Perception on obesity among university students: A case study using factor analysis

Hassan, Suriani; Rahman, Nur Amira Abdol; Ghazali, Khadizah; Ismail, Norlita; Budin, Kamsia

doi:10.1063/1.4887722

Cited by 3 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In relation to other published work, Hassan et al [11] found that the perception of obesity depended on the major of the college student; the science students had higher individual awareness and perceptions of obesity than the art students. In this study, although the participants were all nursing students who had science courses for their chosen major, nutrition training had no impact on how they perceived their healthy eating and activity patterns.…”

Section: Resultsmentioning

confidence: 92%

The Effect of Using a Food and Fitness iPad Application to Increase Obesity Awareness in Pre-licensure Nursing Students

Aul¹

2017

Int J Nurs Clin Pract

View full text Add to dashboard Cite

Background: Obesity is a major health issue that increases medical costs and decreases the quality of life. It is therefore important to try to prevent obesity by adhering to a healthy lifestyle. The definition of a healthy lifestyle may be very different for individuals in their perception of obesity. The purpose of this study was to examine the perceptions of healthy eating and activity patterns of pre-licensure nursing students in order to increase their obesity awareness. Methods: A pre-test questionnaire on healthy eating and activity patterns was administered to a convenience sample of 44 pre-licensure nursing students. The students then tracked their eating and activity patterns for one week using the iPad application of ChooseMyPlate Tracker by Live strong.com and a post-test was given to those nursing students who used the application. Only half of the students who tracked their eating and activity patterns had received nutrition training (n=22). The data was analyzed for variances using descriptive statistics and the Mann-Whitney U test. Results: There was a significant difference (p<.05) between the pre-test and post-test scores. Nursing students had higher perceptions of healthy eating and activity after tracking their own eating and activity patterns for a week. There was no significant difference in the scores between the groups of nursing students who were provided nutrition training and those that did not receive the training. Conclusion: Findings from this study indicate that nursing students had a greater awareness of a healthy lifestyle when they actively tracked their own eating and activity patterns. Nutrition training had no impact on how they perceived their healthy eating and activity patterns. These findings highlight how an iPad applicationcan promote healthy eating and activity in individuals to increase obesity awareness. This awareness has significant implications for the prevention of obesity. Research ArticleOpen Access IntroductionObesity has been a rising epidemic over the past couple of decades with its prevalence increasing in both developed and developing nations. Globally, it is a major contributor to chronic disease and disability [1]. It remains one of the biggest threats to the health of the children and to America as a country. Obesity rates in America rose sharply from 1980 to 2000; adult rates doubled and children's rates tripled [2]. Change has been slow to decrease the rates of obesity, and obesity rates remain very high; in national surveys, more than 30% of adults and nearly 17% of children were found to be obese.Reversing the obesity epidemic in America will require the encouragement of healthy eating and active living. Better policies are needed to change the course of the nation's obesity epidemic so that children will have the opportunity to grow into healthy adults. A culture of health needs to be built. Organizations need to advocate for healthier lifestyles, such as good nutrition and daily exercise, to curb this growing epidemic. One type of organiz...

show abstract

Section: Resultsmentioning

confidence: 92%

The Effect of Using a Food and Fitness iPad Application to Increase Obesity Awareness in Pre-licensure Nursing Students

Aul¹

2017

Int J Nurs Clin Pract

View full text Add to dashboard Cite

show abstract

“…Too many fat cells or increased body fat lead to obesity. As a result of the abundance of food consumed, sedentary lifestyles, and lack of physical activity, obesity is a complex issue [ 2 , 3 ]. Approximately 13% of the adult population worldwide was obese in 2016, according to the World Health Organization (WHO) [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

Ayub,

Khan,

Shehryar Ali Naqvi

et al. 2024

Bioengineering

View full text Add to dashboard Cite

The global prevalence of obesity presents a pressing challenge to public health and healthcare systems, necessitating accurate prediction and understanding for effective prevention and management strategies. This article addresses the need for improved obesity prediction models by conducting a comprehensive analysis of existing machine learning (ML) and deep learning (DL) approaches. This study introduces a novel hybrid model, Attention-based Bi-LSTM (ABi-LSTM), which integrates attention mechanisms with bidirectional Long Short-Term Memory (Bi-LSTM) networks to enhance interpretability and performance in obesity prediction. Our study fills a crucial gap by bridging healthcare and urban planning domains, offering insights into data-driven approaches to promote healthier living within urban environments. The proposed ABi-LSTM model demonstrates exceptional performance, achieving a remarkable accuracy of 96.5% in predicting obesity levels. Comparative analysis showcases its superiority over conventional approaches, with superior precision, recall, and overall classification balance. This study highlights significant advancements in predictive accuracy and positions the ABi-LSTM model as a pioneering solution for accurate obesity prognosis. The implications extend beyond healthcare, offering a precise tool to address the global obesity epidemic and foster sustainable development in smart cities.

show abstract

Population Growth using Data Analytics

Gunjan Shah,

Dr. V P Dhandhukiya

2019

IJARSCT

View full text Add to dashboard Cite

India contributes about 20% of this population making it as the second most populous country in the world. Originally, most of the important predictions were made using the Malthusian growth models. The science of data analytics has opened up new possibilities in the creation of prediction graphs. Prediction graphs give useful information about tackling the problem of increasing population. R programming language is used to identify factors that impact the rate of change of population. Important factors such as literacy rate, death rate, religion and so on, deeply impact the rate of population growth. From the Kaiser-Meyer-Olkin test and Factor Analysis found that out of all factors that were considered, religious differences and migration rate were the most important factors affecting the rate of population growth.

show abstract

Perception on obesity among university students: A case study using factor analysis

Cited by 3 publications

References 5 publications

The Effect of Using a Food and Fitness iPad Application to Increase Obesity Awareness in Pre-licensure Nursing Students

The Effect of Using a Food and Fitness iPad Application to Increase Obesity Awareness in Pre-licensure Nursing Students

Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities

Population Growth using Data Analytics

Contact Info

Product

Resources

About