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

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

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT03643692
Acronym
ARISES
Enrollment
12
Registered
2018-08-23
Start date
2019-02-26
Completion date
2019-07-01
Last updated
2020-08-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetes Mellitus, Type 1

Keywords

Diabetes Mellitus, blood glucose, environmental case parameters

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.

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.

Interventions

DEVICEARISES

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.

Sponsors

Imperial College London
Lead SponsorOTHER

Study design

Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DEVICE_FEASIBILITY
Masking
NONE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
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.

Design outcomes

Primary

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

Countries

United Kingdom

Participant flow

Participants by arm

ArmCount
ARISES
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
12
Total12

Baseline characteristics

CharacteristicARISES
Age, Continuous38 years
Insulin Modality: Insulin pump (CSII), Multiple daily injections (MDI)
CSII
6 Participants
Insulin Modality: Insulin pump (CSII), Multiple daily injections (MDI)
MDI
6 Participants
Race (NIH/OMB)
American Indian or Alaska Native
0 Participants
Race (NIH/OMB)
Asian
0 Participants
Race (NIH/OMB)
Black or African American
1 Participants
Race (NIH/OMB)
More than one race
0 Participants
Race (NIH/OMB)
Native Hawaiian or Other Pacific Islander
0 Participants
Race (NIH/OMB)
Unknown or Not Reported
0 Participants
Race (NIH/OMB)
White
11 Participants
Sex: Female, Male
Female
6 Participants
Sex: Female, Male
Male
6 Participants

Adverse events

Event typeEG000
affected / at risk
deaths
Total, all-cause mortality
0 / 12
other
Total, other adverse events
1 / 12
serious
Total, serious adverse events
0 / 12

Outcome results

Primary

Time in Range (%)

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

Time frame: 6 weeks

ArmMeasureValue (MEDIAN)
ARISESTime in Range (%)64 percentage of time (minutes)

Source: ClinicalTrials.gov · Data processed: Feb 12, 2026