Factors influencing mental health outcomes among university students: a cross-sectional study in Bangladesh

Abstract

Introduction

Mental health issues, particularly anxiety and depression, are on the rise among university students globally, including in Bangladesh. However, comprehensive data on the factors influencing mental health outcomes in this group remain limited, hindering the development of effective programmes and interventions.

Objectives

This study aims to assess the mental health status of university students in Bangladesh and examine the key factors influencing mental health outcomes.

Design

A cross-sectional online survey was conducted in Bangladesh from December 2022 to March 2023.

Setting

Universities in Bangladesh.

Participants

University students aged 18 and older.

Outcome measures

Data were collected through a structured survey that assessed depression and anxiety using the Patient Health Questionnaire and the Generalized Anxiety Disorder scale, as well as dietary diversity through the Individual Dietary Diversity Score.

Results

The results showed that while female students exhibited greater dietary diversity, they also had higher obesity rates, whereas male students reported more physical activity. Mental health assessments revealed that 36.1% of participants experienced mild anxiety, 11.5% severe anxiety, 39.8% mild depression and 8.3% severe depression. Binary logistic regression analysis identified significant predictors of anxiety and depression, including gender, personal income, body mass index and screen time. Females were less likely to experience anxiety (crude odds ratios (COR): 0.531, p =0.034) and depression (COR: 0.591, p =0.023) compared with males. Furthermore, low intake of wheat, rice (COR: 2.123, p=0.050) and pulses (COR: 1.519, p=0.050), as well as high consumption of fats, oils (COR: 2.231, p=0.024) and sugary foods (COR: 2.277, p=0.001), were associated with anxiety, while inadequate intake of vitamin A- and C-rich fruits (COR: 1.435, p =0.018) was linked to depression. Overweight students were found to be more susceptible to depression.

Conclusion

The findings of the study emphasise the necessity for targeted interventions that promote healthier lifestyles to enhance mental health outcomes among university students in Bangladesh.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• The study employs a cross-sectional design, allowing for a broad assessment of variables influencing mental health among university students in a short time.

• The sample size was sufficiently large, and the study used standardised, widely accepted and validated tools such as Patient Health Questionnaire, Generalized Anxiety Disorder, Food Insecurity Experience Scale and Food Frequency Questionnaire.

• The lack of quantitative data on food intake and exercise duration limits a comprehensive assessment of their impact on mental health.

• The cross-sectional nature of the study restricts causal inferences.

• Convenience sampling may introduce selection bias, potentially affecting the generalisability of the findings.

Introduction

Psychiatric disorders are among the leading causes of disability worldwide, with mental health commonly assessed through levels of anxiety, depression and stress.¹ Anxiety is characterised by a state of inner turmoil, often accompanied by nervous behaviours such as pacing, physical symptoms and excessive worry.² Depression, another key determinant of mental health, is one of the most prevalent psychiatric issues globally.³ Mental health problems are widespread, with reports indicating that around 22% of young adults experience mental illness. Depression and anxiety, the most common disorders, have prevalence rates of 10.9% and 22.3%, respectively.⁴ A study at the Jahangirnagar University in Bangladesh revealed concerning mental health statistics among students, with 45% experiencing moderate-to-severe stress and 50% reporting moderate-to-severe anxiety. Additionally, over 44% faced extreme anxiety, while more than half showed signs of moderate depression. Severe depression affected 22% of students, and 7% experienced extreme depression.¹

Young adults, particularly university students, are especially vulnerable to depression and anxiety as they navigate the critical phase of identity formation.⁵ The combined pressures of academics, social adaptation and future career planning contribute to heightened anxiety, stress and depression among university students.⁶,⁸ Moreover, newly enrolled university students face the additional burden of psychosocial adjustments while trying to balance academic and social expectations, further intensifying their mental health challenges.⁶ Furthermore, unbalanced dietary habits and insufficient nutrient intake are becoming a global challenge, significantly impacting mental health and ultimately contributing to disorders such as anxiety, depression and reduced academic performance.^{9 10} Many students gravitate towards calorie-dense, nutrient-poor foods such as fast food, sugary snacks and processed products, resulting in deficiencies in essential nutrients like vitamins and minerals.^{9 11} This poor diet contributes to a range of issues, including fatigue, decreased concentration, weakened immunity and mental health challenges such as anxiety and depression.^{11 12} The situation is often aggravated by the limited availability of affordable, nutritious food options on university campuses, making it difficult for students to make healthier choices.^{9 11 13} Consequently, many students resort to skipping meals, leading to energy deficits and nutritional imbalances that further impair academic performance.¹⁴ Late-night studying often exacerbates unhealthy eating behaviours, with students frequently consuming high-calorie snacks like pizza, chips and ice cream, which can contribute to weight gain and disrupt sleep patterns.¹⁵ In Bangladesh, poor dietary habits also significantly contribute to widespread mental health issues among university students, who, as they transition from youth to adulthood, face new lifestyle habits, academic pressures and greater independence in their food choices.¹⁶ Bangladeshi university students often face nutritional challenges due to time constraints, financial limitations and reliance on fast food, leading to unhealthy dietary habits. Those from lower socio-economic backgrounds are at a higher risk of malnutrition due to limited access to nutritious food, while wealthier students may still develop poor eating habits. Financial hardships in Bangladesh further hinder students' ability to maintain a balanced diet, underscoring the impact of socio-economic status on nutrition and mental health.^{17 18}

Along with dietary habits and consumption patterns, prolonged screen time, driven by online learning and social media use, significantly affects the mental health of university students, impairing brain function, cognitive abilities and sleep quality.¹⁹ Excessive screen exposure is known to reduce sleep quality by suppressing melatonin production through blue light emitted by electronic devices, leading to circadian rhythm disruptions.²⁰,²² Research indicates that students who use social media late at night often experience sleep disturbances, which in turn can lead to conditions like depression and anxiety.²¹ Sleep disturbances can exacerbate mental health disorders, creating a negative feedback loop that further deteriorates overall well-being.²³ A study in Bangladesh found that 79% of adolescents had high recreational screen time, with boys averaging 4.3 hours/day and girls 3.6 hours/day. Factors associated with increased screen time included commuting by car, frequent fast-food consumption, sleep disturbances and coming from high-income families. Boys were more likely to have higher screen time than girls.²⁴ Various studies indicate that university students in Bangladesh spend significant amounts of screen time for multiple reasons, including academic purposes, online education, digital addiction and other recreational activities.²⁴,²⁷ Given the significant challenges faced by university students, it is crucial to implement interventions that address their dietary, lifestyle and mental health needs. Initiatives aimed at promoting healthier eating habits, reducing excessive screen time and improving access to nutritious food on campus are essential for mitigating the negative effects of poor dietary choices and lifestyle behaviours. In this context, universities can play a crucial role by providing access to nutritious food options and educating students about the importance of proper nutrition.^{28 29} Additionally, universities are well-positioned to address mental health issues among students, recognising the strong connection between diet and mental well-being.³⁰

Despite several studies conducted in Bangladesh to assess the mental health and its associated factors among university students,³¹,³⁵ particularly during or after the COVID-19 pandemic,³⁶,⁴⁰ there remains a significant research gap or lack of data in understanding the full scope of mental health issues and the factors influencing them.⁴¹ Despite growing awareness in some universities, mental health and well-being programmes for students remain limited, with few interventions targeting this critical issue. Many institutions still overlook the mental health needs of their students, and there is a significant lack of data to inform effective policies and support systems.⁴² Understanding the prevalence of anxiety, depression and other mental health challenges among different university students is essential to identifying the most vulnerable groups and their unique psychological difficulties. This knowledge is key to developing tailored interventions and support systems.

This study aims to address these gaps by assessing the prevalence of anxiety and depression among different university students in Bangladesh and identifying key factors influencing their mental health outcomes. The findings will provide valuable insights into the specific mental health challenges faced by students and contribute to the development of more targeted policies and support systems. By bridging the gap in both data and mental health interventions, the study will guide future efforts to improve mental health services for students in Bangladesh.

Materials and methods

Study design and participants

A cross-sectional study was conducted from December 2022 to March 2023 to identify the predictors influencing mental health outcomes among university students in Bangladesh. The research included students from various universities, such as the University of Dhaka, Government College of Applied Human Science, National College of Home Economics, Jahangirnagar University, Noakhali Science and Technology University, Chattogram Veterinary and Animal Sciences University, and North South University, both within and outside Dhaka. Prior to data collection, verbal consent was obtained from the universities. Participation was open to both undergraduate and postgraduate students from any department, provided they were over 18 years of age. Data were collected from 410 participants through personal interviews using a combination of open-ended and closed-ended questions via a Google Form. The Google Form link was shared with students using convenience sampling methods through social media platforms, including Facebook and WhatsApp, specifically targeting university groups, student forums and class groups. The surveyed electronic questionnaire was meticulously organised into multiple sections, which included sections on demographic information such as age, gender, religion, marital status, residence, educational background, university name and academic year. It also covered economic status, including personal and family income, as well as questions on food consumption patterns and dietary diversity. Additionally, it gathered information on height, weight, anxiety and depression using relevant indicators, along with screen time and sleep quality. The survey questionnaire used for data collection is provided in online supplemental file 1.

Sample size calculation

The sample size for this study was determined using Fisher’s formula:

$$n=\frac{Z^{2}pq}{d^2}$$

where n is the sample size; Z is the critical value at 95% CI or 5% level of significance, 1.96; p is the prevalence, 0.5; q=1 p; d is the margin of error, 5%; and non-response rate=10%.

The initial calculated sample size for this study was 384. However, to account for a 10% non-response rate, the sample size was adjusted using the formula adjusted sample size=384/(1 – 0.10),⁴³ resulting in a final target sample size of 427 participants. Once 427 responses were obtained, the online survey was closed. However, after data cleaning, 17 participants were excluded due to incomplete responses and missing values, resulting in a final analysed sample of 410 participants. To mitigate multiple participation in the web-based survey, a unique identifier, such as a student ID or university email address, was employed to ensure that each participant could submit the survey only once. To address non-response error, several strategies were implemented. First, multiple reminders were sent to participants via social media platforms, including Facebook and WhatsApp, to encourage participation and enhance the response rate. Second, the survey was designed to be concise and user-friendly, with a clear explanation of the study’s purpose and significance to foster engagement. Additionally, participants were provided with flexibility in completing the survey at their convenience, ensuring accessibility at any time.

Patient and public involvement

University students were not involved in setting the research question or selecting outcome measures; however, they played a crucial role in the study’s design and implementation. Students were instrumental in disseminating baseline information and raising awareness, fostering broader student and community engagement throughout the study and beyond. Their involvement not only supported data collection but also encouraged interest and participation, highlighting the importance of addressing mental health challenges within the university community.

Study measures

Socio-economic and demographic characteristics

Socio-economic and demographic data collection was adapted from the methodologies of Oyshi et al⁴⁴ and Martinez-Lacoba et al,⁴⁵, incorporating certain modifications for this study. The information gathered included a range of socio-economic and demographic variables such as gender, age, marital status, division, religion, family and personal monthly income, sources of income, expenditure, academic degree pursued, type of accommodation during the academic year (hall, home or mess) and room occupancy. Additionally, data on the participants' field of study, categorised into agriculture, biological sciences, humanities, business studies, physical science and others, along with their academic year across various public and private universities, were also meticulously collected.

Dietary assessment

Data on dietary intake were collected using a self-reported annual Food Frequency Questionnaire (FFQ), which was modelled after the FFQs used in prior studies among university students.^{46 47} This FFQ encompassed various food groups, including food grains, fruits, vegetables, roots and tubers, dark green leafy vegetables, vitamin A- and C-rich fruits and vegetables, fish and other seafood, meat and organ meat, eggs, milk and dairy products, legumes and seeds, oils and fats, as well as sweet or sugary foods and fast foods. The Individual Dietary Diversity Score (IDDS) was calculated following the guidelines established by the Food and Agriculture Organization of the United Nations (FAO).⁴⁸

Lifestyle-related factors

Data were collected on participants' height and weight, from which body mass index (BMI) was calculated. BMI results were categorised into four groups: underweight (BMI<18.5), normal weight (18.5≤BMI ≤24.9), overweight (25≤BMI≤29.9) and obesity (BMI≥30).⁴⁹ Participants were deemed to have a healthy level of physical activity if they engaged in at least 60 min of moderate-intensity physical activity each day, such as brisk walking, bicycling or swimming.⁵⁰ In addition, information was gathered on how participants spent their leisure time and their daily screen time (such as watching television, using mobile phones or playing computer games). The questionnaire also addressed tobacco and alcohol consumption.

Food Insecurity Experience Scale

Data on the frequency and effects of food insecurity experienced by the participants and their families were analysed using the Food Insecurity Experience Scale (FIES).⁵¹ The FIES comprises eight questions, escalating from mild concerns about food availability (question 1) to instances of extreme food insecurity, such as enduring a whole day without food due to financial or other constraints (question 8). These items cover a spectrum from anxiety over insufficient food supplies to actual hunger. The questionnaire was directed at participants asking them to report on any experiences of food insecurity at varying degrees of severity over the past year.⁵²

Primary outcome measures

Patient Health Questionnaire

Previous Bangladeshi studies^{53 54} have demonstrated the reliability of the Patient Health Questionnaire (PHQ-9) depression assessment screening tool. Comprising nine items scored from 0 (‘not at all’) to 3 (‘nearly every day’), the questionnaire aligned with the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) depression diagnostic criteria. Higher scores indicated more severe depression symptoms, with the overall score ranging from 0 to 27, calculated as the sum of the nine components. A PHQ-9 score of ≥10 was used to identify depressive criteria among study participants.^{55 56}

Generalized Anxiety Disorder-7

The prevalence of anxiety disorders was assessed using the Generalized Anxiety Disorder-7 (GAD-7), a self-report measure comprising seven items that inquire about participants' experiences over 2 weeks, such as excessive worrying and irritability.⁵⁷ Previous studies have validated the Bangla version of the GAD-7, confirming its suitability for Bangladeshi research.^{53 54 58} Respondents rated their experiences on a 4-point scale from 0 (‘not at all’) to 3 (‘nearly every day’), and scores were totalled to produce a scale ranging from 0 to 21.⁵⁹ Scores exceeding 10 were considered indicative of positive screening for generalised anxiety disorder in this study.⁶⁰

Statistical analysis

The data were initially stored in Microsoft Excel and then imported into IBM SPSS Statistics 26.0 for further analysis. Parametric techniques were applied to continuous variables, while non-parametric methods were used for categorical variables. Continuous data are presented as mean±SD when appropriate, and categorical data are reported as counts and percentages. The χ² test was used for categorical variables to detect associations with mental health. Additionally, binary logistic regressions were employed to assess the relationship between the dependent variable (mental health status) and independent variables (gender, age group, marital status, education level, income, residence, dietary intake, BMI, physical activity, smoking status and sleep quality). Furthermore, variables with p values below 0.2 in the binary logistic regression analysis were included in a multivariable logistic regression model.⁶¹ If strong associations existed between explanatory variables, the one with the lowest p value in the bivariate analysis was selected. Potential multicollinearity issues were assessed by examining variance inflation factors, with no instances exceeding a threshold of 10, indicating no multicollinearity among exposure variables.⁶²

Result

The demographic and socio-economic characteristics of the students are summarised in table 1. The study included 410 university students, with a mean age of approximately 23 years. The sample was evenly split by gender, with 51% male students. Housing arrangements varied by gender: 73.2% of male students lived in university halls or dormitories, while 54.2% of female students resided at home. Most male students (40.2%) lived with three to five roommates, while 31.6% lived with six or more roommates. In terms of socio-economic status, more than half of male students reported a monthly family income between BDT3000 and 25 000, while 47.8% of female students had a monthly family income between BDT25 001 and 50 000. A large majority of both male (63.6%) and female (80.1%) students reported a monthly income of BDT0–8000, primarily from tuition.

Table 1. Demographic and socio-economic characteristics of respondents (n=410).

Characteristics	Malen=209 (51%)	Femalen=201 (49%)
Age (in years)
Mean±SD	23.38±1.89	22.80±2.10
	N (%)	N (%)
Age category (in years)
18–22	63 (30.1%)	92 (45.8%)
23–24	81 (38.8%)	73 (36.3%)
≥25	65 (31.1%)	36 (17.9%)
Marital status
Unmarried	201 (96.2%)	172 (85.6%)
Married	7 (3.3%)	26 (12.9%)
Divorced	0 (0)	3 (1.5%)
Widow/widower	0 (0)	1 (0.5%)
Residential place
Hall	153 (73.2%)	68 (33.8%)
Home	24 (11.5%)	109 (54.2%)
Bachelor room	32 (15.3%)	23 (11.4%)
Sublet	0 (0)	1 (0.5%)
Number of roommates
1	17 (8.1%)	39 (19.4%)
2	42 (20.1%)	70 (34.8%)
3–5	84 (40.2%)	52 (25.9%)
≥6	66 (31.6%)	40 (19.9%)
Family monthly income (in BDT)
3000–25 000	118 (56.5%)	72 (35.8%)
25 001–50 000	63 (30.1%)	96 (47.8%)
50 001–75 000	19 (9.1%)	17 (8.5%)
75 001–100 000	9 (4.3%)	16 (8.0%)
Personal monthly income (in BDT)
0–8000	133 (63.6%)	161 (80.1%)
8001–16 000	57 (27.3%)	26 (12.9%)
16 001–24 000	9 (4.3%)	13 (6.5%)
24 001–32 000	10 (4.8%)	1 (0.5%)
Sources of income
Tuition	149 (71.3%)	93 (46.3%)
Freelancing	5 (2.4%)	2 (1.0%)
Business	3 (1.4%)	4 (2.0%)
Others	52 (24.9%)	102 (50.7%)
Food insecurity
Food secured to mild insecurity	117 (56.0%)	146 (72.6%)
Moderate-to-severe insecurity	92 (44.0%)	55 (27.4%)

Religion: others, Hinduism, Christianity and Buddhism.

Anxiety and depression among students were assessed using the GAD-7 and PHQ-9 screening tools, respectively. Among the 410 participants, 29.0% (119 students) were identified with anxiety, while 33.2% (136 students) experienced depression. As shown in figure 1a, 36.1% of students reported mild anxiety, whereas 11.5% exhibited severe anxiety. Similarly, figure 1b highlights that 39.8% of students had mild depression, while 8.3% suffered from severe depression.

Figure 1. Prevalence of anxiety (a) and depression (b) among university students in Bangladesh. The figure shows the distribution of mild, moderate and severe anxiety and depression based on Generalized Anxiety Disorder and Patient Health Questionnaire scores.

Online supplemental table S1 outlines participants’ nutritional status, lifestyle behaviours and mental health by gender and age. According to the Asian BMI classification, 50% of students had a normal weight, while 21.7% were obese and 18.8% overweight. Obesity was more common in females (23.1%) than males (20.4%). Over half of the students exercised <5 days/week, and excessive screen time (>3 hours daily) was prevalent. Sleep inadequacy affected 60.3% of males and 51.7% of females. Moreover, significant gender differences were observed in depression (p=0.001) and anxiety (p=0.002). Mild depression was more common in males (44.5%), while severe depression was more frequent in females (10.9%). Minimal anxiety was reported by 42.1% of males, while mild anxiety was most common among 18–22-year-olds (40%), followed by 23–24-year-olds (36.4%).

Table 2 presents the prevalence of anxiety and depression among Bangladeshi university students. The findings reveal that males had significantly higher rates of anxiety (36.8%) and depression (43.3%) compared with females. Among different age groups, students aged 18–22 exhibited the highest prevalence of anxiety (31.6%) and depression (40.6%). Furthermore, students who engaged in physical activity <5 days/week reported elevated levels of anxiety (31.3%) and depression (35.2%). Similarly, high anxiety (30.0%) and depression (32.2%) were also observed among students with inadequate sleep.

Table 2. Prevalence of anxiety and mental health disorders among university students.

Variables	Anxiety		P value	Depression		P value
	Yes	No		Yes	No
Gender			0.001			<0.0001
Male	74 (36.8%)	127 (63.2%)		87 (43.3%)	114 (56.7%)
Female	45 (21.5%)	164 (78.5%)		49 (23.4%)	160 (76.6%)
Age category			0.496			0.042
18–22	49 (31.6%)	106 (68.4%)		63 (40.6%)	92 (59.4%)
23–24	45 (29.2%)	109 (70.8%)		45 (29.2%)	109 (70.8%)
≥25	25 (24.8%)	76 (75.2%)		28 (27.7%)	73 (72.3%)
Body mass index			0.986			0.439
Normal	56 (29.5%)	134 (70.5%)		62 (32.6%)	128 (67.4%)
Underweight	12 (30.8%)	27 (69.2%)		16 (41.0%)	23 (59.0%)
Overweight	21 (28.8%)	52 (71.2%)		20 (27.4%)	53 (72.6%)
Obese	29 (27.9%)	75 (72.1%)		38 (36.5%)	66 (63.5%)
Weekly physical activity (at least 60 min/day)			0.274			0.343
<5 days	72 (31.3%)	158 (68.7%)		81 (35.2%)	149 (64.8%)
≥5 days	47 (26.1%)	133 (73.9%)		55 (30.6%)	125 (69.4%)
Screen time (hours)			0.221			0.018
<3	41 (25.5%)	120 (74.5%)		42 (26.1%)	119 (73.9%)
≥3	78 (31.3%	171 (68.7%)		94 (37.8%)	155 (62.2%)
Average sleep			0.662			0.673
Inadequate	69 (30.0%)	161 (70.0%)		74 (32.2%)	156 (67.8%)
Adequate	50 (27.8%)	130 (72.2%)		62 (34.4%)	118 (65.6%)
Alcohol consumption	1 (33.3%)	2 (66.7%)	0.971	2 (66.7%)	1 (33.3%)	0.941
Smoking	9 (31.0%)	20 (69.0%)	0.837	11 (37.9%)	18 (62.1%)	0.494

Table 3 provides a summary of participants' dietary habits, highlighting the consumption of various food groups based on sex and age group. Participants were categorised by sex (male or female) and age groups (18–22, 23–24 and ≥25). The findings indicated that, aside from meat, a significant proportion of female participants consumed a diverse range of food groups. Specifically, 61.7% of female students reported eating fats and oils, while 45.3% consumed sugar-sweetened foods and beverages. Furthermore, variations in food consumption were noted across different age categories, with the 23–24 age group demonstrating the highest intake of most food groups. Around 50.2% of male students displayed the lowest dietary diversity, while 33.3% of female students reported similar consumption patterns across the lowest, medium and highest dietary diversity categories. Table 3 also illustrates the weekly consumption patterns of participants based on sex and age group (18–22, 23–24 and ≥25). The findings indicated that approximately 43.8% of females and 38.8% of males consumed 6–8 food groups >3 days/week. Notably, a majority of males (42.6%) reported consuming wheat or rice on >3 days each week compared with females. However, about 37.3% of male participants consumed vitamin A- and C-rich fruits <3 days/week, whereas 35.8% of females consumed these fruits >3 days/week. Both male and female participants generally consumed leafy and non-leafy vegetables, fish or meat, pulses and fats and oils >3 days/week. In contrast, around 43.5% of males and 42.8% of females reported consuming milk <3 days/week. Additionally, variations in food consumption patterns across different food groups were observed among various age categories. The results also indicated that, for all food groups, intake was comparatively lower among students aged 25 and older than in the younger age groups.

Table 3. Dietary habits and the weekly food consumption patterns of respondents by sex and age group.

Dietary habits	Gender		P value	Age category			P value
	Male	Female		18–22	23–24	≥25
Cereals and grains	169 (80.9%)	174 (86.6%)	0.142	136 (87.7%)	126 (81.8%)	81 (80.2%)	0.206
White roots and tubers	60 (28.7%)	93 (46.3%)	<0.0001	49 (31.6%)	67 (43.5%)	37 (36.6%)	0.905
Vitamin A-rich vegetables and tubers	56 (26.8%)	66 (36.8%)	0.196	38 (24.5%)	51 (33.1%)	33 (32.7%)	0.194
Dark green leafy vegetables	47 (22.5%)	75 (37.3%)	0.01	40 (25.8%)	45 (29.2%)	37 (36.6%)	0.177
Other vegetables	56 (26.8%)	88 (43.8%)	<0.0001	54 (34.8%)	42 (27.3%)	48 (47.5%)	0.004
Fruits rich in vitamin A	52 (24.9%)	82 (40.8%)	0.001	42 (27.1%)	50 (32.5%)	42 (41.6%)	0.054
Other fruits	86 (42.8%)	54 (25.8%)	<0.0001	45 (29.0%)	58 (37.7%)	37 (36.6%)	0.231
Organ meat	11 (5.3%)	20 (10.0%)	0.092	14 (9.0%)	8 (5.2%)	9 (8.9%)	0.371
Meat	99 (47.4%)	85 (42.3%)	0.321	62 (40.0%)	67 (43.5%)	55 (54.5%)	0.069
Eggs	98 (46.9%)	110 (54.7%)	0.115	76 (49.0%)	76 (49.4%)	56 (55.4%)	0.550
Fish and seafood	76 (36.4%)	84 (41.8%)	0.267	48 (31.0%)	70 (45.4%)	42 (41.6%)	0.028
Pulses and nuts	36 (17.2%)	50 (24.9%)	0.069	32 (20.6%)	31 (20.1%)	23 (22.8%)	0.874
Milk and dairy products	69 (33.0%)	94 (46.8%)	0.005	59 (38.1%)	60 (39.0%)	44 (43.6%)	0.658
Fats and oils	76 (36.4%)	124 (61.7%)	<0.0001	72 (46.5%)	74 (48.1%)	54 (53.5%)	0.534
Sugar-sweetened foods and beverages	54 (25.8%)	91 (45.3%)	<0.0001	42 (27.1%)	63 (40.9%)	40 (39.6%)	0.024
Fast food consumption			0.030				0.155
1	138 (66.0%)	107 (53.2%)		86 (55.5%)	91 (59.1%)	68 (67.3%)
2–3	60 (28.7%)	79 (39.3%)		58 (37.4%)	50 (32.5%)	31 (30.7%)
>3	11 (5.3%)	15 (7.5%)		11 (7.1%)	13 (8.4%)	2 (2.0%)
Dietary diversity score			0.001				0.099
Lowest	105 (50.2%)	67 (33.3%)		72 (46.5%)	67 (43.5%)	33 (32.7%)
Medium	62 (29.7%)	67 (33.3%)		48 (31.0%)	41 (26.6%)	40 (39.6%)
Highest	42 (20.1%)	67 (33.3%)		35 (22.6%)	46 (29.9%)	28 (27.7%
Consumption patterns
Consume 6–8 food groups of the food pyramid (days/week)			0.329				0.092
3	49 (23.4%)	51 (25.4%)		47 (30.3%)	28 (18.2%)	25 (24.8%)
<3	79 (37.8%)	62 (30.8%)		54 (34.8%)	52 (33.8%)	35 (34.7%)
>3	81 (38.8%)	88 (43.8%)		54 (34.8%)	74 (48.1%)	41 (40.6%)
Consumption of different foods
Wheat/rice (days/week)			0.865				0.119
3	48 (23.0%)	26 (22.9%)		46 (29.7%)	31 (20.1%)	17 (16.8%)
<3	72 (34.4%)	74 (36.8%)		51 (32.9%)	54 (35.1%)	41 (40.6%)
>3	89 (42.6%)	81 (40.3%)		58 (37.4%)	69 (44.8%)	43 (42.6%)
Vitamin A- and C-rich fruits (days/week)			0.224				0.434
3	72 (34.4%)	66 (32.8%)		56 (36.1%)	52 (33.8%)	30 (29.7%)
<3	78 (37.3%)	63 (31.3%)		57 (36.8%)	52 (33.8%)	32 (31.7%)
>3	59 (28.2%)	72 (35.8%)		42 (27.1%)	50 (32.5%)	39 (38.6%)
Leafy and non-leafy vegetables (days/week)			0.255				0.505
3	73 (34.9%)	55 (27.4%)		50 (32.3%)	49 (31.8%)	29 (28.7%)
<3	44 (21.1%)	47 (23.4%)		39 (25.2%)	34 (22.1%)	18 (17.8%)
>3	92 (44.0%)	99 (49.3%)		66 (42.6%)	71 (46.1%)	54 (53.5%)
Fish/meat (days/week)			0.308				0.064
3	51 (24.4%)	52 (25.9%)		45 (29.0%)	33 (21.4%)	25 (24.8%)
<3	19 (9.1%)	27 (13.4%)		24 (15.5%)	15 (9.7%)	7 (6.9%)
>3	139 (66.5%)	122 (60.7%)		86 (55.5%)	106 (68.8%)	69 (68.3%)
Pulses (days/week)			0.140				0.657
3	60 (28.7%)	62 (30.8%)		43 (27.7%)	46 (29.9%)	33 (32.7%)
<3	52 (24.9%)	64 (31.8%)		49 (31.6%)	44 (28.6%)	23 (22.8%)
>3	97 (46.4%)	75 (37.3%)		63 (40.6%)	64 (41.6%)	45 (44.6%)
Fats and oils (days/week)			0.338				0.868
3	67 (32.1%)	54 (26.9%)		44 (28.4%)	48 (31.2%)	29 (28.7%)
<3	32 (15.3%)	40 (19.9%)		30 (19.4%)	27 (17.5%)	15 (14.9%)
>3	110 (52.6%)	107 (53.2%)		81 (52.3%)	79 (51.3%)	57 (56.4%)
Sugar-sweetened foods (days/week)			0.983				0.616
3	68 (32.5%)	65 (32.3%)		49 (31.6%)	52 (33.8%)	32 (31.7%)
<3	70 (33.5%)	69 (34.3%)		58 (37.4%)	45 (29.2%)	36 (35.6%)
>3	71 (34.0%)	67 (33.3%)		48 (31.0%)	57 (37.0%)	33 (32.7%)
Milk and dairy products (days/week)			0.926				0.597
3	52 (24.9%)	48 (23.9%)		38 (24.5%)	42 (27.3%)	20 (19.8%)
<3	91 (43.5%)	86 (42.8%)		70 (45.2%)	64 (41.6%)	43 (42.6%)
>3	66 (31.6%)	76 (33.33%)		47 (30.3%)	48 (31.2%)	38 (37.6%)

Other vegetablesOther vegetables: onion, tomato, brinjal, pumpkin, gourd, sweet potato, bitter gourd, snake gourd, string beans, cucumber, teasle gourd, beans, radish; other fruits: guava, jackfruit, litchi, banana, pineapple, dates, star fruit; sugar-sweetened foods and beverages: sugar, honey, molasses, lozenges, jellies, soft drinks.

Binary logistic regression models were employed to identify the predictors influencing the primary outcomes, that is, mental health status of university students in Bangladesh.

Table 4 presents the parameter estimates from the logistic regression model that evaluates the influence of various factors on the mental health status (anxiety disorders) of university students, including the corresponding p values and crude odds ratios (CORs). The findings indicated that female students had a 53% less likelihood of having anxiety (COR: 0.531, 95% CI 0.295, 0.954, p =0.034) compared with their male counterparts. Additionally, students with a personal income between BDT8001 and 16 000 exhibited a 46% lower likelihood of experiencing anxiety (COR: 0.458, 95% CI 0.223, 0.939, p =0.033) compared with those earning BDT8000 or less. Dietary habits significantly influenced anxiety levels among students. Those who consumed wheat or rice <3 days/week had 2.12 times higher odds of experiencing anxiety (COR: 2.123, 95% CI 0.998, 4.456, p=0.050) compared with those who consumed these staples at least 3 days/week. Conversely, students who consumed pulses <3 days/week had a 1.52 times higher likelihood of developing anxiety (COR: 1.519, 95% CI 0.247, 1.012, p=0.050) than those who ate pulses 3 days or more. Additionally, students who consumed 6–8 food groups from the food pyramid >3 days/week had a 46.3% lower likelihood of suffering from anxiety (COR: 0.463, 95% CI 0.232, 0.924, p=0.029) compared with those who consumed fewer. In contrast, those who consumed fats and oils >3 days/week were found to have 2.23 times higher odds of anxiety disorders (COR: 2.231, 95% CI 1.111, 4.478, p=0.024) compared with those who consumed them less frequently. Similarly, students consuming sugar-sweetened foods >3 days/week had 2.28 times higher odds of developing anxiety disorders (COR: 2.277, 95% CI 0.131, 0.587, p=0.001) compared with their peers who consumed such foods less often. The study also revealed that students with >3 hours of screen time—engaging in activities such as watching television, mobile phones or playing computer games—had a 1.70 times greater likelihood of suffering from anxiety (COR: 1.697, 95% CI 1.033, 2.788, p =0.037) compared with those with <3 hours of screen time. Table 4 also presents the parameter estimates from the logistic regression model assessing the influence of various factors on the mental health status (depression) of university students, along with the corresponding p values and ORs. The findings indicated that female students were 51.9% less likely to experience depression (COR: 0.591, 95% CI 0.295, 0.914, p =0.023) compared with male students. Additionally, students with a personal income between BDT8001 and 16 000 had a 34.6% lower likelihood of suffering from depression (COR: 0.346, 95% CI 0.168, 0.713, p =0.004) compared with those earning BDT8000 or less. Regarding dietary habits, students who consumed vitamin A- and C-rich fruits <3 days/week had a 1.435 times greater odds of experiencing depression (COR: 1.435, 95% CI 0.218, 0.866, p =0.018) compared with those who consumed these fruits at least 3 days/week. Furthermore, students categorised as overweight had a 1.5 times higher odds of developing depression (COR: 1.495, 95% CI 0.247, 0.993, p =0.036) compared with their peers with a normal BMI. Consistent with the findings on anxiety, students who engaged in 3 or more hours of screen time had a 2.22 times greater odds of experiencing depression (COR: 2.217, 95% CI 1.357, 3.622, p =0.001) than those with less screen time. Additional variables are available in online supplemental table 2.

Table 4. Estimates of parameters from logistic regression model for factors influencing mental health status (anxiety and depression).

Variables	Anxiety				Depression
	Unadjusted model		Adjusted model		Unadjusted model		Adjusted model
	COR (95% CI)	P value	COR (95% CI)	P value	COR (95% CI)	P value	COR (95% CI)	P value
Age category (in years)
18–22	Reference	Reference			Reference	Reference	Reference	Reference
23–24	0.893 (0.550, 1.451)	0.648			0.603 (0.376, 0.967)	0.036	1.129 (0.534, 2.388)	0.750
≥25	0.712 (0.405, 1.252)	0.238			0.560 (0.326, 0.962)	0.036	1.099 (0.396, 3.047)	0.856
Gender
Male	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
Female	0.471 (0.304, 0.729)	0.001	0.531 (0.295, 0.954)	0.034	0.401 (0.262, 0.614)	<0.0001	0.519 (0.295, 0.914)	0.023
Personal monthly income (in BDT)
0–8000	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
8001–16 000	0.425 (0.228, 0.794)	0.007	0.458 (0.223, 0.939)	0.033	0.358 (0.195, 0.657)	0.001	0.346 (0.168, 0.713)	0.004
16 001–24 000	1.197 (0.485, 2.951)	0.696	2.601 (0.862, 7.852)	0.080	0.758 (0.300, 1.917)	0.559	1.044 (0.344, 3.168)	0.939
24 001–32 000	0.465 (0.099, 2.197)	0.334	1.571 (0.266, 9.291)	0.863	0.361 (0.077, 1.702)	0.198	0.959 (0.173, 5.328)	0.702
Consume 6–8 food groups of food pyramid (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
<3	1.241 (0.727, 2.119)	0.428	1.188 (0.569, 2.479)	0.647	1.140 (0.674, 1.927)	0.624	1.613 (0.774, 3.359)	0.202
>3	0.419 (0.237, 0.742)	0.003	0.463 (0.232, 0.924)	0.029	0.506 (0.296, 0.866)	0.013	0.572 (0.290, 1.130)	0.108
Vitamin A- and C-rich fruits (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
<3	1.772 (1.061, 2.960)	0.029	1.140 (0.578, 2.248)	0.704	1.029 (0.631, 1.679)	0.908	1.435 (0.218, 0.866)	0.018
>3	0.912 (0.523, 1.591)	0.746	1.526 (0.753, 3.093)	0.241	0.688 (0.410, 1.156)	0.158	0.828 (0.421, 1.628)	0.545
Leafy and non-leafy vegetables (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
<3	1.577 (0.901, 2.761)	0.110	0.923 (0.455, 1.875)	0.825	1.956 (1.119, 3.418)	0.018	1.194 (0.583, 2.443)	0.628
>3	0.601 (0.361, 1.000)	0.050	0.531 (0.271, 1.039)	0.065	0.923 (0.566, 1.506)	0.748	0.842 (0.444, 1.597)	0.598
Fish/meat (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
<3	1.796 (0.868, 3.716)	0.115	1.466 (0.595, 3.612)	0.406	1.860 (0.915, 3.782)	0.086	1.198 (0.496, 2.896)	0.688
>3	0.954 (0.573, 1.586)	0.855	1.143 (0.591, 2.210)	0.692	0.897 (0.551, 1.461)	0.662	0.962 (0.507, 1.825)	0.684
Pulses (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference
<3	0.757 (0.437, 1.312)	0.322	1.519 (0.247, 1.012)	0.050	1.252 (0.739, 2.120)	0.404
>3	0.655 (0.394, 1.087)	0.101	0.775 (0.407, 1.476)	0.439	0.737 (0.447, 1.216)	0.233
Fats and oils (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
<3	2.114 (1.120, 3.988)	0.021	1.548 (0.702, 3.416)	0.279	1.657 (0.897, 3.060)	0.107	0.968 (0.446, 2.103)	0.935
>3	1.359 (0.812, 2.272)	0.243	2.231 (1.111, 4.478)	0.024	1.220 (0.752, 1.980)	0.421	1.065 (0.559, 2.050)	0.847
Sugar-sweetened foods (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
<3	1.330 (0.808, 2.189)	0.262	0.889 (0.467, 1.692)	0.719	1.665 (1.009, 2.748)	0.046	1.575 (0.816, 3.039)	0.176
>3	0.397 (0.222, 0.711)	0.002	2.277 (0.131, 0.587)	0.001	0.884 (0.522, 1.495)	0.645	0.986 (0.497, 1.958)	0.969
Milk and dairy products (days/week)
3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
<3	2.789 (1.556, 4.998)	0.001	1.646 (0.789, 3.433)	0.184	2.766 (1.570, 4.872)	<0.0001	1.803 (0.877, 3.705)	0.109
>3	1.242 (0.652, 2.365)	0.510	1.363 (0.618, 3.003)	0.443	1.618 (0.881, 2.970)	0.120	1.771 (0.848, 3.696)	0.128
Body mass index
Normal	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
Underweight	0.972 (0.463, 2.041)	0.941	0.715 (0.302, 1.619)	0.445	1.391 (0.690, 2.805)	0.356	0.945 (0.426, 2.098)	0.889
Overweight	0.620 (0.335, 1.146)	0.127	0.611 (0.302, 1.235)	0.170	0.610 (0.334, 1.115)	0.108	1.495 (0.247, 0.993)	0.036
Obese	0.903 (0.524, 1.556)	0.713	1.155 (0.602, 2.217	0.738	1.236 (0.737, 2.074)	0.421	1.644 (0.879, 3.075)	0.161
Weekly physical activity (at least 60 min/day)
<5 days	Reference	Reference			Reference	Reference
≥5 days	0.775 (0.502, 1.197)	0.251			0.809 (0.533, 1.228)	0.320
Screen time (hours)
<3	Reference	Reference	Reference	Reference	Reference	Reference	Reference	Reference
≥3	1.335 (0.856, 2.082)	0.202	1.697 (1.033, 2.788)	0.037	1.718 (1.112, 2.655)	0.015	2.217 (1.357, 3.622)	0.001
Average sleep
Inadequate	Reference	Reference			Reference	Reference
Adequate	0.897 (0.583, 1.381)	0.623			1.108 (0.732, 1.675)	0.628

Bold values indicate significance level established at p<0.05.

Discussion

Recent decades have seen shifts in diet and lifestyle that have contributed to rising malnutrition and diet-related chronic diseases.⁶³ University students, particularly vulnerable during their transition to independence, often struggle to maintain healthy eating habits.⁶⁴ This period is marked by challenges such as weight management issues, increased screen time and decreased physical activity.⁶⁵ Mental health concerns among university students have also emerged as a critical issue for global health policies and campus services.^{31 66} Despite the rising importance of mental health, research on the mental health status of university students in Bangladesh is still limited. This study aims to identify the factors influencing mental health outcomes, with a specific focus on depression and anxiety among university students.

The study’s assessment of BMI revealed that 50% of participants were classified as having a normal weight, while 21.7% were identified as obese, 18.8% as overweight and 9.5% as underweight. Similar research conducted at various universities in Bangladesh has also reported the presence of overweight and obese students.⁶⁷,⁷² The study also revealed that female students had a higher obesity rate (23.1%) compared with their male counterparts, with the majority of obese individuals falling within the 23–24 age category. Similar findings have been observed in other research studies.^{67 69} A lower obesity rate among male students is anticipated as males tend to be more physically active than females. For instance, research from the University of Tuzla in Bosnia indicated that female students were less active than males.⁷³ This disparity may be attributed to less interest, lack of social support for engaging in physical activities and a lower perception of enjoyment in participating in physical education.⁷⁴,⁷⁶ Similarly, the current study found that male students were more physically active than their female counterparts. In our study, we observed a lower prevalence of underweight compared with the findings of refs.^{70 77} but a higher prevalence than those reported by ref.⁷⁸ These discrepancies may be linked to the varying levels of urbanisation in the cities where the student populations are located.⁷⁹ Notably, our findings indicated a higher prevalence of underweight among male students compared with females, which contrasts with other studies.^{69 80} This difference may be due to greater physical activity levels among male students, which can positively impact their BMI and energy expenditure.^{81 82}

The study used the PHQ-9 and GAD-7, both of which are widely recognised tools for screening depression and anxiety. These instruments are grounded in the DSM-IV criteria and have been validated against clinical interviews, making them suitable for use in both clinical and research settings to inform treatment decisions.^{83 84} The analysis from these tools revealed that 29% of students experienced anxiety, with 36.1% reporting mild anxiety and approximately 11.5% suffering from severe anxiety. Additionally, 33.2% of students were affected by depression, including 39.8% with mild depression and 8.3% experiencing severe depression—rates significantly higher than those observed in other studies.^{85 86} Several factors may contribute to these elevated levels of anxiety and depression among students in Bangladesh. Lifestyle choices, including diet, physical inactivity, sleep patterns and screen time, play a significant role, alongside academic and social pressures.³¹ The study found that male students exhibited higher levels of anxiety and depression than their female peers, a trend consistent with findings by another study in Bangladesh.⁸⁷ This disparity can be attributed to several factors. Male students frequently experience significant stress and pressure to perform well academically, which can contribute to the emergence of depressive symptoms.⁸⁸ Moreover, societal and cultural norms surrounding masculinity can heighten the risk of anxiety and depression in males.⁸⁹ Conventional views of masculinity often discourage men from expressing emotions and showing vulnerability, leading to repressed feelings that may contribute to mental health problems.⁹⁰ Furthermore, our findings indicated that a high proportion of male students reported inadequate sleep and relatively high screen time. These specific stressors, combined with societal expectations, likely contribute to the increased levels of anxiety and depression observed among male students in our study. The dietary habits of university students were evaluated using the IDDS, which serves as a key indicator for assessing the quality of dietary habits and reflects the adequacy of micronutrient intake.⁹¹ The IDDS is particularly appealing due to its ease of measurement and interpretation. In resource-limited settings, there is a critical need for straightforward, user-friendly and accurate methods to assess nutritional status.⁹² Consequently, validating any dietary assessment tool is essential to ensure its effectiveness and reliability.⁹¹,⁹³ Qualitative analysis of the dietary habits and consumption patterns of the university students indicated that, apart from meat, a high proportion of female participants consumed a wide range of food groups, including cereals and grains, white roots and tubers, vitamin A-rich vegetables, dark green leafy vegetables, fruits high in vitamin A, eggs, fish and seafood, milk and dairy products, as well as pulses and nuts compared with males, aligning with findings from other studies.^{94 95} Additionally, the dietary diversity scores indicated that a larger proportion of male students had the lowest scores compared with females. This variety in food choices may stem from women’s more positive attitudes towards fruits and vegetables,⁹⁶ higher perceived behavioural control⁹⁷ and greater nutritional knowledge⁹⁸ compared with men. Additionally, women are more likely to take a deliberate approach to their diets and hold stronger health beliefs.⁹⁹ Conversely, male students tended to consume more meat, likely due to easier access to meat products and societal norms that encourage men to prioritise meat in their diets.¹⁰⁰ Moreover, a larger percentage of female students also reported higher consumption of fats-oils and sugar-sweetened foods and beverages compared with their male peers. Consequently, these dietary patterns contributed to a higher prevalence of overweight and obesity^{101 102} among female students compared with males in our study.

Factors such as gender, personal monthly income and screen time play a significant role in influencing mental health, including anxiety and depression, among university students. The study found that females had a lower likelihood of experiencing anxiety and depression compared with males. This may be attributed to male students facing academic stress and societal pressures related to masculinity, which increase their risk by discouraging emotional expression and leading to repressed feelings that negatively impact mental health.⁸⁸,⁹⁰ The study also identified a significant association between personal income and mental health, showing that students with lower incomes reported higher levels of anxiety and depression compared with their higher-earning peers. Disrupted income and joblessness are critical stressors, with prolonged unemployment and financial insecurity being major contributors to increased rates of anxiety and depression among university students in Bangladesh.⁸⁵ Research indicates that unemployment is significantly linked to mental and somatic disorders, limiting individuals' opportunities for achievement and satisfaction and ultimately impairing psychological functioning.¹⁰³ It was also found that elevated screen time among university students was significantly associated with anxiety and depression, likely due to the adverse effects of excessive screen use on brain function and cognitive abilities.¹⁹ A study from China found that screen time, particularly related to studying, is negatively associated with mood,¹⁰⁴ while another study indicated a correlation between screen time and the severity of anxiety and depression.¹⁰⁵ BMI was significantly associated only with depression, showing that overweight students experienced higher levels of depression compared with those with normal weight. This may be linked to body image dissatisfaction driven by societal pressures, which can lead to internal stress and negative emotions, increasing the risk of mood disorders.¹⁰⁶ Overweight students often face bullying or teasing from peers, further exacerbating anxiety and depressive symptoms.¹⁰⁷ Moreover, excess body fat and unhealthy eating habits can raise inflammatory markers, contributing to a higher risk of depression.¹⁰⁸

The consumption patterns and dietary habits—including the intake of 6–8 food groups, as well as the consumption of wheat or rice, pulses, fats and oils, and sugar-sweetened foods and beverages—were significantly associated with the development of anxiety. The findings revealed that students consuming wheat or rice and pulses <3 days/week were linked to increased anxiety, consistent with another research.¹⁰⁹ This may be connected to biological mechanisms that relate diet to mental health, including pathways associated with inflammation, oxidative stress, mitochondrial dysfunction and gut microbiota.^{110 111} Conversely, higher consumption of fats and oils, as well as sugar-sweetened foods and beverages, >3 days/week was significantly linked to developing anxiety. Diets high in fat can enhance the expression of genes related to serotonin production, potentially leading to anxiety-like symptoms. Similarly, high-sugar diets may alter dopamine receptor expression, negatively impacting mood.¹¹²,¹¹⁴ In the case of depression, only the consumption of vitamin A- and C-rich fruits was significantly associated. Students who consumed fewer of these fruits had a higher likelihood of developing depression compared with those who consumed more, aligning with previous research.^{115 116} Vitamins C and A act as antioxidants that prevent oxidation and neutralise harmful free radicals. Because oxidative stress contributes to depression, higher intake of these vitamins is associated with lower depressive symptoms.¹¹⁷,¹¹⁹

The findings of this study highlight the significant prevalence of moderate-to-severe depression and anxiety among university students, underscoring the urgent need for intervention from both healthcare professionals and university administrators.¹²⁰ To address these concerns, universities should implement proactive strategies to promote mental health and well-being among students. Raising awareness about the benefits of regular physical activity, responsible internet use and good sleep hygiene is crucial in mitigating the risk of mental health issues.³¹ Organising mental health programmes on campus can help educate students about these preventive measures while fostering a supportive environment.⁴² Beyond mental health awareness, universities should equip students with the necessary skills to cope with common stressors, such as loneliness, personal independence, family and peer pressure, academic challenges, studying in a second language, demanding lecture schedules and concerns about future career prospects.³¹ Given the strong connection between mental health and lifestyle factors, universities should prioritise interventions that promote a balanced diet, regular physical activity and healthy sleep habits.^{121 122} Poor diet quality has been linked to adverse mental health outcomes, making nutritional support essential.¹²³ Universities can improve campus dining options, offer diverse and nutritious meals and provide designated spaces for students to prepare and consume home-cooked food. Additional support, such as student discounts at supermarkets and educational resources on nutrition, can encourage healthier choices.¹²⁴ Addressing food insecurity is also essential in ensuring student well-being. Universities can implement multiple strategies, such as providing nutrition education, sharing affordable meal recipes, distributing meal and produce vouchers or facilitating access to food assistance programmes.¹²⁵ By integrating these comprehensive support systems or strategies, universities can create a healthier and more conducive environment for students, ultimately improving their mental well-being and academic performance.

Strengths and limitations

This cross-sectional study offers valuable insights into the mental health of university students in Bangladesh, examining key factors that influence their well-being in a short amount of time. By including students from multiple universities and using both open-ended and closed-ended questions, the study strengthens its geographical representation and provides a broad perspective on mental health challenges. The use of social media platforms such as Facebook and WhatsApp facilitated widespread survey distribution, enhancing student engagement. Validated tools, including PHQ-9 for depression, GAD-7 for anxiety, the FIES for food insecurity and the FFQ for dietary intake, ensured robust, reliable and comparable results. Strict data control measures, such as unique identifiers to prevent duplicate responses and multiple reminders to reduce non-response bias, were also implemented. The findings highlight the impact of dietary habits on mental well-being, laying the foundation for future research and interventions. However, limitations include the use of convenience sampling via social media, which may introduce selection bias and limit generalisability. The lack of quantitative data on food intake and exercise duration restricts the ability to assess the relationship between these factors and mental health. Time and budget constraints further limited the scope of data collection. Additionally, the online survey method may have excluded students with limited internet access, especially in rural or underserved areas, affecting sample diversity. Despite using student IDs or university emails for verification, uncertainty remains regarding the authenticity of some responses, given the survey was shared through social media groups. The reliance on self-reported height and weight for BMI calculation could also introduce measurement errors. Finally, the cross-sectional design prevents causal inferences, though associations between variables can be observed. Despite these limitations, the study provides critical insights into the mental health challenges of university students in Bangladesh, offering evidence to guide targeted interventions for policymakers, educators, and health professionals.

Scope for future research

Given the limitations of this study, future research can expand on several aspects to provide a more comprehensive understanding of the mental health status of university students in Bangladesh. Future studies could incorporate quantitative data on daily food portions, calorie intake and exercise duration to explore the relationship between lifestyle practices, BMI and mental health more thoroughly. Researchers should aim to use a more representative sampling method, such as stratified or random sampling, to better capture the diversity of the student population and improve the generalisability of findings. Additionally, future studies could address the digital divide by ensuring access for students with limited internet connectivity through alternative data collection methods, such as in-person surveys. Expanding the scope of research to include longitudinal studies could also provide a deeper understanding of the long-term effects of mental health interventions and the evolution of mental health trends over time. These steps would enable the development of more targeted and effective mental health support systems for university students.

Conclusion

This study highlights the key factors affecting mental health outcomes among university students in Bangladesh, revealing a concerning prevalence of anxiety and depression, particularly within this demographic. The findings highlight concerning trends of obesity and unhealthy eating patterns, especially among females, which further complicate mental health challenges. Key factors such as gender, personal income, screen time and dietary diversity are crucial for understanding mental health outcomes as inadequate intake of essential food groups and excessive consumption of fats and sugars significantly contribute to anxiety and depression, pointing to the need for targeted interventions. Despite limitations, such as the lack of detailed dietary data, this research offers valuable insights into the dietary and mental health landscape of Bangladeshi university students. It advocates for holistic strategies to promote healthier eating habits and address students' mental health needs, with future research focusing on comprehensive lifestyle data to enhance understanding and inform effective policies and interventions.

Data availability statement

Data are available upon reasonable request.