The Effect of a Machine Learning-Based Mobile Application on Physical Activity in Overweight and Obese Women
- Conditions
- Physical Inactivity
- Interventions
- Behavioral: Individualized physical activity management system
- Registration Number
- NCT06225518
- Lead Sponsor
- Istanbul University - Cerrahpasa (IUC)
- Brief Summary
The goal of this clinical trial is to evaluate the effect of an algorithm-driven mobile application that provides personalized recommendations for increasing physical activity, which is an important health behavior, in the prevention of obesity and many other related non-communicable diseases in overweight and obese women. Hypotheses of this study are:
* The physical activity level of overweight and obese adult women in the intervention group increases.
* Body Mass Index decreases in overweight and obese adult women in the intervention group.
* The daily step count of overweight and obese adult women in the intervention group increases.
Participants will be asked to use the mobile application they received daily and follow their personalized physical activity program.
Researchers will compare the experimental and control groups to see if the mobile application affected the physical activity level.
- Detailed Description
According to the World Health Organization (WHO), physical inactivity is one of the significant public health issues of our time. Health problems associated with this issue lead to an overload of healthcare services. According to the report published by WHO in 2022, the prevalence of overweight and obesity in the world constitutes 60% of the total population and causes 1.2 million deaths in the European region. In Turkey, the prevalence of obesity is 66.8 in all genders and 69.3 in women. The increasing epidemic of excessive weight and obesity, which leads to chronic diseases in the long term, poses a significant public health threat both globally and in our country.
Physical activity is an essential lifestyle measure for maintaining a healthy weight and preventing obesity. In women, physical activity levels decrease during pregnancy, and inactivity continues after childbirth. Therefore, determining the physical activity levels of women at risk for obesity and planning public health initiatives to increase their physical activity levels are also important.
Cognitive Behavioral Theory (CBT) is a theory that suggests thoughts, feelings, and behaviors are interconnected and influence each other. CBT is used in many health improvement interventions, such as improving physical activity levels. On the other hand, Social Cognitive Theory (SCT) is an important theory in planning behavior change interventions related to individuals' changing and sustaining health behaviors. SCT provides a strong perspective in understanding health behaviors related to physical activity by identifying the interaction between individuals, the environment, and behavior. Associating the components of CBT and SCT with the level of physical activity will provide a comprehensive approach by simultaneously addressing cognitive, behavioral, environmental, and social factors that affect the physical activity levels of middle-aged women.
Increasing physical activity is an effective intervention in reducing the prevalence of obesity and overweight, which are significant public health problems worldwide and in our country. There is an urgent need for behavior change interventions to determine and increase physical activity levels in the entire society and especially in risk groups to promote healthy lifestyles. This research is designed to evaluate the impact of a machine learning-based mobile application that provides personalized recommendations to increase physical activity, which is an essential health behavior in preventing obesity and many other non-communicable diseases in overweight and obese women.
After obtaining institutional and ethical approvals, data will be collected through face-to-face interviews with women aged 35-60 who apply to Family Health Centers in Istanbul. The height and weight of the women will be measured, and their Body Mass Index (BMI) will be calculated. Women with a BMI value of 25 or higher and no medical condition or health issue that would impede their physical activity status will be included in the study.
The data for the study will be collected using the following tools and measures: Identifying Characteristics Form, Visual Analog Scale (VAS), Anthropometric Measurements, International Physical Activity Questionnaire (Short Form), Women's Physical Activity Self-Efficacy Scale, Physical Activity Barriers Scale, Cognitive Behavioral Physical Activity Scale, Exercise Self-Efficacy Scale, and a smart wristband.
After data collection, the data will be transferred to the Statistical Package for the Social Sciences (SPSS) 25.0 software package for analysis. The data analysis will include percentages, mean values, standard deviations and chi-square test, independent sample t-test, repeated measures ANOVA test, and the corrected Bonferroni test for advanced analyses.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- Female
- Target Recruitment
- 80
- BMI>25
- Who do not have any obstacle to participating in physical activities
- Who have previously used a smart band to increase their physical activity levels
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- PARALLEL
- Arm && Interventions
Group Intervention Description Individualized physical activity management system Individualized physical activity management system The mobile application will be downloaded to the smartphones of the participants in the experimental group and the application will be introduced by the nurse at the family health center. Participants will receive daily and weekly goals with personalized physical activity recommendations, using the exercise recommendations determined by the decision system by public health nursing and physiotherapy and rehabilitation experts in the mobile application. With the initial data collected, a personalized physical activity program will be created according to each participant's lifestyle, physical activity level and physical activity barriers. The physical activity program will include a daily step count goals, exercises and stretching movements for each participant, and this program will be offered to the participants via the mobile application. The exercises that the participants are expected to complete will be shown in the application as videos with animated characters.
- Primary Outcome Measures
Name Time Method Daily step count 3 months Participants' daily step counts measured with their smart bands
International Physical Activity Questionnaire (IPAQ) score 3 months Participants' International Physical Activity Questionnaire (IPAQ) score.
BMI 3 months Weight and height will be combined to report BMI in kg/m\^2
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Istanbul University - Cerrahpasa (IUC)
🇹🇷Istanbul, Turkey