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    Prevalence of Hand Hygiene in Post-Disaster Environments and Affecting Factors: A Study on Earthquake-Affected Populations in Turkey
    (Cambridge Univ Press, 2025) Yoltay, Hatice Eda; Ozsezer, Gozde; Mermer, Gulengul; van Giersbergen, Meryem Yavuz
    Introduction: Natural disasters can increase the risk of infection by severely disrupting access to basic needs, including clean water and sanitation. Hand hygiene, one of the simplest and most effective ways to prevent infections, often becomes a challenge in such situations. The study focused on individuals living in temporary housing following the earthquakes in Turkey on February 6, 2023.Objective: The main objective of this study was to assess the prevalence of hand hygiene practices and the factors affecting these behaviors among individuals affected by disasters.Methods: Data were collected from more than 3,600 randomly selected participants living in container cities in four provinces: Adana, Osmaniye, Hatay, and Gaziantep. Both quantitative and qualitative research methods were used to ensure a comprehensive understanding of hand hygiene behaviors. A detailed questionnaire was used to assess factors such as frequency of hand washing, access to water, and use of hygiene products. In addition, focus group discussions were conducted to explore individual and environmental factors influencing hygiene practices.Results: The results showed that although most individuals were aware of the importance of hand hygiene, several barriers, such as limited access to clean water, psychological stress, and a lack of hygiene supplies, hindered their ability to maintain proper hygiene. The frequency of hand washing increased slightly after the disaster, but challenges such as forgetfulness, time constraints, and skin irritation from inadequate hygiene products were common.Conclusion: This study provides important insights into the prevalence of and factors influencing hand hygiene practices in post-earthquake container cities in Turkey. Findings suggest that although individuals have a basic awareness of the importance of hand hygiene, multiple barriers, including access to water, hygiene supplies, and psychological stress, significantly impact their ability to maintain proper hygiene practices after a disaster. This study highlights the critical need for continued education, improved access to hygiene supplies, and psychosocial support to sustain hygiene behaviors in post-disaster settings. By addressing both physical and psychological barriers, public health interventions can be more effective in reducing the risk of infectious diseases in disaster-affected populations. Furthermore, the study emphasizes the importance of preparedness for future disasters by ensuring hygiene resources are readily available and individuals are equipped with the knowledge and skills to maintain hygiene under adverse conditions.
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    Real-Time Prediction of Correct Yoga Asanas in Healthy Individuals With Artificial Intelligence Techniques: A Systematic Review for Nursing
    (Wiley, 2025) Ozsezer, Gozde; Mermer, Gulengul
    AimThis study aims to systematically review the real-time prediction of yoga asanas using artificial intelligence (AI) techniques to improve the quality of life in healthy individuals.DesignSystematic review.MethodsA comprehensive literature review was conducted in English using the keywords 'yoga', 'asana', 'pose', 'posture', 'machine learning', 'deep learning' and 'prediction' in the Web of Science, Google Scholar, PubMed and Scopus databases. The objective was to identify all relevant studies on the topic. Two independent researchers screened the titles and abstracts of the retrieved publications, applying the JBI Critical Appraisal Checklist for Diagnostic Test Accuracy Studies for quality assessment. The initial search yielded 3250 studies (Google Scholar: 3190, PubMed: 19, Scopus: 27, Web of Science: 14). After applying inclusion criteria, 15 studies were included in the final systematic review.ResultsAmong the included studies, nine employed deep learning (DL) models, three utilised machine learning (ML) and three applied a combination of both DL and ML techniques. The primary statistical evaluation method for real-time prediction was accuracy across all studies. The highest accuracy rates were observed in studies using DL models alone (min = 92.34%, max = 99.92%), followed by studies that combined DL and ML (min = 91.49%, max = 99.58%), and those using only ML (min = 90.9%, max = 98.51%). These findings indicate that integrating DL and ML models can enhance the accuracy of real-time yoga asana prediction.Patient or Public ContributionThe findings advocate for the implementation of DL and ML models in clinical and community settings to improve the real-time and precise prediction of yoga asanas, a well-established evidence-based nursing intervention for healthy individuals.