ARISES: Adaptive, Real-time, Intelligent System to Enhance Self-care of Chronic Disease

• Sponsor: Imperial College London (Other)
• Overall Status: Completed
• CT.gov ID: NCT03643692
• Collaborator: (none)
• Enrollment: 12
• Location: 1
• Arm: 1
• Actual Duration (Months): 4.1
• Patients Per Site Per Month: 2.9

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Diabetes Mellitus, Type 1	Device: ARISES	N/A

Detailed Description

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 Design

• Study Type: Interventional
• Actual Enrollment: 12 participants
• Allocation: N/A
• 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: Feb 26, 2019
• Actual Primary Completion Date: Jul 1, 2019
• Actual Study Completion Date: Jul 1, 2019

Arms and Interventions

Arm

Intervention/Treatment

Experimental: ARISES 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.

Device: ARISES; 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.

Outcome Measures

Primary Outcome Measures

1. Time in Range (%) [6 weeks]

   % time in target range (3.9 - 10 mmol/L) without insulin dose increase

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years and Older
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Inclusion Criteria:

• Adults ≥18years of age

• Diagnosis of T1DM for > 1 year

• Structured education completed in last 3 years and capable of CHO counting

• CBG measured at least twice daily for CGM calibration

• Capacity to follow the protocol and sign the informed consent

• Access to a personal computer/laptop

Exclusion Criteria:

• Severe episode of hypoglycaemia (requiring 3rd party assistance) in last 6 months

• Diabetic ketoacidosis in the last 6 months prior to enrolment

• Impaired awareness of hypoglycaemia (based on Gold score)

• Pregnant or planning pregnancy over time of study procedures

• Breastfeeding

• Enrolled in other clinical trials

• Active malignancy or being investigated for malignancy

• Suspected or diagnosed endocrinopathy like adrenal insufficiency, unstable thyroidopathy, endocrine tumour

• Gastroparesis

• Autonomic neuropathy

• Macrovascular complications (acute coronary syndrome, transient ischaemic attack, cerebrovascular event within the last 12 months prior to enrolment in the study)

• Visual impairment including unstable proliferative retinopathy

• Reduced manual dexterity

• Inpatient psychiatric treatment

• Abnormal renal function test results (calculated GFR <40 mL/min/1.73m2)

• Liver cirrhosis

• Not tributary to optimization to insulin therapy

• Abuse of alcohol or recreational drugs

• Oral steroids

• Regular use of the paracetamol, beta-blockers or any other medication that the investigator believes is a contraindication to the participant's participation.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Imperial College Clinical Research Facility	London		United Kingdom

Sponsors and Collaborators

• Imperial College London

Investigators

• Principal Investigator: Nick Oliver, Imperial College London

Study Documents (Full-Text)

• Study Protocol and Statistical Analysis Plan - Feb 16, 2018

More Information

Publications

None provided.

• Responsible Party: Imperial College London
• ClinicalTrials.gov Identifier: NCT03643692
• Other Study ID Numbers: 18HH4410
• First Posted: Aug 23, 2018
• Last Update Posted: Aug 6, 2020
• Last Verified: Aug 1, 2020

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Keywords provided by Imperial College London: Diabetes Mellitus; blood glucose; environmental case parameters
• Additional relevant MeSH terms: Diabetes Mellitus; Diabetes Mellitus, Type 1; Chronic Disease

Study Results

Participant Flow

Arm/Group Title	ARISES
Arm/Group Description	ARISES: 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 complic
Period Title: Overall Study
STARTED	12
COMPLETED	12
NOT COMPLETED	0

Baseline Characteristics

Arm/Group Title	ARISES
Arm/Group Description	ARISES: 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 complic
Overall Participants	12
Age (years) [Median (Inter-Quartile Range) ]
Median (Inter-Quartile Range) [years]	38
Sex: Female, Male (Count of Participants)
Female	6 50%
Male	6 50%
Race (NIH/OMB) (Count of Participants)
American Indian or Alaska Native	0 0%
Asian	0 0%
Native Hawaiian or Other Pacific Islander	0 0%
Black or African American	1 8.3%
White	11 91.7%
More than one race	0 0%
Unknown or Not Reported	0 0%
Insulin Modality: Insulin pump (CSII), Multiple daily injections (MDI) (Count of Participants)
CSII	6 50%
MDI	6 50%

Outcome Measures

1. Primary Outcome

Title	Time in Range (%)
Description	% time in target range (3.9 - 10 mmol/L) without insulin dose increase
Time Frame	6 weeks

Outcome Measure Data

Analysis Population Description

[Not Specified]

Arm/Group Title	ARISES
Arm/Group Description	ARISES: 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 complic
Measure Participants	12
Median (Inter-Quartile Range) [percentage of time (minutes)]	64

Adverse Events

Time Frame	6 weeks
Arm/Group Title	ARISES
Arm/Group Description	ARISES: 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 complic
All Cause Mortality
	ARISES
	Affected / at Risk (%)	# Events
Total	0/12 (0%)
Serious Adverse Events
	ARISES
	Affected / at Risk (%)	# Events
Total	0/12 (0%)
Other (Not Including Serious) Adverse Events
	ARISES
	Affected / at Risk (%)	# Events
Total	1/12 (8.3%)
Skin and subcutaneous tissue disorders
Rash	1/12 (8.3%)	1

Limitations/Caveats

[Not Specified]

More Information

Certain Agreements

All Principal Investigators ARE employed by the organization sponsoring the study.

There is NOT an agreement between Principal Investigators and the Sponsor (or its agents) that restricts the PI's rights to discuss or publish trial results after the trial is completed.