Analysis of Obesity among Malaysian University Students: A Combination Study with the Application of Bayesian Structural Equation Modelling and Pearson Correlation

Radzi, Che Wan Jasimah Wan Mohamed; Jenatabadi, Hashem Salarzadeh; Alanzi, Ayed R. A.; Mokhtar, Mohd Istajib; Mamat, Mohd Zufri; Abdullah, Nor Aishah

doi:10.3390/ijerph16030492

Cited by 39 publications

(42 citation statements)

References 61 publications

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“…Dietary risk scores of students were largely consistent across all countries with few differences observed. These findings are consistent with the current literature [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]. Students from the USA reported the highest absolute dietary risk score, which is consistent with the higher prevalence of overweight and obesity among American students, and higher BMI compared to students in the other countries studied [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ].…”

Section: Discussionsupporting

confidence: 94%

“…These findings are consistent with the current literature [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]. Students from the USA reported the highest absolute dietary risk score, which is consistent with the higher prevalence of overweight and obesity among American students, and higher BMI compared to students in the other countries studied [ 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 ]. Even though the prevalence of overweight and obesity was higher among USA students, previous work reported that the majority of students from the USA and other countries did not meet the daily recommended servings of fruit and vegetable intake [ 77 , 78 , 79 , 80 , 81 ].…”

Section: Discussionsupporting

confidence: 94%

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Health Behaviors of Higher Education Students from 7 Countries: Poorer Sleep Quality during the COVID-19 Pandemic Predicts Higher Dietary Risk

Zan

Cho

et al. 2021

Clocks & Sleep

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Health behaviors of higher education students can be negatively influenced by stressful events. The global COVID-19 pandemic presents a unique opportunity to characterize and compare health behaviors across multiple countries and to examine how these behaviors are shaped by the pandemic experience. Undergraduate and graduate students enrolled in universities in China, Ireland, Malaysia, South Korea, Taiwan, the Netherlands and the United States (USA) were recruited into this cross-sectional study. Eligible students filled out an online survey comprised of validated tools for assessing sleep quality and duration, dietary risk, alcohol misuse and physical activity between late April and the end of May 2020. Health behaviors were fairly consistent across countries, and all countries reported poor sleep quality. However, during the survey period, the COVID-19 pandemic influenced the health behaviors of students in European countries and the USA more negatively than Asian countries, which could be attributed to the differences in pandemic time course and caseloads. Students who experienced a decline in sleep quality during the COVID-19 pandemic had higher dietary risk scores than students who did not experience a change in sleep quality (p = 0.001). Improved sleep quality was associated with less sitting time (p = 0.010). Addressing sleep issues among higher education students is a pressing concern, especially during stressful events. These results support the importance of making education and behavior-based sleep programming available for higher education students in order to benefit students’ overall health.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionsupporting

confidence: 94%

Health Behaviors of Higher Education Students from 7 Countries: Poorer Sleep Quality during the COVID-19 Pandemic Predicts Higher Dietary Risk

Zan

Cho

et al. 2021

Clocks & Sleep

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“…The elements of lifestyle are one of the leading research variables that are included in the obesity modelling. This was present in some of the previous studies related to children [40] and youth [41]. Those studies showed that the links between the demographics had allowed the BMI to be examined through lifestyle, food intake, and mental health.…”

Section: Aim Of the Study And Research Frameworkmentioning

confidence: 83%

“…This latent variable is been used for the first time to measure the impact of this latent variable on postpartum obesity modeling. Mental health has been used in previous studies but in a different context [41,62]. Yet, there is a lack of studies in postpartum obesity modeling that used mental health as a latent variable.…”

Section: Mental Health (Third Mediator)mentioning

confidence: 99%

Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach

Jenatabadi

Radzi

Samsudin

2020

IJERPH

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As postpartum obesity is becoming a global public health challenge, there is a need to apply postpartum obesity modeling to determine the indicators of postpartum obesity using an appropriate statistical technique. This research comprised two phases, namely: i) development of a previously created postpartum obesity modeling; ii) construction of a statistical comparison model and introduction of a better estimator for the research framework. The research model displayed the associations and interactions between the variables that were analyzed using the Structural Equation Modeling (SEM) method to determine the body mass index (BMI) levels related to postpartum obesity. The most significant correlations obtained were between BMI and other substantial variables in the SEM analysis. The research framework included two categories of data related to postpartum women: living in urban and rural areas in Iran. The SEM output with the Bayesian estimator was 81.1%, with variations in the postpartum women’s BMI, which is related to their demographics, lifestyle, food intake, and mental health. Meanwhile, the variation based on SEM with partial least squares estimator was equal to 70.2%, and SEM with a maximum likelihood estimator was equal to 76.8%. On the other hand, the output of the root mean square error (RMSE), mean absolute error (MSE) and mean absolute percentage error (MPE) for the Bayesian estimator is lower than the maximum likelihood and partial least square estimators. Thus, the predicted values of the SEM with Bayesian estimator are closer to the observed value compared to maximum likelihood and partial least square. In conclusion, the higher values of R-square and lower values of MPE, RMSE, and MSE will produce better goodness of fit for SEM with Bayesian estimators.

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“…(7)(8)(9) . Nevertheless, to the best of our knowledge, very few studies (11) have assessed the aetiology of obesity in university student populations from a broad perspective (that includes different determinants and their possible interactions), even though this population is particularly vulnerable to inadequate lifestyles, which have consequences on their health status (12,13) .…”

mentioning

confidence: 99%

The EHU12/24 cohort: survey design, instruments and participants

et al. 2019

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The EHU12/24 (code of a survey from the University of the Basque Country/Euskal Herriko Unibertsitatea, UPV/EHU) study was designed to investigate the risk factors of overweight/obesity-related lifestyles, particularly those associated with diet, their psychosocial influences and the interactions among these factors. This observational cohort study was carried out according to a standardised protocol and involved a representative sample of the UPV/EHU student population. Anthropometric measurements, direct behavioural determinants, such as physical activity and diet, and indirect determinants, such as social/psychological factors, are considered. In this paper, we present the survey design, instruments, measurements and related quality management. We describe the study sample in terms of its socio-economic and demographic factors and knowledge area and summarise the methodology used to collect the data and obtain the anthropometric measurements. The participants were 603 students (59·5 % female) aged 18–28 years. The crude participation proportion was 53·5 %. Regarding the knowledge area, the lowest response proportions were obtained from the Health Sciences (38·6 %) compared with the Non-Health Sciences (48·3 %) (P = 0·003). The mean age was 20·9 years, and 83·1 % of the sample were from Basque Country. Regarding the socio-economic characteristics, there were significant differences by sex and knowledge area in most studied variables. Moreover, the Health Sciences students were more likely younger, from outside Basque Country, to have parents with university degrees and to have a higher social status. In conclusion, the EHU12/24 cohort provides valuable data for analysing the complexity and multidimensionality of obesity in university students.

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Analysis of Obesity among Malaysian University Students: A Combination Study with the Application of Bayesian Structural Equation Modelling and Pearson Correlation

Cited by 39 publications

References 61 publications

Health Behaviors of Higher Education Students from 7 Countries: Poorer Sleep Quality during the COVID-19 Pandemic Predicts Higher Dietary Risk

Health Behaviors of Higher Education Students from 7 Countries: Poorer Sleep Quality during the COVID-19 Pandemic Predicts Higher Dietary Risk

Associations of Body Mass Index with Demographics, Lifestyle, Food Intake, and Mental Health among Postpartum Women: A Structural Equation Approach

The EHU12/24 cohort: survey design, instruments and participants

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