Comparison of Traditional and Artificial Intelligence-Assisted Yoga
- Conditions
- Healthy People
- Interventions
- Other: Traditional yogaOther: AI-Assisted Yoga
- Registration Number
- NCT06186635
- Lead Sponsor
- Gözde Özsezer
- Brief Summary
The goal of the study was to compare the effects of traditional yoga and AI-assisted yoga practice on quality of life and mental well-being in students in a randomized controlled trial. The main question\[s\] it aims to answer are:
1. What is the level of quality of life of students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences?
2. What is the level of mental well-being of students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences?
3. Does the traditional yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect the quality of life?
4. Does the artificial intelligence-supported yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect the quality of life?
5. Does the traditional yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect mental well-being?
6. Does the artificial intelligence-supported yoga program applied to students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences affect mental well-being?
Participants will take part in a traditional and AI-assisted yoga practice. The researchers will compare the effect of traditional yoga and AI-assisted yoga on quality of life and mental well-being.
- Detailed Description
Yoga is a holistic life science with a history of approximately 5000 years, designed to prepare the individual for meditation by strengthening the body and calming the mind. Looking at the national and international literature, no study comparing the effects of traditional and artificial intelligence-based practices on quality of life and mental well-being has been found before. The aim of the study was to compare the effects of traditional yoga and artificial intelligence-supported yoga practice on quality of life and mental well-being in students in a randomized controlled trial. The population of the study consisted of approximately 880 students studying at Çanakkale Onsekiz Mart University, Faculty of Health Sciences, Departments of Nursing (n=460), Midwifery (n=310) and Emergency Aid and Disaster Management (n=110). In the study, a simple random sampling method was selected according to the literature to ensure homogeneity of the sample. With power analysis, the effect size was 0.35, the α value was 0.05, and the power was 0.90, and the number of samples was found to be 54 people in total, including 18 in the 1st intervention group, 18 in the 2nd intervention group, and 18 in the control group. The research will be collected face-to-face between February 1, 2024, and November 30, 2025, using the Introductory Information Form, Multidimensional Quality of Life Scale, and Mental Well-Being Scale. In the study, two intervention and control groups will be pre-tested. The first intervention group will receive traditional yoga practice, and the second intervention group will receive AI-supported yoga practice. The control group will not receive any intervention during the data collection process. After the interventions are completed, post-tests (Introductory Information Form, Multidimensional Quality of Life Scale, and Mental Well-Being Scale) will be applied to all three groups. In order to eliminate ethical problems in the control group, days and hours suitable for individuals will be determined after the data collection process is completed, and traditional yoga will be practiced preferably once a week for an average of 2 hours or AI-assisted yoga for 1 hour a week for 12 weeks. In this research, at the same time, while applying AI-assisted yoga to the 2nd intervention group, images will be captured with the help of Mediapipe, correct posture will be determined, the angles between the joint points will be determined and recorded, and correct posture predictions will be made with the image processing technique of artificial intelligence.
Recruitment & Eligibility
- Status
- NOT_YET_RECRUITING
- Sex
- All
- Target Recruitment
- 60
- Being a student at Çanakkale Onsekiz Mart University, Faculty of Health Sciences, Department of Nursing, Midwifery, Emergency Aid and Disaster Management.
- Volunteering to participate in the study
- Not having any physical dysfunction that would prevent yoga (COPD, septum deviation, etc.)
- Not having chronic diseases that would prevent yoga (Breast CA, epilepsy, chemotherapy, radiotherapy, stage 3 hypertension >180/110)
- Not having done yoga before
- Being open to communication
- Having internet access.
- Having a computer/tablet/phone with a camera.
- Having a physical or mental health problem that will prevent and/or limit practicing yoga.
- Having had a surgical operation within the last month
- Having had a cancer-related surgical operation within the last year
- Not volunteering to participate in the study
- Not wanting to turn on his camera.
- Doing yoga regularly
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Traditional yoga group Traditional yoga This group will practice traditional yoga for an average of 2 hours, preferably once a week, for 12 weeks. AI-Assisted Yoga AI-Assisted Yoga This group will practice AI-assisted yoga for an average of 2 hours, preferably once a week, for 12 weeks.
- Primary Outcome Measures
Name Time Method The Multidimensional Quality of Life Scale 6 months after interventions 1. The Multidimensional Quality of Life Scale total score varies between 35-245. Higher scores represent better functional status.
Waarwick-Edinburg Mental Well-Being Scale 6 months after interventions 2. A minimum of 14 and a maximum of 70 points are obtained from the Waarwick-Edinburg Mental Well-Being Scale. High scores from the scale indicate high mental (psychological) well-being.
- Secondary Outcome Measures
Name Time Method