Introduction: Body mass index (BMI) and physical activity are the controversial risk factors that cause hemorrhoids. This study was conducted to determine the association between body mass index and physical activity.Methods: This was a case-control study which involved two hundred and two patients using convenience sampling. They had to answer a questionnaire consisting of the International Physical Activity Questionnaire (IPAQ) which monitored their physical activity and their BMI was also measured. For the descriptive analysis, Chi square and an odd ratio were carried out.Results: There was a significant association between age and hemorrhoids (p=0.02), in which the patients who were younger than 50 years old were more likely to have hemorrhoids compared to those who were older than 50 years old (OR=2.268, 95%CI: 1.107-4.630). For the risk estimation calculation, the Chinese individuals were found to have a higher risk compared to non-Chinese individuals (OR=2.056, 95% CI: 1.174-3.601). BMI was proven to be significantly associated with hemorrhoids (p=0.043). Physical activities were found to not be statistically significant (p=0.209). Those with a low and moderate physical activity level were 1.24 times more likely to have hemorrhoids compared to those with a high level of physical activity (OR=1.243, 95%CI: 0.697-2.217). The confidence interval was between 0.697 and 2.217, therefore it was not statistically significant.Conclusion: Physical activity was not associated with the hemorrhoids. However, it was shown that good physical activity could help to regulate bowel function and therefore, the occurrence of hemorrhoids would be less likely. BMI was significantly associated with hemorrhoids.
This paper presents a robust and high-capacity video steganography framework using a hybrid Speeded Up Scale Invariant Robust Features (h-SUSIRF) keypoints detection algorithm. There are two main objectives in this method: (1) determining the dynamic Region of Interest (ROI) keypoints in video scenes and (2) embedding the appropriate secret data into the identified regions. In this work, the h-SUSIRF keypoints detection scheme is proposed to find keypoints within the scenes. These identified keypoints are dilated to form the dynamic ROI keypoints. Finally, the secret images are embedded into the dynamic ROI keypoints’ locations of the scenes using the substitution method. The performance of the proposed method (PM) is evaluated using standard metrics Structural Similarity Index Measure (SSIM), Capacity (Cp), and Bit Error Rate (BER). The standard of the video is ensured by Video Quality Measure (VQM). To examine the efficacy of the PM some recent steganalysis schemes are applied to calculate the detection ratio and the Receiver Operating Characteristics (ROC) curve is analyzed. From the experimental analysis, it is deduced that the PM surpasses the contemporary methods by achieving significant results in terms of imperceptibility, capacity, robustness with lower computational complexity.
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