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临床试验/NCT06650098
NCT06650098尚未招募不适用

The Effect of Web-Based and Artificial Intelligence-Assisted Personalized Applications on Knowledge Levels Compliance with Treatment and Self-Management Among Diabetic Individuals

Uludag University1 个研究点 分布在 1 个国家目标入组 156 人开始时间: 2025年4月1日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
尚未招募
发起方
入组人数
156
试验地点
1
主要终点
Diabetes Self-Management Scale (DSMS)

研究概览

简要总结

Patients in the AI-supported mobile application group will be able to log in with a username and password that will be defined specifically for them. Patients will be informed about how the application is used during their first interview. They will enter their personal and disease characteristics (age, gender, height, weight, HbA1C, HDL, LDL) into the application at the entrance. Other sections of the application will include exercise, nutrition, medication tracking, complication tracking and diabetic foot care sections. The person will be asked to enter relevant information in these fields according to their own life and condition (for example; how many times do you use insulin per day, what are your medication times, how do you spend your day in terms of exercise, how many meals do you eat, what is your diet, do you urinate frequently, are you extremely thirsty, are you hungry often, do you have numbness in your hands and feet, etc.). After the patient enters the necessary information, they will also be asked to enter their daily blood sugar measurement values into the system. Thus, the individual's hypo/hyperglycemia risk, risk analysis, nutrition recommendations, medication reminder system, exercise reminder and incentive warnings will be communicated to the individual thanks to the AI-based mobile application. The aim of this application is to reduce the risk of complications and improve the individual's quality of life by providing personalized recommendations for all the needs of the individual, including alarms and reminders, and to support patients to continue their diabetes education and disease management more actively.

详细描述

pre-test post-test control group design

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Parallel
主要目的
Supportive Care
盲法
Single (Participant)

入排标准

性别
All
接受健康志愿者
否

入选标准

  • •Having been diagnosed with diabetes for at least 1 year
  • •Being between the ages of 18-65
  • •Being open to verbal communication
  • •Being able to read and write and speak Turkish
  • •Having a smart android phone and being able to use mobile applications
  • •Being willing to participate in the study

排除标准

  • •Having a perception disorder and psychiatric disorder that prevents the patient from communicating,
  • •Having a condition that prevents them from using a smart phone (advanced retinopathy and neuropathy, internet problems)
  • •Being on intensive insulin treatment
  • •Having a condition that prevents them from continuing the application phase of the study
  • •Wanting to leave the study

研究组 & 干预措施

WEB based application

Experimental

The content plan for the web-based mobile application group will be prepared with technical support as specified. Patients will be able to log in to the mobile application with a username and password that will be defined specifically for them. Patients will be informed about how the website is used during the first meeting. They will be able to access all the information they need about diabetes with the web-based mobile application. Statistical data such as the frequency of individuals visiting the site, which sections they use more often and how much time they spend will be calculated.

干预措施: WEB based application (Other)

artificial intelligence-supported mobile application

Experimental

It is aimed that an artificial intelligence-based mobile application that includes information, nutrition, exercise programs, complications and medication tracking, personalized suggestions, alarms and reminders, which will enable diabetic individuals to follow their glucose targets, support patients in their diabetes education, awareness and disease management to continue more actively. In addition, it is aimed that patients can easily access information, prevent acute and chronic complications, present physical activity and nutrition suggestions in accordance with the person's lifestyle, follow up on medications with alarms and reminders, prevent the negative results of complications in advance, and improve individuals' diabetes-specific knowledge levels, compliance with treatment, self-management and care with information and guidance about foot care to reduce the risk of diabetic feet, which is particularly risky for diabetic patients.

干预措施: artificial intelligence-supported mobile application (Other)

control group

No Intervention

No intervention will be applied to the control group, and they will receive routine clinical and outpatient training.

结局指标

主要结局

Diabetes Self-Management Scale (DSMS)

时间窗: 6 months

Diabetes Self-Management Scale (DSMS): This scale was used to measure the behavioral component of individuals in the IMB model. This scale was developed by Schmitt et al. (2013) to examine the relationship between diabetes self-management and glycemic control in diabetic patients (Schmitt et al., 2013). The validity and reliability study of the Turkish Diabetes Self-Management Scale (DSMS) was conducted by Eroğlu and Sabuncu (2018) (Eroğlu and Sabuncu, 2018). The scale consists of 16 items and 4 sub-dimensions and is a 4-point Likert-type. The scale is answered as 3. It suits me very much, 2. It suits me a lot, 1. It suits me a little, 0. It does not suit me at all. Glucose Management subdimension: Items 1, 4, 6, 10, 12 (Items 4 and 12 are about medication use, items 1, 6, and 10 are about blood sugar monitoring). Diet Control subdimension: Items 2, 5, 9, 13. Physical Activity subdimension: Items 8, 11, 15. Use of Health Services subdimension: Items 3, 7, and 14. Item 16 is not includ

次要结局

  • Adult diabetes knowledge scale (ADSL)(6 months)
  • Morisky Medication Adherence Scale (MMAS-8)(6 months)

研究者

发起方
Uludag University
申办方类型
Other
责任方
Principal Investigator
主要研究者

Nilhan Toyer Sahin

Phd Student

Uludag University

研究点 (1)

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