Understanding patterns of patient compliance in remote, technology-based clinical trials requires analysis of data collected using digital technologies. The insights from our work suggest that such remote trials are feasible, even when comprising multiple protocol requirements. Beyond our observations, examination of additional factors that impact compliance such as the impact of support, intervention, alternative application design interfaces, and additional covariates can further influence study design.

In the broader perspective, this report supports the growing trend of using mobile applications and wearable technologies to monitor, prompt, and encourage patient compliance with medication intake and performance of clinical assessments. This effort strengthens the notion that these data have the potential to provide further insight regarding patients’ daily lives outside the clinic and potentially evolve into novel endpoints for regulatory purposes.

Read the scientific paper.

Citation: "Characterizing patient compliance over six months in remote digital trials of Parkinson’s and Huntington disease." Shani Cohen, Zeev Waks, Jordan J. Elm, Mark Forrest Gordon, Igor D. Grachev, Leehee Navon-Perry, Shai Fine, Iris Grossman, Spyros Papapetropoulos and Juha-Matti Savola. BMC Medical Informatics and Decision Making. 2018 18:138. https://doi.org/10.1186/s12911-018-0714-7. © The Author(s). 2018

In a recent industry survey for the ICON whitepaper ‘Improving Pharma R&D Efficiency’ respondents cited big data, predictive analytics, smartphones and wearables & sensors as amongst the top disruptive technology trends which will have the greatest impact on clinical trial operations.

So the question remains, in the context of drug development studies, why has the use of this technology been limited to a relatively small number of pilots? It is clear that concerns still remain about implementation of this technology in a clinical trial. These concerns focus on a number of key areas: Patient Acceptance, Device Suitability, Data Complexity and Insight Generation, Operationalisation, Privacy and Security Issues, and Regulatory Acceptance.

Read this article by ICON and Intel Corporation

Authors: Marie McCarthy, Senior Director Product Innovation, ICON; Chen Admati, Head of Intel® Pharma Analytics Platform, Intel

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Read ICON whitepaper “Improving Pharma R&D Efficiency” (registration required)

Read this scientific study which assessed the feasibility of using a wearable platform for long-term data collection in a large sample of PD patients, focused on recruitment success, attrition rates, compliance and system usability.

Citation: Silva de Lima AL, Hahn T, Evers LJW, de Vries NM, Cohen E, Afek M, et al. (2017) Feasibility of large-scale deployment of multiple wearable sensors in Parkinson's disease. PLoS ONE 12(12): e0189161. https://doi.org/10.1371/journal.pone.0189161

Read this scientific study whose objective is to implement and validate quantification of motor dysfunction in a Huntington’s disease (HD) clinical trial using machine learning algorithmic analysis derived from biometric monitoring through a smartphone and wearable sensor combination.

View the Teva Pharmaceuticals press release.

Citation: Implementation and Validation of a Biometric Solution for Remote Monitoring of Motor Symptoms in Patients with Huntington’s Disease in a Phase II Clinical Trial (P2.013). Spyros Papapetropoulos, Shai Fine, Shayna Taylor, Kathleen Blatt, Eli Cohen, Chen Admati, Yonatan Dolan, Josh Lemieux, Igor Grachev, Iris Grossman, Michael Hayden. Neurology Apr 2017, 88 (16 Supplement) P2.013