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Abstract

Introduction

Nursing students are future healthcare leaders who play a significant role in promoting community health. A deeper understanding of obesity and related lifestyle factors enables them to develop better awareness regarding the prevention and management of obesity among individuals.

Objective

This study aimed to determine the prevalence of overweight and obesity and to assess the association with lifestyle practices among undergraduate nursing students.

Method

A descriptive cross-sectional study was conducted at RAKCON University, from February to April 2024. Using convenience sampling, 187 undergraduate nursing students were recruited from a total population of 357. Data were collected using a semistructured questionnaire, including sociodemographic characteristics and lifestyle practices. The researchers measured the height and weight of the students initially to calculate the Body Mass Index. Statistical analyses included chi-square tests or Fisher's exact tests to examine associations between variables. Multiple linear regression was used to explore the predictors.

Results

More than half of the participants (54%) were aged 20 or younger; 67.9% were females. The majority (94.1%) were Arab. More than half (51.3%) had a normal BMI, while 15.5% were obese. There were statistically significant associations in weight change over the last 6 months (X2 = 8.69, p = .03), fast-food consumption (FET = 19.1, p = .01), consumption of soft/sugar-sweetened drinks (FET = 18.4, p = .02), and exercise habits (X2 = 13.90, p = .03). The regression analysis, the model explained 4.3% of BMI variance (R² = .043) and was not statistically significant, F (4, 182) = 2.03, p = .092; no individual predictor reached p < .05.

Conclusion

Overweight and obesity were present among nursing students and associated with selected lifestyle practices, particularly fast-food and carbonated drinks/sweetened drinks, and exercise habits. Targeted health-promotion strategies and educational programs are needed to improve healthy lifestyle behaviors and reduce future obesity-related risks.

Keywords

prevalence overweight obesity lifestyle nursing students cross-sectional study

Introduction/Background

Overweight and obesity occur due to abnormal accumulation of excessive fat in the human body, and it affect the health of the individual in various ways. The main cause of obesity and excess weight is due to the imbalance of energy intake and expenditure (Ibrahim et al., 2022). According to the World Health Organization (WHO), the overweight and obesity among children and adults are continuously increasing. The prevalence of obesity among children in the age group from 5–19 raised fourfold from 2% to 8% worldwide, whereas it is more than doubled from 7% to 16% among those 18 years of age and older from 1990 to 2022. Further, 1.9 billion people will be living with obesity in 2035 around the world. The estimated global economic impact of overweight and obesity in 2035 is $4.32 trillion (World Health Organization, 2023). Childhood obesity is expected to increase by 100% between 2020 and 2035. It is likely that one in four individuals will be living with obesity by 2035 (World Health Organization, 2023).

This is due to the influence of different factors such as environmental, societal, and individual factors. It includes sedentary work, lack of sleep, genetics, medications, health conditions (National Heart, Lung and Blood Institute, 2022a) family income, educational status and place of work (Kerkadi et al., 2019; Lee et al., 2019) regular consumption of high caloric diet, taking food mostly from outside restaurants, changes in society causing harder to find healthy diet and fast foods are available easily for lower cost, consuming large amount of sugary drinks, stress, physical inactivity like sitting in front of desk for longer time, traveling by car even for short distance, habits of having more screen time and spending more time with video games (National Health Service, 2023). A study reported that the individual is risk of getting heart diseases, stroke, diabetes, musculoskeletal disorders, and cancers is increased due to gaining excess weight (Mathew et al., 2021).

While pursuing higher education, students are mostly engaged with unhealthy habits due to academic pressure, having high goals, and the influence of peer groups, which can affect health and lifestyle practices. It is essential that behavioral modifications are more difficult to change in later life. The university students are the future community, and they need to follow healthy life practices such as consuming a good diet, taking adequate naps, regular exercise, preventive behaviors, and mental stability (Aceijas et al., 2016; Shekhar et al., 2022) to decrease the incidence of noncommunicable diseases (Rahati et al., 2014). The previous study reported that the prevalence of obesity among health sciences students were noticeable that 23.7% of participants being overweight, and 11% were obese, and it was higher among males (Makkawy et al., 2021).

Obesity among children and adults is a serious concern in the Arabian Gulf, and it is one of the community burdens across the region. In the United Arab Emirates (UAE), about one-third of university students are overweight or obese, the research says, and scientists have issued a warning that the rate is likely to increase as this generation grows older (Baniissa et al., 2020; The National, 2022; National Heart, Lung, and Blood Institute, 2022a, b). In response, the UAE introduced a monitoring system for noncommunicable disease (NCD) and associated risk factors, with the aim of promoting healthy lifestyles and battling NCDs to support national strategic plans and policies (Saleh, 2023).

Healthcare students are the future healthcare providers and should actively participate in preventive and health promotion activities within the community. Specifically, those pursuing nursing studies are required to balance academic schedules with theory, practical, and clinical duties. These demands often create challenges to maintain healthy lifestyle practices. Previous studies have highlighted that being overweight among nursing students is related to not following healthy dietary habits and a reduction of physical activity (Fun et al., 2021; Tufail et al., 2025). Further research evidence also indicates a rising prevalence of an increasing number of obese and overweight individuals among the healthcare students (Alqassimi et al., 2024; Makkawy et al., 2021). As role models, nursing students must adhere to healthy lifestyle habits to maintain their health as well as to educate others. The literature on the prevalence of obesity in relation to lifestyle factors among nursing students in the UAE is limited to the place where the researcher conducted the study. Therefore, this study aimed to fill this gap by determining the prevalence of overweight and obesity and assessing their association with lifestyle practices among undergraduate nursing students, to identify the most effective strategies for the prevention and reduction of overweight and obesity within this population.

Review of Literature

Obesity is a major concern around the world, especially among the younger generation, due to many environmental changes. The transition from school to university life brings significant challenges in lifestyle behavior as students experience more autonomy and are away from their family. A cross-sectional study was conducted in the Middle East among female nursing students to measure the prevalence of obesity and overweight. The findings revealed that significantly obesity were found among persons living alone and overweight among married participants. Whereas a family with high income is not significantly associated with obesity (Mustafa & Elfaki, 2021). A similar study conducted among 214 nursing students found that 27 students were obese and 50 were overweight. Also revealed a significant association between family history of obesity and Body Mass Index (BMI; Balu et al., 2021).

Another study identified a higher number of obese students among male students than female students among healthcare science college students (Makkawy et al., 2021). A descriptive quantitative study conducted among nursing students showed that 25% were overweight and 13.7% were obese. In addition, eating behaviors significantly influenced BMI distribution (Tufail et al., 2025). Based on BMI, a systematic review on the global prevalence of overweight and obesity among nurses revealed that overweight and obesity were 31.2% and 16.3%. It was noted that the highest prevalence of overweight was in the Eastern Mediterranean (37.2%) and of obesity in South-East Asia (26.4%; Sadali et al., 2023). The research study reported the prevalence of obesity and increased body weight among nurses who did not follow a balanced diet (Blake et al., 2021). A cross-sectional study reported that overweight/obesity was significantly associated with students who visited fast food restaurants regularly. The identified lifestyle factors among students included regular skipping of breakfast, intake of snacks, fried food and less consumption of fruits and vegetables (Bede et al., 2020; Benaich et al., 2021; Yun et al., 2018).

Method

Study Design

A descriptive cross-sectional study was conducted.

Research Question

What is the incidence of obesity among undergraduate nursing students?

What are the Lifestyle Factors among undergraduate nursing students?

Is there any association between obesity Incidences and lifestyle factors among undergraduate nursing students?

Setting

The study was conducted among nursing students at RAK college of Nursing (RAKCON) of RAK Medical and Health Sciences University (RAKMHSU), RAS AL Khaimah, United Arab Emirates.

Participants

The participants in this study were undergraduate nursing students enrolled in the Bachelor of Science in Nursing (BSN) program at RAK College of Nursing (RAKCON). Students from Years 1, 2, 3, and 4. The total number of students was 357, who were full-time and attending on-campus classes during the study period, were eligible to participate.

Sample Size and Technique

The sample size was calculated based on the total population of undergraduate BSN students across the 4 academic years (N = 357). The Raosoft sample size calculator (Raosoft, Inc., 2014) was used, with a 95% confidence level, 5% margin of error, and an assumed response distribution of 50%. Additionally, the researcher confirmed the sample size using Cochran's sample size formula for proportions and adjusted using a finite population correction. Assuming a 95% confidence level (Z = 1.96), 5% margin of error (e = 0.05), and maximum variability (p = .50) with a population size of N = 357 (SocSciStatistics, 2025).

n = \frac{N Z^{2} p (1 - p)}{e^{2} (N - 1) + Z^{2} p (1 - p)}

Z = Z-value for confidence level (95% → $Z = 1.96$ ) 1.96 (95% confidence), P = estimated proportion/response distribution (use $0.50$ when unknown) = 0.50, and e = margin of error (precision; e.g., 5% → 0.05) = 0.05.

N = 357.

Based on these parameters, the required sample size was approximately 185.3 and was rounded up to 186 (n = 186). A convenience sampling technique was employed to recruit eligible students.

Inclusion/Exclusion Criteria

Inclusion

Undergraduate BSN Students who were given voluntary consent to participate in the study, and were full-time and on campus during data collection time, and both genders were included in the study.

Exclusion Criteria

Year 3 BSN students were excluded from the study because they were involved in continuous clinical postings and were unable to approach the students due to the limited data-collection period. Student nurses who were on sick leave and absent during data collection were excluded.

Study Instrument/Measurement

A semistructured self-administered questionnaires were used to collect the data from the student participants. The survey questionnaire consisted of two sections, including:

Section I: Sociodemographic characteristics: include gender, age, nationality, year of study, marital status, family status, residence, presence of medical illness, family history of obesity, height, and weight. The BMI was calculated using the formula (weight (kg)/[Height (m)]². With the metric system, the formula for BMI is weight in kilograms divided by height in meters squared. According to guidelines stated by the National Institute of Health, weight status was classified into four categories: underweight (BMI ≤ 18.5), normal weight (BMI = 18.5–24.9), overweight (BMI = 25–29.9), and obesity (BMI = 30 or greater).

Section II: The Researchers Developed a Semistructured Lifestyle Questionnaire after an extensive literature review (Rathod et al., 2018) to assess the student lifestyle practices. This questionnaire was designed to evaluate the students’ lifestyle practices, including dietary habits (14 items), physical activity (five items), sleep patterns (four items), and smoking (three items).

The content validity of sections I and II was submitted to an expert of the faculty to assess the validity of the questionnaire. Experts checked the consistency, objectivity, and language clarity of the tool sections. A pilot of 19 participants (10% of the sample size) was invited for the questionnaire, and then reliability testing was done. The reliability of the tool was checked using Cronbach's alpha test of reliability, and it was 0.72, which indicates a reliable scale.

Procedure of Data Collection

The study was initiated after obtaining approval from the Research and Ethics Committee. The participants who were willing to participate were informed about the study, and informed written consent was taken. The data was collected face-to-face in the clinical skills lab or in the classroom during their free time. The researchers measured the height and weight of the students initially using a calibrated portable scale, and then the questionnaire was administered in English. The student had spent approximately 10–15 min completing the questionnaire. The researcher read the questions with the student to clarify the questionnaire. Data collection started from February to April 2024 among undergraduate first, second, and fourth-year nursing students.

Statistical Analysis/Methods

Statistical Package for the Social Sciences version 26 was used to analyze the data. All variables were checked for any missing data before starting the analysis. The categorical variables were presented as frequencies and percentages. The chi-square test was used to assess the association between the BMI and lifestyle practices. p<.05 was considered statistically significant. Regression analysis was used to find out the predictors of BMI and lifestyle practices.

Results

A total of 187 undergraduate BSN students participated in the study. The demographic profile of the study sample, as shown in Table 1, reveals that the mean age of participants is 20.70 years with an SD of 2.41, and more than half of the participants (54%) were aged 20 or younger, while 3.7% of them were aged 26 or older. Females constituted 67.9% of the sample. The majority (94.1%) were single and of Arab nationality (91.4%). Most participants (41.2%) were in their first year of study. Most of the participants (87.7%) lived with their parents. The majority of participants (93.6%) did not have a chronic disease, and 23% had a family history of chronic illness.

Table 1.

Demographic Data of the Nursing Students (n = 187).

Variables	Frequency ( n = 187)	Percentage
Age mean ± SD 20.70 ± 2.41
Age
≤ 20	101	54
21–25	79	42.2
≥ 26	7	3.7
Gender
Female	127	67.9
Male	60	32.1
Marital status
Single	176	94.1
Married	11	5.9
Nationality
Arab	171	91.4
Non-Arab	16	8.6
Year of study
1	77	41.2
2	60	32.1
4	50	26.7
Living status
Parents	164	87.7
Relative	7	3.7
Hostel	8	4.3
Friends	2	1.1
Alone	6	3.2
Presence of chronic disease
No	175	93.6
Cardiovascular	2	1.1
Diabetes	5	2.6
Asthma	3	1.6
Endocrine	2	1.1
Family history
Yes	43	23.0
No	144	77.0

Figure 1 shows the distribution of BMI categories among nursing students; more than half of the total students (51.3%) had a normal BMI category; however, the underweight students comprised 10.2% of the total sample. Additionally, overweight students accounted for 23.0%, and obesity was observed in 15.5% of the total students.

Figure 1.

Distribution of body mass index category among nursing students (n = 187).

The Table 2 presents the nutritional and lifestyle habits of nursing students and the association between BMI categories and various nutritional assessment factors among nursing students. The majority (65.2%) of students consumed fewer than three main meals per day. Additionally, 47.6% of the students drank 1.5–2 L of water per day. Regarding breakfast, 55.6% of students sometimes skipped it. More than half (58.3%) of the participants watched TV while eating, and 33.2% ate when stressed. Over half of the students (58.3%) reported weight changes in the past 6 months, with 62.6% eating with their families. Regarding fast food consumption, 54.0% of participants consumed fast food more than 5 days a week, while 39.6% consumed chocolate and sweets fewer than three days a week. Moreover, 44.4% drank soft drinks 1–2 days a week. In terms of fruit and vegetable intake, 39.0% consumed fruit 1–2 days a week, while 30.5% ate vegetables daily. Moreover, 42.8% exercised sometimes, and the majority of students (84.0%) were nonsmokers.

Table 2.

Association Between Body Mass Index (BMI) and Lifestyle Practices Among Nursing Students.

Items	BMI Category				Total Frequency ( n = 187)	Test
Items	UnderWeight	Normal	Over Weight	Obesity	Total Frequency ( n = 187)	Test
Number of main meals taken daily						FET = 4.93 p = .55
<3/day	9 (7.4%)	64 (52.5%)	30 (24.6%)	19 (15.6%)	122 (65.2%)
3 times/day	1 (6.3%)	8 (50.0%)	4 (25.0%)	3 (18.8%)	16 (8.6%)
>3/day	9 (18.4%)	24 (49.0%)	9 (18.4%)	7 (14.3%)	49 (26.2%)
How much water do you drink per day?						X² = 3.52 p = .75
Less than 1 L	7 (11.1%)	36(57.1%)	12 (19.0%)	8 (12.7%)	63 (33.7%)
1.5–2 L	8 (9.0%)	46 (51.7%)	20 (22.5%)	15 (16.9%)	89 (47.6%)
More than 2 L	4 (11.4%)	14 (40.0%)	11 (31.4%)	6 (17.1%)	35 (18.7%)
Breakfast skipping						X² = 1.37p = .97
Never	3 (8.6%)	17 (48.6%)	9 (25.7%)	6 (17.1%)	35 (18.7%)
Sometimes	12 (11.5%)	55 (52.9%)	23 (22.1%)	14 (13.5%)	104 (55.6%)
Daily or almost daily	4 (8.3%)	24 (50.0%)	11 (22.9%)	9 (18.8%)	48 (25.7%)
Do you watch TV while eating?						X² = 3.13 p = .81
Regularly	5 (9.1%)	30 (54.5%)	12 (21.8%)	8 (14.5%)	55 (29.4%)
Sometime	13 (11.9%)	55 (50.5%)	23 (21.1%)	18 (16.5%)	109 (58.3%)
Never	1 (4.3%)	11 (47.8%)	8 (34.8%)	3 (13.0%)	23 (12.3%)
Do you eat when you have psychological stress?						X² = 9.07 p = .17
Yes	5 (8.1%)	26 (41.9%)	20 (32.3%)	11 (17.7%)	62 (33.2%)
No	12 (13.2%)	53 (58.2%)	15 (16.5%)	11(12.1%)	91 (48.7%)
I don’t know	2 (5.9%)	17 (50.0%)	8 (23.5%)	7 (20.6%)	34 (18.2%)
Weight change (last 6 months)						X² = 8.69 p = .03*
Yes	6 (5.5%)	54 (49.5%)	30 (27.5%)	19 (17.4%)	97 (57.4%)
No	13 (16.7%)	42 (53.8%)	13 (16.7%)	10 (12.8%)	78 (41.7%)
Do you eat with your family?						FET = 5.07 p = .67
Yes	12 (10.3%)	66 (56.4%)	23 (19.7%)	16 (13.7%)	117 (62.6%)
No	2 (14.3%)	6 (42.9%)	3 (21.4%)	3 (21.4%)	14 (7.5%)
Sometimes	5 (8.9%)	24 (42.9%)	17 (30.4%)	10 (17.9%)	56 (29.9%)
How many times do you consume fast food?						FET = 19.1 p = .01*
>5 days/week	5 (5.0%)	57 (56.4%)	24 (23.8%)	15 (14.9%)	101 (54.0%)
3–4 days/week	11 (17.5%)	33 (52.4%)	12 (19.0%)	7 (11.1%)	63 (33.7%)
< 3 days/week	2 (11.1%)	6 (33.3%)	4 (22.2%)	6 (33.3%)	18 (9.6%)
Never	1 (20.0%)	0 (0.0%)	3 (60.0%)	1 (20.0%)	5 (2.7%)
How often do you consume chocolate and sweets consumption?						FET = 11.8 p = .20
>5 days/week	4 (9.8%)	24 (58.5%)	8 (19.5%)	5 (12.2%)	41 (21.9%)
3–4 days/week	7 (12.1%)	29 (50.0%)	14 (24.1%)	8 (13.8%)	58 (31.0%)
< 3 days/week	6 (8.1%)	41 (55.4%)	14 (18.9%)	13 (17.6%)	74 (39.6%)
Never	2 (14.3%)	2 (14.3%)	7 (50.0%)	3 (21.4%)	14 (7.5%)
How often do you consume soft drinks/sugar-sweetened drinks?						FET = 18.4 p = .02*
1–2 days/week	6 (7.2%)	48 (57.8%)	18 (21.7%)	11 (13.3%)	83 (44.8%)
3–4 days/week	9 (21.4%)	24 (57.1%)	7 (16.7%)	2 (4.8%)	42 (22.5%)
> 5 days/week	2 (7.1%)	11 (39.3%)	7 (25.0%)	8 (28.6%)	28 (15.0%)
Never	2 (5.9%)	13 (38.2%)	11 (32.4%)	8 (23.5%)	34 (18.2%)
How often do you consume fruits?						FET = 8.92 p = .44
Daily	2 (4.9%)	22 (53.7%)	14 (34.1%)	3 (7.3%)	41 (21.9%)
5–6 days/week	4 (16.7%)	13(54.2%)	4 (16.7%)	3 (12.5%)	24 (12.8%)
3–4 days/week	4 (8.2%)	24 (49.0%)	12 (24.5%)	9 (18.4%)	49 (26.2%)
1–2 days/week	9 (12.3%)	37 (50.7%)	13 (17.8%)	14 (19.2%)	73 (39.0%)
How often do you consume vegetables?						X² = 6.84 p = .67
Daily	4 (7.0%)	29 (50.9%)	17 (29.8%)	7 (12.3%)	57(30.5%)
5–6 days/week	5 (11.9%)	18 (42.9%)	12 (28.6%)	7 (16.7%)	42 (22.5%)
3–4 days/week	5 (10. 4%)	25 (52.1%)	9 (18.8%)	9 (18.8%)	48 (25.7%)
1–2 days/week	5 (12.5%)	24 (60.0%)	5 (12.5%)	6 (15.0%)	40 (21.4%)
Exercise?						X² = 13.90 p = .03*
Yes	4 (6.3%)	40 (63.5%)	15 (23.8%)	4 (6.3%)	63 (33.7%)
No	3 (6.8%)	24 (54.5%)	7 (15.9%)	10 (22.7%)	44 (23.5%)
Sometime	12 (15.0%)	32 (40%)	21 (26.3%)	15 (18.8%)	80 (42.8%)
Smoking status						FET = 5.17 p = 49
Current	2 (14.3%)	8 (57.1%)	3 (21.4%)	1 (7.1%)	14 (7.5%)
Former	1 (6.3%)	5 (31.3%)	6 (37.5%)	4 (25.0%)	16 (8.6%)
Never	16 (10.2%)	83 (52.9%)	34 (21.7%)	24 (15.3%)	157 (84.0%)

Additionally, there were no statistically significant associations in most nutritional assessments, including the number of main meals taken daily, water consumption, breakfast skipping, eating with family, watching TV while eating, eating habits during psychological stress, and the frequency of sweet consumption, fruits, vegetables, and smoking status. However, statistically significant associations were observed in weight change over the last 6 months (X2=8.69, p = .03), frequency of fast food consumption (FET = 19.1, p = .01), consumption of soft drinks/sugar-sweetened drinks (FET = 18.4, p = .02), and exercise habits (X2 = 13.90, p = .03).

Multiple linear regression with four predictors (exercise, soft drinks frequency, chocolate/sweets frequency, fast-food frequency) explained 4.3% of the variance in BMI (kg/m²; R = .207, R² = .043, adjusted R² = .022; SE of estimate = 5.91). The overall model was not statistically significant, F (4, 182) = 2.03, p = .092. None of the individual predictors reached p < .05. The largest coefficient was for soft drinks intake (B = 0.662, SE = 0.385, β = .127, t = 1.72, p = .088, 95% CI [–0.10, 1.42]). Exercise (B = 0.565, p = .263), chocolate/sweets (B = 0.687, p = .156), and fast food (B = 0.389, p = .497) showed positive but not significant associations (Table 3 ).

Table 3.

Multiple Linear Regression Predicting BMI (kg/m²).

Model summary^b
Model	R	R square	Adjusted R square		Std. error of the estimate
1	.207^a	.043	.022		5.9110
a. Predictors: (constant), consuming fast food, consuming chocolate and sweets, exercise, consuming soft drinks/sugar-sweetened drinks
b. Dependent variable: BMI (kg/m²)
ANOVA^a
Model		Sum of squares	df	Mean square	F	Sig.
1	Regression	284.040	4	71.010	2.032	.092^b
	Residual	6359.107	182	34.940
	Total	6643.147	186

a. Predictors: (constant), consuming fast food, consuming chocolate and sweets, exercise, consuming Soft drinks/sugar-sweetened drinks.

b. Dependent variable: BMI category.

Predictor variable	B	SE	β	T	p-value	95% CI lower	95% CI upper
(Constant)	19.9220	1.861		10.704	.000	16.248	23.592
Exercise	.565	.503	.082	1.123	.263	−.428	1.558
Soft drinks	.662	.385	.127	1.717	.088	−.099	1.422
Chocolate and sweets	.687	.482	.104	1.436	.156	−.264	1.638
Fast food	.389	.571	.050	.681	.497	−.737	1.515

Discussion

Overweight and obesity are major public health problems because of their impact on comorbidity. The increasing need to study different determinants of obesity, especially among university students, is very crucial because they are likely to be occupied with their studies and tend to spend more time outside their homes. Consequently, they are prone to fall victim to unhealthy lifestyle practices, which gradually lead to an increase in body weight and the development of obesity.

The findings of the current study, which showed a prevalence of obesity (15.5%) and overweight (23.0%), are relatively consistent with a study by Sahin et al. (2015) that found the rate of obese and overweight students to be 28.6%. The observed prevalence rate aligns as well with the findings of a study done by Hamam et al. (2017), who reported that the prevalence of overweight and obesity among 228 health sciences students was 25.9% and 10.9%, respectively, with an overall prevalence of 36.8%. Furthermore, a study conducted by Ahmed et al. (2015) in the UAE found that 34.6% of male students were overweight and obese compared to 21.5% of females.

The everyday unhealthy habits of college students included increased consumption of sugar-sweetened beverages and fast foods. These habits significantly impacted their mental and physical health. This study highlights that more than half of the participants (54.0%) consumed fast food more than 5 days a week, while 39.6% consumed chocolate and sweets fewer than 3 days a week, and nearly half (44.4%) drank soft drinks 1–2 days a week. This trend can be linked to several factors, such as being away from home for a considerable number of hours, the palatability, accessibility, and convenience of this type of food, peer pressure, academic stress, fewer choices of other foods on campus, the influence of social media, and a low level of nutrition knowledge. This finding is concurrent with Alibabić et al. (2014), who found that most of the participants, 78.2% consume daily hot or cold sandwiches and burgers.

Furthermore, statistically significant associations were found between the frequency of fast food consumption and BMI. This is in line with the previous study (De Vogli et al., 2014), which revealed that fast food consumption is positively associated with BMI in high-income countries. According to Winpenny et al. (2018), who conducted a longitudinal health behavior study on 1,100 participants from the Norwegian, reported data on diet and life transitions on up to eight occasions from age 14 to age 30, found a decline in fruit and vegetable intakes from age 14 to ages 23 and 21 respectively, before increasing to age 30. This finding is concurrent with the findings of the present study, which indicated that 39.0% consumed fruit 1–2 days a week and 21.4% consumed vegetables 1 to 2 days only per week. This could be explained by the fact that students are transitioning from traditional, healthy food to a more modern, westernized, yet unhealthy diet, largely due to the easy availability and limited choices of such foods in campus restaurants. Although water is readily available in the classroom's water dispenser, the present study revealed that nearly 33.7% of students consumed less than 1 L of water per day. In contrast, only 18.7% of students adopted healthy water consumption habits, exceeding 2 L per day. This may have been because students were not concerned about consuming water adequately.

It's highlighted in the present study that the majority (65.2%) of the participants consumed fewer than three main meals daily, and more than half (55.6%) skipped breakfast, with no statistically significant differences observed between meal skipping and BMI. This may be due to insufficient time before coming to the university, or the student's inability to manage their time for a healthy lifestyle. In this regard, a cross-sectional study among undergraduate students of the medical college found that 24% of the participants skipped breakfast 4–5 times a week, 21% skipped 2–3 times a week, 13% skipped occasionally, with a significant association between frequency of skipping breakfast and overweight/obesity. Furthermore, multiple observational studies have reported that low eating frequency is associated with overweight or obesity (Forkert et al., 2019).

Shedding light on physical exercise among college students is very valuable. We presume that health sciences students are aware of the consequences of unhealthy lifestyles. Nevertheless, knowledge is only a part of expressing behaviors and providing health promotion. The present study revealed that 42.8% of the participants only exercised sometimes, with statistically significant associations observed between physical activity and BMI categories. Anupama et al. (2017) mentioned that 63.5% of the students were found to have inadequate physical activity; however, the association between inadequate physical activity and overweight/obesity was not significant. Moreover, the research identified that 13.3% students stated that they did regular exercise, and 30.3% never exercised. Additionally, a study found that 44.4% of medical students had low physical activity, with no significant association found between physical activity level and BMI. This could be interpreted as a lack of time and laziness, or a lack of motivation among college students to exercise. Additionally, it emphasized the association between psychological stress and BMI.

In the current study, 33.2% of the study sample ate more when they were psychologically stressed. These findings were supported by Cheikh Ismail et al. (2022), who found that nearly half of the participants had eaten when they were psychologically stressed out. With regards to other eating behaviors, it was seen in the current research that 29.4% of the participants watched TV regularly while they were eating, with no statistically significant association. All of the abovementioned characteristics of university students’ lifestyle place them at an increased risk for overweight and obesity. Our study confirms the global observations that the trend of unhealthy lifestyles and poor dietary habits among university students is increasing. It is an important role to identify and reduce the occurrence of obesity among college students by educating them about healthy lifestyle practices.

In contrast to the bivariate associations in the current study, the multiple linear regression model that included exercise, soft drink intake, chocolate/sweets intake, and fast-food intake explained only a small proportion of the variance in BMI (R = .207, R² = .043, adjusted R² = .022), and the overall model was not statistically significant, F(4, 182) = 2.03, p = .092. None of the individual predictors reached conventional significance (p < .05), although soft drinks showed the largest standardized coefficient (β = .127, p = .088). These findings suggest that, within this sample of nursing students, the selected lifestyle behaviors—when considered together—have only a modest and statistically nonsignificant unique contribution to BMI. Similar low R² values have been reported in other studies of obesity, reflecting the complex, multifactorial nature of weight status where genetic, metabolic, psychosocial, and broader environmental determinants interact with diet and physical activity behaviors (Hruby & Hu, 2015). The nonsignificant trend for soft drink intake in the present regression is directionally consistent with a substantial body of evidence showing that regular consumption of sugar-sweetened beverages increases the risk of weight gain and obesity in young people and adults (Luger et al., 2017; Malik et al., 2013). However, the lack of statistical significance in the current study model may be related to limited statistical power, the use of frequency categories rather than precise volume measurements, and the omission of other dietary and lifestyle variables, including total energy intake and snacking patterns, which are known to influence BMI (Aljefree et al., 2022; Hruby & Hu, 2015).

Similarly, exercise did not emerge as a significant predictor of BMI in the current study regression, despite the descriptive association between physical activity and BMI categories. Cross-sectional research among university students has shown that physical activity alone often shows weak associations with BMI when not combined with dietary changes and when other behaviors such as sedentary time, sleep, and stress are not simultaneously modeled (Arnetz et al., 2020; El Ansari et al., 2018). These findings reinforce the idea that single behaviors, examined in isolation or with limited covariates, may not fully capture the behavioral risk profile that underlies unhealthy weight gain in this population. Consequently, while the descriptive findings point to concerning levels of fast-food and soft drink consumption, the multivariable analysis suggests that effective obesity prevention will require comprehensive, multicomponent interventions addressing diet quality, physical activity, stress management, sleep, and the broader campus food environment rather than focusing on a single behavior.

Strengths and Limitations

This study provides valuable insights into the prevalence of overweight among undergraduate nursing students in one of the Emirates in the UAE, by focusing on nursing students, the study addresses a crucial group who are future healthcare providers and role models for healthy lifestyle behaviors. The use of direct anthropometric measurements, including height and weight, rather than self-reported data, strengthens the accuracy of the BMI assessment. Moreover, the study's findings highlight modifiable lifestyle factors, which can inform targeted health promotion interventions at the university level to mitigate future obesity-related health risks.

This study was conducted among nursing students only and cannot be generalized to other programs. The number of male students was fewer than that of female students. Some students were refused permission to participate as they were revealing their weight. The sample size was small due to a shortage of time to complete the study. Only willing participants were included; hence, the representation of the actual population might be somewhat affected.

Implications for Practice

The findings of this study emphasize the critical need for targeted health promotion programs within nursing education settings to address overweight and obesity among nursing students. Although the multivariate model was not statistically significant, the descriptive findings still point to concerning patterns of unhealthy eating and suboptimal physical activity among nursing students. In practice, this indicates that interventions should adopt a comprehensive, multicomponent approach rather than focusing narrowly on a single behavior (e.g., only fast food or only soft drink consumption). Additionally, embedding practical, evidence-based interventions into the nursing curriculum could enhance students’ awareness and encourage the adoption of sustainable healthy habits.

As future healthcare professionals, nursing students are expected to model healthy behaviors in both their personal and professional lives; therefore, supporting them through broad, holistic lifestyle interventions is likely to be more effective than targeting isolated behaviors. Nursing educators and university administrators should integrate comprehensive lifestyle education that emphasizes balanced nutrition, reduced consumption of fast food and sugar-sweetened beverages, and the promotion of regular physical activity.

Finally, the limited explanatory power of the regression model highlights the importance of future research incorporating additional variables (e.g., sleep duration, mental health, socioeconomic status, and total energy intake) and, where possible, longitudinal study designs. Such research would provide a more comprehensive understanding of the determinants of BMI in this population and inform the development of more precise, evidence-based interventions within nursing education settings.

Conclusions

The present study findings suggest that certain dietary behaviors and physical activities are linked to BMI differences among nursing students. This study not only enriches our understanding of the determinants of obesity among nursing students but also provides valuable insights for educators, administrators and policymakers of the university to form an intervention with the aim of promoting healthy eating habits and encouraging the students to engage in regular physical activity to prevent or reduce the problem of obesity among university students in the future. Additionally, creating supportive campus environments that facilitate access to healthy food options and opportunities for exercise may further reduce obesity risk.

Footnotes

Acknowledgments

The researcher would like to thank the administrators of the college and the ethical committee of the respected University for granting permission to conduct this study. I would like to acknowledge the student participants for their time and valuable contribution to complete the questionnaire. We would like to acknowledge our student coinvestigators’ contribution to write research proposal, data collection and writing manuscript. We would like to acknowledge the student research board for reviewing the research manuscript.

ORCID iD

Eman Abdelaziz Ahmed Rashad Dabou

Institutional Review Board Approval,Informed Consent

The ethical clearance was obtained from the Research and Ethics Committee of RAKCON (RAK-REC-09-2023/24-F-M). Approval was obtained from the college administrators on 25th November 2023. Written informed consent was obtained from all the participants after explaining the objectives of the study using a participant information sheet. Confidentiality of the data was assured by not collecting any personal identity.

Authors’ Contribution

All the faculty authors contributed equally to designing the research proposal, data collection, data analysis, and writing the manuscript. The student coinvestigators’ contribution to write the research proposal, data collection, and writing the manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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Prevalence of Overweight and Obesity and Its Association With Lifestyle Practices as Predictors Among Nursing Students: A Cross-Sectional Study

Abstract

Introduction

Objective

Method

Results

Conclusion

Keywords

Introduction/Background

Review of Literature

Method

Study Design

Setting

Participants

Sample Size and Technique

Inclusion/Exclusion Criteria

Inclusion

Exclusion Criteria

Study Instrument/Measurement

Procedure of Data Collection

Statistical Analysis/Methods

Results

Discussion

Strengths and Limitations

Implications for Practice

Conclusions

Footnotes

Acknowledgments

ORCID iD

Institutional Review Board Approval,Informed Consent

Authors’ Contribution

Funding

Declaration of Conflicting Interests

References