Uncertainty or uncertain situations in one’s life are perceived as significant sources of stress for many individuals (Carleton, 2016). However, there are individual differences in the way that people respond to or react to uncertainty resulting in different mental health outcomes (Carleton, 2016; Shapiro et al., 2020). Intolerance of uncertainty (IU) is conceptualized as a cognitive bias representing emotional, behavioral, and cognitive reactions towards uncertain events regardless of whether they will occur or not in the future (Freeston et al., 1994). Previous research on IU indicated that it plays a crucial role in the development and maintenance of generalized anxiety disorder (GAD) and major depression (Carleton, 2016; Shapiro et al., 2020). For high IU individuals, processing of information during uncertain situations may become biased, leading to the appraisal of the uncertainties as more threatening than they really are (Dugas et al., 2001). This cognitive bias eventually leads to a tendency to be highly reactive to negative life events, especially in the absence of sufficient information (Carleton, 2016). Similarly, Holaway et al. (2006) also argued that elevated levels of IU is associated with underestimating one’s coping resources and perceiving the outside world as overly dangerous (Carleton, 2016). This leads to a greater sense of vulnerability to threatening and uncertain situations, increasing the individual’s vulnerability to psychopathology (Shapiro et al., 2020).

Although there is a robust association between IU and measures of psychological distress, which indicates that IU is a vulnerability factor for various psychological disorders (Rosser, 2019; Shapiro et al., 2020), available research also implies that certain factors may be capable of either increasing or decreasing its negative impact on psychological distress and wellbeing (Ouellet et al., 2019; Dar et al., 2017). More specifically, Dar et al. (2017) reported worry to enhance the negative impact of IU. Likewise, Liao and Wei (2011) highlighted a similar relationship between rumination and IU. On the other hand, psychological flexibility (Inozu et al., 2022; Smith et al., 2020), attentional control capacity (Saulnier et al., 2021), and perceived effectiveness (Dai et al., 2021) were observed to buffer the negative impact of IU, leading to decreased levels of depression and anxiety. In the current study, we aim to examine the moderator role of another resilience characteristic, mindful awareness, in the association of IU with anxiety and depression.

Mindfulness can be referred to as a dispositional trait and a skill originating from Eastern traditions of psychology and philosophy (Brown et al., 2007). It is defined as controlling the focus of attention and bringing awareness to the present moment (Brown & Ryan, 2003). Mindfulness enhances self-regulatory skills in attentional processes (e.g., inhibiting, sustaining, shifting) and cognitive flexibility (Bishop et al., 2004; Moore & Malinowski, 2009). It also allows for the greater awareness of the present moment and remaining focused on tasks at hand which form the basis for the conceptualization of mindfulness as a resilience factor (Collins et al., 2018). According to Garland et al. (2015), awareness of the present moment facilitates well-being through increasing the use of adaptive ways of interpreting especially uncertain or negative situations. That is, an individual high in mindfulness may find it easier to re-evaluate the stressful situation due to their capacity to focus on the here and now in an accepting, non-reactive way (Brown & Ryan, 2003). Therefore, such reductions in reactivity may be responsible for the significant decreases in the level of psychological distress (Bergin & Pakenham, 2016; Calvete et al., 2017).

According to Creswell and Lindsay (2014), individuals with elevated levels of mindfulness experience lower levels of psychological distress since it has the potential for buffering the negative impact of stressful life events. Creswell and Lindsay (2014) explain the buffering role of mindfulness through its capacity for reducing reactivity. From a similar vein, Randal et al. (2015) suggested mindfulness as a crucial characteristic that allow individuals to approach their thoughts as only mental processes rather than facts, allowing for the buffering of the negative impact of certain trait-based psychological vulnerability factors such as low self-esteem, maladaptive perfectionism and chronotype (being evening type or morning type) (Gu et al., 2022; Gorgol et al., 2022). All these studies indicate that mindfulness can decrease the negative impact of the biased cognitions associated with these trait-based vulnerabilities. Previous studies also focused on the interaction between IU and mindfulness and concluded that the association of IU with psychological distress was more robust for individuals who have lower levels of mindfulness (Matta et al., 2022; Papenfuss et al., 2021). However, the same strong association between IU and psychological distress was not observed for the individuals who are high in mindfulness since it allows individuals to remain in a non-reactive mode, re-evaluating uncertain situation as non-threatening and acceptable, eventually leading to significant reductions in psychological distress (Kraemer et al., 2016). In line with this Boelen and Lenferink (2018) argued that high levels of IU may prevent individuals from experiencing the benefits of mindfulness. Although several studies suggest a significant pattern regarding the implications of the association between IU and mindfulness, they are cross-sectional and do not allow for the ruling out of alternative interpretations of the relationship between the variables. Relying on data collected from the same individuals at two different time points will allow for making inferences about the temporal precedence between IU, mindfulness, and psychological distress (Cole & Maxwell, 2003).

The previous research on the trait-based factors that play a role in the etiology of anxiety and depression indicate that the vulnerability factors for psychopathology interact with each other and the protective factors and modify the effect of each other on mental health outcomes (Shahar et al., 2004). Mindfulness was proposed as a protective factor that is influential in buffering especially stressful life events by allowing the individuals to respond more adaptively to difficult situations (Creswell & Lindsay, 2014; Randal et al., 2015). Although some recent studies indicated a similar relationship between mindfulness and trait-based vulnerability factors (Feltman et al., 2009; Nyklíček & Irrmischer, 2017), this function of mindfulness has received less interest from the researchers. The current study, utilizing a two-wave longitudinal design, aimed to examine whether the interaction between IU and mindfulness is able to prospectively predict depression and anxiety and, in addition to understand whether mindfulness can significantly buffer IU by weakening its association with depression and anxiety. It is expected that the association of IU with depression and anxiety will change depending on the level of mindfulness, and mindfulness will act as a buffer in the relationship of IU with both depression and anxiety. In specific, we expect IU to prospectively predict lower levels of depression and anxiety at time 2, if participants also have high levels of mindfulness through the enhanced use of self-regulatory skills and adaptive coping skills in the face of uncertainty. On the other hand, it was hypothesized that high levels of IU will prospectively predict higher levels of depression and anxiety at time 2, if participants also have lower levels of mindfulness.

Method

Participants

Participants were recruited from the students enrolled in an introductory psychology course. The announcement regarding the first step the study was provided during the third week of the semester and 315 (150 female) Turkish university students between the ages 18 and 30 (M = 18.56, SD = 1.68) participated in the first phase of the study. However, only 243 of the original sample (114 males and 129 females) aged between 18 and 29 years old (M = 19.88, SD = 1.52) participated in both phases. Thus, only participants that completed both phases were included in the analyses and received 1.5 extra course credits for their participation.

Measures

Intolerance of Uncertainty Scale-12 (IUS-12, Carleton et al., 2007)

IUS-12 was used to measure the levels of intolerance to uncertainty. It measures IU through 12 items, such as “unforeseen events upset me greatly” that are evaluated on a 5-point-Likert-type scale. Past research has shown that IUS-12 has good psychometric properties with good levels of internal consistency (α = 0.96) and associations with well-established anxiety and worry measures (Carleton et al., 2007). The Turkish adaptation of the IUS-12 was used in the current study and was also found to have good psychometric properties among university students with good levels of internal consistency (α = 0.88), and test-retest reliability (r =.74) (Sarıçam et al., 2014).

Mindful Attention Awareness Scale (MAAS, Brown & Ryan, 2003)

MAAS is a 15-item self-report scale used to assess the degree of mindful attention and awareness of both internal and external experiences on a 6-point Likert-type scale. A sample item from MAAS is “I find it difficult to stay focused on what’s happening in the present.” Previous research has shown that MASS has good psychometric properties with good levels of internal consistency (α = 0.82; α = 0.87), test-retest reliability (r =.81), and validity in various samples (Brown & Ryan, 2003). The Turkish version of the MAAS, which was used in the current study, also yielded good psychometric properties indicated by satisfactory levels of internal consistency (α = 0.85), and test-retest reliability (α = 0.83), besides evidence for validity (moderate correlations with scales of impulsivity, thought suppression, and psychological distress) (Catak, 2012).

Patient Health Questionnaire-9 (PHQ-9, Kroenke et al., 2001)

Participants’ levels of depression at both times 1 and 2 were assessed using the PHQ-9, which is a short 9-item self-report measure. It helps identify the presence of major depression and assess the severity of depressive symptoms according to the diagnostic criteria presented in the diagnostic statistical manual of mental disorders-4 (DSM-IV). Participants specified the frequency with which they experienced depressive symptoms during the last week using a 4-point Likert scale. Previous research has found that within a primary care setting, this measure has good psychometric properties with good levels of internal reliability (α = 0.89) and test-retest reliability, as well as good levels of criterion, construct, and external validity (Kroenke et al., 2001). The Turkish version of the PHQ-9, which was used in the current study, had satisfactory levels of internal reliability (α = 0.84) (Sari et al., 2016).

Generalized Anxiety Disorder-7 (GAD-7, Spitzer et al., 2006)

Participants’ anxiety levels at both times 1 and 2 were measured with GAD-7, composed of 7 items evaluated on a 4-point Likert scale. GAD-7 was designed to assess the severity of anxiety symptoms (e.g., Not being able to stop or control worrying). GAD-7 has yielded acceptable levels of internal consistency ​(α = 0.92) and test-retest reliability (r =.83). This measure has also been shown to have good levels of procedural, criterion, and construct validity (Spitzer et al., 2006). The Turkish version of the GAD-7 yielded good internal consistency (α = 0.85), test-retest reliability, and convergent validity (Konkan et al., 2013).

Procedure

Before data collection, Institutional Review Board (IRB) approval was obtained from the authors’ institution. During the first stage of data collection, participants were first presented with a consent form that included important information about the current study and participants’ rights. If participants consented to participate in the research study, they were provided with the link to the online Qualtrics survey composed of IU-12, MAAS, PHQ-9, and GAD-7 (T1). Five weeks after the first stage, participants received an email inviting them to participate in the second stage of the study, which required the completion of PHQ-9 and GAD-7 (T2).

Data analysis

All analyses were performed using IBM SPSS Statistics (Version 26). All predictors were mean centered prior to data analysis. Pearson correlation analysis was conducted to assess the relationship between IU, MAAS, PHQ-9, and GAD-7. Two separate moderation analyses were conducted by using the PROCESS macro for SPSS with 5000 bootstraps resamples and a 95% confidence interval (Model 1; Hayes, 2017). The first moderation analysis examined the moderator role of MAAS on the association between IU and PHQ-9 (T2), while the second analysis tested the same moderating effect of MAAS in the association between IU and GAD-7 (T2). Gender, PHQ-9 (T1), and GAD-7 (T1) were included as control variables during both analyses.

Results

Analyses showed that all study variables are significantly correlated with each other. However, MAAS is negatively, and IU is positively correlated with PHQ-9, and GAD-7 measured at both T1 and T2. This indicates that individuals with higher mindfulness scores are less likely to experience depression or anxiety symptoms. On the other hand, people with a high IU got higher scores on scales assessing depression and anxiety (see Table 1). Furthermore, age did not have significant associations with any of the variables. A series of t-tests also revealed that there were no significant gender differences in any of the study variables, except for T1 anxiety scores [t (241) = 2.529, p =.01, d = 0.33] with women reporting significantly elevated anxiety (M = 15.68, SD = 5.99), when compared with men (M = 13.80, SD = 5.57).

Table 1 Means, standard deviations, internal consistencies and correlations of the scales and variables
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Moderation analyses

The first moderation analysis testing whether the interaction between IU and MAAS is associated with PHQ-9 (T2) while controlling for gender and PHQ-9 (T1) revealed that only MAAS has significant associations with depression measured at T2 (β = − 0.07, SE = 0.033, p =.025). Similar significant results were not found for IU (β = 0.04, SE = 0.036, p =.226). Furthermore, the interaction of MAAS with IU-12 was significantly associated with PHQ-9 (T2), (β = 0.006, SE = 0.003, p <.05). The results revealed that the interaction term prospectively predicts depression.

Table 2 Findings for depression and anxiety respectively
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Further probing of the significant interaction between IU and MAAS in explaining T2 levels of depression indicated that IU is not associated with depression for individuals who have low levels of MAAS (b = − 0.02, se = 0.04, p =.68, 95% bootstrap CIs − 0.11 0.07). Nevertheless, higher IU scores were associated with higher levels of depression for the participants who have low MAAS scores (b = 0.11, se = 0.05, p =.02, 95% bootstrap CIs 0.02 0.21) (Fig. 1).

Fig. 1
figure 1

Depression levels at low/high MAAS and IU-12 levels (N = 243)

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The same analysis was conducted with GAD-7 (T2) as the outcome variable and T1 scores of GAD-7 included as covariate instead of PHQ. Like the first analysis, MAAS was significantly associated with T2 GAD scores (β = − 0.07, SE = 0.028, p =.02). However, neither IU (β = 0.03, SE = 0.035, p =.39), nor the interaction term (β = 0.004, SE = 0.003, p =.14) were significantly associated with GAD-7 (T2). The second moderation analysis indicated that MAAS scored did not moderate the relationship between T1 IU-12 scores and T2 GAD-7 scores (see Table 2).

Discussion

The current study aimed to examine whether IU, dispositional mindfulness, and their interaction prospectively predict depression and anxiety. Although mindfulness measured at T1 was significantly negatively associated with anxiety and depression measured at T1, we did not observe significant associations of IU with either anxiety or depression. Partially confirming the hypotheses, results indicate that the level of IU is not associated with depression for individuals who have high levels of mindfulness. However, having difficulty in tolerating uncertain situations was associated with more severe symptoms of depression when the individual’s capacity to remain focused on the present moment without being distracted is low. A similar pattern was not observed for anxiety.

Although the results revealed a significant interaction and showed that mindfulness moderated the association of IU with depression, the findings are not directly in line with the results of the previous studies, which highlighted mindfulness as a resilience factor that is able to buffer the impact of various risk factors (Matta et al., 2022; Papenfuss et al., 2021). Quite the contrary, our results indicate that high intolerance of uncertainty is associated with elevated levels of depression for individuals who are both high and low in mindfulness. However, for individuals who experience low levels of discomfort while interacting with uncertainties is associated with significantly lower levels of depression, only if they also have high levels of mindfulness. In other words, mindfulness is more likely to exert its impact and prospectively predict lower levels of depression only in the absence of significant concerns regarding uncertain situations in life. Furthermore, having a low capacity for tolerating uncertainty cannot be compensated by the ability to focus on current tasks without distraction. In short, the current results were not in line with previous literature showing mindfulness as a potential protective factor against the negative impact of several risk factors similar to IU (Creswell & Lindsay, 2014). The pattern that has emerged in the present study can be related to the nature of IU, which is a cognitive style different from other risk factors due to its negative impact on attentional processes (Morriss & McSorley, 2019). Notably, Boelen and Lenferink (2018) suggested that high IU individuals are less likely to benefit from adaptive regulatory strategies including mindfulness. That is, IU may be cognitively taxing, preventing individuals from fully utilizing their capacity to focus on the here and now, not leading to decreases in depression.

One other factor that may explain the current findings is the scale we utilized to assess mindfulness. Although MAAS is one of the most frequently used measures of mindfulness, it was also criticized for focusing too much on the awareness aspect of mindfulness, neglecting the other crucial components such as acceptance and observing the situation from a non-reactive perspective (Baer et al., 2006; Sauer et al., 2012), which according to Shapiro and Carlson (2017), forms the essence of mindfulness. Thus, the lack of significant findings regarding the buffering effect of mindfulness in the current study can be related to the use of MAAS instead of other mindfulness scales. Furthermore, being aware of current activities and not getting distracted while performing even the simplest tasks may not be by itself effective in the presence of certain robust cognitive risk factors such as IU.

The current results also indicate a significant difference between the results for depression and anxiety, revealing that the interaction between IU and mindfulness does not prospectively predict anxiety measured within a five-week time interval. This finding can be explained by the difference between anxiety and depression. That is, depression is a state that has a temporal focus on the negative events that took place in the past; however, anxiety is more about the threats that the individual can get exposed to in the future (Beck et al., 1987). Therefore, there is good reason to assume that anxiety is more open to the negative impact of certain cognitive risk factors such as IU (Jensen et al., 2016), which is likely to make dispositional mindfulness less effective.

Finally, the findings regarding the association of mindfulness with both anxiety and depression are in line with the previous studies which suggest mindfulness to be one of the protective factors, responsible for lower levels of maladjustment. However, the current results indicating IU as not predictive of the mental health outcomes are in contradiction with the literature. The lack of significant associations may be associated with the current analytic strategy that involves controlling of T1 levels of anxiety and depression.

To our knowledge, the current study is the first to examine whether the interaction between IU and mindfulness prospectively predicts psychological distress. Despite significant results, the current study is not free from limitations. First, we used self-report measures, which may have led to self-report bias. Secondly, the participants were university students. Thus, the findings cannot be generalized to the general population. Finally, the time interval between the two data collection phases is five weeks, which is a period selected for practical reasons and may not be enough for observing the buffering role of mindfulness. Therefore, future research could utilize experimental designs and target community samples. Also, it may be useful to observe the implications of the interaction between IU and mindfulness on a longer time interval. Future research could also examine the impact of participants’ prior knowledge of and experience with mindfulness on the relationship between IU and psychological distress.

Overall, the findings indicate that mindfulness interacts with trait-based vulnerability factors in prediction of mental health outcomes. Furthermore, the interaction between IU and mindful awareness can prospectively predict individual differences in depression but not anxiety. More specifically, mindful awareness skills may have a beneficial effect only for individuals who have lower IU levels, indicating the importance of intervention methods that target IU. Utilization of such interventions can increase the efficacy of mindfulness skills.