The Effect of a ChatGPT-Supported Online Group-Guided Self-Help Program on Uncontrolled and Emotional Eating Behaviors and Body Image in Overweight and Obese Woman Nurses
- Conditions
- Overweight (BMI > 25)Obesity and OverweightEmotional EatingBinge Eating
- Registration Number
- NCT06653673
- Lead Sponsor
- Dokuz Eylul University
- Brief Summary
Nurses with overweight and female gender characteristics often experience disruptions in their eating behaviors due to stressful working conditions. These disruptions can lead to physical and psychosocial problems (Nicholls et al., 2017). Studies have shown that guided self-help programs, based on cognitive behavioral therapy interventions, are effective in addressing uncontrolled and emotional eating behaviors (Carrard et al., 2011; Cachelin et al., 2015). However, the use of supportive tools to assist the facilitator in group-based interventions of these evidence-based self-help programs and their impact on individual recovery processes remain unexplored. Additionally, no studies have been found investigating the application of these AI-supported guided self-help programs among female nurses, who are frequently exposed to stress and at high risk of obesity.
Given the demanding workloads of nurses, cost-effective and easily accessible online programs have been found to promote recovery (Jenkins et al., 2021). Therefore, it is essential to test this AI-supported program in an online group format with overweight and obese woman nurses.
The aim of this study is to evaluate the effect of a ChatGPT-supported online group-guided self-help program on uncontrolled and emotional eating behaviors and body image in overweight and obese woman nurses through a randomized controlled trial. Participants will be recruited via an announcement shared on social media platforms (Instagram). Nurses who respond to the announcement will be asked to complete a survey through Google Forms. Those randomly assigned to the intervention group will participate in online group sessions conducted via Google Meets. Pre- and post-test data will be collected using Google Forms.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- Female
- Target Recruitment
- 50
- Hold at least a bachelor's degree in nursing from a nursing school.
- Work as a nurse in an institution.
- Be a woman.
- Have a BMI (Body Mass Index) greater than 24.99.
- Not have a hearing impairment that would prevent them from understanding and completing verbal instructions.
- Have access to a smartphone or computer with a camera.
- Agree to participate in the study.
- Score 50 or higher on the emotional eating and external eating subscales of the Dutch Eating Behavior Questionnaire (DEBQ).
- Refusal to participate in the study.
- Diagnosis of chronic diseases such as diabetes, hyperlipidemia, hypertension, or hyperthyroidism.
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Primary Outcome Measures
Name Time Method Dutch Eating Behavior Questionnaire (DEBQ) 8 weeks The Dutch Eating Behavior Questionnaire (DEBQ) was developed by Van Strien, Frijters, Jan, Bergers, and Defares (1986) to measure emotional, external, and restrained eating behaviors. The questionnaire consists of 33 items divided into three subscales:
Restrained Eating (10 items) - Example: "Do you deliberately eat foods that are slimming?" Emotional Eating (13 items) - Example: "Do you have a desire to eat when someone upsets you?" External Eating (10 items) - Example: "Do you feel like eating when you pass by a café or snack bar?" Each item is rated on a 5-point Likert scale (1 = Never, 5 = Very often). The 31st item is reverse-scored.
The psychometric properties of the DEBQ were validated in Turkish by Bozan, Baş, and Aşçı (2011), confirming the original three-factor structure. In their study, the Cronbach's alpha coefficients for the subscales were found to be:
Emotional Eating: .90 Restrained Eating: .94 External Eating: .96 These reliability values indicate high internal con
- Secondary Outcome Measures
Name Time Method