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临床试验/NCT03643692
NCT03643692已完成不适用

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

Imperial College London1 个研究点 分布在 1 个国家目标入组 12 人开始时间: 2019年2月26日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
12
试验地点
1
主要终点
Time in Range (%)

研究概览

简要总结

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.

详细描述

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.

研究设计

研究类型
Interventional
分配方式
Na
干预模型
Single Group
主要目的
Device Feasibility
盲法
None

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • 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

排除标准

  • 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.

结局指标

主要结局

Time in Range (%)

时间窗: 6 weeks

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

次要结局

未报告次要终点

研究者

申办方类型
Other
责任方
Sponsor

研究点 (1)

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