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. Author manuscript; available in PMC: 2024 Mar 28.
Published in final edited form as: Pers Individ Dif. 2023 Jun 29;213:112322. doi: 10.1016/j.paid.2023.112322

Associations between sleep quality and irritability: Testing the mediating role of emotion regulation

Claire Whiting a,*, Nellia Bellaert b,c, Christen Deveney d,1, Wan-Ling Tseng b,1
PMCID: PMC10978035  NIHMSID: NIHMS1933094  PMID: 38549685

Abstract

Objectives:

Irritability and sleep problems are common symptoms that span a range of internalizing and externalizing mental health disorders. While poor sleep has been associated with symptoms related to irritability (e.g., anxiety and depression), few studies have directly tested the association between sleep quality and irritability and whether the association is direct or mediated by a separate mechanism.

Method:

The present study used self-report measures to test whether sleep is associated with irritability in 458 adults aged 19–74 years (58 % female; 79 % White), and whether this association is mediated by emotion regulation. Confirmatory factor analyses were carried out to support the use of scores from these measures.

Results:

Controlling for anxiety and depression symptoms, results showed a direct association between poorer sleep quality and increased irritability (β = 0.25, p < .001) that was not mediated by emotion regulation.

Conclusions:

Our findings underscore the important link between sleep and irritability, both of which are common features of mental health difficulties, prompting further inquiry into the directionality of the findings and potential mediators. This work has notable clinical implications for sleep as a possible intervention target for individuals with high irritability.

Keywords: Sleep, Irritability, Emotion regulation, Factor analysis

1. Introduction

Irritability, defined as an increased propensity to experience anger and frustration (Brotman et al., 2017), is a feature of both internalizing and externalizing mental health disorders (American Psychiatric Association, 2013). Severe irritability has been associated with long-term adverse outcomes in youth and adults, including depression, anxiety, and suicidality (Fava et al., 2010; Pickles et al., 2010; Stringaris et al., 2009). While research into the neurobiological mechanisms of irritability has been increasing, limited work has been conducted in adults and on the environmental contributors to irritability. A greater understanding of these is needed to identify intervention targets.

One such environmental factor is sleep. Short sleep duration and poor sleep quality have consistently been implicated as causal contributors to irritability-related constructs including anxiety, depression, anger, aggression, and psychological well-being (Alvaro et al., 2013; Freeman et al., 2017; Van Veen et al., 2022). The handful of studies on irritability in adults suggest that poor sleep correlates with higher irritability. For example, university students with insomnia and, therefore, poorer sleep quality and quantity, reported more irritability in class (Fernández-Mendoza et al., 2009). Among fire service workers, higher self-reported, but not objectively measured, sleep duration was associated with lower irritability levels (Kelly et al., 2021). Poor sleep quality also predicted the onset of subsyndromal depression with irritability in adults starting treatment for cancer and viral infections (Franzen et al., 2010). Although this literature is limited, it is consistent with studies conducted in younger populations. For example, in elementary school students, sleep quality is negatively associated with irritability (Rubens et al., 2017). In adolescents, shorter self-reported sleep duration (Yeo et al., 2019) and greater sleep discrepancies between weekdays and weekends (Tamura et al., 2022) have been associated with increased irritability. Furthermore, experimental sleep restriction has been found to worsen oppositionality/irritability and emotion regulation in adolescents (Baum et al., 2014).

Despite the consistency of these findings, only one study used a validated irritability scale to assess irritability (Rubens et al., 2017). Most studies derived measures of irritability from oppositionality subscales (e.g., Baum et al., 2014) or used a single item (e.g., “do you feel irritable in class?”; Fernández-Mendoza et al., 2009). As such, prior work may not have appropriately captured the construct of irritability, and the only study using a validated measure was conducted in children. While irritability is typically stable across early adolescence through early adulthood (Caprara et al., 2007; Leadbeater & Homel, 2015), there is a need for direct tests, using reliable and valid measures, of whether sleep and irritability are associated in the adult population.

In addition, it is unclear whether poor sleep directly increases irritability or whether another factor mediates the association between sleep and irritability. One possible mediator is emotion regulation, i.e., the “processes by which individuals influence which emotions they have, when they have them, and how they experience and express these emotions” (Gross, 1998, p. 275). Robust evidence links poor sleep (in terms of duration, continuity, or quality) with impaired emotion regulation, measured at neurobiological, cognitive, and behavioral levels, in both children and adults (Palmer & Alfano, 2017; Tomaso et al., 2021). Given that irritability is conceptualized as a heightened propensity for experiencing anger in response to frustration (Brotman et al., 2017), emotion regulation likely plays a key role in the experience and persistence of anger, as well as manifestations of verbal and physical aggression, in highly irritable individuals. In adults, emotion regulation difficulties have been found to mediate the relationship between poor sleep quality and verbal, physical, and interpersonal aggression (Kirwan et al., 2019). However, although some individuals with elevated irritability display heightened aggression, irritability has been empirically and theoretically distinguished from aggression (e.g., Toohey & DiGiuseppe, 2017; Zik et al., 2022). This necessitates a specific exploration of sleep, irritability, and emotion regulation in adults. Such work may also inform pediatric irritability research as populations characterized by irritability-related characteristics such as severe temper outbursts or oppositional defiant disorder exhibit impaired emotion regulation (Cavanagh et al., 2017; Roy et al., 2013). Together, these findings suggest that sleep may increase irritability by disrupting emotion regulation systems, but this hypothesis has not been tested in adults or children. Advancing our knowledge of the mediating processes through which sleep impacts irritability is critical for informing theory, understanding individual differences in this association, and developing intervention strategies.

The present study is the first, to our knowledge, to simultaneously examine sleep, emotion regulation, and irritability in adults. We hypothesized that poorer sleep quality would be associated with greater irritability, with emotion regulation difficulties partially mediating this link. Due to limited or mixed findings regarding the factor structures of the sleep, emotion regulation, and irritability measures (Bardeen et al., 2012; Hallion et al., 2018; Malhi et al., 2017; Manzar et al., 2018), confirmatory factor analyses were performed prior to running the mediation model. Given that anxiety and depression often co-occur with both sleep problems and irritability (Brotman et al., 2017; Cox & Olatunji, 2016; Franzen & Buysse, 2008), we controlled for these symptoms in our analyses.

2. Materials and methods

2.1. Participants

Data were drawn from a study examining irritability in adults; details about the recruitment and sample have been described by Deveney et al. (2019). The parent study was approved by the Institutional Review Board at Wellesley College. Data on sleep quality and emotion regulation have not been published. Briefly, participants recruited through Amazon Mechanical Turk completed a series of questionnaires and tasks online. The present sample includes the 458 participants aged 19–74 years (Mean ± SD = 40.54 ± 11.44) reported in Deveney et al. (2019). Participants were US residents. There were 265 (57.86 %) females and 189 (41.27 %) males. 6.11 % were African American, 4.80 % were Asian, 79.48 % were White, 2.84 % were Hispanic/Latinx, 3.49 % had multiple ethnicities, 0.87 % were Native American or Hawaiian/Pacific Islander, and 2.40 % were designated as “other/unknown”. The median annual income was between $30,000–$49,999, and 43.02 % had achieved a college or postgraduate degree as their highest level of education.

2.2. Measures

2.2.1. Sleep quality

We measured sleep quality using the Pittsburgh Sleep Quality Index (PSQI; Buysse et al., 1989), a self-rated 19-item scale assessing seven components of sleep over the past month: quality, latency, duration, efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. Components are scored from 0 to 3 and summed to yield a total score. Higher scores indicate worse sleep quality. The PSQI has excellent psychometric properties (Mollayeva et al., 2016) and showed good internal consistency in the current sample (Cronbach’s α = 0.79). The total score was used in the main analyses.

2.2.2. Emotion regulation

The Difficulties with Emotion Regulation Scale (DERS; Gratz & Roemer, 2004), a 36-item self-report questionnaire, was used to assess emotion regulation. Each item is rated on a five-point Likert scale. There are six subscales: non-acceptance, goals, impulse, awareness, strategies, and clarity. Higher scores indicate greater difficulties with emotion regulation. The DERS demonstrates good psychometric properties (Gratz & Roemer, 2004) and showed good internal consistency in the current sample (Full DERS: Cronbach’s α = 0.95; DERS without Awareness items: Cronbach’s α = 0.96). Based on the results of our CFA (see below), the total score of the DERS without the Awareness subscale was used in the analyses.

2.2.3. Irritability

The five-item Brief Irritability Test (BITe; Holtzman et al., 2015) assesses irritability over the past two weeks. Each item is rated on a six-point Likert scale. Greater total scores indicate higher levels of irritability. The BITe has good internal consistency and validity (Holtzman et al., 2015), and the internal consistency in our sample was excellent (Cronbach’s α = 0.94). The total score was used in the analyses.

2.2.4. Depression and anxiety

We assessed depression and anxiety symptoms, and controlled for these symptoms in the analyses given their high co-occurrence with irritability (Brotman et al., 2017). The Depression, Anxiety and Stress Scale (DASS; Antony et al., 1998), a 21-item self-report scale that assesses symptoms over the past week, was used. The DASS shows excellent psychometric properties (Coker et al., 2018). The depression and anxiety subscales showed good internal consistency in our sample (Cronbach’s α = 0.94 and 0.85, respectively).

2.3. Data analyses

Prior to conducting the mediation analysis, we performed confirmatory factor analyses on the primary measures, i.e., sleep (PSQI), emotion regulation (DERS), irritability (BITe), given the limited or mixed data regarding their factor structures as discussed below. To confirm that total scores were appropriate to use in our mediation model, confirmatory factor analyses were conducted using R (R Core Team, 2022) to test the unidimensionality of the PSQI, DERS, and BITe. Relative χ2 (χ2/df) < 2, Confirmatory Fit Index (CFI) > 0.95; Root Mean Square Error of Approximation (RMSEA) < 0.07, and Standardized Root Mean Square Residual (SRMR) values<0.08 were indicative of acceptable fit (Hooper et al., 2008).

The factor structure of the PSQI has been debated in the literature (Manzar et al., 2018). We tested for a single-factor structure, including the seven subscales as indicators. Subscales were modeled as ordinal data, and diagonally weighted least squares estimation was used, as this performs well for data with four response categories (Li, 2016). Errors for sleep efficiency and sleep duration were allowed to correlate, as duration is included in the calculation for efficiency. Errors for sleep efficiency and sleep latency, which measure overlapping constructs, were also allowed to correlate.

The six DERS subscale scores were included as indicators and modeled as continuous data. CFA with maximum likelihood estimation was used to assess whether the subscales loaded onto a single factor. Errors for Goals and Strategies subscales were allowed to correlate based on content overlap (e.g., “When I’m upset, I have difficulty focusing on other things.”; “When I’m upset, I believe that wallowing in it is all I can do.”). Previous studies have found that the Awareness subscale shows lower internal consistency, weaker subscale intercorrelations, and contributes substantially less to DERS than the other subscales (Bardeen et al., 2012; Hallion et al., 2018; Malhi et al., 2017). Therefore, we tested whether removing the Awareness subscale improved model fit.

The five BITe items were included as indicators in CFA to test for a single-factor structure. Only the initial development study has evaluated the factor structure of this questionnaire (Holtzman et al., 2015). Item responses were modeled as ordinal data and diagonally weighted least squares estimation was used, as this approach is recommended for ordinal data with six response categories (Li, 2016).

Means and standard deviations were computed for all scales. Bivariate correlations between PSQI, DERS, BITe, depression, and anxiety total scores were examined. Given the large age range of our sample, associations between age and PSQI, DERS, and BITe total scores were also assessed through bivariate correlations.

Our main analysis examined the association between sleep quality and irritability with emotion regulation as a mediator and age, depression, and anxiety as covariates. Mediation analysis was conducted using the lavaan package in R. 5000 bootstrap samples were run, with biascorrected bootstrap confidence intervals.

3. Results

3.1. Confirmatory factor analyses

A one-factor structure for the PSQI showed good model fit (χ2/df = 1.82, CFI = 0.996, RMSEA = 0.043, SRMR = 0.053, factor loadings ≥ 0.539). Results also supported a one-factor structure for the BITe (χ2/df = 1.54, CFI = 1.000, RMSEA = 0.034, SRMR = 0.014, factor loadings ≥ 0.881). The DERS unidimensional factor structure including all six subscales showed poor model fit (χ2/df = 23.70, CFI = 0.872, RMSEA = 0.223, SRMR = 0.089). The Awareness subscale showed a low factor loading (0.391) compared to other subscales (≥ 0.643). A second one-factor model, with the Awareness subscale removed, showed an acceptable model fit (χ2/df = 3.18, CFI = 0.993, RMSEA = 0.069, SRMR = 0.020, factor loadings ≥ 0.609). Therefore, we removed the DERS Awareness subscale from subsequent analyses.

Descriptive statistics and correlations between variables are presented in Table 1. Results indicated significant associations between sleep, emotion regulation, irritability, depression, and anxiety.

Table 1.

Correlations and descriptive statistics.

Measure 1 2 3 4 5 N M SD Range
1. PSQI – 454 5.60 3.80 0–18
2. DERS 0.391* – 458 58.59 21.21 30–125
3. BITe 0.553* 0.619* – 458 11.73 5.99 5–30
4. Depression 0.536* 0.646* 0.634* – 458 7.89 9.75 0–40
5. Anxiety 0.509* 0.539* 0.570* 0.667* – 458 5.18 6.68 0–34
6. Age −0.015 −0.192* −0.077 −0.062 −0.143* 456 40.54 11.44 19–74
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Note. PSQI = Pittsburgh Sleep Quality Index; DERS = Difficulties with Emotion Regulation Scale (without Awareness subscale); BITe = Brief Irritability Test. Higher scores reflect poorer sleep quality and emotion regulation and higher irritability, depression, and anxiety, respectively.

*

p < .001.

3.2. Associations with age

Given the wide age range of the sample, we tested for correlations between age and our primary variables. Age was negatively correlated with DERS scores, r(454) = −0.19, p < .001, and anxiety scores, r(454) = −0.14, p = .002. Therefore, we included age as a covariate in the subsequent mediation model. There were no significant correlations between age and PSQI, BITe, or depression scores (−0.08 ≤ rs ≥ −0.02, ps ≥ 0.099).

3.3. Mediation analysis

Path coefficients and standard errors for the mediation model are shown in Fig. 1. There was a direct effect of sleep quality on irritability, b = 0.40, 95 % CI [0.27, 0.53], p < .001, standardized β = 0.25, indicating that worse sleep quality was associated with higher levels of irritability. There was also a direct effect of emotion regulation on irritability, b = 0.09, 95 % CI [0.06, 0.12], p < .001, standardized β = 0.32, indicating that greater difficulty in emotion regulation was associated with higher levels of irritability. The direct effect of sleep quality on emotion regulation was not significant, b = 0.22, 95 % CI [−0.29, 0.72], p = .388, standardized β = 0.04. The indirect (i.e., mediation) effect of emotion regulation was not significant, b = 0.02, 95 % CI [−0.02, 0.07], p = .408, standardized β = 0.01. The total effect was significant, b = 0.42, 95 % CI [0.28, 0.55], p < .001, standardized β = 0.26. The model accounted for 53.7 % of the variance in BITe scores.

Fig. 1.

Fig. 1.

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Mediation model.

Note. Standardized path coefficients are shown. Values in parentheses are standard errors. PSQI = Pittsburgh Sleep Quality Index; DERS = Difficulties with Emotion Regulation Scale (without Awareness subscale); BITe = Brief Irritability Test. Age, depression, and anxiety were included as covariates. * p < .001.

4. Discussion

This is the first study to test whether sleep quality is associated with irritability in adults and whether emotion regulation mediates this link. After controlling for depression and anxiety, which were moderately correlated with irritability, poorer sleep quality was directly associated with elevated irritability, and this association was not mediated by emotion dysregulation. These findings highlight the possibility that targeting sleep hygiene may improve irritability and its associated clinical impairments.

Our findings are consistent with prior pediatric research linking poor sleep and elevated irritability (Rubens et al., 2017). Furthermore, this association held after controlling for the wide range of ages in our sample, and age was not correlated with sleep or irritability scores. These results suggest that poor sleep may increase irritability in adults regardless of their age, extending findings in pediatric samples regarding the role of sleep on mental health (Freeman et al., 2017). The present results may have promising clinical implications. Specifically, sleep interventions may provide a low-cost first-line treatment target for those with high irritability. Practices that promote quality sleep may decrease levels of irritability, across the adult population. Given the high prevalence of irritability and sleep problems across psychiatric disorders (Alvaro et al., 2013; Brotman et al., 2017; Freeman et al., 2017) and their contribution to outcomes such as quality of life and suicidality (Pickles et al., 2010; Reimer & Flemons, 2003), this line of work holds importance for efforts to reduce the global burden of mental disorders.

Although experimental work suggests that sleep restriction increases irritability (Baum et al., 2014), our correlational design does not allow us to rule out the possibility that the reverse association exists. For example, irritability may reduce sleep quality by increasing stress and arousal. If so, improving both sleep quality and irritability may facilitate a reinforcing cycle that improves psychological well-being. Future longitudinal and experimental research examining the associations between sleep and irritability is necessary to clarify the potential bidirectional associations between these constructs.

Unexpectedly, emotion regulation did not mediate the association between sleep quality and irritability. It is possible that specific emotion regulation strategies, not captured by one broad scale, are implicated in this link. Future work could use ecological momentary assessment to probe temporal associations between sleep, irritability, and emotion regulation with finer-grained assessments. Alternatively, the association between sleep and irritability may be mediated by other mechanisms. Key candidates are aberrant reward and threat processing, which are implicated in individuals with severe irritability (Brotman et al., 2017; Stringaris et al., 2018). Sleep deprivation impacts activation in reward neural circuitry (Mullin et al., 2013) and processing of threatening social stimuli (Goldstein-Piekarski et al., 2015). Other functional networks involved in irritability and impacted by sleep, such as those engaged in executive control, face perception, and motor behavior (de Almondes et al., 2020; Nielsen et al., 2021), are also promising areas for exploration in future work.

A number of limitations to this study are worth noting. First, measures were collected at a single time point and ratings were based on varying time intervals. Future research using experimental or time-series designs, with consistent questionnaire time frames, are needed to confirm and elucidate the directionality of these findings. Secondly, questionnaires were self-reported, meaning that findings are subject to common method variance bias. However, some work has found weak correlations between self-reported and objective measures of sleep (Jackowska et al., 2016). Therefore, it is possible that the links between sleep quality and irritability may not exist when sleep is measured objectively. Finally, although BITe scores showed wide distributions and several individuals scored in the upper extreme, participants were not drawn from a clinical population. Given prior work implicating sleep as an important factor for clinical disorders (Freeman et al., 2017), research with clinical populations is warranted and may reveal stronger associations between the variables.

5. Conclusions

This study expands the limited research on sleep and irritability by examining their associations in an adult sample using a validated irritability scale. Our findings demonstrated a link between disrupted sleep and increased irritability. Although the mechanism mediating this link remains unclear, this study emphasizes that sleep is a potential target for clinical interventions. Given the associations between irritability and numerous psychiatric disorders and poor clinical outcomes, identifying effective interventions for irritability could have a substantial impact on societal mental health.

Funding

This work was supported by funds from Wellesley College to C.D. WL.T is supported by grants from the NIMH (R00MH110570), Charles H. Hood Foundation, and Fund to Retain Clinical Scientists from the Yale School of Medicine and the Yale Center for Clinical Investigation.

Footnotes

CRediT authorship contribution statement

Claire Whiting: Conceptualization, Formal analysis, Writing – original draft. Nellia Bellaert: Conceptualization, Writing – original draft. Christen Deveney: Methodology, Investigation, Writing – review & editing, Supervision. Wan-Ling Tseng: Conceptualization, Writing – review & editing, Supervision.

Declaration of competing interest

The authors declare that there were no conflicts of interest with respect to the authorship or the publication of this article.

Data availability

This study was not pre-registered as it involved analyses of existing data. Study materials and analysis code are available at https://osf.io/qb28y/. Data are not openly available because participants were not asked for permission to share their anonymized data publicly. Upon request, we will share data to researchers who have completed appropriate ethics training, who agree to confidentiality restrictions, and whose research is consistent with the purpose of this study.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

This study was not pre-registered as it involved analyses of existing data. Study materials and analysis code are available at https://osf.io/qb28y/. Data are not openly available because participants were not asked for permission to share their anonymized data publicly. Upon request, we will share data to researchers who have completed appropriate ethics training, who agree to confidentiality restrictions, and whose research is consistent with the purpose of this study.

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