Çocukların Fiziksel Aktivite, Obezite Düzeylerinin İncelenmesi: Kayseri İli Örneği

Yazıcı, Meltem; KORKMAZ, Zübeyde; Erten, Zeliha Kaya; Gürbüz, Kaan

doi:10.51123/jgehes.2021.32

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…It can be claimed that the forces acting on the bones during trauma in overweight and obese children are higher than in normal weight children, which may increase the risk of fracture in overweight and obese children [22]. However, the fact that normal weight children have higher physical activity levels and participation in games and sports than obese and overweight children may increase the risk of fracture in normal weight children compared to obese and overweight children [23,24].…”

Section: Discussionmentioning

confidence: 99%

Effect of Family Income Level on Pediatric Upper Extremity Fracture Rate

Arslan,

Gültekin,

Arslan

et al. 2024

Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi

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Objective: The aim of the study was to compare the sociodemographic characteristics of children with upper extremity fractures with those of children without upper extremity fractures. Materials and Methods: A total of 161 participants (age:11.24±2.95, female:53, male:108), 100 of whom were admitted to the orthopedics and traumatology clinic in 2022 with upper extremity fractures and 61 without upper extremity fractures, were included in the study. Participants; age, height, weight, mother's education level, mother's age, father's education level, father's age and monthly income of the family were recorded. Fracture and recurrent fracture rates of the participants who were members of a family whose monthly income was below the minimum wage and those who were included in a family with a monthly income above the minimum wage were compared. Results: The age, height and weight of the participants without upper extremity fractures were greater than those with upper extremity fractures (p<0.01). The body mass index of those with upper extremity fractures was lower than those without upper extremity fractures (p=0.001). While the number of upper extremity fractures in the participants who were members of families with a monthly income below the minimum wage was 79 (63.70%), the number of fractures in the participants who were members of families with an income above the minimum wage was 21 (56.80%) (p=0.44). In addition, when those with upper extremity fractures were evaluated independently from those without upper extremity fractures; While the number of recurrent upper extremity fractures was 7 (33.3%) in the participants who grew up in families with an income above the minimum wage, the number of recurrent upper extremity fractures was 11 (13.9%) in the participants who grew up in families with an income above the minimum wage (p= 0.04). Conclusion: Low body mass index may increase the rate of upper extremity fractures in children. For this reason, families should be informed about the possibility of physical development of children at risk for upper extremity fractures. In addition, the rate of recurrent upper extremity fractures is higher in children of families with a relatively high monthly income. Families with relatively high incomes should be made aware of the safety measures to be taken against accidents in their living spaces.

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Section: Discussionmentioning

confidence: 99%

Effect of Family Income Level on Pediatric Upper Extremity Fracture Rate

Arslan,

Gültekin,

Arslan

et al. 2024

Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi

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Using Artificial Intelligence Techniques for the Analysis of Obesity Status According to the Individuals' Social and Physical Activities

Koklu,

Sulak

2024

Sinop Üniversitesi Fen Bilimleri Dergisi

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Obesity is a serious and chronic disease with genetic and environmental interactions. It is defined as an excessive amount of fat tissue in the body that is harmful to health. The main risk factors for obesity include social, psychological, and eating habits. Obesity is a significant health problem for all age groups in the world. Currently, more than 2 billion people worldwide are obese or overweight. Research has shown that obesity can be prevented. In this study, artificial intelligence methods were used to identify individuals at risk of obesity. An online survey was conducted on 1610 individuals to create the obesity dataset. To analyze the survey data, four commonly used artificial intelligence methods in literature, namely Artificial Neural Network, K Nearest Neighbors, Random Forest and Support Vector Machine, were employed after pre-processing. As a result of this analysis, obesity classes were predicted correctly with success rates of 74.96%, 74.03%, 74.03% and 87.82%, respectively. Random Forest was the most successful artificial intelligence method for this dataset and accurately classified obesity with a success rate of 87.82%.

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Çocukların Fiziksel Aktivite, Obezite Düzeylerinin İncelenmesi: Kayseri İli Örneği

Cited by 2 publications

References 24 publications

Effect of Family Income Level on Pediatric Upper Extremity Fracture Rate

Effect of Family Income Level on Pediatric Upper Extremity Fracture Rate

Using Artificial Intelligence Techniques for the Analysis of Obesity Status According to the Individuals' Social and Physical Activities

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