Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson’s Disease

Hill, Derek L. G.; Stephenson, Diane; Brayanov, Jordan; Claes, Kasper; Badawy, Reham; Sardar, Sakshi; Fisher, Katherine; Lee, Susan J.; Bannon, Anthony W.; Roussos, George; Kangarloo, Tairmae; Terebaite, Viktorija; Müller, Martijn; Bhatnagar, Roopal; Adams, Jamie L.; Dorsey, E. Ray; Cosman, Josh

doi:10.3390/s22062136

Cited by 13 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As sensitivity, reliability, and interoperability of these data are refined, there is high expectation that they will factor into standard practice and serve as clinical outcome assessment tools relied upon to inform decisions made by health care providers (HCPs), health authorities, and payers. Consortia are already actively advocating for standardized approaches and parallel efforts are focusing on data security and regulations [ 44 ]. While multiple approaches can and will add value to the PD community at large, there seems to be logic in consideration of low-cost, ubiquitous devices that have a low burden of friction for the user.…”

Section: The Role Of Digital Devicesmentioning

confidence: 99%

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Khanna,

Jones

2023

JMIR Form Res

View full text Add to dashboard Cite

Parkinson disease (PD) is a complex neurodegenerative disorder that afflicts over 10 million people worldwide, resulting in debilitating motor and cognitive impairment. In the United States alone (with approximately 1 million cases), the economic burden for treating and caring for persons with PD exceeds US $50 billion and myriad therapeutic approaches are under development, including both symptomatic- and disease-modifying agents. The challenges presented in addressing PD are compounded by observations that numerous, statistically distinct patient phenotypes present with a wide variety of motor and nonmotor symptomatic profiles, varying responses to current standard-of-care symptom-alleviating medications (L-DOPA and dopaminergic agonists), and different disease trajectories. The existence of these differing phenotypes highlights the opportunities in personalized approaches to symptom management and disease control. The prodromal period of PD can span across several decades, allowing the potential to leverage the unique array of composite symptoms presented to trigger early interventions. This may be especially beneficial as disease progression in PD (alongside Alzheimer disease and Huntington disease) may be influenced by biological processes such as oxidative stress, offering the potential for individual lifestyle factors to be tailored to delay disease onset. In this viewpoint, we offer potential scenarios where emerging diagnostic and monitoring strategies might be tailored to the individual patient under the tenets of P4 medicine (predict, prevent, personalize, and participate). These approaches may be especially relevant as the causative factors and biochemical pathways responsible for the observed neurodegeneration in patients with PD remain areas of fluid debate. The numerous observational patient cohorts established globally offer an excellent opportunity to test and refine approaches to detect, characterize, control, modify the course, and ultimately stop progression of this debilitating disease. Such approaches may also help development of parallel interventive strategies in other diseases such as Alzheimer disease and Huntington disease, which share common traits and etiologies with PD. In this overview, we highlight near-term opportunities to apply P4 medicine principles for patients with PD and introduce the concept of composite orthogonal patient monitoring.

show abstract

Section: The Role Of Digital Devicesmentioning

confidence: 99%

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Khanna,

Jones

2023

JMIR Form Res

View full text Add to dashboard Cite

show abstract

“…The work has begun under Critical Path Initiative leadership to develop DHT data standards, starting with standards for metadata. 77 Moreover, a number of precompetitive collaborations -typically observational clinical trials -are underway in neurological diseases, such as AD, Parkinson's disease, and other disorders. 7,[78][79][80] They include efforts to design new studies, collect data, understand data behavior (both standalone and in conjunction with conventional clinical assessments), and characterize early disease stages.…”

Section: Other Considerationsmentioning

confidence: 99%

“…It is important to recognize the challenges the DHT community is facing as well as the initial work that is being done to address them. The work has begun under Critical Path Initiative leadership to develop DHT data standards, starting with standards for metadata 77 . Moreover, a number of precompetitive collaborations – typically observational clinical trials – are underway in neurological diseases, such as AD, Parkinson's disease, and other movement disorders 7,78–80 .…”

Section: Other Considerationsmentioning

confidence: 99%

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

Izmailova¹,

Maguire

McCarthy

et al. 2022

Clinical Translational Sci

Self Cite

View full text Add to dashboard Cite

The US Food and Drug Administration (FDA) has publicly recognized the importance of improving drug development efficiency, deeming translational biomarkers a top priority. The use of imaging biomarkers has been associated with increased rates of drug approvals. An appropriate level of validation provides a pragmatic way to choose and implement these biomarkers. Standardizing imaging modality selection, data acquisition protocols, and image analysis (in ways that are agnostic to equipment and algorithms) have been key to imaging biomarker deployment. The best known examples come from studies done via precompetitive collaboration efforts, which enable input from multiple stakeholders and data sharing. Digital health technologies (DHTs) provide an opportunity to measure meaningful aspects of patient health, including patient function, for extended periods of time outside of the hospital walls, with objective, sensor‐based measures. We identified the areas where learnings from the imaging biomarker field can accelerate the adoption and widespread use of DHTs to develop novel treatments. As with imaging, technical validation parameters and performance acceptance thresholds need to be established. Approaches amenable to multiple hardware options and data processing algorithms can be enabled by sharing DHT data and by cross‐validating algorithms. Data standardization and creation of shared databases will be vital. Pre‐competitive consortia (public‐private partnerships and professional societies that bring together all stakeholders, including patient organizations, industry, academic experts, and regulators) will advance the regulatory maturity of DHTs in clinical trials.

show abstract

“…Contactless sensing however suffers from lower accuracy than wearable devices due to a disconnect from the body and more emphasis on sensing the environmental changes caused by human motion [15]. The wearable device used in this paper is the Panoramic Digital Health activity bracelet [16]. Low Power (LP) accelerometer; High Energy (HE) accelerometer, Gyroscope, Magnetometer, and Pressure sensors are contained within the bracelet.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Sensing for Fatigue Detection Using Wearables and RF

Taylor,

Hill,

Adam

et al. 2024

IEEE Sensors J.

View full text Add to dashboard Cite

In the field of smart healthcare, wearable and sensing devices are connected to the Internet of Things (IoT) to assess patients within their own homes. Fatigue is a multidimensional experience that can be characterised by exhaustion and reduced physical performance. Monitoring people within their homes to detect slower pace could be a promising method to objectively measure aspects of human fatigue. This paper makes use of both a wearable bracelet and Radio Frequency (RF) sensing to detect simulated fatigue from human activity monitoring. Different activities are collected at a normal pace to represent no fatigue and then repeated at a slower pace to represent fatigue. Artificial intelligence (AI) is used to detect if there is fatigue present or not regardless of activity taking place as well as identifying which activity took place and if fatigue is present in said activity. When using the data from both the bracelet and RF sensing, Random Forest and ResNet algorithms achieved 100 % in detecting fatigue as opposed to non-fatigue using algorithms. When using only the bracelet, only the Random Forest algorithm was able to achieve 100 % accuracy. Using only RF data, 94.80 % accuracy was achieved with a Convolutional Neural Network (CNN). When detecting individual activities with fatigue and no fatigue, the Random Forest algorithm achieved an accuracy score of 97.40 % using both the bracelet and RF sensing and with only the bracelet data. CNN was again the best algorithm for RF sensing only with an accuracy score of 89.84 %.

show abstract

Metadata Framework to Support Deployment of Digital Health Technologies in Clinical Trials in Parkinson’s Disease

Cited by 13 publications

References 14 publications

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies

Empowering drug development: Leveraging insights from imaging technologies to enable the advancement of digital health technologies

Hybrid Sensing for Fatigue Detection Using Wearables and RF

Contact Info

Product

Resources

About