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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| ARISES | Experimental | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| ARISES | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Time in Range (%) | % time in target range (3.9 - 10 mmol/L) without insulin dose increase | 6 weeks |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Nick Oliver | Imperial College London | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Imperial College Clinical Research Facility | London | United Kingdom |
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| ID | Title | Description |
|---|---|---|
| FG000 | 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 |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
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| ID | Title | Description |
|---|---|---|
| BG000 | 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 |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Continuous | Median |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Time in Range (%) | % time in target range (3.9 - 10 mmol/L) without insulin dose increase | Posted | Median | Inter-Quartile Range | percentage of time (minutes) | 6 weeks |
|
6 weeks
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | 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 |
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| Term | Organ System | Source Vocabulary | Assessment Type | Notes | Statistical Information |
|---|---|---|---|---|---|
| Rash | Skin and subcutaneous tissue disorders | Non-systematic Assessment | Rash from empatica watch |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Nick Oliver | Imperial College London | 02033111093 | nick.oliver@imperial.ac.uk |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Feb 16, 2018 | Jul 15, 2020 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| D003920 | Diabetes Mellitus |
| ID | Term |
|---|---|
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
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|
| years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Race (NIH/OMB) | Count of Participants | Participants |
|
| Insulin Modality: Insulin pump (CSII), Multiple daily injections (MDI) | Count of Participants | Participants |
|
| Units | Counts |
|---|---|
| Participants |
|
|
| 0 |
| 12 |
| 0 |
| 12 |
| 1 |
| 12 |
|
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| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |