The Effect of a Machine Learning-Based Mobile Application on Physical Activity in Overweight and Obese Women
NCT ID: NCT06225518
Last Updated: 2024-10-28
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
80 participants
INTERVENTIONAL
2024-04-05
2024-08-05
Brief Summary
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* 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.
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Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
PARALLEL
PREVENTION
TRIPLE
Study Groups
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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.
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.
Control
The mobile application will be downloaded to the smartphones of the participants in the experimental and control groups and the application will be introduced by the nurse at the family health center to which the participants are affiliated. Participants in the control group will use the mobile application only to enter and track daily step counts and other data.
No interventions assigned to this group
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* Who do not have any obstacle to participating in physical activities
Exclusion Criteria
35 Years
60 Years
FEMALE
Yes
Sponsors
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Istanbul University - Cerrahpasa
OTHER
Responsible Party
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Ezgi Hasret Kozan Cikirikci
Lecturer
Locations
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Istanbul University - Cerrahpasa (IUC)
Istanbul, , Turkey (Türkiye)
Countries
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References
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World Health Organization. Burden: mortality, morbidity and risk factors. Global status report on non communicable diseases. 2010.
WHO European Regional Obesity Report. Copenhagen: WHO Regional Office for Europe. 2022. Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/igo
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; 2025 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK539893/
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.
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.
Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004 Apr;31(2):143-64. doi: 10.1177/1090198104263660.
Bandura A. Social cognitive theory: an agentic perspective. Annu Rev Psychol. 2001;52:1-26. doi: 10.1146/annurev.psych.52.1.1.
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.
Other Identifiers
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ETKU10/201
Identifier Type: -
Identifier Source: org_study_id
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