The Effect of a Machine Learning-Based Mobile Application on Physical Activity in Overweight and Obese Women

Physical Inactivity Clinical Trial

Official title:

The Effect of a Machine Learning-Based Mobile Application on Physical Activity in Overweight and Obese Women

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT06225518
• Other study ID #: ETKU10/201
• Status: Not yet recruiting
• Phase: N/A
• Start date: May 4, 2024
• Est. completion date: September 4, 2024

Study information

• Verified date: March 2024
• Source: Istanbul University - Cerrahpasa (IUC)
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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 information / eligibility

• Status: Not yet recruiting
• Enrollment: 80
• Est. completion date: September 4, 2024
• Est. primary completion date: August 4, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 35 Years to 60 Years

Eligibility

Inclusion Criteria:

• BMI>25
• Who do not have any obstacle to participating in physical activities

Exclusion Criteria:

• Who have previously used a smart band to increase their physical activity levels

Related Conditions & MeSH terms

• Overweight
• Physical Inactivity

Intervention

Behavioral:
• Individualized physical activity management system
Participants will be provided with personalized exercise recommendations determined by a decision system by public health nursing and physiotherapy and rehabilitation experts via the mobile application. Targets will be determined for participants based on their completion of physical activity recommendations every day and every week in the mobile application. The initial program will be individually created based on the initial data collected and each participant's lifestyle, physical activity level and barriers to physical activity. Then, depending on the participants' ability to achieve their goals, the duration and intensity of the suggestions given will be individualized to a level that the person can complete.

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Istanbul University - Cerrahpasa (IUC)

References & Publications (9)

Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004 Apr;31(2):143-64. doi: 10.1177/1090198104263660. — View Citation

Bandura A. Social cognitive theory: an agentic perspective. Annu Rev Psychol. 2001;52:1-26. doi: 10.1146/annurev.psych.52.1.1. — View Citation

Evenson KR, Aytur SA, Borodulin K. Physical activity beliefs, barriers, and enablers among postpartum women. J Womens Health (Larchmt). 2009 Dec;18(12):1925-34. doi: 10.1089/jwh.2008.1309. — View Citation

Fadhil, A. Towards Automatic and Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective. arXiv preprint arXiv:1803.01842. 2018

Niemiro GM, Rewane A, Algotar AM. Exercise and Fitness Effect on Obesity. 2023 Nov 17. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK539893/ — View Citation

Pinto BM, Floyd A. Theories underlying health promotion interventions among cancer survivors. Semin Oncol Nurs. 2008 Aug;24(3):153-63. doi: 10.1016/j.soncn.2008.05.003. — View Citation

Shamizadeh T, Jahangiry L, Sarbakhsh P, Ponnet K. Social cognitive theory-based intervention to promote physical activity among prediabetic rural people: a cluster randomized controlled trial. Trials. 2019 Feb 4;20(1):98. doi: 10.1186/s13063-019-3220-z. — View Citation

WHO European Regional Obesity Report. Copenhagen: WHO Regional Office for Europe. 2022. Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/igo

World Health Organization. Burden: mortality, morbidity and risk factors. Global status report on non communicable diseases. 2010.

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	International Physical Activity Questionnaire (IPAQ) score	Participants' International Physical Activity Questionnaire (IPAQ) score.	3 months
Primary	Daily step count	Participants' daily step counts measured with their smart bands	3 months
Primary	BMI	Weight and height will be combined to report BMI in kg/m^2	3 months