Introduction

Night eating syndrome (NES) is a type of eating disorder characterized by the consumption of at least one-third of one’s daily caloric intake after the last meal of the day or throughout the nocturnal evening on at least two separate occasions per week (Kucukgoncu et al. 2014). (NES) also characterized by recurrent episodes of night eating, evident through excessive food consumption after the evening meal or eating after awakening from sleep, often associated with significant distress and/or impairment in functioning (Sakthivel et al. 2023).

The term “NES” was first used in 1955 (Stunkard et al. 1955). However, because the definition of NES has changed over time, there is no standardized definition for it. As a result, it has been difficult to obtain precise information about NES prevalence and to compare the outcomes of different studies (Kucukgoncu et al. 2014). The first diagnostic criteria of NES was established in 1955 (Stunkard et al. 1955). NES is now classified in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) under the heading “Other Specified Feeding or Eating Disorder” (Association 2013). The presence NES does not appear to be secondary to any psychiatric disorder or dependency. Moreover, the night eating behavior is recognized in NES unlike the sleep-related eating disorder (SRED) (Vinai et al. 2012). NES is characterized by decreased appetite during the daytime and increased appetite (hyperphagia) during the evening and nighttime hours. Additionally, NES has been linked to changes in circadian rhythm, changes in mood, and other eating disorders (Night Eating Syndrome (NES), 2021). Development of NES in university students is dependent on several factors, not limited to, transition from late adolescence to early adulthood, developmental stress, unhealthy eating habits (Riccobono et al. 2020), peer pressure, gender identity (Guo et al. 2020). The performance stress in academics, combined with emotional vulnerability and the uncertainty of prospective career, increases the propensity for depression, anxiety, and stress (Mohammad Miraj et al. 2022). The precise prevalence of NES in the general population remains somewhat elusive, with estimates ranging from 1.5 to 4.6%. This prevalence is notably higher in those with obesity (3–15%) and individuals with psychiatric conditions, particularly depression (up to 15%) (Sara Haneef et al., 2024). Early adulthood has been identified as the typical age for the onset of NES (Vander Wal 2012). NES prevalence among university students ranged from 4.2% in the United States and Malaysia (Runfola et al. 2014);(Dzulkafli et al., 2020) to 15% in Brazil (Borges et al. 2017).

The relationship between NES and BMI is up for debate. In some studies, obesity has been found to be associated with NES. Other studies, however, found no link between BMI and NES (Shoar et al. 2019);(Kaur et al. 2021).

A cross-sectional study conducted in Malaysia to study the association between NES, psychological distress, and sleep quality among university students showed that NES was significantly associated with psychological distress and sleep quality (Chow 2023). Similarly, NES was found to be significantly associated with poor sleep quality among university students in Malaysia (Gan et al. 2019). Sleep quality is a common term in sleep medicine that refers to a group of sleep measurements such as total sleep time, total wake time, sleep onset latency, efficiency of sleep, degree of fragmentation, and in some cases sleep disruptive events (Krystal and Edinger 2008). A recent systematic review looked at the sociodemographic factors related to NES. It has been discovered that the presence of NES was not affected by age. NES was also found to have no relationship with gender, educational level, income, having children, living with a spouse, or smoking (Kaur et al. 2021). Because of the psychological distress caused by the COVID-19 pandemic, it has been reported that the psychosocial impact of the disease has contributed to disordered eating behaviors such as uncontrolled and emotional eating (Ramalho et al. 2021). A cross-sectional research was conducted among 568 students (78.7% women) aged 18–25 years. Students completed a survey including demographic information, Pittsburgh Sleep Quality Index (PSQI), Eating Attitude Test-26 (EAT-26), Night Eating Questionnaire (NEQ), and Beck Depression Inventory (BDI). Anthropometric measurements were taken. Students were grouped based on poor (PSQI > 5) and good (PSQI ≤ 5) sleep quality. The results showed that there was a significant association between PSQI > 5 and NES after adjusting for age, sex, class standing, residency, smoking status, and alcohol consumption on logistic regression. Those results suggest that PSQI > 5 is a significant risk for the NES, but not other disordered eating behaviors or obesity (Suna, G et al., 2022). The primary goal of this study is to determine the prevalence of NES among Palestinian university students and the quality of their sleep during the second year of COVID-19 pandemic. The second objective is to investigate the relationship between NES, sleep quality, body mass index socio-economic and lifestyle factors.

Methods

Data and method

Study design

This cross-sectional study was conducted in June and July 2021 during the online taught summer semester, which is the shortest semester in the academic year. A structured online questionnaire was filled by students from the largest university in West Bank, Palestine, An-Najah National University. The online questionnaire was made on Google forms and shared via the university web portal “https://zajel.najah.edu” and courses web pages.

At the start of the questionnaire, a statement clarified that participation is voluntary.

Collected data included: socio-demographics, medical history and lifestyle, the Night Eating Questionnaire (NEQ), and Pittsburgh Sleep Quality Index (PSQI) questionnaire. Simple random sampling was used. Once the calculated sample size was reached accepting responses was stopped. The total number of completed responses was 336. After removing duplicated responses, 333 participants’ responses were included in the data analysis.

Participant’s characteristics

Palestinian students from An-Najah National University were included in the study. Participants who reported using psychiatric medication or experiencing psychological problems were excluded from the study, as were participants who were not registered for the summer semester.

The sample size was calculated using G Power software. An alpha level of (0.05) was considered, as were two-sided p-values of (0.05) and (80%) power. The required sample size is 300 students. Considering the dropout rate of 5% and missing data, the sample size is considered to be 320 participants.

Collected data and study instruments

Socio-demographic information included: age, gender, marital status, living place, living nature, university year, college, family income, university fee payment. medical history and lifestyle information included: presence of chronic disease, surgery, medication, smoking, smoking type, weight, height, diet, diet time, diet reason, diet satisfaction, working out, walking, walking times per week, walking time duration (min), screen time for studying (hour), and screen time for leisure (hour). In this study, we used a back-to-back translated Arabic version of PSQI and NEQ.

The NEQ was used to assess the presence of NES among study participants. NEQ items include: hunger in the morning and timing of first meal (2 items), food cravings and control of eating behaviour before bedtime (2 items) and at the night-time awakenings (2 items), food eaten after dinner percentage (1 item), initial insomnia (1 item), nocturnal awakenings and ingestion of food frequency (3 items), and mood disturbance (2 items), and realization of nocturnal eating episodes (1 item). Each item has a 0–4 Likert’s scale, except for item 7 that has a zero scored option: check here if your mood does not change during the day. The total NEQ score is calculated by reversing 1, 4, and 14 items’ code. Then, By summing all items’ scores except for item 13 as it does not assess a NES symptomatology degree. A total score equal or higher than 25 indicates the presence of NES (Allison et al. 2008). In this study NES had a high reliability with a Cronbach’s alpha of 0.697.

For sleep quality and disturbances measurement, The Pittsburgh Sleep Quality Index (PSQI) was used. PSQI contains 19 self-rated items and additional 5 questions answered by by bed- or roommate (if exists). Only the self-rated questions are used for scoring. These questions’ scores produce 7 “Component Scores”. In this study we included the following component scores: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, use of sleeping medication, and daytime dysfunction. Each of these component score has a 0–3 score range. Where a score of 0 indicates “No difficulty”, and a score of 3 indicates “Severe difficulty (Buysse et al. 1989).

Data analysis

Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 23. Mean values and frequencies were calculated for sample description. Chi-square test and independent t-test were applied to evaluate the relationship between the presence NES and study variables. Pearson correlation and multiple linear regression tests were used to evaluate the relationship between NEQ scores and study variables. In the regression model, age and gender were not included in the model. This decision was made since the study sample consisted of homogeneous group in terms of age. Therefore, there was no need to adjust for age. Additionally, gender wasn’t associated with NES, so it was not included in the model as well.

Results

Participants’ characteristics

A total of 335 respondents participated in the study, only 333 included in the final analysis; two responses were excluded due to: one of them reported that he was take regular sleep medication and the other one reported he had a psychiatric problem. The participants’ average age was 21 ± 2.66 years (range: 16 to 33).

Tables 1 and 2 indicate the sociodemographic and lifestyle characteristics of the participants.

Table 1 Participants’ socio-demographics
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Table 2 Participants’ lifestyle
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NEQ scores and NES prevalence

The NEQ scores of the participants varied from 14 to 56, with a mean of 36.12 ± 9.15. According to NEQ, the prevalence of NES was 82.6% across the study population, with 275 persons having NES. Only 17.4% (58) of those surveyed said they didn’t have it.

Sleep quality

Sleep quality index subscales mean scores were (1.12 ± 0.94) for subjective sleep quality, (1.49 ± 1.04) for sleep latency, (0.74 ± 1.02) for sleep duration, (0.65 ± 1.03) for habitual sleep efficiency, (0.26 ± 0.7) for use of sleep medications, and (1.25 ± 0.82) for daytime dysfunction.

NES and participants’ characteristics

The relationships between NES and participants’ sociodemographic, medical histroy and lifestyle factors are shown in Table 3. There was no relationship between NES and any sociodemographic or lifestyle factor. Correlation test was done between NEQ scores and participants’ characteristics continuous variables (Table 4). NEQ scores were only significantly correlated with walking times per week (p < .05).

Table 3 Relationship between NEQ and participants’ socio-demographics and lifestyle
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Table 4 Association between NES and participants’ characteristics
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NES and sleep quality

Table 5 shows the relationships between the NES and sleep quality subscales. Subjective sleep quality (p < .01), sleep latency (p < .01), and daytime dysfunction (p < .05) were all significantly associated with NES. The NEQ scores were compared to the sleep quality subscale scores in a correlation test (Table 6). Subjective sleep quality (p < .01), sleep latency (p < .01), sleep duration (p < .05), and daytime dysfunction (p < .01) were all significantly linked with NEQ scores.

The prediction of the NEQ score based on sleep quality subscales was evaluated using multiple linear regression. A significant regression equation was found (F (4, 351) = 21.200, p < .01), with an R2 = 0.135.

Table 5 Relationships between NES and sleep quality subscales
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Table 6 Correlations between NEQ and sleep quality subscales
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Discussion

NES prevalence

This study successfully established the prevalence of NES and its relationship to socio-demographics, lifestyle, and sleep quality in a representative sample of Palestinian university students. NES was discovered in 82.6% of the research participants. This figure is considerably greater than the reported NES prevalence rates. NES was only found in 1.5% of an Omani Arab adult’s sample (Zadjali et al. 2015). According to two studies, NES was identified in 10.3% and 9.5% of Saudi medical students (Ahmed et al. 2019);(Ahmad et al. 2019). In Malaysian university students, the prevalence of NES was 12.2% and 4.2% (Gan et al. 2019);(Dzulkafli et al., 2020). University students in the United States had a NES prevalence of 5.69% and 4.2% (Nolan and Geliebter 2012);(Runfola et al. 2014). 15% of Brazilian students enrolling in higher education institutions have NES. Furthermore, they discovered a link between NES and depression, anxiety, and stress in pupils (Borges et al. 2017).

It’s important to keep in mind that this research took place in the middle of 2021, during the COVID-19 pandemic. This could have an impact on the presence of NES in the study sample. During the COVID-19 pandemic, NES was discovered to be linked to exhaustion, depression, and anger in Turkish athletes (Turgut et al. 2020). According to a Portuguese study, the COVID-19 pandemic’s psychosocial impact may lead to disordered eating behaviors such as uncontrolled and emotional eating as a result of psychological distress (Ramalho et al. 2021). A cross-sectional study conducted in United Arab Emirates (UAE) during COVID-19 period found that, during the pandemic, 31% reported weight gain and 72.2% had less than eight cups of water per day. Furthermore, the dietary habits of the participants were distanced from the Mediterranean diet principles and closer to “unhealthy” dietary patterns. Moreover, 38.5% did not engage in physical activity and 36.2% spent over five hours per day on screens for entertainment. A significantly higher percentage of participants reported physical exhaustion, emotional exhaustion, irritability, and tension “all the time” during the pandemic compared to before the pandemic. Sleep disturbances were prevalent among 60.8% of the participants during the pandemic. Although lockdowns are an important safety measure to protect public health, results indicate that they might cause a variety of lifestyle changes, physical inactivity, and psychological problems among adults in the UAE (Cheikh Ismail et al., 2020). Furthermore, it is hypothesized that the unusual lifestyle imposed by COVID-19 quarantine resulted in cardician misalignment, altering eating and sleeping habits (Da Silva et al. 2020).

NES and BMI

According to BMI, 44.7% of the study participants were normal weight, 23% were overweight, 18.7% were underweight, and 13.6% were obese. In our sample, there was no evidence of a link between NES and BMI categories or scores. Furthermore, there was no significant relationship between NEQ and BMI. Similarly, among university students in the United States, Saudi Arabia, and Malaysia, there was no significant association between NES and BMI (Runfola et al. 2014);(Ahmed et al. 2019);(Gan et al. 2019);(Dzulkafli et al., 2020). This was also observed in women in the United States (Rogers et al. 2006). Higher BMI, on the other hand, was associated with NES among Saudi university students (Ahmad et al. 2019). Furthermore, particular populations such as depressed patients (Kucukgoncu et al. 2014) and obese adults with metabolic syndrome showed a significant association between NES and BMI (Ali et al. 2020).

It has been claimed that NES may play a role in obesity development, however this has yet to be proved (Shoar et al. 2019). Because it entails excessive calorie eating at night, NES might be considered a risk factor for obesity and an increase in BMI. Furthermore, it has been discovered that NES is more common and linked to weight gain among obese adults (Muscatello et al. 2022). However, the literature on the link between NES and BMI is inconsistent (Shoar et al. 2019);(Kaur et al. 2021);(Muscatello et al. 2022). The conflicting results regarding the link between NES and BMI are most likely owing to differences in measuring methodologies as well as the involvement of several moderators such as age, socioeconomic status, and others that were not systematically researched in available studies (Bruzas and Allison 2019). Obesity is also a complicated and multifaceted condition in which genetic, behavioral, environmental, and socioeconomic variables all play a role (Hruby and Hu 2015). As a result, more research into the link between BMI and NES should be done.

NES and sleep quality

In this study, NES was significantly related to higher scores of subjective sleep quality (p < .01), sleep latency (p < .01), and daytime dysfunction (p < .05). Additionally, NEQ scores were significantly correlated with these scores in addition to sleep duration (p < .05) score. In multiple linear regression, subjective sleep quality (p < .01) and sleep latency (p < .01) predicted NEQ score.

Similarly, among Malaysian university students, poor sleep quality was linked to NES (Gan et al. 2019). People with NES reported greater subjective sleep disturbances, such as short sleep, poor sleep quality, and difficulties falling asleep, than their non-NES counterparts, according to a study conducted in the United States (Birketvedt et al. 1999). Likewise, American women with NES reported more sleep disturbances than women without NES, including worse sleep quality, shorter sleep duration, and more awakenings. Furthermore, NES in women was linked to decreased total sleep time and sleep efficiency when their sleep patterns were compared using polysomnography (PSG) (Rogers et al. 2006). In an Egyptian study, the NEQ was found to have a statistically significant positive connection with sleep latency and minutes spent awake in obese patients with metabolic syndrome (Ali et al. 2020).

In our research, neither habitual sleep efficiency nor the usages of sleep medicine subscale scores were shown to be significantly linked with NEQ. NEQ was found to be significantly associated with all PSQI subscale scores in a UK study (Cleator et al. 2013). The link between NES and sleep disruption is unquestionably complicated. Aside from the psychological aspects outlined above, it’s possible that sleep deprivation-induced metabolic changes have a role in the occurrence of NES. Alterations in glucose regulation and appetite neuroendocrine control are two of the negative impacts of prolonged partial sleep loss (Knutson et al. 2007).

Strengths and limitation

Within a representative sample of Palestinian university students, this study was successful in establishing the prevalence of NES and the relationship between it and socio-demographics, lifestyle, and sleep quality. The study is a cross-sectional study, examining only the relationship, not cause and effect. There is a possibility that the COVID-19 pandemic influenced the results, and we do not have any previous data to compare them with.

Conclusion

The presence of NES was identified in 82.6% of the study subjects. No indication of a connection between NES and BMI categories or scores could be found in our study population. In addition, there was no statistically significant association between NEQ and body mass index (BMI). Additionally, no sociodemographic or lifestyle component was found to be statistically significant in relation to NES. The presence of NES was found to be associated with higher scores on subjective sleep quality (p < .01), sleep latency (p < .01), and daytime dysfunction (p < .05) measures. Additional to this, the NEQ scores were shown to be substantially connected with these scores, as well as the sleep duration scores (p < .05). Subjective sleep quality (p < .01) and sleep latency (p < .01) were found to be significant predictors of the NEQ score in multiple linear regression.