The effect of anxiety levels on attentional networks in high-altitude migrants: the role of sleep quality and haemoglobin concentration

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The effect of anxiety levels on attentional networks in high-altitude migrants: the role of sleep quality and haemoglobin concentration

https://doi.org/10.21203/rs.3.rs-3450364/v1

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To explore the effect of anxiety levels on attentional networks in high altitude migrants and to verify the mediating and moderating role of sleep quality and haemoglobin concentration(HGB). One hundred and forty high altitude transplants living on the plateau for two years were selected and anxiety levels were captured by the SCL-90 questionnaire, behavioural data by the ANT experimental procedure, sleep quality by the Pittsburgh Sleepiness Index and HGB by means of venous blood sampling. The results revealed a significant difference between the high and low anxiety groups in the attentional executive network and no difference in the alertness and orientation networks; a difference in sleep quality between the high and normal HGB groups.Sleep quality was fully mediated between anxiety level and attentional executive network, accounting for 45% of the total effect; sleep quality and HGB were moderated mediation between anxiety level and attentional executive network (β = 0.19, t = 2.03, p < 0.05), and only for individuals with high HGB did the sleep quality of individuals with high anxiety levels differ from that of individuals with low anxiety levels. It was only for individuals with high HGB that differences existed. It is concluded that as anxiety levels increase, this affects the sleep quality of high altitude migrants and thus their attentional network efficiency, and that as HGB increase, this alleviates the poor sleep quality of high altitude migrants with increased anxiety levels and thus improves their attentional network efficiency.

HGB

Anxiety levels

attention networks

sleep quality

mediating effects

Attention is the pointing and focusing of mental activity or consciousness on a certain object (Peng, 2004), which devotes limited cognitive resources to processing goal-related information (ZHOU et al., 2017) and is an important mental property that enables all mental processes to occur and proceed. Attentional function has been found to be an important cognitive function susceptible to anxiety (Bar-Haim et al., 2007), as well as being sensitive to hypoxia, and one study found that chronic high altitude exposure reduced attentional resources in migrants (Wang et al., 2014). As attentional function is sensitive to both anxiety and hypoxic conditions, it is unclear whether anxiety may enhance the impairment of attentional function in migrants at high altitude, and it is therefore worth exploring this question.

Posner et al. (Posner et al., 1900; Fossella et al., 2002) divided attention into three sub-networks, namely alerting, orienting and executive control. "Alerting" refers to the ability to maintain an alert state to receive information input; "orienting" refers to the ability to select information from a large amount of sensory input; and "executive control" is the ability to coordinate and control cognitive 'executive control' is the ability to coordinate and control cognitive operations and resolve response conflicts.Fan et al. devised the attention network test (ANT) based on Posner's theory (Fan et al., 2002), a paradigm that combines cueing with a flanker task to examine the alerting, orienting and executive control functions of the attention network by varying the type of cue and target stimulus. It is a valid method for measuring the function of the brain's attentional network (Wang et al., 2005).The paradigm can also be used to measure specific attentional network deficits in special populations, to guide the treatment of specific attentional network deficits and to measure differences in the development of attentional networks in normal populations (Rothbart et al., 2015).The ANT paradigm has also been applied to the study of attention in high altitude hypoxic conditions, as found in the study by Ma Hailin et al. (Wang et al., 2014) on the attentional networks of migrants exposed to prolonged high altitude, which found that high altitude hypoxia can negatively affect attention.

1.1 The effect of anxiety levels on attentional networks in high altitude transplants

For example, through the ANT paradigm, it was found that the attentional networks of high altitude migrants are deficient compared to the attentional executive control networks of individuals in the plains (Wang et al., 2014), whereas the attentional orienting and vigilance networks do not differ, which may be caused by the apparent competition for cognitive resources among individual attentional systems in the highlands (Zhang et al., 2018).As for the factors influencing individual attentional network deficits, some studies have found a correlation between anxiety levels and impaired attentional networks, meaning that when anxiety levels progress to generalised anxiety disorder, individual attentional functions are partially impaired and mainly in attentional executive functions (MO et al., 2019).The reason for this may be related to brain areas associated with the executive control network, with studies identifying the anterior cingulate cortex, lateral prefrontal brain regions and the basal ganglia as important brain areas for executive function (Bush et al., 2000; MacDonald et al., 2000). In fMRI studies of individuals with high levels of anxiety, it was found that the anterior cingulate cortex was activated in normal individuals during negative word stimulation, but not in individuals with high levels of anxiety (Shin et al., 2001).Wuetal (1991) used positron emission tomography to find a reduced rate of glucose metabolism in the frontal and basal ganglia in individuals with high anxiety.This all suggests that when anxiety levels reach a certain level, it is sufficient to affect the efficiency of an individual's attentional network and may even cause impairment. This means that anxiety can reinforce the negative effects of hypoxia on the efficiency of the attentional network in individuals under the same conditions of hypoxia. Based on the above studies, this study proposes that H1: High altitude migrants with low anxiety levels pay attention to network efficiency faster than high altitude migrants with high anxiety levels.

1.2 Mediation of sleep quality

Good sleep may improve the efficiency of attentional networks in high-altitude migrants, and it has been found that the quality of sleep affects the attentional networks of individuals, and there is conclusive evidence that sleep-disordered individuals have significantly lower efficiency in attentional vigilance networks and attentional executive control networks compared to sleep-quality individuals (Liu et al., 2020).The reason why sleep quality improves the efficiency of the attentional network is that sleep is an important physiological phenomenon that contributes to functional integration, physical recovery and memory consolidation (STICKGOLD et al., 2005), and that good sleep even helps to remove dementia-related proteins from the brain (SABIA et al., 2021).

An individual's level of anxiety is one of the main factors influencing an individual's sleep quality, with much research evidence showing that an individual's sleep quality is positively correlated with anxiety and depression (WANG et al., 2016; ZHAO et al., 2022). It is possible that cognitive errors and control beliefs play an important role in this (ALFANO CA et al.,2009). Studies have found that the poorer the quality of sleep, the poorer the cognitive assessment (MAUSS et al., 2013). It may also be influenced by genetic and environmental factors (GREGORY et al., 2011).Thus the anxiety levels of individuals under hypoxic conditions in the plateau also have a negative impact on their sleep quality due to factors such as cognitive errors and control beliefs, which in turn further act on their attentional network efficiency. On this basis, it is proposed that H2: sleep quality mediates the relationship between anxiety levels and attentional network efficiency in high-altitude residents.

1.3 Modulation of HGB

Haemoglobin is a protein responsible for the transport of oxygen in higher organisms. One important role of haemoglobin is to indicate the level of hypoxia in individuals in highland environments. Since haemoglobin plays an important physiological role in the binding, utilisation, transport and release of oxygen in the body (Wang et al., 2010), it has also been found that the higher the level of haemoglobin in the blood, the greater its ability to carry oxygen within the physiological range (QIU et al., 2009; LI et al., 2014), so haemoglobin concentration acts as an indicator of the body's ability to carry oxygen, and is able to reflect whether or not highland migrants are in a hypoxic state.There is evidence that haemoglobin is associated with the sleep quality of individuals in highland environments, for example, studies have found that the strong oxygen-carrying capacity of haemoglobin means the availability of sufficient oxygen, which affects the sleep quality of highland migrants (AINSLIE et al., 2013; Bain et al., 2018; Cho et al., 2016). The reason for this is that hypoxia can disrupt human biological rhythms (Bosco et al., 2003) affecting sleep regulation and thus causing sleep disturbances, whereas an appropriate haemoglobin concentration ensures oxygen supply and thus alleviates sleep disturbances caused by hypoxia. A higher haemoglobin level within the physiological norm (non-pathological haemoglobin increase) therefore means better oxygenation of the brain, which is conducive to improving sleep quality in plateau environments. However, when anxiety levels are too high in the plateau environment, higher hemoglobin oxygen levels within the physiological normative range may not have a positive protective effect on sleep quality as anxiety increases the oxygen consumption of attentional network functions.Based on this, we propose that H3 and HGB regulate the relationship between anxiety levels and sleep quality in plateau environments, and further propose the hypothesis of a regulated mediator model in which HGB regulates anxiety in plateau environments and affects the attentional network through sleep quality.The hypothetical model is shown in Fig. 1:

2.1 Object of study

The sample size was calculated according to the G-Power software, with a moderate effect size (0.3) set in the a priori test, giving an estimated total sample size of 111 (f = 0.3, α = 0.05, power = 0.95). One hundred and forty high altitude migrants were selected, all of whom had lived on the plateau for more than 2 years. Twenty-three subjects were excluded for data quality; five subjects withdrew from the physical examination due to haemophobia. We conducted behavioural tests on the remaining 112 subjects (63 of whom were female, mean age 22.33 ± 1.23). All subjects were physically and mentally healthy and had no brain injury or drug/alcohol addiction. All subjects signed an informed consent form and all processes of this experiment met the standards of the local ethics association.

2.2 Experiment design

To investigate the effect of anxiety levels on the attentional network of high altitude migrants, a one-way (subject type: high anxiety group, low anxiety group) within-group experimental design was used. The independent variable was anxiety level as measured by the SCL-90 questionnaire in highland migrants. The dependent variable indicators were the efficiency of the attentional vigilance network, the orienting network and the executive control network as measured by the extent of the ANT experiment.

To investigate the effect of haemoglobin concentration on sleep quality in high-altitude migrants, we used a one-way (subject type: normal group, high group) within-group experimental design. The independent variable was whether the haemoglobin concentration was classified within the medical reference normal range. The dependent variable indicator was sleep quality as measured by the Pittsburgh Sleepiness Index.

2.3 Survey instruments

To investigate the anxiety levels of high altitude migrants, the SCL-90 scale (Chinese version) was used (WANG et al., 1984): it was used to assess the mental health status of the patients. A version translated by Wang Zhenyu was used, and the scale consists of nine factors, including somatisation, obsessive-compulsive symptoms, interpersonal sensitivity, depressive symptoms, anxiety symptoms, hostility, phobic symptoms, paranoid symptoms and psychoticism. A 5-point scale from 1 to 5 was used ( 1 from none, 2 mild, 3 moderate, 4 quite severe, 5 severe), with a factor score ≥ 2 being a positive result. The total score reflects the severity of the condition and the factor score may reflect the characteristics of the symptom cluster. The correlation coefficients between the factor scores and the total scores ranged from 0.76 to 0.92, reaching a moderate correlation or higher, the internal consistency coefficient of the total scale was 0.954, and the mean internal consistency coefficient of each factor was 0.765, which indicate that the SCL-90 scale (Chinese version) has good reliability and validity (Chen et al., 2003; JIN et al., 1986).

In order to investigate the sleep quality of high altitude migrants, the Pittsburgh Sleep Quality Index (PSQI) was used, which was revised by Liu, Hsien-Chen et al. (1996). Each dimension is scored on a scale of 0 to 3, with a total score of 0 to 21. A higher total score means poorer sleep quality. The PSQI score classifies sleep quality into three levels: PSQI ≤ 4 as good, 5–7 as moderate and ≥ 8 as poor (LI Wei et al., 2003). The Cronbach alpha coefficient for the PSQI scale in this study was 0.83, indicating good reliability, and the KMO coefficient was 0.74. The questionnaire had good construct validity by Bartlett's spherical test.

2.4 ANT experimental procedures

Ponser et al. 1990 proposed a theory of attentional networks, which divides attention into three sub-networks: alerting, orienting and executive control (conflict). Alerting refers to maintaining a certain state of arousal to process external stimulus information. Orienting refers to the selection of target information and the targeting of attention to the event of interest. Executive control is the accomplishment of the target task by suppressing distractions and resolving conflicts. Based on Posner's theory of attentional networks, Van Zin et al. designed the Attention Network Test (ANT) to measure the efficiency of the brain's attentional network function by combining cueing and the Flanker task.

Attentional network efficiency was derived by subtracting the average response time of the subjects. The alerting effect = RTno-cue - RTdouble-cue; the orienting effect = RTcenter-cue-RTspatial-cue; and the executive control effect = RTincongruent- RTcongruent. higher scores for alerting and orienting indicate more efficient alerting and orienting networks, and lower scores for executive control indicates that the executive control network is more efficient.

Three cue conditions (no cue, central cue and spatial cue) and two stimulus conditions (congruent stimulus and incongruent stimulus) were included. In the congruent stimulus condition, the five arrows pointed in the same orientation, and in the incongruent stimulus condition, the middle arrow and the arrows on either side were oriented in opposite directions. The clue appeared before the presentation of the target stimulus, all stimuli were white on a black background, each small arrow had a viewing angle of 0.58°, the distance between adjacent arrows had a viewing angle of 0.06°, and the whole stimulus, i.e. the 5 arrows, had a viewing angle of 3.27°. The arrows had two orientations facing left or right, 1.06° from the central point of gaze, and below or above the central point of gaze. The design of all trials was modular, with each module having the same probability of occurrence for each of the six conditions.The subject's task was to determine the orientation of the middle arrow of the target stimulus by pressing the "F" key when the middle arrow was facing left and the "J" key when the middle arrow was facing right; the experiment was preceded by a practice module, which was programmed using E-Prime 2.0. The flow chart is shown in Fig. 2.

2.5 Venous blood physiological index data collection

In this study, physiological indicators were collected by venous blood sampling in a hospital in Lhasa, and data on the subjects' routine blood indicators were collected. The subjects were divided into normal and high groups according to whether the values of the red blood system indicators were within the normal medical reference range for plateau areas. Normal medical reference values: 1. red blood cell RBC (female: 3.8–5.1 10^12/L male: 4.3–5.8 10^12/L) 2. haemoglobin HGB 120–165 g/L 3. red blood cell pressure HCT (40–60%) 4. mean red blood cell volume MCV (80–100 fL) 5. mean haemoglobin content MCH (27-31pg) 6.Mean haemoglobin concentration MCHC (320–360 g/L) 7.Standard deviation of erythrocyte distribution width RDW-SD (35–56 fL) 8.Coefficient of variation of erythrocyte distribution width RDW-CV (11–16%)

3.1 Differences in anxiety levels of high altitude migrants compared between attention networks

The migrants were divided into low and high anxiety groups based on their SCL-90 scores, and their attentional networks of alertness, orientation and execution were subjected to an independent samples t-test to compare the differences, which revealed a significant difference between the low and high anxiety groups in the attentional execution network (t = -3.96, p < 0.01), meaning that the low anxiety group had a more efficient attentional execution network than the high anxiety group, while there was no difference in the attentional alerting network and orienting network did not differ, and the results are shown in Table 1 and Fig. 3. The results of this study partially validated research hypothesis 1.

Table 1

Comparison of differences in anxiety levels between attention networks
	Anxiety level
	Low anxiety group(n = 58)	High anxiety group(n = 54)
	M ± SD	M ± SD	t
Attention Network(alerting)	8.63 ± 10.88	9.42 ± 13.02	−0.35
Attention Network(orienting)	58.98 ± 18.64	58.77 ± 20.09	−0.06
Attention Network(conflict)	76.29 ± 19.46	95.81 ± 32.01	−3.96^*
Note: p<0.05.p<0.01.**p<0.001.

3.2 Comparison of differences in haemoglobin concentration in sleep quality

The experimentally observed haemoglobin concentrations were divided into normal and high groups according to whether the values were within the normal medical reference range. The results of the independent samples t-test showed that the sleep quality scores in the normal group of haemoglobin concentrations (HGB) were significantly higher than those in the high group (t = 2.31, p < 0.01). The results are shown in Table 2 and Fig. 4.

Table 2

Differences in haemoglobin concentration between sleep quality
	HGB
	Normal group(n = 56)	Higher group(n = 56)
	M ± SD	M ± SD	t
Sleep Quatliy	5.19 ± 4.21	3.67 ± 2.51	2.31^**

3.3 Analysis of the correlation between anxiety levels, sleep quality and attention networks in high altitude migrants

Correlating anxiety levels, sleep quality and alertness, orientation and execution of the attention network in high altitude migrants revealed a significant positive correlation between anxiety levels and sleep quality (r = 0.36, p < 0.01), a significant negative correlation between anxiety levels and haemoglobin concentration (r = -0.22, p < 0.05), a significant positive correlation between anxiety levels and execution of the attention network (r = 0.21, p < 0.05), sleep quality and execution of the attentional network were positively correlated significantly (r = 0.31, p < 0.01), while both positive and negative correlations between anxiety level and alertness and orientation of the attentional network were not significant, nor were both positive and negative correlations between sleep quality and alertness and orientation of the attentional network. The results are shown in Table 3.

Table 3

Correlation analysis between anxiety levels, sleep quality, haemoglobin concentration and attention network
	M ± SD	1	2	3	4	5	6
1Anxiety Level	20.77 ± 6.22	——
2Sleep Quality	4.52 ± 3.93	0.36^**	——
3HGB	162.96 ± 20.93	−0.22^*	−0.17	——
4Attention Network(alerting)	8.08 ± 11.51	0.09	0.16	−0.13	——
5Attention Network(orienting)	58.73 ± 19.18	−0.11	−0.01	−0.03	−0.12	——
6Attention Network(conflict)	89.02 ± 29.18	0.21^*	0.31^**	−0.07	0.14	−0.05	——

Table 4

Regression analysis of the mediating effects of sleep quality on anxiety levels and attentional executive networks
	Predictor variables	Resulting variables	R	R²	β	Bootstrap LLCI	Bootstrap ULCI	t
Model 1	Anxiety Level	Sleep quality	0.36	0.13	0.36	0.18	0.54	4.06^**
Model 2	Anxiety Level×Sleep Quality	Attention Network(conflict)	0.32	0.10	0.11	−0.08	0.30	1.14
Model 2	Anxiety Level×Sleep Quality	Attention Network(conflict)	0.32	0.10	0.26	0.07	0.45	2.73^**

3.4 A test of the mediating effect of sleep quality on anxiety and attentional executive networks in High Seas transplants

Based on the correlation analysis, which found that vigilance orientation was not significant, attentional executive network efficiency was used as the dependent variable and sleep quality was used as the mediating variable between anxiety level and attentional executive network efficiency in high altitude migrants, and in order to standardise the criteria, Z-scores for each variable were used; regression analysis of mediating effects was conducted, and the results found that anxiety level positively predicted sleep quality significantly (β = 0.36, p < 0.01),anxiety level positively predicted attentional executive network efficiency insignificantly (p > 0.05) and sleep quality positively predicted significantly (β = 0.26, p < 0.01).

In a test of the mediating effect of sleep quality, the results found a significant and fully mediated role for sleep quality in mediating anxiety levels and attentional executive network efficiency. This study tested the hypothesis of H2 and the results are shown in Table 5 and Fig. 5.

Table 5

Test of the mediating role of sleep quality between anxiety levels and attentional executive networks
Paths	Indirect effects	0.95 confidence interval	Intermediary effect volume
Anxiety Level—Sleep Quality—Attention Network(conflict)	0.09	[0.02;0.19]	45%

3.5 A test of moderating mediating effects of sleep quality and haemoglobin concentration on anxiety levels and attentional executive networks moderated in high sea migrants

Using anxiety level as the independent variable, attentional executive network efficiency as the dependent variable, sleep quality as the mediating variable, and hemoglobin concentration as the moderating variable, and Bootstrap analyses using z-scores of each variable for the purpose of standardization, it was found that the moderating mediating roles of sleep quality and hemoglobin concentration were established in the effect of anxiety level on the efficiency of the attentional executive network of this altitude migrant (see Fig. 6).Specifically, a significant moderating effect of haemoglobin concentration between anxiety level and sleep quality (coeff = 0.197, SE = 0.097, p = 0.044, 95% CI = [0.005, 0.396]) and a significant mediating effect of sleep quality between anxiety level and attentional executive network efficiency (coeff = 0.265, SE = 0.097, p = 0.007, 95% CI = [0.072, 0.457]), whereas the direct effect of the independent variable anxiety level on the dependent variable attentional executive network validity was not significant after controlling for the mediating variable sleep quality, with the interval containing 0 (Effect = 0.112, SE = 0.097, 95% CI = [-0.080, 0.305]).

The effect of haemoglobin concentration on sleep quality was tested according to simple slope analysis, and the mean +/-1SD of haemoglobin concentration was compared. Figure 7 shows the regression lines for data collected at high (+ 1SD) and low (-1SD) levels of haemoglobin concentration under the four experimental treatments. This comparison revealed significant differences at high haemoglobin concentrations (Effect = 0.154, SE = 0.071, p < 0.05, 95% CI = [0.032, 0.309]), indicating that there were differences in sleep quality between individuals with high and low levels of anxiety moderated by high haemoglobin concentrations. The difference was not significant at low haemoglobin concentrations (Effect = 0.049, SE = 0.035, p > 0.05, 95% CI = [-0.001, 0.134]), indicating that there was no difference in sleep quality between individuals with high and low levels of anxiety when moderated by low haemoglobin concentrations.

There was a moderating mediating effect, with the indirect effect of the mediation test not containing 0 when haemoglobin concentration was high (M + 1SD = 1) (Effect = 0.154, SE = 0.071, 95% CI = [0.032, 0.309]). That is, for individuals with high haemoglobin concentrations, sleep quality fully mediated the effect between anxiety levels and attentional executive network efficiency; when haemoglobin concentrations were low (M − 1SD = -1), the indirect effect of the mediation test contained 0 (Effect = 0.049, SE = 0.035, 95% CI = [-0.001, 0.134]). That is, for individuals with low haemoglobin concentrations, sleep quality did not fully mediate the effects of anxiety levels and attentional executive network efficiency. The results of this study validate the hypothesis of H3.

4.1Moderating mediating effects of sleep quality and hemoglobin concentration between anxiety levels and attentional network efficiency

Based on the experiments, the results of this study found that anxiety levels were able to influence the attentional network of high-altitude migrants, and that the efficiency of the attentional network of high-altitude migrants with low anxiety levels was faster than that of high-altitude migrants with high anxiety levels, specifically in the executive control network, while there were no differences in the vigilance and orientation networks, and anxiety levels only differed in the efficiency of the executive network. This result may be related to the fact that the 3 subnetworks are independent yet interconnected, perform different functions and have specific anatomical regions and biochemical mechanisms within the brain (MO et al., 2019; MacDonald et al., 2000; Shin et al., 2001); it may also be related to the type of anxiety, as there studies have found that trait anxiety interferes with the goal-directed attention system (executive function) and state anxiety interferes with the stimulus-driven attention system (alerting, orienting function) (AI et al., 2017; Pacheco-Unguetti et al., 2010); and finally, it may be that increased amygdala activity and thus prefrontal attentional control are related to increased anxiety levels (Bishop et al., 2009).Thus, it is only the level of anxiety that has an effect on the attentional executive network, whereas a reduction in the effectiveness of the executive control network can lead to a reduction in the individual's ability to escape from invalid cues and even to a reduction in emotional neutrality when faced with information (Pacheco-Unguetti et al., 2011). This suggests that as anxiety levels increase it does affect the efficiency of the attentional executive network and also brings about a reduction in the ability to process information.

The results of this study found a significant positive correlation between sleep quality and the attentional executive network only, suggesting that improvements in sleep quality may only affect improvements in the executive network, and the reason for this result may lie in the brain regions associated with the attentional executive network, as numerous studies (LIU et al., 2020; Giovagnoli et al., 2020) have indicated that the prefrontal The prefrontal cortex and anterior cingulate cortex are closely associated with executive control functions.Previous functional magnetic resonance imaging-based studies (Altena et al., 2008) have shown structural atrophy and functional changes in the prefrontal cortex, anterior cingulate gyrus, temporal lobe and frontal-striatal loop cortex of individuals with poor sleep quality, including reduced volume, reduced blood flow, reduced activity and decreased metabolism. Furthermore, studies of neurotransmitters (Ott et al., 2019) have shown that a deficiency of dopamine transmitters in the prefrontal cortex and reduced expression of DRD1 are closely associated with impairment of cognitive functions such as executive control functions.This is why there are cases of structural, functional and neurotransmitter changes in brain regions such as the frontal lobe caused by chronic sleep deprivation and disturbed sleep rhythms, which ultimately lead to a decrease in the efficiency of the executive control network. Future studies could focus on brain regions involved in the processing of the alerting and orienting networks, such as the frontoparietal, thalamic and temporoparietal junctional areas, in individuals with poor sleep quality (Markett et al., 2022), thus corroborating that sleep quality selectively improves the efficiency of the attentional network. This also suggests that as sleep quality becomes poorer, so does the efficiency of the attentional executive network.

The results of this study found that sleep quality and anxiety levels were also positively and significantly correlated, suggesting that a decrease in anxiety levels was able to improve the sleep quality of highland migrants. This means that anxiety has a direct and strong effect on sleep quality, with the more anxious the person, the worse the sleep quality (YANG et al., 2008). This may be because sleep is an important component of the body's physiological needs, and anxiety triggered by environmental factors (YANG et al., 2016) further exacerbates poor sleep, which leads to reduced functional and stimulus connectivity within brain regions associated with executive function, thereby reducing individual autonomic regulation (Verweij et al. 2014), thus creating a vicious cycle. This would suggest that as anxiety levels increase it can lead to problems with sleep quality in highland migrants.

Therefore, based on the results of the above study, the present study found that the mediating effect of sleep quality may be due to the fact that sleep quality is affected as anxiety levels increase, thus reducing the efficiency of the attentional executive network in high altitude migrants, thus also suggesting the possibility of improving sleep quality by reducing anxiety levels, thus increasing the efficiency of the attentional executive network.

Based on the results of this study, it was found that haemoglobin concentration plays a moderating role in mediating sleep quality and the efficiency of anxiety levels and attentional executive networks in high-altitude migrants, due to the fact that hypoxia is an important factor affecting sleep quality in high-altitude migrants (WANG et al., 2022), and that within the physiological range, the higher the level of haemoglobin in the blood, the greater its ability to carry oxygen (Wang et al., 2010), thus alleviating the sleep disturbance caused by anxiety levels in high-altitude migrants.However, when anxiety levels are too high for individuals in an altitude environment, the higher hemoglobin oxygen content within the physiological norm may not have a positive protective effect on sleep quality due to the increased oxygen consumption of the attentional network function caused by anxiety. Therefore, with higher haemoglobin concentrations, there are limitations to the alleviating effect on sleep disturbances in high altitude migrants caused by high anxiety levels. However, with the modulation of haemoglobin concentration, it is possible to alleviate the poor sleep quality associated with anxiety levels and thus improve the efficiency of the attentional executive network in high altitude migrants.

4.2Limitations and outlook

This study only used physiological indicators and behavioural data to investigate the relationship between anxiety levels, haemoglobin, sleep quality and attentional networks, but did not investigate the underlying cognitive neural mechanisms. Based on this study, we will continue to investigate the factors that influence sleep quality and attention network deficits in high altitude migrants, find ways to improve the attention network of high altitude migrants, and further investigate the deeper cognitive neural mechanisms behind the effect of haemoglobin concentration on sleep quality in high altitude migrants. The aim is to find practical ways to improve the sleep quality of high-altitude migrants and thus improve the efficiency of their attention networks.

Anxiety levels can have an impact on the efficiency of the attention network of high altitude migrants. As anxiety levels increase, it affects the quality of sleep of high altitude migrants, thus reducing the efficiency of the attentional network, and when haemoglobin concentrations increase, it alleviates the poor sleep of high altitude migrants caused by increased anxiety levels, thus improving the efficiency of the attentional network.

Acknowledgments

We thank the children who participated in this study, as well as their parents and the teachers and schools who assisted with the study and provided ANT data. We thank the members of the Highland Brain Science Research Center who assisted with data collection and processing.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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The effect of anxiety levels on attentional networks in high-altitude migrants: the role of sleep quality and haemoglobin concentration

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Abstract

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1 INTRODUCTION

1.1 The effect of anxiety levels on attentional networks in high altitude transplants

1.2 Mediation of sleep quality

1.3 Modulation of HGB

2 OBJECTS AND METHODS

2.1 Object of study

2.2 Experiment design

2.3 Survey instruments

2.4 ANT experimental procedures

2.5 Venous blood physiological index data collection

3 RESULTS

3.1 Differences in anxiety levels of high altitude migrants compared between attention networks

3.2 Comparison of differences in haemoglobin concentration in sleep quality

4 DISCUSSION

4.1Moderating mediating effects of sleep quality and hemoglobin concentration between anxiety levels and attentional network efficiency

4.2Limitations and outlook

5 CONCLUSION

Declarations

References

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