telecommunications, Torino, Italy
| | - Eran Gazit
- grid.413449.f0000 0001 0518 6922Tel Aviv Sourasky Medical Center, Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv-Yafo, Israel
| | - Anisoara Paraschiv-Ionescu
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Abolfazl Soltani
- grid.5333.60000000121839049Laboratory of Movement Analysis and Measurement, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - Felix Kluge
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Arne Küderle
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Martin Ullrich
- grid.5330.50000 0001 2107 3311Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
| | - Cameron Kirk
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK
| | - Hugo Hiden
- grid.1006.70000 0001 0462 7212Newcastle University, School of Computing, Newcastle, UK
| | - Ilaria D’Ascanio
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy
| | - Clint Hansen
- grid.412468.d0000 0004 0646 2097Neurogeriatrics Kiel, Department of Neurology, University Hospital Schleswig-Holstein, Kiel, Germany
| | - Lynn Rochester
- grid.1006.70000 0001 0462 7212Newcastle University, Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle, UK ,The Newcastle upon Tyne NHS Foundation Trust, Newcastle, UK
| | - Claudia Mazzà
- grid.11835.3e0000 0004 1936 9262The University of Sheffield, INSIGNEO Institute for in silico Medicine, Sheffield, UK ,grid.11835.3e0000 0004 1936 9262The University of Sheffield, Department of Mechanical Engineering, Sheffield, UK
| | - Lorenzo Chiari
- grid.6292.f0000 0004 1757 1758University of Bologna, Department of Electrical, Electronic and Information Engineering ‘Guglielmo Marconi’, Bologna, Italy ,grid.6292.f0000 0004 1757 1758University of Bologna, Health Sciences and Technologies—Interdepartmental Center for Industrial Research (CIRI-SDV), Bologna, Italy
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Lyden K, Abraham N, Boucher R, Wei G, Gonce V, Carle J, Hartsell SE, Christensen J, Beddhu S. Predicting hospitalization from real-world measures in patients with chronic kidney disease: A proof-of-principle study. Digit Health 2023; 9:20552076231181234. [PMID: 37361437 PMCID: PMC10286549 DOI: 10.1177/20552076231181234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 05/24/2023] [Indexed: 06/28/2023] Open
Abstract
Objective To investigate if in-clinic measures of physical function and real-world measures of physical behavior and mobility effort are associated with one another and to determine if they predict future hospitalization in participants with chronic kidney disease (CKD). Methods In this secondary analysis, novel real-world measures of physical behavior and mobility effort, including the best 6-minute step count (B6SC), were derived from passively collected data from a thigh worn actigraphy sensor and compared to traditional in-clinic measures of physical function (e.g. 6-minute walk test (6MWT). Hospitalization status during 2 years of follow-up was determined from electronic health records. Correlation analyses were used to compare measures and Cox Regression analysis was used to compare measures with hospitalization. Results One hundred and six participants were studied (69 ± 13 years, 43% women). Mean ± SD baseline measures for 6MWT was 386 ± 66 m and B6SC was 524 ± 125 steps. Forty-four hospitalization events over 224 years of total follow-up occurred. Good separation was achieved for tertiles of 6MWT, B6SC and steps/day for hospitalization events. This pattern persisted in models adjusted for demographics (6MWT: HR = 0.63 95% CI 0.43-0.93, B6SC: HR = 0.75, 95% CI 0.56-1.02 and steps/day: HR = 0.75, 95% CI 0.50-1.13) and further adjusted for morbidities (6MWT: HR = 0.54, 95% CI 0.35-0.84, B6SC: HR = 0.70, 95% CI 0.49-1.00 and steps/day: HR = 0.69, 95% CI 0.43-1.09). Conclusion Digital health technologies can be deployed remotely, passively, and continuously to collect real-world measures of physical behavior and mobility effort that differentiate risk of hospitalization in patients with CKD.
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Affiliation(s)
- Kate Lyden
- Department of Kinesiology, University of Massachusetts, Amherst, MA, USA
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, USA
| | - Nikita Abraham
- Division of Nephrology & Hypertension, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Robert Boucher
- Division of Nephrology & Hypertension, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Guo Wei
- Division of Nephrology & Hypertension, University of Utah School of Medicine, Salt Lake City, UT, USA
- Division of Biostatistics, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Victoria Gonce
- Division of Nephrology & Hypertension, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Judy Carle
- Division of Nephrology & Hypertension, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Sydney E. Hartsell
- Division of Nephrology & Hypertension, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jesse Christensen
- Medical Service, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Srinivasan Beddhu
- Division of Nephrology & Hypertension, University of Utah School of Medicine, Salt Lake City, UT, USA
- Medical Service, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA
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Nobbs D, Piwko W, Bull C, Cormack F, Ahmaniemi T, Holst SC, Chatterjee M, Maetzler W, Avey S, Ng WF. Regulatory Qualification of a Cross-Disease Digital Measure: Benefits and Challenges from the Perspective of IMI Consortium IDEA-FAST. Digit Biomark 2023; 7:132-138. [PMID: 37901363 PMCID: PMC10601930 DOI: 10.1159/000533189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 07/11/2023] [Indexed: 10/31/2023] Open
Abstract
Background Innovative Medicines Initiative (IMI) consortium IDEA-FAST is developing novel digital measures of fatigue, sleep quality, and impact of sleep disturbances for neurodegenerative diseases and immune-mediated inflammatory diseases. In 2022, the consortium met with the European Medicines Agency (EMA) to receive advice on its plans for regulatory qualification of the measures. This viewpoint reviews the IDEA-FAST perspective on developing digital measures for multiple diseases and the advice provided by the EMA. Summary The EMA considered a cross-disease measure an interesting and arguably feasible concept. Developers should account for the need for a strong rationale that the clinical features to be measured are similar across diseases. In addition, they may expect increased complexity of study design, challenges when managing differences within and between disease populations, and the need for validation in both heterogeneous and homogeneous populations. Key Messages EMA highlighted the challenges teams may encounter when developing a cross-disease measure, though benefits potentially include reduced resources for the technology developer and health authority, faster access to innovation across different therapeutic fields, and feasibility of cross-disease comparisons. The insights included here can be used by project teams to guide them in the development of cross-disease digital measures intended for regulatory qualification.
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Affiliation(s)
- David Nobbs
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Wojciech Piwko
- Takeda Pharmaceuticals International, Zurich, Switzerland
| | - Christopher Bull
- Open Lab, School of Computing, Newcastle University, Newcastle upon Tyne, UK
| | | | - Teemu Ahmaniemi
- VTT Technical Research Center of Finland Ltd., Espoo, Finland
| | - Sebastian C. Holst
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | | | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany
| | - Stefan Avey
- Janssen Research and Development, Spring House, PA, USA
| | - Wan Fai Ng
- Translational and Clinical Research Institute, Newcastle University and NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - IDEA-FAST Consortium
- Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
- Takeda Pharmaceuticals International, Zurich, Switzerland
- Open Lab, School of Computing, Newcastle University, Newcastle upon Tyne, UK
- Cambridge Cognition, Cambridge, UK
- VTT Technical Research Center of Finland Ltd., Espoo, Finland
- Janssen Research and Development, Cambridge, MA, USA
- Department of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany
- Janssen Research and Development, Spring House, PA, USA
- Translational and Clinical Research Institute, Newcastle University and NIHR Newcastle Biomedical Research Centre, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
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13
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Wirtz AL, Logie CH, Mbuagbaw L. Addressing Health Inequities in Digital Clinical Trials: A Review of Challenges and Solutions From the Field of HIV Research. Epidemiol Rev 2022; 44:87-109. [PMID: 36124659 PMCID: PMC10362940 DOI: 10.1093/epirev/mxac008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Revised: 08/10/2022] [Accepted: 09/12/2022] [Indexed: 12/29/2022] Open
Abstract
Clinical trials are considered the gold standard for establishing efficacy of health interventions, thus determining which interventions are brought to scale in health care and public health programs. Digital clinical trials, broadly defined as trials that have partial to full integration of technology across implementation, interventions, and/or data collection, are valued for increased efficiencies as well as testing of digitally delivered interventions. Although recent reviews have described the advantages and disadvantages of and provided recommendations for improving scientific rigor in the conduct of digital clinical trials, few to none have investigated how digital clinical trials address the digital divide, whether they are equitably accessible, and if trial outcomes are potentially beneficial only to those with optimal and consistent access to technology. Human immunodeficiency virus (HIV), among other health conditions, disproportionately affects socially and economically marginalized populations, raising questions of whether interventions found to be efficacious in digital clinical trials and subsequently brought to scale will sufficiently and consistently reach and provide benefit to these populations. We reviewed examples from HIV research from across geographic settings to describe how digital clinical trials can either reproduce or mitigate health inequities via the design and implementation of the digital clinical trials and, ultimately, the programs that result. We discuss how digital clinical trials can be intentionally designed to prevent inequities, monitor ongoing access and utilization, and assess for differential impacts among subgroups with diverse technology access and use. These findings can be generalized to many other health fields and are practical considerations for donors, investigators, reviewers, and ethics committees engaged in digital clinical trials.
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Affiliation(s)
- Andrea L Wirtz
- Correspondence to Dr. Andrea L. Wirtz, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street, Baltimore, MD 21205 (e-mail: )
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14
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Suman A, van Es J, Gardarsdottir H, Grobbee DE, Hawkins K, Heath MA, Mackenzie IS, van Thiel G, Zuidgeest MGP. A cross-sectional survey on the early impact of COVID-19 on the uptake of decentralised trial methods in the conduct of clinical trials. Trials 2022; 23:856. [PMID: 36203202 PMCID: PMC9535935 DOI: 10.1186/s13063-022-06706-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 09/01/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic significantly impacted the conduct of clinical trials through delay, interruption or cancellation. Decentralised methods in clinical trials could help to continue trials during a pandemic. This paper presents the results of an exploratory study conducted early in the pandemic to gain insight into and describe the experiences of organisations involved in clinical trials, with regard to the impact of COVID-19 on the conduct of trials, and the adoption of decentralised methods prior to, and as mitigation for the impact, of COVID-19. METHODS A survey with 11 open-ended and four multiple choice questions was conducted in June 2020 among member organisations of the public-private "Trials@Home" consortium. The survey investigated (1) the impact and challenges of COVID-19 on the continuation of ongoing clinical trials, (2) the adoption of decentralised methods in clinical trials prior to and as a mitigation strategy for COVID-19, (3) the challenges of conducting clinical trials during COVID-19, (4) the expected permanency of COVID-19-driven changes to the adoption of decentralised methods in clinical trials, and (5) lessons learned from conducting clinical trials during the COVID-19 pandemic. A thematic, inductive analysis of open survey questions was performed, complemented with descriptive statistics (frequencies and distributions). RESULTS The survey had a response rate of 81%. All organisations included in the analysis (n = 18) implemented (some) decentralised methods in their clinical trials prior to COVID-19, and 15 (83%) implemented decentralised methods as mitigation for COVID-19. Decentralised methods for IMP supply, patient-health care provider interaction and communication, clinic visits and source document verification were used more often as mitigation strategies than they were used prior to COVID-19. Many respondents expect to maintain those decentralised methods they implemented during COVID-19 in ongoing trials, as well as implement them in future trials. CONCLUSIONS Decentralised methods are a widely implemented mitigation strategy for trial conduct in the face of the COVID-19 pandemic. The results of this survey show that there is an interest to continue the use of decentralised methods in future trials, but important points of attention have been identified that need solutions to help guide the transition from the traditional trial model to a more decentralised trial model.
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Affiliation(s)
- Arnela Suman
- grid.7692.a0000000090126352Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands ,grid.413711.10000 0004 4687 1426Amphia Academy, Amphia Hospital, Breda, The Netherlands
| | - Jasmijn van Es
- grid.7692.a0000000090126352Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Helga Gardarsdottir
- grid.5477.10000000120346234Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Faculty of Science, Utrecht University, Utrecht, The Netherlands
| | - Diederick E. Grobbee
- grid.7692.a0000000090126352Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Kimberly Hawkins
- grid.417924.dSanofi-Aventis Recherche & Development, Chilly Mazarin, Île-de-France France
| | - Megan A. Heath
- grid.417924.dSanofi-Aventis Recherche & Development, Chilly Mazarin, Île-de-France France
| | - Isla S. Mackenzie
- grid.8241.f0000 0004 0397 2876MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Ghislaine van Thiel
- grid.7692.a0000000090126352Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mira G. P. Zuidgeest
- grid.7692.a0000000090126352Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
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Ferrer-Mallol E, Matthews C, Stoodley M, Gaeta A, George E, Reuben E, Johnson A, Davies EH. Patient-led development of digital endpoints and the use of computer vision analysis in assessment of motor function in rare diseases. Front Pharmacol 2022; 13:916714. [PMID: 36172196 PMCID: PMC9510779 DOI: 10.3389/fphar.2022.916714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
Digital health technologies are transforming the way health outcomes are captured and measured. Digital biomarkers may provide more objective measurements than traditional approaches as they encompass continuous and longitudinal data collection and use of automated analysis for data interpretation. In addition, the use of digital health technology allows for home-based disease assessments, which in addition to reducing patient burden from on-site hospital visits, provides a more holistic picture of how the patient feels and functions in the real world. Tools that can robustly capture drug efficacy based on disease-specific outcomes that are meaningful to patients, are going to be key to the successful development of new treatments. This is particularly important for people living with rare and chronic complex conditions, where therapeutic options are limited and need to be developed using a patient-focused approach to achieve the biggest impact. Working in partnership with patient Organisation Duchenne UK, we co-developed a video-based approach, delivered through a new mobile health platform (DMD Home), to assess motor function in patients with Duchenne muscular dystrophy (DMD), a genetic, rare, muscular disease characterized by the progressive loss of muscle function and strength. Motor function tasks were selected to reflect the “transfer stage” of the disease, when patients are no longer able to walk independently but can stand and weight-bear to transfer. This stage is important for patients and families as it represents a significant milestone in the progression of DMD but it is not routinely captured and/or scored by standard DMD clinical and physiotherapy assessments. A total of 62 videos were submitted by eight out of eleven participants who onboarded the app and were analysed with pose estimation software (OpenPose) that led to the extraction of objective, quantitative measures, including time, pattern of movement trajectory, and smoothness and symmetry of movement. Computer vision analysis of video tasks to identify voluntary or compensatory movements within the transfer stage merits further investigation. Longitudinal studies to validate DMD home as a new methodology to predict progression to the non-ambulant stage will be pursued.
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Wang J, Battioui C, McCarthy A, Dang X, Zhang H, Man A, Zou J, Kyle J, Munsie L, Pugh M, Biglan K. Evaluating the Use of Digital Biomarkers to Test Treatment Effects on Cognition and Movement in Patients with Lewy Body Dementia. JOURNAL OF PARKINSON'S DISEASE 2022; 12:1991-2004. [PMID: 35694933 PMCID: PMC9535589 DOI: 10.3233/jpd-213126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: PRESENCE was a Phase 2 trial assessing mevidalen for symptomatic treatment of Lewy body dementia (LBD). Participants received daily doses (10, 30, or 75 mg) of mevidalen (LY3154207) or placebo for 12 weeks. Objective: To evaluate if frequent cognitive and motor tests using an iPad app and wrist-worn actigraphy to track activity and sleep could detect mevidalen treatment effects in LBD. Methods: Of 340 participants enrolled in PRESENCE, 238 wore actigraphy for three 2-week periods: pre-, during, and post-intervention. A subset of participants (n = 160) enrolled in a sub-study using an iPad trial app with 3 tests: digital symbol substitution (DSST), spatial working memory (SWM), and finger-tapping. Compliance was defined as daily test completion or watch-wearing ≥23 h/day. Change from baseline to week 12 (app) or week 8 (actigraphy) was used to assess treatment effects using Mixed Model Repeated Measures analysis. Pearson correlations between sensor-derived features and clinical endpoints were assessed. Results: Actigraphy and trial app compliance was > 90% and > 60%, respectively. At baseline, daytime sleep positively correlated with Epworth Sleepiness Scale score (p < 0.01). Physical activity correlated with improvement on Movement Disorder Society –Unified Parkinson Disease Rating Scale (MDS-UPDRS) part II (p < 0.001). Better scores of DSST and SWM correlated with lower Alzheimer Disease Assessment Scale –Cognitive 13-Item Scale (ADAS-Cog13) (p < 0.001). Mevidalen treatment (30 mg) improved SWM (p < 0.01), while dose-dependent decreases in daytime sleep (10 mg: p < 0.01, 30 mg: p < 0.05, 75 mg: p < 0.001), and an increase in walking minutes (75 mg dose: p < 0.001) were observed, returning to baseline post-intervention. Conclusion: Devices used in the LBD population achieved adequate compliance and digital metrics detected statistically significant treatment effects.
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Affiliation(s)
- Jian Wang
- Eli Lilly and Company, Indianapolis, IN, USA
| | | | | | | | - Hui Zhang
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Albert Man
- Eli Lilly and Company, Indianapolis, IN, USA
| | - Jasmine Zou
- Eli Lilly and Company, Indianapolis, IN, USA
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Day JO, Smith S, Noyce AJ, Alty J, Jeffery A, Chapman R, Carroll C. Challenges of Incorporating Digital Health Technology Outcomes in a Clinical Trial: Experiences from PD STAT. JOURNAL OF PARKINSON'S DISEASE 2022; 12:1605-1609. [PMID: 35466954 PMCID: PMC9398088 DOI: 10.3233/jpd-223162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Digital health technologies (DHTs) have great potential for use as clinical trial outcomes; however, practical issues need to be addressed in order to maximise their benefit. We describe our experience of incorporating two DHTs as secondary/exploratory outcome measures in PD STAT, a randomised clinical trial of simvastatin in people with Parkinson's disease. We found much higher rates of missing data in the DHTs than the traditional outcome measures, in particular due to technical and software difficulties. We discuss methods to address these obstacles in terms of protocol design, workforce training and data management.
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Affiliation(s)
- Jacob O. Day
- Faculty of Health, University of Plymouth, Plymouth, UK,Correspondence to: Jacob Day, Faculty of Health, University of Plymouth, Plymouth, PL4 8AA, UK. Tel.: +01752 432028; E-mail:
| | - Stephen Smith
- Department of Electronic Engineering, University of York, York, UK
| | - Alastair J. Noyce
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, UK,Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London, UK
| | - Jane Alty
- Wicking Dementia Research and Education Centre, College of Health and Medicine, University of Tasmania, Hobart, Australia,Department of Neurology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Alison Jeffery
- Peninsula Clinical Trials Unit, Faculty of Health, University of Plymouth, Plymouth, UK
| | - Rebecca Chapman
- Peninsula Clinical Trials Unit, Faculty of Health, University of Plymouth, Plymouth, UK
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Demanuele C, Lokker C, Jhaveri K, Georgiev P, Sezgin E, Geoghegan C, Zou KH, Izmailova E, McCarthy M. Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies. Digit Biomark 2022; 6:47-60. [PMID: 35949223 PMCID: PMC9294934 DOI: 10.1159/000525080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/07/2022] [Indexed: 12/21/2022] Open
Abstract
Background Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own “device” (BYOD) manner where participants use their technologies to generate study data. Summary The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving. Key Messages We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.
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Affiliation(s)
| | | | - Krishna Jhaveri
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania, USA
| | | | - Emre Sezgin
- The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
| | | | - Kelly H. Zou
- Global Medical Analytics and Real-World Evidence, Viatris Inc, Canonsburg, Pennsylvania, USA
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Berwanger O, Machline-Carrion MJ. Digital Health-Enabled Clinical Trials in Stroke: Ready for Prime Time? Stroke 2022; 53:2967-2975. [PMID: 35770670 DOI: 10.1161/strokeaha.122.037378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
As stroke continues to represent a major global health care problem, advancing our knowledge of new effective and safe stroke interventions represents a public health priority. The identification of these therapies requires the conduct of high-quality and well-powered randomized clinical trials. Despite its potential to inform clinical practice, traditional randomized clinical trial models have their drawbacks, including elevated costs, long completion times, failure to recruit the target sample sizes, lack of diversity, and complex operational procedures. Therefore, improving the participants' experience and trials' overall efficiency constitutes an important unmet need. Innovative models such as virtual and decentralized patient-centric trials have been proposed as a valuable strategy in this pursuit. In this narrative review, we discuss the limitations of traditional randomized clinical trial models and present the concept, advantages, and challenges of decentralized digitally enabled approaches to the conduct of stroke clinical trials.
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Affiliation(s)
- Otavio Berwanger
- Academic Research Organization (ARO), Hospital Israelita Albert Einstein, São Paulo, Brazil (O.B.)
| | - M Julia Machline-Carrion
- Department of Medical Affairs, epHealth Primary Care Solutions, Florianópolis, Santa Catarina, Brazil (M.J.M.-C.)
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20
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Garcia A, Balasubramanian V, Lee J, Gardner R, Gummidipundi S, Hung G, Ferris T, Cheung L, Granger C, Kowey P, Rumsfeld J, Russo A, Hills MT, Talati N, Nag D, Stein J, Tsay D, Desai S, Mahaffey K, Turakhia M, Perez M, Hedlin H, Desai M. Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials. J Biopharm Stat 2022; 32:496-510. [PMID: 35695137 DOI: 10.1080/10543406.2022.2080698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
The digital clinical trial is fast emerging as a pragmatic trial that can improve a trial's design including recruitment and retention, data collection and analytics. To that end, digital platforms such as electronic health records or wearable technologies that enable passive data collection can be leveraged, alleviating burden from the participant and study coordinator. However, there are challenges. For example, many of these data sources not originally intended for research may be noisier than traditionally obtained measures. Further, the secure flow of passively collected data and their integration for analysis is non-trivial. The Apple Heart Study was a prospective, single-arm, site-less digital trial designed to evaluate the ability of an app to detect atrial fibrillation. The study was designed with pragmatic features, such as an app for enrollment, a wearable device (the Apple Watch) for data collection, and electronic surveys for participant-reported outcomes that enabled a high volume of patient enrollment and accompanying data. These elements led to challenges including identifying the number of unique participants, maintaining participant-level linkage of multiple complex data streams, and participant adherence and engagement. Novel solutions were derived that inform future designs with an emphasis on data management. We build upon the excellent framework of the Clinical Trials Transformation Initiative to provide a comprehensive set of guidelines for data management of the digital clinical trial that include an increased role of collaborative data scientists in the design and conduct of the modern digital trial.
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Affiliation(s)
- Ariadna Garcia
- Department of Medicine, Stanford University, California, USA
| | | | - Justin Lee
- Department of Medicine, Stanford University, California, USA
| | - Rebecca Gardner
- Department of Medicine, Stanford University, California, USA
| | | | - Grace Hung
- Department of Medicine, Stanford University, California, USA
| | - Todd Ferris
- Department of Medicine, Stanford University, California, USA
| | - Lauren Cheung
- Department of Medicine, Stanford University, California, USA
| | | | - Peter Kowey
- Department of Medicine, Stanford University, California, USA
| | - John Rumsfeld
- Department of Medicine, Stanford University, California, USA
| | - Andrea Russo
- Department of Medicine, Stanford University, California, USA
| | | | - Nisha Talati
- Department of Medicine, Stanford University, California, USA
| | - Divya Nag
- Department of Medicine, Stanford University, California, USA
| | - Jeffrey Stein
- Department of Medicine, Stanford University, California, USA
| | - David Tsay
- Department of Medicine, Stanford University, California, USA
| | - Sumbul Desai
- Department of Medicine, Stanford University, California, USA
| | | | - Mintu Turakhia
- Department of Medicine, Stanford University, California, USA
| | - Marco Perez
- Department of Medicine, Stanford University, California, USA
| | - Haley Hedlin
- Department of Medicine, Stanford University, California, USA
| | - Manisha Desai
- Department of Medicine, Stanford University, California, USA
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21
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Olaye IM, Belovsky MP, Bataille L, Cheng R, Ciger A, Fortuna KL, Izmailova ES, McCall D, Miller CJ, Muehlhausen W, Northcott CA, Rodriguez-Chavez IR, Pratap A, Vandendriessche B, Zisman-Ilani Y, Bakker JP. Recommendations for Defining and Reporting Adherence Measured by Biometric Monitoring Technologies: Systematic Review. J Med Internet Res 2022; 24:e33537. [PMID: 35436221 PMCID: PMC9052021 DOI: 10.2196/33537] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/03/2021] [Accepted: 01/14/2022] [Indexed: 11/13/2022] Open
Abstract
Background Suboptimal adherence to data collection procedures or a study intervention is often the cause of a failed clinical trial. Data from connected sensors, including wearables, referred to here as biometric monitoring technologies (BioMeTs), are capable of capturing adherence to both digital therapeutics and digital data collection procedures, thereby providing the opportunity to identify the determinants of adherence and thereafter, methods to maximize adherence. Objective We aim to describe the methods and definitions by which adherence has been captured and reported using BioMeTs in recent years. Identifying key gaps allowed us to make recommendations regarding minimum reporting requirements and consistency of definitions for BioMeT-based adherence data. Methods We conducted a systematic review of studies published between 2014 and 2019, which deployed a BioMeT outside the clinical or laboratory setting for which a quantitative, nonsurrogate, sensor-based measurement of adherence was reported. After systematically screening the manuscripts for eligibility, we extracted details regarding study design, participants, the BioMeT or BioMeTs used, and the definition and units of adherence. The primary definitions of adherence were categorized as a continuous variable based on duration (highest resolution), a continuous variable based on the number of measurements completed, or a categorical variable (lowest resolution). Results Our PubMed search terms identified 940 manuscripts; 100 (10.6%) met our eligibility criteria and contained descriptions of 110 BioMeTs. During literature screening, we found that 30% (53/177) of the studies that used a BioMeT outside of the clinical or laboratory setting failed to report a sensor-based, nonsurrogate, quantitative measurement of adherence. We identified 37 unique definitions of adherence reported for the 110 BioMeTs and observed that uniformity of adherence definitions was associated with the resolution of the data reported. When adherence was reported as a continuous time-based variable, the same definition of adherence was adopted for 92% (46/50) of the tools. However, when adherence data were simplified to a categorical variable, we observed 25 unique definitions of adherence reported for 37 tools. Conclusions We recommend that quantitative, nonsurrogate, sensor-based adherence data be reported for all BioMeTs when feasible; a clear description of the sensor or sensors used to capture adherence data, the algorithm or algorithms that convert sample-level measurements to a metric of adherence, and the analytic validation data demonstrating that BioMeT-generated adherence is an accurate and reliable measurement of actual use be provided when available; and primary adherence data be reported as a continuous variable followed by categorical definitions if needed, and that the categories adopted are supported by clinical validation data and/or consistent with previous reports.
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Affiliation(s)
- Iredia M Olaye
- Department of Medicine Division of Clinical Epidemiology and Evaluative Sciences Research, Weill Cornell Medical College Cornell University, New York, NY, United States
| | - Mia P Belovsky
- Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA, United States
| | - Lauren Bataille
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States
| | - Royce Cheng
- Health Platforms, Verily Life Sciences, Cambridge, MA, United States
| | | | - Karen L Fortuna
- Giesel School of Medicine at Dartmouth College, Hanover, NH, United States
| | | | | | | | | | | | | | - Abhishek Pratap
- CAMH Krembil Center for Neuroinformatics, Toronto, ON, Canada
- Vector Institute, Toronto, ON, Canada
- Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States
- Institute of Psychiatry, Psychology, and Neuroscience, Kings College London, London, United Kingdom
| | - Benjamin Vandendriessche
- Byteflies, Antwerp, Belgium
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Yaara Zisman-Ilani
- Department of Social and Behavioral Sciences; College of Public Health, Temple University, Philadelphia, PA, United States
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22
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Nourani A, Ayatollahi H, Solaymani Dodaran M. Data management in diabetes clinical trials: a qualitative study. Trials 2022; 23:187. [PMID: 35241149 PMCID: PMC8895796 DOI: 10.1186/s13063-022-06110-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 02/15/2022] [Indexed: 11/16/2022] Open
Abstract
Background Clinical trials play an important role in expanding the knowledge of diabetes prevention, diagnosis, and treatment, and data management is one of the main issues in clinical trials. Lack of appropriate planning for data management in clinical trials may negatively influence achieving the desired results. The aim of this study was to explore data management processes in diabetes clinical trials in three research institutes in Iran. Method This was a qualitative study conducted in 2019. In this study, data were collected through in-depth semi-structured interviews with 16 researchers in three endocrinology and metabolism research institutes. To analyze data, the method of thematic analysis was used. Results The five themes that emerged from data analysis included (1) clinical trial data collection, (2) technologies used in data management, (3) data security and confidentiality management, (4) data quality management, and (5) data management standards. In general, the findings indicated that no clear and standard process was used for data management in diabetes clinical trials, and each research center executed its own methods and processes. Conclusion According to the results, the common methods of data management in diabetes clinical trials included a set of paper-based processes. It seems that using information technology can help facilitate data management processes in a variety of clinical trials, including diabetes clinical trials.
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Affiliation(s)
- Aynaz Nourani
- Department of Health Information Technology, Urmia University of Medical Sciences, Urmia, Iran
| | - Haleh Ayatollahi
- Health Management and Economics Research Center, Health Management Research Institute, Iran University of Medical Sciences, Tehran, Iran. .,Department of Health Information Management, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran.
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23
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Abstract
Internet-connected devices, including personal computers, smartphones, smartwatches, and voice assistants, have evolved into powerful multisensor technologies that billions of people interact with daily to connect with friends and colleagues, access and share information, purchase goods, play games, and navigate their environment. Digital phenotyping taps into the data streams captured by these devices to characterize and understand health and disease. The purpose of this article is to summarize opportunities for digital phenotyping in neurology, review studies using everyday technologies to obtain motor and cognitive information, and provide a perspective on how neurologists can embrace and accelerate progress in this emerging field.
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Affiliation(s)
- Anoopum S. Gupta
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
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24
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Parziale A, Mascalzoni D. Digital Biomarkers in Psychiatric Research: Data Protection Qualifications in a Complex Ecosystem. Front Psychiatry 2022; 13:873392. [PMID: 35757212 PMCID: PMC9225201 DOI: 10.3389/fpsyt.2022.873392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/13/2022] [Indexed: 11/13/2022] Open
Abstract
Psychiatric research traditionally relies on subjective observation, which is time-consuming and labor-intensive. The widespread use of digital devices, such as smartphones and wearables, enables the collection and use of vast amounts of user-generated data as "digital biomarkers." These tools may also support increased participation of psychiatric patients in research and, as a result, the production of research results that are meaningful to them. However, sharing mental health data and research results may expose patients to discrimination and stigma risks, thus discouraging participation. To earn and maintain participants' trust, the first essential requirement is to implement an appropriate data governance system with a clear and transparent allocation of data protection duties and responsibilities among the actors involved in the process. These include sponsors, investigators, operators of digital tools, as well as healthcare service providers and biobanks/databanks. While previous works have proposed practical solutions to this end, there is a lack of consideration of positive data protection law issues in the extant literature. To start filling this gap, this paper discusses the GDPR legal qualifications of controller, processor, and joint controllers in the complex ecosystem unfolded by the integration of digital biomarkers in psychiatric research, considering their implications and proposing some general practical recommendations.
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25
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Bortolani S, Brusa C, Rolle E, Monforte M, De Arcangelis V, Ricci E, Mongini TE, Tasca G. Technology-outcome measures in neuromuscular disorders: a systematic review. Eur J Neurol 2021; 29:1266-1278. [PMID: 34962693 DOI: 10.1111/ene.15235] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 12/20/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Portable and wearable devices can monitor a number of physical performances and have been lately applied to patients with neuromuscular disorders (NMD). METHODS We performed a systematic search of literature databases following PRISMA principles, including all studies reporting the use of technological devices for motor function assessment in NMDs from 2000 to 2021. We also summarized the evidence on measurement properties (validity, reliability, responsiveness) of the analyzed technological outcome measures. RESULTS One-hundred studies fulfilled the selection criteria, most of them published in the last ten years. We defined four categories that gathered similar technologies: gait analysis tools, for clinical assessment of pace and posture; continuous monitoring of physical activity with inertial sensors, that allow "unsupervised" activity assessment; upper limb evaluation tools, including Kinect-based outcome measures to assess the reachable workspace; and new muscle strength assessment tools, such as Myotools. Inertial sensors have the evident advantage of being applied in the "in-home" setting, which has become especially appealing with the Covid-19 pandemic, although poor evidence from psychometric property assessment and results of the analyzed studies may limit their research application. Both Kinect-based outcome measures and Myotools have been already validated in multicenter studies and different NMDs, showing excellent characteristics for application in clinical trials. CONCLUSION This overview is intended to raise awareness on the potential of the different TOMs in the neuromuscular field and be an informative source for the design of future clinical trials, particularly in the era of telemedicine.
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Affiliation(s)
- Sara Bortolani
- Department of Neuroscience, Rita Levi Montalcini", University of Turin, Via Cherasco 15, 10126, Turin, Italy.,Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Chiara Brusa
- Department of Neuroscience, Rita Levi Montalcini", University of Turin, Via Cherasco 15, 10126, Turin, Italy
| | - Enrica Rolle
- Department of Neuroscience, Rita Levi Montalcini", University of Turin, Via Cherasco 15, 10126, Turin, Italy
| | - Mauro Monforte
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Valeria De Arcangelis
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Enzo Ricci
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Istituto di Neurologia, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Tiziana Enrica Mongini
- Department of Neuroscience, Rita Levi Montalcini", University of Turin, Via Cherasco 15, 10126, Turin, Italy
| | - Giorgio Tasca
- Unità Operativa Complessa di Neurologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
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26
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Rogers A, De Paoli G, Subbarayan S, Copland R, Harwood K, Coyle J, Mitchell L, MacDonald TM, Mackenzie IS. A Systematic Review of Methods used to Conduct Decentralised Clinical Trials. Br J Clin Pharmacol 2021; 88:2843-2862. [PMID: 34961991 PMCID: PMC9306873 DOI: 10.1111/bcp.15205] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 12/15/2021] [Accepted: 12/17/2021] [Indexed: 12/02/2022] Open
Abstract
Aims To evaluate, using quantitative and qualitative approaches, published data on the design and conduct of decentralised clinical trials (DCTs). Methods We searched MEDLINE, EMBASE, CENTRAL, PsycINFO, ProQuest Dissertations and Theses, ClinicalTrials.gov, OpenGrey and Google Scholar for publications reporting, discussing, or evaluating decentralised clinical research methods. Reports of randomised clinical trials using decentralised methods were included in a focused quantitative analysis with a primary outcome of number of randomised participants. All publications discussing or evaluating DCTs were included in a wider qualitative analysis to identify advantages, disadvantages, facilitators, barriers and stakeholder opinions of decentralised clinical trials. Quantitative data were summarised using descriptive statistics, and qualitative data analysed using a thematic approach. Results Initial searches identified 19 704 articles. After removal of duplicates, 18 553 were screened, resulting in 237 eligible for full‐text assessment. Forty‐five trials were included in the quantitative analysis; 117 documents were included in the qualitative analysis. Trials were widely heterogeneous in design and reporting, precluding meta‐analysis of the effect of DCT methods on the primary recruitment outcome. Qualitative analysis formulated 4 broad themes: value, burden, safety and equity. Participant and stakeholder experiences of DCTs were incompletely represented. Conclusion DCTs are developing rapidly. However, there is insufficient evidence to confirm which methods are most effective in trial recruitment, retention, or overall cost. The identified advantages, disadvantages, facilitators and barriers should inform the development of DCT methods. We recommend further research on how DCTs are experienced and perceived by participants and stakeholders to maximise potential benefits.
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Affiliation(s)
- Amy Rogers
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Giorgia De Paoli
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Selvarani Subbarayan
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Rachel Copland
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Kate Harwood
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Joanne Coyle
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Lyn Mitchell
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Thomas M MacDonald
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
| | - Isla S Mackenzie
- MEMO Research, Division of Molecular and Clinical Medicine, University of Dundee, Dundee, UK
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27
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Kruizinga MD, Essers E, Stuurman FE, Yavuz Y, de Kam ML, Zhuparris A, Janssens HM, Groothuis I, Sprij AJ, Nuijsink M, Cohen AF, Driessen GJA. Clinical validation of digital biomarkers for pediatric patients with asthma and cystic fibrosis - Potential for clinical trials and clinical care. Eur Respir J 2021; 59:13993003.00208-2021. [PMID: 34887326 DOI: 10.1183/13993003.00208-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 10/10/2021] [Indexed: 11/05/2022]
Abstract
BACKGROUND Digital biomarkers are a promising novel method to capture clinical data in a home-setting. However, clinical validation prior to implementation is of vital importance. The aim of this study was to clinically validate physical activity, heart rate, sleep and FEV1 as digital biomarkers measured by a smartwatch and portable spirometer in children with asthma and cystic fibrosis (CF). METHODS This was a prospective cohort study including 60 children with asthma and 30 children with CF (age 6-16). Participants wore a smartwatch, performed daily spirometry at home and completed a daily symptom questionnaire for 28-days. Physical activity, heart rate, sleep and FEV1 were considered candidate digital endpoints. Data from 128 healthy children was used for comparison. Reported outcomes were compliance, difference between patients and controls, correlation with disease-activity and potential to detect clinical events. Analysis was performed with linear mixed effect models. RESULTS Median compliance was 88%. On average, patients exhibited lower physical activity and FEV1 compared to healthy children, whereas the heart rate of children with asthma was higher compared to healthy children. Days with a higher symptom score were associated with lower physical activity for children with uncontrolled asthma and CF. Furthermore, FEV1 was lower and (nocturnal) heart rate was higher for both patient groups on days with more symptoms. Candidate biomarkers and showed a distinct pattern before- and after a pulmonary exacerbation. CONCLUSION Portable spirometer- and smartwatch-derived digital biomarkers show promise as candidate endpoints for use in clinical trials or clinical care in pediatric lung disease.
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Affiliation(s)
- Matthijs D Kruizinga
- Centre for Human Drug Research, Leiden, the Netherlands .,Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands.,Leiden University Medical Centre, Leiden, the Netherlands
| | - Esmée Essers
- Centre for Human Drug Research, Leiden, the Netherlands.,Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Frederik E Stuurman
- Centre for Human Drug Research, Leiden, the Netherlands.,Leiden University Medical Centre, Leiden, the Netherlands
| | - Yalçin Yavuz
- Centre for Human Drug Research, Leiden, the Netherlands
| | | | | | - Hettie M Janssens
- Division of Respiratory Medicine and Allergology, Department of Pediatrics, Erasmus Medical Centre/Sophia Children's Hospital, University Hospital Rotterdam, Rotterdam, The Netherlands
| | - Iris Groothuis
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Arwen J Sprij
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Marianne Nuijsink
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands
| | - Adam F Cohen
- Centre for Human Drug Research, Leiden, the Netherlands.,Leiden University Medical Centre, Leiden, the Netherlands
| | - Gertjan J A Driessen
- Juliana Children's Hospital, Haga teaching Hospital, the Hague, the Netherlands.,Department of pediatrics, Maastricht University Medical Centre, Maastricht, the Netherlands
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28
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Gaudelet T, Day B, Jamasb AR, Soman J, Regep C, Liu G, Hayter JBR, Vickers R, Roberts C, Tang J, Roblin D, Blundell TL, Bronstein MM, Taylor-King JP. Utilizing graph machine learning within drug discovery and development. Brief Bioinform 2021; 22:bbab159. [PMID: 34013350 PMCID: PMC8574649 DOI: 10.1093/bib/bbab159] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 04/01/2021] [Accepted: 04/05/2021] [Indexed: 12/15/2022] Open
Abstract
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.
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Affiliation(s)
| | - Ben Day
- Relation Therapeutics, London, UK
- The Computer Laboratory, University of Cambridge, UK
| | - Arian R Jamasb
- Relation Therapeutics, London, UK
- The Computer Laboratory, University of Cambridge, UK
- Department of Biochemistry, University of Cambridge, UK
| | | | | | | | | | | | | | - Jian Tang
- Mila, the Quebec AI Institute, Canada
- HEC Montreal, Canada
| | - David Roblin
- Relation Therapeutics, London, UK
- Juvenescence, London, UK
- The Francis Crick Institute, London, UK
| | | | - Michael M Bronstein
- Relation Therapeutics, London, UK
- Department of Computing, Imperial College London, UK
- Twitter, UK
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29
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Maetzler W, Pilotto A. Digital assessment at home - mPower against Parkinson disease. Nat Rev Neurol 2021; 17:661-662. [PMID: 34611337 DOI: 10.1038/s41582-021-00567-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Kiel University, Kiel, Germany.
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.,Parkinson's disease rehabilitation Centre, FERB Onlus, S. Isidoro Hospital Trescore Balneario, Bergamo, Italy
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30
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Gelinas L, Morrell W, White SA, Bierer BE. Navigating the ethics of remote research data collection. Clin Trials 2021; 18:606-614. [PMID: 34231414 DOI: 10.1177/17407745211027245] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
COVID-19 has accelerated broad trends already in place toward remote research data collection and monitoring. This move implicates novel ethical and regulatory challenges which have not yet received due attention. Existing work is preliminary and does not seek to identify or grapple with the issues in a rigorous and sophisticated way. Here, we provide a framework for identifying and addressing challenges that we believe can help the research community realize the benefits of remote technologies while preserving ethical ideals and public trust. We organize issues into several distinct categories and provide points to consider in a table that can help facilitate ethical design and review of research studies using remote health instruments.
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Affiliation(s)
- Luke Gelinas
- Advarra IRB, Columbia, MD, USA.,Multi-Regional Clinical Trials Center of Brigham and Women's Hospital and Harvard, Cambridge, MA, USA
| | - Walker Morrell
- Multi-Regional Clinical Trials Center of Brigham and Women's Hospital and Harvard, Cambridge, MA, USA
| | - Sarah A White
- Multi-Regional Clinical Trials Center of Brigham and Women's Hospital and Harvard, Cambridge, MA, USA
| | - Barbara E Bierer
- Multi-Regional Clinical Trials Center of Brigham and Women's Hospital and Harvard, Cambridge, MA, USA.,Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
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31
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Bakker JP, Ross M, Vasko R, Cerny A, Fonseca P, Jasko J, Shaw E, White DP, Anderer P. Estimating sleep stages using cardiorespiratory signals: validation of a novel algorithm across a wide range of sleep-disordered breathing severity. J Clin Sleep Med 2021; 17:1343-1354. [PMID: 33660612 DOI: 10.5664/jcsm.9192] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
STUDY OBJECTIVES We have developed the CardioRespiratory Sleep Staging (CReSS) algorithm for estimating sleep stages using heart rate variability and respiration, allowing for estimation of sleep staging during home sleep apnea tests. Our objective was to undertake an epoch-by-epoch validation of algorithm performance against the gold standard of manual polysomnography sleep staging. METHODS Using 296 polysomnographs, we created a limited montage of airflow and heart rate and deployed CReSS to identify each 30-second epoch as wake, light sleep (N1 + N2), deep sleep (N3), or rapid eye movement (REM) sleep. We calculated Cohen's kappa and the percentage of accurately identified epochs. We repeated our analyses after stratification by sleep-disordered breathing (SDB) severity, and after adding thoracic respiratory effort as a backup signal for periods of invalid airflow. RESULTS CReSS discriminated wake/light sleep/deep sleep/REM sleep with 78% accuracy; the kappa value was 0.643 (95% confidence interval, 0.641-0.645). Discrimination of wake/sleep demonstrated a kappa value of 0.711 and accuracy of 89%, non-REM sleep/REM sleep demonstrated a kappa of 0.790 and accuracy of 94%, and light sleep/deep sleep demonstrated a kappa of 0.469 and accuracy of 87%. Kappa values did not vary by more than 0.07 across subgroups of no SDB, mild SDB, moderate SDB, and severe SDB. Accuracy increased to 80%, with a kappa value of 0.680 (95% confidence interval, 0.678-0.682), when CReSS additionally utilized the thoracic respiratory effort signal. CONCLUSIONS We observed substantial agreement between CReSS and the gold-standard comparator of manual sleep staging of polysomnographic signals, which was consistent across the full range of SDB severity. Future research should focus on the extent to which CReSS reduces the discrepancy between the apnea-hypopnea index and the respiratory event index, and the ability of CReSS to identify REM sleep-related obstructive sleep apnea.
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Affiliation(s)
- Jessie P Bakker
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | - Marco Ross
- Philips Sleep and Respiratory Care, Vienna, Austria
| | - Ray Vasko
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | | | - Pedro Fonseca
- Philips Research, Eindhoven, the Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Jeff Jasko
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | - Edmund Shaw
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
| | - David P White
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania
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Li M, Chen S, Lai Y, Liang Z, Wang J, Shi J, Lin H, Yao D, Hu H, Ung COL. Integrating Real-World Evidence in the Regulatory Decision-Making Process: A Systematic Analysis of Experiences in the US, EU, and China Using a Logic Model. Front Med (Lausanne) 2021; 8:669509. [PMID: 34136505 PMCID: PMC8200400 DOI: 10.3389/fmed.2021.669509] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/04/2021] [Indexed: 12/11/2022] Open
Abstract
Real world evidence (RWE) and real-world data (RWD) are drawing ever-increasing attention in the pharmaceutical industry and drug regulatory authorities (DRAs) all over the world due to their paramount role in supporting drug development and regulatory decision making. However, there is little systematic documentary analysis about how RWE was integrated for the use by the DRAs in evaluating new treatment approaches and monitoring post-market safety. This study aimed to analyze and discuss the integration of RWE into regulatory decision-making process from the perspective of DRAs. Different development strategies to develop and adopt RWE by the DRAs in the US, Europe, and China were reviewed and compared, and the challenges encountered were discussed. It was found that different strategies on development of RWE were applied by FDA, EMA, and NMPA. The extent to which RWE was adopted in China was relatively limited compared to that in the US and EU, which was highly related to the national pharmaceutical environment and development stages. A better understanding of the overall goals, inputs, activities, outputs, and outcomes in developing RWE will help inform actions to harness RWD and leverage RWE for better health care decisions.
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Affiliation(s)
- Meng Li
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Shengqi Chen
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Yunfeng Lai
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Zuanji Liang
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Jiaqi Wang
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Junnan Shi
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Haojie Lin
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Dongning Yao
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Hao Hu
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
| | - Carolina Oi Lam Ung
- State Key Laboratory in Quality Research of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macao, China
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Kruizinga MD, Moll A, Zhuparris A, Ziagkos D, Stuurman FE, Nuijsink M, Cohen AF, Driessen GJA. Postdischarge Recovery after Acute Pediatric Lung Disease Can Be Quantified with Digital Biomarkers. Respiration 2021; 100:979-988. [PMID: 34004601 DOI: 10.1159/000516328] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/10/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Pediatric patients admitted for acute lung disease are treated and monitored in the hospital, after which full recovery is achieved at home. Many studies report in-hospital recovery, but little is known regarding the time to full recovery after hospital discharge. Technological innovations have led to increased interest in home-monitoring and digital biomarkers. The aim of this study was to describe at-home recovery of 3 common pediatric respiratory diseases using a questionnaire and wearable device. METHODS In this study, patients admitted due to pneumonia (n = 30), preschool wheezing (n = 30), and asthma exacerbation (AE; n = 11) were included. Patients were monitored with a smartwatch and a questionnaire during admission, with a 14-day recovery period and a 10-day "healthy" period. Median compliance was calculated, and a mixed-effects model was fitted for physical activity and heart rate (HR) to describe the recovery period, and the physical activity recovery trajectory was correlated to respiratory symptom scores. RESULTS Median compliance was 47% (interquartile range [IQR] 33-81%) during the entire study period, 68% (IQR 54-91%) during the recovery period, and 28% (IQR 0-74%) during the healthy period. Patients with pneumonia reached normal physical activity 12 days postdischarge, while subjects with wheezing and AE reached this level after 5 and 6 days, respectively. Estimated mean physical activity was closely correlated with the estimated mean symptom score. HR measured by the smartwatch showed a similar recovery trajectory for subjects with wheezing and asthma, but not for subjects with pneumonia. CONCLUSIONS The digital biomarkers, physical activity, and HR obtained via smartwatch show promise for quantifying postdischarge recovery in a noninvasive manner, which can be useful in pediatric clinical trials and clinical care.
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Affiliation(s)
- Matthijs D Kruizinga
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Allison Moll
- Centre for Human Drug Research, Leiden, The Netherlands.,Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | | | | | - Frederik E Stuurman
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Marianne Nuijsink
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands
| | - Adam F Cohen
- Centre for Human Drug Research, Leiden, The Netherlands.,Leiden University Medical Centre, Leiden, The Netherlands
| | - Gertjan J A Driessen
- Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands.,Maastricht University Medical Centre, Leiden, The Netherlands
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Roux de Bézieux H, Bullard J, Kolterman O, Souza M, Perraudeau F. Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study. JMIR Form Res 2021; 5:e20175. [PMID: 33661120 PMCID: PMC7974765 DOI: 10.2196/20175] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 10/22/2020] [Accepted: 01/24/2021] [Indexed: 12/31/2022] Open
Abstract
Background Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1 diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps to monitor and manage care. Objective We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits. Methods Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual diet log. Results All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction with sensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10. Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future. Conclusions We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits. CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely. Trial Registration ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888.
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Affiliation(s)
- Hector Roux de Bézieux
- Pendulum Therapeutics, Inc, San Francisco, CA, United States.,Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, United States.,Center for Computational Biology, University of California, Berkeley, CA, United States
| | - James Bullard
- Pendulum Therapeutics, Inc, San Francisco, CA, United States
| | | | - Michael Souza
- Pendulum Therapeutics, Inc, San Francisco, CA, United States
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Kruizinga MD, Stuurman FE, Exadaktylos V, Doll RJ, Stephenson DT, Groeneveld GJ, Driessen GJA, Cohen AF. Development of Novel, Value-Based, Digital Endpoints for Clinical Trials: A Structured Approach Toward Fit-for-Purpose Validation. Pharmacol Rev 2021; 72:899-909. [PMID: 32958524 DOI: 10.1124/pr.120.000028] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Novel digital endpoints gathered via wearables, small devices, or algorithms hold great promise for clinical trials. However, implementation has been slow because of a lack of guidelines regarding the validation process of these new measurements. In this paper, we propose a pragmatic approach toward selection and fit-for-purpose validation of digital endpoints. Measurements should be value-based, meaning the measurements should directly measure or be associated with meaningful outcomes for patients. Devices should be assessed regarding technological validity. Most importantly, a rigorous clinical validation process should appraise the tolerability, difference between patients and controls, repeatability, detection of clinical events, and correlation with traditional endpoints. When technically and clinically fit-for-purpose, case building in interventional clinical trials starts to generate evidence regarding the response to new or existing health-care interventions. This process may lead to the digital endpoint replacing traditional endpoints, such as clinical rating scales or questionnaires in clinical trials. We recommend initiating more data-sharing collaborations to prevent unnecessary duplication of research and integration of value-based measurements in clinical care to enhance acceptance by health-care professionals. Finally, we invite researchers and regulators to adopt this approach to ensure a timely implementation of digital measurements and value-based thinking in clinical trial design and health care. SIGNIFICANCE STATEMENT: Novel digital endpoints are often cited as promising for the clinical trial of the future. However, clear validation guidelines are lacking in the literature. This paper contains pragmatic criteria for the selection, technical validation, and clinical validation of novel digital endpoints and provides recommendations for future work and collaboration.
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Affiliation(s)
- M D Kruizinga
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
| | - F E Stuurman
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
| | - V Exadaktylos
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
| | - R J Doll
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
| | - D T Stephenson
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
| | - G J Groeneveld
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
| | - G J A Driessen
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
| | - A F Cohen
- Centre for Human Drug Research, Leiden, The Netherlands (M.D.K., F.E.S., V.E., R.J.D., G.J.G., A.F.C.); Juliana Children's Hospital, HAGA Teaching Hospital, The Hague, The Netherlands (M.D.K., G.J.A.D.); Leiden University Medical Center, Leiden, The Netherlands (M.D.K., F.E.S., G.J.G., A.F.C.); and Critical Path for Parkinson's Consortium, Critical Path Institute, Tucson, Arizona (D.T.S.)
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Leroux A, Rzasa-Lynn R, Crainiceanu C, Sharma T. Wearable Devices: Current Status and Opportunities in Pain Assessment and Management. Digit Biomark 2021; 5:89-102. [PMID: 34056519 PMCID: PMC8138140 DOI: 10.1159/000515576] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 03/01/2021] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION We investigated the possibilities and opportunities for using wearable devices that measure physical activity and physiometric signals in conjunction with ecological momentary assessment (EMA) data to improve the assessment and treatment of pain. METHODS We considered studies with cross-sectional and longitudinal designs as well as interventional or observational studies correlating pain scores with measures derived from wearable devices. A search was also performed on studies that investigated physical activity and physiometric signals among patients with pain. RESULTS Few studies have assessed the possibility of incorporating wearable devices as objective tools for contextualizing pain and physical function in free-living environments. Of the studies that have been conducted, most focus solely on physical activity and functional outcomes as measured by a wearable accelerometer. Several studies report promising correlations between pain scores and signals derived from wearable devices, objectively measured physical activity, and physical function. In addition, there is a known association between physiologic signals that can be measured by wearable devices and pain, though studies using wearable devices to measure these signals and associate them with pain in free-living environments are limited. CONCLUSION There exists a great opportunity to study the complex interplay between physiometric signals, physical function, and pain in a real-time fashion in free-living environments. The literature supports the hypothesis that wearable devices can be used to develop reproducible biosignals that correlate with pain. The combination of wearable devices and EMA will likely lead to the development of clinically meaningful endpoints that will transform how we understand and treat pain patients.
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Affiliation(s)
- Andrew Leroux
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA
| | - Rachael Rzasa-Lynn
- Department of Anesthesiology, University of Colorado, Aurora, Colorado, USA
| | - Ciprian Crainiceanu
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Tushar Sharma
- Department of Anesthesiology, University of Colorado, Aurora, Colorado, USA
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Rochester L, Mazzà C, Mueller A, Caulfield B, McCarthy M, Becker C, Miller R, Piraino P, Viceconti M, Dartee WP, Garcia-Aymerich J, Aydemir AA, Vereijken B, Arnera V, Ammour N, Jackson M, Hache T, Roubenoff R. A Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach. Digit Biomark 2020; 4:13-27. [PMID: 33442578 DOI: 10.1159/000512513] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 10/23/2020] [Indexed: 12/19/2022] Open
Abstract
Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.
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Affiliation(s)
- Lynn Rochester
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom.,The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Claudia Mazzà
- Department of Mechanical Engineering, The University of Sheffield, Sheffield, United Kingdom.,INSIGNEO Institute for in Silico Medicine, The University of Sheffield, Sheffield, United Kingdom
| | - Arne Mueller
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Brian Caulfield
- Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.,School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland
| | | | - Clemens Becker
- Robert Bosch Foundation for Medical Research, Stuttgart, Germany
| | - Ram Miller
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Paolo Piraino
- Research and Early Development Statistics, Bayer, Berlin, Germany
| | | | | | - Judith Garcia-Aymerich
- ISGlobal, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,CIBER Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Aida A Aydemir
- EMD Serono, Billerica, MA, a Business of Merck KGaA, Darmstadt, Germany
| | - Beatrix Vereijken
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Nadir Ammour
- Sanofi R&D, Clinical Sciences and Operations, Chilly-Mazarin, France
| | | | - Tilo Hache
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
| | - Ronenn Roubenoff
- Translational Medicine, Novartis Institutes for Biomedical Research, Basel, Switzerland
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Digital Health Applications for Pharmacogenetic Clinical Trials. Genes (Basel) 2020; 11:genes11111261. [PMID: 33114567 PMCID: PMC7692850 DOI: 10.3390/genes11111261] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/20/2020] [Accepted: 10/24/2020] [Indexed: 12/15/2022] Open
Abstract
Digital health (DH) is the use of digital technologies and data analytics to understand health-related behaviors and enhance personalized clinical care. DH is increasingly being used in clinical trials, and an important field that could potentially benefit from incorporating DH into trial design is pharmacogenetics. Prospective pharmacogenetic trials typically compare a standard care arm to a pharmacogenetic-guided therapeutic arm. These trials often require large sample sizes, are challenging to recruit into, lack patient diversity, and can have complicated workflows to deliver therapeutic interventions to both investigators and patients. Importantly, the use of DH technologies could mitigate these challenges and improve pharmacogenetic trial design and operation. Some DH use cases include (1) automatic electronic health record-based patient screening and recruitment; (2) interactive websites for participant engagement; (3) home- and tele-health visits for patient convenience (e.g., samples for lab tests, physical exams, medication administration); (4) healthcare apps to collect patient-reported outcomes, adverse events and concomitant medications, and to deliver therapeutic information to patients; and (5) wearable devices to collect vital signs, electrocardiograms, sleep quality, and other discrete clinical variables. Given that pharmacogenetic trials are inherently challenging to conduct, future pharmacogenetic utility studies should consider implementing DH technologies and trial methodologies into their design and operation.
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Artusi CA, Imbalzano G, Sturchio A, Pilotto A, Montanaro E, Padovani A, Lopiano L, Maetzler W, Espay AJ. Implementation of Mobile Health Technologies in Clinical Trials of Movement Disorders: Underutilized Potential. Neurotherapeutics 2020; 17:1736-1746. [PMID: 32734442 PMCID: PMC7851293 DOI: 10.1007/s13311-020-00901-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Mobile health technologies (mHealth) are patient-worn or portable devices aimed at increasing the granularity and relevance of clinical measurements. The implementation of mHealth has the potential to decrease sample size, duration, and cost of clinical trials. We performed a review of the ClinicalTrials.gov database using a standardized approach to identify adoption in and usefulness of mHealth in movement disorders interventional clinical trials. Trial phase, geographical area, availability of data captured, constructs of interest, and outcome priority were collected. Eligible trials underwent quality appraisal using an ad hoc 5-point checklist to assess mHealth feasibility, acceptability, correlation with patient-centered outcome measures, and clinical meaningfulness. A total of 29% (n = 54/184) registered trials were using mHealth, mainly in Parkinson's disease and essential tremor (59.3% and 27.8%). In most cases, mHealth were used in phase 2 trials (83.3%) as secondary outcome measures (59.3%). Only five phase 3 trials, representing 9.3% of the total, used mHealth (1 as primary outcome measure, 3 as secondary, and 1 as tertiary). Only 3.7% (n = 2/54) of all trials used mHealth for measuring both motor and non-motor symptoms, and 23.1% (n = 12/52) used mHealth for unsupervised, ecologic outcomes. Our findings suggest that mHealth remain underutilized and largely relegated to phase 2 trials for secondary or tertiary outcome measures. Efforts toward greater alignment of mHealth with patient-centered outcomes and development of a universal, common-language platform to synchronize data from one or more devices will assist future efforts toward the integration of mHealth into clinical trials.
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Affiliation(s)
- Carlo Alberto Artusi
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Gabriele Imbalzano
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Andrea Sturchio
- Gardner Family Center for Parkinson's disease and Movement Disorders, Department of Neurology, University of Cincinnati Academic Health Center, 260 Stetson St., Suite 2300, Cincinnati, OH, 45267-0525, USA
| | - Andrea Pilotto
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- FERB Onlus, Ospedale S. Isidoro, Trescore Balneario, Bergamo, Italy
| | - Elisa Montanaro
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Alessandro Padovani
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Leonardo Lopiano
- Department of Neuroscience "Rita Levi Montalcini", University of Torino, Torino, Italy
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Alberto J Espay
- Gardner Family Center for Parkinson's disease and Movement Disorders, Department of Neurology, University of Cincinnati Academic Health Center, 260 Stetson St., Suite 2300, Cincinnati, OH, 45267-0525, USA.
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Geoghegan C, Nido V, Bemden ABV, Hallinan Z, Jordan L, Kehoe LS, Morin SL, Niskar A, Okubagzi PG, Wood WA. Learning from patient and site perspectives to develop better digital health trials: Recommendations from the Clinical Trials Transformation Initiative. Contemp Clin Trials Commun 2020; 19:100636. [PMID: 32913915 PMCID: PMC7473867 DOI: 10.1016/j.conctc.2020.100636] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/27/2020] [Accepted: 08/09/2020] [Indexed: 01/22/2023] Open
Abstract
In order to harness the potential of digital health technologies to enhance the quality of clinical research, it is critical to first understand how to engage patients and research sites when planning and conducting digital health trials. To pave the way for the more effective use of digital health technologies in trials, the Clinical Trials Transformation Initiative has developed the first comprehensive, evidence-based set of recommendations for incorporating patient and site perspectives in digital health trials. While directed primarily at sponsors, these recommendations are expected to be valuable for all stakeholders including investigators.
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Affiliation(s)
| | | | | | | | | | | | - Steve L. Morin
- Office of Health and Constituent Affairs, US Food and Drug Administration, Silver Spring, MD, USA
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Taylor KI, Staunton H, Lipsmeier F, Nobbs D, Lindemann M. Outcome measures based on digital health technology sensor data: data- and patient-centric approaches. NPJ Digit Med 2020; 3:97. [PMID: 32715091 PMCID: PMC7378210 DOI: 10.1038/s41746-020-0305-8] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 06/26/2020] [Indexed: 11/08/2022] Open
Abstract
Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients' functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson's disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.
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Affiliation(s)
- Kirsten I. Taylor
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
- Faculty of Psychology, University of Basel, Missionsstrasse 60/62, 4055 Basel, Switzerland
| | - Hannah Staunton
- Patient-Centered Outcomes Research, Biometrics, Product Development, Roche Products Limited, Hexagon Place, 6 Falcon Way, Shire Park, Welwyn Garden City, AL7 1TW UK
| | - Florian Lipsmeier
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - David Nobbs
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Michael Lindemann
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Grenzacherstrasse 124, 4070 Basel, Switzerland
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42
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Choosing a Mobile Sensor Technology for a Clinical Trial: Statistical Considerations, Developments and Learnings. Ther Innov Regul Sci 2020; 55:38-47. [PMID: 32557010 DOI: 10.1007/s43441-020-00188-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Accepted: 06/09/2020] [Indexed: 10/24/2022]
Abstract
The DIA Study Endpoints Community Working Group on Mobile Sensor Technology (MST) series addresses considerations that may be useful for selecting MST for use in a clinical trial. This article describes considerations regarding the selection of MST for clinical trials including expectations around technology specifications, verification (bench testing), regulatory clearance and certification status. We identify useful statistical methods needed to establish agreement of the MST with respect to a clinical 'gold' standard technology in terms of accuracy and precision, and to combine data across trials, data types or device versions. In addition to describing key considerations, this manuscript also serves as a central location citing those resources where additional detail can be found.
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