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Compared with dietary behavior and physical activity risk, sedentary behavior risk is an important factor in overweight and obesity: evidence from a study of children and adolescents aged 13–18 years in Xinjiang, China

BMC Pediatrics volume 22, Article number: 582 (2022) Cite this article

Abstract

Background

Malnutrition or insufficient physical activity (PA) is a risk factor for obesity and chronic disease in children and adolescents. Affected by different economic circumstance, ethnic, dietary behavior, physical activity and other factors, children and adolescents in Xinjiang, China are facing a severe situation of overweight and obesity prevention and control. It is necessary to analyze the dietary behavior and physical activity of children and adolescents with different nutritional status and the relationship between them.

Methods

Using a stratified cluster sampling method in Xinjiang, China, 4833 middle school students aged 13–18 were selected., and tests for height and weight were conducted. Self-assessment questionnaires were completed for Dietary Behaviors, Physical Activity, and Sedentary Behaviors as well. Chi-square test, Logistic regression analysis and other methods were used to analyze the relationship between Dietary Behaviors, Physical Activity, Sedentary Behaviors and other health behavior risk factors and Weight and BMI.

Results

Children and adolescents aged 13–18 in Xinjiang, China, girls had a lower BMI compared with boys(19.49 VS. 20.41). The proportions of Underweight, Overweight and Obese among girls were lower (Underweight: 11.8 VS. 14.5; Overweight: 7.6 VS. 12.7; Obese 2.3 VS. 7.0).Boys with higher risk of sedentary had a 1.46-fold higher risk of developing Overweight/Obese than those with lower risk of sedentary (95%CI: 1.07–2.01).Girls with higher risky diet had a 1.56-fold higher risk of developing Underweight than those with lower risky diet (95%CI: 1.11–2.19). For all participants, the risk of Overweight/Obese in children and adolescents with higher risk of sedentary was 1.45 times more than that of children and adolescents with lower Risk sedentary (95%CI: 1.12 ~ 1.88). Overall, Weight had a significant correlation with PA risk (r = 0.076, P < 0.01) and sedentary behavior risk (r = 0.035, P < 0.05). BMI had a key correlation with PA risk (r = 0.064, P < 0.01) and sedentary behavior risk (r = 0.037, P < 0.05).

Conclusions

The detection rate of Underweight among children and adolescents aged 13–18 in Xinjiang, China is higher, while the detection rate of Overweight and Obese is lower than that of the whole country. Static behavior was an important factor affecting the occurrence of Overweight and Obese in children and adolescents, and the performance of boys was more obvious than that of girls.The results further improve the data on the weight status of Chinese children and adolescents and their influencing factors, and call on Chinese society and schools to continue their efforts to prevent and reduce malnutrition and obesity among children and adolescents in Xinjiang.

Peer Review reports

Introduction

Meta-analyses have suggested that long-term healthy dietary habits and adequate PA can not only promote human health and the body's immune capacity but can also reduce the incidence and all-cause mortality of obesity, cardiovascular disease, type 2 diabetes, and cancer [1,2,3]. Overweight and obesity have become serious public health problems in recent years, and childhood obesity increases the odds of problems such as obesity, early death, and disability in adulthood. As of April 2020, more than 340 million children and adolescents aged 5 and 19 years worldwide were overweight or obese, and the prevalence of overweight or obesity in this age group increased sharply from 4% in 1975 to more than 18% in 2016. In 2020, statistics showed that the proportion of obese or overweight children and adolescents in the United States was 41% [4]. In China, the detection rate of overweight and obesity among school children aged 6–17 increased year by year and reached 20% in 2020 [5] and is expected to be 28.0% in 2030 [6].

A proper diet contributes to health, and one reason for the high incidence of obesity and overweight is the increased intake of energy-rich foods, such as sugar and fat, including among children and adolescents. Nearly half (46.7%) of children and adolescents aged 2–18 years in Australia consume drink sugary drinks [7]. In China, the consumption rate of sugary milk drinks and sugar-sweetened beverages among children and adolescents is more than 30% and 25%, respectively, which is significantly higher than the rates in adults [8]. The WHO meta-analysis of 5 prospective cohort studies in children showed that after 1 year of follow-up, higher SSB intake was associated with a 55% higher risk of overweight/obesity than those with the lowest intake. In free-living, free-eating populations, free sugar or ssb intake is associated with body weight [9].A high intake of fruit juices and soft drinks contributes to excessive weight gain and obesity in children.Recent research has found a positive association between regular SSBs consumption and weight gain [10]. The World Health Organization (WHO) recommends increasing the daily intake of fruits, vegetables, legumes, whole grains and nuts; however, in 2014, intakes among European children and adolescents aged 12.5–17.5 years were only 50% of recommended levels, and intake of and milk and dairy products was 66.6% [11]. From 2013 to 2016, nearly 90% of people in the United States did not reach the recommended standard for vegetable and milk intake; average daily intake of fruits and vegetables among children aged 14–18 years was 50% of the recommended intake, and milk intake was approximately 65% of recommendations [4]. In terms of the effect of dairy consumption on growth and development in children aged 6–18 years, it has been shown that those who consume sufficient milk and dairy products are more likely to have a lean body composition [12]. Between 2016 and 2017, the consumption rate of milk and dairy products among Chinese middle school students was 82.5%; only 44.1% consumed milk and dairy products daily, and only 20.4% of these students reached the intake level recommended in the Dietary Guidelines for Chinese Residents (2016) [13]. The average intake of milk and dairy products among Chinese residents has been at a low level, but the consumption rates among children and adolescents are higher than rates among adults [8]. In 2019, the per capita milk consumption in Xinjiang Uygur Autonomous Region was 21.1 kg, which is much higher than the national per capita consumption of 12.5 kg, ranking third in China after in Beijing and Inner Mongolia [14].

In 2009, physical inactivity was identified as the fourth leading risk factor for NCDs, causing more than 3 million preventable deaths.[]Current estimation of global physical inactivity suggests that 27.5% of adults and over 80% of adolescents aged 11–17 years do not meet the existing WHO recommendations for physical exercise to promote health [15,16,17]. In the past 20 years, the overall PA among Chinese residents has shown a declining trend each year. In particular, the PA level of children and adolescents is generally insufficient. The average time engaged in medium- and high-intensity PA among Chinese children and adolescents is 37.66 min/d, which is far lower than these times in Canada (59 min/d), the United Kingdom (60.5 min/d), and the United States (48 min/d) [18]. An objective and subjective measure of physical activity in the United States gave qualitatively similar results in terms of gender and age activity patterns: 42% of children aged 6–11 received the recommended minimum daily amount of 60 min of physical activity, while only 8% of Teens achieve this. Among adults, 30 min of daily physical activity is less than 5% [19]. For economical, equipment, and technical reasons, we chose a self-reported method to assess physical activity.With the development of electronic technologies, digital media devices have increasingly become the main cause of reduced PA in children and adolescents. Among Australian children and young adults, only 30% of those aged 5 to 17 years meet the guideline for daily screen-based sedentary behavior [20]. Screen overuse in children and adolescents is associated with physical and mental health risks [21]. In 2016, the prevalence of obesity among Chinese children and adolescents aged 9–17 years was approximately 12%, and around 37% used a screen for more than 2 h per day [22]. Epidemiological studies have shown that children who use more screen media also eat less fruits and vegetables, consume more high-energy snacks, high-energy drinks and fast food, and consume more calories per hour [23]. During the COVID-19 pandemic, nearly 80% of children and adolescents in China significantly increased their daily screen use time, with 42.3%–48.2% of students using digital devices for more than 5 h a day to study online, especially high school students [24]. This means a significantly increased risk of diseases such as obesity owing to screen overuse. The COVID-19 pandemic has accelerated the evolution of this trend. Both the physical quality index (body mass index, BMI) and obesity increased owing to the decline in PA levels during the pandemic [25, 26].

Xinjiang Uygur Autonomous Region is located at the northwest border of China. There are 13 ethnic groups in Xinjiang, including Uygur, Han, Kazak, and Hui. The special regional environment in Xinjiang results in a variety of eating habits. The diet mainly comprises wheat, beef, mutton, dairy products, and nuts. According to China's 2020 Obesity Prevention and Control Implementation Plan for Children and Adolescents, Xinjiang is listed as a middle-level area of overweight and obesity prevalence among children and adolescents. In view of the severe situation facing children and adolescents in terms of overweight and obesity, we conducted an investigation among 5044 children and adolescents aged 13–18 years in Xinjiang, China to analyze the dietary behavior and PA of children and adolescents with different nutritional status and the relationship between them. We hypothesized that both dietary behavior and physical activity could influence the nutritional status of children and adolescents in Xinjiang, China. Our research will provide help for the physical and mental development of children and adolescents in Xinjiang, China. At the same time, it will also provide effective public health policies for local government departments and education administrative departments.

Materials and methods

Study design and participants

This study was conducted in Urumqi, Altay, Karamay, Aksu, and Kashgar, in Xinjiang, China from September to December 2021 (Fig. 1). Using a stratified cluster sampling method, two to four middle schools were selected in each region, and another two to three classes were randomly selected in each grade. In this study, 5044 middle school students aged 13–18 years were selected and asked to complete a questionnaire. After excluding 211 invalid questionnaires, 4833 valid questionnaires were finally collected; the effective recovery rate was 95.82%.

Fig. 1
figure 1

Schematic diagram of the study area in Xinjiang Uygur Autonomous Region

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This study belongs to the status quo investigation research, and the research index is enumeration data, so the sample estimation formula is:\(n=\frac{Z{a/2}^{2}\times p\left(1-p\right)}{{d}^{2}}\), d = 0.15 × p, α = 0.05 (two-sided), = 1.96. The number of middle school students in Xinjiang is about 150,000, and the total population of Xinjiang is 25.89 million, accounting for 0.58%. Therefore, it is 0.58, and the calculated sample is about 60 people. Considering the loss rate of 10%, about 66 people need to be tested. This study tested a total of 5 regions in Xinjiang, 6 age groups, and urban and rural distribution. Therefore, this study should test a total of about 3960 people. This study used a class cluster sampling survey, and a total of 4833 valid data were investigated.See the selection process in Fig. 2.

Fig. 2
figure 2

Participant selection process

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Prior to the questionnaire survey, students, parents or legal guardians had obtained written informed consent. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Human Subjects Protection Committee of Chizhou University (0236–2020).

Measures

This study was part of the Key Laboratory of Adolescent Health Assessment and Exercise Intervention, Ministry of Education, East China Normal University research project. The study questionnaire was finalized with reference to the Chinese National Student Physical Health Survey questionnaire and the US Centers for Disease Control and Prevention Youth Risk Behavior Surveillance System questionnaire [27, 28]. The overall questionnaire included eight parts: basic information, physical exercise, eating habits, sleep status, health risk behavior, mental health, PA, and physical examination. Students were required to complete the total of 108 items within 20–30 min.The questionnaire was determined after expert discussion and analysis, and 202 middle school students were selected for pre-investigation at 15-day intervals, which indicated that the questionnaire had good reliability and validity, cronbach's α coefficient is 0.85.

This survey included six questions addressing sociodemographic characteristics (age, urban or rural residence, school type, living on campus, father's education, and mother's education), four questions addressing dietary behaviors (consumption of sugar-sweetened drinks, breakfast, dairy or soy milk, and eggs), six questions addressing physical activity (low intensity PA, moderate-to-vigorous PA [MVPA], after school PA, means of transportation to and from school, time from home to school, and muscle strength training), three questions on sedentary behaviors (sitting to study after school, watching TV, using a phone or tablet) and two anthropometric measures (height, weight).

Each student was required to choose a response to each item according to their own situation. With reference to previous studies and related behavior guidelines [29,30,31,32], the categories of dietary behaviors, PA, and sedentary behaviors were scored 0 point for healthy behaviors or 1 points for unhealthy behaviors, and the number of risk behaviors was calculated. The categories dietary behaviors, PA, and sedentary behaviors included four, six, and three risk behaviors, respectively. According to the median number of risk behaviors in each category, for dietary behaviors, ≤ 1 risk behavior was defined as “low dietary risk” and > 1 risk behavior was defined as “high dietary risk.” For PA, ≤ 2 risk behaviors were defined as “low PA risk” and > 2 risk behaviors was defined as “high PA risk.” Similarly, ≤ 1 sedentary risk behavior was defined as “low sedentary risk” and > 1 risk behavior was defined as “high sedentary risk.”

Students’ height and weight were measured according to the methods and instruments required in the national physical health test for Chinese students. Height was measured to the nearest 0.1 cm and weight to the nearest 0.1 kg [27]. BMI was calculated based on height and weight as weight (kg)/height (m)2. Chinese school-aged children were categorized as underweight, normal weight, overweight, or obese [33].

Statistical analysis

Demographic characteristics are expressed as mean ± standard deviation or number and percentage. We used the t-test for comparisons of continuous variables by sex and the chi-square test for comparisons among PA, sedentary behavior, and dietary behavior by sex and nutritional status. We adjusted for sex, urban or rural residence, school type, father's education, mother's education, and living on campus in logistic regression analysis to analyze the effect of dietary behaviors, PA, and sedentary behaviors on body weight. The crude odds ratio (COR), adjusted odds ratio (AOR), and 95% confidence interval (CI) for children and adolescents with different nutritional statuses were calculated using GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA). Correlation analysis of child and adolescent risk behaviors, body weight, and BMI by sex and school type was conducted using Pearson's correlation analysis. Statistical analysis of the data was performed using IBM SPSS 25.0 (IBM Corp., Armonk, NY, USA), and the significance level was set to P < 0.05.

Results

Participant characteristics

According to Table 1, 50.49% (2440/4833) of children aged 13–18 years in Xinjiang, China were male. Those in middle school accounted for 70.1% (3390/4833) of the study population. Compared with boys, girls had a lower BMI (19.49 VS. 20.41). The proportions of Underweight, Overweight and Obese among girls are all lower (Underweight: 11.8 VS. 14.5; Overweight: 7.6 VS. 12.7; Obese 2.3 VS. 7.0).

Table 1 Comparison of different demographic characteristics of children and adolescents aged 13–18 years in Xinjiang, China
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Assessment of dietary behaviors, physical activity, and sedentary behaviors

Table 2 shows that for dietary behaviors, boys and girls showed differences in the frequency of weekly egg consumption (χ2=6.977, P < 0.05). Children and adolescents with different nutritional statuses had differences in the number of times per day sugar-sweetened drinks were consumed (χ2=45.641, P < 0.001) as well as differences in the weekly frequency of eating eggs (χ2=8.698, P < 0.05).

Table 2 Comparison of dietary behaviors, physical activities, and sedentary behaviors detection rates among children and adolescents by sex and nutritional status
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For PA, there was a difference in the proportions of low-intensity PA per day by sex (χ2=16.058, P < 0.001), as well as in the proportions of MVPA per day (χ2=32.704, P < 0.001) and muscle strength training per week (χ2=23.157, P < 0.001). Children and adolescents with different nutritional statuses differed in terms of after school PA, means of transportation to and from school, time from home to school, and muscle strength training (χ2=94.472, 11.485, 84.710, 12.248, respectively; P < 0.05 or 0.001).

For sedentary behaviors, there is a difference in the proportion of sitting to study after school each day by sex (χ2=22.627, P < 0.001). Children and adolescents with different nutritional statuses differed for this variable as well as for watching TV and using a phone or tablet (χ2=64.211, 20.842, and 26.083, respectively; P < 0.001).

Logistic regression analysis of risk factors among children and adolescents with different nutritional status

According to Table 3, the AOR for risk of being overweight/obese was 1.46 times higher among children with high sedentary risk than that of children with low sedentary risk (95% CI: 1.07–2.01). For girls, the risk for underweight was 1.56 times higher among those with high dietary risk than girls low dietary risk (95% CI: 1.11– 2.19). For all participants, the risk of being overweight/obese was 1.45 times higher among children with high sedentary risk than that for children with low sedentary risk (95% CI: 1.12– 1.88), as shown in Fig. 3.

Table 3 Logistic regression analysis of the risk factors among children and adolescents with different nutritional statuses
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Fig. 3
figure 3

Schematic of adjusted odds ratio (OR) in children and adolescents with different nutritional statuses. Abbreviations: PA, physical activity

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Correlation analysis of health behavior risk factors with weight and BMI

Table 4 showed that overall, weight had a significant correlation with PA risk (r = 0.076, P < 0.01) and sedentary behavior risk (r = 0.035, P < 0.05). BMI had a significant correlation with PA risk (r = 0.064, P < 0.01) and sedentary behavior risk (r = 0.037, P < 0.05). By sex, boys performed significant differences than girls. Correlations were also found for school type.

Table 4 Correlations among unhealthy dietary, physical activity, and sedentary behaviors and weight status, by sex and school type
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Discussion

In this study, we aimed to assess the prevalence of overweight and obesity and its association with dietary behavior and PA in children and adolescents aged 13–18 years in Xinjiang, China. Our results showed that 13.2% of the study population was underweight, 10.2% was overweight, and 4.7% was obese. Underweight and overnutrition often coexist in low- and middle-income countries (LMICs), which is referred to as the “double burden of malnutrition” [29, 34,35,36,37]. A study among 177,325 children and adolescents aged 12–15 years in 58 LMICs showed that 10% of the study population in approximately 30 countries had underweight, compared with 10% with overweight and obesity in 45 countries [38]. The detection rate of overweight (10.2%) and obesity (4.7%) among children and adolescents in our study was lower than that of overweight (14.4%) and obesity (11.9%) among children and adolescents in 2016 and lower than the rate of overweight (12.7%) and obesity (4.9%) among children and adolescents in northeast China in 2018 [22, 39]. Another study reported a rate of overweight (13.81%) and obesity (8.03%) among children aged 6–17 years in China from 2016 to 2018 [40], which was greater than the rates in our study population. The proportion of underweight (13.2%) in our study was slightly higher than reported rates of 11.7% in western China in 2014, 7.9% in children age 7–18 years, 5.47% in Xinjiang in 2016, and 7.4% in China in 2018 [41,42,43]. Our study was carried out at numerous study sites among different ethnic groups in Xinjiang, and the scope was middle and high school age students. Poor economic development and increased school pressure might cause a change of dietary behavior of children and adolescents in Xinjiang and lead to the increased proportion of underweight. This is also associated with lower PA levels owing to the COVID-19 pandemic, especially among boys, which is in line with the findings of several studies reporting that low PA is associated with underweight in children and adolescents [44,45,46]. Several studies have reported the rebound rate of malnutrition among children and adolescents in both Guangxi and Wuhan, China [47, 48]. Whether underweight among children 13–18 years old in Xinjiang is common in the development of children and adolescents needs further investigation.

As for dietary behaviors, studies have confirmed that the occurrence of obesity and multiple chronic diseases is closely associated with unhealthy eating behaviors [49]. Consistent with findings regarding a low-fat diet among children aged 6–18 years [12], 91.7% of underweight children aged 13–18 years in Xinjiang, China consume dairy or soy milk ≥ 1 d/week. Every 100 g of whole milk provides 64 kcal of energy and 3.5 g of protein. Therefore, milk makes an important contribution to the nutritional intake of children and adolescents [30]. Xinjiang is an economically underdeveloped region, with economic resources mainly involving agriculture and animal husbandry. Thus, the annual per capita consumption of milk in Xinjiang is higher than the national level. A larger proportion of children and adolescents in Xinjiang consume milk compared with other regions that have low dairy intake, and high milk intake may be an influencing factor for underweight. There are few investigations of underweight status in the study region. Greater attention is needed regarding underweight and healthy brain development in children and adolescents in this area [28].

Excessive consumption of sugary drinks can increase the risk of overweight or obesity in children and adolescents, and consumption of sweetened drinks has become a common behavior in these age groups globally. With the world's overweight and obese population continuing to increase, in 2015, the WHO called for a reduction in sugar intake worldwide [50]. In a study in 2013 among 53,000 Chinese children and adolescents aged 6–17 years, 66.6% of participants consumed sugar-sweetened beverages and 9.6% reported consuming more than seven sugar-sweetened beverage per week [30]. We found that 81.1% and 85.0% of overweight and obese children and adolescents, respectively, consumed sugar-sweetened drinks daily over 1 time, which was higher than the proportions in the normal and underweight groups. Our study confirmed that excessive consumption of sugary drinks is a risk factor for overweight and obesity among children and adolescents, which is consistent with the conclusions of other studies [51,52,53]. In 2019, the per capita sugar consumption in Xinjiang Uygur Autonomous Region was 1.8 kg, much higher than China's per capita consumption of 1.3 kg and ranking third in the country [14]. Sugar intake in Xinjiang is high, among which children and adolescents might be important groups with high sugar intake. Schools, families, and society should take measures to reduce sugar intake in the population.

There is a positive correlation between PA and energy expenditure [54, 55]. In terms of MVPA, this study showed that 92.9% of children and adolescents aged 13–18 years in Xinjiang had MVPA time of 60 min/d at school, and participants met the recommended MVPA activity standard of the WHO and for Chinese children and adolescents. The results of this study differed from those previous studies in China and abroad. In 2016, Roman-Vinas used acceleration sensors and found that only 15% of children and adolescents objectively measured in Tianjin, China met the daily MVPA recommendations [56]. In 2016, only around 30% of children and adolescents in China met the recommended MVPA volume. Middle and high school students were more likely to meet the recommended MVPA than primary school students [31]. Physical inactivity was positively associated with socioeconomic level [57]. Parental educational and profession levels have been also associated with adolescents’ sedentary behaviour [58]. The proportion of Chinese children and adolescents meeting MVPA between 2016 and 2018 did not exceed 40% [40]. Regarding the reason for these differences, differences exist in the questionnaires or instruments used for PA assessment among different studies. This may also be related to different survey populations, ages, regional economic development levels, school types, and levels of academic pressure. Our study also showed that 20.2% of students engaged in 60 min/d PA daily after school, which is consistent with studies reporting that Chinese children and adolescents engaged in more MVPA on campus than off campus [32, 33]. In terms of sex, we found that the proportion of boys participating in 60 min/d MVPA per day was 95%, significantly higher than the 90.7% of girls. Boys were also more likely to meet MVPA recommendations than girls, which is consistent with results obtained in China and abroad [31, 59,60,61,62].

It is well known that economic and technological advancements have increased screen time among young people [63,64,65]. In China, 41.5% of teens spend 2 h per day using a screen on weekends [66]. The influencing factors of body function include sedentary behaviors in addition to dietary behaviors and PA. The analysis of sedentary behaviors in this study included three categories: sitting to study after school, watching TV, and using a phone or tablet. Logistic regression analysis also showed that children and adolescents with high sedentary risk had a 1.59-fold and 1.45-fold greater risk of developing overweight and obesity, respectively, compared with their counterparts who had low sedentary risk. Additionally, correlation analysis indicated that the risk of sedentary behavior showed a positive correlation with weight (r = 0.035) and BMI (r = 0.037). The conclusions of this study are consistent with the results of previous related studies. Reducing stationary behavior is an important means to prevent and treat overweight and obesity among children and adolescents [21, 67,68,69,70]. Because screen-based behavior is often accompanied by decreased PA, snack intake, and consumption of sugary drinks, which affect the energy balance, this can lead to the occurrence of overweight or obesity. Therefore, another benefit of reducing static behavior is a reduction in calorie intake [71]. Current evidence suggests that screen media exposure leads to obesity in children and adolescents through increased eating while viewing; exposure to high-calorie, low-nutrient food and beverage marketing that influences children’s preferences, purchase requests, consumption habits; and reduced sleep duration [23]. Greater participation in PA and limiting screen time are important interventions to prevent obesity and regulate physical health.

There are several advantages of this study. First, ours is the first extensive survey of nutritional status, dietary behavior, and PA among children and adolescents in Xinjiang, China. Second, the study region is a multi-ethnic area with a unique geographic environment and varied eating and lifestyle habits. Our survey of children and teenagers in this region has distinct regional characteristics.Third, this study includes a large, diverse sample of students recruited using multistage and stratified cluster random sampling that provides an adequate representation of Xinjiang children and adolescents. We adjusted for several confounders that may influence the studied associations. Our study also has some limitations. First, we used self-report questionnaires, which can be affected by recall bias and may deviate from the actual values, for example, MVPA may be somewhat overestimated. Objective measurement of PA is challenging and costly in a large sample, necessitating the use of specialized equipment and technical staff. Second, the sample size and number of dietary behaviors investigated in this study may be insufficient. Improved future studies are needed to achieve more accurate results.Third, such assessments may not accurately reflect true nutritional status and may underestimate the magnitude of the observed associations. Finally, this study is a cross-sectional study, which can only understand the association between different factors, but cannot understand the causal relationship between them.

Conclusions

The detection rate of underweight was high among children aged 13–18 years in Xinjiang, China but low for overweight and obesity, compared with the whole country. Sedentary behavior is an important factor impacting the occurrence of overweight and obesity in children and adolescents in Xinjiang.This test is the first step in understanding the current status of overweight and obesity, physical activity and nutritional status among children and adolescents of various ethnic groups in Xinjiang, and has obtained extensive available data for China. Taking this as an opportunity, continued adherence to monitoring of physical activity will help guide the development of policies and programs to increase the activity levels of children and adolescents in Xinjiang and reduce the burden of NCDs.

Availability of data and materials

The datasets used during the current study cannot be made publicly available as per ethics approval at Chizhou University. Readers can obtain them from the corresponding author on reasonable request.

References

  1. Ekelund U, Brown WJ, Steene-Johannessen J, Fagerland MW, Owen N, Powell KE, Bauman AE, Lee I. Do the associations of sedentary behaviour with cardiovascular disease mortality and cancer mortality differ by physical activity level? A systematic review and harmonised meta-analysis of data from 850 060 participants. BRIT J SPORT MED. 2019;53(14):886–94.

    Article  Google Scholar 

  2. Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, Bauman A, Lee I. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. 2016;388(10051):1302–10.

    Article  PubMed  Google Scholar 

  3. Owen N, Healy GN, Dempsey PC, Salmon J, Timperio A, Clark BK, Goode AD, Koorts H, Ridgers ND, Hadgraft NT, et al. Sedentary Behavior and Public Health: Integrating the Evidence and Identifying Potential Solutions. ANNU REV PUBL HEALTH. 2020;41(1):265–87.

    Article  Google Scholar 

  4. McGuire S. U.S. Department of Agriculture and U.S. Department of Health and Human Services, Dietary Guidelines for Americans, 2010. 7th Edition, Washington, DC: U.S. Government Printing Office, January 2011. Dietary Adv Nutr. 2011;2(3):293–4.

    Article  PubMed  Google Scholar 

  5. China NHCO. Report on Nutrition and Chronic Disease Status of Chinese Residents. Beijing: National Health Commission of the People’s Republic of China; 2020.

    Google Scholar 

  6. MaGuansheng: Report on childhood obesity in China. Beijing: People’s Medical Publishing House; 2017, p. 15. https://max.book118.com/html/2018/0606/171068350.shtm.

  7. Brand-Miller JC, Barclay AW. Declining consumption of added sugars and sugar-sweetened beverages in Australia: a challenge for obesity prevention. Am J Clin Nutr. 2017;105(4):854–63.

    Article  CAS  PubMed  Google Scholar 

  8. Scientific Research Report on Dietary Guidelines for Chinese Residents. In.: The Chinese Nutrition Society; 2021. https://www.cnsoc.org/latesachie/422120204.html.

  9. Fidler MN, Braegger C, Bronsky J, Campoy C, Domellof M, Embleton ND, Hojsak I, Hulst J, Indrio F, Lapillonne A, et al. Sugar in Infants, Children and Adolescents: A Position Paper of the European Society for Paediatric Gastroenterology, Hepatology and Nutrition Committee on Nutrition. J Pediatr Gastroenterol Nutr. 2017;65(6):681–96.

    Article  Google Scholar 

  10. Deren K, Weghuber D, Caroli M, Koletzko B, Thivel D, Frelut ML, Socha P, Grossman Z, Hadjipanayis A, Wyszynska J, et al. Consumption of Sugar-Sweetened Beverages in Paediatric Age: A Position Paper of the European Academy of Paediatrics and the European Childhood Obesity Group. Ann Nutr Metab. 2019;74(4):296–302.

    Article  CAS  PubMed  Google Scholar 

  11. Moreno LA, Gottrand F, Huybrechts I, Ruiz JR, González-Gross M, DeHenauw S. Nutrition and Lifestyle in European Adolescents: The HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) Study. Adv Nutr. 2014;5(5):615S-623S.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kang K, Sotunde OF, Weiler HA. Effects of Milk and Milk-Product Consumption on Growth among Children and Adolescents Aged 6–18 Years: A Meta-Analysis of Randomized Controlled Trials. Adv Nutr. 2019;10(2):250–61.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Xu X, Cheng X, Zhao L. Consumption of milk and dairy products among junior high and senior high school students — China, 2016 – 2017. 2022. PMID: 35586460.

  14. Per Capita Consumption of Major Food Products by Region (2019). In.: China Statistical Yearbook; 2020. p. 43–6.

  15. Guthold R, Stevens GA, Riley LM, Bull FC. Worldwide trends in insufficient physical activity from 2001 to 2016: a pooled analysis of 358 population-based surveys with 1·9 million participants. Lancet Glob Health. 2018;6(10):e1077–86.

    Article  PubMed  Google Scholar 

  16. Guthold R, Stevens GA, Riley LM, Bull FC. Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health. 2020;4(1):23–35.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Global Recommendations on Physical Activity for Health. Geneva: World Health Organization; 2010. https://www.who.int/publications/i/item/9789241599979.

  18. Zhang ZH, Li HJ, Slapsinskaite A, Zhang T, Zhang L, Gui CY. Accelerometer-measured physical activity and sedentary behavior in Chinese children and adolescents: a systematic review and meta-analysis. Public Health. 2020;186:71–7.

    Article  CAS  PubMed  Google Scholar 

  19. Troiano RP, Berrigan D, Dodd KW, Masse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc. 2008;40(1):181–8.

    Article  PubMed  Google Scholar 

  20. Australian Health Survey: Physical activity, 2011–12. Australian Bureau of Statistics (ABS); 2013. https://www.abs.gov.au/statistics/health/health-conditions-and-risks/australian-health-survey-physical-activity/latest-release#data-download.

  21. Stiglic N, Viner RM. Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open. 2019;9(1):e23191.

    Article  Google Scholar 

  22. Cai Y, Zhu X, Wu X. Overweight, obesity, and screen-time viewing among Chinese school-aged children: National prevalence estimates from the 2016 Physical Activity and Fitness in China—The Youth Study. J Sport Health Sci. 2017;6(4):404–9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Fekel H, Fiser F, Tomanek A. A simple exposure meter for endoscopic photography and cinematography. Cesk Otolaryngol. 1969;18(2):90–2.

    CAS  PubMed  Google Scholar 

  24. Guo YF, Liao MQ, Cai WL, Yu XX, Li SN, Ke XY, Tan SX, Luo ZY, Cui YF, Wang Q, et al. Physical activity, screen exposure and sleep among students during the pandemic of COVID-19. Sci Rep. 2021;11(1):8529.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Nagata JM, Abdel Magid HS, Pettee Gabriel K. Screen Time for Children and Adolescents During the Coronavirus Disease 2019 Pandemic. Obesity. 2020;28(9):1582–3.

    Article  CAS  PubMed  Google Scholar 

  26. Rubio Herrera MA, BretónLesmes I. Obesidad en tiempos de COVID-19. Un desafío de salud global. Endocrinol Diabetes Nutr. 2021;68(2):123–9.

    Article  Google Scholar 

  27. Notice of the Ministry of Education and other six departments on carrying out the 2019 National Student Physical Health Research and spot Inspection and Review of the National Student Physical Health Standards-Government Portal of the Ministry of Education. vol. 2022. 2022. http://www.moe.gov.cn/srcsite/A17/moe_943/moe_946/201907/t20190724_392121.html.

  28. Chengye J. China’s urban primary and middle school students malnutrition status and 20 years of dynamic changes. Chin Child Health J. 2008;16(06):622–5.

    Google Scholar 

  29. Piernas C, Wang D, Du S, Zhang B, Wang Z, Su C, Popkin BM. The double burden of under- and overnutrition and nutrient adequacy among Chinese preschool and school-aged children in 2009–2011. Eur J Clin Nutr. 2015;69(12):1323–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Gui Z, Zhu Y, Cai L, Sun F, Ma Y, Jing J, Chen Y. Sugar-Sweetened Beverage Consumption and Risks of Obesity and Hypertension in Chinese Children and Adolescents: A National Cross-Sectional Analysis. Nutrients. 2017;9(12):1302.

    Article  PubMed Central  Google Scholar 

  31. Fan X, Cao Z. Physical activity among Chinese school-aged children: National prevalence estimates from the 2016 Physical Activity and Fitness in China—The Youth Study. J Sport Health Sci. 2017;6(4):388–94.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Wang Chao HGAC. Relationship between spatial characteristics of fitness places and physical activity of adolescents —— An empirical study based on GIS and accelerometer. J Shanghai Inst Phys Educ. 2018;42(03):36–43.

    Google Scholar 

  33. Silva DAS, Chaput J, Katzmarzyk PT, Fogelholm M, Hu G, Maher C, Olds T, Onywera V, Sarmiento OL, Standage M, et al. Physical Education Classes, Physical Activity, and Sedentary Behavior in Children. Med Sci Sports Exerc. 2018;50(5):995–1004.

    Article  PubMed  Google Scholar 

  34. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents, and adults. Lancet 2017;390(10113):2627–42.

  35. Dearth-Wesley T, Wang H, Popkin BM. Under- and overnutrition dynamics in Chinese children and adults (1991–2004). Eur J Clin Nutr. 2008;62(11):1302–7.

    Article  CAS  PubMed  Google Scholar 

  36. Poskitt EM. Countries in transition: underweight to obesity non-stop? Ann Trop Paediatr. 2009;29(1):1–11.

    Article  CAS  PubMed  Google Scholar 

  37. Tzioumis E, Adair LS. Childhood Dual Burden of Under- and Overnutrition in Low- and Middle-inCome Countries: A Critical Review. Food Nutr Bull. 2014;35(2):230–43.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Yang L, Bovet P, Ma C, Zhao M, Liang Y, Xi B. Prevalence of underweight and overweight among young adolescents aged 12–15 years in 58 low-income and middle-income countries. Pediatr Obes. 2019;14(3):e12468.

    Article  CAS  PubMed  Google Scholar 

  39. Duan R, Kou C, Jie J, Bai W, Lan X, Li Y, Yu X, Zhu B, Yuan H. Prevalence and correlates of overweight and obesity among adolescents in northeastern China: a cross-sectional study. BMJ Open. 2020;10(7):e36820.

    Article  Google Scholar 

  40. Zheng Z. Epidemiological research on physical activity and physical health of children and teenagers aged 9–17 years in China. Shanghai Institute of Physical Education; 2021. p. 23–29.

  41. Yanhui D. analysis of the prevalence status and trend of malnutrition among children aged 7 to 18 in China from 2005 to 2014. J Peking Univ (Medical edition). 2017;49(03):424–32.

    Google Scholar 

  42. Al LXE. dynamic analysis of the nutrition status of Han students aged 7 to 18 in Urumqi for 30 years. China School Health. 2017;38(09):1311–3.

    Google Scholar 

  43. Baoguo L, Tong H, Ke M. Physical health analysis of Xinjiang Ethnic teenagers under the perspective of healthy China. J Guangzhou Inst Phys Educ. 2018;38(06):23–6.

    Google Scholar 

  44. Kantanista A, Osinski W. Underweight in 14 to 16 year-old girls and boys: prevalence and associations with physical activity and sedentary activities. Ann Agric Environ Med. 2014;21(1):114–9.

    PubMed  Google Scholar 

  45. Elinder LS, Sundblom E, Rosendahl KI. Low Physical Activity Is a Predictor of Thinness and Low Self-rated Health: Gender Differences in a Swedish Cohort. J Adolesc Health. 2011;48(5):481–6.

    Article  PubMed  Google Scholar 

  46. Ochiai H, Shirasawa T, Nanri H, Nishimura R, Nomoto S, Hoshino H, Kokaze A. Lifestyle factors associated with underweight among Japanese adolescents: a cross-sectional study. Arch Public Health. 2017;75(1):45.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Yuzhu C, Zhifeng F, Zhenzhu T, Hong Y, Qichun W, Lin Z, Xuanhua L, Wutao L, Zhongyou L. the malnutrition status and change trend of children and adolescents in Guangxi from 1989 to 2011. Chin Child Health J. 2015;23(06):588–91.

    Google Scholar 

  48. Fang L, Jia T, Weidong L, Qiong W, Jie G. epidemic analysis of malnutrition from 2000–2010. School Health China. 2012;33(05):631–3.

    Google Scholar 

  49. Janicke DM, Mitchell TB, Pinto S, Wolock ER, Ding K, Moorman EL, Gonzalez-Louis R, Lim CS. Latent profiles of the feeding practices of caregivers of rural children with overweight and obesity and associations with child eating behaviors. Appetite. 2022;171:105911.

    Article  PubMed  Google Scholar 

  50. Guideline: Sugars intake for adults and children. Geneva: World Health Organization; 2015. https://www.who.int/publications/i/item/9789241549028.

  51. Malik VS, Schulze MB, Hu FB. Intake of sugar-sweetened beverages and weight gain: a systematic review1–3. Am J Clin Nutr. 2006;84(2):274–88.

    Article  CAS  PubMed  Google Scholar 

  52. van der Horst K, Timperio A, Crawford D, Roberts R, Brug J, Oenema A. The School Food Environment. Am J Prev Med. 2008;35(3):217–23.

    Article  PubMed  Google Scholar 

  53. Asawa K, Sen N, Bhat N, Tak M, Sultane P, Patil V. Association of sugary foods and drinks consumption with behavioral risk and oral health status of 12- and 15-year-old Indian school children. J Educ Health Promot. 2018;7(1):19.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Parikh T, Stratton G. Influence of Intensity of Physical Activity on Adiposity and Cardiorespiratory Fitness in 5–18 Year Olds. Sports Med. 2011;41(6):477–88.

    Article  PubMed  Google Scholar 

  55. Collings PJ, Westgate K, Väistö J, Wijndaele K, Atkin AJ, Haapala EA, Lintu N, Laitinen T, Ekelund U, Brage S, et al. Cross-Sectional Associations of Objectively-Measured Physical Activity and Sedentary Time with Body Composition and Cardiorespiratory Fitness in Mid-Childhood: The PANIC Study. Sports Med. 2017;47(4):769–80.

    Article  PubMed  Google Scholar 

  56. Roman-Viñas B, Chaput J, Katzmarzyk PT, Fogelholm M, Lambert EV, Maher C, Maia J, Olds T, Onywera V, Sarmiento OL, et al. Proportion of children meeting recommendations for 24-hour movement guidelines and associations with adiposity in a 12-country study. Int J Behav Nutr Phy. 2016;13(1):123.

    Article  Google Scholar 

  57. Moraes AC, Fernandes CA, Elias RG, Nakashima AT, Reichert FF, Falcao MC. Prevalence of physical inactivity and associated factors in adolescents. Rev Assoc Med Bras (1992). 2009;55(5):523–8.

    Article  Google Scholar 

  58. Bibiloni MM, Pich J, Cordova A, Pons A, Tur JA. Association between sedentary behaviour and socioeconomic factors, diet and lifestyle among the Balearic Islands adolescents. BMC Public Health. 2012;12:718.

    Article  PubMed Central  Google Scholar 

  59. Wang C, Chen P, Zhuang J. A National Survey of Physical Activity and Sedentary Behavior of Chinese City Children and Youth Using Accelerometers. Res Q Exercise Sport. 2013;84(sup2):S12–28.

    Article  Google Scholar 

  60. Duan J, Hu H, Wang G, Arao T. Study on Current Levels of Physical Activity and Sedentary Behavior among Middle School Students in Beijing, China. PLoS ONE. 2015;10(7):e133544.

    Article  Google Scholar 

  61. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57.

    Article  PubMed  Google Scholar 

  62. Iannotti RJ, Janssen I, Haug E, Kololo H, Annaheim B, Borraccino A. Interrelationships of adolescent physical activity, screen-based sedentary behaviour, and social and psychological health. Int J Public Health. 2009;54(S2):191–8.

    Article  PubMed  Google Scholar 

  63. Saunders TJ, Chaput J, Tremblay MS. Sedentary Behaviour as an Emerging Risk Factor for Cardiometabolic Diseases in Children and Youth. Can J Diabetes. 2014;38(1):53–61.

    Article  PubMed  Google Scholar 

  64. LeBlanc AG, Katzmarzyk PT, Barreira TV, Broyles ST, Chaput J, Church TS, Fogelholm M, Harrington DM, Hu G, Kuriyan R, et al. Correlates of Total Sedentary Time and Screen Time in 9–11 Year-Old Children around the World: The International Study of Childhood Obesity, Lifestyle and the Environment. PLoS ONE. 2015;10(6):e129622.

    Article  Google Scholar 

  65. Rideout V. Measuring time spent with media: the Common Sense census of media use by US 8- to 18-year-olds. J Child Media. 2016;10(1):138–44.

    Article  Google Scholar 

  66. Wu XY, Tao SM, Zhang SC, Zhang YK, Huang K, Tao FB. Analysis on risk factors of screen time among Chinese primary and middle school students in 12 provinces. Zhonghua Yu Fang Yi Xue Za Zhi. 2016;50(6):508–13.

    CAS  PubMed  Google Scholar 

  67. Oude Luttikhuis H, Baur L, Jansen H, Shrewsbury VA, O’Malley C, Stolk RP, Summerbell CD. Interventions for treating obesity in children. Cochrane Db Syst Rev. 2009;1:D1872.

    Google Scholar 

  68. Robinson TN. Reducing Children’s Television Viewing to Prevent Obesity. JAMA. 1999;282(16):1561.

    Article  CAS  PubMed  Google Scholar 

  69. Epstein LH, Valoski AM, Vara LS, McCurley J, Al E. Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol. 1995;14(2):108–9.

    Article  Google Scholar 

  70. Waters E, de Silva-Sanigorski A, Burford BJ, Brown T, Campbell KJ, Gao Y, Armstrong R, Prosser L, Summerbell CD. Interventions for preventing obesity in children. Cochrane Db Syst Rev. 2011;12:D1871.

    Google Scholar 

  71. Epstein LH, Roemmich JN, Paluch RA, Raynor HA. Influence of changes in sedentary behavior on energy and macronutrient intake in youth. Am J Clin Nutr. 2005;81(2):361–6.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank all the participants and their parents for their cooperation in our research.

Funding

This work was supported by”Anhui Province Higher Education Provincial Quality Engineering Major Project (2021jyxm1013)”.

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Authors and Affiliations

  1. Research Department of Physical Education, Xinjiang University, Urumqi, 800002, China

    He Liu

  2. School of Physical Education, Chizhou University, Chizhou, 247000, China

    Cunjian Bi, Hongniu Lin & Jie Zhang

  3. Sports Health Promotion Center, Chizhou University, Chizhou, 247000, China

    Cunjian Bi, Hongniu Lin & Jie Zhang

  4. Xinjiang Sports Vocational and Technical College, Urumqi, 830007, China

    Wei Ma

  5. Research Department of Physical Education, Xinjiang Institute of Engineering, Urumqi, 830023, China

    Yan-Yan Hu & Jing-Zhi Liu

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Contributions

Conception or design of the work, H.L., C.-J.B., Y.-Y.H.;the acquisition and analysis of data, J.-Z.L., W.M.,C.-J.B.,H.-N.L.,J.Z.; writing—original draft preparation, C.-J.B.,H.-N.L.,J.Z.;writing—review and editing,C.-J.B.,H.-N.L.,J.Z.; All authors have read and agreed to the published version of the manuscript.

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Correspondence to Cunjian Bi.

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Ethics approval and consent to participate

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Human Subjects Protection Committee of ChiZhou University (0236–2020). Prior to the questionnaire survey, students, parents or legal guardians had obtained written informed consent.

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Not applicable.

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The authors declare no conflict of interest.

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Liu, H., Bi, C., Lin, H. et al. Compared with dietary behavior and physical activity risk, sedentary behavior risk is an important factor in overweight and obesity: evidence from a study of children and adolescents aged 13–18 years in Xinjiang, China. BMC Pediatr 22, 582 (2022). https://doi.org/10.1186/s12887-022-03646-y

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Keywords

BMC Pediatrics

ISSN: 1471-2431

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