Differences in Coping Strategies Between Young and Older Adults: The Role of Executive Functions

Abstract

Executive functions (EFs) have been identified as processes in the ability to select and apply adaptive strategies for coping with stress. This study compares executive functioning, short-term memory, and coping in a sample of young and older adults with no prior diagnosis of depression and with normal cognitive function (N = 216). The study collected measures of depression, EFs, short-term memory, and coping. Young participants scored higher than older adults on EFs and short-term memory. Moreover, in young adults, there was a prevalence of avoidance coping strategies. Scores on depressive symptomatology were found to be related to avoidant coping strategies. Older adults with higher score on inhibition used less avoidant coping. Thus, it seems that executive deficits might contribute to depression, as they affect processes for coping with stress. This finding may have implications for the role of EFs and coping in psychological well-being and successful adaptation of individuals to stressful situations.

Keywords

executive functions coping depressive symptoms young adults older adults

Coping has been defined as constantly changing cognitive and behavioral efforts to manage specific external or internal demands that are appraised as taxing or exceeding the resources of a person (Folkman & Moskowitz, 2004; Lazarus & Folkman, 1984). Following this definition, coping has been classified into three essential domains: cognitive coping (process focused on finding a meaning from the stressful event and evaluating and rendering it less aversive), behavioral coping (behavior focused on tackling and managing the consequences of the stressful event), and emotional coping (process focused on maintaining affective balance or diminishing the emotional impact of the stressful situation).

In light of these domains, three perspectives on coping strategies have been developed: (a) problem-focused coping versus emotion-focused coping (Compas et al., 2015; Folkman & Lazarus, 1980), that is, strategies aimed at managing or modifying the problem versus strategies focused on regulating emotional responses to the problem; (b) approach versus avoidance (Roth & Cohen, 1986), in other words, an emotional and cognitive response that is oriented either toward or away from threat; and (c) behavioral versus cognitive coping (Latack & Havlovic, 1992), that is, action strategies versus mental strategies and self-talk. Furthermore, for the analysis of the factorial structure of coping categories, the use of hierarchical systems of action types (e.g., proximity seeking, accommodation) has been recommended (Skinner, Edge, Altman, & Sherwood, 2003).

Coping and Aging

Age-related differences in the adoption of coping strategies have frequently been reported, as there are inherent changes in the ways people cope as they age. It has been suggested that during the aging process, individuals shift from problem-focused coping toward more emotion-focused coping (Cavanaugh & Blanchard-Fields, 2011; Chen, Peng, Xu, & O’Brain, 2018; Mélendez, Benlluire, & Mayordomo, 2017; Mélendez, Mayordomo, Sancho, & Tomás, 2012). Similarly, a contextual interpretation has been proposed, which says that age differences in coping result from changes in what people must cope with as they age rather than normative, stage-related, developmental changes (Chen et al., 2018; Folkman, Lazarus, Pimley, & Novacek, 1987). For example, aging is a period of increased exposure to health problems, disability, loneliness, or grief (e.g., Blandin & Pepin, 2017; Cheng, Lau, & Chan, 2014; Mick, Parfyonov, Wittich, Phillips & Pichora-Fuller, 2018), while the young adult age is a stage mostly oriented to the consolidation of one’s identity and the making of personal and professional decisions (Kroger, Martinussen, & Marcia, 2010; Wolf & Zimprich, 2015).

A number of researchers have shown that age strengthens the ability to distance oneself from stressful situations and reassess them positively (Folkman et al., 1987; Schryer & Ross, 2012). Consequently, older adults report enhanced emotional well-being compared with younger adults (Carstensen et al., 2011; Ricarte, Navarro, Latorre, Ros, & Watkins, 2016; Samanez-Larkin, Worthy, Mata, McClure, & Knutson, 2014). Temporal perspective drives age differences in social preferences. This effect has been theoretically conceptualized in the Socioemotional Selectivity Theory, which proposes that as the temporal horizons of mortality become more salient in older age, motivation shifts away from the pursuit of knowledge and information and toward an emphasis on satisfying social goals and maximizing emotional well-being (Carstensen, 2006; Carstensen, Isaacowitz, & Charles, 1999; Mather & Knight, 2006). In line with this approach, older adults are better at solving problems with a social component (Blanchard-Fields, Jahnke, & Camp, 1995) and employ cognitive strategies to improve emotion regulation because they are more focused on emotional goals (Mather & Carstensen, 2005).

Executive Functions and Aging

Executive functions (EFs) have been identified as important mechanisms involved in successful adaptation to stress, given that coping is a part of the overall set of EFs (Compas, 2006). The EFs are a family of mental processes associated with the function of the prefrontal cortex (Müller & Kerns, 2015). They specifically describe high-level cognitive domains responsible for activation, maintenance, and selection of different courses of action required to perform complex behaviors to achieve different goals (Miyake & Friedman, 2012). EFs are regarded as essential variables for mental and physical health; academic success; and cognitive, social, and psychological development (Diamond, 2013). Their importance in the everyday lives and well-being of older adults has also recently been highlighted (Dawson, Rehman, McAuley, & Schryer, 2015; Karr, Areshenkoff, Rast, & Garcia-Barrera, 2015).

With regard to the attentional processes, three basic EFs have been distinguished (Garon, Bryson, & Smith, 2008): (a) inhibition (suppression of prepotent or affectively driven behaviors), (b) working memory (WM; to hold information active in mind and to mentally work with that information as a platform for guiding our behavior), and (c) shifting (switching flexibly between tasks or mental sets). These underlie more complex processes such as planning, problem-solving (PS), and reasoning (Collins & Koechlin, 2012).

The emergence and development of EFs has been situated around preschool age (Garon et al., 2008; Nieto, Ros, Medina, Ricarte, & Latorre, 2016), continuing to evolve through middle childhood and adolescence (Best, Miller, & Jones, 2009). In late adolescence, the EFs begin to stabilize (Friedman et al., 2015), and during normal aging, these variables have been considered vulnerable to age-related changes in brain structures and functioning (Di, Rypma, & Biswal, 2014). Cognitive evolution and the changes associated with it have been demonstrated in studies showing that older adults’ performance on inhibition, WM, short-term memory, and shifting tasks changes is affected accordingly (Albinet, Boucard, Bouquet, & Audiffren, 2012; see Diamond, 2013, for a review; McFall et al., 2014; Roberts & Allen, 2016; Ros, Latorre, & Serrano, 2010; Verhaegen, Collette, & Majerus, 2014). Executive deficits have specifically been related to aspects of information processing (Mewborn, Renzi, Hammond, & Miller, 2015; Zimprich & Kurtz, 2013), auditory distractions (Alain & Woods, 1999), visual distractions (Darowski, Helder, Zacks, Hasher, & Hambrick, 2008), or the antisaccade task (Peltsch, Hemraj, Garcia, & Munoz, 2011). Nonetheless, EFs are relatively independent functions (Miyake et al., 2000) and hence are not equally affected; genetics or lifestyle may influence age-related EF decline (Thibeau, McFall, Wiebe, Anstey, & Dixon, 2016). The meta-analyses reviewed by Verhaeghen (2011) suggest that findings in this field are not conclusive enough to support clear, specific age-related limitations in selective attention tasks (Stroop inhibition of return, negative priming, and flanker) or tasks tapping local task-shifting costs (reading with distractors is an exception). However, this relation is significant for divided attention (global task-shifting costs and dual tasking).

Coping and EFs

Stress is generally thought to impair executive functioning (Diamond, 2013; Shansky & Lipps, 2013). The recent meta-analysis by Shields, Sazma, and Yonelinas (2016) concludes that stress deteriorates WM, cognitive flexibility, and cognitive inhibition, whereas stress enhances response inhibition, that is, stress contributes to a cognitive state of reactive and automatic processing while also enhancing executive motor control, which should facilitate engagement with or escape from the current stressor. Furthermore, neuropsychological abilities may affect learning and adoption and use of different coping strategies (Krpan, Levine, Stuss, & Dawson, 2007). More specifically, the ability to hold and manipulate different information (i.e., WM) requires the application of coping strategies such as those based on PS or cognitive restructuring (e.g., Andreotti et al., 2013; Connor-Smith, Compas, Wadsworth, Thomsen, & Saltzman, 2000; Fletcher, Marks, & Hine, 2011). Moreover, individual differences in executive functioning (including response inhibition, memory updating, and task switching) predict differences in success at emotion regulation (Schmeichel & Tang, 2015). According to Hofmann, Schmeichel, and Baddeley (2012), inhibition, shifting, and WM can be considered underlying variables involved in self-regulation strategies. For example, to overcome maladaptive, impulsive tendencies, it has been suggested that WM training may help at-risk drinkers to reduce their impulsive alcohol intake (Houben, Wiers, & Jansen, 2011), and that training inhibitory control can help to reduce problematic eating behavior (Houben & Jansen, 2011). Finally, studies on emotion regulation have found an association between coping, cognitive function, and depression (e.g., Gotlib & Joorman, 2010).

The Current Study

EFs have been identified as mechanisms involved in successful adaptation to stress (Compas, 2006), and the interrelation between coping strategies and EFs across the life span is accepted (e.g., Andreotti et al., 2013; Campbell et al., 2009; Connor-Smith et al., 2000; Evans, Kouros, Samanez-Larkin, & Garber, 2015; Fletcher et al., 2011; Gotlib & Joorman, 2010; Schmeichel & Tang, 2015). This relation has been analyzed in different studies, for example, in samples of children and adolescents with depressive symptoms (Evans et al., 2015; Morris, Evans, Rao, & Garber, 2015), in adult patients with neuropsychiatric symptoms after acquired brain injury (Gregório et al., 2015), in persons with multiple sclerosis (Grech et al., 2016), or in returning war veterans (Martindale et al., 2016). However, to the best of our knowledge, this study is the first to compare executive functioning (inhibition and WM tasks), short-term memory, and adoption of coping strategies (avoidance vs. approach) in a sample of young and older adults without prior diagnosis of depression and with normal cognitive function. Hence, first, considering that during normal aging, EFs are regarded as vulnerable to age-related changes (e.g., Di et al., 2014), we expect the group of older adults to exhibit impaired decreased executive performance and short-term memory, in comparison with the group of young adults. Second, considering that executive deficits may contribute to depression by influencing the cognitive processes required for coping with stress (e.g., Campbell et al., 2009; Gotlib & Joorman, 2010), we expect the effect of depressive symptoms to explain the use of coping strategies in both groups. Finally, in line with the ideas of the Socioemotional Selectivity Theory proposed by Carstensen and colleagues, we expect to find age-related differences in the adoption of coping strategies in our study sample.

Method

Participants

The study sample comprised 216 participants (64.8% females; ages 19–81 years; M = 40.94, standard deviation [SD] = 22.96) from different public facilities in Castilla–La Mancha: universities, universities for older adults, care centers for older adults, reading clubs, and women’s centers. Participants were grouped by age: young adults (n = 119; 66.4% females; ages 19–22 years; M = 20.45, SD = 0.62; education level: completed secondary school, 0.8% and attended university, 99.2%) and older adults (n = 97; 62.9% females; ages 60–81 years; M = 66.09, SD = 4.55; education level: completed primary school, 14.4%, completed secondary school, 43.3% and attended university, 42.3%). Inclusion criteria for both groups were as follows: (a) minimum of high school level education and (b) no active signs of depressive disorder or cognitive deficit at the time of assessment. The following tests were used to measure the second inclusion criterion: the Spanish adaptation by Latorre and Montañés (1997) of the Center for Epidemiologic Studies-Depression Scale (CES-D; Radloff, 1977) and the Spanish version of the Mini-Mental Status Examination (Mini-Examen Cognoscitivo; Lobo, Saz, Marcos, & Zaracemp, 2002). Consequently, three participants were excluded from the group of young adults and six from the group of older adults, as they met the CES-D criteria for depression. No participants exhibited cognitive deterioration. There were no differences with regard to gender variable, χ²(1) = 0.29, p = .592, but we found significant statistical differences regarding the educational variable, χ²(2) = 89.07, p < .001.

The response rate for this study was 94.98%.

Materials

Depression score

CES-D translated into Spanish (Latorre & Montañés, 1997) is a short self-report scale designed to measure depressive symptoms in the general population. It is widely used in research with adults of all ages, with high reliability, internal consistency, and good discriminant and construct validity (Radloff, 1977). The Cronbach’s α in this study was 0.88. The total score ranges from 0 to 60 with the higher scores indicating more symptoms of depression.

Short-term memory and WM

Digit span tasks

Digit span subtest from the Wechsler Memory Scale-Revised (Wechsler, 1987) includes digit span forward (DSF) and digit span backward (DSB), which are frequently used tasks to measure short-term memory and WM (e.g., Baddeley, 2012; Engle, Tuholski, Laughlin, & Conway, 1999; Snyder, 2013). DSF assess the processes of short-term maintenance of information, and DSB assess the handling of information in the WM. In the DSF, the examiner reads increasingly longer number sequences and after each sequence asks the subject to recall the numbers in the same order. The procedure for the DSB is the same as the DSF, but the subjects are asked to recall the sequence in the reverse order. For both measures, the task is terminated when the subject is no longer able to successfully recall either of two sequences of the same length.

Inhibition

Stroop task

The Stroop verbal task, an adaptation of the original task designed by Stroop (1935), was used to assess inhibitory processes in attention. Participants are asked to judge the color in which each in a series of words is written. Using the E-Prime 1.1 software (Schneider, Eschman, & Zuccolotto, 2002), words written in different colors (red, blue, green, or yellow; size 50 ppi) appear one by one on a 15-in. screen. The words are of two types: names of colors or invented words. The written word may be incongruent with the font color (RED, written in blue) or neutral (invented words written in any color). Participants are asked to name the color the words are written in as quickly as possible, ignoring their meaning. Responses are given orally and recorded using a recorder and microphone connected to the E-Prime software. Each block comprises 192 randomly presented words, half of which are neutral and half are incongruent. This task was scored as the difference in reaction time between incongruent and neutral conditions.

Coping strategies

Coping Response Inventory-Adult Form

To evaluate coping strategies, the Spanish adaptation by Kirchner and Forns (2010) of the Coping Response Inventory-Adults Form (CRI-A; Moos, 1993). CRI-A is a 48 questions self-reporting scale which assesses coping responses to stressful life experiences. The criteria are as follows: no never, once or twice, quite often, yes, and almost always. The scale identifies four approach strategies and four avoidance strategies. Approach strategies: (a) logical analysis: cognitive attempts to prepare mentally for a stressor and its consequences; (b) positive reappraisal: cognitive attempts to restructure a problem in a positive way while accepting the reality of the situation; (c) seeking guidance and support: behavioral attempts to seek information, guidance, or support; (d) PS: behavioral attempts to take action to deal directly with the problem. Avoidance strategies: (e) cognitive avoidance: cognitive attempts to avoid realistically thinking about the problem; (f) resigned acceptance: cognitive attempts to react to the problem by accepting it; (g) seeking alternative rewards: behavioral attempts to cope by finding substitute activities or creating new sources of satisfaction; and (h) emotional discharge: behavioral attempts to reduce tension by expressing negative feelings. The Spanish adaptation of the scale presents adequate psychometric properties and validity (Moos, 1993). The internal consistency of the Spanish version of CRI-A yields Cronbach’s α values ranging from .50 to .70 for each of the subscales. While these values are not excellent, they are consistent with those reported in the literature on coping questionnaires, most of which tend not to present especially high values (Mikulic & Crespi, 2008).

Procedure

Informational meetings were organized with the management teams of the centers. Once approval had been given, we requested the collaboration of participants from all the organizations involved: (a) Older participants were sent a letter requesting their collaboration in the study. They were then telephoned individually to check the letter had been received. Once their participation was confirmed and according to the availability of each volunteer, we scheduled the study sessions at the premises of the collaborating organizations; and (b) the sample of younger participants was recruited from among undergraduate medical students studying psychology at Albacete Medical School. Once written informed consent had been obtained, we proceeded to collect the data. Data were gathered by five experimenters. Two individual assessment sessions were conducted. The protocol was as follows: (a) in the first session, data on sociodemographic variables were collected, CES-D, Mini-Examen Cognoscitivo, and CRI-A (approximate duration of 1 hour); and (b) in the second session, we administered the digit span and Stroop (approximate duration of 1 hour). All the assessment sessions were conducted in the facilities of the different centers, during normal attendance hours and according to the availability of participants.

Data Analysis

The Statistical Package for Social Sciences 23.0 was used for all analyses. First, an exploratory factor analysis (EFA) was conducted for approach and avoidance coping strategies using the CRI-A. The Kaiser–Meyer–Olkin (KMO) measure of sampling was calculated to assess sampling adequacy. Bartlett’s test of sphericity was also conducted. Factors were considered those with eigenvalues >1. Using EFA, we obtained individualized scores for each of the factors extracted through the regression method.

After obtaining these factors, t tests were performed to determine whether there were any significant differences in variables between groups (younger and older adults). An analysis of covariance was also performed to determine what effect group age had on coping strategies. CES-D score was used as covariate because a correlation between depressive symptoms and coping strategies has been demonstrated in several studies (e.g., Aldao & Nolen-Hoeksema, 2012; Hori et al., 2014; Moritz et al., 2016). In addition, given the statistical significant differences in the educational level between groups, this variable was also included as a covariate in the analysis.

Finally, hierarchical regressions were performed to assess to what extent the executive variables, controlled for the effect of depressive symptoms, predict coping strategies in each group.

Results

Exploratory Factor Analysis

The EFA was estimated using maximum likelihood, and a subsequent Varimax rotation and Kaiser Normalization with 25 iterations were used to determine the factor structure of the items. Factors were retained on the basis of the scree plot elbow and total variance explained. The scree plot elbow suggested a two-factor solution, which explained 40.69% of the variance. The results obtained in the KMO sampling adequacy test (KMO = .60) and Bartlett’s sphericity test, χ²(21) = 289.84, p<.001, were adequate. Variables were considered to load onto a factor if the variable had a loading >±.30 (seeking alternative rewards coping strategy was excluded for having loadings lower than .30). Table 1 displays the item loadings for the two factors coping factor (avoidance vs. approach strategies).

Table 1.
Factor Analysis of Coping Measured by CRI-A Scale.

Factor

CRI-A variables Approach coping strategies Avoidance coping strategies

Logical analysis .48 —

Positive reappraisal .45 —

Problem-solving .90 —

Seeking guidance and support .33 —

Cognitive avoidance — .84

Resigned acceptance — .42

Emotional discharge — .59

Note. CRI-A = Coping Response Inventory-Adults Form.

Preliminary Analyses

Table 2 shows the descriptive statistics for the main study variables by age-group—young people and older adults. The t test results yield significant differences between the groups in the EFs analyzed: The young group performed better than the older group on DSF, DSB, and inhibition. Regarding coping strategies, the adolescents scored higher on avoidance strategies than the older adults. Finally, we found no significant differences between groups on depressive symptoms.

Table 2.
Means (Standard Deviations) of Main Study Variables.

Variables Young group (n = 119) Older group (n = 97) t Cohen’s d

CES-D 14.68 (8.44) 12.66 (8.61) 1.71 0.24

Digit span backward 8.31 (1.60) 6.53 (1.56) 8.26 1.13

Digit span forward 7.54 (1.97) 4.52 (1.44) 12.60 1.75

Inhibition^a 39.30 (35.68) 98.59 (98.29) −6.11 −0.80*

Approach strategies factor −0.09 (0.91) 0.11 (0.93) −.1.53 −0.22

Avoidance strategies factor 0.29 (0.83) −0.38 (0.81) 5.81 0.82**

Note. CES-D = Center for Epidemiologic Studies-Depression Scale.

^aInhibition = Stroop effect.

**p < .001.

Analyses of Variance

We used scores on coping strategies factors as the dependent variable in a 2 (type of coping strategies: approach, avoidance) × 2 (age-group: younger, older) analysis of covariance, with the CES-D scores and educational level as covariate variables. In these analyses, a statistically significant effect of type of coping strategies × CES-D scores was found, F(1, 204) = 10.68, p = .001, partial η² = 0.05. A correlational analysis showed that CES-D scores was positively correlated with avoidance coping strategies (r = .23, p = .001), while a nonsignificant correlation was found between CES-D and approach coping strategies (r = −.11, p = .104).

We also found a significant interaction between type of strategies × age-group, F(1, 204) = 12.08, p = .001, partial η² = 0.06. Post hoc Bonferroni test results showed that young participants use more avoidant coping strategies (B = 0.56, p < .001) than older participants, while nonsignificant differences were found between both groups regarding the use of approach coping strategies.

No significant effects of type of coping strategies × education level were found (p = .57).

Hierarchical Regression Analyses

Separately for each age-group, hierarchical regressions with EFs (DSB and inhibition), short-term memory (DSF), and depressive symptoms (CES-D) scores were entered as predictors of coping strategies factors: first, approach strategies and, second, avoidance strategies. In each analysis, cognitive factors were entered in the regression in Step 1 and, in Step 2, CES-D scores were added.

Regarding the young group (see Table 3), the results showed that depressive symptomatology measured on the CES-D Scale was the only statistically significant predictor of coping strategies: participants with higher scores in CES-D used more avoidant coping strategies and fewer approach coping strategies.

Table 3.
Hierarchical Regression Results (Unstandardized B Weights, Standard Error, Standardized Beta Weights, R², and ΔR²) for Coping Strategies in Young Group (n = 133).

Dependent variable Step Predictors B 95% CI for B SE B β R ² ΔR²

Approach strategies Step 1 DSF .049 [−.069, .167] .060 0.09 .021 .021

DSB −.063 [−.159, .032] .048 −1.31

INHIB .002 [−.003, .006] .002 0.661

Step 2 DSF .066 [−.050, .182] .059 0.116 .075 .05*

DSB −.070 [−.163, .024] .047 −0.152

INHIB .001 [−.004, .006] .002 0.039

CES-D −.25 [−.044, −.006] .10 −0.234*

Avoidance strategies Step 1 DSF −.019 [−.128, .090] .055 −0.036 .012 .012

DSB .051 [−.037, .139] .045 0.121

INHIB .001 [−.004, .005] .055 −0.036

Step 2 DSF −.038 [−.143, .067] .053 −0.073 .092 .08

DSB .058 [−.027, .143] .043 0.138

INHIB .001 [−.003, .005] .002 0.054

CES-D .028 [.010, .046] .009 0.286

Note. INHIB = inhibition; DSF = digit span forward; DSB = digit span backward; CES-D = Center for Epidemiologic Studies-Depression Scale; SE = standard error; CI = confidence interval.

*p ≤.05. **p ≤ .01.

With regard to the older group ( Table 4), none of the independent variables used were significant predictors of the approach coping strategies. Nevertheless, inhibition was the only significant predictor of avoidance coping strategies: participants with higher scores in inhibition used less avoidance coping strategies.

Table 4.
Hierarchical Regression Results (Unstandardized B Weights, Standard Error, Standardized Beta Weights, R², and ΔR²) for Coping Strategies in Older Group (n = 109).

Dependent variable Step Predictors B 95% CI for B SE B β R ² ΔR²

Approach strategies Step 1 DSF −.122 [−.278, .034] .078 −0.207 .046 .046

DSB .031 [−.136, .198] .084 0.048

INHIB −.001 [−.003, .001] .001 −0.070

Step 2 DSF −.118 [−.279, .043] .081 −0.200 .047 .001

DSB .026 [−.146, .199] .087 0.041

INHIB −.001 [−.003, .001] .001 −0.74

CES-D .003 [−.020, .026] .012 0.026

Avoidance strategies Step 1 DSF −.086 [−.218, .045] .066 −0.169 .094 .094*

DSB .021 [−.121, .162] .071 0.037

INHIB −.002 [−.004, .000] .001 −0.229*

Step 2 DSF −.067 [−.202, .068] .068 −0.131 .109 .016

DSB .001 [−.144, .145] .073 0.002

INHIB −.002 [−.004, .000] .001 −0.247*

CES-D .012 [−.007, .031] .010 0.129

Note. INHIB = inhibition; DSF = digit span forward; DSB = digit span backward; CES-D = Center for Epidemiologic Studies-Depression Scale; SE = standard error; CI = confidence interval.

*p ≤ .05; **p ≤ .01.

Discussion

The main aim of this study was to compare executive functioning (inhibition and WM), short-term memory, and adoption of coping strategies (avoidance vs. approach), in a sample of young and older adults with no prior diagnosis of depression and with normal cognitive function.

Regarding our first hypothesis, performance on executive and short-term memory tasks was higher in the young group than in that of the older adults. In this sense, age-related losses in certain executive processes have frequently been reported (e.g., aspects of information processing, visual and auditory tasks; see Diamond, 2013, for a review). In the line of our results for WM capacity, deficits in storage capacity have been suggested as one of the possible explanations for aging impairments in WM (Bopp & Verhaeghen, 2005). An alternative to this theory is the inhibition deficit theory (Hasher & Zacks, 1988), according to which older adults show deficits not because their WM capacity is reduced, but because as individuals age they find it increasingly difficult to manage irrelevant information (e.g., Rozek, Kemper, & McDowd, 2012; Verhaeghen, 2011). Likewise, it has been well documented that short-term memory decline in older adulthood (Hale et al., 2011; Verhaegen et al., 2014). With regard to inhibition, although becoming older is characterized by a gradual loss of inhibitory efficacy (inhibition deficit theory; Hasher & Zacks, 1988), the results on these losses are not conclusive, as they depend on the type of inhibitory process examined (Maylor, Schlaghecken, & Watson, 2005). It has been suggested that inhibition involves diverse inhibitory processes that may differentially associate with other cognitive abilities, such as WM (Friedman & Miyake, 2004). The Stroop test is also regarded as a task that is sensitive to the cognitive decline associated with normal aging, as demonstrated by the fact that the behavioral response to congruent and to incongruent stimuli is slower, and the Stroop effect is larger in older people than in young people (e.g., Peña-Casanova et al., 2009; Zurrón, Lindín, Galdo-Álvarez, & Díaz, 2014).

Regarding our second hypothesis, the correlation between depressive symptoms and coping strategies has been demonstrated (e.g., Moritz et al., 2016). More specifically, avoidance coping strategies have been related to more depressive symptoms and approach coping strategies related to less depressive symptoms (see Bj⊘rkl⊘f, Engedal, Selbæk, Kouwenhoven, & Helvik, 2013, for a review). Furthermore, depression is known to be associated with executive deficits (Snyder, 2013). Consequently, coping strategies are considered to include domains such as inhibition, cognitive flexibility, WM, sequencing, and planning (Compas, 2006), which are required to deal with stress factors that might contribute to depression (Compas, 2009). In our study sample, scores on the CES-D were associated with avoidant coping strategies, and, more specifically, the participants with lower scores on the CES-D showed less use of avoidance strategies. In this vein, the findings of Morris et al. (2015) suggest that lower inhibitory capacity and less use of primary control coping¹ were associated with lower depressive symptoms at baseline but predicted increases in depressive symptoms at follow-up. According to Gotlib and Joorman (2010), inhibitory deficits might increase the risk of depression, as they reinforce negative affectivity (e.g., incapacity to disengage from negative stimuli or rumination). However, results in this regard are not conclusive because, although it has been suggested that inhibitory control might serve as a protective factor against depression, it has also been found that higher primary control and lower inhibition scores were not associated with increments in depression, that is, primary strategies such as PS could compensate for inhibitory deficits (Morris et al., 2015). WM has also been found to be related to secondary control coping² (Andreotti et al., 2013). Evidence has also been found for the role of secondary control coping and cognitive reappraisal as predictors of positive and negative affect and symptoms of depression and anxiety (e.g., Andreotti et al., 2013; Reich, Zautra, & Hall, 2010).

In the analysis of the adoption of coping strategies by age-group, our findings show that in the young participants, there is a prevalence of avoidance strategies. In this regard, it has been suggested that lower executive performance is related to maladaptive coping styles (i.e., avoidant strategies), and that adequate executive functioning predicts approach coping strategies (e.g., Gregório et al., 2015). Carver and Connor-Smith (2010) and Trouillet, Gana, Loure, and Fort (2009) found, across ages, a coping style focused mainly on emotion as a response to changes in coping resources throughout life span. Aging is associated with an increased ability to regulate emotions in the context of reduced perceived lifetime (Carstensen, 2006). Hence, as people age, they experience fewer negative emotions (Mather & Carstensen, 2005). When the adoption of coping strategies has been compared between young and older adults, it has been suggested that the increase in PS in aging could be a result of the adoption of the emotion regulation strategy of reappraisal (Scheibe & Carstensen, 2010). Modulating variables regarding age effects on coping styles have also been found, although the findings are not conclusive (Aldwin, Sutton, Chiara, & Spiro, 1996; Lazarus & Folkman, 1984). For example, Trouillet et al. (2009) suggested that the age effect on adoption of coping strategies may be mediated by different coping resources, such as social support satisfaction and perceived stress.

This study has some limitations. The first is related to the sociocultural characteristics of our sample: (a) the sample was composed completely of Spanish participants of Caucasian origin; thus, our findings may not be generalized to other cultures; (b) the educational level of the study sample because one of the inclusion criteria was having completed high school; the inclusion of participants from other cultures or races and with a lower educational level in future research could help us to analyze whether this variable has a significant effect on executive performance and, consequently, its association with the adoption of coping strategies, controlling for age effect; and (c) there is a big difference between the two age groups. It would therefore be desirable for future research to include a group of middle adulthood participants to compare their performance in the variables of interest. Second, given that our participants presented no depressive disorder, but depressive symptoms have been used as a study covariate and, in previous works, depression, coping, and EF have been related, an interesting line of research would be to extend our sample to depressive patients, which would allow us to compare the executive performance and coping between participants diagnosed with depression and a control group. Third, the assessment of EF is limited to WM tasks and inhibition. The inclusion of further executive domains would complete our findings. The last limitation is related to the reliability data for the CRI-A Questionnaire; an alternative could be the Responses to Stress Questionnaire (Connor-Smith et al., 2000), which has shown high levels of internal consistency.

In conclusion, to the best of our knowledge, this is the first work to compare EF and adoption of coping strategies in a sample of young adults and older adults with no prior diagnosis of depression. This line of research is important due to the role played by the EFs in adequate cognitive, social, and psychological functioning (Diamond, 2013) and their importance for successful daily functioning and well-being in aging (Dawson et al., 2015; Karr et al., 2015). Furthermore, the identification of EFs as mechanisms involved in successful adaptation to stress (Compas, 2006) underlines the importance of the study of coping strategies in obtaining adaptive results, and more specifically, in the final stage of the life span, which tends to be identified with personal and material losses, which, in turn, hinder adaptation to unfavorable situations, such as health problems, dependency, loss of loved ones, or financial readjustments (Martin, Kliegel, Rott, Poon, & Johnson, 2008). However, it is worth noting that older adults are able to maintain their coping resources to successfully adapt to life changes (Freund & Riediger, 2001) and reduce the negative impact of stressful events (Taylor & Stanton, 2007). In this sense, it could be beneficial to implement programs to develop coping strategies for successful adaptation in older adults, such as that proposed by Mayordomo, Sales, Satorres, and Blasco (2015) to promote psychological well-being and life satisfaction in aging.

	Factor
Logical analysis	.48	—
Positive reappraisal	.45	—
Problem-solving	.90	—
Seeking guidance and support	.33	—
Cognitive avoidance	—	.84
Resigned acceptance	—	.42
Emotional discharge	—	.59

Variables	Young group (n = 119)	Older group (n = 97)	t	Cohen’s d
CES-D	14.68 (8.44)	12.66 (8.61)	1.71	0.24
Digit span backward	8.31 (1.60)	6.53 (1.56)	8.26	1.13**
Digit span forward	7.54 (1.97)	4.52 (1.44)	12.60	1.75**
Inhibition^a	39.30 (35.68)	98.59 (98.29)	−6.11	−0.80*
Approach strategies factor	−0.09 (0.91)	0.11 (0.93)	−.1.53	−0.22
Avoidance strategies factor	0.29 (0.83)	−0.38 (0.81)	5.81	0.82**

Dependent variable	Step	Predictors	B	95% CI for B	SE B	β	R ²	ΔR²
Approach strategies	Step 1	DSF	.049	[−.069, .167]	.060	0.09	.021	.021
DSB	−.063	[−.159, .032]	.048	−1.31
INHIB	.002	[−.003, .006]	.002	0.661
Step 2	DSF	.066	[−.050, .182]	.059	0.116	.075	.05*
DSB	−.070	[−.163, .024]	.047	−0.152
INHIB	.001	[−.004, .006]	.002	0.039
CES-D	−.25	[−.044, −.006]	.10	−0.234*
Avoidance strategies	Step 1	DSF	−.019	[−.128, .090]	.055	−0.036	.012	.012
DSB	.051	[−.037, .139]	.045	0.121
INHIB	.001	[−.004, .005]	.055	−0.036
Step 2	DSF	−.038	[−.143, .067]	.053	−0.073	.092	.08**
DSB	.058	[−.027, .143]	.043	0.138
INHIB	.001	[−.003, .005]	.002	0.054
CES-D	.028	[.010, .046]	.009	0.286**

Dependent variable	Step	Predictors	B	95% CI for B	SE B	β	R ²	ΔR²
Approach strategies	Step 1	DSF	−.122	[−.278, .034]	.078	−0.207	.046	.046
DSB	.031	[−.136, .198]	.084	0.048
INHIB	−.001	[−.003, .001]	.001	−0.070
Step 2	DSF	−.118	[−.279, .043]	.081	−0.200	.047	.001
DSB	.026	[−.146, .199]	.087	0.041
INHIB	−.001	[−.003, .001]	.001	−0.74
CES-D	.003	[−.020, .026]	.012	0.026
Avoidance strategies	Step 1	DSF	−.086	[−.218, .045]	.066	−0.169	.094	.094*
DSB	.021	[−.121, .162]	.071	0.037
INHIB	−.002	[−.004, .000]	.001	−0.229*
Step 2	DSF	−.067	[−.202, .068]	.068	−0.131	.109	.016
DSB	.001	[−.144, .145]	.073	0.002
INHIB	−.002	[−.004, .000]	.001	−0.247*
CES-D	.012	[−.007, .031]	.010	0.129

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Regional Government of Castilla La Mancha and Ministry of Science and Innovation (PII1I09-0274-8863 and PSI2010-20088).

Notes

Author Biographies

Marta Nieto, PhD by University of Castilla-La Mancha. Professor at Faculty of Medicine, Albacete, Spain. Researcher in autobiographical memory and executive functions, mainly in preschool age.

Dulce Romero, PhD by University of Castilla-La Mancha. Professor of health sciences in University of Granada, Spain. Researcher in Neroscience and cognitive development.

Laura Ros, PhD by University of Castilla-La Mancha. Professor at Faculty of Medicine, Albacete, Spain. Researcher in autobiographical memory and executive functions, mainly in older age.

Carmen Zabala, PhD by University of Castilla-La Mancha. Professor at Faculty of Nursing (Talavera de la Reina, Toledo, Spain). Researcher in emotion.

Manuela Martínez, PhD by University of Castilla-La Mancha. Professor at Faculty of Nursing (Talevera de la Reina, Toledo, Spain). Researcher in autobiographical memory and mental disorders.

Jorge J. Ricarte, PhD by University of Castilla-La Mancha. Professor at Faculty of Education (Albacete, Spain). Researcher in autobiographical memory in schizophrenia and executive functions.

Juan P. Serrano, PhD by University of Castilla-La Mancha. Professor at Faculty of Medicine, Albacete, Spain. Researcher in autobiographical memory in older age, executive functions and reminiscence therapy.

Jose M. Latorre, PhD by University of Castilla-La Mancha. Principal researcher of Applied Cognitive Psychology Unit. Professor at Faculty of Medicine. Researcher in autobiographical memory across life span and executive functions.

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	Factor
CRI-A variables	Approach coping strategies	Avoidance coping strategies
Logical analysis	.48	—
Positive reappraisal	.45	—
Problem-solving	.90	—
Seeking guidance and support	.33	—
Cognitive avoidance	—	.84
Resigned acceptance	—	.42
Emotional discharge	—	.59