Sound Distraction and Mental Health in the Workplace: The Role of Coping Strategies

Abstract

Objective:

Guided by Person-Environment Fit theory, this study examines how perceived sound distraction and disturbance, coping strategies and personal characteristics relate to mental health outcomes in office environments.

Materials and Methods:

Data were collected from two organisations (N = 214). Structural equation modelling was used to analyse the complex relationships between sound distraction, coping strategies, personal characteristics and a broad set of mental health outcomes.

Results:

The measurement model confirmed that three items loaded onto each mental health factor, which were strongly interrelated. Stressful mood was significantly associated with exhaustion (β = 0.57, t = 6.64) and fatigue (β = 0.21, t = 4.86) and also correlated with poor sleep quality (β = −0.59, t = −4.42) and disengagement (β = −0.34, t = −2.88). Employees reporting low concentration and poor sleep tended to feel more disengaged. Perceived distraction was negatively related to concentration (β = −0.67, t = −5.38). Disturbance from unintelligible background sounds was linked to higher exhaustion (β = 0.10, t = −2.99), whereas disturbance from intelligible speech showed an inverse relationship with exhaustion (β = −0.11, t = −2.63). Both disturbance types were strongly correlated and associated with increased distraction. Coping strategies were predominantly avoidance-based; notably, working more slowly than usual was associated with disturbance from speech (β = 0.18, t = 3.17), while trying to be quieter was linked to disturbance from unintelligible sounds (β = 0.28, t = 3.44). Greater effort was paradoxically related to higher fatigue (β = 0.24, t = 2.41) but lower disengagement (β = −0.098, t = −2.46). Noise-sensitive employees reported higher stressful mood (β = 0.19, t = 3.62) and were more likely to adopt avoidance coping strategies (β = 0.42, t = 6.88). The lack of concentration spaces and frequent individual-focused tasks amplified the distraction and disturbance.

Conclusion:

The findings highlight the complex interplay among sound, coping and mental health. Organisations should reduce chronic noise stressors and, based on theory and prior research, encourage approach-oriented coping to mitigate long-term risks. Providing acoustically optimised spaces and interventions such as sound masking can improve person-environment fit and support employee well-being.

KEY MESSAGES

• (1)Environmental discomfort, particularly sound disturbance, is related to employees’ coping strategies and mental health outcomes.
• (2)Avoidance coping strategies are more commonly used than approach strategies in response to sound-related stressors.
• (3)Moderate background noise may reduce exhaustion, but distraction remains a key barrier to concentration.
• (4)Tailored workplace design and mental health support can help mitigate long-term risks.

Introduction

The Person-Environment Fit theory posits that stress arises when the environment fails to provide sufficient resources to meet an individual’s needs. This misalignment can lead to perceived psychological, physiological, or behavioural strain.[1] In the work environment, the physical environment should also meet the individual’s needs. These needs depend on the nature of the performed work activity and the individual’s preferences.[2] Radun and Hongisto highlight that satisfaction with sound is the most critical determinant of how well different office environments support tasks requiring concentration, confidentiality or remote communication.[3]

Despite this, employees in open-plan offices continue to report high levels of sound disturbance, particularly from irrelevant speech noise, which remains one of the most distracting sources.[4,5] The open-plan office generally lacks visual and acoustic privacy,[6] and sound levels are higher due to intelligible speech (e.g., conversations, telephone calls and laughter).[4] As a result, employees may experience productivity loss, increased stress, and reduced comfort.[7]

Open-plan offices consistently show the lowest perceived fit across work activities, mainly due to dissatisfaction with sound.[3] This dissatisfaction increases the likelihood that employees will develop coping strategies to manage sound disturbance.[8,9] Because extended exposure to sound disturbance can impair physical and psychological well-being, as well as cognitive performance,[10,11] offices should be designed as ‘a supportive acoustic environment that can match people’s physiological, psychological, and behavioral demands in context, and that also fits the criteria and standards’. [10] Satisfaction with the sound environment has become a critical factor influencing work performance,[12] health, well-being and productivity.[6]

Historically, sound-related studies have adopted a pathogenic approach by focusing on adverse health outcomes, such as sick-building syndrome or a loss of work performance.[13] To design a healthy office environment, we need to shift from a pathogenic to a salutogenic orientation.[14] In a salutogenic work environment, environmental resources are aligned to promote employees’ positive health outcomes.[15] Recent studies show that employees value acoustic environments that make them feel comfortable and relaxed, especially during cognitively demanding tasks.[10] This suggests that office design should not only reduce sound distractions and associated health risks but also enhance conditions that support work activities and mental well-being.[14]

Torresin et al.[14] advocate conducting scientific studies and examining how people’s perceptions and experiences of sound environments influence their health and well-being. For instance, Roskams et al.[16] explore how the experience of speech disturbance and acoustical quality influence concentration, stress, engagement and productivity. However, few studies simultaneously consider multiple mental health indicators. To understand the influence of workplace design, it is essential to consider both positive and negative indicators of mental health, including stress, depression, burnout, fatigue, well-being, engagement, concentration, mood, sleep quality and productivity.[17] This study adopts a holistic approach to examine how individual characteristics and noise disturbance relate to these mental health indicators.

In addition, limited research has explored how coping strategies are used to mitigate noise disturbance and their effectiveness in supporting productivity.[18,19] Although coping is widely acknowledged as a key factor in long-term mental health,[20] its specific impact within workplace settings is underexplored. Furthermore, little is known about whether job activities shape coping strategies, for example, how individuals adapt to noise when performing tasks that demand high concentration.[21] This study also contributes to understanding how personal characteristics (including people’s work activities) and sound disturbance relate to the use of coping strategies. The study employs structural equation modelling (SEM) to assess direct and indirect relationships among personal characteristics, sound disturbance, coping strategies and mental health indicators.

LITERATURE REVIEW

Mental Health

The World Health Organization (WHO)[22] defines mental health as ‘a state of well-being in which every individual realizes his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community’. This definition emphasises that mental health at work goes beyond the mere presence or absence of mental diseases and should also include health-promoting or motivational factors.[23]

The environmental demands-resources (ED-R) model provides a theoretical framework for understanding how workplace conditions are related to mental health. It identifies two underlying processes, namely a health-depleting process and a motivational process.[15] Environmental demands can initiate a health-impairing process, leading to short-term reversible effects such as fatigue[24] or long-term irreversible consequences, such as burnout.[25] Conversely, environmental resources trigger a motivational process that promotes short-term consequences (e.g., improved mood) and long-term benefits such as work engagement.[26]

These processes are not mutually exclusive and may interact over time. For instance, stress can lead to changes in mood, indicating that varying levels of stress appraisal are associated with shifts in emotional state and productivity.[27] Prolonged exposure to high demands without adequate recovery may result in chronic fatigue, exhaustion or depression.[28,29] Based on this framework, the following hypothesis is proposed:

H1. Mental health indicators are interrelated, reflecting the interplay of health-depleting and motivational processes as proposed by the ED-R model.

Environment

Sound Disturbance

Sound disturbance refers to the degree of annoyance or irritation caused by harmful or undesirable stimuli.[30] It can have detrimental effects on people’s psychological well-being, physiological responses to stress, cognitive performance and ability to concentrate.[31,16] Recent research confirms that irrelevant speech is the most disruptive sound in open-plan offices, impairing memory and concentration.[32]

Four sound categories can be distinguished, namely speech and conversations, the sound of office equipment, the sound of installations and ventilation and background sound.[31] The changing-state hypothesis suggests that sounds with high variability, such as speech and conversation, are more distracting than repetitive, steady-state sounds, such as repeating tones.[33,34]

Among these, intelligible speech from colleagues is consistently identified as the most disturbing sound in open-plan offices.[11] It affects both the speaker and the listener, as the speaker may be afraid of a lack of speech privacy, and the listener may be disturbed while doing concentrated tasks.[33] Two mechanisms explain why intelligible speech conversations are perceived as disturbing.[35] First, the interference-by-process mechanism suggests that auditory stimuli involuntarily engage cognitive processes that overlap with those required for task performance, thereby reducing efficiency.[36,37] Second, the attentional capture mechanism shows that unexpected sounds divert attention away from the task at hand, impairing performance.[36]

Sudden auditory changes are especially detrimental to the cognitive performance of employees who perform complex work tasks.[32,38] Moreover, Radun et al.[39] argue that both intelligible speech and steady-state sound hurt employees’ physiological and psychological stress responses. From a theoretical perspective, sound disturbance can be conceptualised as an environmental demand within the Environmental Demands-Resources model. It contributes to the health-depleting pathway by increasing cognitive load and stress responses, potentially leading to fatigue, mood disturbance, and burnout.[24] Based on these findings, the following hypothesis is drawn:

H2. Perceived sound disturbance, as an environmental demand, may negatively relate to mental health indicators via the health-depleting pathway of the ED-R model.

Coping Strategies

Employees will try to manage stressful situations in a noisy work environment by using several coping strategies.[40] In general, coping strategies can be divided into approach/vigilant (i.e., adaptive) coping and avoidance (i.e., maladaptive) coping.[20] Approach coping focuses on the source of the threat and aims to deal with the resulting emotion or pain.[41,42] This strategy is used when individuals feel that sufficient resources are available to cope with noise,[43] thereby mitigating psychological stress.[14] Examples of approach coping strategies include discussing the noise problem with colleagues, changing the workstation and proposing to management to improve acoustics or putting on headphones.[19]

In contrast, avoidance coping involves distancing oneself from the stressor, either by ignoring it or redirecting attention away from it.[41,42] Examples of avoidance strategies are trying to make an even greater effort, putting work off till another time or trying to be quieter so that others will do the same.[19] These strategies are more common when the indoor work environment does not meet individuals’ expectations.[14] Fuller and Conner[41] argue that avoidance coping may be more effective in the short term, but that vigilant coping has more positive long-term effects. Nevertheless, avoidance strategies are frequently used to manage sound disturbance,[19] which may contribute to long-term, chronic mental illnesses, including depressive symptoms or anxiety.[20]

From a theoretical perspective, coping strategies can be understood within the ED-R model. Environmental demands, such as sound disturbance, may activate coping strategies, which in turn influence whether the health-depleting or motivational pathway is triggered. Avoidance coping may intensify the health-depleting process by increasing strain and reducing recovery, while approach coping may buffer stress and promote engagement. The following hypothesis is, therefore, drawn:

H3. Adaptive coping strategies are positively related to mental health indicators, whereas maladaptive strategies are negatively related, consistent with the ED-R model’s motivational and health-depleting pathways.

Oseland and Hodsman[21] further explain that employees can learn to use coping strategies to limit sound disturbance. They found that workers who perform highly concentrated jobs may have taught themselves to cope with background noise. Employees may thus adapt their work behaviour according to several environmental factors.[18] For instance, employees who experience sound-related distraction near their desks are more likely to use avoidance coping strategies, such as interrupting their work or trying to be quieter.[19]

From a theoretical perspective, the ED-R model suggests that environmental demands, such as sound disturbance, can trigger coping responses aimed at managing stress. When demands exceed available resources, employees may resort to avoidance coping, which may offer short-term relief but contribute to the health-depleting pathway over time.[15] Especially in open-plan offices, employees are exposed to many irrelevant stimuli (e.g., noise) that may increase the incentive to develop effective coping strategies.[8,9] Conversely, when resources are perceived as sufficient, approach coping may be activated, supporting the motivational pathway. Hypothesis 4 is, therefore, formulated as:

H4. Perceived sound disturbance is related to coping strategies.

Person

Noise-sensitive individuals are more easily annoyed by noise[44] and experience greater discomfort at work.[45] Noise sensitivity can be defined as an individual’s natural response to stimuli in the external environment. At the physiological level, noise sensitivity heightens individuals’ reactions to auditory stimuli, increasing their respiratory rate and electrodermal activity (i.e., skin conductance) and reducing their heart rate.[16] As a result, they may spend more of their working time compensating for noise,[18] which may further decrease their cognitive performance, productivity and concentration.[16]

Next to noise sensitivity, research shows that employees with neurotic personality traits are more prone to physiologically arousing states that reduce sleep quality, hedonic tone and increase stress.[46] While employees who score high on neuroticism generally report lower subjective well-being, people with agreeable and extroverted personality traits are more positive about their well-being.[47] Especially among individuals with introverted and neurotic personality traits, sound might influence well-being, stress, productivity and concentration more than among extroverted and emotionally stable individuals.[21] Other personal characteristics, such as gender and age, are also found to influence employees’ mental health. For instance, males and employees younger than 24 are generally more resilient to work-related stress,[48] whereas females are more prone to stress and burnout complaints.[49]

From a theoretical standpoint, the Person-Environment Fit theory posits that well-being is influenced by the degree of alignment between individual needs and environmental conditions. A mismatch, such as an introverted person working in a noisy, open-plan office, can lead to increased stress. Based on this reasoning, the following hypothesis is proposed:

H5. Personal characteristics are related to mental health indicators.

Females are generally more sensitive to noise than males.[18] Female, neurotic and introverted individuals experience greater annoyance from noise at low sound-intensity levels than their counterparts.[50] At sound levels below 65 dBA, females outperform their male colleagues on medium-workload tasks, while males perform better at high workloads and at sound levels above 65 dBA.[51] These findings suggest that both biological sensitivity and task context interact with gender and personality traits to shape sound perception.

The complexity and cognitive demand of a work task also influence the occurrence of sound distraction. Jahncke et al.[11] argue that employees are less disturbed by irrelevant speech if they perform relatively simple tasks. Moreover, for cognitively demanding tasks, such as memory or reasoning, highly intelligible speech is the most disturbing.[52] Because intelligible speech is highly variable, employees cannot habituate to it, which makes it harder to ignore. It is, therefore, experienced as highly disturbing, especially when performing cognitive and concentrative tasks.[53]

This reasoning aligns with the ED-R model, which posits that environmental demands, such as sound disturbance, interact with personal characteristics to influence stress responses. For instance, noise-sensitive individuals may perceive the same acoustic environment as more demanding, thereby intensifying the health-depleting pathway. Similarly, the P-E fit theory suggests that when the acoustic environment does not match an individual’s preferences or cognitive needs, misfit occurs, leading to increased strain and reduced well-being. Therefore, the following relationship is expected:

H6. Personal characteristics are related to perceived sound disturbance.

Personal characteristics also influence the choice of specific coping strategies for managing sound disturbance. Men tend to cope with noise by listening to music or relocating their workspace. In contrast, women are extroverts and are more likely to address the issue through conversation with colleagues or by reducing their own noise in the hope that others will follow. Moreover, employees aged 26–35 are more likely to put on music or change their desks to cope with noise than those aged 36–45, who are more likely to put on earphones, while those aged 46–55 are more inclined to discuss the noise problem with colleagues.[19] It thus seems that extroverts, females and older-aged employees are more likely to use approach coping styles. Noise-sensitive employees often use a broader range of coping strategies to deal with noise[18] and may benefit more from acoustic improvements.[37]

These findings suggest that personal characteristics shape not only the experience of sound disturbance and mental health outcomes, but also the behavioural responses to environmental stressors. Within the ED-R model, coping strategies may mediate the relationship between environmental demands and health outcomes, and personal traits influence which strategies are selected. From a P-E fit perspective, coping can be seen as an attempt to restore fit between the individual and the environment. Therefore, the following hypothesis is formulated:

H7. Personal characteristics are related to the coping strategies used.

Figure 1 shows the conceptual model that follows from the hypotheses. It shows that personal characteristics might relate to experienced sound disturbance, coping strategies, and mental health indicators directly, while they might also have an indirect relationship with mental health indicators via disturbance. Furthermore, noise disturbance is expected to relate directly to coping strategies and mental health indicators, or indirectly via the coping strategies used.

Figure 1.

Conceptual model of sound disturbance, coping strategies and mental health outcomes.

Note : The conceptual model shows the variables personal characteristics, disturbance, coping strategies, and mental health outcomes in boxes, and the hypotheses posed along the arrows connecting the boxes.

RESEARCH APPROACH

Data Collection

To examine the relationships among personal characteristics, sound disturbance, coping strategies and mental health, a cross-sectional online survey was conducted in two Dutch organisations between September 2022 and January 2023. The initial sample consisted of 256 respondents. The primary reason for exclusion was incomplete survey responses; participants who did not fully answer the survey were removed from the dataset to ensure data quality and consistency. The final sample comprised 214 office employees, including 127 from a large technology firm and 87 from a real estate services company. These organisations were targeted because they actively support employee mental well-being and financially contributed to the overarching research project to which this study belongs. Inclusion criteria were employees should be knowledgeable workers at one of the two participating organisations and worked at least 1 day per week in the organisation’s office. They should also be above 18 years old. Employees were excluded if they did not work at the organisation (i.e., worked exclusively from home or other locations) or did not provide informed consent.

This study was conducted in accordance with ethical guidelines for research involving human participants. Ethical approval was obtained from the Ethics Review Board of Eindhoven University of Technology under reference number ERB2022BE12, and all participants provided informed consent before participation. Participants were assured that their responses would remain confidential and that all data would be anonymised during analysis and reporting. To further ensure anonymity, completed questionnaires were submitted directly to the authors of this study, and raw data were not shared with the participating organisations. No personally identifiable information was collected, and data were stored securely in compliance with the general data protection regulation (GDPR), the European Union’s framework for data protection and privacy. This means data handling followed principles of confidentiality, security, and minimization to protect participants’ rights. Participants were informed of their right to withdraw from the study at any time without consequences.

Measures

This study used a combination of validated psychometric scales and context-specific items to assess personal characteristics, perceived sound disturbance and distraction, coping strategies and mental health indicators (see Table 1 for an overview). Most scales were initially developed in English. To ensure linguistic and cultural appropriateness for Dutch participants, a forward-backwards translation procedure was conducted by bilingual researchers familiar with workplace research terminology. Both Dutch and English versions were reviewed for conceptual and semantic equivalence, and respondents could choose their preferred version. This approach aimed to minimise potential bias due to language comprehension or cultural interpretation.

Table 1.

Overview of measurement instruments for personal characteristics, coping strategies, distraction and disturbance and mental health outcomes

Variable	Scale/ items	References
Personal characteristics
Age	–	–
Gender	–	–
Personality	Big five inventory (BFI) (10 items)	[65]
Workhours	–	–
Sensitivity to noise	GABO Questionnaire (4 items)	[45]
I frequently wear headphones		–
There are sufficient concentration spots		–
There are sufficient phone booths		–
Work tasks	Individual-focused work Planned meetings Telephone conversations Informal unplanned meetings Collaborating on focused work Relaxing/taking a break Reading Individual routine tasks	[66]
Approach coping strategies	Put on the radio or earphones Change workstation or do work at home Discuss the noise problem with colleagues Try to be quieter in hope others will be too Make a proposal to management to improve acoustics	[18]
Avoidance coping strategies	Do work more slowly than usual Put work off till another time Interrupt work or leave desk Make an even greater effort Do work more quickly than usual	[18]
Perceived distraction	Distraction Scale (5 items)	[58]
Perceived sound disturbance	Intelligible speech conversations Intelligible speech telephone conversations Unintelligible background conversations People passing by Noise outside Printers, fax, ventilation	[19]
Well-being	Health at Work Survey of WHO	[59]
Stress	Stress and Worry (2 items) Four-item Patient and Health Questionnaire for Depression and Anxiety (PHQ-4) (2 items)	[67] [68]
Hedonic tone	UWIST Mood Adjective Checklist (4 items)	[62]
Tense arousal	UWIST (4 items)	[62]
Depressive symptoms	PHQ-4 (2 items)	[68]
Sleep quality	Single-item sleep quality scale (PSQ) Health at Work Survey of WHO (4 items)	[60] [59]
Exhaustion	Oldenburg Burnout Inventory (OLBI) (8 items)	[63]
Fatigue	Checklist Individual Strength (CIS) (8 items)	[25]
Productivity	Health at Work Survey of WHO	[59]
Job performance	Health at Work Survey of WHO (2 items)	[59]
Concentration	Checklist Individual Strenght (CIS) (5 items)	[25]
Disengagement	Oldenburg Burnout Inventory (OLBI) (8 items)	[63]

Personal Characteristics

Respondents reported their age, gender (male, female, other) and weekly work hours. Personality was measured using the 10-item Big Five Inventory, a brief measure of five major personality traits: openness, conscientiousness, extraversion, agreeableness and neuroticism. This scale has been validated for Dutch-speaking populations, showing good psychometric properties and conceptual equivalence with the original English version.[54]

The scale includes 10 items (2 per dimension) rated on a 5-point Likert scale (1. Strongly disagree to 5. Strongly agree). For each personality dimension, the score is calculated as the average of its two item scores, resulting in subscales that range from 1 to 5, with higher scores indicating stronger expression of that trait. For scales with only two items, the inter-item correlation is calculated, which should be between 0.2 and 0.4.[55] The inter-item correlation is a measure of internal consistency that helps determine whether the items measure the same underlying construct. Table 2 shows that the inter-item correlations of openness and conscientiousness were low, and therefore, they were excluded from further analysis.

Table 2.

Inter-item correlation for two-item constructs measuring personality traits and mental health outcomes

Two-item construct	Inter-item correlation
Personality
Agreeableness	0.21
Neuroticism	0.44
Openness	0.001
Conscientiousness	0.16
Extraversion	0.39
Mental health
Depressive symptoms	0.40
Job performance	0.29

Noise sensitivity was assessed using four items from the GABO questionnaire, originally developed to assess general auditory sensitivity. It was initially developed by INRS in France and translated into Dutch.[56] The full GABO includes 12 items covering work, sleep and habituation contexts. The present study used only the work-related items (‘I need a quiet environment to be able to carry out new tasks’, ‘When people around me are noisy, I find it hard to do my work’, ‘I perform significantly worse in noisy environments’, and ‘I need quietness in order to carry out a difficult task’), rated on a 5-point scale (1. Not sensitive to 5. Very sensitive). While the original scale proposes cut-off scores to classify individuals as noise-sensitive (<1.11) or not (>1.63), these thresholds were not applied here due to the adapted scale. Instead, scores were averaged, with higher means indicating greater work-related noise sensitivity. To measure internal consistency for multi-item scales, Cronbach α can be calculated. These should be between 0.7 and 0.9 for high internal consistency.[57] For noise sensitivity, the Cronbach α was 0.85, indicating good internal consistency.

Participants also indicated whether they frequently wear headphones (yes/no) and whether there were sufficient concentration spots and phone booths in their workplace (yes/no). Furthermore, respondents reported how often they engaged in each of the following work tasks: individual-focused work, planned meetings, telephone conversations, informal unplanned meetings, collaborating on focused work, relaxing/taking a break, reading and individual routine tasks, rated on a 5-point scale (1. Never to 5. Almost every day).

Coping Strategies

Coping strategies in response to workplace noise were assessed using 10 items, each describing a specific behavioural response. Respondents rated how frequently they used each strategy on a 5-point Likert scale (1. None of the time to 5. All of the time). Each item was treated as a separate indicator of coping behaviour to explore which strategies were most commonly used and how they related to personal characteristics and mental health outcomes. Approach coping strategies include: discussing the noise problem with colleagues, proposing to management to improve acoustics, changing the workstation or working from home, putting on the radio or earphones and trying to be quieter in the hope that others would do too. Avoidance coping strategies include the following: doing work more slowly than usual, putting work off until another time, interrupting work or leaving the desk, making an even greater effort and doing work more quickly than usual.[18,19]

Perceived Distraction and Sound Disturbance

Distraction was measured using the Distraction Scale by Lee and Brand,[58] which assesses perceived cognitive interference due to environmental stimuli. The scale includes five items, each rated on a 5-point scale (1. Low to 5. High distraction). Higher scores indicate greater perceived distraction. The scale demonstrated acceptable reliability (Cronbach α = 0.73), and previous research supports its construct validity in office settings (e.g., Kaarlela-Tuomaala et al. [18].

Sound disturbance was assessed using multiple items targeting specific noise sources in the office environment, including intelligible speech and telephone conversations, unintelligible background conversations, people passing by, noise outside and printers or ventilation. Respondents rated the degree to which each source disturbed them on a 5-point scale (1. None of the time to 5. All of the time). Higher mean scores represent greater perceived sound disturbance. Each source was analysed separately, rather than combined into a single sound disturbance score, allowing the distinct impact of different sound types to be captured.

Mental Health Indicators

Mental health was evaluated using multiple validated scales, and total scores were calculated according to each scale’s guidelines.

The WHO Health at Work Survey was used to measure well-being, productivity and sleep quality.[59] Well-being and productivity were each assessed on a 10-point scale (1. Low to 10. High). Sleep quality was assessed using four items, rated on a 5-point scale (1. Low to 5. High). For sleep quality, a single-item scale was also used to indicate respondents’ general sleep quality (1 = very bad to 4 = very good).[60] For these measures, higher scores indicate better outcomes. The 4-item sleep quality scale showed acceptable reliability (Cronbach α = 0.69). Total scores for well-being and productivity were computed from raw item scores, while sleep quality was calculated as the mean of the four items.

The Patient Health Questionnaire-4 (PHQ-4) is an ultra-brief measure of anxiety and depression, consisting of four items rated on a 4-point scale (1. Not at all to 4. Nearly every day). This scale has been translated and validated for Dutch-speaking populations.[61] Higher scores indicate greater psychological distress. In this study, two items of the PHQ-4 were combined with two items to measure stress and worry, which combined show good reliability (Cronbach α = 0.85). The other two items of the PHQ-4 were used to measure depressive symptoms. Table 2 shows that the inter-item correlation for depressive symptoms is acceptable, indicating good internal consistency.

The University of Wales Institute of Science and Technology (UWIST) Mood Adjective Checklist was used to assess transient affective states along two dimensions: hedonic tone and tense arousal. Each subscale consists of four items rated on a 4-point scale (1. Negative to 4. Positive mood). Higher scores reflect a more positive mood and lower arousal, respectively.[62] Subscales were calculated as the mean of the four items, and reliability was acceptable (Cronbach α = 0.77).

The Oldenburg Burnout Inventory (OLBI) includes two 8-item subscales assessing exhaustion and disengagement on a 4-point scale (1. Strongly disagree to 4. Strongly agree). This scale has been validated in Dutch occupational settings.[63] Higher scores indicate a higher risk of burnout. Subscales were computed as the mean of the items, and internal consistency was strong (Cronbach α = 0.82).

The Checklist Individual Strength (CIS) measures subjective fatigue and concentration issues, and was initially developed and validated in Dutch.[64] The fatigue subscale includes eight items (Cronbach α = 0.90), and the concentration subscale consists of five items (Cronbach α = 0.79), both rated on a 7-point scale (1. Low to 7. High). Higher scores indicate greater fatigue or concentration problems. Subscale scores were calculated as the mean of the items within each subscale.

Analytical Approach

The present study employed a two-step SEM procedure following the approach of Anderson and Gerbing.[69] In the first step, the measurement model was validated using confirmatory factor analysis (CFA) to assess the adequacy of reliability and convergent validity of the latent constructs. The CFA confirmed a six-factor structure comprising 18 observed indicators, consistent with Bergefurt et al.[70] Reliability and convergent validity were assessed using Cronbach α, composite reliability (CR > 0.70) and average variance extracted (AVE > 0.50).

In the second step, the structural model was tested to examine the hypothesised relationships between latent constructs. Path coefficients were estimated using maximum likelihood, and their significance was assessed using standardised regression weights and critical ratios (CR > 1.96, P < 0.05). The structural model was evaluated using the same fit indices as the measurement model, and model parsimony was further supported by inspecting Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. The results provided empirical support for the proposed theoretical framework, confirming the directional relationships between sound disturbance, coping strategies and employee outcomes.

Model fit was evaluated using multiple indices: χ²/df, Goodness of Fit Index (GFI), Non-Normed Fit Index (NNFI), Comparative Fit Index (CFI) and Root Mean Square Error of Approximation (RMSEA). Acceptable model fit was defined as χ ²/df < 3.0, CFI ≥ 0.90 and RMSEA ≤ 0.08.[71] Insignificant paths (P > 0.05) were removed sequentially to prevent model overfitting, and this backward stepwise process was repeated until all remaining paths were statistically significant.

Confirmatory Factor Analysis and SEM were conducted in R (Version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) using the Lavaan package (Version 0.6-15; Rosseel, Belgium). Analyses were performed in RStudio (Version 2023.09; Posit Software, PBC, Boston, MA, USA). [Figures 12] were created using Microsoft Visio (Version 2023; Microsoft Corporation, Redmond, WA, USA).

Figure 2.

Structural Equation Model showing standardised significant path coefficients.

Note : SEM model, showing the standardised significant relationships between the variables along the arrows. Positive relationships are indicated with black arrows and negative relationships with grey arrows.

FINDINGS

Descriptive Statistics

Table 3 summarises the descriptive characteristics of the sample (N = 214). Categorical variables are presented as n (%), and continuous variables as mean (M) and standard deviation (SD). Approximately 59% of participants worked at Company 1 and 41% at Company 2. The majority were male, with an average age of 38 (SD = 10.13). Employees typically worked full-time, averaging 39 hours per week (SD = 3.13). Headphone use was common, and many respondents indicated insufficient availability of concentration spots or phone booths. Noise sensitivity was moderate (M = 3.40, SD = 0.82). Personality scores were highest for agreeableness, followed by extraversion and neuroticism. Individual-focused tasks dominated work activities, while planned meetings and phone calls were also frequent; reading and breaks were less common.

Table 3.

Descriptive statistics of the sample population. Categorical variables are presented as frequencies and percentages (n, %) and continuous variables as means (M) and standard deviations (SD)

Variable names and underlying items/constructs	Sample (N)	Sample (%)	Mean	SD
Personal characteristics
Gender Male Female	161 53	75.2 24.8
Wear headphones Yes No	145 69	67.8 32.2
Sufficient concentration spots Yes No	66 148	30.8 69.2
Sufficient phone booths Yes No	54 160	25.2 74.8
Age			38.01	10.13
Work hours			38.61	3.13
Noise sensitivity (1. Not sensitive − 5. Sensitive)			3.40	0.82
Personality Extraversion Agreeableness Neuroticism			3.81 3.88 2.35	0.76 0.66 0.75
Work tasks (1. Never − 5. Almost every day)
Individual-focused work			4.22	0.82
Planned meetings			3.99	0.91
Telephone conversations			3.84	1.08
Informal unplanned meetings			3.61	0.96
Collaborating on focused work			3.14	0.94
Relaxing/taking a break			3.18	0.99
Reading			3.20	1.08
Individual routine tasks			3.75	0.89
Perceived sound disturbance
Distraction (1. Low − 5. High distraction)			3.26	0.64
Intelligible speech conversations (1. None of the time − 5. All of the time)			3.65	0.82
Intelligible speech telephone conversations			3.53	0.90
Unintelligible background conversations			2.98	1.00
People passing by			2.61	1.03
Noise outside			2.07	0.93
Printers/ fax/ ventilation			1.67	0.92
Coping strategies (1. None of the time − 5. All of the time)
Do work more slowly than usual			2.98	0.84
Put on radio or earphones			2.98	1.22
Make an even greater effort			2.74	0.94
Change workstation or do work at home			2.62	1.14
Interrupt work or leave desk			2.49	0.94
Put work off till another time			2.48	1.02
Discuss noise problem with colleagues			2.39	0.91
Try to be quieter in hope others will be too			2.12	1.05
Do work more quickly than usual			1.96	0.77
Make proposal to management to improve acoustics			1.23	0.61
Mental health
Productivity (1. Low − 10. High productivity)			7.06	0.99
Hedonic tone (1. Negative − 4. Positive mood)			3.09	0.54
Well-being (1. Low − 10. High well-being)			6.84	1.32
Sleep quality (1. Low − 5. High sleep quality)			3.80	0.74
Overall sleep quality (1. Very bad − 4. Very good)			2.81	0.64
Concentration (1. Low − 7. High concentration)			4.43	1.12
Job performance (1. Low − 4. High performance)			3.34	0.50
Stress (1. Low − 4. High stress)			1.72	0.58
Fatigue (1. Low − 7. High fatigue)			3.39	1.21
Exhaustion (1. Low − 4. High exhaustion)			2.21	0.45
Depressive symptoms (1. Low − High depressive symptoms)			1.44	0.46
Tense arousal (1. Negative − 4. Positive mood)			2.81	0.54
Disengagement (1. Low − 4. High disengagement)			2.18	0.50

Table 3 also presents perceived sound disturbance. Distraction levels were moderate, with speech − both conversational and telephone − rated as most disturbing. Sounds from office equipment (e.g., printers, ventilation, fax) were the least disturbing. The most frequently reported coping strategies included ‘working more slowly than usual’, ‘using radio or earphones’ and ‘making an extra effort’. Strategies such as ‘working faster than usual’ and ‘suggesting acoustic improvements to management’ were less common.

Finally, mental health indicators suggest generally favourable outcomes. Employees rated their job performance, sleep quality, concentration, well-being and productivity rather positively. They reported feeling calmer, relaxed, happy and satisfied than tense, nervous, sad or low-spirited. Average scores for depressive symptoms, stress and fatigue were low, though some exhaustion (M = 2.21, SD = 0.45) and disengagement (M = 2.18, SD = 0.50) were noted.

Measurement Model

To assess the reliability and validity of the latent constructs, standardised factor loadings were examined alongside Cronbach α, CR and AVE. All constructs demonstrated acceptable internal consistency, with Cronbach α values ranging between 0.67 and 0.86. CR values exceeded the recommended threshold of 0.70, and AVE values were above 0.50, indicating adequate convergent validity of the measurement model. Table 4 presents the standardised factor loadings and reliability statistics for each construct.

Table 4.

Standardised factor loadings, internal consistency and convergent validity statistics for latent constructs

Variable	Latent constructs	Factor loadings
		Stressful mood	Concentration	Sleep quality	Fatigue	Disengagement	Exhaustion
Stress	Feeling nervous, anxious or on edge	0.82	-0.30	-0.17	0.30	0.12	0.30
Stress	Not being able to stop or control worrying	0.81	-0.28	-0.21	0.30	0.069	0.30
Mood	Nervous	0.81	-0.18	0.11	0.19	-0.026	-0.11
Concentration	I have trouble concentrating	-0.24	0.85	0.18	-0.27	-0.17	-0.17
Concentration	My thoughts easily wander	-0.17	0.84	0.093	0.15	-0.20	-0.25
Productivity	How often did you find yourself not working as carefully as you should?	-0.30	0.63	-0.090	0.30	-0.013	0.002
Sleep quality	Staying asleep, when you woke up nearly every night and it took an hour or more to get back to sleep?	-0.14	0.24	0.86	-0.23	-0.18	-0.27
Overall sleep quality	Overall sleep quality	-0.17	0.22	0.83	-0.13	-0.19	-0.30
Sleep quality	Waking too early, when you woke up nearly every night at least two hours earlier than you wanted to?	-0.19	0.047	0.69	-0.30	-0.090	-0.10
Fatigue	Physically, I feel in a good shape	0.30	-0.28	-0.15	0.92	0.080	0.36
Fatigue	I feel fit	0.20	-0.26	-0.16	0.87	0.12	0.18
Fatigue	Physically, I feel in a bad condition	0.30	-0.28	-0.13	0.71	0.22	0.28
Disengagement	I find my work to be a positive challenge	-0.11	-0.17	-0.19	0.12	0.81	0.12
Disengagement	I always find new and interesting aspects in my work	0.098	-0.11	-0.067	0.16	0.80	-0.029
Disengagement	I feel more and more engaged in my work	0.21	-0.16	-0.16	0.042	0.78	0.13
Exhaustion	During my work, I often feel emotionally drained	0.30	-0.20	-0.19	0.30	0.13	0.77
Exhaustion	After my work, I usually feel worn out and weary	0.30	-0.30	0.007	0.30	-0.004	0.72
Exhaustion	There are days when I feel tired before I arrive at work	-0.17	-0.28	0.14	-0.29	-0.090	0.66
Cronbach's α		0.73	0.67	0.68	0.86	0.73	0.68
Average variance extracted (AVE)		0.66	0.66	0.62	0.69	0.63	0.52
Composite reliability (CR)		0.85	0.84	0.83	0.87	0.84	0.76

Structural Equation Modelling

The SEM analysis followed a structured, theory-driven approach. The initial model was constructed based on prior literature and theoretical assumptions about the hypothesised relationships. Model refinement was conducted iteratively, by adding and removing relationships based on statistical significance and theoretical plausibility. Non-significant paths were removed to improve model parsimony.

Table 5 presents the fit indices for the final structural model. The GFI (0.84), NNFI (0.90) and CFI (0.91) indicate an acceptable fit, as values closer to 1 reflect better alignment between the hypothesised and observed data. GFI represents the proportion of variance explained by the model. At the same time, NNFI adjusts the bias of the Normed Fit Index by measuring the discrepancy between the χ2 values of the hypothesised and null models. CFI evaluates the discrepancy between the data and the hypothesised model, accounting for small sample bias. The RMSEA (0.045) falls below the recommended threshold of 0.05, suggesting minimal misfit per degree of freedom. Information criteria (AIC and BIC) provide estimates of prediction error penalised for model complexity; lower values indicate superior fit, which the hypothesised model achieved.

Table 5.

Model fit indices for Structural Equation Model: Goodness-of-fit statistics and information criteria.

Model fit indices	Values
Degrees of freedom	357
Chi-square (χ2)	513.66
Root mean square error of approximation (RMSEA)	0.045
Akaike information criterion (AIC)	11,971.78
Bayesian information criterion (BIC)	12,264.62
Comparative fit index (CFI)	0.91
Non-normed fit index (NNFI)	0.90
Goodness of fit index (GFI)	0.84
χ2 / df	1.44

Direct Relationships

[2] and Table 6 present the structural equation model, including standardised path coefficients (β) and corresponding t-values (in parentheses). The measurement model confirmed that three items loaded onto each of the six mental health factors, which were strongly interrelated. Stressful mood was associated with the long-term factor exhaustion (β = 0.57, t = 6.64), fatigue (β = 0.21, t = 4.86), poor sleep quality (β = −0.59, t = −4.42), disengagement (β = −0.34, t = −2.88) and lower concentration (β = −0.47, t = −2.71). Sleep quality was negatively related to fatigue (β = −0.34, t = −2.68) and exhaustion (β = −0.14, t = −3.01), while fatigue predicted exhaustion (β = 0.15, t = 5.07). Concentration was negatively associated with disengagement (β = −0.10, t = −2.48). These findings confirm Hypothesis 1.

Table 6.

Significant unstandardised direct and indirect effects with corresponding t-values

	Stressful mood	Concentration		Sleep quality	Fatigue		Disengagement	Exhaustion		Distraction		Intelligible speech conversations	Unintelligible background sound	Tried to be quieter in hope others would be too		Done work more slowly than usual	Discussed noise problem with colleagues
Variables	Direct	Direct	Indirect	Direct	Direct	Indirect	Direct	Direct	Indirect	Direct	Indirect	Direct	Direct	Direct	Indirect	Direct	Direct
Personal characteristics
Noise sensitivity	0.19 (3.62)									0.15 (3.31)	0.020 (2.66)		0.29 (3.61)	0.28 (3.44)	0.065 (2.91)	0.42 (6.88)
Concentration spots										−0.30 (−3.78)	−0.041 (−2.84)		-0.50 (-3.54)				-0.46 (-3.70)
Work tasks
Collaborating on focussed work							−0.12 (−3.05)						0.23 (3.35)			-0.14 (-2.81)
Individual focussed work								0.098 (2.61)					-0.18 (-2.35)
Coping strategies
Made an even greater effort					0.24 (2.41)		−0.098 (−2.46)									0.13 (2.46)	0.21 (3.40)
Done work more quickly than usual															0.37 (4.42)
Perceived distraction		−0.67 (−5.38)
Perceived sound disturbance
Unintelligible background sound								0.10 (2.99)		0.14 (3.40)	0.029 (3.38)	0.32 (6.17)			0.24 (3.56)
Intelligible speech conversations								−0.11 (−2.63)		0.21 (4.57)						0.18 (3.17)
Mental health
Stressful mood		−0.47 (−2.71)	0.095 (2.89)	−0.59 (−4.42)	0.21 (4.86)	−0.34 (−2.88)		0.57 (6.64)	0.084 (4.66)
Concentration							−0.10 (-2.48)
Sleep quality					−0.34 (−2.68)		−0.14 (−3.01)
Fatigue		−0.20 (−3.04)						0.15 (5.07)
Disengagement
Exhaustion

Perceived distraction was negatively related to concentration (β = −0.67, t = −5.38). Disturbance from unintelligible background sounds was associated with higher exhaustion (β = 0.10, t = 2.99), and greater distraction (β = 0.14, t = 3.40). Disturbance from intelligible speech conversations showed a negative association with exhaustion (β = −0.11, t = −2.63). Both disturbance types were positively correlated (β = 0.32, t = 6.17). Disturbance from unintelligible background (β = 0.14, t = 3.40) and intelligible speech conversations (β = 0.21, t = 4.57) were also both related to distraction. These findings support Hypothesis 2.

Regarding coping, employees who exerted greater effort reported higher fatigue (β = 0.24, t = 2.41), but paradoxically, lower disengagement (β = −0.098, t = −2.46). Although only one coping strategy showed a significant direct relationship with mental health, Hypothesis 3 is supported. Disturbance from speech was associated with working more slowly than usual (β = 0.18, t = 3.17), confirming Hypothesis 4.

Personal characteristics also influenced outcomes. Noise sensitivity predicted stressful mood (β = 0.19, t = 3.62), distraction (β = 0.15, t = 3.31), disturbance from both unintelligible sounds (β = 0.29, t = 3.61) and avoidance coping strategies, including working more slowly than usual (β = 0.42, t = 6.88) and trying to be quieter (β = 0.28, t = 3.44). Lack of concentration spaces predicted higher distraction (β = −0.30, t = −3.78) and disturbance from both unintelligible sounds (β = −0.50, t = −3.54) and was also negatively related to the coping strategy discussed the noise problem with colleagues (β = −0.46, t = −3.70).

Employees who frequently engaged in individual-focused work reported higher exhaustion (β = 0.098, t = 2.61) but less disturbance from unintelligible background sounds (β = −0.18, t = −2.35). Collaborating on focused work predicted lower disengagement (β = −0.12, t = −3.05) and more disturbance from unintelligible background sound (β = 0.23, t = 3.35), as well as less frequent use of doing work more slowly than usual (β = −0.14, t = −2.81). These findings support Hypotheses 5–7.

Indirect Relationships

Several indirect associations between personal characteristics and mental health outcomes were mediated by perceived sound disturbance and distraction. First, distraction was indirectly related to noise sensitivity through perceived disturbance caused by unintelligible background sounds, indicating that individuals who were more sensitive to noise were also more likely to experience such disturbances, which in turn heightened distraction.

Employees who reported insufficient concentration spots also tended to feel more disturbed by unintelligible background sounds, which subsequently increased distraction. Furthermore, perceived disturbance from unintelligible sounds was indirectly associated with distraction through perceived disturbance from intelligible speech conversations.

Additional indirect pathways were observed among mental health factors. A highly stressful mood was associated with greater fatigue, which negatively related to concentration. Stressful mood also predicted poorer sleep quality, which, in turn, contributed to increased fatigue.

DISCUSSION

This study examined how personal characteristics, perceived sound disturbance, perceived distraction, coping strategies and a broad range of mental health indicators are related. Results show that mental health factors − stressful mood, sleep quality, fatigue, exhaustion and disengagement − are significantly interrelated, forming an intricate network of associations. The results also suggest that short-term feelings, such as stressful mood, are associated with long-term consequences like exhaustion. As Schulz et al.[72] argue, organisations should, therefore, proactively address potential workplace stressors. Regular monitoring of employees’ mental health may help mitigate long-term consequences and enhance organisational awareness of employees’ needs early on.

While employees in this study generally adopted a mix of coping strategies, several avoidance strategies, such as working more slowly (M = 2.98) and making greater effort (M = 2.74), were used more frequently than most approach strategies, such as discussing the noise problem with a colleague (M = 2.39) or proposing acoustic improvements to management (M = 1.23). This tendency towards avoidance-based responses aligns with prior research indicating that employees are often reluctant to voice concerns about their office environment.[19] Organisations should consider implementing intervention programs to improve communication and equip employees with strategies to manage work-related stressors effectively.

Interestingly, employees who exerted greater effort (a form of avoidance coping) reported increased fatigue but lower disengagement. This pattern is consistent with prior research linking emotion-focused coping to higher fatigue levels (e.g., Shirom[73]. Yet, the current study adds nuance by showing that such coping may still be associated with sustained engagement. As Tobin[74] explains, coping strategies can be categorised into problem-focused (approach strategies) and emotion-focused (avoidance strategies), each with engagement and disengagement subtypes. When stressors persist, they may be perceived as threats rather than challenges, potentially leading to disengagement.[75] However, employees in this study who relied on avoidance coping may have maintained engagement by expending additional effort to manage stressors. While this engagement may be beneficial in the short term, it underscores the importance of reducing chronic stressors to protect long-term mental health.

The current findings highlight how coping unfolds in everyday work environments, particularly in relation to environmental discomfort. This contributes to a growing body of literature on workplace well-being by emphasising the role of the physical factors, such as sound disturbance, in shaping coping responses and fatigue outcomes.[76]

Interestingly, the model indicates that perceived disturbance from intelligible speech conversations was linked to lower levels of exhaustion. This finding implies that maintaining a moderate level of background disturbance may help keep employees engaged and prevent mental fatigue or monotony. The Yerkes–Dodson law posits that optimal cognitive performance occurs at moderate levels of arousal.[77] Occasional disruptions in the work environment may break repetitive sensory patterns and reduce boredom,[78] thereby alleviating exhaustion.

However, the strong negative relationship between perceived distraction and concentration, coupled with reports of insufficient concentration spots, suggests that the current office layouts do not fully support employees’ work activities. Employees engaged in individual-focused tasks reported higher exhaustion, reinforcing the need for workplace designs that accommodate diverse work activities. Facility managers and designers should enhance spatial flexibility by incorporating phone booths and concentration areas to minimise sound disturbance. The availability of dedicated workspaces for specific tasks may no longer align with employees’ post-pandemic needs, potentially intensifying perceived noise. As noted by Radun and Hongisto,[3] dissatisfaction with office sound levels is a critical factor influencing the perceived alignment between work tasks and office layout. Although quiet workspaces can serve as valuable job resources,[79] implementing such solutions is often challenging.[3] Alternative measures, such as shared quiet offices or strategically placed phone booths, warrant consideration.

To further improve person–environment fit and support mental well-being, organisations must account for individual differences. Traditional office designs often overlook specific employee needs.[80] Noise-sensitive employees are more vulnerable to distraction, sound disturbance and stress. HR and workplace managers should prioritise understanding these individual needs and tailor environments accordingly. Acoustic solutions, such as sound-absorbing panels, sound-reflecting screens and sound-masking systems, can help minimise sound-related distractions, particularly for employees who are more sensitive to noise.[38] In addition, organisations may benefit from promoting approach-oriented coping strategies. This could be achieved through targeted training programs on stress communication, the establishment of anonymous feedback channels and encouragement for managers to model constructive coping behaviours. These practices may help shift workplace culture towards more open dialogue and proactive problem-solving, especially in response to environmental discomfort.

Overall, these findings indicate that workplace well-being initiatives should address both environmental and behavioural factors. By reducing chronic stressors, enhancing spatial flexibility and supporting individual coping styles, organisations can foster a healthier and more engaging work environment. Future interventions should be tailored to diverse employee needs, particularly in post-pandemic office settings where traditional layouts may no longer suffice.

Limitations and Future Directions

While this study provides valuable insights into the complex interplay between personal characteristics, workplace acoustics and mental health, several limitations should be acknowledged. The sample consisted of 214 participants from two Dutch firms in the technology and real estate sectors, with over 75% identifying as male. Although this reflects the gender distribution of the Dutch technology sector, the gender imbalance may have influenced findings related to noise sensitivity and mental health. Future studies should examine whether similar patterns emerge in more gender-balanced work environments or explicitly investigate how gender may moderate these relationships.

Moreover, the study did not include participants from industries with different acoustic profiles, such as manufacturing or service sectors. This limits the generalisability of the findings to other work environments where noise characteristics and exposure may differ significantly. Another limitation of the study is that employees diagnosed with depression, anxiety or sleep disorders in the past 6 months were not excluded from participation. As a result, the sample may include individuals with recent mental health diagnoses. Nonetheless, no extreme outliers were detected in the data, suggesting that the inclusion of these individuals did not substantially distort the overall results.

Although the sample satisfied the recommended minimum of 10 observations per estimated parameter, its relatively modest size may have limited statistical power and reduced the robustness of the results. Future research should aim for a larger sample (>500 participants) and include employees from both public and private organisations across different cultural contexts, to explore potential cross-cultural differences in workplace distractions and mental health outcomes.

Moreover, several potential confounding factors were not controlled for. Variables such as job demands and resources, organisational culture, tenure, job role and position level may influence both noise perception and mental health outcomes. Their omission limits the ability to isolate the effects of environmental factors from broader occupational and contextual influences. Future research should incorporate these variables to better account for individual and organisational differences and to strengthen the validity and generalisability of the findings.

This study used cross-sectional data to assess perceptions of mental health. However, self-reported perceptions may differ from physiological measures of stress. As Vaessen et al.[81] indicate the correlation between self-reported stress and physiological stress indicators is often weak. Future studies should integrate both subjective (survey-based) and objective (wearable sensor-based) measures to assess stress responses more comprehensively. Physiological indicators such as heart rate variability, muscle tension and blood pressure could offer deeper insights into how workplace distractions influence employee well-being. Incorporating these measures may help guide design strategies that address both conscious and unconscious needs in the work environment.

CONCLUSION

This study explores how individual characteristics, sound distraction, sound disturbance, coping strategies and mental health relate to each other. The findings highlight that short-term mental health consequences, potentially due to perceived sound disturbance, could develop into more long-term mental health issues. Based on these insights, several recommendations are made to enhance the person-environment fit in office settings. Employees should be encouraged to adopt approach coping strategies rather than avoidance strategies, as avoidance coping is linked to increased fatigue. Furthermore, organisations should ensure that meeting and concentration spaces align with employees’ work demands, particularly in hybrid work environments where workspace requirements have shifted. Lastly, while traditional office designs often cater to a broad workforce, individual differences should not be overlooked. This study highlights the importance of designing office environments that cater to employees’ diverse needs to support their mental health and overall job satisfaction.

Availability of Data and Materials

The data and materials generated during this study are not publicly available due to confidentiality agreements and ethical considerations regarding participant privacy.

Author Contributions

Lisanne Bergefurt: conceptualisation, methodology, formal analysis, investigation, data curation, writing – original draft, visualisation.

Rianne Appel-Meulenbroek: writing – review & editing, supervision.

Theo Arentze: writing – review & editing, supervision.

Ethics Approval and Consent to Participate

This study was approved by the Ethics Review Board of Eindhoven University of Technology under reference number ERB2022BE12. All participants provided informed consent before their inclusion in the study. Participation was voluntary, and respondents could withdraw at any time without consequence.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

None.