Abstract
Background
Sleep duration has been shown to affect blood pressure, yet conflicting results persist. The present study aimed to investigate whether sleep duration was associated with high blood pressure in adolescents from the Thai National Health Examination Survey, VI (2020).
Materials and methods
The survey was a nationwide, cross-sectional survey that used a multistage, stratified sample of the Thai population in both urban and rural areas across 5 regions, including Bangkok, Northern, Central, North-eastern, and Southern regions. Data from 4053 adolescents (2,029 females) aged 10–19, including demographic data, blood pressure, fasting lipid and glucose levels, sleep duration, and outdoor activity time, were collected. High blood pressure was diagnosed using the 2017 American Academy of Pediatrics guidelines. The results are presented as odds ratios (95% CI, p-values) for logistic regression and β coefficient (95% CI, p-values) for linear regression.
Results
The prevalence of high blood pressure was about 10% (weighted prevalence of 12.6%). Average sleep duration was 8.65 ± 1.28 h. The high blood pressure group had a higher body mass index (BMI), higher triglycerides (TG), longer weekday sleep duration, and lower high-density lipoprotein cholesterol than the normotensive group. In the multivariable logistic regression, BMI and TG were independently associated with high blood pressure, with ORs of 1.15 (95% CI: 1.11–1.20, p-value < 0.001) and 1.003 (95% CI 1.000-1.006, p-value = 0.048), respectively. Although sleep duration was not associated with high blood pressure (OR = 0.99, 95% CI 0.87–1.13), it was positively associated with systolic blood pressure in a multivariable linear regression model (β = 0.41, 95% CI 0.064– 0.764, p-value = 0.023).
Conclusions
The prevalence of high blood pressure was about 10% in Thai adolescents. Although longer sleep duration was associated with higher systolic blood pressure, its effect was modest.
Keywords: Prevalence, Blood pressure, Adolescents, Sleep duration, Hypertension, Body mass index
Introduction
High blood pressure is a significant public health risk, typically associated with adults, but is now increasingly recognized in adolescents [1, 2]. Previous studies have shown that the prevalence of high blood pressure in adolescents ranges from 7.4 to 15.4% [3–6]. The early beginning of high blood pressure in adolescents is concerning since it frequently lasts into adulthood, increasing the risk of cardiovascular disease, stroke, and other significant health disorders [7]. Identifying modifiable risk factors during adolescence is crucial for early intervention and prevention to reduce the burden of high blood pressure in adulthood.
Sleep plays a critical role in maintaining overall health, and inadequate sleep has been linked to a variety of adverse health outcomes, including obesity, diabetes, and cardiovascular disorders [8]. Adolescence is a critical period characterized by significant physiological, psychological, and behavioral changes, including changes in sleep habits. Despite the well-established need for regular sleep, many teenagers fail to meet the recommended sleep duration due to academic stress, social activities, and excessive screen time.
Emerging data suggest that insufficient sleep may be associated with high blood pressure in adolescents. Javaheri et al. investigated 238 teenagers aged 13–16 years and discovered that shorter sleep duration was substantially linked with higher systolic and diastolic blood pressure [9]. The national survey of 1187 adolescents from Korea found that short sleepers (≤ 5 h/day) also had an increased risk of high blood pressure [10]. The biological mechanisms linking short sleep duration to increased blood pressure in teenagers are not fully understood, although they appear to involve multiple pathways. Insufficient sleep can increase sympathetic nervous system activity, raise stress hormone levels, and alter glucose metabolism, all of which can contribute to higher blood pressure [11]. However, another study reported a non-linear relationship between blood pressure and sleep duration in youth, meaning that longer sleep duration (> 10 h) increased the risk of high systolic blood pressure [12]. Inconsistent findings might be due to variations in the methods used to measure sleep, differences in the criteria for adequate sleep and high blood pressure, and differences in the ethnicities of the populations studied. The only study to explore the association between sleep duration and high blood pressure in Thai adolescents was a report from the fifth National Health Examination Survey (V, 2014), which found that neither short nor long sleep duration was associated with high blood pressure among 3505 adolescents [5]. These inconsistent findings still need to be investigated to understand better the underlying pathological mechanism linking sleep and cardiovascular interactions. The present study aimed to investigate whether sleep duration was associated with an increased risk of high blood pressure in adolescents using the sixth National Health Examination Survey, VI (2020).
Materials and methods
This study was conducted with approval from the Ramathibodi Hospital Ethics Committee for Human Research (MURA 2022/114). Participants in the present study were recruited from the Thai National Health Examination Survey VI (NHES-VI) database, conducted in 2020. In brief, the NHES-VI was a nationwide, cross-sectional survey that used a multistage, stratified sample of the Thai population in both urban and rural areas across 5 regions, including Bangkok, Northern, Central, North-eastern, and Southern regions. The sampling method was described in detail previously [13]. A total of 4237 adolescents aged 10–19 years were examined in the NHES-VI survey. One hundred eighty-four participants were excluded due to missing blood pressure, height, and blood chemistry data. Thirty-seven participants who previously had high blood pressure were also included, as their blood pressure was high at the time of measurement. Therefore, a total of 4053 adolescents were included.
Demographic data were obtained, including age, weight, height, waist circumference (WC), and blood pressure. Kilograms (kg) were used to measure weight, rounded to the nearest decimal. The nearest decimal point for measuring height was one centimeter (cm). WC was measured at the midpoint between the lower rib and the top of the iliac crest, in centimeters, rounded to the nearest decimal. Body mass index (BMI) was calculated using the following formula: weight (kg) divided by height (m)2. BMI was categorized into BMI z-score based on the World Health Organization growth reference for age and gender [14]. Obesity was defined as a BMI z-score > 2.
The 2017 American Academy of Pediatrics (AAP) recommendations were followed for the standardized blood pressure measurement [15]. The blood pressure monitor used in this study was the Omron HEM-7117 (Kyoto, Japan), which has been validated in accordance with the European Society of Hypertension guidelines [16]. Following a 5-minute rest period, blood pressure was taken on the right arm while sitting. Each participant’s cuff size was at least 40% wider and 80% longer than their mid-arm circumference. Each participant received three blood pressure measurements at 5-minute intervals. The first measurement was discarded, and each participant’s blood pressure was calculated as the average of the last two measurements. High blood pressure was defined as systolic or diastolic blood pressure (SBP) > the 95th percentile for age, gender, and height in those aged 10–12 years, or ≥ 130/80 mmHg in those aged ≥ 13 years [15]. The term “hypertension” was not used in the present study because each participant’s blood pressure was measured at a single visit, rather than three, as the current guidelines recommend. Heart rate (HR) was also the average of the last two measurements.
Sleep duration data (hours/day) for weekdays (WD) and weekends (WE) were collected via direct interviews with trained researchers. Average sleep duration (hours) was calculated as follows: [(WD x 5) + (WE x 2)] / 7. Based on the American Academy of Sleep Medicine (AASM) recommendation, participants aged 10–12, 13–18, and 19 years should have a sleep duration of 9–12, 8–10, and 7–9 h/day, respectively [17]. Therefore, sleep duration was categorized into three groups: short sleep (less than the recommended amount), normal sleep (within the recommended range), and long sleep (more than the recommended amount). Outdoor activity time was an average of weekly outdoor activity time reported in minutes/day.
Biochemical tests were analyzed in a central laboratory in the Faculty of Medicine Ramathibodi Hospital. The analyses were performed using a Dimension ExL 200 analyzer (Siemens Healthcare Diagnostics, USA). Glucose was analyzed using the enzymatic method (Hexokinase/G6-PDH). Serum lipids were analyzed using the lipase/glyceryl kinase/glyceryl-3-phosphate oxidase assay. The laboratory tests were standardized according to the Centers for Disease Control and Prevention Lipid Standardization Program.
Fasting blood samples were available in 3856 participants, including glucose (GLU), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG). Low-density lipoprotein cholesterol (LDL-C) was calculated using the formula: LDL-C = TC - HDL-C – (TG/5). Abnormal fasting glucose is defined as a pre-diabetes range (100–125 mg/dl) and the diabetes range (≥ 126 mg/dl). An abnormal lipid profile in adolescents is defined as TG ≥ 130 mg/dL, LDL-C ≥ 130 mg/dL, or HDL-C ≤ 40 mg/dL, according to recommendations from the National Heart, Lung, and Blood Institute [18].
All data were weighted for statistical analysis. Demographic data are presented as frequencies and means (standard deviations). A t-test was used to detect differences in continuous data between the two groups. The Chi-square test was used to detect differences in categorical data between two or three groups. Multivariable logistic regression was used to evaluate the parameters associated with overall high blood pressure, systolic high blood pressure, and diastolic high blood pressure as dichotomous variables across five models. Multivariable linear regression was used to analyze systolic blood pressure as a continuous variable. Independent variables in Model 1 included age, sex, BMI, and average sleep duration (hours/day). This model was used based on the already known factors associated with blood pressure. Model 2 was further adjusted for GLU. Model 3 was further adjusted for TG. Models 2 and 3 were further augmented with the biochemical parameters that showed significant differences between the normotensive and high blood pressure groups in the univariate analysis. Models 4 and 5 were added by adjusting for outdoor activity time as a continuous and a categorical variable with 60-minute intervals, respectively. Outdoor activity time was not included in the first 3 models, as it was reported by only 60% of participants. The results are presented as odds ratios (95% CI, p-values) for logistic regression and β coefficient (95% CI, p-values) for linear regression. A p-value of ≤ 0.05 was considered statistically significant. The analysis was conducted using institutional-licensed STATA version 18.
Results
Demographic, anthropometric, blood pressure, and biochemistry data
A total of 4,053 participants (2024 males, 49.9%) were included in the present study. Demographic data, anthropometric measurements, fasting blood chemistry, and average sleep duration are presented in Table 1. The prevalence of high blood pressure was 10% (12.9% in males and 7.1% in females; weighted prevalence: 12.6%). Fasting blood in the pre-diabetic range was detected in 2.6% (3.5% in males and 1.7% in females), while it was in the diabetic range in 0.6% (0.5% in males and 0.7% in females). A high TC (≥ 200 mg/dl) was detected in 31% (25.9% in males and 35.9%), a high LDL-C (≥ 130 mg/dl) was detected in 22.3% (18.4% in males and 26.3%), and a high TG (≥ 130 mg/dl) was detected in 19.5% (19.9% in males and 19% in females). A low HDL-C (≤ 40 mg/dl) was detected in 9.7% (25.9% in males and 35.9% in females).
Table 1.
Characteristics of all participants
| Parameters | All participants (N = 4,053) |
Normotension (N = 3,647) |
High blood pressure (N = 406) |
p-value |
|---|---|---|---|---|
| Age (years), mean (SD) | 14.16 (2.7) | 14.13 (2.6) | 14.39 (3.1) | 0.073 |
| Male, N (%) | 2,024 (49.9) | 1,762 (48.3) | 262 (64.5) | < 0.001 |
| Height (cm), mean (SD) | 155.8 (11.8) | 155.4 (11.6) | 159.2 (13.0) | < 0.001 |
| Weight (kg), mean (SD) | 51.2 (17.3) | 49.8 (16.0) | 63.4 (22.5) | < 0.001 |
| BMI (kg/m2), mean (SD) | 20.7 (5.4) | 20.3 (5.0) | 24.6 (7.0) | < 0.001 |
| BMI z-score, mean (SD) | 0.2 (1.6) | 0.1 (1.55) | 1.2 (1.8) | < 0.001 |
| WC (cm), mean (SD) | 69.2 (13.3) | 68.2 (12.4) | 78.2 (17.0) | < 0.001 |
| Obesity, N (%) | 615 (15.2) | 460 (12.6) | 155 (38.2) | < 0.001 |
| SBP (mmHg), mean (SD) | 108.1 (11.7) | 105.9 (9.6) | 127.8 (10.2) | < 0.001 |
| DBP (mmHg), mean (SD) | 63.9 (8.5) | 62.6 (7.2) | 76.1 (9.4) | < 0.001 |
| GLUa (mg/dl), mean (SD) | 84.6 (9.6) | 84.4 (9.4) | 87.1 (10.7) | < 0.001 |
| TCa (mg/dl), mean (SD) | 185.6 (36.5) | 185.4 (36.6) | 187.3 (35.1) | < 0.001 |
| HDL-Ca (mg/dl), mean (SD) | 55.8 (12.7) | 56.2 (12.7) | 52.9 (12.6) | < 0.001 |
| LDL-Ca (mg/dl), mean (SD) | 109.8 (32.4) | 109.8 (32.5) | 109.9 (31.2) | 0.962 |
| TGa (mg/dl), mean (SD) | 99.7 (55.4) | 97.1 (52.0) | 122.8 (75.6) | < 0.001 |
| Average sleep durationa (hours), mean (SD) | 8.7 (1.3) | 8.6 (1.3) | 8.8 (1.3) | 0.061 |
| Weekday sleep durationa (hours), mean (SD) | 8.5 (1.4) | 8.5 (1.4) | 8.6 (1.4) | 0.022 |
| Weekend sleep durationa (hours), mean (SD) | 9.1 (1.6) | 9.1 (1.6) | 9.2 (1.7) | 0.688 |
| Outdoor activity timeb (minutes/day) | 70.5 (54.3) | 70.5 (54.4) | 70.7 (53.8) | 0.958 |
BMI, body mass index; WC, waist circumference; WHR, waist to height ratio; SBP, systolic blood pressure; DBP, diastolic blood pressure; HR, heart rate; GLU, glucose; TC, total cholesterol; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; TG, triglyceride
aTotal participants = 3,856
bTotal participants = 2,568
Bold text, statistical significance (p-value ≤ 0.05)
Compared with the normal blood pressure group, the high blood pressure group had a significantly greater proportion of males, higher BMI, BMI z-score, WC, GLU, TC, and TG, but significantly lower HDL-C. LDL-C and outdoor activity time were not different between the two groups.
Sleep duration and high blood pressure
There was no difference in the average sleep duration between the high blood pressure and normotensive groups in male participants. However, female participants with high blood pressure had a significantly longer average sleep duration than those with normotension (8.9 vs. 8.6 h, p-value = 0.002). After analyzing average sleep duration according to the AASM recommendations, participants were classified into three groups, as shown in Table 2. Overall, about 21.9% and 10.7% had short and long average sleep durations, respectively. When stratified by sex, female participants in the long sleep group had a significantly greater percentage of high blood pressure than in the short sleep group (9.6% vs. 4.1%, p-value = 0.006), and the normal sleep group had a significantly greater risk of high blood pressure than in the short sleep group (7.6% vs. 4.1%, p-value = 0.011). However, the percentages of high blood pressure were not different between the three groups of female participants when stratified by age group (10–12, 13–15, and 16–19 years) as shown in Tables 3, 4 and 5. In males, there were no differences in the percentage with high blood pressure across the three groups based on average sleep duration.
Table 2.
Sleep duration and risk of high blood pressure categorized by average sleep duration
| Average sleep duration group | All participants (N = 3,856) |
p- value |
Males (N = 1,906) |
p- value |
Females (N = 1,950) |
p- value |
|||
|---|---|---|---|---|---|---|---|---|---|
| Normotension (N = 3,479) |
High blood pressure (N = 377) |
Normotension (N = 1,665) |
High blood pressure (N = 241) |
Normotension (N = 1,814) |
High blood pressure (N = 136) |
||||
|
Short sleep N (%) |
782 (92.4) | 64 (7.6) | 0.026 | 342 (88.4) | 45 (11.6) | 0.782 | 440 (95.9) | 19 (4.1) | 0.014 |
|
Normal sleep N (%) |
2,335 (89.8) | 264 (10.2) | 1,130 (87.3) | 165 (12.7) | 1,205 (92.4) | 99 (7.6) | |||
|
Long sleep N (%) |
362 (88.0) | 49 (11.9) | 193 (86.2) | 31 (13.8) | 169 (90.4) | 18 (9.6) | |||
Sleep duration group: Short, less than recommendation; Normal, within recommendation; Long, more than recommendation
All participants: p-value between group Short and Normal = 0.026, p-value between group Normal and Long = 0.276, p-value between group Short and Long = 0.011
Male participants: p-value between group Short and Normal = 0.561, p-value between group Normal and Long = 0.651, p-value between group Short and Long = 0.425
Female participants: p-value between group Short and Normal = 0.011, p-value between group Normal and Long = 0.333, p-value between group Short and Long = 0.006
Bold text, statistical significance (p-value ≤ 0.05)
Table 3.
Sleep duration and risk of high blood pressure categorized by average sleep duration (Ages 10–12)
| Average Sleep duration group | All participants (N = 1,493) |
p- value |
Males (N = 763) |
p- value |
Females (N = 730) |
p- value |
|||
|---|---|---|---|---|---|---|---|---|---|
| Normotension (N = 1,328) |
High blood pressure (N = 165) |
Normotension (N = 675) |
High blood pressure (N = 88) |
Normotension (N = 653) |
High blood pressure (N = 77) |
||||
|
Short sleep N (%) |
114 (92.7) | 9 (7.3) | 0.383 | 64 (94.1) | 4 (5.9) | 0.311 | 50 (90.9) | 5 (9.1) | 0.918 |
|
Normal sleep N (%) |
1,032 (88.7) | 132 (11.3) | 516 (87.9) | 71 (12.1) | 516 (89.4) | 61 (10.6) | |||
|
Long sleep N (%) |
182 (88.4) | 24 (11.7) | 95 (88.0) | 13 (12.0) | 87 (88.8) | 11 (11.2) | |||
Sleep duration group: Short, less than recommendation; Normal, within recommendation; Long, more than recommendation
All participants: P-value between group Short and Normal = 0.174, P-value between group Normal and Long = 0.897, P-value between group Short and Long = 0.206
Male participants: P-value between group Short and Normal = 0.128, P-value between group Normal and Long = 0.986, P-value between group Short and Long = 0.178
Female participants: P-value between group Short and Normal = 0.731, P-value between group Normal and Long = 0.847, P-value between group Short and Long = 0.679
Bold text, statistical significance (p-value ≤ 0.05)
Table 4.
Sleep duration and risk of high blood pressure categorized by average sleep duration (Ages 13–15)
| Average Sleep duration group | All participants (N = 1,320) |
p- value |
Males (N = 654) |
p- value |
Females (N = 666) |
p- value |
|||
|---|---|---|---|---|---|---|---|---|---|
| Normotension (N = 1,234) |
High blood pressure (N = 86) |
Normotension (N = 598) |
High blood pressure (N = 56) |
Normotension (N = 636) |
High blood pressure (N = 30) |
||||
|
Less sleep N (%) |
343 (94.0) | 22 (6.0) | 0.845 | 149 (91.4) | 14 (8.6) | 0.943 | 194 (96.0) | 8 (4.0) | 0.588 |
|
Normal sleep N (%) |
793 (93.4) | 56 (6.6) | 387 (91.3) | 37 (8.7) | 406 (95.5) | 19 (4.5) | |||
|
More sleep N (%) |
98 (92.5) | 8 (7.6) | 62 (92.5) | 5 (7.5) | 36 (92.3) | 3 (7.7) | |||
Sleep duration group: Short, less than recommendation; Normal, within recommendation; Long, more than recommendation
All participants: p-value between group Short and Normal = 0.711, p-value between group Normal and Long = 0.712, p-value between group Short and Long = 0.573
Male participants: p-value between group Short and Normal = 0.958, p-value between group Normal and Long = 0.731, p-value between group Short and Long = 0.778
Female participants: p-value between group Short and Normal = 0.769, p-value between group Normal and Long = 0.365, p-value between group Short and Long = 0.307
Bold text, statistical significance (p-value ≤ 0.05)
Table 5.
Sleep duration and risk of high blood pressure categorized by average sleep duration (Ages 16–19)
| Average Sleep duration group | All participants (N = 1,043) |
p- value |
Males (N = 489) |
p- value |
Females (N = 554) |
p- value |
|||
|---|---|---|---|---|---|---|---|---|---|
| Normotension (N = 917) |
High blood pressure (N = 126) |
Normotension (N = 392) |
High blood pressure (N = 97) |
Normotension (N = 525) |
High blood pressure (N = 29) |
||||
|
Less sleep N (%) |
325 (91.0) | 33 (9.2) | 0.060 | 129 (82.7) | 27 (17.3) | 0.365 | 196 (97.0) | 6 (3.0) | 0.170 |
|
Normal sleep N (%) |
510 (87.0) | 76 (12.9) | 227 (79.9) | 57 (20.1) | 283 (93.7) | 19 (6.3) | |||
|
More sleep N (%) |
82 (82.8) | 17 (17.2) | 36 (73.5) | 13 (26.5) | 46 (92.0) | 4 (8.0) | |||
Sleep duration group: Short, less than recommendation; Normal, within recommendation; Long, more than recommendation
All participants: p-value between group Short and Normal = 0.080, p-value between group Normal and Long = 0.259, p-value between group Short and Long = 0.025
Male participants: p-value between group Short and Normal = 0.481, p-value between group Normal and Long = 0.305, p-value between group Short and Long = 0.155
Female participants: p-value between group Short and Normal = 0.092, p-value between group Normal and Long = 0.651, p-value between group Short and Long = 0.103
Bold text, statistical significance (p-value ≤ 0.05)
Multivariable analysis for parameters associated with blood pressure
Multivariable analyses of parameters associated with overall high blood pressure, systolic high blood pressure, and diastolic high blood pressure are presented in Tables 6, 7 and 8. Only BMI and TG were significantly associated with high blood pressure across all three models. When analyzing systolic blood pressure as a continuous variable, age, male sex, BMI, TG, and average sleep duration were positively associated with systolic blood pressure (Table 9). However, outdoor activity time was not associated with blood pressure in any models.
Table 6.
Multivariable analysis for parameters associated with high blood pressure (Overall HT)
| Variables | Model | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| Age (year) |
1.060 [1.013, 1.109] (0.015) |
1.064 [1.016, 1.114] (0.012) |
1.062 [1.013, 1.113] (0.016) |
0.936 [0.836, 1.048] (0.235) |
0.939 [0.837, 1.055] (0.270) |
| Sex (male) |
2.750 [2.276, 3.321] (< 0.001) |
2.713 [2.247, 3.275] (< 0.001) |
2.677 [2.216, 3.233] (< 0.001) |
1.186 [0.844, 1.668] (0.305) |
1.202 [0.851, 1.697] (0.276) |
| BMI |
1.114 [1.088, 1.140] (< 0.001) |
1.107 [1.081, 1.133] (< 0.001) |
1.099 [1.070, 1.127] (< 0.001) |
1.148 [1.107, 1.190] (< 0.001) |
1.152 [1.110, 1.196] (< 0.001) |
| Average sleep duration (hours/day) |
1.135 [1.030, 1.251] (< 0.001) |
1.119 [1.016, 1.233] (0.025) |
1.107 [1.007, 1.216] (0.037) |
0.986 [0.868, 1.119] (0.812) |
0.991 [0.870, 1.129] (0.887) |
| GLU |
1.024 [1.011, 1.036] (0.001) |
1.023 [1.010, 1.036] (0.001) |
1.001 [0.989, 1.014] (0.823) |
1.002 [0.988, 1.015] (0.789) |
|
| TG |
1.003 [1.001, 1.004] (0.01) |
1.003 [1.000,1.006] (0.039) |
1.003 [1.000, 1.006] (0.048) |
||
| Outdoor activity time (minutes/day) |
1.005 [1.000, 1.010] (0.065) |
||||
| Outdoor activity time (base: <60 min/day) | |||||
| − 60–119 min/day |
1.275 [0.940, 1.730] (0.111) |
||||
| − 120 min/day and above |
1.843 [0.987, 3.443] (0.055) |
||||
| N | 3856 | 3856 | 3856 | 2335 | 2335 |
BMI Body mass index, GLU Glucose, LDL-C Low-density lipoprotein cholesterol
Results shown as adjusted OR [95% CI], (p-value) from logistic regression. Bold text, statistical significance (p-value ≤ 0.05)
Table 7.
Multivariable analysis for parameters associated with high systolic blood pressure
| Variables | Model | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| Age (year) |
1.044 [0.980, 1.112] (0.169) |
1.048 [0.985, 1.1416] (0.130) |
1.045 [0.979, 1.115] (0.171) |
0.915 [0.814, 1.028] (0.126) |
0.912 [0.814, 1.021] (0.102) |
| Sex (male) |
3.784 [2.942, 4.867] (< 0.001) |
3.766 [2.916, 4.864] (< 0.001) |
3.718 [2.872, 4.811] (< 0.001) |
1.344 [0.878, 2.057] (0.161) |
1.314 [0.865, 1.994] (0.186) |
| BMI |
1.092 [1.070, 1.114] (< 0.001) |
1.083 [1.060, 1.107] (< 0.001) |
1.074 [1.049, 1.099] (< 0.001) |
1.097 [1.059, 1.137] (< 0.001) |
1.099 [1.063, 1.137] (< 0.001) |
| Average sleep duration (hours/day) |
1.018 [0.927, 1.118] (0.692) |
1.003 [0.914, 1.100] (0.950) |
0.988 [0.902, 1.082] (0.779) |
0.900 [0.775, 1.045] (0.154) |
0.895 [0.768, 1.041] (0.140) |
| GLU |
1.025 [1.011, 1.039] (0.001) |
1.024 [1.010, 1.039] (0.002) |
0.997 [0.984, 1.011] (0.682) |
0.998 [0.984, 1.011] (0.705) |
|
| TG |
1.003 [1.001, 1.005] (0.011) |
1.004 [1.001,1.007] (0.023) |
1.003 [1.001,1.007] (0.018) |
||
| Outdoor activity time (minutes/day) |
1.001 [0.996, 1.006] (0.603) |
||||
| Outdoor activity time (base: <60 min/day) | |||||
| − 60–119 min/day |
1.005 [0.708, 1.427] (0.976) |
||||
| − 120 min/day and above |
1.466 [0.766, 2.807] (0.231) |
||||
| N | 3856 | 3856 | 3856 | 2335 | 2335 |
BMI Body mass index, GLU Glucose, LDL-C Low-density lipoprotein cholesterol
Results shown as adjusted OR [95% CI], (p-value) from logistic regression. Bold text, statistical significance (p-value ≤ 0.05)
Table 8.
Multivariable analysis for parameters associated with high diastolic blood pressure
| Variables | Model | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| Age (year) |
1.170 [1.099, 1.246] (< 0.001) |
1.173 [1.101, 1.250] (< 0.001) |
1.170 [1.096, 1.249] (< 0.001) |
1.099 [0.933, 1.293] (0.241) |
1.111 [0.935, 1.320] (0.216) |
| Sex (male) |
2.066 [1.514, 2.821] (< 0.001) |
2.059 [1.503, 2.820] (< 0.001) |
1.993 [1.462, 2.717] (< 0.001) |
0.866 [0.579, 1.295] (0.461) |
0.949 [0.613, 1.471] (0.805) |
| BMI |
1.107 [1.085, 1.129] (< 0.001) |
1.099 [1.078, 1.121] (< 0.001) |
1.089 [1.066, 1.112] (< 0.001) |
1.190 [1.160, 1.220] (< 0.001) |
1.194 [1.160, 1.228] (< 0.001) |
| Average sleep duration (hours/day) |
1.355 [1.178, 1.559] (< 0.001) |
1.333 [1.157, 1.535] (< 0.001) |
1.311 [1.134, 1.517] (0.001) |
1.177 [0.949, 1.460] (0.128) |
1.200 [0.975, 1.478] (0.082) |
| GLU |
1.018 [1.008, 1.029] (0.002) |
1.017 [1.005, 1.028] (0.006) |
1.001 [0.992, 1.010] (0.788) |
1.002 [0.993, 1.011] (0.637) |
|
| TG |
1.003 [1.001, 1.005] (0.002) |
1.004 [1.002, 1.006] (0.002) |
1.004 [1.001, 1.006] (0.008) |
||
| Outdoor activity time (minutes/day) |
1.006 [0.999, 1.014] (0.096) |
||||
| Outdoor activity time (base: <60 min/day) | |||||
| − 60–119 min/day |
1.098 [0.617, 1.954] (0.737) |
||||
| − 120 min/day and above |
1.666 [0.733, 3.791] (0.207) |
||||
| N | 3856 | 3856 | 3856 | 2335 | 2335 |
BMI Body mass index, GLU Glucose, LDL-C Low-density lipoprotein cholesterol
Results shown as adjusted OR [95% CI], (p-value). Bold text, statistical significance (p-value ≤ 0.05)
Table 9.
Multivariable analysis for parameters associated with systolic blood pressure (linear regressions)
| Variables | Model | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| Age (year) |
1.039 [0.844, 1.234] (< 0.001) |
1.076 [0.882, 1.270] (< 0.001) |
1.077 [0.855, 1.268] (< 0.001) |
1.625 [1.370, 1.879] (< 0.001) |
1.631 [1.373, 1.889] (< 0.001) |
| Sex (male) |
7.606 [6.812, 8.400] (< 0.001) |
7.456 [6.654, 8.258] (< 0.001) |
7.431 [6.621, 8.242] (< 0.001) |
2.588 [1.492, 3.684] (< 0.001) |
2.578 [1.487, 3.669] (< 0.001) |
| BMI |
0.732 [0.670, 0.793] (< 0.001) |
0.700 [0.641, 0.760] (< 0.001) |
0.622 [0.556, 0.688] (< 0.001) |
0.759 [0.645, 0.844] (< 0.001) |
0.762 [0.680, 0.844] (< 0.001) |
| Average sleep duration (hours/day) |
0.420 [0.165, 0.675] (0.003) |
0.360 [0.105, 0.615] (0.008) |
0.270 [0.013, 0.526] (0.040) |
0.407 [0.058, 0.756] (0.025) |
0.414 [0.064, 0.764] (0.023) |
| GLU |
0.119 [0.078, 0.160] (< 0.001) |
0.115 [0.072, 0.157] (< 0.001) |
0.028 [-0.021, 0.078] (0.243) |
0.027 [-0.023, 0.077] (0.270) |
|
| TG |
0.029 [0.020, 0.038] (< 0.001) |
0.021 [0.013, 0.030] (< 0.001) |
0.022 [0.013, 0.030] (< 0.001) |
||
| Outdoor activity time (minutes/day) |
0.003 [-0.012, 0.013] (0.961) |
||||
| Outdoor activity time (base: <60 min) | |||||
| − 60–119 min |
0.686 [-0.240, 1.613] (0.137) |
||||
| − 120 min and above |
0.284 [-1.522, 2.090] (0.744) |
||||
| N | 3856 | 3856 | 3856 | 2335 | 2335 |
BMI, body mass index; GLU, glucose; LDL-C, low-density lipoprotein cholesterol
Results shown as adjusted coefficient [95% CI], (p-value) from linear regressions; Bold text, statistical significance (p-value ≤ 0.05)
Discussion
The present study revealed that the prevalence of high blood pressure in Thai adolescents was about 10% (weighted prevalence of 12.6%). After adjusting for potential risk factors, BMI and TG were the primary factors associated with overall, systolic, and diastolic high blood pressure. In addition, longer sleep duration was associated with higher systolic blood pressure.
The prevalence of high blood pressure in the present study was higher than the previous report from the Thai National Health Examination Survey, V (2014), which reported 9.4% [5]. This finding was consistent with many reports from the Asian countries. A systematic review of blood pressure studies in 200,000 Asian adolescents from 39 countries revealed that the prevalence of hypertension ranged from 0.7 to 24.5%, and the trend increased over the past 5 years [19]. The increase in the hypertension rate could be due to the rise in obesity prevalence, which increased from 14.5% to 15.2% between the 2014 and 2020 reports, respectively [5]. Another national survey of Korean adolescents also showed an increase in BMI z-score from 0.09 in 2007 to 0.2 in 2013 [20]. The rate of increase was expected to be faster in the post-COVID-19 era, as adolescents are accustomed to sedentary lifestyles due to restrictions on outdoor activities during the COVID-19 outbreak [21]. This would increase the prevalence of obesity and consequently increase the prevalence of hypertension in adolescents in the next decade.
Previous studies reported the association between blood pressure and sleep duration in adolescents. Most studies found that short sleep duration was associated with higher blood pressure in adolescents [22–27]. Morgan et al. reported a recent survey of 3,907 children and adolescents from the US and showed that longer sleep duration was associated with lower systolic blood pressure. Moreover, longer sleep duration was also associated with lower odds of obesity [28]. Another study from Korea, involving 1,272 adolescents, reported that short sleep duration and obesity were associated with an increased risk of high blood pressure. The pathological mechanism underlying the association between sleep duration and blood pressure is not fully understood, but it has been postulated that increased sympathetic activity in individuals with inadequate sleep may contribute to elevated blood pressure [11, 23]. Nonetheless, the other studies [5, 29] did not reveal any association between sleep duration and blood pressure. On the other hand, univariate analysis in the present study showed that, for both genders, the long sleep group had a higher risk of high blood pressure, but there was no difference in blood pressure risk with different sleep durations in the subgroup analysis by age group. However, this might be due to smaller sample sizes for each sex in each age group. The multivariable analysis also found no association between sleep duration and high blood pressure, but a positive association between sleep duration and systolic blood pressure as a continuous variable was noted. This could be due to a higher proportion of females in the long sleep group with high blood pressure than in the short sleep group. This finding was consistent with a previous study investigating sleep duration and blood pressure in 1771 adolescents, which found that females who slept more than 9.5 h/day had an OR of 1.83 for high blood pressure compared to those who slept less than 8.5 h/day [22]. A possible explanation is that Thai male adolescents engage in more physical activity than their female counterparts, which may lead to shorter sleep duration in male than in female adolescents [30]. However, the exact mechanism behind this finding remains unclear. On the other hand, although the association was small, TG was associated with high blood pressure. This was consistent with previous studies [31, 32]. High TGs are commonly seen in adolescents with insulin resistance, meaning that they are more commonly seen in those with a more severe degree of obesity [33]. This could explain a small association between high TG and high blood pressure. Nonetheless, its association was less than that of age, male sex, and BMI.
Outdoor activity time is one of the parameters used to represent physical activity. Schaefer et al. reported a study of 306 adolescents who self-reported the time spent outdoors and measured activity by actigraphy, and showed that time spent outdoors was positively associated with moderate-to-vigorous physical activity. Numerous studies also showed that increased physical activity was beneficial in decreasing cardiovascular and metabolic risk [34, 35]. A study from Canada revealed that engaging in physical activity was associated with a lower odds of having high blood pressure [35]. A systematic review of 27 studies, including 15,220 adolescents, found that physical activity reduced both systolic and diastolic blood pressure [36]. However, the present study did not find significant differences in outdoor activity time between the normotensive and high blood pressure groups. In multivariable analysis, the present study did not reveal any significant association between outdoor activity time and high blood pressure. The most likely explanation is that BMI substantially affects blood pressure, making it impossible to examine the relationship between blood pressure and other factors in the present study. Another explanation could be that the sample size in the present study was not large enough to detect a trivial association between physical activity and blood pressure.
The present study reported a prevalence of high blood pressure from the National Health Examination Survey of Thailand, which included more than 4,000 adolescents. Statistical analysis was weighted to reflect the National Survey’s sampling design. However, the present study also had some limitations. First, blood pressure was measured at a single visit, not three. Therefore, the true prevalence of hypertension cannot be determined. Second, sleep duration was measured by self-report rather than actigraphy. So, the sleep duration may not be that accurate for each participant. Third, as normative tables for Thai blood pressure and BMI are unavailable, the present study used the WHO normative table for BMI and the AAP normative blood pressure table to categorize obesity and high blood pressure, respectively. In conclusion, the present study reported that the prevalence of high blood pressure was about 10%. Although longer sleep duration was associated with higher systolic blood pressure in Thai adolescents. However, its effect was less pronounced compared to those of age, male sex, and BMI. To reduce the burden of high blood pressure in adolescents, which can lead to NCDs in adulthood, public health authorities may need to launch a campaign promoting healthy eating and sufficient physical activity to maintain a normal BMI.
Acknowledgements
The authors thank the Bureau of Policy and Strategy, Ministry of Public Health, Thai Health Promotion Foundation, National Health Security Office, Thailand, and Health System Research Institute for financial support.
Abbreviations
- BMI
Body mass index
- DBP
Diastolic blood pressure
- HDL-C
High-density lipoprotein cholesterol
- SBP
Systolic blood pressure
- TC
Total cholesterol
- TG
Triglyceride
- WC
Waist circumference
Authors’ contributions
KP, SBo, SS, SB, NN, SC, SA, ST, and WA had substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data; KP, SBo, and WA had substantial contributions to drafting the article and revising it critically for important intellectual content; KP, SBo, SS, SB, NN, SC, SA, ST, and WA had approved the manuscript of the version to be published.
Funding
The sixth Thai National Health Examination Survey was supported by the Bureau of Policy and Strategy, Ministry of Public Health, Thai Health Promotion Foundation, National Health Security Office, and Health System Research Institute, Thailand.
Data availability
The datasets used and/or analyzed during the current study are available from the Department of Community Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University on reasonable request.
Declarations
Ethics approval and consent to participate
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the ethics committee for human research (MURA 2022/114) of the Faculty of Medicine Ramathibodi Hospital, Mahidol University. We received permission to access and use the 6th Thai National Health Examination Survey database from the Department of Community Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University; e-mail: headracm@mahidol.ac.th.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Haseler E, Sinha MD. Hypertension in Children and Young Adults. Pediatr Clin North Am. 2022;69(6):1165–80. [DOI] [PubMed] [Google Scholar]
- 2.Tran AH, Urbina EM. Hypertension in children. Curr Opin Cardiol. 2020;35(4):376–80. [DOI] [PubMed] [Google Scholar]
- 3.Díaz A, Calandra L. High blood pressure in school children and adolescents in Argentina over the past 25 years: A systematic review of observational studies. Arch Argent Pediatr. 2017;115(1):5–11. [DOI] [PubMed] [Google Scholar]
- 4.Soua S, Ghammam R, Maatoug J, Zammit N, Ben Fredj S, Martinez F, Ghannem H. The prevalence of high blood pressure and its determinants among Tunisian adolescents. J Hum Hypertens. 2024;38(4):371–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Pirojsakul K, Aekplakorn W, Siwarom S, Paksi W, Kessomboon P, Neelapaichit N, Chariyalertsak S, Assanangkornchai S, Taneepanichskul S. Sleep duration and risk of high blood pressure in Thai adolescents: the Thai National Health Examination Survey V, 2014 (NHES-V). BMC Public Health. 2022;22(1):1983. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Akbari M, Moosazadeh M, Ghahramani S, Tabrizi R, Kolahdooz F, Asemi Z, Lankarani KB. High prevalence of hypertension among Iranian children and adolescents: a systematic review and meta-analysis. J Hypertens. 2017;35(6):1155–63. [DOI] [PubMed] [Google Scholar]
- 7.Song P, Zhang Y, Yu J, Zha M, Zhu Y, Rahimi K, Rudan I. Global Prevalence of Hypertension in Children: A Systematic Review and Meta-analysis. JAMA Pediatr. 2019;173(12):1154–63. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Eshera YM, Gavrilova L, Hughes JW. Sleep is Essential for Cardiovascular Health: An Analytic Review of the Relationship Between Sleep and Cardiovascular Mortality. Am J Lifestyle Med. 2024;18(3):340–50. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Javaheri S, Storfer-Isser A, Rosen CL, Redline S. Sleep quality and elevated blood pressure in adolescents. Circulation. 2008;118(10):1034–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Lee JA, Park HS. Relation between sleep duration, overweight, and metabolic syndrome in Korean adolescents. Nutr Metab Cardiovasc Dis. 2014;24(1):65–71. [DOI] [PubMed] [Google Scholar]
- 11.Makarem N, Alcántara C, Williams N, Bello NA, Abdalla M. Effect of Sleep Disturbances on Blood Pressure. Hypertension. 2021;77(4):1036–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhao Q, He M, Zhang M, Chu Y, Ban B. Non-linear relationship between sleep duration and blood pressure in children with short stature. Front Pead. 2023;6;11:1302750. [DOI] [PMC free article] [PubMed]
- 13.Aekplakorn W, Chariyalertsak S, Kessomboon P, Sangthong R, Inthawong R, Putwatana P, Taneepanichskul S. Prevalence and management of diabetes and metabolic risk factors in Thai adults: the Thai National Health Examination Survey IV, 2009. Diabetes Care. 2011;34(9):1980–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.World Health Organization. Growth reference 5–19 years. Accessed 24 Feb 2024. [http://www.who.int/growthref/who2007_bmi_for_age/en/].
- 15.Flynn JT, Kaelber DC, Baker-Smith CM, Blowey D, Carroll AE, Daniels SR, de Ferranti SD, Dionne JM, Falkner B, Flinn SK et al. Clinical Practice Guideline for Screening and Management of High Blood Pressure in Children and Adolescents. Pediatrics. 2017;140(3):e20171904 10.1542/peds.2017-1904. [DOI] [PubMed]
- 16.Topouchian J, Agnoletti D, Blacher J, Youssef A, Ibanez I, Khabouth J, Khawaja S, Beaino L, Asmar R. Validation of four automatic devices for self-measurement of blood pressure according to the international protocol of the European Society of Hypertension. Vasc Health Risk Manag. 2011;7:709–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Paruthi S, Brooks LJ, D’Ambrosio C, Hall WA, Kotagal S, Lloyd RM, Malow BA, Maski K, Nichols C, Quan SF, et al. Recommended Amount of Sleep for Pediatric Populations: A Consensus Statement of the American Academy of Sleep Medicine. J Clin Sleep Med. 2016;12(6):785–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.The National Heart, lung and Blood Institute. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adolescents: summary report. Pediatrics. 2011;128(Suppl 5):S213–256. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Islam B, Ibrahim TI, Tingting W, Wu M, Jiabi Q. Current status of elevated blood pressure and hypertension among adolescents in Asia: a systematic review. J Glob Health. 2025;15:04115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Cho H, Kim JH. Secular trends in hypertension and elevated blood pressure among Korean children and adolescents in the Korea National Health and Nutrition Examination Survey 2007–2015. J Clin Hypertens (Greenwich). 2020;22(4):590–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Zaccagni L, Gualdi-Russo E. Reduced Physical Activity and Increased Weight Status in Children and Adolescents During the COVID-19 Pandemic: A Systematic Review. Child (Basel). 2025;30;12(2):178. [DOI] [PMC free article] [PubMed]
- 22.Paciência I, Barros H, Araújo J, Ramos E. Association between sleep duration and blood pressure in adolescents. Hypertens Res. 2013;36(8):747–52. [DOI] [PubMed] [Google Scholar]
- 23.Jiang W, Hu C, Li F, Hua X, Zhang X. Association between sleep duration and high blood pressure in adolescents: a systematic review and meta-analysis. Ann Hum Biol. 2018;45(6–8):457–62. [DOI] [PubMed] [Google Scholar]
- 24.Santos E, Souza OF, EVIDENCE OF THE ASSOCIATION BETWEEN SLEEP DURATION AND BLOOD PRESSURE IN ADOLESCENTS. A SYSTEMATIC REVIEW. Rev Paul Pediatr. 2021;39:e2019225. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Quan SF, Combs D, Parthasarathy S. Impact of Sleep Duration and Weekend Oversleep on Body Weight and Blood Pressure in Adolescents. Southwest J Pulm Crit Care. 2018;16(1):31–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Meininger JC, Gallagher MR, Eissa MA, Nguyen TQ, Chan W. Sleep duration and its association with ambulatory blood pressure in a school-based, diverse sample of adolescents. Am J Hypertens. 2014;27(7):948–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mezick EJ, Hall M, Matthews KA. Sleep duration and ambulatory blood pressure in black and white adolescents. Hypertension. 2012;59(3):747–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Morgan T, Basalely A, Singer P, Castellanos L, Sethna CB. The association between sleep duration and cardiometabolic risk among children and adolescents in the United States (US): A NHANES study. Child Care Health Dev. 2024;50(3):e13273. [DOI] [PubMed] [Google Scholar]
- 29.Shaikh WA, Patel M, Singh S. Association of sleep duration with arterial blood pressure profile of gujarati Indian adolescents. Indian J Community Med. 2010;35(1):125–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Katewongsa P, Pongpradit K, Widyastari DA. Physical activity level of Thai children and youth: Evidence from Thailand’s 2018 report card on physical activity for children and youth. J Exerc Sci Fit. 2021;19(2):71–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Vasudevan A, Thomas T, Kurpad A, Sachdev HS. Prevalence of and Factors Associated With High Blood Pressure Among Adolescents in India. JAMA Netw Open. 2022;5(10):e2239282. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Oni O, Amador E, Orekoya O, Olanipekun T. Triglyceride to Direct High Density Lipoprotein Cholesterol Ratio and Risk for Elevated Systolic Blood Pressure Among Adolescent - Results from the 2005–2016 National Health and Nutrition Examination Survey. Pediatrics. 2021;147(3MeetingAbstract):372–3. [Google Scholar]
- 33.Jung MK, Yoo EG. Hypertriglyceridemia in Obese Children and Adolescents. J Obes Metab Syndr. 2018;27(3):143–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Silva DR, Werneck AO, Collings PJ, Fernandes RA, Barbosa DS, Ronque ERV, Sardinha LB, Cyrino ES. Physical activity maintenance and metabolic risk in adolescents. J Public Health. 2018;40(3):493–500. [DOI] [PubMed] [Google Scholar]
- 35.Wellman RJ, Sylvestre MP, Abi Nader P, Chiolero A, Mesidor M, Dugas EN, Tougri G, O’Loughlin J. Intensity and frequency of physical activity and high blood pressure in adolescents: A longitudinal study. J Clin Hypertens (Greenwich). 2020;22(2):283–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Hassan MA, Zhou W, Ye M, He H, Gao Z. The effectiveness of physical activity interventions on blood pressure in children and adolescents: A systematic review and network meta-analysis. J Sport Health Sci. 2024;13(5):699–708. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the Department of Community Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University on reasonable request.