Ignite Creation Date:
2025-12-25 @ 4:32 AM
Ignite Modification Date:
2026-01-03 @ 2:18 PM
Study NCT ID:
NCT06225518
Status:
COMPLETED
Last Update Posted:
2024-10-28
First Post:
2023-09-25
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
The Effect of a Machine Learning-Based Mobile Application on Physical Activity in Overweight and Obese Women
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D057185', 'term': 'Sedentary Behavior'}], 'ancestors': [{'id': 'D001519', 'term': 'Behavior'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'TRIPLE', 'whoMasked': ['PARTICIPANT', 'INVESTIGATOR', 'OUTCOMES_ASSESSOR']}, 'primaryPurpose': 'PREVENTION', 'interventionModel': 'PARALLEL', 'interventionModelDescription': 'This research was planned as an experimental type study. Randomization will be achieved by randomly selecting women from the study population and randomly assigning from the sample group women to the experimental and control groups by two people other than the researcher. Supervision in the research will be provided by creating a control group. Management will be provided by evaluating and comparing the effect of the mobile application on the physical activity level of women in experimental and control groups.'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 80}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-04-05', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-10', 'completionDateStruct': {'date': '2024-08-05', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-10-24', 'studyFirstSubmitDate': '2023-09-25', 'studyFirstSubmitQcDate': '2024-01-16', 'lastUpdatePostDateStruct': {'date': '2024-10-28', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-01-26', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-07-05', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'International Physical Activity Questionnaire (IPAQ) score', 'timeFrame': '3 months', 'description': "Participants' International Physical Activity Questionnaire (IPAQ) score."}, {'measure': 'Daily step count', 'timeFrame': '3 months', 'description': "Participants' daily step counts measured with their smart bands"}, {'measure': 'BMI', 'timeFrame': '3 months', 'description': 'Weight and height will be combined to report BMI in kg/m\\^2'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Physical Inactivity']}, 'referencesModule': {'references': [{'type': 'BACKGROUND', 'citation': 'World Health Organization. Burden: mortality, morbidity and risk factors. Global status report on non communicable diseases. 2010.'}, {'type': 'BACKGROUND', 'citation': 'WHO European Regional Obesity Report. Copenhagen: WHO Regional Office for Europe. 2022. Licence: https://creativecommons.org/licenses/by-nc-sa/3.0/igo'}, {'type': 'BACKGROUND', 'citation': 'Fadhil, A. Towards Automatic and Personalised Mobile Health Interventions: An Interactive Machine Learning Perspective. arXiv preprint arXiv:1803.01842. 2018'}, {'pmid': '30969715', 'type': 'BACKGROUND', 'citation': '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/'}, {'pmid': '20044854', 'type': 'BACKGROUND', '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.'}, {'pmid': '18687261', 'type': 'BACKGROUND', '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.'}, {'pmid': '15090118', 'type': 'BACKGROUND', 'citation': 'Bandura A. Health promotion by social cognitive means. Health Educ Behav. 2004 Apr;31(2):143-64. doi: 10.1177/1090198104263660.'}, {'pmid': '11148297', 'type': 'BACKGROUND', 'citation': 'Bandura A. Social cognitive theory: an agentic perspective. Annu Rev Psychol. 2001;52:1-26. doi: 10.1146/annurev.psych.52.1.1.'}, {'pmid': '30717779', 'type': 'BACKGROUND', '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.'}]}, 'descriptionModule': {'briefSummary': '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:\n\n* The physical activity level of overweight and obese adult women in the intervention group increases.\n* Body Mass Index decreases in overweight and obese adult women in the intervention group.\n* The daily step count of overweight and obese adult women in the intervention group increases.\n\nParticipants will be asked to use the mobile application they received daily and follow their personalized physical activity program.\n\nResearchers will compare the experimental and control groups to see if the mobile application affected the physical activity level.', 'detailedDescription': "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.\n\nPhysical 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.\n\nCognitive 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.\n\nIncreasing 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.\n\nAfter 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.\n\nThe 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.\n\nAfter 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."}, 'eligibilityModule': {'sex': 'FEMALE', 'stdAges': ['ADULT'], 'maximumAge': '60 Years', 'minimumAge': '35 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* BMI\\>25\n* Who do not have any obstacle to participating in physical activities\n\nExclusion Criteria:\n\n* Who have previously used a smart band to increase their physical activity levels'}, 'identificationModule': {'nctId': 'NCT06225518', 'briefTitle': 'The Effect of a Machine Learning-Based Mobile Application on Physical Activity in Overweight and Obese Women', 'organization': {'class': 'OTHER', 'fullName': 'Istanbul University - Cerrahpasa'}, 'officialTitle': 'The Effect of a Machine Learning-Based Mobile Application on Physical Activity in Overweight and Obese Women', 'orgStudyIdInfo': {'id': 'ETKU10/201'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Individualized physical activity management system', 'description': "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.", 'interventionNames': ['Behavioral: Individualized physical activity management system']}, {'type': 'NO_INTERVENTION', 'label': 'Control', 'description': '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.'}], 'interventions': [{'name': 'Individualized physical activity management system', 'type': 'BEHAVIORAL', 'description': "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.", 'armGroupLabels': ['Individualized physical activity management system']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Istanbul', 'country': 'Turkey (Türkiye)', 'facility': 'Istanbul University - Cerrahpasa (IUC)', 'geoPoint': {'lat': 41.01384, 'lon': 28.94966}}]}, 'ipdSharingStatementModule': {'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'ICF', 'CSR'], 'timeFrame': 'The data will be available after the statistical analysis have completed. All the relevant data will be kept without any time restriction', 'ipdSharing': 'YES', 'description': 'The data of this research will be shared with researchers who request it via e-mail.', 'accessCriteria': 'The data of this research will be available for the researchers who are planning a systematic review/metanalysis on this issue and have a PROSPERO record.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Istanbul University - Cerrahpasa', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Lecturer', 'investigatorFullName': 'Ezgi Hasret Kozan Cikirikci', 'investigatorAffiliation': 'Istanbul University - Cerrahpasa'}}}}