Wang, Blain et al (2021) Cognition and Emotion

Cognition and Emotion

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Variability in emotion regulation strategy use is negatively associated with depressive symptoms Xiaoqin Wang , Scott D. Blain , Jie Meng , Yuan Liu & Jiang Qiu To cite this article: Xiaoqin Wang , Scott D. Blain , Jie Meng , Yuan Liu & Jiang Qiu (2020): Variability in emotion regulation strategy use is negatively associated with depressive symptoms, Cognition and Emotion, DOI: 10.1080/02699931.2020.1840337 To link to this article: https://doi.org/10.1080/02699931.2020.1840337

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COGNITION AND EMOTION https://doi.org/10.1080/02699931.2020.1840337

Variability in emotion regulation strategy use is negatively associated with depressive symptoms Xiaoqin Wanga,b*, Scott D. Blaind*, Jie Menga,b*, Yuan Liu

a,b

and Jiang Qiua,b,c

a

Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, People’s Republic of China; Department of Psychology, Southwest University, Chongqing, People’s Republic of China; cSouthwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, People’s Republic of China; dPsychology Department, University of Minnesota Twin Cities, Minneapolis, MI, USA b

ABSTRACT

ARTICLE HISTORY

Variability in the emotion regulation (ER) strategies one uses throughout daily life has been suggested to reflect adaptive ER ability and to act as a protective factor in mental health. Moreover, psychological inflexibility and persistent negative affect (or affective inertia) are key features of depression and other forms of mental illness and are often further exacerbated by rigid or overly passive regulatory behaviours.

The current study investigated the hypothesis that ER variability might serve as a protective factor against depressive symptoms and affective inertia. Using experience-sampling (N = 213), we tested whether two indictors of ER variability (between- and within-strategy SDs) were related to depressive symptoms and affective inertia. We found that people with higher between-strategy variability and within-strategy variability (specifically for reappraisal and distraction) reported fewer depressive symptoms. Both within- and between-strategy variability were negatively related to negative affective inertia. Between-strategy variability and negative affective inertia had unique effects on depression, when used as simultaneous predictors. Altogether, this study provides further evidence for the utility of ER as a factor buffering against depressive symptoms and particularly for the use of variable ER strategies.

Received 5 March 2020 Revised 4 October 2020 Accepted 17 October 2020

Introduction In order to successfully function in life, humans must balance the need to adaptatively respond to salient emotional signals with the ability to regulate the intensity and duration of their emotional experiences.

Emotion regulation (ER) refers to the ways that individuals manage the trajectories of their emotions to adapt to dynamically changing contexts and environments, through the use of various specific strategies (e.g. situational selection, situational modification, attentional deployment, cognitive change and response modulation). The ability to successfully regulate one’s emotions is an important part of overall mental health and ER ability has been proposed as one fundamental protective factor against

KEYWORDS

Emotion regulation; depression; experience sampling; affective inertia; psychological flexibility

depression (Gross, 1998; Gross & Muñoz, 1995; Marroquín, 2011). The majority of past research linking ER abilities to mental health and psychopathology has focused on trait approaches to ER; such frameworks emphasise cross-situationally consistent influences of specific ER strategies on modifying emotional outcomes (e.g. depression and anxiety) and classify ER strategies as either adaptive (e.g. problem-solving and acceptance) or maladaptive (e.g. rumination and expressive suppression) (Aldao et al., 2010; Aldao & Nolen-Hoeksema, 2010). However, this framework has recently drawn scrutiny, as a number of studies suggest that the effectiveness of ER strategies in mental health varies across different situations and contexts (Bonanno & Burton, 2013; Webb et al., 2012).

CONTACT Jiang Qiu qiuj318@swu.edu.cn Faculty of Psychology, Southwest University, Chongqing 400715, People’s Republic of China *These authors contributed equally to this work.

Supplemental data for this article can be accessed https://doi.org/10.1080/02699931.2020.1840337 © 2020 Informa UK Limited, trading as Taylor & Francis Group

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The interactions between specific ER strategies and situations may also be an important element in influencing mental health outcomes (Aldao et al., 2015; Bonanno & Burton, 2013). For example, reappraisal, an ER strategy typically framed as adaptive, has been suggested to be negatively associated with depressive symptoms only in uncontrollable situations (Haines et al., 2016; Troy et al., 2013). Rather than focusing on consistent and narrow patterns of ER strategy used across all situations, an increasing amount of literature has proposed that flexibly deploying strategies (or coping styles) across different situations might promote optimally effective ER (Cheng et al., 2014; Kato, 2012). In other words, variability and flexibility of ER strategy use may be one of the most important factors through which ER can buffer against mental illness.

Emotion regulation flexibility and depression Several theories for explaining and treating depression have focused on the importance of psychological inflexibility across a number of specific domains, including inflexible affective responses (e.g. experiencing low-level sadness for extended periods of time), cognitive inflexibility (e.g. poor attentional shifting ability), inflexible attributional style (e.g. making internal and stable attributions about the causes of negative events) and inflexible regulatory behaviour (e.g. rumination) (Cheng et al., 2014; Koval et al., 2012; Miyake et al., 2000). Psychological flexibility – the ability to adapt to shifting situational demands, reconfigure mental resources, switch perspectives, and balance among important life domains – plays a fundamental role in buffering against depression and mental illness broadly (Kashdan & Rottenberg, 2010). As one specific aspect of psychological flexibility, ER flexibility refers to an individual’s ability to deploy various ER strategies to meet the demands of situational changes (Aldao et al., 2015; Bonanno & Burton, 2013). Given that depression involves not only looping and negative thoughts, but also rigid and passive responding, flexible regulatory behaviours – and flexible ER in particular – may be adaptive and potentially reduce depressive symptoms.

There is considerable heterogeneity in how researchers have conceptualised and computed ER flexibility (Cheng et al., 2014), but negative associations with depression symptoms have been shown across various ER flexibility operationalizations.

Research focused on situation-strategy fit has shown that people who are good at deploying emotionfocused strategies in uncontrollable situations and problem-focused strategies in controllable situations have lower levels of depressive symptoms (Cheng, 2001). The ability to abandon ineffective ER strategies and shift to new ER strategies has also been related to lower levels of depression (Kato, 2012, 2015). One other promising conceptualisation, which has not been explored directly in relation to depression, focuses on cross-situational variability of ER strategies.

Individual differences in ER variability refer to one’s ability to variably deploy ER strategies in order to achieve changing personal goals and to meet situational demands that vary over time (Aldao et al., 2015; Blanke et al., 2019; Lazarus & Folkman, 1987).

ER variability has been suggested as a necessary but not sufficient condition for ER flexibility (Aldao et al., 2015). These models assume that individuals who tend to contextually and flexibly use a variety of ER strategies will be more likely to successfully deal with negative life events and thus foster better mental health. Previous findings have shown that greater ER variability in daily life is associated with reduced intensity of negative affect (Blanke et al., 2019). The current study sought to investigate whether depressive symptoms are negatively associated with variability of ER strategy use in daily life.

Emotion regulation flexibility and affective inertia Resistance of mood states to change – or the maintenance of emotions across long periods of time – has been proposed as a core depressive symptom, known as affective inertia (Kuppens et al., 2010; Suls et al., 1998). Affective inertia is characterised by reduced reactivity to one’s changing environment and it is a facet of affective inflexibility (Brose et al., 2015; Gilbert et al., 2019; Koval et al., 2012). The typical method of assessing affective inertia is to compute autocorrelation of a given affect at successive time points, without taking into consideration any particular life events that may have happened between assessments (Kuppens et al., 2010). Research based on this construct has demonstrated that higher affective inertia – for both negative affect and positive affect – is associated with psychological maladjustment (Brose et al., 2015; Koval et al., 2013, 2016) and predicts the onset of clinical depression (Kuppens et al., 2012). Perhaps not surprisingly – as affective

COGNITION AND EMOTION

inertia reflects an inflexible emotional response to the environment – previous studies have found that regulatory strategies characterised by cognitive rigidity (e.g. rumination) are associated with higher inertia of negative affect and maladaptive behaviours (Koval et al., 2012). Variability in ER strategy use might indicate high sensitivity to changing environments and the ability to shift to appropriate regulatory strategies that are synchronised with specific situational changes (Aldao et al., 2015; Cheng et al., 2014; Hollenstein, 2015). In contrast to rumination and its associated affective inertia – or a rigidity of affective states – ER variability may reflect flexible switching of thoughts to manage affective experiences. In light of the interplay between affect and cognition (Forgas, 2008), it is likely that ER variability would be negatively related to affective inertia.

Emotion regulation flexibility, affective inertia and depression As mentioned above, both emotion regulation flexibility and affective inertia are related to depression (Brose et al., 2015; Cheng et al., 2014; Kashdan & Rottenberg, 2010; Kato, 2012, 2015; Koval et al., 2012, 2016; Kuppens et al., 2012; R. J. Thompson et al., 2012). Moreover, emotion regulation flexibility and affective inertia may be highly correlated with one another since the same basic cognitive abilities may be involved in the regulatory processing of affect and cognition (Zelazo & Cunningham, 2007). Therefore, it remains unknown whether emotion regulation flexibility would contribute to individual differences in depression symptoms independent of affective inertia. Initial evidence has shown that affective inertia was associated with depression independent of rumination, which suggests that cognitive and affective inflexibility uniquely contribute to depression (Koval et al., 2012). It remains worthwhile, however, to assess whether general variability in ER strategy use would predict depression, independent of affective inertia.

Computing emotion regulation variability To assess individual differences in ER strategy variability, many researchers have turned to existing selfreport measures. For instance, measures designed to assess coping flexibility – such as the Coping Flexibility Questionnaire (COFLEX) (Vriezekolk et al., 2012) – can serve as a proxy for ER variability, as coping skills and ER strategies are analogous constructs that

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share many underlying processes (Tamir, 2016).

Another example is the Ways of Coping Checklist (WCC), where researchers can calculate ER variability scores based on the scope of an individual’s deployed ER strategies (Lester et al., 1994; Williams, 2002). Similarly, the Coping Styles and Flexibility Inventory (CSFI) measures ER variability by calculating the standard deviations of an individual’s ratings of various coping strategies in response to various hypothetical stressful events (Katz et al., 2005). Though useful, these measures lack ecological validity since participants’ self-reported patterns of ER strategy use in response to hypothetical events may be very different from actual daily behaviour. A more representative way to assess ER variability would be to actually measure individuals’ ER strategy use in response to stressful events throughout their daily lives. The experience-sampling method (ESM) – asking participants to report on their thoughts, emotions, or behaviours at multiple time points throughout their daily life – has been suggested as an ideal way to study ER variability (Aldao et al., 2015; Burr & Samanez-Larkin, 2020).

Using ESM to assess ER variability, various metrics can be derived, often foscusing on the constructs of between- and within- ER strategy variability (Aldao et al., 2015). Within-strategy variability reflects an individual’s variation in their use of a specific strategy across different time points (Aldao et al., 2015; Aldao & Nolen-Hoeksema, 2012). High scores on within-strategy variability would be obtained when someone differs in how much they use a certain strategy across different situations and times. Conversely, between-strategy variability refers to variation in the endorsement of different strategies on given occasions. High scores on between-strategy variability indicate that an individual neither tried to use all strategies simultaneously to a similar extent nor strongly prioritised a single strategy, which may reflect a tendency to search for the most appropriate regulatory strategy from their regulatory repertoire, for use in different specific situations (Blanke et al., 2019). Previous findings have suggested that both betweenand within- strategy variability are positively related to mental health (Aldao & Nolen-Hoeksema, 2012; Birk & Bonanno, 2016; Blanke et al., 2019). More thoroughly investigating whether ER variability can predict depressive symptoms seems like a worthwhile effort for better understanding the possible adaptiveness of ER variability, and we believe ESM is an ideal method for doing so.

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The current study

Method

The main purpose of the current study was to investigate the role of ER variability as a possible protective factor against depressive symptoms and affective inertia. We also examined whether ER variability was associated with depression independent of affective inertia, given the possible association between these two predictor variables. To this end, we recruited a general population sample of college students to complete self-report measures of depressive symptoms and ER variability, as well as a ten-day-long ESM protocol. Participants completed five-times-daily reports of negative events, positive and negative affect, and ER strategy use.

In our analyses of ER variability, we calculated standard deviations (SDs) to quantify participant-level metrics of within- and between- strategy ER variability (Aldao et al., 2015; Blanke et al., 2019). We then tested several hypotheses regarding associations among these ESM and single-time-point questionnaire variables.

First, we hypothesized that ER variability assessed during daily life would be related to self-reported ER flexibility assessed using the COFLEX. We also hypothesized that ER variability would negatively predict affective inertia, as ER variability is associated with flexible strategy deployment and sensitivity to varying environments, factors which could prevent affective states from persisting (Aldao et al., 2015; Cheng et al., 2014); to test for potential associations of ER variability with affective inertia, we used multilevel models accounting for the nested structure of data collected using ESM. Finally, we hypothesized that ER variability and affective inertia would be uniquely and independently related to depression, and thus we computed further hierarchical regression models to test these potential relations. Many previous studies have shown that high levels of psychopathology may lead to greater mean ER strategy endorsement (Aldao et al., 2015; Dixon-Gordon et al., 2015b). Since the method we used to calculate ER variability (SDs) can be confounded with mean levels of ER strategy use, we controlled for the effects of mean ER strategy use in all statistical analyses. Additionally, given the shared variance of affective inertia and mean levels of affect, highlighted in previous studies (Dejonckheere et al., 2019; Houben et al., 2015; Houben & Kuppens, 2020; Koval et al., 2013), we controlled for mean levels of affect in all of our affect-related analyses.

Participants We initially planned to recruit 200 undergraduate students from Southwest University in Chongqing, People’s Republic of China, via online advertising.

The sample size was decided by referring to previous studies (Blanke et al., 2019, study 3 and study 4). In order to balance the gender ratio of participants, we recruited males in the later stages of recruitment, which resulted in a total of 223 participants for the current study. From the initial sample, ten participants were excluded from data analysis after data collection because of equipment malfunction (n = 3) and poor compliance (n = 7, > 30% missing data), resulting in a final sample of 213 participants (55.4% females).

Their mean age was 19.81 years (SD = 1.37, range: 18-26). Participants received a fixed reimbursement according to the number of ESM questionnaires they had completed by the end of the study, with a bonus of 50 RMB for having completed 35 or more beeps. This study was approved by the Human Participants Review Board and ethics committee of Southwest University.

During an initial session, participants completed demographic information and a series of self-report questionnaires online. Then, they were invited to the lab to receive the Structured Clinical Interview for DSM-IV Axis I disorders that was used to exclude subjects who had a history of psychiatric or neurological disorders, had taken psychiatric medications, or had received mental health treatment. At this session, participants who met inclusion requirements were also given instructions on how to complete the experience sampling portion of the study.

Experience sampling method ESM procedure overview At the end of the in-person research session, participants were instructed on how to use the experience sampling programme and completed a practice trial on the Wenjuanxing platform (https://www.wjx.cn/) via their smart phones. In the subsequent weeks, participants answered questionnaires on the platform 5 times a day for 10 consecutive days, between 10 am and 10 pm, whenever they received a beep. The minimum time interval between every two beeps was 120 min. Participants were required to complete each response within 20 min. The

COGNITION AND EMOTION

questions presented at each time point included: (1) filling out name and ID; (2) rating the current intensity of negative and positive affect; (3) a negative event description and rating of negativity from 0 to 100 (0 = not at all, 100 = extremely strong); and (4) rating the intensity of seven ER strategies possibly used. Participants were told to aim for a total of 50 answered beeps (5 beeps a day over 10 days). We prolonged the programme by 3 days for a portion of participants, in order to allow participants who had missed beeps more than 5 times to reach a sufficient number of beeps. This resulted in a total of 182 participants having answered the questionnaires for 13 days, which allowed 16 participants who had missed beeps more than 5 times to reach the minimum beeps (35 beeps) and 161 participants to finish more than 50 beeps after the extra 3 days. A total of 11,545 beeps were collected throughout the duration of the ESM programme.

Participants answered 54.53 beeps on average (SD = 5.41; range: 39–65). If answering 50 beeps or more is considered a response rate of 100%, the average response rate was 98.1% (SD = 0.04) with a range of 78–100% of all beeps. (In this calculation, all participants with ≥ 50 beeps responded were counted as having 100% response rate and all participants with <50 beeps responded were counted as having their raw response rate.)

Negative affect (NA) and positive affect (PA) ratings We selected affect items for participants to rate at each beep, including eleven NA items (“nervous”, “ashamed”, “sad”, “depressed”, “hostile”, “tired”, “upset”, “angry”, “dysphoric”, “anxious” and “afraid”) and seven PA items (“happy”, “active”, “inspired”, “attentive”, “relaxed”, “alert” and “determined”).

These items were chosen based on their presence in both the revised Positive Affect Negative Affect Scale (Qiu et al., 2008) and the International Positive and Negative Affect Schedule short form (I-PANASSF) (Karim et al., 2011; E. R. Thompson, 2007). At each beep, participants were asked to rate their current level of each affect item using a continuous sliding scale from 0 (not at all) to 100 (extremely strong). A mixed model was used to test the between- and within- person reliabilities of NA and PA (Csikszentmihalyi, 2012). The between- and within-person reliabilities of NA were 0.89 and 0.86, respectively. The between- and within-person reliabilities of PA were 0.87 and 0.82, respectively.

5

We calculated average levels of NA and PA for each individual, at each beep, for use in our subsequent analyses.

Emotion regulation strategies We selected seven items to assess ER strategy use in daily life (from which we were able to compute metrics of both individual mean ER strategies use and ER variability). English-translated versions of the specific items are presented in our supplemental materials (Part 1: Experience sampling items). Based on the widely accepted process model of emotion regulation (Gross, 1998), our items for ER strategies were adopted to reflect various specific stages of the emotion regulation process. For example, we included items for distraction (attentional deployment), reappraisal (cognitive change) and expressive suppression (response modulation). We also had items for acceptance, expressive engagement and social sharing, which have been reported as common forms of response modulation (Flett et al., 2003; Forman et al., 2007; Vincke & Bolton, 1994).

Rumination – a self-focused attention toward negative affect or negative events – is a maladaptive ER strategy and was also included in this study because it has been shown to be a common response in people with major depressive disorder (Cooney et al., 2010; Papageorgiou & Siegle, 2003; Papageorgiou & Wells, 2003). Individuals were asked to rate the degree of each ER strategy they used, from 1 (did not apply at all) to 100 (applied to a great deal), at each beep.

Non-ESM self-report measures Beck depression Inventory-II The Beck Depression Inventory-II (BDI-II) is a reliable and well-validated scale for measuring individual differences in depressive symptoms (Beck et al., 1996). It has been widely used to assess the severity of cognitive, motivational, affective, and somatic symptoms of depression. The BDI-II asks individuals to consider their experiences in the past week and answer a series of 21 items. Participants rate each item on a four-point Likert scale (ranging from 0 to 3), with total scores ranging from 0 to 63. Higher scores indicate more severe depressive symptoms.

In the present study, the BDI-II had an acceptable internal consistency reliability (α = .89).

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Coping flexibility questionnaire

Hierarchical regression analyses

The Coping Flexibility Questionnaire (COFLEX) is a self-report measure designed to assess individual differences in coping flexibility (Vriezekolk et al., 2012). The scale consists of 13 items and two subscales including (1) versatility and (2) reflective coping. Participants are asked to rate how often they engage in the strategies described in each of these items (e.g. “I think of different options when a solution is not successful”) on a four-point Likert scale from 1 (seldom or never) to 4 (almost always).

Higher scores on each subscale indicate greater coping flexibility. In our sample, the COFLEX had an acceptable Cronbach’s alpha reliability for its total scale (α = 0.834) and Cronbach’s alpha for versatility and reflective coping were 0.785 and 0.719, respectively.

To test the effects of ER variability on individuals’ levels of depressive symptoms, we implemented hierarchical regression analysis, using IBM’s SPSS Version 24 for Windows. Variables that explain depression were entered in two steps. Considering the high correlation between ER variability and mean ER strategy use reported in existing studies (Blanke et al., 2019; Dixon-Gordon et al., 2015a), we included mean ER strategy use, age and gender as independent variables and BDI-II scores as our dependent variable at step 1. In step 2, within-strategy and between-strategy SDs were also entered into the regression.

Finally, to test whether ER variability was a significant predictor of depression independent of affective inertia, we ran another hierarchical regression model with BDI-II scores as dependent variable. Age and gender were included as independent variables at step 1. Mean ER strategy use and mean level of affect were included as independent variables at step 2. In step 3, we entered within-strategy, between-strategy SDs and affective inertia into the regression as additional independent variables.

Statistical analyses Computation of participant-level ER variables Consistent with previous work (Blanke et al., 2019), we computed two indicators of ER variability: betweenstrategy and within-strategy SDs. For between-strategy variability, first we calculated the mean level and standard deviation (SD) across all the ER strategies at each occasion. Then, the SDs obtained at all the beeps were averaged to compute the mean between-strategy variability at the participant level.

Higher values of between-strategy variability reflect modifying strategies and the extent of their use across different occasions and situations. Next, we calculated the mean intensity and SD of each strategy across all occasions. Within-strategy variability for each participant was computed by averaging the SDs across seven strategies. Individuals with a high value of global within-strategy variability indicate that they used given strategies to different extents across time. Reliabilities using Cronbach’s alpha were calculated for between-strategy (α = 0.904) and within-strategy variability (α = 0.882). Mean ER strategy use at the participant level was calculated by averaging the means of all strategies’ endorsement level across all measurement occasions.

Descriptive statistics Between-person (person level) means and standard deviations were calculated for each of our ESM variables. We also calculated standard descriptive statistics for the BDI-II and COFLEX.

Multilevel analyses Consistent with Kuppens et al.’s approach (2010), we operationalised affective inertia by examining the autocorrelation of affect with itself at a time-lagged interval. The “lme4” package in R (Bates, 2010) was used to fit linear mixed effects models and examine the moderating role of ER variability in affective inertia, accounting for the nested data structure (measures in each occasion were nested within individuals). The last measurement of each day and time points with missing data were removed. We ran separate models for NA and PA. At Level 1 of the model, we predicted either NA or PA at time (t+1) by itself at the previous measurement time (t) shown in Equation (1): Affecti(t+1) (either NA or PA) = b0i + b1i (Affectti ) + b2i (Timeti ) + b3i (Eventti ) + rti

(1)

Here, the outcome (Affect i(t＋1)) reflects person i’s NA or PA at time (t＋1). The intercept (β0i) indicates the average level of NA or PA for person i. Of particular interest, the slope (β1i) indicates the within-person association between person i’s NA or PA at time (t) and itself at time (t+1). Possible linear trends in the affect at t+1 are captured by the slope of time (β2i).

COGNITION AND EMOTION

In some cases, higher affective inertia might be caused by sequentially occurring emotional events (Waugh et al., 2017); to account for the influence of negative events that may have occurred during the measurement period involving the affective autocorrelation, we controlled for the effects of event (0 = no negative event happened, 1 = negative event happened), which are captured by β3i. The unexplained component of person i’s NA or PA at time (t+1) is captured by the within-person residual (rti). At Level 2 of the model, the intercept and slope values of NA and PA were allowed to vary randomly across persons and were predicted by person-level ER variability, after controlling for mean level of ER strategy use (reflecting each person i’s mean ER strategy use score across all ESM measures), as shown in Equations (2) to (5):

b0i = g00 + g01 (Between-strategy SDsi ) (2)

b1i = g10 + g11 (Between-strategy SDsi ) + g12 (Within-strategy SDsi ) + g13 (Mean ER strategy Usei ) + u1i

parameter in Equation 1, for each person. The slopes γ01, γ11, γ21 and γ31 indicate between-person associations of between-strategy SDs, for each withinperson parameter modelled in Equation 1. The slopes γ02, γ12, γ22 and γ32 indicate between-person associations between within-strategy SDs and each within-person parameter modelled in Equation 1. Parameters u0i, u1i, u2i and u3i reflect person-specific variance in each within-person parameter that is unexplained by between-strategy SDs and withinstrategy SDs. We were primarily interested in the γ11 and γ12 slopes, which represent the associations of affective inertia with between-strategy SDs and within-strategy SDs, respectively. According to our hypothesis, higher between-strategy SDs and withinstrategy SDs should be associated with a more negative Affect (t⍰1) - Affect (t) slope.

Sensitivity power analyses

+ g02 (Within-strategy SDsi ) + g03 (Mean ER strategy Usei ) + u0i

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(3)

b2i = g20 + g21 (Between-strategy SDsi )

G*power version 3.1 was used to conduct sensitivity power analyses to estimate our statistical power for doing hierarchical regression analyses (Erdfelder et al., 2009). We conducted Monte Carlo power simulations (with 5,000 simulations) in Mplus version 7.0 to estimate the post-hoc power of multilevel models by using the parameter estimates from the results of actual analyses (Bolger & Laurenceau, 2013; Muthén & Muthén, 2012).

+ g22 (Within-strategy SDsi ) + g23 (Mean ER strategy Usei ) + u2i

(4)

b3i = g30 + g31 (Between-strategy SDsi )

Descriptive statistics

+ g32 (Within-strategy SDsi ) + g33 (Mean ER strategy Usei ) + u3i

(5)

In Equations (2) to (5), the intercepts γ00, γ10, γ20 and γ30 indicate estimates of each within-person Table 1. Descriptive statistics.

Age Mean ER strategy use Between-strategy SDs Within-strategy SDs NA PA BDI-II COFLEX_Versatility COFLEX_Reflective

α – 0.947 0.904 0.882 0.890 0.870 0.890 0.785 0.719

Mean ± SD 19.81 ± 1.37 35.85 ± 14.47 21.12 ± 7.29 18.88 ± 5.99 0.269 ± 7.637 0.275 ± 0.237 10.380 ± 7.637 14.06 ± 5.12 14.62 ± 4.05

Results

Range 18–26 2.99–67.32 3.30–40.55 7.06–36.32 −0.24–0.96 −0.38–0.87 0–31 6.00–28.00 5.00–25.00

N = 213, 117 females, ER = emotion regulation, SD = standard deviation. NA = negative affect, PA = positive affect, BDI-II = Beck Depression Inventory-II, COFLEX = Coping Flexibility Questionnaire

Descriptive statistics and correlation coefficients for our two ER variability indicators and self-perceived coping flexibility are displayed in Table 1. Our between-strategy SD variable was positively correlated with versatility (r = 0.194, p = 0.005) and reflective coping (r = 0.206, p = 0.002). Similarly, our within-strategy SD variable was positively related to versatility (r = 0.236, p = 0.001) and reflective coping (r = 0.140, p = 0.042). In line with previous results (Aldao & Nolen-Hoeksema, 2012; Blanke et al., 2019), mean ER strategy use was positively related to between-strategy SD (r = 0.451, p < 0.001) and within-strategy SD (r = 0.176, p = 0.010), suggesting that people who used a great deal of strategies also variably switched strategies and endorsed strategies at different levels across different contexts. We also found a strong positive correlation between the two ER variability indicators (r = 0.591, p < 0.001),

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indicating that people who have less preference for any particular strategy at a given situation also did not use particular strategies to the same extent over time.

Associations between ER variability and depression We implemented a two-stage hierarchical regression model to test whether between-strategy and withinstrategy variability could predict depression symptom severity. Results of the variance inflation factor (all less than 2.0), and collinearity tolerance (all greater than 0.52) suggested that the estimated βs were robust. The results of step 1 revealed that the variance accounted for (R 2) with the first three independent variables (Age, Gender and Mean ER strategy use) equalled 0.044 (adjusted R 2 = 0.031), which was significantly different from zero (F (3, 209) = 3.232, p = 0.023). Mean ER strategy use was the only statistically significant independent variable, β = 0.036, p = 0.006. In step 2, within-strategy and between-strategy SDs were entered into the regression equation. The change in variance accounted for (ΔR 2) was equal to 0.027, which was significantly different from zero (F (2, 207) = 3.178, p = 0.009). Only between-strategy SDs, β = −0.227, p Table 2. Unstandardised regression coefficients (B), std. error, tvalues, and p-values for variables as predictor of depression.

Variability

B

Std. Error

t-value

pValue

Step 1 Intercept

8.769

8.007

1.095

.275

Age

−.137

.386

−.355

.723

Gender

1.421

1.056

1.345

.180

.099

.036

2.751

.006

Mean ER strategy use Adjusted R 2 Step2 Intercept

7.873

8.007

.954

.275

Age

−.064

.386

−.165

.869

Gender

1.320

1.048

1.260

.209

95% CI [−7.014, 24.553] [−.898, .624] [−.662, 3.503] [.028, .169]

.044 [−8.402, 24.149] [−.824, .697] [−.745, 3.386] [.063, .221]

Mean ER .142 .040 3.532 < .001 strategy use Between−.238 −.227 −2.451 .015 [−.430, strategy SDs −.047] Within.158 .124 1.468 .144 [−.054, strategy SDs .370] 0.071 Adjusted R 2 N = 213; R 2 = 0.044 for Step 1; ΔR 2 = 0.027 for Step 2;

= 0.015, but not within-strategy SDs, significantly contributed to the explanation of depression. The unstandardised regression coefficients (B) and standardised regression coefficients (β) for the full model are reported in Table 2. A sensitivity power analysis was conducted in the software package, G*power 3.0 (Erdfelder et al., 2009). The alpha level used for this analysis was p < .05 and the power was set at 0.8. Given our sample size of 213 individuals, the minimum effect size (ƒ2) that we had 80% power to detect was 0.046.

Taking possibly inconsistent effects of each strategy on depression into account, we ran another hierarchical regression model to examine whether variability within each strategy could predict depression. Similar to the first model, age, gender and mean ER strategy use were added as independent variables in step 1, and the SDs of each strategy were added in step 2. The results of step 1 were the same as for our first hierarchical regression model. In step 2, SDs of seven ER strategies were entered into the regression equation. The change in variance accounted for (ΔR2) was equal to 0.087, which was significantly different from zero (F (10, 202) = 3.010, p = 0.001). Results showed that distraction and reappraisal SDs significantly contributed to the explanation of lower depression symptoms, while expressive suppression SDs significantly contributed to the explanation of higher depression symptoms. However, we did not find any association between depression and the SDs of the other four strategies. The unstandardised regression coefficients (B) and standardised regression coefficients (β) for the full model are reported in Table S2.

Associations between ER variability and affective inertia In a set of preliminary analyses, we estimated means, SDs and ICC for NA and PA at time t using null models.

For NAt, the mean level was 17.06 (B = 0.84, 95% CI [15.41, 18.72]), with SDs of 9.09 and 12.17 at the within- and between-person levels, respectively. The ICC for NAt was 0.64, indicating that 64% of the total variability in NAt was between-persons and 36% was within-persons. For PAt, the mean level was 43.34 (B = 1.09, 95% CI [41.19, 45.47]), with SDs of 14.92 and 15.67 at the within- and betweenperson levels, respectively. The ICC for PAt was 0.52, indicating that 52% of the total variability in PAt was between-persons and 48% was within-persons.

Two within-person models as shown in Equation 1, which were conducted without any predictors at the

COGNITION AND EMOTION

between-person level, to examine the autocorrelations of NA and PA. The average NA (t+1)-NA (t) slope was 0.36 (95% CI [0.34, 0.38], p < 0.001), while the average PA(t+1) – PA(t) slope was 0.33 (95% CI [0.31, 0.35], p < 0.001). These slopes are consistent with previous findings, suggesting our current participants’ levels of affective inertia are normative and in line with typical samples (Kuppens et al., 2010; R. J. Thompson et al., 2012).

The main results (e.g. standardised γ01, γ02, γ11 and γ12 estimates and 95% CIs) are summarised in Table 3. At the second-level model of NA, the γ01 and γ02 estimates were significant, revealing that both between-strategy SDs and within-strategy SDs were associated with NA at the immediately following time point. Examination of the γ11 and γ12 estimates, representing associations between ER variability and within-person NA (t+1)-NA (t) slope (e.g. NA inertia), revealed that both between-strategy SDs and within-strategy SDs were associated with NA inertia.

At the second-level model of PA, we found that only the γ01 estimate was significant, suggesting that between-strategy SDs were negatively associated with PA at lagged time points. The Monte Carlo simulation results showed an adequate power to detect the effect of between-strategy SDs (85.2%) and within-strategy SDs (88.5%) on NA (t+1)-NA (t) slope.

Examination of the γ11 and γ12 estimates, representing the associations between ER variability and within-person PA (t+1)-PA (t) slope (e.g. PA inertia), revealed that neither between-strategy SDs nor within-strategy SDs were associated with PA inertia.

To further explore the association between ER variability and the NA(t+1)-NA(t) slope (representing NA inertia), we conducted simple slopes analyses using the “reghelper” package in R (Hughes & Team, 2017). Simple slopes were calculated at ± 1 SD around the mean score on each ER variability measure (e.g. between-strategy SDs and withinstrategy SDs), based on multilevel models using within-person standardised NA(t) scores as a predictor of NA(t+1). As expected, simple slopes were significantly lower at –1 SD for between-strategy SDs and within-strategy SDs than at +1 SD for between-strategy SDs and within-strategy SDs. As shown in Panel a of Figure 1, individuals scoring higher (+1 SD) than average on between-strategy SDs tend to have lower NA inertia. Similarly, individuals scoring higher (+1 SD) than average on withinstrategy SDs tend to have lower NA inertia, as shown in Panel b of Figure 1.

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We also ran several supplementary multilevel models (e.g. removing all covariates from the Level 1 model, removing event from the Level 1 model, removing mean ER strategy use from the Level 2 model, etc.) to test the robustness of our findings.

Alternate model specifications are described in the Supplemental Materials (Part 2). Across all of these supplementary models, results were in line with our main findings, indicating that between-strategy SDs and within-strategy SDs were negatively related to NA inertia, but not related to PA inertia. The standardised estimates and 95% CIs for all supplementary models are summarised in Table S1.

Predicting depression from ER variability and affective inertia To test the unique effect of ER variability on depression, we entered affective inertia and ER variability into a hierarchical regression model simultaneously. Affective inertia scores were extracted by using the slopes from the above-mentioned multilevel analyses without considering the predictors in level 2. Negative inertia and positive inertia were calculated separately. In the regression model, results of the variance inflation factor (all less than 4.0), and collinearity tolerance (all greater than 0.36) suggested that the estimated βs were robust. In step 3, NA inertia, PA inertia, within-strategy and between-strategy SDs were entered into the regression equation.

The change in variance accounted for (ΔR 2) was equal to 0.082, which was significantly different from zero (F (4, 205) = 3.691, p = 0.006). As shown in the Table 4, NA inertia was a significant independent predictor of depression, β = 0.198, p = 0.006. Importantly, between-strategy SDs was also linked to depression independent of the effect of affective inertia, β = −0.220, p = 0.025.

Discussion The current study was the first we know of that used ESM to investigate how ER variability is associated with depressive symptoms. We believe this is an important step toward understanding the effects of dynamic changes in ER on emotional health in daily life. In our first main study aim – given that ER variability reflects a set of adaptive regulatory behaviours involving flexible deployment of (or switching between) strategies to meet situational demands (Aldao et al., 2015; Blanke et al., 2019) – we

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Table 3. Fixed effect estimates reflecting associations between ER variability with mean affect at next time point (γ01 and γ02) and affective inertia (γ11 and γ12).

Mean level

Outcome

NA(t⍰1)

PA(t⍰1)

Parameter

Estimate (SE)

Betweenstrategy SDs (γ01) Within-strategy SDs (γ02) Betweenstrategy SDs (γ01) Within-strategy SDs (γ02)

−0.402 (0.051) 0.321 (0.102) 0.329(0.1401) −0.144(0.140)

Inertia 95% CI

pvalue

[−0.513, −0.225]

<0.001

[0.137, 0.458]

<0.001

[0.054, 0.604]

0.019

[−0.451, 0.163]

0.385

Parameter

Estimate (SE)

Betweenstrategy SDs (γ11) Within-strategy SDs (γ12) Betweenstrategy SDs (γ11) Within-strategy SDs (γ12)

−0.010(0.0023)

[−0.014, −0.006]

95% CI

p-value

−0.018(0.002)

<0.001

0.000(0.001)

[−0.022, −0.013] [−0.004, 0.004]

−0.001(0.002)

[−0.006, 0.003]

0.597

0.008

0.982

N = 213, NA = negative affect; PA = positive affect; CI = confidence interval; SD = standard deviation

hypothesised that ER variability would be negatively associated with depression symptom severity. As predicted, our findings showed that people with higher overall between-strategy variability (and within-strategy variability for reappraisal and distraction) reported lower levels of depressive symptoms.

Paired with previous work, this suggests that ER variability may play a protective role against depression symptomatology, as measured in a general population sample. Our second primary aim was to investigate the possible role of affective inertia in ER flexibility and depression. As we hypothesised, both within- and between-strategy variability were negatively related to NA inertia. Neither within-strategy variability nor between-strategy variability were related to PA inertia. Given that ER flexibility and affective flexibility are established as two different components of psychological flexibility and are both believed to influence depression (Stange et al., 2017), we tested and found that ER variability (between-strategy SDs) and NA inertia were both

independently related to depression. Altogether, our findings suggest that ER variability is an adaptive psychological feature that is negatively related to depression symptom severity and inertia of negative affect. Additionally, our findings offer evidence of independent effects of cognitive flexibility and affective flexibility on depression.

Our results suggested that only between-strategy variability (but not within-strategy variability) was negatively associated with depressive symptoms when controlling for mean ER strategy use.

Between-strategy variability was also related to self-reported ER flexibility. Variability in ER strategy use across time might involve the ability to inhibit one’s use of certain strategies that are currently not working and shift to alternative, more appropriate strategies; such abilities are adaptive, allowing one to achieve their regulatory goals and improve mental health (Pruessner et al., 2020; Sheppes, 2020). Depression is an affective disorder which involves impaired cognitive control, and may be

Figure 1. The relation between ER variability and NA inertia. Simple slopes reflecting NA (t+1) in situations rated as low (–1 SD) versus high (+1 SD) in NA (t), among individuals scoring low (–1 SD) versus high (+1 SD) on between-strategy SDs (Panel A) and within-strategy SDs (Panel B).

COGNITION AND EMOTION

Table 4. Regression models predicting depression severity from affective inertia and ER variability. Unstandardised regression coefficients (B), std. error, t-values, and p-values for variables as predictor of depression.

Variability

B

Std. Error

t-value

pValue

Step 1 Intercept

13.763

7.949

1.732

.085

Age

−.207

.394

−.526

.600

1.368

1.078

1.269

.206

4.134

7.018

.589

.556

.219

.340

.644

.521

1.504

.928

1.620

.107

.146

.051

2.832

.005

[−17.679, 21.029] [−1.329, .728] [−.979, 4.994] [.044, .268]

.290

.046

6.260

< .001

[.150, .432]

−.253

7.018

−5.616

< .001

[−.471, −.199]

1.957

7.449

.263

.793

.241

.344

.701

.484

1.502

.932

1.611

.109

.144

.060

2.379

.018

[−23.848, 19.838] [−.558, 1.420] [−.138, 5.219] [.036, .398]

.272

.054

4.994

< .001

[.106, .440]

−.255

.045

−5.640

< .001

−.220

.097

−2.265

.025

[−.377, −.134] [−.397, −.032]

.201

.170

2.006

.089

6.120

0.198

2.796

0.006

−2.008

−.062

−.899

0.370

Gender Adjusted R Step 2 Intercept

2

Age Gender Mean ER strategy use Mean level of NA Mean level of PA Adjusted R 2 Step3 Intercept Age Gender Mean ER strategy use Mean level of NA Mean level of PA Betweenstrategy SDs Withinstrategy SDs NA inertia PA inertia 2

95% CI [−2.565, 39.797] [−.898, .624] [−1.329, 2.978]

.010

.038

[−.050, .306] [2.214, 14.580] [−7.351, 4.759]

.065 Adjusted R N = 213; R 2 = .010 for Step 1; ΔR2 = .034 for Step 2; ΔR2 = .082 for Step 3; NA = negative affect; PA = positive affect.

associated with an excessive use of rumination and impoverished abilities to opt for using inhibition and reappraisal when processing negative stimuli (Joormann, 2010; Joormann & D’Avanzato, 2010; Joormann & Gotlib, 2010; Joormann & Michael Vanderlind, 2014). Previous findings have suggested

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those with higher levels of depressive symptoms use a limited number of different ER strategies, in line with the current ESM findings (Ehring et al., 2008; Lougheed & Hollenstein, 2012; Slee et al., 2008). One potential explanation for the relation between ER variability and depression is that individuals with higher ER variability may have a wider repertoire of regulatory strategies – one of the foundations of ER strategy switching (Bonanno & Burton, 2013). However, given the design of the current study, the causal relation between ER variability and depression cannot be directly tested. Future studies could use longer-term longitudinal designs to test whether ER variability would be an effective predictor of the onset of depressive disorders and/ or to test whether depressive symptoms result in less flexible regulatory behaviours.

Some previous findings have suggested a weak positive relation between within-ER strategy variability and depression (Blanke et al., 2019). Though we did not find any significant relation between withinstrategy variability and depression, the observed coefficient was in the positive direction. It could be that variability of ER strategy use across time is optimised at a moderately high level, with low variability reflecting a rigid regulatory style but excessively high variability reflecting unstable or erratic strategy use.

Additionally, considering that the relations between depression and variability in different specific strategies are inconsistent (Aldao & Nolen-Hoeksema, 2012), averaging within-strategy SDs across all strategies may obscure the differential effects of different ER strategy types on depression. For example, in the current sample, within-strategy variabilities for distraction and reappraisal were found to negatively correlate with depression. Specific situational contexts may also matter. A growing number of studies have emphasised that the positive role of reappraisal on mental health is most apparent when one’s situation is controllable (Haines et al., 2016; Troy et al., 2013). In contrast, distraction is a more effective ER strategy when regulating negative stimuli at a high level of intensity (Birk & Bonanno, 2016; Dorman Ilan et al., 2020). These findings suggest that research into the efficacy of ER strategies needs to take regulatory context into consideration, which further supports ER flexibility theory (Aldao et al., 2015; Bonanno & Burton, 2013). Future research should further investigate how environmental characteristics interact with different ER strategies to downregulate depressive symptoms.

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Our findings suggested that individuals higher in ER variability have lower NA inertia, but not PA inertia. NA inertia has been suggested to be more predictive of various indicators of mental health than PA (Haines et al., 2016; Houben et al., 2015). ER variability as a protective factor against depression may have little to do with persistence of positive affect. Besides reflecting emotional rigidity, NA inertia has also been shown to be related to poor emotion regulation (Brose et al., 2015; Koval et al., 2012) and impaired recovery from NA (Clasen et al., 2013; Koval et al., 2015). We did not investigate mechanisms possibly underlying the association between ER variability and NA inertia. Based on previous findings, we conjecture that cognitive control ability may at least partially explain ER variability, NA inertia and their association.

Previous studies have shown that decreased affective inertia was predicted by cerebral blood flow in the lateral prefrontal cortex (LPFC), suggesting an inhibitory effect of the prefrontal cortex (PFC) on the persistence of emotional responses (Waugh et al., 2017). The underlying cognitive processes involved in ER flexibility may include stopping, switching or maintenance strategies synchronised with relevant situational changes, all of which are reliant on cognitive control (Pruessner et al., 2020; Sheppes, 2020). The specific negative correlation of between-strategy variability and NA inertia may be explained by an ability to discontinue ineffective strategies and switch to alternative strategies, preventing negative affect from persisting over time. The possible adaptive value of within-strategy variability remains less clear. Withinstrategy variability may reflect an ability to inhibit habitual maladaptive strategy use (i.e. rumination), which could result in positive changes of persistent low mood. However, we cannot rule out the possibility that high within-strategy variability could also indicate pathological instability of strategy use, rather than a more adaptive form of intentional flexibility (Blanke et al., 2019; Houben et al., 2015). More closely monitoring situational contexts and possible cognitive mechanisms during participants’ regulatory behaviours could help to distinguish between ER flexibility and instability, in future research.

Consistent with previous findings (Brose et al., 2015; Koval et al., 2012), our current results suggested that cognitive and affective flexibility play unique roles in depression, demonstrated by the independent predictive effects of between-strategy SDs and NA inertia on depression. However, the causal relations of these variables and their protective role

against depressive symptoms remain unclear. For instance, ER variability might serve to inhibit affective inertia (or vice versa), which might in turn protect against depression. Better understanding the temporal and causal relations among these variables might give us a better understanding of depression symptomatology and possible pathways for intervention. Future research into this topic is particularly promising since the core symptoms of depression share conceptual similarities with aspects of psychological flexibility (Kashdan & Rottenberg, 2010; Stange et al., 2017). Importantly, though, the current design does not give direct support for any particular causal relation among ER variability, NA inertia, and depression. Future work could better establish what causal pathways might be in play, by using intervention designs or longer-term longitudinal data, with each variable (including depression) tested at multiple time points. Additionally, future work could examine other possible protective factors and covariates of interest in the depression-ER strategy association, for instance, cognitive control (Gotlib & Joormann, 2010; Joormann & Gotlib, 2010). Aside from considering the drawbacks of our short-term ESM design, there are also a number of other specific limitations and future directions to consider in the current work.

Limitations and future directions Despite its various strengths, the current study also had a number of limitations worth noting. First, although we observe associations between ER variability and depressive symptoms in a sample of healthy college students, future work is needed to see whether these findings would also hold true for those with higher levels of depression, including those diagnosed with depressive disorders and other forms of psychopathology. Future research on ER variability in clinical populations is worth pursuing and would be in line with frameworks such as the National Institute of Mental Health’s Research Domain Criteria initiative (Insel et al., 2010) and the Hierarchical Taxonomy of Psychopathology (Kotov et al., 2017). Second, given that dynamic changes in ER strategy use and affective experience across different time scales may reflect distinctive cognitive mechanisms (Hollenstein et al., 2013; Kuppens et al., 2010), further work could extend the current findings by observing ER variability and affective inertia at multiple time scales (e.g. hours, days and

COGNITION AND EMOTION

months). Third, we assumed that ER variability provides a metric of one’s ability to dynamically and effectively deploy ER strategies. However, the current conceptualisation also includes random components of one’s ER strategy use, in addition to those actually reflecting flexible decision making based on contextual demands (Aldao et al., 2015; Cheng et al., 2014). Therefore, future work should assess how specific situations and demands in daily life interact with patterns of ER strategy use to influence mental health. Fourth, we did not estimate the autoregressive effect by using time-varying autoregressive (AR) models in the current study, which could have taken into account nonstationarity in mood change over time. Reasons for this omission include the following: (1) lack of a flexible smoothing function for estimating both intra- and inter-individual variation (Bringmann et al., 2017); (2) lack of an R package to test the cross-level moderating effects on autoregressive effects of affect at the moment level based on generalised additive modelling (GAM) framework; (3) less than 100 time beeps per person and possible abrupt changes in mood caused by negative events included in our current dataset would limit the precision of estimates made possible by TV-AR models (Bringmann et al., 2018; Haslbeck et al., 2020). We carried out analyses using the TV-AR model based on the GAM framework for empirical data from one individual, as a sample of this method (see. supplemental materials Part 4). The results suggested that time-varying parameters is especially important in AR models and that GAM could be a useful tool for detecting the changing dynamics of affect. Future studies should extend GAM by using a multilevel approach, which could allow for flexibly estimating both intra- and inter-individual effects (De Haan-Rietdijk et al., 2016; Schuurman et al., 2016).

Also worth noting is that we did not examine the cognitive mechanisms underlying ER flexibility.

Recent research has proposed frameworks that emphasise the role of conscious decision making in ER; in order to successfully engage in ER, individuals must identify whether ER is needed, choose an optimal regulatory strategy from the available options, stop ineffective strategies and switch to adaptive strategies (Pruessner et al., 2020; Sheppes, 2020). As part of this ER process, individuals must weigh the costs and benefits associated with each regulatory option, in different contexts (Sheppes et al., 2014). Research suggests individuals who prefer switching from ineffective to effective

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strategies report higher life satisfaction (Birk & Bonanno, 2016), and the cognitive approach to ER flexibility has been suggested as important for understanding emotion dysregulation in post-traumatic stress disorder (Levy-Gigi et al., 2016) and depression (Millgram et al., 2019), as well as the treatment of anxiety (Alkoby et al., 2019). Looking into these regulatory decision-making behaviours in daily life might provide a direct way to more thoroughly explore the adaptiveness of ER flexibility and its specific contributing mechanisms. Finally, future research could also work to integrate the present findings with other important mechanisms and traits that might be associated with and contribute to both impaired ER flexibility and depression. These include alexithymia – or the inability to label and describe one’s emotions (Aaron et al., 2018; Aaron et al., 2020) – reward sensitivity (Blain et al., 2020), and the general factor of psychopathology (DeYoung & Krueger, 2018; Lahey et al., 2017).

Conclusion In this study, we investigated whether two indicators of ER variability were correlated with depressive symptoms and affective inertia in daily life. Our findings suggested that ER variability appears to have adaptive value, indicated by its negative association with depressive symptoms and NA inertia in daily life. Moreover, between-strategy variability and NA inertia played independent roles in predicting depression. In sum, our research significantly contributes to ER theory, further demonstrating the importance of flexible ER strategy use. This work may pave the way for more detailed research on the cognitive pathways and clinical utility of ER variability.

Disclosure statement No potential conflict of interest was reported by the author(s).

Funding This research was supported by the National Natural Science Foundation of China [grant number31771231; 31800950], the General Program of National Natural Science Foundation of China [32071070], Natural Science Foundation of Chongqing [grant number cstc2019jcyj-msxmX0520], Social Science Planning Project of Chongqing [grant number 2018PY80] and Fundamental Research Funds for the Central Universities [grant number SWU119007], Chang Jiang Scholars Program, National

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X. WANG ET AL.

Outstanding Young People Plan, Chongqing Talent Program.

SDB was supported by the National Science Foundation Graduate Research Fellowship Program (1348264).

Authorship contribution statement Xiaoqin Wang: Conceptualization, Investigation, Data curation, Methodology, Formal analysis, Visualization, Writing original draft. Scott Blain: Extensive revision and editing of the written article, including substantive contributions to the framing of research questions and interpretation of findings. Jie meng: Conceptualization, Data curation, Methodology.

Yuan Liu: Conceptualization, Writing – review & editing. Jiang Qiu: Conceptualization, Investigation, Resources, Project administration.

Data availability statement Part of the data from this study are available online at the Open Science Framework (https://osf.io/yuh8b/). The other data (ESM self-report measures) used in this article are available upon request. Those data are being used in an unfinished longitudinal study, and so will not be made publicly available until that project is finished.

ORCID Yuan Liu

http://orcid.org/0000-0002-6859-2802

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