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Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities in daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases

Periodic Reporting for period 1 - IDEA-FAST (Identifying Digital Endpoints to Assess FAtigue, Sleep and acTivities in daily living in Neurodegenerative disorders and Immune-mediated inflammatory diseases)

Reporting period: 2019-11-01 to 2021-04-30

For patients with chronic diseases such as neurodegenerative disorders (NDD) and immune-mediated inflammatory diseases (IMID), a key attribute for any successful therapeutic intervention is its ability to improve the patients’ activities of daily living (ADL) and health-related quality of life (HRQoL). Current evaluations of ADL and HRQoL rely mainly on subjective reports, typically using standardized questionnaires provided by patients every few months. The approach is often prone to recall bias, reliability issues and poor sensitivity to change.
The advances in digital technology, data analytics and wearable devices provide unparalleled opportunities to identify digital correlates of ADL/HRQoL that are objective, reliable, quantifiable, individualized, can be used semi-continuously over prolonged periods of time and responsive to day to day variations, therefore, potentially more sensitive to change. These technologies allow more frequent recording (e.g. daily) of their self-scored health states in their habitual environment, often referred to as electronic PRO (ePRO).
The objective of IDEA-FAST is to identify digital endpoints that provide reliable, objective and sensitive evaluation of ADL/disability/HRQoL relevant for the following NDD: Parkinson's disease (PD), Huntington's disease (HD) and the following IMID: Rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjögren's syndrome (PSS), and inflammatory bowel disease (IBD).
Within Period 1 IDEA-FAST is undertaking a Feasibility Study the aims of which are to: (i) identify the most promising digital measures of fatigue and sleep; (ii) test the feasibility, acceptability and utility of the various digital technologies that will be used to profile sleep, fatigue and ADLs in the various patient populations.
To prepare for the study a number of measure devices for use across the disease cohorts have been evaluated against a defined selection criteria. From this devices were selected to be included in the study. To support their use and provide a pipeline to transfer data to the Data Management Platform operations methods for data transfer were documented and API’s developed. In addition, a support system has been introduced to deal with any issues that recruitment site may have in the use of devices or data transfer. The data management strategy has ben defined together with a set of data standards. A first version of the Data Management Platform (DMP) has been developed and released which includes Access Control and data provenance mechanisms for compliance with legal and data privacy requirements (e.g. GDPR), and to ensure data integrity and security.
Various relevant datasets (either extant datasets from EFPIA partners or datasets generated during the FS) have been compiled and analysed to assess the data variability and a comprehensive statistical analysis plan for the FS data has been created.
In collaboration with recruitment sites and disease cohorts the FS protocol has been developed and ethics approval gained, this includes a model of informed consent. The recruitment sites and their respective disease cohorts for the FS are: UKSH (PD), GHI (HD), EMC (IBD) and UNEW (PSS, SLE, RA). At the end pf the period only 75% of the targeted recruitment was reach due to the COVID-19 pandemic which has closed or limited recruitment across Europe. The involvement and engagement with patients and patients groups has been embedded within the feasibility study. A Patient Specialist Advisory Board (PSAB) and the Patient Involvement and Engagement (PIE) group have been setup to enable this.
The assessment protocol for device-specific digital endpoints and candidate digital endpoints for fatigue, sleep disturbances and other ADL and HRQoL measures in NDD and IMID) have been completed and all required candidate digital endpoints are available.
A regulatory strategy has been developed which led to the submission to the EMA requesting qualification advice. IDEA-FAST is currently waiting for feedback on this.
IDEA-FAST has developed and is implementing a dissemination plan. This includes during the period a website www.idea-fast.eu a twitter account and presentations at conferences as well as peer reviewed publications. In addition IDEA-FAST has developed collaborations with other IMI funded projects, Mobilise-D and Neuronet.
IDEA-FAST aims to build a clinically valid digital fatigue and sleep disturbances assessment system that is uniformly applicable across six chronic diseases, with the possibility to also identify disease-specific ADL digital signatures of these symptoms. In NDD digital endpoints are expected to reliable assess circadian changes in severity and disease progression over 1 year. Thus accurate assessment of fatigue will add a new dimension beyond the current assessment tools available for these diseases, enabling a more complete/comprehensive assessment of these conditions. This advancement will have significant impact on industry and the provision of healthcare:
(i) Industry relevance: IDEA-FAST will contribute to the definition of fatigue and sleep disturbances parameters that can be used as new endpoints for clinical trials in drug development. Moreover, it will provide novel tools for better stratification of patient cohorts according to the presence and severity of disabilities.
(ii) Healthcare relevance: This project, together with comparable activities, is an ambitious contribution to identify and refine the use of computationally-supported real-world patient outcomes in the currently under-investigated non-motor disability field that will considerably advance care for these patients.
Within the first period of the project the following key advances beyond the state of the art have been achieved:
1 – Assessment of sensor types and devices for use with patients to capture data on fatigue and sleep
2 – Analysis using extent data for validation of potential algorithms to provide digital biomarkers for the disease of interest in the project.
3 – Development and implementation of a clinical feasibility study to evaluate the sensors and algorithms
4 – Development of a data pipeline and data management platform to enable the study and allow sharing of data.
5 – Informal qualification advice received from EMA.
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