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. 2025 Jan 15;65(3):777–796. doi: 10.1111/bjc.12529

Negative interpretation bias and repetitive negative thinking as mechanisms in the association between insomnia and depression in young adults

Isabel Clegg 1,2, Lies Notebaert 1, Amy Whittle‐Herbert 2, Cele Richardson 2,✉
PMCID: PMC13456444  PMID: 39815420

Abstract

Objectives

Despite evidence supporting sleep's role in the development and maintenance of depression, mechanisms underlying this association in young people are less established. Negative interpretation bias (the tendency to interpret ambiguous situations negatively) and repetitive negative thinking (RNT) are important candidate mechanisms. Whilst negative interpretation bias is implicated in depression development, it is a transdiagnostic process and may result from insomnia. Yet, research relating to these constructs is lacking. RNT is another transdiagnostic process implicated in association between negative interpretation bias, depression and insomnia. However, an elaborated model that includes both mechanisms is yet to be tested. It was hypothesised that negative interpretation bias and RNT would sequentially mediate the relationship between sleep/insomnia and depressive symptoms in young people.

Design

The associations predicted by this hypothesis were tested via cross‐sectional mediation in a sample of 214 participants (Mage = 19.19 years, SD = 1.67, Rangeage = 17–24 years, 20% male).

Methods

Participants completed questionnaire measures of insomnia symptoms, depression symptoms and RNT, an ambiguous scenarios task and a 1‐week sleep diary.

Results

Results were consistent with negative interpretation bias and RNT as sequential mechanisms which partially account for the relationship between sleep (i.e., insomnia severity and sleep parameters) and depression.

Conclusions

This study supports negative interpretation bias and RNT as mechanisms linking insomnia and depression in young people, as the predicted associations between these variables were observed. Future research should investigate the causal/directional associations. However, results support theoretical models, and suggest sleep, interpretation bias and RNT may be important processes to target in preventing and treating depression.

Keywords: cognitive bias, depression (emotion), insomnia, repetitive negative thinking, youth mental health

Practitioner points.

  • Insomnia and depression relate to negative interpretation bias and repetitive negative thinking.

  • Negative interpretation bias and repetitive negative thinking partially explain associations between insomnia and depression.

  • Negative interpretation bias and repetitive negative thinking are transdiagnostic mechanisms.

  • Interventions for depression may target all three transdiagnostic mechanisms.

INTRODUCTION

Insomnia and depression are debilitating, prevalent and commonly comorbid disorders amongst adolescents (10–19 years) and emerging adults (19–24 years), collectively referred to as young people (de Zambotti et al., 2018; Kessler et al., 2007). Insomnia and depression are both associated with lasting social, psychological and functional consequences in young people (Gradisar et al., 2022; Sawyer et al., 2012), with worse outcomes for those with both disorders (Blank et al., 2015).

Although insomnia has traditionally been conceptualized as a symptom of depression, meta‐analytic evidence suggests that insomnia is a stronger predictor, rather than an outcome, of depression in young people (Lovato & Gradisar, 2014). For example, research has shown that (i) those with insomnia are three times more likely to develop depression compared to good sleepers (Baglioni et al., 2011); (ii) residual insomnia is a strong predictor of depression relapse (Benca & Peterson, 2008) and (iii) improvements in sleep lead to improvements in depression (Cunningham & Shapiro, 2018).

Despite strong evidence supporting poor sleep's causal role in the development and maintenance of depression, the mechanisms underlying this association in young people are not well established (Blake et al., 2018). Understanding the mechanisms that link symptoms of insomnia and depression could inform theoretical models explaining the high comorbidity between the disorders in young people. Further, this knowledge could be leveraged to prevent the development, maintenance and exacerbation of these disorders, and lead to more efficacious treatments.

Cognitive biases have been suggested as one important candidate mechanism linking insomnia with depression (Blake et al., 2018). A negative interpretation bias refers to the systematic selection of negative or threatening interpretations, over neutral or positive interpretations, when resolving ambiguous situations (Wessa et al., 2023). Negative interpretation bias has been implicated in the development and maintenance of depression (Everaert et al., 2017; Platt et al., 2017). Below, we first review the literature relating insomnia and depression (separately) to interpretation bias, before presenting repetitive negative thinking (RNT) as a candidate mechanism that may explain how insomnia may lead to a negative interpretation bias, and depression in turn.

Insomnia and interpretation bias

A sleep‐related interpretation bias is emphasized in prominent cognitive models of insomnia and is defined by the resolution of ambiguity biased towards the experience of poor sleep (Espie et al., 2006; Harvey, 2002; Lundh & Broman, 2000). For example, Harvey's (2002) cognitive model of insomnia proposes that those with perceptions of a sleep deficit tend to worry about and catastrophize the consequences of disrupted sleep, resulting in elevated autonomic arousal. Once a sleep‐related cue is detected, those with insomnia are more likely to interpret the cue in a manner that confirms their disorder (i.e., a sleep‐related interpretation bias), ultimately leading to actual sleep disturbance. For example, individuals with a bias towards sleep‐related interpretations may interpret difficulty concentrating in the afternoon as a ‘sign’ of insufficient sleep, rather than recognizing other potential contributing factors (such as boredom) that could influence this experience. Other models of insomnia (Espie et al., 2006; Lundh & Broman, 2000), similarly view interpretation bias as interacting with arousal to exacerbate subjective experiences of a sleep deficit, ultimately leading to insomnia.

In an early, smaller‐scale (N = 78) study, Ree and Harvey (2006) found that sleepiness, but not insomnia symptom severity, was positively related to sleep‐related interpretation bias. However, since then, evidence for a sleep‐related interpretation bias in those with insomnia symptoms compared to normal sleepers has been shown, and replicated, in several studies using the same task (Insomnia Ambiguity Task; Akram et al., 2021; Ellis et al., 2010; Gerlach et al., 2020; Ree et al., 2006). In this task, participants are asked to interpret ambiguous sentences, with a greater number of insomnia‐consistent interpretations indicating greater sleep‐related interpretation bias (Ree & Harvey, 2006). Additionally, the use of a face‐morphing paradigm demonstrated that those with insomnia – as opposed to normal sleepers – perceived their own face as significantly more tired than a baseline neutral photograph (Akram et al., 2016). Ultimately, meta‐analytic evidence supports the presence of a sleep‐related interpretation bias in those with poor sleep or insomnia compared to normal sleepers, with a moderately weighted pooled effect size (Akram et al., 2023).

Whilst insomnia is robustly associated with a sleep‐related interpretation bias, growing evidence suggests that cognitive biases transcend diagnostic categories (Lavigne et al., 2024). Yet, little research has examined the association between symptoms of insomnia and non‐sleep‐related interpretation biases. Blake et al. (2018) described a global negative cognitive style (e.g., negative interpretation bias) may result from several neurobiological processes also related to the experience of poor sleep. For example, the model links cognitive styles, such as interpretation biases, to changes in sleep architecture, corticolimbic regulation and arousal that occur in young people (Blake et al., 2018).

In support of the hypothesis that insomnia may be linked with other negative interpretation biases, one study has shown that those with insomnia not only display a bias towards insomnia‐consistent interpretations but also a bias towards threatening interpretations more generally (Ree et al., 2006). A relationship between sleep quality and an anxiety‐related interpretation bias was not found in a second study (Gerlach et al., 2020), however, this study was underpowered to detect small effects and recruited participants who had relatively good sleep quality. In addition, other experimental research has shown that sleep‐deprived university students interpret neutral pictures more negatively, and display increases in negative mood compared to non‐deprived students (Tempesta et al., 2010). Nonetheless, the association between insomnia and non‐sleep‐related interpretation bias remains relatively unexplored, with research yet to robustly test for an association between insomnia and a general negative interpretation bias (i.e., characteristic of depression). In addition, existing research has not yet considered if this negative interpretation bias may partially account for the association between symptoms of insomnia and depression in young people.

Depression and interpretation bias

Similar to insomnia, a negative interpretation bias is also emphasized in cognitive theories of depression (Beck, 1967; Beck & Clark, 1988; Ingram, 1984). For example, Beck (1967) proposed that once depressive schemas are activated, cognitive biases, including negative interpretation bias, arise to validate the schema content. These biases distort the individual's perspective, perpetuating depressive symptoms. For example, those with a defectiveness schema (i.e., I am unlovable), may more readily interpret experiences (i.e., a friend not immediately replying to a text message) as negative (i.e., they must not like me) rather than neutral (i.e., they must be busy), perpetuating feelings of sadness. There is robust empirical evidence that negative interpretation bias is a characteristic of depression (Everaert et al., 2017), and is causally implicated in the development and maintenance of depression in adults (Chen et al., 2022; Gober et al., 2021; Sugita & Yoshimura, 2022). Likewise, there is emerging evidence for an association between depression and negative interpretation bias in samples of young people (Platt et al., 2017). For example, studies have shown significant correlations between negative interpretation bias scores and depressive symptoms in unselected young people (Klein et al., 2018; Orchard et al., 2016a; Smith et al., 2018). In addition, young people with depression display a greater negative interpretation bias than those without depression (Micco et al., 2014; Orchard et al., 2016b; Orchard & Reynolds, 2018). Finally, a review offers preliminary support for negative interpretation bias as a vulnerability factor in the development and maintenance of depression in young people (Platt et al., 2017).

Given there is sound evidence for a casual role of negative interpretation bias in the development and maintenance of depression, research has turned to understand the link between negative interpretation bias and depression (Everaert et al., 2017). RNT refers to difficult‐to‐control, repetitive thinking about negative topics (Ehring & Watkins, 2008). RNT is an important risk factor for depression in young people (McEvoy et al., 2017), and has been implicated in the association between negative interpretation bias and depression (Krahé et al., 2019). As such, we propose that negative interpretation bias and RNT may in part explain the association between symptoms of insomnia and depression in young people.

The role of repetitive negative thinking

Negative interpretation bias is thought to underlie RNT (Hirsch et al., 2018; Hirsch & Mathews, 2012). Krahé et al. (2019) proposed that given resolving ambiguous information is a necessary component of everyday life, those with a systematic tendency to select negative or threatening interpretations when faced with ambiguous information (i.e., a negative interpretation bias), will have more frequent perceptions of negativity and threat, than those without this bias. As such, this negative interpretation bias leads to rumination (e.g., on past situations that have been interpreted negatively) and worry (e.g., over future events that are perceived as threatening, or likely to have a negative outcome; Krahé et al., 2019). In other words, negative interpretation bias fuels RNT. Whilst no studies have been conducted with young people to date, adults with clinical depression display a greater negative interpretation bias than healthy controls, and this negative interpretation bias was moderately to strongly correlated with RNT (Krahé et al., 2019). In addition, a cognitive bias modification paradigm designed to reduce negative interpretation bias was found to subsequently reduce RNT and symptoms of depression (Hirsch et al., 2020).

More broadly, RNT is also implicated in the association between insomnia and depression. Conceptualizations of insomnia propose that extended wakefulness in bed creates an ideal environment for RNT, given there is an absence of visual and auditory stimuli (Lovato & Gradisar, 2014). Extended wakefulness in bed is common in young people due to a misalignment between biological sleep processes and ideal sleep timing (Roenneberg et al., 2004). In particular, young people may experience sleep‐onset difficulties due to delayed circadian timing and slower accumulation of sleep pressure (Gradisar et al., 2022). An alternate, yet complimentary, model suggests that insomnia may disrupt neural connectivity in the default mode network, which subsequently increases mentation and RNT (Akbar et al., 2022).

There is strong empirical evidence to support a pathway from insomnia to depression, through RNT in young people. For example, in older adolescents (16–18 years), sleep disturbance predicted depressive symptoms 1 year later, and this relationship was partially mediated by RNT (Danielsson et al., 2013). Similarly, a 5‐year longitudinal study spanning early‐to‐mid adolescence found that worse sleep partially predicted increases in symptoms of depression through RNT in young people (Richardson et al., 2024). Of note, symptoms of depression did not predict worsening sleep (Richardson et al., 2024). Finally, sleep treatments (specifically, Bright Light Therapy and Digital Cognitive Behavioural Therapy for Insomnia) lead to improvements in depressive symptoms in young people, via reduced RNT (Li et al., 2023; Richardson & Gradisar, 2022). Thus, there is strong support for RNT as a mediator of the relationship between sleep/insomnia and depression in young people. Whilst there are various potential explanations for this (e.g., time spent awake in bed and altered neural connectivity in regions associated with mentation; Richardson et al., 2024), interpretation bias may also play a key role. However, the relationships between sleep/insomnia, negative interpretation bias, RNT and symptoms of depression are yet to be explored, in young people or otherwise.

The current study

The current study aimed to address several gaps in the existing literature. Namely, Blake et al. (2018) proposed that cognitive biases, including interpretation biases, may play a crucial role in linking insomnia and depression in young individuals. However, only one study has examined how insomnia relates to other, non‐sleep‐related interpretation biases to date (Ree et al., 2006) and links between sleep/insomnia and a more general negative interpretation bias (characteristic of depression) are yet to be explored. Moreover, RNT is known to underlie the relationship between negative interpretation bias and depression (Krahé et al., 2019), and insomnia and depression in young people (Richardson et al., 2024; Richardson & Gradisar, 2022), yet no research has tested an elaborated mediation model where negative interpretation bias and RNT sequentially mediate the association between insomnia and depression. Testing this elaborated model may lead to a greater theoretical understanding of the nature of the association between insomnia and depression in young people and may elucidate more specific intervention targets. In sum, the current study hypothesised that negative interpretation bias and RNT are mechanisms that explain the association between symptoms of insomnia and symptoms of depression. This hypothesis was supported by the theory that interpretation biases and RNT represent transdiagnostic mechanisms that may explain comorbidity between disorders (Garland & Howard, 2013; Lavigne et al., 2024). Given the association between insomnia and general negative interpretation bias has not yet been explored, the current study aimed to examine associations between symptoms of insomnia, general negative interpretation bias, RNT and symptoms of depression, cross‐sectionally. Although this research design cannot establish temporal precedence, or causality, cross‐sectional data can provide support for predictions derived from the hypothesis. This hypothesis predicts that, in a cross‐sectional design, patterns of associations and effects will be observed consistent with a model in which negative interpretation bias and RNT explain the association between symptoms of insomnia and depression.

Prior research has tended to measure insomnia symptoms through questionnaire reports or diagnosis (Akram et al., 2023), which does not permit insight into which specific aspects of sleep may convey risk for interpretation bias. Only one study has explored the association between specific sleep parameters and sleep‐related interpretation bias and found that total sleep time and sleep onset latency were not related to interpretation bias (Gerlach et al., 2020). However, this study was likely limited by a relatively small sample size (N = 76) and did not explore the association between interpretation bias and other sleep parameters characteristic of insomnia (e.g., sleep efficiency and wake after sleep onset). Thus, the current study will explore all key sleep parameters.

Based on previous literature, we expected that we would replicate the robust positive association between symptoms of insomnia and symptoms of depression (Blake et al., 2018). Further, we expected patterns of results would be consistent with RNT as a mediator of this association, given past research has reliably found support for this model (Richardson et al., 2024; Richardson & Gradisar, 2022). Finally, we proposed that negative interpretation bias would be positively associated with RNT, and depression in turn, given evidence that RNT underlies the association between negative interpretation bias and depression (Hirsch et al., 2020; Krahé et al., 2019).

Beyond replicating and extending upon these past findings in a sample of young people, this study aimed to test the predictions generated by the above‐mentioned hypothesis, such that, in a cross‐sectional design, patterns of associations would be consistent with negative interpretation bias and RNT as sequential mediators of the relationship between insomnia symptoms and depressive symptoms (see Figure 1). Specifically, our hypothesis predicts that symptoms of insomnia will be positively associated with negative interpretation bias, and RNT in turn. Further, it predicts that this pathway will partially explain the relationship between symptoms of insomnia and depression in young people. We adopted an exploratory approach to understanding how specific sleep parameters (i.e., total sleep time, sleep onset latency, sleep efficiency and wake after sleep onset) were related to the other variables of interest.

FIGURE 1.

FIGURE 1

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The proposed mediation model where negative interpretation bias and RNT partially explain the relationship between sleep and symptoms of depression.

METHOD

Participants

Participants were recruited in Western Australia via university networks and received course credit for participation. Inclusion criteria required that participants were aged between 17 and 24 years. Two hundred and fourteen individuals were included in the final sample (male = 42, female = 169, non‐binary = 3, Mage = 19.19 years, SD = 1.67, Rangeage = 17–24). In terms of ethnicity, 50% of the sample identified as Australian, 13.6% as British, 11.7% as Mainland Southeast Asian and 10.3% as Chinese Asian.

Materials and measures

Sleep

The Insomnia Severity Index, (ISI; Bastien et al., 2001; Morin, 1993) is a 7‐item scale designed to assess subjective symptoms of insomnia, with each item (e.g., ‘How satisfied/dissatisfied are you with your current sleep pattern?’) rated on a 0–4 scale. Item scores are summed to produce a total score ranging from 0 to 28, with higher scores suggesting greater insomnia severity. The ISI has shown satisfactory psychometric properties including good convergence with clinician's evaluation of sleep disturbance severity (r = .57–.71; Bastien et al., 2001). In the present sample, the internal consistency for the ISI was good (Cronbach's α = .85).

The Consensus Sleep Diary, (CSD; Carney et al., 2012) is a standardized tool designed to track nightly subjective sleep. The eight core items that permitted a quantitative response were included (e.g., ‘What time did you get out of bed?’) and were supplemented with four additional items from the extended version of the CSD (e.g., ‘How many drinks containing alcohol did you have?’). Responses to daily sleep questions can be used to calculate average sleep parameters including nightly total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL) and wake after initial sleep onset (WASO) which were the focus of the current study. Average sleep parameters were calculated when the participant had responded to at least four sleep diary entries (Griffiths et al., 2022). The CSD has been shown to differentiate those with good sleep from those with insomnia, and improvement in insomnia symptom severity is significantly related to improvement in CSD indices (Maich et al., 2018; Natale et al., 2015).

Depression

The Patient Health Questionnaire, (PHQ‐9: Kroenke et al., 2001) is a 9‐item questionnaire designed to assess subjective symptoms of depression. Each item (e.g., ‘How often have you been bothered by feeling down, depressed, or hopeless?’) is rated on a 0 ‘not at all’ to 3 ‘nearly every day’ scale, and item scores are summed to produce a total score ranging from 0 to 27. A higher score suggests more severe symptoms of depression. The PHQ‐9 has been shown to differentiate those with and without major depression and demonstrates good concurrent validity (correlation with General Health Questionnaire = .89; Kroenke et al., 2001). In the present sample, the internal consistency for the PHQ‐9 was good (Cronbach's α = .87).

Repetitive negative thinking

The Persistent and Intrusive Negative Thoughts Scale (PINTS: Magson et al., 2019) is a 5‐item measure of RNT. The scale is designed to measure transdiagnostic characteristics of maladaptive repetitive thoughts which are intrusive and difficult to disengage from. Each item (e.g., ‘When I have a problem, I can't get it out of my head’) is rated on a 1 ‘never’ to 5 ‘almost always’ scale, and item scores are averaged to produce a mean score (with at least four responses required). A higher score suggests a greater tendency to engage in RNT. The PINTS has been shown to demonstrate good concurrent validity (correlation with Rumination‐Reflection Questionnaire‐rumination subscale = .67; Magson et al., 2019). In the present sample, internal consistency for the PINTS was good (Cronbach's α = .87).

Interpretation bias

The Ambiguous Scenarios Test for Depression in Adolescents (AST‐DA: Orchard et al., 2016a) provided a basis for measuring negative interpretation bias. Young people were presented with 20 ambiguous scenarios from the AST‐DA (e.g., ‘You buy a present for your sister's birthday. When she opens it, her face shows you how she feels’) and rated each item on a 1 ‘extremely unpleasant’ to 9 ‘extremely pleasant’ scale. In addition, a further 30 ambiguous scenarios were created based on the original items to increase power by reducing noise. Participants were randomly presented with all 50 items. Item scores were averaged to produce a mean score, with lower scores indicating greater negative interpretation bias. There was no time limit for completion. The original scale has been shown to have good construct validity (Orchard et al., 2016a). In the present sample, the internal consistency for the adapted AST‐DA (original and novel items) was good (Cronbach's α = .93).

Procedure

During an in‐person lab session, participants provided informed consent and completed demographic questions. Participants then completed the adapted ASD‐DA and other self‐report measures, including the ISI, PINTS and PHQ‐9 via Qualtrics (Qualtrics, Provo, UT). These measures were completed within a battery of cognitive tests and questionnaires that formed a larger study. At the end of the session, participants were asked to indicate whether their data had integrity, whilst being assured of no negative consequences if they responded negatively.

Following this, the 1‐week sleep diary was explained to participants, with the opportunity to ask questions. Participants received a weblink to complete their daily sleep diary via email at 8 AM every day for 1 week and were asked to complete this as close to their wake‐up time as possible. On Day 7, participants were provided with study debriefing information.

Statistical analysis

There were 286 individuals who commenced the study. In the preparation of data, 10 individuals who indicated that their data did not have integrity were excluded from the final analytic sample. Sleep diary data were monitored as completed, and any non‐sensical responses (i.e., lights‐off time preceded bedtime) were clarified with the participant. If the response was unable to be clarified or resolved, then this entry was removed and was considered missing data when computing average sleep parameters. Average sleep parameters were only calculated when the participant had responses to at least four sleep diary entries (Griffiths et al., 2022). A sensitivity analysis compared the strength of associations between all variables included in the final analysis when those who were missing at least one variable (N = 62) were included or excluded. The pattern of associations between variables was similar in each set of analyses. If participants were missing a response to an item, they were typically missing a response to the scale due to technical error. Since (i) those with missing or complete data did not differ significantly on several characteristics, (ii) the sensitivity analysis showed a similar pattern of association when those with incomplete data were included or excluded and (iii) it is not practical to impute sleep diary data or data where the individual is missing the complete scale, only those with complete data were retained (N = 214). Upon visual examination, data were non‐normally distributed. As such, non‐parametric analyses, and bootstrapping (described below) were used.

To assess the simple associations between variables Spearman's correlation analyses were conducted. Correlation effect sizes were interpreted in line with guidelines by Gignac and Szodorai (2016) for individual differences research (where r = .1 indicates a weak correlation, r = .2 indicates a medium correlation and r = .3 indicates a strong correlation).

To examine whether patterns of associations are consistent with negative interpretation bias and RNT being mediators of the relationship between sleep and depression symptom severity, the indirect effect of insomnia/sleep on depression symptoms through RNT and negative interpretation bias (independently and sequentially) was examined using the bias‐corrected non‐parametric bootstrapping procedures outlined by Preacher and Hayes (2004). Mediation models were conducted separately for insomnia symptom severity and for each sleep parameter metric. Mediation was tested using the Process macro in SPSS (Hayes, 2017), with 5000 bootstrap resamples and 95% confidence intervals. The indirect path is considered significant when the confidence interval of the effect does not cross zero. A correction for multiple comparisons was not applied as an individual hypothesis approach was adopted, and not disjunction testing (Rubin, 2021).

RESULTS

Descriptive statistics

Descriptive statistics for key variables are presented in Table 1. In terms of insomnia symptom severity, approximately 36.0% of the sample reported no clinically significant insomnia symptoms, 37.4% screened for sub‐threshold insomnia symptoms, 23.8% screened for moderate insomnia symptoms and 2.8% screened for severe insomnia symptoms. In terms of depression symptom severity, approximately 25.7% of the sample reported minimal symptoms of depression, 29.9% screened for mild symptoms of depression, 24.3% screened for moderate symptoms of depression, 11.7% screened for moderately severe symptoms of depression and 8.4% screened for severe symptoms of depression.

TABLE 1.

Descriptive statistics for insomnia symptom severity, negative interpretation bias, RNT, depression symptom severity, TST, SE, SOL and WASO.

Variable Mean SD Range
Insomnia symptom severity 10.43 5.71 .00–28.00
Negative interpretation bias 5.40 .91 2.38–8.08
RNT 3.70 .82 1.00–5.00
Depression symptom severity 9.63 6.16 .00–27.00
TST (h) 7.29 .91 4.79–10.15
SE (%) 82.40 8.59 54.18–97.49
SOL (min) 22.36 17.68 .00–120.00
WASO (min) 6.52 9.40 .00–52.14
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Note: For the measure of negative interpretation bias, lower scores indicate greater negative interpretation bias.

Abbreviations: RNT, repetitive negative thinking; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.

Relationships between variables

Spearman's correlations were first examined to evaluate the relationships between all key variables of interest (see Table 2). Consistent with expectations, worse insomnia symptom severity was significantly associated with worse depression symptom severity, with a strong effect size. In addition, both worse insomnia symptom severity and depression symptom severity were associated with greater negative interpretation bias, with a strong effect size. Likewise, both worse insomnia symptom severity and depression symptom severity showed significant and strong associations with greater RNT. Finally, greater RNT was also significantly associated with greater negative interpretation bias, with a strong effect size.

TABLE 2.

Spearman's correlations between insomnia symptom severity, negative interpretation bias, RNT, depression symptom severity, TST, SE, SOL and WASO.

1. Insomnia symptom severity 2. Negative interpretation bias 3. RNT 4. Depression symptom severity 5. TST 6. SE 7. SOL 8. WASO
1. –
2. −.41** –
3. .63** −.41** –
4. .72** −.53** .61** –
5. −.28** .26** −.28** −.33** –
6. −.34** .36** −.34** −.36** .48** –
7. .29** −.23** .22** .24** −.19** −.58** –
8. .34** −.14* .17* .23** −.15* −.34** .23** –
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Note: For the measure of negative interpretation bias, lower scores indicate greater negative interpretation bias.

Abbreviations: RNT, repetitive negative thinking; SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.

*p < .05; **p < .01.

A similar pattern of results emerged when considering sleep parameters. Reduced TST, poorer SE and greater SOL and WASO were all significantly associated with worse depression symptom severity (with medium‐to‐strong effects). In addition, reduced TST, poorer SE and greater SOL and WASO were also associated with greater negative interpretation bias (with medium‐to‐strong effects for TST, SE and SOL and weak effects for WASO). Finally, reduced TST, poorer SE, greater SOL and WASO were associated with greater RNT (with medium‐to‐strong effects).

Mediation models

Cross‐sectional serial mediation models for measures of sleep (insomnia symptom severity, TST, SE, SOL and WASO) to depression symptom severity through negative interpretation bias and RNT are presented in Table 3. All indirect effects were significant (i.e., through negative interpretation bias alone, through RNT alone and through these mediators in serial). Partial mediation was observed when insomnia symptom severity, TST and SE were entered as predictors, and complete mediation was observed when SOL and WASO were entered as predictors. Regarding ‘Serial Mediation Model 1’, insomnia symptom severity was a significant predictor of depression symptom severity, F(1, 212) = 217.25, p < .001 and explained 51% of the variance in these scores. The amount of variance explained increased to 60% when RNT and negative interpretation bias were included in the model, F(3, 210) = 106.79, p < .001. Regarding ‘Serial Mediation Model 2’, TST was a significant predictor of depression symptom severity, F(1, 212) = 30.94, p < .001 and explained 13% of the variance in these scores. The amount of variance explained increased to 47% when RNT and negative interpretation bias were included in the model, F(3, 210) = 62.89, p < .001. Regarding ‘Serial Mediation Model 3’, SE was a significant predictor of depression symptom severity, F(1, 212) = 35.64, p < .001 and explained 14% of the variance in these scores. The amount of variance explained increased to 47% when RNT and negative interpretation bias were included in the model, F(3, 210) = 61.45, p < .001. Regarding ‘Serial Mediation Model 4’, SOL was a significant predictor of depression symptom severity, F(1, 212) = 13.16, p < .001 and explained 6% of the variance in these scores. The amount of variance explained increased to 46% when RNT and negative interpretation bias were included in the model, F(3, 210) = 59.81, p < .001. Regarding ‘Serial Mediation Model 5’, WASO was a significant predictor of depression symptom severity, F(1, 212) = 10.23, p < .01 and explained 5% of the variance in these scores. The amount of variance explained increased to 46% when RNT and negative interpretation bias were included in the model, F(3, 210) = 60.19, p < .001.

TABLE 3.

Cross‐sectional serial mediation models for various measures of sleep (x) to depression symptom severity (y) through negative interpretation bias (m1) and RNT (m2).

Type Effect Estimate SE t p Lower 95% CI Upper 95% CI Standardized coefficient
Serial Mediation Model 1 (insomnia symptom severity)
Indirect Insomnia symptom severity → Negative interpretation bias → RNT → depression symptom severity .02 .01 – – .005 .04 .02
Indirect Insomnia symptom severity → Negative interpretation bias → Depression symptom severity .11 .03 – – .06 .18 .11
Indirect Insomnia symptom severity → RNT → Depression symptom severity .10 .03 – – .03 .17 .09
Component Insomnia symptom severity → Negative interpretation bias −.06 .01 −6.36 <.0001 −.08 −.04 −.40
Component Insomnia symptom severity → RNT .07 .01 9.00 <.0001 .06 .09 .52
Component Negative interpretation bias → RNT −.21 .05 −4.03 .0001 −.31 −.11 −.23
Component Negative interpretation bias → Depression symptom severity −1.80 .33 −5.39 <.0001 −2.45 −1.14 −.27
Component RNT → Depression symptom severity 1.35 .43 3.17 .002 .51 2.19 .18
Direct Insomnia symptom severity → Depression symptom severity .53 .06 8.89 <.0001 .42 .65 .50
Total Insomnia symptom severity → Depression symptom severity .77 .05 14.74 <.0001 .67 .87 .71
Serial Mediation Model 2 (total sleep time)
Indirect TST → negative interpretation bias → RNT → Depression symptom severity −.29 .10 – – −.50 −.13 −.04
Indirect TST → negative interpretation bias → depression symptom severity −.59 .16 – – −.91 −.28 −.09
Indirect TST → RNT → depression symptom severity −.55 .21 – – −.99 −.15 −.08
Component TST → negative interpretation bias .27 .07 4.13 .0001 .14 .40 .27
Component TST → RNT −.18 .06 −3.16 .002 −.29 −.07 −.20
Component Negative interpretation bias → RNT −.35 .06 −6.11 <.0001 −.46 −.23 −.38
Component Negative interpretation bias → depression symptom severity −2.18 .38 −5.70 <.0001 −2.94 −1.43 −.32
Component RNT → Depression symptom severity 3.10 .43 7.25 <.0001 2.26 3.95 .41
Direct TST → depression symptom severity −.97 .36 −2.70 .008 −1.68 −.26 −.14
Total TST → depression symptom severity −2.41 .43 −5.56 <.0001 −3.26 −1.55 −.36
Serial Mediation Model 3 (sleep efficiency)
Indirect SE → negative interpretation bias → RNT → depression symptom severity −.04 .01 – – −.06 −.02 −.06
Indirect SE → negative interpretation bias → depression symptom severity −.08 .02 – – −.12 −.05 −.11
Indirect SE → RNT → depression symptom severity −.06 .02 – – −.11 −.02 −.08
Component SE → negative interpretation bias .04 .01 5.77 <.0001 .03 .05 .37
Component SE → RNT −.02 .01 −3.11 .002 −.03 −.01 −.20
Component Negative interpretation bias → RNT −.33 .06 −5.59 <.0001 −.44 −.21 −.36
Component Negative interpretation bias → depression symptom severity −2.12 .39 −5.40 <.0001 −2.90 −1.35 −.31
Component RNT → depression symptom severity 3.15 .43 7.33 <.0001 2.30 3.99 .42
Direct SE → depression symptom severity −.09 .04 −2.22 .028 −.17 −.01 −.12
Total SE → depression symptom severity −.27 .05 −5.97 <.0001 −.36 −.18 −.38
Serial Mediation Model 4 (sleep onset latency)
Indirect SOL → negative interpretation bias → RNT → depressionsymptom severity .01 .01 – – .005 .02 .04
Indirect SOL → negative interpretation bias → depression symptom severity .03 .01 – – .01 .05 .07
Indirect SOL → RNT → depression symptom severity .01 .01 – – <.0001 .04 .05
Component SOL → negative interpretation bias −.01 .003 −3.26 .001 −.02 −.004 −.22
Component SOL → RNT .01 .003 1.87 .063 −.0003 .01 .12
Component Negative interpretation bias → RNT −.37 .06 −6.56 <.0001 −.48 −.26 −.41
Component Negative interpretation bias → depression symptom severity −2.27 .39 −5.87 <.0001 −3.03 −1.51 −.33
Component RNT → depression symptom severity 3.27 .43 7.66 <.0001 2.43 4.11 .44
Direct SOL → depression symptom severity .03 .02 1.49 .137 −.01 .06 .08
Total SOL → depression symptom severity .08 .02 3.63 .0004 .04 .13 .24
Serial Mediation Model 5 (wake after sleep onset)
Indirect WASO → negative interpretation bias → RNT → depression symptom severity .02 .01 – – .003 .04 .03
Indirect WASO → negative interpretation bias → depression symptom severity .03 .02 – – .01 .07 .05
Indirect WASO → RNT → depression symptom severity .03 .02 – – .01 .07 .05
Component WASO → negative interpretation bias −.01 .01 −2.09 .038 −.03 −.0008 −.14
Component WASO → RNT .01 .01 1.98 .049 <.0001 .02 .12
Component negative interpretation bias → RNT −.38 .06 −6.79 <.0001 −.49 −.27 −.42
Component Negative interpretation bias → Depression symptom severity −2.30 .38 −6.03 <.0001 −3.06 −1.55 −.34
Component RNT → depression symptom severity 3.25 .43 7.63 <.0001 2.41 4.09 .43
Direct WASO → depression symptom severity .06 .03 1.69 .093 −.01 .12 .09
Total WASO → depression symptom severity .14 .04 3.20 .002 .05 .23 .21
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Note: For the measure of negative interpretation bias, lower scores indicate greater negative interpretation bias.

Abbreviations: SE, sleep efficiency; SOL, sleep onset latency; TST, total sleep time; WASO, wake after sleep onset.

DISCUSSION

Insomnia is known to be a robust predictor of depression in young people (Blake et al., 2018; Lovato & Gradisar, 2014), yet the mechanisms underlying this association are less well established (Blake et al., 2018). The current study tested predictions concerning observed associations generated by the hypothesis that negative interpretation bias and RNT are mechanisms linking symptoms of insomnia and depression in young people. It was hypothesised that these mechanisms would sequentially mediate the association between symptoms of insomnia and depression. Consistent with this hypothesis, medium to strong correlations were observed between all variables of interest, except for WASO and negative interpretation bias where a weak effect was observed. Further, in the cross‐sectional mediation models, all indirect effects were significant. Partial mediation was observed when insomnia symptom severity, TST and SE were entered as predictors, whilst complete mediation was observed when SOL and WASO were entered as predictors.

To the best of authors' knowledge, this is the first study to demonstrate a link between sleep/insomnia and a more general, depression‐related negative interpretation bias in young people. As such, we build on previous work by Ree et al. (2006) which provided preliminary evidence of an association between insomnia and a threat‐related (i.e., anxiety) interpretation bias. Collectively, these results support the theory that interpretation biases represent transdiagnostic mechanisms in mental health (Garland & Howard, 2013; Lavigne et al., 2024). Since results were consistent with patterns of associations predicted by a model in which general negative interpretation bias acts as a mediator in the association between each measure of insomnia/sleep and depression severity, results from the current study also support the suggestion that interpretation biases play an important role in linking insomnia with depression in young people (Blake et al., 2018). Similarly, indirect effects from measures of insomnia/sleep to symptoms of depression through RNT were also significant, consistent with the notion that RNT plays an important role in linking insomnia and depression in young individuals (Richardson et al., 2024; Richardson & Gradisar, 2022; Werner‐Seidler et al., 2023).

Beyond these simple mediation pathways, this study also tested novel, elaborated mediation models where negative interpretation bias and RNT sequentially mediate the association between insomnia and depression. The results were consistent with negative interpretation bias and RNT as serial mechanisms operating in the relationship between sleep and depression in young people. This result was replicated across all measures of insomnia/sleep, permitting greater confidence in the importance of negative interpretation bias and RNT in this relationship.

In contrast to prior research that has almost exclusively measured insomnia symptoms through questionnaire reports or diagnosis (Akram et al., 2023), the multi‐method measurement of sleep in the current study permits insight into which aspects of sleep disturbance specifically may convey risk for interpretation bias, RNT and symptoms of depression. Numerically, insomnia symptom severity had the strongest relationship with depression symptom severity, followed by TST and SE, and to a lesser extent SOL and WASO. Negative interpretation bias and RNT partially explained the association between ISI, TST and SE and symptoms of depression. As such, there may be other important mechanistic processes (e.g., emotion regulation) to consider in the associations between these constructs (Akbar et al., 2022; Blake et al., 2018; Lovato & Gradisar, 2014). Complete mediation was observed when SOL and WASO were entered as predictors. Statistically, there is less variance to explain in these models because SOL and WASO had a weaker relationship with depression symptom severity. Research has shown that the agreement between sleep diary data and actigraphy (an objective sleep measure based on accelerometer data) is weakest for WASO (Kearns et al., 2023). Consequently, the high variability in WASO estimates may indicate greater measurement error for this index compared to other sleep indicators, potentially contributing to the weaker association between negative interpretation bias and WASO. Conceptually, SOL and WASO directly measure time spent awake in bed and previous research has established that this time, which is void of visual and auditory stimuli, is particularly conducive to RNT, and thus symptoms of depression (Lovato & Gradisar, 2014). Results from the current study support the suggestion that this environment may also encourage negative interpretations. For example, when replaying the day's events in bed, young people may select negative interpretations, fuelling repetitive negative thinking and symptoms of depression in turn.

Implications

There are several different theoretical models that propose candidate explanations for the relationship between sleep and mental health in young people (e.g., Akbar et al., 2022; Blake et al., 2018; Lovato & Gradisar, 2014). Empirical support for these proposed mechanisms is rising, for example, RNT is becoming a well‐established mechanism in the association between sleep and metal health (Danielsson et al., 2013; Leung et al., 2022; Li et al., 2023; Richardson et al., 2024; Richardson & Gradisar, 2022; Werner‐Seidler et al., 2023), with this finding replicated in the current study. There is also emerging evidence for the role of other cognitive mechanisms (e.g., attention biases; Clegg et al., 2024), with the current study supporting the role of interpretation bias. Support for other biological (e.g., reward processing; Burani et al., 2021; Wieman et al., 2022) and social mechanisms (e.g., childhood trauma and interpersonal disturbance; Burani et al., 2021) is also rising. As evidence of mechanisms builds, future research should consolidate and revise these models. In addition, there is increasing appreciation that young people are predisposed to a specific set of disorders (e.g., depression, anxiety and eating disorders; Rapee et al., 2019). Sleep, negative interpretation bias and RNT are conceptualized as transdiagnostic mechanisms in mental health (Harvey, 2016; Lavigne et al., 2024; McEvoy et al., 2017), thus, the development of a mechanistic model that describes the association between sleep and several psychological disorders may be needed.

Currently, only 60% of young people who engage with the most common psychological treatment for symptoms of depression (i.e., cognitive behavioural therapy (CBT)) experience symptom reduction (Barth et al., 2013). However, targeting mechanistic processes may increase efficacy. Treatments that target mechanistic processes may combine interventions that target sleep, negative interpretation bias and RNT to improve symptoms of depression. For example, interventions for sleep (e.g., CBT for insomnia), for interpretation bias (e.g., cognitive bias modification (CMB) or cognitive restructuring) and for RNT (e.g., Funk et al., 2023, 2024; Joubert et al., 2021). Based on the theory that time spent awake in bed fuels RNT in young people (Lovato & Gradisar, 2014), there is evidence that sleep treatments that reduce wakefulness in bed (e.g., sleep restriction therapy, stimulus control therapy and bright light therapy) improve symptoms of depression via reducing RNT (Li et al., 2023; Richardson & Gradisar, 2022). However, it is not yet clear whether these treatment components also reduce negative interpretation bias.

There are several other potential benefits to these interventions targeting sleep, cognitive biases and RNT, beyond improving treatment efficacy. First, they can be administered online and are thus highly accessible to young people with depression. Second, these strategies could be easily included in modular prevention approaches. Finally, they may be less stigmatizing for young people, given sleep interventions are less stigmatizing than those that directly target mental health difficulties (Tadros et al., 2023). Ultimately, the use of effective and accessible interventions for depression may not only improve outcomes for those with depression but also encourage help‐seeking behaviour to prevent the development of depression.

Limitations and future directions

The current study has several strengths, including the multi‐method measurement of sleep parameters. The results are consistent across several sleep parameters providing confidence that these mechanisms indeed are associated with several components of insomnia, and not only one particular sleep parameter. In addition, this study provides greater insight into which sleep parameters are most important in understanding the associations between sleep, negative interpretation bias, RNT and symptoms of depression. Finally, the current study was sufficiently powered and implemented a measure of interpretation bias that is specifically designed for young people.

However, there are several limitations that are important to consider when interpreting our findings. Firstly, the current sample was unselected, and primarily comprised of undergraduate university students, which limits the generalizability of these findings to clinical samples and young people more broadly. As such, replication with clinical samples and representative community samples represents a direction for future research. Further, the current study was cross‐sectional in nature, and whilst the pattern of results supports a model consistent with RNT and negative interpretation bias as sequential mechanisms, longitudinal and/or experimental research is needed to test the causal and directional associations between variables. Whilst there is a theoretical basis for the direction of associations proposed in the current study, it is possible that these processes (particularly the mediators) may unfold in a different temporal order. Longitudinal studies could examine feedback loops between processes, providing a more nuanced understanding of the associations between these constructs. Future research should also consider other mechanisms which may link the disorders (e.g., emotion regulation and behavioural avoidance; Blake et al., 2018), given there was variance in depression symptoms which was unaccounted for by negative interpretation bias and RNT. Further, this research may also consider the association between these other mechanisms and the cognitive mechanisms proposed in the current study to provide a more complete model.

In the current study, we did not measure, nor control for the mood state, or sleepiness of participants during testing. Philippot et al. (2023) described that judgements of negativity when measuring RNT may be biased by the mood state of the respondent, introducing circularity in the assessment of mood and RNT. This reasoning could also apply to mood state and the measurement of negative interpretation bias, as well as sleepiness and the measurement of insomnia symptoms/sleep. As such, future research may choose to assess state mood and sleepiness as control variables and include actigraphy or polysomnography to provide an objective measure of sleep, which may be less subject to self‐report biases. Further, extensions of the current research may incorporate measures of conditions commonly comorbid with depression and insomnia, such as anxiety, to clarify the unique and shared relationships between symptom presentation, negative interpretation bias and RNT.

This study employed a measure of explicit interpretation bias, meaning that participants had an opportunity to reflect on the ambiguous scenario prior to responding (Hirsch et al., 2016). Whilst this is a common and valid measure of interpretation bias, it may be susceptible to other biases (e.g., demand effects, selection and reporting biases; Wessa et al., 2023). These biases are mitigated in implicit measures of interpretation bias, where participants must resolve ambiguity when ambiguous stimuli are encountered. For example, previous methods of measuring implicit interpretation bias have involved response latencies (Lawson et al., 2002) and could be included in future research. Understanding if implicit interpretation bias also operates in the associations between variables may have implications for the interventions selected to target interpretation bias. For example, cognitive restructuring may be a more appropriate intervention for an explicit interpretation bias, given this is generated from a reflection on an ambiguous scenario. Alternatively, cognitive bias modification may be more appropriate for implicit interpretation biases, where positive reinforcement is used to encourage positive or neutral resolutions of ambiguity (Beard, 2011).

Conclusions

This study examined whether negative interpretation bias and RNT are mechanisms that may link symptoms of insomnia with symptoms of depression in a community sample of young people. We found cross‐sectional support consistent with negative interpretation bias and RNT as serial and transdiagnostic mechanisms linking symptoms of these disorders. As such, these results inform theoretical models linking insomnia with depression in young people and contribute to the growing body of literature exploring mechanisms that underpin these disorders. As evidence of mechanisms builds, future research should consolidate and revise models to better understand the association between sleep and depression. This theoretical and empirical knowledge can be leveraged to improve the prevention and treatment of insomnia and depression in young people given mechanistic processes represent novel intervention targets.

AUTHOR CONTRIBUTIONS

Isabel Clegg: Writing – original draft; visualization; methodology; investigation; formal analysis; conceptualization. Lies Notebaert: Writing – review and editing; supervision; resources; methodology; conceptualization. Amy Whittle‐Herbert: Writing – review and editing; investigation; conceptualization. Cele Richardson: Writing – review and editing; software; resources; methodology; conceptualization.

CONFLICT OF INTEREST STATEMENT

The authors have no conflict of interest to declare.

ACKNOWLEDGEMENTS

The authors acknowledge the support provided by an Australian Government Research Training Program (RTP) Stipend and Joy Schapper Postgraduate Research Scholarship in Clinical Psychology to Isabel Clegg.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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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

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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