Lifestyle,meal times,and sleep patterns changes in higher education professors during COVID-19: Association with non-communicable chronic diseases

Abstract

BACKGROUND:

In response to the COVID-19 pandemic, educational institutions had to swiftly adapt and transition to remote teaching in order to maintain academic activities. However, these changes presented a number of challenges for professors, which could have negative effects on their health.

OBJECTIVE

To analyze the association between changes in dietary and sleep habits, physical activity level, and sedentary behavior with the development of non-communicable diseases (NCDs) among Brazilian higher education professors during the pandemic period.

METHODS

This is a cross-sectional and retrospective study conducted using an online form. Generalized linear models, adjusted for age, sex, and body mass index, were used to verify the difference between pre-pandemic and pandemic periods. Logistic regression models were used to predict the odds ratio (OR) for the development of NCDs according to physical activity time, sedentary behavior time, dietary and sleep patterns.

RESULTS

A total of 936 professors residing across Brazil participated in the survey. The duration of sedentary behavior increased, sleep duration slightly decreased, and meal times shifted to earlier during the pandemic. A total of 22.9%of the participants reported the diagnosis of some NCDs during this period. Physical activity practice was associated with a lower risk of diseases during the pandemic, regardless of the intensity performed. On the other hand, late eating habits and excessive food consumption during the pandemic were associated with a higher risk.

CONCLUSION

The results provide data that can help in the development of public policies that promote health actions to minimize the consequences associated with the pandemic period.

Keywords

Physical activity sedentary behavior feeding behavior chrononutrition university professor coronavirus

1 Introduction

In December 2019, a new disease, COVID-19, was identified in China, caused by the transmission of the virus called SARS-CoV-2. As it spread around the world, the World Health Organization officially declared it a pandemic and an international public health crisis [1]. The health issue prompted immediate responses from government institutions to establish coping measures, such as recommendations for wearing masks, hand hygiene, social distancing, and isolation [2].

The COVID-19 pandemic compelled universities worldwide to rapidly reorganize and adopt remote learning to sustain their academic activities. However, these changes presented numerous challenges for professors, including learning to deal with technology, adapting to the new virtual learning reality, and reinventing and innovating their pedagogical strategies while preserving the quality of teaching. Additionally, they faced the absence of practical scenarios and the complexity of training new professionals [3,4, 3,4]. With these changes came increased working hours, challenges in balancing personal and professional life, and dealing with emotions triggered by the current atypical moment [3]. It is believed that these changes are associated with increased levels of stress, anxiety, and depression, as shown in previous studies [5,6, 5,6]. It is assumed that these changes may also affect the lifestyle, sleep quality, and eating behavior of these individuals.

Among various work activities, teaching is considered one of the most stressful, and it is known that this was heightened during the pandemic period [3,6, 3,6]. Prolonged stress, marked by an increase in cortisol levels in the body, leads to an augmented sensation of hunger, especially in the pursuit of sugary foods. This desire to consume a specific type of food is defined as “food craving”, a multidimensional concept that encompasses emotional, behavioral, cognitive, and physiological axes [7, 8]. Thus, the psychoemotional and environmental changes instituted during the pandemic are closely related to changes in eating behavior, potentially leading to weight gain and, consequently, the development of non-communicable diseases (NCDs), such as obesity [9].

Previous studies have reported insufficient physical activity and high levels of sedentary behavior among the teaching population [10, 11]. In addition, time constraints were among the most reported barriers for these professionals [12]. Therefore, given these findings and the current pandemic scenario, it is believed that there has been an increase in sedentary lifestyles due to remote work and the greater workload that these professionals have been subjected to during the pandemic. A study by McDowell et al. [13] reported that individuals who began working from home during the COVID-19 pandemic spent more time sitting and viewing screens compared to those whose jobs remained unchanged. This is a worrying fact since a sedentary lifestyle is associated with harmful health outcomes, such as the development of cardiovascular disease, obesity, and type 2 diabetes [14, 15].

Additionally, remote work during the pandemic has also exposed professors to increased computer use, which is related to the development of musculoskeletal disorders [16, 17]. Moreover, greater exposure to computer and cellphone screen light, often during nighttime, negatively impacts sleep and the circadian rhythm [18, 19]. Sleep is one of the main pillars to the proper functioning and well-being of the human body, and its deprivation can affect the immune system, metabolism, and mental health, causing deleterious effects on health [20, 21]. Studies have shown that sleep timing and duration are associated with the development of NCDs [20 , 23]. In addition, an association has also been found between increased screen time, poor sleep quality, decreased efficiency, and depression [24,25, 24,25].

In this manner, the surge in demands placed on educators during the pandemic –ranging from adapting to remote teaching, increased workloads, providing emotional support to students in an uncertain scenario, to the impacts of the pandemic on personal life –might contribute to alterations in lifestyle habits. These changes can manifest as disrupted sleep patterns, physically inactive lifestyles, and less healthy dietary choices, fostering an environment conducive to the onset of NCDs. The dearth of prior studies investigating these shifts in the context of the pandemic among educators underscores the pressing need to explore and comprehend the repercussions of this period on this population. Thus, the present research aimed to analyze the association between changes in dietary patterns, sleep patterns, physical activity, and sedentary behavior, and the development of NCDs among professors in Brazilian higher education during the COVID-19 pandemic.

2 Methods

A cross-sectional and retrospective study was conducted from September to December 2021 with 936 professors from Higher Education Institutions (HEIs) residing throughout Brazil, through an online and anonymous form. The criteria adopted for the inclusion of volunteers in the research were being a resident in Brazil, being a professor at an HEI, and practicing the profession during the research period. The exclusion criteria were refusal to agree to the Free and Informed Consent Form (FICF) before filling out the questionnaire and incomplete and/or unanswered questions in the form.

The sample size calculation was performed using G*Power software. The analysis of variance (ANOVA) test for independent groups was selected, with a statistical power (probability of error 1 β) of 0.95 and a medium effect size of 0.25. A minimum sample size of 210 participants was determined as large enough to make significant inferences. The protocol and the consent form were approved by the Ethics Committee of the Federal University of Uberlândia (Ethical approval number: 51479821.0.0000.5152; approved September 16, 2021).

2.1 Questionnaire

Data were collected through an online and anonymous Google Forms® questionnaire, consisting of five sections:

2.1.1 Socioeconomic, demographic, and clinical data

Data such as age, gender, Brazilian region of residence, body mass, height, and self-reported morbidity were collected. Additionally, it was also asked whether there was a change in body mass during the pandemic (gain or loss) and how many kg resulted from that change. Body Mass Index (BMI) was calculated (body mass in kg/height in m²) and individuals were classified as eutrophic (18.5–24.9), overweight (25.0–29.9), and obese (30.0-above) [26]. To evaluate the presence of self-reported morbidity, the question “Has a doctor ever diagnosed you with any of the following illnesses?” was asked. Diseases such as hypertension, obesity, diabetes mellitus, high cholesterol, kidney disease, depression, respiratory, and musculoskeletal diseases were investigated, with the possibility to describe any additional alternatives in the “others” option. Furthermore, respondents were asked whether any of the diseases mentioned above were diagnosed during the pandemic, with the option to specify the disease.

2.1.2 Evaluation of physical activity level

The level of physical activity was evaluated by the Nordic Physical Activity Questionnaire-short (NPAQ-short). The NPAQ-short is reliable (test-retest reliability of 0.80 to 0.82) and valid for monitoring compliance with WHO recommendations on physical activity [27]. With its two questions, the instrument retrospectively measures the time (minutes) spent on moderate to vigorous physical activity (MVPA) and vigorous physical activity (VPA) both in leisure and occupational time. In addition to NPAQ-short, changes in the total physical activity level, including light activities, were evaluated through a five-point Likert scale (small reduction, large reduction, small increase, large increase, and no change).

2.1.3 Evaluation of sedentary behavior

To assess the daily duration of sedentary behavior as a linear variable, an objective question was asked, encompassing the time spent on sedentary activities performed both in a sitting and lying position, as well as in a reclining position on one weekday. The question was: “On a WEEKDAY (Monday to Sunday), on average how many hours/minutes PER DAY do you sit, lie down, or recline while watching television, using a computer and/or cell phone, playing video games, talking to friends, or doing other activities?”.

2.1.4 Evaluation of the dietary pattern

Changes in dietary behavior resulting from social distancing were assessed through objective questions such as “Do you usually have breakfast?”, and so on for all other meals, and the approximate amount consumed during meals with the options to mark “It is the same as before and during the pandemic”, “It was larger before the pandemic”, “It is larger during the pandemic”, “I do not usually have this meal”. In addition, objective questions were asked about the timing of daily meals, consumption of food after 11 p.m., and excessive food intake before and during the pandemic.

2.1.5 Evaluation of sleep habits and quality

To evaluate sleep habits and quality, objective questions that included the time of going to bed on weekdays and weekends, the time between lying down in bed to sleep and being able to fall asleep on weekdays and weekends, and the time of waking up on weekdays and weekends were developed. All questions were asked referring to the period before and during the pandemic. The sleep assessment was based on the assessment carried out by Mota et al. [28]. Sleep duration was computed using the weighted average of self-reported sleep duration taking into consideration both weekdays and weekends: [(Reported current weekday sleep duration×5) + (Reported current weekend sleep duration×2)]/7 [29].

The questions related to the assessment of physical activity level (minutes), sedentary behavior (minutes), dietary pattern (yes/no), and sleep pattern (h:min) were presented in a distinctive format. For each question, two responses were required –one regarding the period before the pandemic (retrospective information) and the other reflecting the volunteer’s behavioral habits at the time of questionnaire completion (pandemic period).

2.2 Statistical analysis

Data normality was verified by the Kolmogorov-Smirnov test. Descriptive analyses were presented as frequency and percentage, mean and standard deviation for normally distributed data, or median and interquartile range for non-normally distributed data. For proportion variables, Pearson’s chi-square test was performed. Generalized linear models (GzLM) (mean±standard error) with gamma distribution adjusted for age, sex, and BMI, were used to verify the difference between pre-pandemic and during the pandemic periods regarding physical activity time (minutes), sedentary behavior time (minutes), meal times (h:min), and sleep pattern (h:min). In addition, Sidak’s post-hoc test was applied. Logistic regression models were used to predict the odds ratio (OR) for the risk of development of NCDs according to physical activity practice (yes/no), physical activity time (minutes), sedentary behavior time (minutes), dietary pattern (yes/no), and adequate sleep pattern (yes/no). The analyses were adjusted following models 1 (unadjusted), model 2 (adjusted for age and sex), and model 3 (adjusted for age, sex, and BMI), and the results were expressed as OR with a 95%confidence interval (CI). All analyses were performed using SPSS software version 23.0, and p < 0.05 was considered statistically significant.

3 Results

A total of 936 professors were evaluated in this study. The participants were between 35 and 56 years old (45.77±10.65 years old), with 569 (60.8%) being male and 42.4%residing in the Southeast region of Brazil. They had an average weight of 73.82 kg (±15.87) and a BMI of 25.98 kg/m² (±4.48). Regarding changes in body mass during the pandemic, 54.9%of participants reported weight gain and 28.8%reported weight loss (Table 1).

Table 1
Sociodemographic and anthropometric aspects of the adult population during the COVID-19 pandemic (n = 936)

Variables n (%) mean±SD median

[interquartile range]

Sociodemographic variables

Age (years) – 45.77±10.65 –

Male gender 569 (60.8) – –

Female gender 367 (39.2) – –

Brazilian region

North 84 (9.0) – –

Northeast 153 (16.3) – –

Midwest 158 (16.9) – –

South 144 (15.4) – –

Southeast 397 (42.4) – –

Anthropometric variables

Weight (kg) – 73.82±15.87 –

BMI (kg/m²) – 25.98±4.48 –

BMI classification

Underweight 16 (1.70) – –

Normal weight 425 (45.4) – –

Overweight 343 (36.6) – –

Obesity 152 (16.3) – –

Weight gain during pandemic (yes) 514 (54.9) – –

Weight gain during pandemic (kg) – – 4.0 [0.0 –40.0]

Weight loss during pandemic (yes) 270 (28.8) – –

Weight loss during pandemic (kg) – – 3.0 [0.0 –25.0]

Physical activity

Change in the level physical activity (yes) 765 (81.4) – –

Decrease in the level of physical activity 541 (57.8) – –

Increase in the level of physical activity 224 (23.6) – –

Comorbidities

Pre-pandemic hypertension (yes) 152 (16.3) – –

Pre-pandemic diabetes mellitus (yes) 39 (4.2) – –

Pre-pandemic musculoskeletal disorders (yes) 59 (6.3) – –

Pre-pandemic high cholesterol (yes) 201 (21.5) – –

Pre-pandemic anxiety/depression (yes) 142 (15.2) – –

Pre-pandemic respiratory diseases (yes) 62 (6.6) – –

Other pre-pandemic diseases (yes) 166 (17.4) – –

Variables	n (%)	mean±SD	median
Sociodemographic variables
Age (years)	–	45.77±10.65	–
Male gender	569 (60.8)	–	–
Female gender	367 (39.2)	–	–
Brazilian region
North	84 (9.0)	–	–
Northeast	153 (16.3)	–	–
Midwest	158 (16.9)	–	–
South	144 (15.4)	–	–
Southeast	397 (42.4)	–	–
Anthropometric variables
Weight (kg)	–	73.82±15.87	–
BMI (kg/m²)	–	25.98±4.48	–
BMI classification
Underweight	16 (1.70)	–	–
Normal weight	425 (45.4)	–	–
Overweight	343 (36.6)	–	–
Obesity	152 (16.3)	–	–
Weight gain during pandemic (yes)	514 (54.9)	–	–
Weight gain during pandemic (kg)	–	–	4.0 [0.0 –40.0]
Weight loss during pandemic (yes)	270 (28.8)	–	–
Weight loss during pandemic (kg)	–	–	3.0 [0.0 –25.0]
Physical activity
Change in the level physical activity (yes)	765 (81.4)	–	–
Decrease in the level of physical activity	541 (57.8)	–	–
Increase in the level of physical activity	224 (23.6)	–	–
Comorbidities
Pre-pandemic hypertension (yes)	152 (16.3)	–	–
Pre-pandemic diabetes mellitus (yes)	39 (4.2)	–	–
Pre-pandemic musculoskeletal disorders (yes)	59 (6.3)	–	–
Pre-pandemic high cholesterol (yes)	201 (21.5)	–	–
Pre-pandemic anxiety/depression (yes)	142 (15.2)	–	–
Pre-pandemic respiratory diseases (yes)	62 (6.6)	–	–
Other pre-pandemic diseases (yes)	166 (17.4)	–	–

BMI: body mass index. Values are presented as frequency and percentage, mean and standard deviation for normally distributed data, or median and interquartile range for non-normally distributed data.

Table 1 shows the self-reported prevalence of diseases in the pre-pandemic period. The diagnosis of diseases during the pandemic was reported by 22.9%of participants, being hypertension 2.5%(23), diabetes mellitus 1.6%(15), musculoskeletal disease 3.1%(29), anxiety/depression 9.3%(87), and other diseases 8.4%(78) (Table 2).

Table 2

Report on the development of NCDs in professors during the COVID-19 pandemic (n = 936)

Morbidities	n (%)
Diagnosed with some morbidity during pandemic (yes)	214 (22.9)
Diagnosed with arterial hypertension during pandemic (yes)	23 (2.5)
Diagnosed with diabetes mellitus during pandemic (yes)	15 (1.6)
Diagnosed with musculoskeletal diseases during pandemic (yes)	29 (3.1)
Diagnosed with anxiety/depression during pandemic (yes)	87 (9.3)
Diagnosed with other diseases during pandemic (yes)	78 (8.4)

Values are expressed as absolute number and percentage.

As presented in Table 3, there was no significant difference between the pre-pandemic and pandemic periods in moderate (p = 0.712) and vigorous (p = 0.601) physical activity time. However, an increase in sedentary behavior was found (p < 0.001). Regarding changes in meal times, we observed that meals were eaten later during the pandemic compared to before the pandemic (p < 0.05), with the exception of the evening snack (p = 0.457) (Table 3).

Table 3

Difference between pre-pandemic and during pandemic regarding physical activity time, sitting time, meal timing, and sleep pattern

Variables	Pre-pandemic	During pandemic
	Mean±SE	Mean±SE	p-value^*
Physical activity
Moderate PA time (minutes)	192.71±6.26	189.39±6.45 0	0.712
Vigorous PA time (minutes)	154.80±5.96	150.27±6.28 0	0.601
Sedentary behavior (minutes)	373.46±6.76	586.76±10.70	<0.001
Meal timing
Breakfast (h:min)	06 : 37±00 : 00	07 : 04±00 : 00	<0.001
Mid-morning snack (h:min)	09 : 22±00 : 00	09 : 31±00 : 00	0.006
Lunch (h:min)	11 : 57±00 : 00	12 : 09±00 : 00	<0.001
Afternoon snack (h:min)	15 : 37±00 : 00	15 : 43±00 : 00	0.024
Dinner (h:min)	19 : 31±00 : 00	19 : 40±00 : 00	0.017
Evening snack (h:min)	21 : 05±00 : 01	20 : 21±00 : 01	0.457
Sleep pattern
Weekday bedtime (h:min)	21 : 21±00 : 00	19 : 49±00 : 00	0.004
Weekend bedtime (h:min)	19 : 08±00 : 00	18 : 07±00 : 00	0.010
Weekday wake-up time (h:min)	06 : 11±00 : 00	06 : 27±00 : 00	<0.001
Weekend wake-up time (h:min)	07 : 48±00 : 00	07 : 44±00 : 00	0.435
Weekday sleep latency (h:min)	00 : 23±00 : 00	00 : 30±00 : 00	<0.001
Weekend sleep latency (h:min)	00 : 23±00 : 00	00 : 29±00 : 00	<0.001
Weekday sleep duration (h)	07.5±0.04	7.30±0.04	0.002

Values are presented as mean and standard error. SE = Standard Error. Statistical analysis: Generalized linear models. The value in bold is statistically significant at p≤0.05.

In the comparison of sleep patterns, during the pandemic, bedtime changed to earlier on both weekdays (p = 0.004) and weekends (p = 0.010), while wake-up time during the week has become later week (p < 0.001). Moreover, there was an increase in sleep latency during the week (p < 0.001) and on weekends (p < 0.001) and a decrease in sleep duration during the week (p = 0.002) (Table 3).

When analyzing the results of logistic regression, it is observed that the practice of physical activity, both before (OR = 0.67; 95%CI: 0.45–0.98) and during the pandemic period (OR = 0. 51; 95%CI: 0.36–0.71) was associated with a lower risk of development of NCDs during the pandemic. In addition, the time spent on moderate (OR = 1.91; 95%CI: 1.32–2.75) and vigorous (OR = 0.56; 95%CI: 0.36–0.87) physical activities during the pandemic was also associated with a lower risk of being diagnosed with NCDs (Table 4).

Table 4

Logistic regression for the occurrence of non-communicable chronic diseases according to physical activity time, sedentary behavior, food intake, and sleep pattern

Variables	Model 1			Model 2			Model 3
	Unadjusted			Adjusted for age and sex			Adjusted for age, sex, and BMI
	OR	95%CI	p-value	OR	95%IC	p-value	OR	95%IC	p-value
Physical activity
Physical activity practice pre-pandemic (yes/no)	0.66	0.45–0.97	0.036	0.67	0.45–0.98	0.042	0.72	0.48–1.06	0.102
Physical activity practice during pandemic (yes/no)	0.47	0.33–0.65	<0.0001	0.47	0.34–0.66	<0.0001	0.51	0.36–0.71	<0.0001
Time spent on M/V physical activity pre-pandemic (minutes)	1.05	0.77–1.44	0.715	1.03	0.75–1.41	0.820	0.95	0.69–1.31	0.769
Time spent on M/V physical activity during pandemic (minutes)	2.08	1.45–2.99	<0.0001	2.02	1.40–2.90	<0.0001	1.91	1.32–2.75	<0.0001
Time spent on V physical activity pre-pandemic (minutes)	0.84	0.58–1.21	0.372	0.87	0.60–1.26	0.474	0.93	0.64–1.35	0.741
Time spent on V physical activity during pandemic (minutes)	0.51	0.33–0.79	0.003	0.53	0.34–0.82	0.005	0.56	0.36–0.87	0.010
Delta of physical activity M/V (minutes)	2.18	1.55–3.06	<0.0001	2.17	1.54–3.05	<0.0001	2.09	1.48–2.95	<0.0001
Sedentary behavior
Sedentary behavior pre-pandemic (minutes)	1.00	0.99–1.00	0.410	1.00	0.99–1.00	0.222	1.00	0.99–1.00	0.099
Sedentary behavior during pandemic (minutes)	1.00	0. 99–1.00	0.523	1.00	0.99–1.00	0.226	1.00	0.99–1.00	0.072
OR	95%CI	p-value	OR	95%IC	p-value	OR	95%IC	p-value
Food consumption
Breakfast consumption (yes/no)	1.18	0.69–2.01	0.542	1.23	0.72–2.11	0.438	1.15	0.67–1.98	0.603
Late-night/midnight consumption pre-pandemic (yes/no)	1.08	0.64–1.81	0.766	1.11	0.65–1.87	0.689	1.07	0.63–1.83	0.788
Late-night/midnight consumption during pandemic (yes/no)	2.14	1.46–3.14	<0.0001	2.18	1.48–3.21	<0.0001	2.04	1.38–3.02	<0.0001
Excessive intake pre-pandemic (yes/no)	1.79	1.26–2.54	0.001	1.81	1.27–2.57	<0.0001	1.61	1.12–2.31	0.009
Excessive intake during pandemic (yes/no)	2.53	1.85–3.48	<0.0001	2.47	1.79–3.39	<0.0001	2.27	1.64–3.15	<0.0001
Dinner after 7 p.m. pre-pandemic (yes/no)	0.85	0.61–1.19	0.349	0.89	0.63–1.25	0.529	0.88	0.63–1.25	0.502
Dinner after 7 p.m. during pandemic (yes/no)	1.20	0.86–1.69	0.269	1.24	0.88–1.74	0.215	1.19	0.84–1.68	0.315
Dinner after 9 p.m. pre-pandemic (yes/no)	1.49	1.00–2.23	0.048	1.54	1.03–2.32	0.034	1.52	1.01–2.29	0.043
Dinner after 9 p.m. during pandemic (yes/no)	1.51	1.03–2.21	0.034	1.53	1.04–2.25	0.030	1.46	0.98–2.15	0.056
Sleep pattern
Sleep time adequate on weekdays pre-pandemic (yes/no)	1.10	0.79–1.53	0.550	1.08	0.77–1.50	0.647	1.07	0.77–1.50	0.653
Sleep time adequate on weekdays during pandemic (yes/no)	1.19	0.86–1.66	0.277	1.16	0.83–1.62	0.357	1.14	0.82–1.60	0.419
Sleep time adequate on weekends pre-pandemic (yes/no)	1.16	0.81–1.67	0.411	1.14	0.79–1.64	0.460	1.14	0.79–1.64	0.482
Sleep time adequate on weekends during pandemic (yes/no)	1.47	1.05–2.06	0.022	1.46	1.04–2.04	0.024	1.40	1.00–1.97	0.048

M/V: moderate/vigorous; V: vigorous. Model 1: unadjusted; model 2: adjusted for age and sex; model 3: adjusted for age, sex, and body mass index. The value in bold is statistically significant at p≤0.05. Adequate sleep: ≥7 hours; Inadequate sleep: <7 hours.

Regarding dietary consumption, the consumption of food after 11 p.m. and during the early morning hours during the pandemic (OR = 2.04; 95%CI: 1.38–3.02), excessive food intake before (OR = 1.61; 95%CI: 1.12–2.31) and during the pandemic (OR = 2.27; 95%CI: 1.64–3.15), and dinner after 9 p.m. before (OR = 1.52; 95%CI: 1.01–2.29) and during the pandemic (OR = 1.53; 95%CI: 1.04–2.25) were predictors of the development of NCDs (Table 4). Additionally, inadequate sleep time on weekends during the pandemic was statistically associated with the development of diseases during the pandemic (OR = 1.40; 95%CI: 1.00–1.97) (Table 4).

4 Discussion

To the best of the author’s knowledge, this study is among the earliest to evaluate changes in lifestyle, dietary patterns, and sleep patterns associated with the development of NCDs in the Brazilian higher education teaching population during the COVID-19 pandemic. These results showed that there were changes in the lifestyle, dietary patterns, and sleep patterns of the teaching population during the pandemic. Physical activity was negatively associated with the risk of development of NCDs during the pandemic. Additionally, late food consumption, excessive food consumption, and inadequate sleep time on weekends during the pandemic were associated with a higher risk of a diagnosis of NCDs.

In the present study, although 57.8%of participants reported a decrease in their level of physical activity during the pandemic, no significant differences were found in physical activity time before and during the pandemic. However, previous studies have found that teachers/professors have insufficient physical activity before and during the pandemic [10, 11]. On the other hand, we found a significant increase in sedentary behavior in our sample during the pandemic. These findings corroborate the study of Sousa et al. [30], which showed an increase in sedentary behavior among teachers in Brazilian state schools, although they did not find a significant change in the level of physical activity.

In our sample, a 3-hour increase in sedentary activity time was observed during the pandemic, resulting in a daily total of 9 hours. This increase in sedentary behavior may be attributed to the remote work implemented during the pandemic. Worryingly, this also increases the risk of metabolic and cardiovascular diseases associated with excessive time spent sitting. A meta-analysis by Patterson et al. [31] revealed a progressive increase in the risk of diseases and mortality associated with sitting periods exceeding 6 and 8 hours per day. Supporting this finding, Delfino et al. [32] found that teachers who exhibited high levels of sedentary behavior (>8 hours per day) were twice as likely to develop abdominal obesity as those with low levels of sedentary behavior.

Reinforcing the strong current scientific evidence, we found that physical activity during the pandemic was a protective factor for the development of NCDs. Additionally, 54.9%of the sample reported weight gain during the pandemic, while 52.9%were overweight/obesity, and it is known that staying physically active is an important strategy for weight loss and, consequently, reducing the risk of developing other NCDs, such as type 2 diabetes and cardiovascular diseases [33].

The changes found in the timing of almost all meals are an important factor, as modifying only the timing of meals can substantially affect body weight [34]. In this sense, research from chrononutrition area has shown that eating patterns related to late eating may influence nutrient metabolism and be associated with metabolic and nutritional diseases such as obesity [34 –37]. Studies on this subject have shown lower insulin sensitivity [38, 39] and increased triacylglycerol levels [40] at night compared to day, suggesting an impaired food metabolism in the late period of the day. In the present study, excessive food consumption during the pandemic, dinner after 9 p.m., and food consumption after 11 p.m./midnight were associated with a higher risk of a diagnosis of NCDs, which corroborates the literature. A study by Manoogian et al. [41] showed that the intake of certain nutrients and/or meal timing can influence various organic and metabolic functions, promoting a temporal misalignment in various physiological variables that directly affect lifestyle and health. In our study, such a change may be due to the change in routine and work environment, which reflected modifications in eating habits, including meal timing.

Parallel to dietary changes, significant changes in the sleep pattern were observed, such as bedtime, sleep latency, and sleep duration. Sleep-wake parameters depend on other environmental factors to function properly, including exposure to daylight, physical activity, regular meal times, and social interactions. However, in the situation of social distancing, most of these synchronizers were heavily modified, and as a consequence, they can result in daytime sleepiness, fatigue, and anxiety disorders [42, 43]. In our sample, during the pandemic, a decrease in sleep duration and increased sleep latency during the week were observed. This may occur because, to meet work demands and balance them with social and family life, professors deprive themselves of some hours of sleep during the week, which can compromise sleep quality and lead to occupational stress [44]. In addition, disorders such as anxiety can increase sleep latency [45]. Finally, it is known that teaching work represents high cognitive demands, and low sleep quality can directly affect the performance of such activities and consequent professional satisfaction [46].

Another important finding from our study was that 22.9%of participants reported the diagnosis of some disease during the pandemic, and the main diseases diagnosed were related to mental health, such as anxiety and depression. This finding corroborates previous studies that reported worsened mental health of professors during the pandemic, such as the study by Silva et al. [47], which found that 82.3%of professors had at least one mental disorder during the pandemic period. It is believed that the change in the physical space in which activities were carried out, the need for complex adaptation to digital technologies, the difficulty of balancing personal and professional life, as well as the health insecurity due to the public calamity situation regarding an unknown disease at the time, may have had negative consequences on the health of this population, especially regarding mental health [48].

Thus, it is believed that the new demands of the pandemic have brought difficulties in adopting or maintaining a routine considered healthier and protective against NCDs in the Brazilian higher education professor population. Therefore, it is important to regulate the teaching workload and invest in prevention actions to protect the health of teaching mediators. Furthermore, our findings also seek to encourage new research that focuses on the intersection between health and education.

Despite the significant findings, this study has some limitations, such as the use of self-reported data, which may have been affected by bias and low reliability. Moreover, while this is a longitudinal retrospective study, all data were collected during the pandemic. Additionally, the pre-pandemic questions were designed not to specify a particular timeframe. As a result, there is a possibility that memory bias may have affected information pertaining to the period before the pandemic. Finally, due to the cross-sectional design, causal relationships cannot be established in this study. Future studies should consider incorporating longitudinal research and diverse samples from various countries. Furthermore, it is recommended that research be conducted to assess the health and quality of life of the population in question after the pandemic. Effective interventions should also be developed to enhance the health and well-being of university professors in both normal and extraordinary situations, such as pandemics and other emergencies.

5 Conclusion

This study found that sedentary behavior time increased, sleep duration slightly decreased, and meal times shifted to earlier during the pandemic compared to before. During the pandemic, physical activity was associated with a lower risk of development of NCDs, while late eating habits and excessive food consumption were associated with a higher risk. The obtained results can provide data that assist in the treatment and minimization of the consequences of the pandemic period, where there were significant changes in the lifestyle of the population that impacted the development of physical and mental chronic diseases.

Ethical approval

This study was approved by the Human Research Ethics Committee of the Federal University of Uberlândia (Ethical approval number: 51479821.0.0000.5152).

Informed consent

Informed consent was obtained from all respondents.

Conflict of interest

The authors declare that they have no conflict of interest.

Footnotes

Acknowledgments

The authors have no acknowledgments.

Funding

The authors report no funding.

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