Pathological worry, the hallmark symptom of generalized anxiety disorder (GAD), has been reliably linked to deficits in some cognitive skills, such as learning, memory, and facets of executive functioning (EF) (Moran, Reference Moran2016; Pietrzak et al., Reference Pietrzak, Maruff, Woodward, Fredrickson, Fredrickson, Krystal and Darby2012). Pathological worry is defined as the chronic tendency to anticipate potential future threats. EF refers to a group of complex cognitive control processes necessary for organizing tasks, assessing risks, decision-making, managing emotions, and adapting to unanticipated events (Banich, Reference Banich2009). EF relates closely to the central executive of the working memory (WM) which retains and alters task-pertinent information (Baddeley, Reference Baddeley2001), and tends to decline with age (Deary et al., Reference Deary, Corley, Gow, Harris, Houlihan, Marioni and Starr2009). EF deficits have been linked to problems in career, academics, relationships, self-esteem, as well as physical and mental health (Snyder, Miyake, & Hankin, Reference Snyder, Miyake and Hankin2015). This is likely because EF is intertwined with accurately registering and recalling facts or events, processing speed, language, speech fluency, and logical reasoning (Best, Miller, & Jones, Reference Best, Miller and Jones2009; Brown, Brockmole, Gow, & Deary, Reference Brown, Brockmole, Gow and Deary2012). Thus, better understanding how worry leads to EF processes going awry is important.

In the past five decades, neurocognitive theories have increasingly acknowledged that executive dysfunction may be a product of excessive worry. The attentional control theory (Eysenck, Derakshan, Santos, & Calvo, Reference Eysenck, Derakshan, Santos and Calvo2007) posits that worry might negatively impinge on inhibition (refraining from autopilot responses) and set-shifting (switching flexibly between distinct mindsets) (Miyake & Friedman, Reference Miyake and Friedman2012). Likewise, affective neuroscience models have proposed that anxiety disorders are associated with depletion in EF capacities stemming from interference with the capacity to ignore task-unimportant matters (Beaudreau, MacKay-Brandt, & Reynolds, Reference Beaudreau, MacKay-Brandt and Reynolds2013). For instance, GAD might be related to ineffective recruitment of the pregenual anterior cingulate cortex or other EF-linked brain regions that dampen amygdala hyperarousal (Andreescu et al., Reference Andreescu, Sheu, Tudorascu, Gross, Walker, Banihashemi and Aizenstein2015). All in all, as pathological worry is self-perpetuating and emotionally dysregulating, these models propose that worry can reduce accuracy or increase response time (latency) on EF tests.

Although these theories suggest that worry symptom–EF relationships unfold across a short time-scale (Beckwé, Deroost, Koster, De Lissnyder, & De Raedt, Reference Beckwé, Deroost, Koster, De Lissnyder and De Raedt2014), such theories might be extended to encompass more long-term effects using scar models, such as the perseverative cognition hypothesis (Brosschot, Gerin, & Thayer, Reference Brosschot, Gerin and Thayer2006). The perseverative cognition hypothesis proposes that habitual worry is the mechanism by which stress leads to long-term deleterious effects on somatic, cardiovascular, and metabolic health via the buildup of allostatic load. Supporting these ideas, studies have evidenced moderate-to-large relationships between frequent worry and future acute myocardial infarction as well as chronic heart disease, even across multiple decades (Davidson, Mostofsky, & Whang, Reference Davidson, Mostofsky and Whang2010; Kawachi, Sparrow, Vokonas, & Weiss, Reference Kawachi, Sparrow, Vokonas and Weiss1994; Kubzansky et al., Reference Kubzansky, Kawachi, Spiro, Weiss, Vokonas and Sparrow1997). Likewise, increased resting systolic and diastolic blood pressure, cortisol, and body mass index in childhood predicted poorer WM in young adulthood (Evans & Fuller-Rowell, Reference Evans and Fuller-Rowell2013). In addition, excessive morning cortisol secretion predicted decline on tests of set-shifting and WM 2 to 4 years later in community adults (Beluche, Carrière, Ritchie, & Ancelin, Reference Beluche, Carrière, Ritchie and Ancelin2010). Collectively, allostatic load (i.e. accumulating wear and tear on the body) from chronic worry could possibly negatively impact EF abilities over decades.

Regarding short-term effects, ample data lend credence to these theories. After adjusting for anxiety symptoms, two recent meta-analytic studies pooling data across 24 samples showed that worry had significant small-to-moderate between-person relationships with EF (Zetsche, Bürkner, & Schulze, Reference Zetsche, Bürkner and Schulze2018) and WM (Moran, Reference Moran2016). However, the study of worry–EF associations has largely been a cross-sectional enterprise, leaving the question of causality to debate. To date, five experiments offer support for the causal role of worry on EF using non-emotional (or neutral) tasks (Hallion, Ruscio, & Jha, Reference Hallion, Ruscio and Jha2014; Hayes, Hirsch, & Mathews, Reference Hayes, Hirsch and Mathews2008; Leigh & Hirsch, Reference Leigh and Hirsch2011; Stefanopoulou, Hirsch, Hayes, Adlam, & Coker, Reference Stefanopoulou, Hirsch, Hayes, Adlam and Coker2014; Williams, Mathews, & Hirsch, Reference Williams, Mathews and Hirsch2014). For instance, compared to thinking positive or neutral thoughts, worrying impaired WM and inhibition on accuracy and timed tests in persons with GAD (Stefanopoulou et al., Reference Stefanopoulou, Hirsch, Hayes, Adlam and Coker2014). In general, these experiments support the notion that EF deficiencies might be a downstream consequence of excessive worry.

Despite their importance, experiments that induced worry to test its momentary impact on EF might not extend to naturalistic contexts across various stages of adult life. Unlike short-term effects, buildup of allostatic load leading to dysregulation of the hypothalamus–pituitary adrenal axis likely explains the long-term effects of chronic worry on EF across decades. For instance, chronic stress and worry predicted reduced EF-linked prefrontal cortex activity and heightened amygdala reactivity 13 years later (Kim et al., Reference Kim, Evans, Angstadt, Ho, Sripada, Swain and Phan2013). Thus, a way to advance the field on this topic is to examine how excessive worry results in EF problems in adulthood across multiple occasions, beyond a single laboratory visit. Although prospective observational data involve no explicit manipulation of the predictor to permit strong causal inferences (Shadish, Cook, & Campbell, Reference Shadish, Cook and Campbell2002), they move us closer toward causal models.

At present, at least five such studies in adults have been conducted. First, among Swedish community adult twins, neuroticism was negatively correlated with visuospatial WM, processing speed, and recognition memory at baseline (Wetherell, Reynolds, Gatz, & Pedersen, Reference Wetherell, Reynolds, Gatz and Pedersen2002); however, in this study, neuroticism did not predict 9-year cognitive decline. By comparison, even slight worry symptoms in older adults predicted decreased visual attention, recall, and EF 2 to 3 years later (Pietrzak et al., Reference Pietrzak, Maruff, Woodward, Fredrickson, Fredrickson, Krystal and Darby2012, Reference Pietrzak, Scott, Neumeister, Lim, Ames, Ellis and Maruff2014). Similarly, in another study on older adults, increased anxious symptoms predicted verbal memory deterioration across 12 years (Gulpers, Oude Voshaar, van Boxtel, Verhey, & Köhler, Reference Gulpers, Oude Voshaar, van Boxtel, Verhey and Köhler2019). Also, higher GAD symptoms predicted EF decline 3.4 years later in community-dwelling men (Kassem et al., Reference Kassem, Ganguli, Yaffe, Hanlon, Lopez, Wilson and Cauley2017). Based on this emerging body of longitudinal evidence, worsening of GAD symptoms might predict weakened EF across longer time periods.

Yet another limitation is that these studies' use of two-wave regression analyses informs between-person (nomothetic) relationships, but not within-person (idiographic) patterns of growth. Thus, little is known about idiographic, dynamic, prospective effects of change in GAD severity on future change in EF constructs. One cutting-edge method that can achieve this goal is bivariate dual latent change score (LCS) modeling (McArdle, Reference McArdle2009). As anxiety–EF models propose both within- and between-person changes across time, it is essential to apply suitable analyses. Further, the direction and magnitude of between- and within-person relationships among variables might not correspond to each other (Fisher, Medaglia, & Jeronimus, Reference Fisher, Medaglia and Jeronimus2018). LCS techniques allow inferences regarding idiographic change processes by establishing temporal precedence as well as adjusting for between-person variation, regression to the mean, and measurement error (Wright et al., Reference Wright, Calabrese, Rudick, Yam, Zelazny, Williams and Simms2015). Further, relative to multilevel modeling, this latent variable method accounts for lagged dependent variables (Falkenström, Finkel, Sandell, Rubel, & Holmqvist, Reference Falkenström, Finkel, Sandell, Rubel and Holmqvist2017).

However, thus far, at least two prospective anxiety–cognition studies have applied this potent method. Supporting affective neuroscience and scar theories, rise in anxiety severity predicted steeper reductions in cognitive functioning within older adults across 2 years (Tetzner & Schuth, Reference Tetzner and Schuth2016); nonetheless, as the latter study had two time points, it cannot speak to whether growth in symptoms predicted subsequent worsening of EF. Recently, in a relatively healthy, cognitively-intact, twin sample, anxiety proneness predicted more reductions in attention and processing speed across 6 years (Petkus, Reynolds, Wetherell, Kremen, & Gatz, Reference Petkus, Reynolds, Wetherell, Kremen and Gatz2017); however, this study did not include worry symptom or EF measures. Taken together, heightened GAD might forecast larger future decreased EF over long durations.

Accordingly, by using bivariate dual LCS models, the current study aimed to clarify how within-person increased GAD severity might predict future shifts in unique EF components across 18 years in a community-adult sample. This endeavor offers novel contributions. It adds to one-time assessment and experimental designs that have dominated the field and is a step toward causal inferences. Also, most EF–anxiety research has centered on using emotional (or ‘hot’) EF paradigms (Hallion, Tolin, Assaf, Goethe, & Diefenbach, Reference Hallion, Tolin, Assaf, Goethe and Diefenbach2017). This is surprising as the aforementioned neurocognitive models hypothesize specific inverse relationships among worry or anxiety and ‘cold’ EF. Importantly, these theories center on shifting, inhibition, and mixing costs (MCs) i.e. EF domains that are understudied in GAD. Moreover, theories and evidence that highlight the inverse connection between cold EF and worry are growing (e.g. Moran, Bernat, Aviyente, Schroder, & Moser, Reference Moran, Bernat, Aviyente, Schroder and Moser2015). This study thus fills a gap by testing the potentially instrumental effect of increased abnormal worry on change in non-emotional (or ‘cold’) EF aspects across time. Further, in developmental psychopathology, the 18-year duration offers distinct viewpoints for this research aim as between-person negative GAD–EF relationships have been found over similar periods (e.g. Zhang et al., Reference Zhang, Norton, Carriere, Ritchie, Chaudieu and Ancelin2015). Also, assessing for change in GAD symptoms can inform clinicians about persons at risk of EF decline. Based on the theories and evidence reviewed, we hypothesized that elevated GAD severity would be related to decreased inhibition, set-shifting, and MCs.

Method

Participants

This was a secondary analysis using the Midlife Development in the United States (MIDUS) dataset with three waves of data collection: 1995 to 1996 [time 1 (T1)]; 2004 to 2005 [time 2 (T2)]; and 2012 to 2013 [time 3 (T3)] (Ryff & Lachman, Reference Ryff and Lachman2018; Ryff et al., Reference Ryff, Almeida, Ayanian, Carr, Cleary, Coe and Williams2017). This study was exempted from IRB approval as it used a publicly available dataset that can be obtained from the following online data repository: https://www.icpsr.umich.edu/icpsrweb/ICPSR/series/203. Participants (n = 2581) averaged 46.00 years (s.d. = 11.40, range = 24–74 years) at baseline, 54.71% were female, and 41.70% had college education. The sample comprised mostly White participants (92.02%), and the remaining 6.98% were African American, Asian, Native American, or Pacific Islander.

Measures

EF tests were administered as part of a larger battery of neuropsychological assessments (Lachman, Agrigoroaei, Tun, & Weaver, Reference Lachman, Agrigoroaei, Tun and Weaver2014). The BTACT was administered at T2 and T3 (but not at T1), whereas GAD was assessed at all waves. The EF subtests in the BTACT evidenced adequate convergent validity (e.g. rs = 0.41–0.52 with other EF tests) and discriminant validity (e.g. rs = 0.16–0.17 with immediate and delayed recall tests) (Lachman et al., Reference Lachman, Agrigoroaei, Tun and Weaver2014). Strong 4-week retest reliability (r = 0.82–0.83) has also been documented. Moreover, the telephone-administered EF subtests used in this study showed strong convergent validity (r = 0.76) with those administered in person.

Inhibition

The Stop-and-Go Switch Task (SGST; Tun & Lachman, Reference Tun and Lachman2006) – Reverse condition – was used to assess inhibition using accuracy (performance scores) and latency (response time). SGST Single-Task block trials were composed of Normal and Reverse conditions. Respondents replied ‘GO’ or ‘STOP’ in response to ‘GREEN’ or ‘RED’ signs respectively in the normal condition. The opposite rule applied in the reverse condition i.e. respondents replied ‘STOP’ for ‘GREEN’ and ‘GO’ for ‘RED’. Latencies were registered in milliseconds (ms) i.e. duration between the sign and correct answer given. Higher latency marked slower response times. Initially, 20 normal block trials were administered, followed by 20 reverse SGST Single-Task block trials.

Set-shifting

Ability to transit smoothly between unique mental sets was indexed by accuracy and latency (ms) on the SGST Mixed-Task Switch block trials (Tun & Lachman, Reference Tun and Lachman2006). Participants had to respond correctly to shifts in normal and reverse conditions at random times of two to six trials based on the signs ‘NORMAL’ or ‘REVERSE’ across 32 trials. More information on data reduction for this measure can be found in Lachman et al. (Reference Lachman, Agrigoroaei, Tun and Weaver2014).

Mixing costs

MCs, a measure related to common EF (WM ability to facilitate staying on task), was computed. MCs refer to the difference between latencies (ms) during recurring trials of mixed blocks compared to recurring trials of single blocks (Smith, Banich, & Friedman, Reference Smith, Banich and Friedman2019). Consistent with theory (Rubin & Meiran, Reference Rubin and Meiran2005), the MC index has consistently evidenced a large association with the common EF latent factor (r = 0.59) that encapsulates set-shifting, inhibition, and WM updating capacities (Smith et al., Reference Smith, Banich and Friedman2019). Further, using MCs to approximate common EF prevents overlap with set-shifting, a problem inherent with other indices, such as global switch costs (Vandierendonck, Liefooghe, & Verbruggen, Reference Vandierendonck, Liefooghe and Verbruggen2010).

Generalized anxiety disorder severity

GAD severity was measured with the Composite International Diagnostic Interview-Short Form (CIDI-SF; Kessler, Andrews, Mroczek, Ustun, & Wittchen, Reference Kessler, Andrews, Mroczek, Ustun and Wittchen1998) derived from the Diagnostic and Statistical Manual for Mental Disorders-Third Version-Revised (DSM-III-R; Wittchen, Zhao, Kessler, & Eaton, Reference Wittchen, Zhao, Kessler and Eaton1994) criteria. Eight item ratings that were most concordant with current DSM-5 criteria were averaged to form a GAD severity score, ranging from 1 (never experienced symptoms) to 4 (experienced symptoms on most days). These included the frequency at which participants experienced excessive and unrealistic worry about multiple things and six associated symptoms (‘were restless because of your worry’, ‘were irritable because of your worry’, ‘had trouble falling or staying asleep’, ‘had trouble keeping your mind on what you were doing’, ‘were low on energy’, ‘had sore or aching muscles because of tension’) in the past 12 months on a four-point Likert-scale (1 = never, 2 = less than half the days, 3 = about half the days, 4 = on most days). In this study, internal consistencies (Cronbach's α) for the GAD severity scale were 0.71, 0.70, and 0.71 at T1, T2, and T3, respectively. The CIDI-SF was developed to replicate GAD diagnoses using the full CIDI. It has a high degree of sensitivity (96.6%), specificity (99.8%), and global agreement with the GAD diagnosis (99.6%) (Kessler et al., Reference Kessler, Andrews, Mroczek, Ustun and Wittchen1998). Also, 100% of persons who met DSM-III-R-defined GAD criteria also met DSM-IV-defined GAD criteria in a previous study (Abel & Borkovec, Reference Abel and Borkovec1995). Further, the CIDI-SF showed good retest reliability (agreement = 0.89; κ = 0.69) (Kessler et al., Reference Kessler, Andrews, Mroczek, Ustun and Wittchen1998). Based on the CIDI-SF, the proportion of participants who had GAD at each time point was 2.33, 2.03, and 1.96%, respectively. Those with GAD had higher average symptom severity than those without GAD at T1 (3.39 v. 2.34, d = 0.84), T2 (3.28 v. 2.23, d = 0.88), and T3 (3.25 v. 2.21, d = 0.88), indicating that those diagnosed with GAD using the CIDI reportedly worried more days than not in the past year.

Procedure

Interviewers administered the SGST over the phone at a standardized location during times chosen by participants for convenience and to reduce surrounding distraction. A computerized system managed when sound stimuli (e.g. instructions and block trials) were played. Each cognitive testing session was recorded as a digital auditory file and analyzed afterward. On average, the cognitive tests took about 20 min to administer. For the highest sound quality, participants were encouraged to use landlines. Further, during testing, they were asked to not write and to close their eyes to minimize cognitive load (Vredeveldt, Hitch, & Baddeley, Reference Vredeveldt, Hitch and Baddeley2011). Before the test, research personnel briefly examined if participants could hear the interviewer clearly by instructing respondents to repeat a series of numbers back to them. The volume was then altered accordingly as required for 4% of the cases with slight hearing difficulties (Lachman et al., Reference Lachman, Agrigoroaei, Tun and Weaver2014). In the normal condition, participants were instructed, ‘Every time I say RED you will say STOP, and every time I say GREEN you will say GO’. In the reverse condition, they were directed, ‘Every time I say RED you will say GO, and every time I say GREEN you will say STOP’. Before the actual trials, practice runs were given for each condition. The entire cognitive testing script, preprocessing, and stimulus presentation details of the BTACT switch task can be found at the test developers' website (http://www.brandeis.edu/projects/lifespan).

Data analyses

We performed structural equation modeling with the lavaan package (Rosseel, Reference Rosseel2012) using R software. Prior to estimating models, we screened the data for non-normality by applying the rule of skewness ⩾±3 and kurtosis ⩾±7 as markers of univariate outliers, and Mahalanobis distance values to identify multivariate outliers (West, Finch, & Curran, Reference West, Finch, Curran and Hoyle1995). As several non-normal distributions were identified in the data, we opted to use maximum likelihood with robust (MLR) estimators for all of our analyses (Yuan & Bentler, Reference Yuan and Bentler2000). Aberrations from univariate and multivariate normality distributions are not uncommon in health and cognitive data (Cain, Zhang, & Yuan, Reference Cain, Zhang and Yuan2017). Also, as non-normal data could result in biased fit tests and standard errors, MLR estimators adjust for such non-normality and offer more precise parameter and standard error estimates (Brown, Reference Brown2015). Further, prior to analysis, six cases with multivariate outliers were removed from the dataset.

To judge goodness-of-fit of all models, we used the χ² index. Models with χ² values that were not statistically significant indicated that the model had good fit. As five comparisons were made, we applied a Bonferroni correction and set the α value to 0.05/5 = 0.01 to guard against type 1 error (Simes, Reference Simes1986). Further, we assessed if models surpassed heuristic cut-offs of practical fit indices: confirmatory fit index (CFI; Bentler, Reference Bentler1990; CFI ⩾ 0.95); root mean square error of approximation (RMSEA; Steiger, Reference Steiger1990; RMSEA ⩽ 0.050). To handle missing data, we used full information maximum likelihood suitable for our data presumed to be missing at random (Graham, Reference Graham2009). Unstandardized regression estimates were shown.

To test if the measures were assessed along the same scale at all time points, we examined the equivalence of factor loadings (λs), intercepts (τs), and residual variances (ɛs) (Millsap & Yun-Tein, Reference Millsap and Yun-Tein2004). Tests of configural invariance were based on global model fit as well as statistical significance of the λs. To assess metric invariance, we constrained λs to be equal across all waves and compared the fit of the constrained model to the configural model. Next, we tested scalar invariance by comparing a model with both λs and τs constrained to equality to the model with only λs constrained across waves. Last, we evaluated strict invariance by juxtaposing a fully constrained model (i.e. equality constrained λs, τs, and ɛs) to a model with scalar invariance. We used change in fit indices to evaluate measurement invariance at each step (Meade, Johnson, & Braddy, Reference Meade, Johnson and Braddy2008). Change in CFI and RMSEA values of >−0.010 and +0.025, respectively, from the unconstrained to constrained model suggest that the unconstrained model is preferable.

Afterward, to test how T1 − T2 change in GAD severity predicted T2 − T3 change in an EF construct within persons, we used bivariate dual LCS models (McArdle, Reference McArdle2009). Figure 1 displays this multivariate model. By examining latent change of a construct across two consecutive waves, its course can be modeled in terms of its baseline level and differences in scores between each period. The following equation computes for within-person T2 − T3 change in EF:

(1)$$\Delta {\rm E}{\rm F}_{[ T2\ndash T3] } = \alpha _X \times {\rm E}{\rm F}_{\rm S} + \beta _X \times {\rm E}{\rm F}_{[ T2] } + \delta _Y \times \Delta {\rm GA}{\rm D}_{[ T1\ndash T2] }$$

where ΔEF indicates the latent difference in an EF construct between T2 and T3, α_X indicates the between-person constant change parameter linked to the latent slope (EF_S), and β_X indicates the within-person self-feedback processes called the proportional effect. Importantly, the coupling parameter (δ_Y) indicates the within-person effect of 9-year change in GAD severity on future 9-year change in EF domains. The term ‘EF’ in Equation (1) is a placeholder for five separate models that tested unique EF components: inhibition (accuracy and latency); set-shifting (accuracy and latency); and MCs. All models controlled for T1 major depressive disorder (MDD) severity.Footnote ^†Footnote ¹ To achieve parsimonious, theoretically synced, and interpretable models, we fixed residual variances that were negative or not statistically significant to zero. Last, in all our final models, we estimated an additional parameter (covariation between EF variables at T2 and T3) suggested by the modification indices. Inclusion of this parameter was informed by theory and the goal of achieving well-fitting models (Hertzog, Dixon, Hultsch, & MacDonald, Reference Hertzog, Dixon, Hultsch and MacDonald2003). For all data involving response times, the average latencies or MCs of the single- and mixed-task block trials were used (Lachman et al., Reference Lachman, Agrigoroaei, Tun and Weaver2014). Cohen's d effect size was calculated using the formula $d = 2r/\sqrt {1-r^2}$, where $r = \sqrt {t^2/( t^2 + {\rm df}) }$ (Dunlap, Cortina, Vaslow, & Burke, Reference Dunlap, Cortina, Vaslow and Burke1996; Dunst, Hamby, & Trivette, Reference Dunst, Hamby and Trivette2004). Thus, d values of 0.2, 0.5, and 0.8, represent small, moderate, and large effect sizes, respectively (Cohen, Reference Cohen1988).

Fig. 1.

Bivariate dual LCS models of GAD severity and EF. EF, executive function; GAD, generalized anxiety disorder; LCS, latent change score; T1, time 1; T2, time 2; T3, time 3. Note: Of most interest to this study is the coupling parameter (δ_Y) that indicates the effect of T1 − T2 change in GAD severity predicting T2 − T3 change in EF while adjusting for other parameters in the model. In each bivariate LCS model, EF serves as a placeholder for one of the three EF constructs examined in this study (inhibition, set-shifting, and MCs).

Results

Longitudinal measurement invariance

Across all occasions, we observed invariance of factor structure (configural), λs (metric), τs (scalar), and ɛs (strict). Table 1 shows the fit indices for all the measurement invariance models for the one-factor GAD severity model across three time points, as well as six-factor model of GAD severity and the five EF constructs across T2 and T3. The satisfactory absolute and relative fit indices suggested that strict invariance was supported. Thus, across each wave, latent GAD severity and EF scores were measured along the same scale, allowing for meaningful comparisons of latent scores across different waves.

Table 1.

Longitudinal measurement invariance of GAD severity across waves (standardized estimates)

GAD, generalized anxiety disorder; EF, executive functioning; CFI, confirmatory fit index; RMSEA, root mean square error of approximation; λ, factor loading; τ, factor intercept; ɛ, item residual variance; Δ, change in fit statistic.

Inhibition and GAD severity

The LCS models showed good fit for inhibition indexed by accuracy [χ²(df = 13) = 12.398, p = 0.495, CFI = 1.000, RMSEA = 0.000] and latency [χ²(df = 12) = 39.081, p < 0.001, CFI = 0.968, RMSEA = 0.030]. Steeper within-person increase in GAD severity predicted future decline in inhibition (accuracy: b = −0.006, p < 0.001, d = −0.61; latency: b = 0.003, p < 0.001, d = 0.59). Table 2 shows more details.

Table 2.

Two bivariate dual LCS models for inhibition (indexed by accuracy and latency) and GAD symptoms

b, unstandardized parameter estimates; s.e., standard error; INHA, inhibition accuracy; INHL, inhibition latency; GAD, generalized anxiety disorder; MDD, major depressive disorder; CFI, confirmatory fir index; RMSEA, root mean square error of approximation; AIC, Akaike information criteria; SABIC, sample-adjusted Bayesian information criteria.

^a Parameter estimate was fixed to 0 to facilitate model convergence.

***p < 0.001.

Set-shifting and GAD severity

The models showed good fit for set-shifting marked by both accuracy [χ²(df = 13) = 24.982, p = 0.023, CFI = 0.970, RMSEA = 0.022] and latency [χ²(df = 12) = 39.819, p < 0.001, CFI = 0.965, RMSEA = 0.030]. Greater increase in GAD severity significantly predicted future decline in set-shifting (accuracy: b = −0.005, p < 0.001, d = −0.51; latency: b = 0.005, p < 0.001, d = 0.96). Table 3 elaborates on these results.

Table 3.

Two bivariate dual LCS models for set-shifting (indexed by accuracy and latency) and GAD symptoms

b, unstandardized parameter estimates; s.e., standard error; SETA, set-shifting accuracy; SETL, set-shifting latency; GAD, generalized anxiety disorder; MDD, major depressive disorder; CFI, confirmatory fit index; RMSEA, root mean square error of approximation; AIC, Akaike information criteria; SABIC, sample-adjusted Bayesian information criteria.

^a Parameter estimate was fixed to 0 to facilitate model convergence.

***p < 0.001.

Mixing costs and GAD severity

The models demonstrated good fit for MCs [χ²(df = 13) = 6.945, p = 0.905, CFI = 1.000, RMSEA = 0.000]. More growth in GAD severity substantially predicted future increase in MCs (b = 0.005, p < 0.001, d = 0.54). Table 4 expands on this set of findings. Online Supplementary Table S1 presents between-person descriptive statistics of study variables.Footnote ²

Table 4.

Bivariate dual LCS models for MCs (a measure related to common EF) and GAD symptoms

b, unstandardized parameter estimates; s.e., standard error; EF, executive functioning; GAD, generalized anxiety disorder; MDD, major depressive disorder; CFI, confirmatory fit index; RMSEA, root mean square error of approximation; AIC, Akaike information criteria; SABIC, sample-adjusted Bayesian information criteria.

^a Parameter estimate was fixed to 0 to facilitate model convergence.

***p < 0.001.