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Bachman SL, Gomes E, Aryal S, Cella D, Clay I, Lyden K, Leach HJ. Do Measures of Real-World Physical Behavior Provide Insights Into the Well-Being and Physical Function of Cancer Survivors? Cross-Sectional Analysis. JMIR Cancer 2024; 10:e53180. [PMID: 39008350 DOI: 10.2196/53180] [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: 09/28/2023] [Revised: 02/26/2024] [Accepted: 04/24/2024] [Indexed: 07/16/2024] Open
Abstract
BACKGROUND As the number of cancer survivors increases, maintaining health-related quality of life in cancer survivorship is a priority. This necessitates accurate and reliable methods to assess how cancer survivors are feeling and functioning. Real-world digital measures derived from wearable sensors offer potential for monitoring well-being and physical function in cancer survivorship, but questions surrounding the clinical utility of these measures remain to be answered. OBJECTIVE In this secondary analysis, we used 2 existing data sets to examine how measures of real-world physical behavior, captured with a wearable accelerometer, were related to aerobic fitness and self-reported well-being and physical function in a sample of individuals who had completed cancer treatment. METHODS Overall, 86 disease-free cancer survivors aged 21-85 years completed self-report assessments of well-being and physical function, as well as a submaximal exercise test that was used to estimate their aerobic fitness, quantified as predicted submaximal oxygen uptake (VO2). A thigh-worn accelerometer was used to monitor participants' real-world physical behavior for 7 days. Accelerometry data were used to calculate average values of the following measures of physical behavior: sedentary time, step counts, time in light and moderate to vigorous physical activity, time and weighted median cadence in stepping bouts over 1 minute, and peak 30-second cadence. RESULTS Spearman correlation analyses indicated that 6 (86%) of the 7 accelerometry-derived measures of real-world physical behavior were not significantly correlated with Functional Assessment of Cancer Therapy-General total well-being or linked Patient-Reported Outcomes Measurement Information System-Physical Function scores (Ps≥.08). In contrast, all but one of the physical behavior measures were significantly correlated with submaximal VO2 (Ps≤.03). Comparing these associations using likelihood ratio tests, we found that step counts, time in stepping bouts over 1 minute, and time in moderate to vigorous activity were more strongly associated with submaximal VO2 than with self-reported well-being or physical function (Ps≤.03). In contrast, cadence in stepping bouts over 1 minute and peak 30-second cadence were not more associated with submaximal VO2 than with the self-reported measures (Ps≥.08). CONCLUSIONS In a sample of disease-free cancer survivors, we found that several measures of real-world physical behavior were more associated with aerobic fitness than with self-reported well-being and physical function. These results highlight the possibility that in individuals who have completed cancer treatment, measures of real-world physical behavior may provide additional information compared with self-reported and performance measures. To advance the appropriate use of digital measures in oncology clinical research, further research evaluating the clinical utility of real-world physical behavior over time in large, representative samples of cancer survivors is warranted. TRIAL REGISTRATION ClinicalTrials.gov NCT03781154; https://clinicaltrials.gov/ct2/show/NCT03781154.
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Affiliation(s)
| | - Emma Gomes
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, United States
| | | | - David Cella
- Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Ieuan Clay
- VivoSense, Inc, Newport Coast, CA, United States
| | - Kate Lyden
- VivoSense, Inc, Newport Coast, CA, United States
| | - Heather J Leach
- Department of Health and Exercise Science, Colorado State University, Fort Collins, CO, United States
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Al Meslamani AZ. Barriers to digital endpoints in data collection in low and middle-income countries. Expert Rev Pharmacoecon Outcomes Res 2024; 24:701-703. [PMID: 38480011 DOI: 10.1080/14737167.2024.2331047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 03/12/2024] [Indexed: 03/19/2024]
Affiliation(s)
- Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research quality of care Center, Al Ain University, Abu Dhabi, United Arab Emirates
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Sandler RD, Lai L, Dawson S, Cameron S, Lynam A, Sperrin M, Hoo ZH, Wildman MJ. Development of data processing algorithm to calculate adherence for adults with cystic fibrosis using inhaled therapy - a multi-center observational study within the CFHealthHub learning health system. Expert Rev Pharmacoecon Outcomes Res 2024; 24:759-771. [PMID: 38458615 DOI: 10.1080/14737167.2024.2328085] [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: 08/10/2023] [Accepted: 02/28/2024] [Indexed: 03/10/2024]
Abstract
OBJECTIVES To develop a robust algorithm to accurately calculate 'daily complete dose counts' for inhaled medicines, used in percent adherence calculations, from electronically-captured nebulizer data within the CFHealthHub Learning Health System. METHODS A multi-center, cross-sectional study involved participants and clinicians reviewing real-world inhaled medicine usage records and triangulating them with objective nebulizer data to establish a consensus on 'daily complete dose counts.' An algorithm, which used only objective nebulizer data, was then developed using a derivation dataset and evaluated using internal validation dataset. The agreement and accuracy between the algorithm-derived and consensus-derived 'daily complete dose counts' was examined, with the consensus-derived count as the reference standard. RESULTS Twelve people with CF participated. The algorithm derived a 'daily complete dose count' by screening out 'invalid' doses (those <60s in duration or run in cleaning mode), combining all doses starting within 120s of each other, and then screening out all doses with duration < 480s which were interrupted by power supply failure. The kappa co-efficient was 0.85 (0.71-0.91) in the derivation and 0.86 (0.77-0.94) in the validation dataset. CONCLUSIONS The algorithm demonstrated strong agreement with the participant-clinician consensus, enhancing confidence in CFHealthHub data. Publishingdata processing methods can encourage trust in digital endpoints and serve as an exemplar for other projects.
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Affiliation(s)
- Robert D Sandler
- Adult CF Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- Sheffield Centre for Health and Related Research, The University of Sheffield, Sheffield, UK
| | - Lana Lai
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - Sophie Dawson
- Wolfson Adult Cystic Fibrosis Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Sarah Cameron
- Adult CF Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Aoife Lynam
- Cystic Fibrosis Unit, Southampton University Hospitals NHS Trust, Southampton, UK
| | - Matthew Sperrin
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK
| | - Zhe Hui Hoo
- Adult CF Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
| | - Martin J Wildman
- Adult CF Centre, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK
- Sheffield Centre for Health and Related Research, The University of Sheffield, Sheffield, UK
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Al Meslamani AZ. The long-term clinical impact of digital endpoints and biomarkers in data collection. Expert Rev Pharmacoecon Outcomes Res 2024; 24:697-699. [PMID: 38362754 DOI: 10.1080/14737167.2024.2320233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 02/14/2024] [Indexed: 02/17/2024]
Affiliation(s)
- Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research quality of care Center, Al Ain University, Abu Dhabi, United Arab Emirates
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Patel B, Makaryus AN. The implications of cardiac device cybersecurity responsibilities and challenges faced by policymakers, manufacturers, and patients. Expert Rev Pharmacoecon Outcomes Res 2024; 24:743-747. [PMID: 38808954 DOI: 10.1080/14737167.2024.2361076] [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: 08/03/2023] [Accepted: 05/24/2024] [Indexed: 05/30/2024]
Abstract
INTRODUCTION As digital health expands, reliance on digital endpoints is rapidly increasing to improve diagnostic accuracy and management in the healthcare field. Digital endpoints are beneficial to monitor how patient's clinical information is processed outside of a clinical setting. AREAS COVERED Implications of cardiac digital endpoints play a role in allowing patients to track their clinical data outside of a clinical setting. Advances in cardiac digital endpoints involve advanced devices and implants, trackers, and artificial intelligence. We will explore further digital endpoints within cardiology and threats as well as security concerns for policies to focus on the maintenance of safe patient health data analysis, transmission, and processing. EXPERT OPINION As digital endpoints evolve and expand, policymakers must ensure there is adequate cybersecurity surrounding them. We believe guidelines should be in place to make sure data is accessed solely on a secure connection and access to digital literacy for patients should be readily available.
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Affiliation(s)
- Bhakti Patel
- Donald and Barbara Zucker School of Medicine, Hofstra/Northwell Health, Hempstead, NY, USA
| | - Amgad N Makaryus
- Donald and Barbara Zucker School of Medicine, Hofstra/Northwell Health, Hempstead, NY, USA
- Department of Cardiology, Nassau University Medical Center, East Meadow, NY, USA
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Ali A, Clarke DF. Digital measures in epilepsy in low-resourced environments. Expert Rev Pharmacoecon Outcomes Res 2024; 24:705-712. [PMID: 37818647 DOI: 10.1080/14737167.2023.2270163] [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: 07/14/2023] [Accepted: 10/09/2023] [Indexed: 10/12/2023]
Abstract
INTRODUCTION Digital measures and digital health-care delivery have been rarely implemented in lower-and-middle-income countries (LMICs), contributing to worsening global disparities and inequities. Sustainable ways to implement and use digital approaches will help to improve time to access, management, and quality of life in persons with epilepsy, goals that remain unreachable in under-resourced communities. As under-resourced environments differ in human and economic resources, no one approach will be appropriate to all LMICs. AREAS COVERED Digital health and tools to monitor and measure digital endpoints and metrics of quality of life will need to be developed or adapted to the specific needs of under-resourced areas. Portable technologies may partially address the urban-rural divide. Careful delineation of stakeholders and their engagement and alignment in all efforts is critically important if these initiatives are to be successfully sustained. Privacy issues, neglected in many regions globally, must be purposefully addressed. EXPERT OPINION Epilepsy care in under-resourced environments has been limited by the lack of relevant technologies for diagnosis and treatment. Digital biomarkers, and investigative technological advances, may finally make it feasible to sustainably improve care delivery and ultimately quality of life including personalized epilepsy care.
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Affiliation(s)
- Amza Ali
- Department of Medicine, Faculty of Medical Sciences, Mona, Kingston, Jamaica
| | - Dave F Clarke
- Dell Medical School, University of Texas at Austin, Austin, TX, USA
- Department of Pediatric Epilepsy, Dell Children's Medical Center of Central Texas, Austin, TX, USA
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Al Meslamani AZ, Li N. Assessing the economic impact of digital endpoints on medication adherence. Expert Rev Pharmacoecon Outcomes Res 2024:1-3. [PMID: 38520281 DOI: 10.1080/14737167.2024.2334893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/21/2024] [Indexed: 03/25/2024]
Affiliation(s)
- Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Quality of Care Center, Al Ain University, Abu Dhabi, United Arab Emirates
| | - Nannan Li
- Department of Health Services Research, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
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Sinha SD, Chary Sriramadasu S, Raphael R, Roy S. Decentralisation in Clinical Trials and Patient Centricity: Benefits and Challenges. Pharmaceut Med 2024; 38:109-120. [PMID: 38453755 DOI: 10.1007/s40290-024-00518-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2024] [Indexed: 03/09/2024]
Abstract
Decentralised clinical trials (DCTs) encompass various terms such as virtual, home-based, remote and siteless trials. The objectives of DCTs are to enhance the ease of participation for patients in clinical trials by minimising or removing the necessity for trial subjects to travel to the trial sites. This approach has been shown to reduce drop-out rates, increase study effectiveness and ultimately get life-altering drugs to market faster-saving sponsors billions. At the outset, DCTs deploy a wide range of digital technologies to collect safety and efficacy data from study participants, providing study treatments and performing investigations from the comfort of the patient's own home. The aim of decentralised trials includes patient centricity, enhanced efficacy in clinical trial conduct and generating real-world data. This is done by not only making it convenient for the patient to participate in the trial execution, but also involving them from the planning stage and taking their inputs during designing of trials and consenting documentation, understanding their treatment requirements and designing the studies accordingly. Various regulatory authorities have published guidelines governing DCT principles, especially after the coronavirus disease 2019 (COVID-19) experience of undertaking multicentric clinical trials. Both United States Food and Drug Administration (USFDA) and European Medicines Agency (EMA) have newer, recently updated guidelines to capture this growing reality to undertake clinical trials using patient technology or patient-centric technologies. Other regulatory agencies are accepting data generated using decentralised and patient-centric technologies and making an effort to include elements of decentralised trials in their regulatory guidelines. Decentralised trials follow a hybrid approach to have a balanced mix of remote and in-person data collection and trial procedures. Decentralised and patient-centric approaches are the future of any organisation for the conduct of clinical trials. Globally, all sponsor pharmaceutical companies must start undertaking drug development and clinical trials using a decentralised approach while keeping patient centricity in mind.
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Affiliation(s)
- Shubhadeep D Sinha
- Department of Clinical Development and Medical Affairs, Hetero Labs Limited, Hetero Corporate, 7-2-A2, Industrial Estates, Sanath Nagar, Hyderabad, Telangana, 500018, India.
| | - Sreenivasa Chary Sriramadasu
- Department of Clinical Development and Medical Affairs, Hetero Labs Limited, Hetero Corporate, 7-2-A2, Industrial Estates, Sanath Nagar, Hyderabad, Telangana, 500018, India
| | - Ruby Raphael
- Department of Clinical Development and Medical Affairs, Hetero Labs Limited, Hetero Corporate, 7-2-A2, Industrial Estates, Sanath Nagar, Hyderabad, Telangana, 500018, India
| | - Sudeshna Roy
- Department of Clinical Development and Medical Affairs, Hetero Labs Limited, Hetero Corporate, 7-2-A2, Industrial Estates, Sanath Nagar, Hyderabad, Telangana, 500018, India
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Willemse IHJ, Schootemeijer S, van den Bergh R, Dawes H, Nonnekes JH, van de Warrenburg BPC. Smartphone applications for Movement Disorders: Towards collaboration and re-use. Parkinsonism Relat Disord 2024; 120:105988. [PMID: 38184466 DOI: 10.1016/j.parkreldis.2023.105988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Numerous smartphone and tablet applications (apps) are available to monitor movement disorders, but an overview of their purpose and stage of development is missing. OBJECTIVES To systematically review published literature and classify smartphone and tablet apps with objective measurement capabilities for the diagnosis, monitoring, assessment, or treatment of movement disorders. METHODS We systematically searched for publications covering smartphone or tablet apps to monitor movement disorders until November 22nd, 2023. We reviewed the target population, measured domains, purpose, and technology readiness level (TRL) of the proposed app and checked their availability in common app stores. RESULTS We identified 113 apps. Most apps were developed for Parkinson's disease specifically (n = 82; 73%) or for movement disorders in general (n = 17; 15%). Apps were either designed to momentarily assess symptoms (n = 65; 58%), support treatment (n = 22; 19%), aid in diagnosis (n = 16; 14%), or passively track symptoms (n = 11; 10%). Commonly assessed domains across movement disorders included fine motor skills (n = 34; 30%), gait (n = 36; 32%), and tremor (n = 32; 28%) for the motor domain and cognition (n = 16; 14%) for the non-motor domain. Twenty-six (23%) apps were proof-of-concepts (TRL 1-3), while most apps were tested in a controlled setting (TRL 4-6; n = 63; 56%). Twenty-four apps were tested in their target setting (TRL 7-9) of which 10 were accessible in common app stores or as Android Package. CONCLUSIONS The development of apps strongly gravitates towards Parkinson's disease and a selection of motor symptoms. Collaboration, re-use and further development of existing apps is encouraged to avoid reinventions of the wheel.
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Affiliation(s)
- Ilse H J Willemse
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
| | - Sabine Schootemeijer
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Robin van den Bergh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Helen Dawes
- NIHR Exeter BRC, Medical School, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Jorik H Nonnekes
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands; Department of Rehabilitation, Sint Maartenskliniek, Nijmegen, the Netherlands
| | - Bart P C van de Warrenburg
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
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Markey N, Howitt B, El-Mansouri I, Schwartzenberg C, Kotova O, Meier C. Clinical trials are becoming more complex: a machine learning analysis of data from over 16,000 trials. Sci Rep 2024; 14:3514. [PMID: 38346965 PMCID: PMC10861486 DOI: 10.1038/s41598-024-53211-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/29/2024] [Indexed: 02/15/2024] Open
Abstract
The past decade has seen substantial innovation in clinical trials, including new trial formats, endpoints, and others. Also there have been regulatory changes, increasing competitive pressures and other external factors which impact clinical trials. In parallel, trial timelines have increased and success rates remain stubbornly low. This has led many observers to question whether clinical trials have become overly complex and if this complexity is always needed. Here we present a large-scale analysis of protocols and other data from over 16,000 trials. Using a machine learning algorithm, we automatically assessed key features of these trials, such as number of endpoints, number of inclusion-exclusion criteria and others. Using a regression analysis we combined these features into a new metric, the Trial Complexity Score, which correlates with overall clinical trial duration. Looking at this score across different clinical phases and therapeutic areas we see substantial increases over time, suggesting that clinical trials are indeed becoming more complex. We discuss drivers of increasing trial complexity, necessary or helpful ('good') complexity versus unnecessary ('bad') complexity, and we explore mechanisms of how sponsors of clinical trials can reduce trial complexity where appropriate.
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Affiliation(s)
- Nigel Markey
- Boston Consulting Group, 80 Charlotte Street, London, W1T 4DF, UK
| | - Ben Howitt
- Boston Consulting Group, 80 Charlotte Street, London, W1T 4DF, UK
| | - Ilyass El-Mansouri
- Boston Consulting Group, 75 Avenue de la Grande Armée, 75016, Paris, France
| | | | - Olga Kotova
- Boston Consulting Group, 80 Charlotte Street, London, W1T 4DF, UK
| | - Christoph Meier
- Boston Consulting Group, 80 Charlotte Street, London, W1T 4DF, UK.
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Mirelman A, Volkov J, Salomon A, Gazit E, Nieuwboer A, Rochester L, Del Din S, Avanzino L, Pelosin E, Bloem BR, Della Croce U, Cereatti A, Thaler A, Roggen D, Mazza C, Shirvan J, Cedarbaum JM, Giladi N, Hausdorff JM. Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease. Mov Disord 2024; 39:328-338. [PMID: 38151859 DOI: 10.1002/mds.29689] [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: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. OBJECTIVES The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. METHODS Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. RESULTS Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]). CONCLUSIONS Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jana Volkov
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Amit Salomon
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Laura Avanzino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Avner Thaler
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | | | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA
- Yale University School of Medicine, New Haven, Connecticut, USA
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel
- Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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Rego S, Henriques AR, Serra SS, Costa T, Rodrigues AM, Nunes F. Methods for the Clinical Validation of Digital Endpoints: Protocol for a Scoping Review Abstract. JMIR Res Protoc 2023; 12:e47119. [PMID: 37883152 PMCID: PMC10636620 DOI: 10.2196/47119] [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: 03/09/2023] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND Clinical trials often use digital technologies to collect data continuously outside the clinic and use the derived digital endpoints as trial endpoints. Digital endpoints are also being developed to support diagnosis, monitoring, or therapeutic interventions in clinical care. However, clinical validation stands as a significant challenge, as there are no specific guidelines orienting the validation of digital endpoints. OBJECTIVE This paper presents the protocol for a scoping review that aims to map the existing methods for the clinical validation of digital endpoints. METHODS The scoping review will comprise searches from the electronic literature databases MEDLINE (PubMed), Scopus (including conference proceedings), Embase, IEEE (Institute of Electrical and Electronics Engineers) Xplore, ACM (Association for Computing Machinery) Digital Library, CENTRAL (Cochrane Central Register of Controlled Trials), Web of Science Core Collection (including conference proceedings), and Joanna Briggs Institute Database of Systematic Reviews and Implementation Reports. We will also include various sources of gray literature with search terms related to digital endpoints. The methodology will adhere to the Joanna Briggs Institute Scoping Review and the Guidance for Conducting Systematic Scoping Reviews. RESULTS A search for reviews on the existing evidence related to this topic was conducted and has shown that no such review was previously undertaken. This review will provide a systematic assessment of the literature on methods for the clinical validation of digital endpoints and highlight any potential need for harmonization or reporting of methods. The results will include the methods for the clinical validation of digital endpoints according to device, digital endpoint, and clinical application goal of digital endpoints. The study started in January 2023 and is expected to end by December 2023, with results to be published in a peer-reviewed journal. CONCLUSIONS A scoping review of methodologies that validate digital endpoints is necessary. This review will be unique in its breadth since it will comprise digital endpoints collected from several devices and not focus on a specific disease area. The results of our work should help guide researchers in choosing validation methods, identify potential gaps in the literature, or inform the development of novel methods to optimize the clinical validation of digital endpoints. Resolving these gaps is the key to presenting evidence in a consistent way to regulators and other parties and obtaining regulatory acceptance of digital endpoints for patient benefit. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/47119.
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Affiliation(s)
- Sílvia Rego
- Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal
- Faculty of Medicine, University of Porto, Porto, Portugal
| | | | | | - Teresa Costa
- NOVA Medical School, Universidade NOVA de Lisboa, Lisbon, Portugal
| | | | - Francisco Nunes
- Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal
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13
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Price DL, Khan A, Angers R, Cardenas A, Prato MK, Bani M, Bonhaus DW, Citron M, Biere AL. In vivo effects of the alpha-synuclein misfolding inhibitor minzasolmin supports clinical development in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:114. [PMID: 37460603 PMCID: PMC10352257 DOI: 10.1038/s41531-023-00552-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/26/2023] [Indexed: 07/20/2023] Open
Abstract
Direct targeting of alpha-synuclein (ASYN) has emerged as a disease-modifying strategy for Parkinson's disease and other synucleinopathies which is being approached using both small molecule compounds and ASYN-targeted biologics. Minzasolmin (UCB0599) is an orally bioavailable and brain-penetrant small molecule ASYN misfolding inhibitor in clinical development as a disease-modifying therapeutic for Parkinson's disease. Herein the results of preclinical evaluations of minzasolmin that formed the basis for subsequent clinical development are described. Pharmacokinetic evaluations of intraperitoneal 1 and 5 mg/kg minzasolmin in wildtype mice revealed parallel and dose-proportional exposures in brain and plasma. Three-month administration studies in the Line 61 transgenic mouse model of PD were conducted to measure ASYN pathology and other PD-relevant endpoints including markers of CNS inflammation, striatal DAT labeling and gait. Reductions in ASYN pathology were correlated with improved aspects of gait and balance, reductions in CNS inflammation marker abundance, and normalized striatal DAT levels. These findings provide support for human dose determinations and have informed the translational strategy for clinical trial design and biomarker selection for the ongoing clinical studies of minzasolmin in patients living with early-stage Parkinson's disease (ClinicalTrials.gov ID: NCT04658186; EudraCT Number 2020-003265).
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Affiliation(s)
| | - Asma Khan
- Neuropore Therapies, Inc., San Diego, CA, USA
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14
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Brem AK, Kuruppu S, de Boer C, Muurling M, Diaz-Ponce A, Gove D, Curcic J, Pilotto A, Ng WF, Cummins N, Malzbender K, Nies VJM, Erdemli G, Graeber J, Narayan VA, Rochester L, Maetzler W, Aarsland D. Digital endpoints in clinical trials of Alzheimer's disease and other neurodegenerative diseases: challenges and opportunities. Front Neurol 2023; 14:1210974. [PMID: 37435159 PMCID: PMC10332162 DOI: 10.3389/fneur.2023.1210974] [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: 04/23/2023] [Accepted: 05/26/2023] [Indexed: 07/13/2023] Open
Abstract
Alzheimer's disease (AD) and other neurodegenerative diseases such as Parkinson's disease (PD) and Huntington's disease (HD) are associated with progressive cognitive, motor, affective and consequently functional decline considerably affecting Activities of Daily Living (ADL) and quality of life. Standard assessments, such as questionnaires and interviews, cognitive testing, and mobility assessments, lack sensitivity, especially in early stages of neurodegenerative diseases and in the disease progression, and have therefore a limited utility as outcome measurements in clinical trials. Major advances in the last decade in digital technologies have opened a window of opportunity to introduce digital endpoints into clinical trials that can reform the assessment and tracking of neurodegenerative symptoms. The Innovative Health Initiative (IMI)-funded projects RADAR-AD (Remote assessment of disease and relapse-Alzheimer's disease), IDEA-FAST (Identifying digital endpoints to assess fatigue, sleep and ADL in neurodegenerative disorders and immune-mediated inflammatory diseases) and Mobilise-D (Connecting digital mobility assessment to clinical outcomes for regulatory and clinical endorsement) aim to identify digital endpoints relevant for neurodegenerative diseases that provide reliable, objective, and sensitive evaluation of disability and health-related quality of life. In this article, we will draw from the findings and experiences of the different IMI projects in discussing (1) the value of remote technologies to assess neurodegenerative diseases; (2) feasibility, acceptability and usability of digital assessments; (3) challenges related to the use of digital tools; (4) public involvement and the implementation of patient advisory boards; (5) regulatory learnings; and (6) the significance of inter-project exchange and data- and algorithm-sharing.
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Affiliation(s)
- Anna-Katharine Brem
- Department of Old Age Psychiatry, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland
| | - Sajini Kuruppu
- Department of Old Age Psychiatry, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Casper de Boer
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Marijn Muurling
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | | | | | - Jelena Curcic
- Novartis Institutes for Biomedical Research (NIBR), Basel, Switzerland
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Laboratory of Digital Neurology and Biosensors, University of Brescia, Brescia, Italy
- Neurology Unit, Department of Continuity of Care and Frailty, ASST Spedali Civili Brescia Hospital, Brescia, Italy
| | - Wan-Fai Ng
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- NIHR Newcastle Biomedical Research Centre and Clinical Research Facility, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Nicholas Cummins
- Department of Biostats and Health Informatics, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | | | | | - Gul Erdemli
- Novartis Pharmaceuticals Corporations, Cambridge, MA, United States
| | - Johanna Graeber
- Institute of General Practice, University Medical Center Schleswig-Holstein, Kiel University, Kiel, Germany
| | | | - Lynn Rochester
- Faculty of Medical Sciences, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany
| | - Dag Aarsland
- Department of Old Age Psychiatry, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
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15
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Abstract
Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation 'deep' medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence-based medicine.
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16
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Simmatis LER, Robin J, Pommée T, McKinlay S, Sran R, Taati N, Truong J, Koyani B, Yunusova Y. Validation of automated pipeline for the assessment of a motor speech disorder in amyotrophic lateral sclerosis (ALS). Digit Health 2023; 9:20552076231219102. [PMID: 38144173 PMCID: PMC10748679 DOI: 10.1177/20552076231219102] [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: 04/12/2023] [Accepted: 11/20/2023] [Indexed: 12/26/2023] Open
Abstract
Background and objective Amyotrophic lateral sclerosis (ALS) frequently causes speech impairments, which can be valuable early indicators of decline. Automated acoustic assessment of speech in ALS is attractive, and there is a pressing need to validate such tools in line with best practices, including analytical and clinical validation. We hypothesized that data analysis using a novel speech assessment pipeline would correspond strongly to analyses performed using lab-standard practices and that acoustic features from the novel pipeline would correspond to clinical outcomes of interest in ALS. Methods We analyzed data from three standard speech assessment tasks (i.e., vowel phonation, passage reading, and diadochokinesis) in 122 ALS patients. Data were analyzed automatically using a pipeline developed by Winterlight Labs, which yielded 53 acoustic features. First, for analytical validation, data were analyzed using a lab-standard analysis pipeline for comparison. This was followed by univariate analysis (Spearman correlations between individual features in Winterlight and in-lab datasets) and multivariate analysis (sparse canonical correlation analysis (SCCA)). Subsequently, clinical validation was performed. This included univariate analysis (Spearman correlation between automated acoustic features and clinical measures) and multivariate analysis (interpretable autoencoder-based dimensionality reduction). Results Analytical validity was demonstrated by substantial univariate correlations (Spearman's ρ > 0.70) between corresponding pairs of features from automated and lab-based datasets, as well as interpretable SCCA feature groups. Clinical validity was supported by strong univariate correlations between automated features and clinical measures (Spearman's ρ > 0.70), as well as associations between multivariate outputs and clinical measures. Conclusion This novel, automated speech assessment feature set demonstrates substantial promise as a valid tool for analyzing impaired speech in ALS patients and for the further development of these technologies.
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Affiliation(s)
- Leif ER Simmatis
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
| | | | - Timothy Pommée
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Scotia McKinlay
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Rupinder Sran
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Niyousha Taati
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Justin Truong
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | | | - Yana Yunusova
- Department of Speech-Language Pathology, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
- KITE-Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada
- Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
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17
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West NEJ, Juneja M, Pinilla N, De Loose KR, Henry TD, Baumgard CS, Kraineva O. Personalized vascular healthcare: insights from a large international survey. Eur Heart J Suppl 2022; 24:H8-H17. [PMID: 36382003 PMCID: PMC9650465 DOI: 10.1093/eurheartjsupp/suac052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
Fragmentation of healthcare systems through limited cross-speciality communication and intermittent, intervention-based care, without insight into follow-up and compliance, results in poor patient experiences and potentially contributes to suboptimal outcomes. Data-driven tools and novel technologies have the capability to address these shortcomings, but insights from all stakeholders in the care continuum remain lacking. A structured online questionnaire was given to respondents (n = 1432) in nine global geographies to investigate attitudes to the use of data and novel technologies in the management of vascular disease. Patients with coronary or peripheral artery disease (n = 961), physicians responsible for their care (n = 345), and administrators/healthcare leaders with responsibility for commissioning/procuring cardiovascular services (n = 126) were included. Narrative themes arising from the survey included patients' desire for more personalized healthcare, shared decision-making, and improved communication. Patients, administrators, and physicians perceived and experienced deficiencies in continuity of care, and all acknowledged the potential for data-driven techniques and novel technologies to address some of these shortcomings. Further, physicians and administrators saw the 'upstream' segment of the care journey-before diagnosis, at point of diagnosis, and when determining treatment-as key to enabling tangible improvements in patient experience and outcomes. Finally, despite acceptance that data sharing is critical to the success of such interventions, there remains persistent issues related to trust and transparency. The current fragmented care continuum could be improved and streamlined through the adoption of advanced data analytics and novel technologies, including diagnostic and monitoring techniques. Such an approach could enable the refocusing of healthcare from intermittent contacts and intervention-only focus to a more holistic patient view.
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Affiliation(s)
- Nick E J West
- Abbott Vascular, 3200 Lakeside Drive, Santa Clara, CA 95054, USA
| | - Maneesh Juneja
- MJ Analytics Ltd, Hemel Hempstead, Hertfordshire HP1 1FW, UK
| | - Natalia Pinilla
- Division of Cardiology/Population Health Research Institute, McMaster University, Hamilton, ON L8L 2X2, Canada
| | - Koen R De Loose
- AZ Sint Blasius, Sint-Blasius, Kroonveldlaan 50, 9200 Dendermonde, Belgium
| | - Timothy D Henry
- The Carl and Edyth Lindner Research Center at The Christ Hospital, 2123 Auburn Avenue, Suite 424, Cincinnati, OH 45219, USA
| | | | - Olga Kraineva
- Abbott Vascular, 3200 Lakeside Drive, Santa Clara, CA 95054, USA
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18
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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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19
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Mori H, Wiklund SJ, Zhang JY. Quantifying the Benefits of Digital Biomarkers and Technology-Based Study Endpoints in Clinical Trials: Project Moneyball. Digit Biomark 2022; 6:36-46. [PMID: 35949224 PMCID: PMC9297703 DOI: 10.1159/000525255] [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: 03/09/2022] [Accepted: 05/23/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Digital biomarkers have significant potential to transform drug development, but only a few have contributed meaningfully to bring new treatments to market. There are uncertainties in how they will generate quantifiable benefits in clinical trial performance and ultimately to the chances of phase 3 success. Here we have proposed a statistical framework and ran a proof-of-concept model with hypothetical digital biomarkers and visualized them in a familiar manner to study power calculation. Methods A Monte Carlo simulation for Parkinson's disease (PD) was performed using the Captario SUM® platform and illustrative study technology impact calculations were generated. We took inspiration from the EMA-qualified wearable-derived digital endpoint stride velocity 95<sup>th</sup> centile (SV95C) for Duchenne muscular dystrophy, and we imagined a similar measurement for PD would be available in the future. DaTscan enrichment and “SV95C-like” endpoint biomarkers were assumed on a hypothetical disease-modifying drug pivotal trial aiming for an 80% probability of achieving a study p value of less than 0.05. Results Four scenarios with different combinations of technologies were illustrated. The model illustrated a way to quantify the magnitude of the contributions that enrichment and endpoint technologies could make to drug development studies. Discussion/Conclusion Quantitative models could be valuable not only for the study sponsors but also as an interactive and collaborative engagement tool for technology players and multi-stakeholder consortia. Establishing values of digital biomarkers could also facilitate business cases and financial investments.
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20
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Peraza LR, Kinnunen KM, McNaney R, Craddock IJ, Whone AL, Morgan C, Joules R, Wolz R. An Automatic Gait Analysis Pipeline for Wearable Sensors: A Pilot Study in Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2021; 21:8286. [PMID: 34960379 PMCID: PMC8707484 DOI: 10.3390/s21248286] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 12/06/2021] [Accepted: 12/08/2021] [Indexed: 12/29/2022]
Abstract
The use of wearable sensors allows continuous recordings of physical activity from participants in free-living or at-home clinical studies. The large amount of data collected demands automatic analysis pipelines to extract gait parameters that can be used as clinical endpoints. We introduce a deep learning-based automatic pipeline for wearables that processes tri-axial accelerometry data and extracts gait events-bout segmentation, initial contact (IC), and final contact (FC)-from a single sensor located at either the lower back (near L5), shin or wrist. The gait events detected are posteriorly used for gait parameter estimation, such as step time, length, and symmetry. We report results from a leave-one-subject-out (LOSO) validation on a pilot study dataset of five participants clinically diagnosed with Parkinson's disease (PD) and six healthy controls (HC). Participants wore sensors at three body locations and walked on a pressure-sensing walkway to obtain reference gait data. Mean absolute errors (MAE) for the IC events ranged from 22.82 to 33.09 milliseconds (msecs) for the lower back sensor while for the shin and wrist sensors, MAE ranges were 28.56-64.66 and 40.19-72.50 msecs, respectively. For the FC-event detection, MAE ranges were 29.06-48.42, 40.19-72.70 and 36.06-60.18 msecs for the lumbar, wrist and shin sensors, respectively. Intraclass correlation coefficients, ICC(2,k), between the estimated parameters and the reference data resulted in good-to-excellent agreement (ICC ≥ 0.84) for the lumbar and shin sensors, excluding the double support time (ICC = 0.37 lumbar and 0.38 shin) and swing time (ICC = 0.55 lumbar and 0.59 shin). The wrist sensor also showed good agreements, but the ICCs were lower overall than for the other two sensors. Our proposed analysis pipeline has the potential to extract up to 100 gait-related parameters, and we expect our contribution will further support developments in the fields of wearable sensors, digital health, and remote monitoring in clinical trials.
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Affiliation(s)
- Luis R. Peraza
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
| | | | - Roisin McNaney
- Department of Human Centred Computing, Monash University, Clayton, VIC 3800, Australia;
| | - Ian J. Craddock
- Electrical and Electronic Engineering, School of Computer Science, University of Bristol, Bristol BS8 1QU, UK;
| | - Alan L. Whone
- Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK;
- Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK
| | - Catherine Morgan
- Translational Health Sciences, University of Bristol Medical School, Bristol BS8 1QU, UK;
- Movement Disorders Group, North Bristol NHS Trust, Westbury on Trym, Bristol BS10 5NB, UK
| | - Richard Joules
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
| | - Robin Wolz
- IXICO, London EC1A 9PN, UK; (L.R.P.); (K.M.K.); (R.J.); (R.W.)
- Department of Computing, Imperial College London, London SW7 2AZ, UK
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