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Effect of low-frequency noise exposure on cognitive function: a systematic review and meta-analysis



Low-frequency noise may cause changes in cognitive function. However, there is no established consensus on the effect of low-frequency noise on cognitive function. Therefore, this systematic review and meta-analysis aimed to explore the relationship between low-frequency noise exposure and cognitive function.


We conducted a systematic review and identified original studies written in English on low-frequency noise and cognition published before December 2022 using the PsycINFO, PubMed, Medline, and Web of Science databases. The risk of bias was evaluated according to established guidelines. A random-effects meta-analysis was performed where appropriate. To explore the association between low-frequency noise exposure and cognitive function, we reviewed eight relevant studies. These studies covered cognitive functions grouped into four domains: attention, executive function, memory, and higher-order cognitive functions. The data extraction process was followed by a random-effects meta-analysis for each domain, which allowed us to quantify the overall effect.


Our analysis of the selected studies suggested that interventions involving low-frequency noise only had a negative impact on higher-order cognitive functions (Z = 2.42, p = 0.02), with a standardized mean difference of -0.37 (95% confidence interval: -0.67, -0.07). A moderate level of heterogeneity was observed among studies (p = 0.24, I2 = 29%, Tau2 = 0.03).


Our study findings suggest that low-frequency noise can negatively impact higher-order cognitive functions, such as logical reasoning, mathematical calculation, and data processing. Therefore, it becomes important to consider the potential negative consequences of low-frequency noise in everyday situations, and proactive measures should be taken to address this issue and mitigate the associated potential adverse outcomes.

Peer Review reports


Noise is a well-known environmental stressor that can negatively affect physiological, psychological, and behavioral processes [1, 2], making noise pollution a significant global public health concern. In the United States alone, over a quarter of the workers have been negatively impacted by noise. Furthermore, in its 2020 environmental noise report, the European Environment Agency stated that more than 22 million individuals were adversely affected by noise [3]. Low-frequency noise (LFN) refers to a type of environmental noise characterized by sound waves below 200 Hz. It is considered a distinctive environmental noise issue that affects many households [4]. The sources of LFN can be both natural, including wind, thunder, lightning, waves, earthquakes, and volcanic eruptions, and artificial, including life-related noise (e.g., air conditioning, power distribution equipment, elevators, and fans), traffic noise (e.g., railways and aviation), industrial enterprise noise (e.g., substations, wind farms, and converter stations), and construction noise (e.g., piling and decoration). As LFN becomes more prominent, its effects on humans intensify [5]. Studies have demonstrated the negative effect of LFN on the circulatory, endocrine, and nervous systems of the human body as well as on learning and social behavior [6, 7]. The impact of LFN can often be masked by medium and high-frequency noises, leading to people being less aware of the effects of LFN. Moreover, environmental noise measurements typically involve A-weighting, which significantly attenuates low-frequency components, potentially resulting in individuals being strongly disturbed by LFN, even when measurement values are within the standard limits [8]. The World Health Organization has also emphasized the detrimental effects of LFN for the first time in their Guidelines for Community Noise [9]. These recommendations about LFN were developed in the World Health Organization Night Noise Guidelines for Europe [10], and the noise guidelines are constantly improving over time [11, 12]. According to another review, it was reported that about 10% of people lived near infrasound or LFN sources, causing diseases of the central nervous system. LFN in the daily environment constitutes a problem that requires more research attention [13].

Cognition refers to the abilities related to the acquisition, processing, utilization, and understanding of information, which involves various functions, such as attention, learning, memory, execution, reasoning, and calculation [14]. Cognitive ability is not only a measure of learning capacity but also an important indicator of physical and mental health. Studies have demonstrated that environmental noise can negatively impact cognitive function [15, 16]. The potential causes for these negative impacts include distraction, reduced sleep quality, reduced speech perception ability, elevated psychological stress, discomfort, and learned helplessness [17,18,19,20]. Through analysis of brain structure and organization, it was theorized that this change in cognitive function may be related to gray matter decline in cognition-related brain regions such as the cerebellum and angular gyrus [13] and may also be related to the Ca2+ mediated apoptosis pathway in the hippocampal neurons [21].

Previous studies have examined the association between environmental noise and cognitive function, although most studies have included traffic noise rather than LFN as an independent exposure factor for their analysis and research [15, 16]. Some scholars propose that the adverse effects of noise can be attributed to the LFN component, with the rattling or vibrating aspect of LFN potentiating these negative effects [22]. However, research on LFN in occupational environments remains limited, and its negative impacts have not been widely recognized to date [23].

We proposed the question of whether LFN exposure increases the risk of cognitive impairment in humans compared to less LFN exposure. Therefore, this literature review considered LFN as an individual exposure factor. We aimed to systematically analyze and assess the impact of LFN on cognitive function by assessing the risk of bias in the studies and highlighting the primary limitations of existing research. Collectively, these findings can provide a useful theoretical foundation for the development of effective noise protection policies.


Protocol and registration

This systematic review was conducted according to the PRISMA standards [24] (Additional file 1), and the protocol was pre-registered in the PROSPERO database (ID = CRD42022384598) on December 27, 2022.

Eligibility and inclusion and exclusion criteria

This systematic review was performed in strict accordance with the Population, Exposure, Outcome framework [24]. The PECOS (participants, exposure, control/comparison, outcomes, and study design) statement is described in Table 1. For the literature review, only original research articles were included, whereas review papers, conference records, abstracts, editorials, reports, letters, notes, chapters, books, and theses were excluded. No restrictions were imposed on the publication year or geographic area; however, given the need for accurate understanding of the content and unavailability of translation sources, we only included research papers written in English. We also established specific inclusion criteria pertaining to the exposure method and outcome of intervention factors as follows:

  • 1) The presence of clear acoustic calibration instructions for noise exposure as an intervention condition, such as the spectral analysis of noise, and the primary component of noise exposure should be explicitly stated as the LFN component.

  • 2) The reporting of cognitive function domains, including performance on neurocognitive tasks, academic skills, overall IQ, measurements of neurodevelopment, and cognitive decline.

Table 1 The statement of PECOS

Information sources

The literature was sourced from the PsycINFO, PubMed, Medline, and Web of Science databases. Using keywords centered around “low-frequency noise” and “cognition,” searches were conducted to identify all published articles relevant to both concepts. The search period spanned from the earliest available date in each database until December 16, 2022. The complete search strategy for each database can be found in Additional file 2.

Study selection

The selected studies bibliography was created using the NoteExpress reference management software. After consolidating the literature and removing duplicates, two authors independently reviewed the literature based on the inclusion criteria. Initial screening involved reviewing the titles and abstracts of all articles, followed by screening the full text. Any discrepancies or uncertainties regarding eligibility or information extracted were resolved through discussion or consultation with other authors. If more detailed information was required from the original article, the authors were contacted for a joint decision on the inclusion or exclusion of the article.

Data collection process

Data were independently collected by the first author using a standardized data extraction table, primarily including data, such as article’s authors, publication year, study field, research design, noise type and assessment, results, measurements, adjustments, and effect sizes reported. A second researcher reviewed all the extracted data, and discrepancies were resolved through discussion. All reviewers and data extractors have received unified training and learned about the use of the Revised Cochrane risk of bias tool for randomized trials (RoB 2) [25]. Missing data were obtained by contacting the corresponding authors through email. When the data could not be used directly for analysis, corresponding formulas were applied for data conversion, and subgroup data were reasonably combined. For example, in the study of Belojević G et al. [26], participants were classified based on noise sensitivity, but in other articles, study cohorts were randomly selected from the entire population. Therefore, we merged the three subgroups. First, we merged the two subgroups with medium and low sensitivity to noise, and then we merged the obtained data with the subgroup with high sensitivity to noise to obtain the effective data. The specific extraction and conversion of raw data can be found in Additional file 3.

Data characteristics

Data were extracted from all included articles based on the following four characteristics: 1) intervention and control measures, including the intensity and type of LFN and whether the control group was less exposed; 2) basic subject information, including sample size, age range, and gender differences; 3) experimental design methods, including randomization methods, blinding, within/between-subject designs, and other methods; 4) extraction of continuous variable data, including the mean, standard deviation, and sample size of each group. If the original text did not provide corresponding means or standard deviations, other convertible statistical data, including standard errors, 95% confidence intervals (CIs), and t- or p-values, were extracted.

Risk of bias and quality of evidence assessment

Two authors independently assessed the risk of bias in the studies using the RoB 2 for randomized trials [27]. This tool assessed potential threats to the internal validity of randomized controlled trials across six domains of bias: randomization process, deviations from the intended interventions, missing outcome data, measurement of outcomes, selection of the reported result, and overall bias. The possible risk-of-bias judgments for each domain comprised a low risk of bias, some concerns regarding bias, and a high risk of bias [28, 29]. The individual and overall bias risks were strictly evaluated based on the guidelines provided by RoB 2 [30]. Any discrepancies were resolved through discussion or consultation with a third author. We further evaluated the overall quality of evidence and strength of evidence assessments for each outcome using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidelines. The starting level of the quality of evidence was determined according to the study design. This initial level was then increased or decreased considering several factors. Factors that lower the confidence of evidence were: risk of bias, inconsistency of results, indirectness of evidence, imprecision, publication bias, and number of studies. In contrast, a dose–response gradient, a large magnitude of effect, and confounding that underestimate the associations can increase confidence [31].

Summary and synthesis of results and meta-analysis

Due to systematic or random errors, the studies included in the meta-analysis may produce results that deviate from the real population parameters. The purpose of integrating these results into the meta-analysis is to generate estimates that are closer to the real population parameters by minimizing these errors in the study. Meta analysis models are mainly divided into conventional fixed effects and random effects models [32], among which there are also inverse variations, quality effects, and random effects estimators that are used to reduce these errors as much as possible, but there is still a controversy about the most effective estimator [33]. If heterogeneity is expected between relevant research results, a random effects model is usually preferred for analysis [34]. This model handles heterogeneity between data by increasing the weight of large sample data and reducing the weight of large sample data. Although this method carries certain risks, considering the variations in noise intervention, outcome measurement, and target populations across the included studies, a random effects model was still adopted to obtain more conservative results [35, 36]. Using RevMan software, the standardized mean difference (SMD), 95% CI, and p-value for different cognitive tasks were calculated. The Q statistic was used to examine the heterogeneity of study results, with a p-value of < 0.05 indicating heterogeneity across studies. Additionally, the I2 statistic was utilized to quantify the impact of heterogeneity. Low, moderate, and high heterogeneity were represented by an I2 ≤ 25%, 25%–75%, and ≥ 75%, respectively. Heterogeneity was generally considered acceptable if I2 did not exceed 50% [37, 38].


Study selection

This study included a total of eight original randomized control studies from five different countries, with three articles from Sweden and two from Iran. The specific selection process is shown in Fig. 1. The exclusion reasons for 42 full-text articles can be found in Additional file 4.

Fig. 1
figure 1

PRISMA-style flowchart of the study selection process

Study characteristics

In this study, cognitive function assessment was further classified into four domains: attention, executive function, memory, and higher-order cognitive functions [38]. Detailed information on the classification of functions in each included study as well as in-depth descriptions of LFN are outlined in Table 2.

Table 2 Descriptive characteristics of studies

Risk of bias and quality of evidence assessment

Risk of bias assessment conducted by two authors revealed a moderate risk of overall bias in the included studies quality. In terms of overall evaluation, three studies were considered to have a low risk of bias, whereas five were considered to have an uncertain risk of bias. Major uncertainties were noted in the methods of random assignment and selective reporting of study results. The risk of bias in different domains, both in terms of percentages and summary, is shown in Fig. 2. Detailed assessment data on the risk of bias can be found in Additional file 5. The GRADE system provided information about the certainty of the conclusions and strength of evidence. All estimates were of low or very low quality and the decisions made and reasons for these decisions are specified in Additional file 6.

Fig. 2
figure 2

Risk of Bias of individual studies

Impact of LFN exposure on cognitive function

From the perspective of cognitive psychology, cognitive function can be divided into basic and higher-order cognitive functions. Basic cognitive functions include attention, execution, and memory, while higher-order cognitive functions include textual reasoning, decision-making ability, and innovation [39, 40]. Compared to basic cognitive function, in the process of higher-order cognitive function, more brain regions utilize more resources, with the prefrontal cortex and cerebellum playing a core role [41, 42]. For analysis, we subdivided cognitive functions into four domains as mentioned above. The results for each domain are described as follows:

Impact of LFN exposure on attentional functioning

Among the eight included studies, five involved the cognitive function domain of attention. Figure 3 shows high heterogeneity among the studies (p < 0.001, I2 = 91%, Tau2 = 0.56). A random model was utilized for the meta-analysis, revealing no statistically significant impact of LFN intervention on changes in attention levels (Z = 0.13, p = 0.90), with the SMD being -0.05 (95% CI: -0.75, 0.66).

Fig. 3
figure 3

Forest plot of the effect of LFN on Attentional functioning domain

Impact of LFN exposure on executive functioning

Seven of the eight included studies evaluated executive functioning and were analyzed as shown in Fig. 4. The studies exhibited no heterogeneity (p = 0.58, I2 = 0%, Tau2 = 0.00). A random-effects model was employed for the meta-analysis, and results revealed that the impact of LFN intervention on changes in attention levels was not statistically significant (Z = 0.76, p = 0.45), with an SMD of -0.06 (95%CI: -0.22, 0.10).

Fig. 4
figure 4

Forest plot of the effect of LFN on Executive functioning domain

Impact of LFN exposure on memory

Four of the eight included studies were related to memory and analyzed as shown in Fig. 5. The studies demonstrated no heterogeneity (p = 0.90, I2 = 0%, Tau2 = 0.00). A random-effects model was employed for meta-analysis, revealing that the impact of LFN intervention on changes in attention levels was not statistically significant (Z = 0.60, p = 0.55) and demonstrating an SMD of -0.09 (95% CI: -0.38, 0.20).

Fig. 5
figure 5

Forest plot of the effect of LFN on Memory domain

Impact of LFN exposure on higher-order functions

Four of the eight included studies discussed higher-order functions and were subsequently analyzed, as shown in Fig. 6. The studies demonstrated moderate heterogeneity (p = 0.24, I2 = 29%, Tau2 = 0.03). A random-effects model was utilized for the meta-analysis, indicating that LFN intervention had a negative impact on higher-order cognitive functions, with this difference being statistically significant (Z = 2.42, p = 0.02), and an SMD of -0.37 (95% CI: -0.67, -0.07).

Fig. 6
figure 6

Forest plot of the effect of LFN on Higher-order functions domain


Summary of evidence

Noise is a serious global public health problem that deserves our attention for its impact on human life and health. Our initial literature search revealed numerous studies on noise and the relationship between noise and cognitive functions. Nevertheless, most of these studies primarily focused on environmental noise, traffic noise, or medium- to high- frequency noise in a broader sense [43,44,45]. The volume of studies specifically addressing LFN was notably limited, and views on the impact of LFN on cognitive functions were diverse. Some studies have suggested that LFN exposure negatively affects cognitive functions [46,47,48,49], whereas others have suggested no such effect [13]. Some even proposed that LFN exposure can enhance cognitive function [50]. To deepen our understanding of the relationship between LFN and cognitive function, we conducted a meta-analysis.

Cognitive function is a broad and complex concept, therefore, assessing changes in cognitive function from an overall perspective is likely to be one-sided and subjective and could lead to significant heterogeneity issues. To better assess the relationship between LFN exposure and cognitive function, we divided cognitive function into four domains mentioned earlier [39]. In the present study, we evaluated a total of eight studies: five on attention, seven on executive functioning, four on memory, and four on higher-order cognitive functions.

Our meta-analysis concluded that there was a moderate risk of bias and low quality of evidence indicating a negative impact of LFN exposure on higher-order cognitive functions (such as logical reasoning, mathematical calculation, and data processing), with low heterogeneity among the results of related studies. This suggests that responses to LFN within the domain of higher-order cognitive functions are relatively consistent. Previous studies have demonstrated that low-frequency traffic noise slows down reading speed and negatively impacts performance in mathematical tasks [48] and that LFN can reduce mental arithmetic test accuracy [47]. Gao et al. [51] observed a significant longitudinal negative correlation between traffic noise exposure and mathematical performance in cognitive testing. We have demonstrated in a previous study that participants exposed to LFN experience higher levels of cognitive load [52]. Further, LFN exposure does not significantly change basic cognitive functions, such as attention, executive function, and memory. There was no heterogeneity between the results of relevant studies in the domains of executive function and memory, but heterogeneity was higher within the attention domain, which aligns with the findings of Thompson et al. [52], who demonstrated that noise exposure worsens cognitive impairment and reading ability but has no effect on executive function. This may be because basic cognitive functions are more readily achieved and that the influence of LFN is not substantial enough to affect these simpler cognitive functions. Alternatively, the impact of LFN on basic cognitive functions could be too small for our selected detection indicators to determine [39]. We noted that the impact of LFN on cognitive functions may also be related to each individual’s noise sensitivity, which could potentially be the primary reason for changes in cognitive function [6, 26, 53].


In this study, we summarized literature concerning the relationship between LFN and cognition in the general (non-pathological) population up until December 2022. Two authors independently used rigorous scientific methods to assess the risk of bias. We categorized cognitive function into four domains and analyzed each, specifically focusing on LFN, an issue of increasing public health interest. Our findings revealed that, while LFN interventions may not substantially impact basic cognitive function, they could potentially negatively affect higher-order cognitive functions. These results are useful in the development of protective measures against LFN.

This study has some limitations. First, because research solely addressing LFN is relatively scarce and the cognitive tasks among studies are different, standardizing the exposure dose of LFN, including frequency, sound pressure level, exposure method, and intervention time, when incorporating the literature is challenging. This could have potentially affected the quality of evidence concerning the relationship between LFN and cognitive function. Second, cognitive function is broad, and representing changes in cognitive function from a single perspective may be somewhat one-sided. Even though we categorized and analyzed cognitive function, the summarization process may still be subjective. Finally, because of limitations in LFN research, the number of studies and population covered in our study were limited. Meanwhile, considering the GRADE assessment of current evidence is of poor quality, the conclusions demonstrated herein cannot be generalized.

Future research directions

The impact of LFN on basic cognitive functions may be too small to detect or may depend on individual noise sensitivity. Studies delving into the impact of LFN on cognitive function are lacking, with the scope of populations involved in these studies being relatively narrow. First, to conduct research of higher quality on the relationship between LFN interventions and cognitive functions, it is of paramount importance to conduct multi-center studies that comprise the entire population and include larger sample sizes.

Second, a unified standard for the methods and intensity of LFN interventions is yet to be established, and the selection of methods to evaluate cognitive function also varies. Although our study carefully selected situations involving LFN interventions and classified domains of cognitive function, the presence of confounding factors still poses a limitation on comparability across studies.

Third, given the potential role of noise sensitivity in its impact on cognitive function, future research demands a more profound exploration of this contributing factor. Furthermore, it should be incorporated into the inclusion criteria for study participants in a standardized manner.

Finally, it is hoped that the protection standards for LFN can be improved, and the subjective protection awareness of long-term exposure groups to actively protect against LFN can be improved, such as wearing noise reducing earphones and controlling the duration of noise exposure.


Through this meta-analysis, we aimed to explore the influence of LFN intervention on cognitive function. Our research indicated that, to date, there is no evidence supporting the notion that LFN intervention impacts attention levels, executive function, and memory. However, there is evidence of low quality showing that LFN intervention may lead to a reduction in higher-order cognitive functions, including reasoning and mathematical calculation. This impact may be associated with the level of cognitive load and susceptibility to noise. When the future research is conducted, these two factors and their interventions must be considered. In practical scenarios, attention should be directed toward the negative consequences of LFN during noise protection, and relevant protective measures should be implemented.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.



Low-frequency noise


Standardized mean difference


Confidence interval


Participants, exposure, control/comparison, outcomes, and study design

RoB 2:

Revised Cochrane risk of bias tool for randomized trials


Grading of Recommendations Assessment, Development, and Evaluation


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We would like to thank Editage ( for English language editing.


This work was supported by a Priority Project for Army Logistics Research (No. BSW17J029). The funder had no role in the design, conduct, or reporting of this work.

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Authors and Affiliations



PL originated the research idea, carried out the study and prepared the manuscript. Study design: PL, JW. Literature research and screen: PL, JJL. Data extraction: ZLL, JW. Critical appraisal: JW, JL. Critical appraisal: SHZ, SLX. Statistical analysis: PL, ZHL. Manuscript writing: PL, ZHL, JW.All authors have read and approved the final manuscript. Zhaohui Liu and Jin Wang are the co-corresponding authors of this article.

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Correspondence to Zhaohui Liu or Jin Wang.

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Supplementary Information

Additional file 1. 

The PRISMA 2020 checklist of this review.

Additional file 2.

The Literature Search Strategy of this review.

Additional file 3. 

Detailed information for effect sizes transformation.

Additional file 4. 

Exclusion reasons for 42 full-text articles.

Additional file 5. 

The individual study bias analyses of this review.

Additional file 6. 

GRADE summary for quality of evidence from LFN associated with cognitive function.

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Liang, P., Li, J., Li, Z. et al. Effect of low-frequency noise exposure on cognitive function: a systematic review and meta-analysis. BMC Public Health 24, 125 (2024).

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