Development of a Reward-Based Health Behavior Model to Reduce Abdominal Obesity the Working-Age Population
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Abstract
This article aimed to develop a Reward-Based Health Behavior Model (RBHM) to reduce abdominal obesity among the working-age population and to examine its effectiveness before and after implementation. This study employed a quasi-experimental research design. The research instruments consisted of: 1) the Reward-Based Health Behavior Model, 2) a nutrition and exercise knowledge test, and 3) a body composition recording form. The statistical methods used for data analysis included comparing the mean knowledge and body composition scores of the sample group before and after the intervention using the paired-sample t-test, and analyzing factors affecting weight reduction using the Chi-square test.
The research results were found as follows: 1) The Reward-Based Health Behavior Model is a health behavior promotion model developed by the researcher, utilizing reward mechanisms as motivational tools to encourage individuals to modify their health behaviors. The model aims to promote continuous engagement in appropriate health practices, with an emphasis on nutritional behaviors and physical activity, thereby reducing abdominal fat accumulation and abdominal obesity among the working-age population. 2) The effectiveness of the Reward-Based Health Behavior Model can be summarized as follows: 2.1) the mean scores of nutrition and exercise knowledge after the experiment were significantly higher than those before the experiment, and 2.2) the mean body mass index (BMI) and waist circumference after the experiment were significantly lower than those before the experiment at the .05 level of statistical significance. It can be concluded that implementing the Reward-Based Health Behavior Model contributed to reducing abdominal obesity. In addition, the analysis of associations using the Chi-Square test indicated that gender was not significantly associated with nutritional and exercise knowledge or weight reduction.
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