Effect of Artıfıcıal Intellıgence Based Mobıle Vırtual Assıstant

NCT06079450 | Completed DMC

• 1 other identifier: IKCU
• Study Type: observational
• Target: 1
• Locations: 1 country
• Sites: 1

Brief Summary

Aim: This study was conducted experimentally to examine the effect of artificial intelligence-based mobile virtual assistant developed for individuals with diabetes on cost, hospitalization rate, self-care and hypoglycemia. Methods: The research is multi-stage and designed as three stages in itself. According to this; development of the mobile application in the first and second stages and adding artificial intelligence to the application as a project; In the third stage, it was planned to examine the effect of the application on the variables and scales. The data of the study were collected between June 2022 and June 2023 in the Endocrinology Polyclinic of two private hospitals in Izmir and a diabetes association where individuals with diabetes were registered. Power 0.80 was determined by using NCSS PAS statistical software from the population of the research; The minimum number of samples to be included in the study was calculated as n:122 and they were divided into two as intervention and control groups by randomization. The research sample was carried out as intervention (n:60) and control (n:60) lastly due to death and cost. Five data collection tools were used, namely "Individual Introduction Form", "Diabetes Self-Care Scale", "Hypoglycemia Confidence Scale", "Mobile Application Opinion Form" and "Cost Table". An artificial intelligence-based mobile virtual assistant application was applied to the individuals with diabetes in the intervention group, and the data were collected three times, at the 0th, 6th and 12th months, and the costs were recorded. The standard outpatient trainings, which are currently applied, continued to be given to individuals with diabetes in the control group, the data were collected twice, at the beginning (0. month) and 12. months, and the costs were recorded. In the evaluation of the data, number, percentage, arithmetic mean, standard deviation, minimum and maximum median were calculated. Among the variables, chi-square, Kruskal Wallis, Mann Whitney U test and t test were used.

Conditions

Diabetes

Keywords

Diabetes; mobile application; artificial intelligence; self-care; hospitalization; cost

Outcome Measures

Primary Outcomes (2)

• Diabetes Self-Care Scale

  The score consists of 35 items and is a 4-point Likert type, the lowest acceptable score is 92 and the highest score is 140. As the score increases, self-care increases

  12 months

• Hypoglycemic Confidence Scale

  The scale consists of 9 items and is a 4-point Likert type. There is no cut-off value, the average score is used.

  12 months

Secondary Outcomes (1)

• hospitalization rate

  12 months

Study Arms (2)

experimental group

Artificial intelligence-based mobile application initiative was implemented for diabetes patients

Other: artificial intelligence based mobile application

control group

no intervention was applied

Interventions

artificial intelligence based mobile application OTHER

Artificial intelligence-based mobile application developed by me that includes diabetes education for individuals with diabetes.

experimental group

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

* The patient has a perception disorder and psychiatric disorder that prevents communication, * Desire to leave the research, * His death during the research, * Being pregnant, * Individuals with severe retinopathy and neuropathy that prevent smartphone use were excluded from the study.

You may qualify if:

• Having been diagnosed with Type 1 and Type 2 diabetes at least six months ago, according to the criteria of the American Diabetes Association (ADA),
• Using insulin for at least six months,
• Being between the ages of 18- 65,
• Being able to read and write and speak Turkish,
• Having an Android phone and being able to use mobile applications,
• To volunteer to participate in the study.

Sponsors & Collaborators

• Izmir Tinaztepe University: lead

Study Sites (1)

Izmir Katip Celebi University

Izmır, 35620, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Target Duration: 12 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Lecturer of Internal Medicine Nursing

Study Record Dates

• First Submitted: August 10, 2023
• First Posted: October 12, 2023
• Study Start: June 10, 2022
• Primary Completion: January 10, 2023
• Study Completion: August 7, 2023
• Last Updated: October 12, 2023

Record last verified: 2023-10

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)