Adaptive, Real-time, Intelligent System to Enhance Self-care of Chronic Disease (ARISES)
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|ClinicalTrials.gov Identifier: NCT03643692|
Recruitment Status : Completed
First Posted : August 23, 2018
Last Update Posted : October 10, 2019
|Condition or disease||Intervention/treatment||Phase|
|Diabetes Mellitus, Type 1||Device: ARISES||Not Applicable|
ARISES will target self-management to optimise glucose control through insulin dose recommendation (therapeutic advice), exercise and stress support, hypoglycaemia prevention through timely snack recommendation and behavioural change through educational support (lifestyle advice).
Semi-structured focus meetings comprised of patients with T1DM, clinicians, engineers and experts in human-computer interaction will provide a forum to establish the essential usability requirements to incorporate into the ARISES mobile interface. The design will focus on ensuring access to decision support is intuitive and efficient while maintaining sight of real-time glycaemia outcomes. The design and implementation of the user-interface will be assessed in a series of usability validation studies.
Clinical studies will be conducted in two phases. The first phase will be an observational study using wearable technologies to collect data and evaluate blood glucose correlations against physiological and environmental case parameters. Useful associations will assist the development of the CBR/machine learning algorithm and identify wearable devices for the final ARISES platform.
|Study Type :||Interventional (Clinical Trial)|
|Actual Enrollment :||12 participants|
|Intervention Model:||Single Group Assignment|
|Masking:||None (Open Label)|
|Primary Purpose:||Device Feasibility|
|Official Title:||Adaptive, Real-time, Intelligent System to Enhance Self-care of Chronic Disease|
|Actual Study Start Date :||February 26, 2019|
|Actual Primary Completion Date :||July 1, 2019|
|Actual Study Completion Date :||July 1, 2019|
Observational study using wearable technologies to collect data and evaluate blood glucose correlations against physiological and environmental case parameters. Useful associations will assist the development of the CBR/machine learning algorithm and identify wearable devices for the final ARISES platform.
The Adaptive, Real-time, Intelligent System to Enhance Self-care of chronic diseases (ARISES) project will use type 1 diabetes (T1DM) as an exemplary case study to demonstrate safety, technical proof of concept and efficacy of a novel mobile platform. Combining wearable sensors and smartphone technology, a range of biological, environmental and behavioural data will be analysed to provide real-time therapeutic and lifestyle decision support. Using Case-Based-Reasoning (CBR), the system will be adaptive and personalised with the ability to learn from previously encountered scenarios. Ultimately, ARISES aims to empower self-management of chronic illness and limit the complications associated suboptimal treatment.
- Usability Questionnaire [ Time Frame: 6 weeks ]Data Collection
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03643692
|Imperial College Clinical Research Facility|
|London, United Kingdom|
|Principal Investigator:||Nick Oliver||Imperial College London|