pmc.ncbi.nlm.nih.gov

The Role of Delay Discounting in the Generation of Stressful Life Events Across Adolescence

. Author manuscript; available in PMC: 2023 Jun 6.

Published in final edited form as: Res Child Adolesc Psychopathol. 2022 Jun 23;50(12):1529–1541. doi: 10.1007/s10802-022-00950-0

Abstract

Hammen’s (1991) model of stress generation suggests that depressed individuals are more likely to behave in ways that bring about greater exposure to negative life events. More recent research suggests that adolescents with other types of psychological vulnerabilities, including those more likely to make impulsive choices, may also be predisposed to experience greater increases in stress over time. The current study examined whether delay discounting (DD), defined as the tendency to prefer smaller but immediately available rewards relative to larger, delayed rewards, predicts the generation of negative life events across adolescence and whether this is due to the association between DD and depressive symptoms. Participants (n = 213, Mage = 15, range 12–17) completed self-report measures of depressive symptoms and negative life events, as well as a behavioral measure assessing DD annually over four years. Results of latent growth models suggest that both independent and dependent negative life events increased across adolescence. Consistent with a stress generation framework, DD predicted the growth in dependent, but not independent, negative life events over this time period, controlling for baseline levels of depressive symptoms. Further exploratory analyses suggest that DD was associated with increases in depressive symptomology across adolescence, but that the relation between DD and changes in independent negative life events was not better accounted for by increases in depressive symptoms over time. Taken together, these findings suggest the importance of DD in predicting youths’ exposure to dependent negative life events and point to potential avenues for clinical intervention.

Keywords: Stress, Adolescents, Depression, Delay Discounting

Introduction

Adolescence is a period of significant change across environmental, inter- and intrapersonal domains (Petersen et al., 2017). The incidence of stressful life experiences increases precipitously across adolescence (Kessler et al., 2005; Rao et al., 2009; Wickrama et al., 2015) and is, in turn, associated with the onset and exacerbation of mental health problems (Hiremath et al., 2018; White et al., 2016; Wolfe & Mash, 2006), physical disability (Feuerstein & Thebarge, 1991), and suicidal ideation (Chang, 2002; Tan et al., 2018; Wilburn & Smith, 2005). Stress generation refers to the well-described phenomenon by which individuals with psychopathology report greater levels of negative life events, suggesting that people may contribute to the accumulation of stressful events in their own environments (Hammen, 1991, 2006; Liu & Alloy, 2010). For instance, research finds depressed individuals report greater numbers of dependent negative life events, defined as events influenced by an individual’s behavior (e.g., fight with a parent, skipping school), relative to their non-depressed peers (Hammen, 2005). Conversely, independent life events, or events that are not caused by an individual’s actions or behaviors (e.g., natural disaster, death in the family), have not been shown to prospectively predict depressive symptoms (Mezulis et al., 2006; Monroe et al., 2006). This pattern of findings has been replicated among depressed adolescents in both community (Hammen et al., 2011) and clinical populations (Rudolph et al., 2009), and appears to be related to the severity of symptomology, with individuals with sub-clinical and clinical levels of depressive symptoms reporting more dependent stressful life events than adolescents without depressive symptoms (Krackow & Rudolph, 2008).

While most research has focused on depressed mood as the impetus for stress generation (Davila et al., 1995; Rudolph et al., 2000), more recent research suggests that other factors common to adolescence may also be implicated in stress generation (e.g. Dodd et al., 2014; Farmer & Kashdan, 2015; Uhrlass & Gibb, 2007). Given the role of stress in the onset of long-term mental and physical health problems (Cohen et al., 2007; McFarlane, 2010), identifying vulnerabilities that contribute to the development of stressful life events across adolescence above and beyond depressive symptoms (e.g. Bodell et al., 2012) is critical for effectively targeting future prevention and intervention efforts. To that end, the current study examines one such factor, delay discounting, and its role in stress generation in a four-year prospective study of adolescents.

Delay Discounting and Stress Generation

Delay discounting (DD) refers to an individual’s tendency to decrease the subjective value of a reward as a function of the delay to which it will be received. In other words, adolescents with elevated levels of DD are more likely to prioritize immediately available pay-offs even if they have a smaller subjective value compared to larger rewards that take longer to obtain. DD has been found to correlate with personality assessments of impulsivity (Madden et al., 1997; Petry, 2002) and may serve as a predictor of future unhealthy behaviors in which impulsivity is implicated (Anokhin et al., 2015; Audrain-McGovern et al., 2009; Felton et al., 2020).

Research also indicates that DD specifically is associated with a host of negative health outcomes. For instance, individuals with elevated (more problematic) rates of DD have been found to evidence greater incidence of marijuana use (Stanger et al., 2012), smoking (Reynolds & Fields, 2012; Fields et al., 2009), and alcohol use (Field et al., 2007). DD has also been associated with several addictive behaviors in adolescents, including overeating (Fields et al., 2013; Guerrieri et al., 2009), risky sexual behaviors (Chesson et al., 2006), and gambling (Cosenza & Nigro, 2015).

Following from its association with negative health outcomes, emerging research also suggests that DD may also predict increases in stress. For instance, a group of college students were asked to perform a computerized DD task and subsequently had their heart rate monitored as they completed a serial subtraction task. The results from this study showed a significant relationship between DD and stress response, such that steeper rates of DD were associated with greater stress response (Diller et al., 2011) among women only, suggesting that elevations in DD may precede, and put individuals at risk for, maladaptive stress responses.

Several studies also indicate that constructs closely related to DD, including impulsive behaviors, may play an important role in generating negative life events. For instance, negative urgency, defined as the tendency to act impulsively in the context of negative affect, has been shown to predict dependent (but not independent) life events among adults, even after controlling for depressive symptoms (Liu & Kleiman, 2012). Another large-scale (n = 998) longitudinal study of adults found that a broad measure of impulsivity predicted dependent negative life events; however, this study did not control for baseline depressive symptoms (Iacovino et al., 2015).

No study, to our knowledge, has specifically examined the role of DD within a stress generation framework. Moreover, of the existing literature on impulsivity and impulsive choice behaviors and their role in stress generation, few have examined these relations during adolescence, a particularly vulnerable period for increases in negative life events. Finally, it has been suggested that stress generation may be specific to depression (for review, see Liu & Alloy, 2010), indicating that relations between non-depressogenic constructs and the generation of negative life events may be better accounted for by their co-occurrence with depression. Given that DD has been consistently linked to depression (Amlung et al., 2019), it is possible that DD drives stress generation primarily through its shared relation with depressive symptoms.

Current Study

The current study attempts to address gaps in this literature by examining whether DD drives increases in dependent negative life events over time, and what role (if any) depressive symptoms play in these relations. Utilizing a sample of adolescents assessed annually over a four-year period, we evaluated the utility of DD in predicting negative life events across a specifically vulnerable developmental period. We hypothesized that baseline levels of DD would drive increases in dependent negative life events, controlling for depressive symptoms. In an exploratory model, we also examined whether DD would play a role in the depression-stress generation process, by evaluating whether DD would be associated with depressive symptoms which, in turn, drive increases in stressful life events. Finally, we evaluated an alternative model in which we examined whether DD predicted independent life events (converse to what would be hypothesized by stress generation frameworks).

Methods

Participants and Procedures

The current study uses data from a larger eight-year longitudinal study of the development of risk-taking behaviors (see Felton et al., 2020 for further details). Participants were recruited from a suburban metropolitan area using fliers and mailings sent to local area school, libraries, and community centers. Families interested in participating were initially screened for inclusion criteria, including English proficiency, having a child between the ages of 11 and 13 years old, and the ability to participate in annual assessments. No other exclusion criteria was used, to increase the generalizability of the sample. Prior to their first assessment, children under 18 and their parent/guardian provided written assent and consent, respectively. If a participant turned 18 over the course of the study, the youth provided their own consent and no longer required parental permission to take part in the study. Children and their parents received a monetary incentive ranging from $15 to $35 for taking part in each annual assessment. All procedures were approved by the University of Maryland Institutional Review Board.

Because key measures used in this study were not introduced until the fifth year of data collection, data presented below comes from waves 5–8 (relabeled Time 1 through 4, for clarity). Analyses revealed no significant differences between youth who participated in the first and fifth wave of the study on demographic factors (sex, age, race/ethnicity, socioeconomic indicators) or depressive symptoms. The original sample included 277 adolescent participants and their parents. Of those, 213 completed measures of interest in year 5; and 193, 154, and 152 in years 6–8, respectively. The average adolescent age at baseline (Time 1) was 15.02 (range 12–17, SDage = 0.95) and was 45% female. Of these participants, 53% identified as White/Caucasian, 38% as Black/African American, 2% as Asian, and 8% as “other race/ethnicity.” These percentages were representative of the metropolitan area from which the sample was taken. Mothers reported annual family income which ranged from $0 to $375,000 (Mincome = $102.498, SDincome = $56,350). Educational attainment varied widely, with 32% of the sample reported completing up to an Associates Degree, 32% completing a 4-year college degree, and 36% completing an advanced degree. Consistent with recommendations to utilize more than one indicator of socioeconomic status (SES; Galobardes et al., 2006a, b), a composite variable was created by combining the z-scores of parents’ reported annual income and maternal education level.

Measures

Negative Life Events

Negative life events were measured at each wave. A modified, 72-item version of the Coddington’s Life Events Questionnaire (Coddington, 1972) was used to measure both dependent (those events that a person contributes to) and independent (fateful) life events. Adaptations to this scale have been used to measure negative events associated with the onset of pathology (Bailey & Garralda, 1990; Coddington, 1972; Garrison et al., 1987). In this study, 35 items were chosen that were most pertinent to the adolescents’ environment (for example, items such as “getting married” were eliminated because they were not applicable to the current population). Participants endorsed which events on the list they had experienced. Eleven items were then removed from the scale for not reflecting negatively-valanced events (such as “recognition for excelling in a sport or activity”). A post-Baccalaureate research assistant and the lead researcher on the study (a PhD-level psychologist) independently divided the scale into dependent stressful events (events that are influenced by the adolescents’ behavior) and independent stressful events (events that are separate from adolescents’ influence or actions). Any discrepancies between classifications of items were resolved in consultation with a third, independent, PhD-level psychologist colleague. The dependent life events subscale included six items, such as “failing a grade in school,” while the independent life events subscale included 18 items, such as “loss of job by your father or mother.” Items from each subscale were summed to create an index of experienced negative life events.

Delay Discounting

The 27-item Monetary Choice Questionnaire (MCQ; Kirby et al., 1999) was administered at baseline (Time 1) to assess whether the participant preferred smaller immediate rewards (e.g. $15 today) versus larger, delayed rewards (eg.g. $35 in 13 days). The MCQ results in three estimates of delay discount rates (k, Mazur, 1987) associated with small, medium, and large delayed (hypothetical) rewards. DD has been shown to be associated with a broad range of health behaviors (Chapman, 2003; Plazola & Castillo, 2017; Story et al., 2014) relevant during adolescence, valid and reliable in community adolescent samples and among adolescents engaging in maladaptive/risky behaviors (Duckworth & Seligman, 2005; Cosenza et al., 2017; Hendrickson & Rasmussen, 2017), and has strong test–retest reliability and temporal stability (Kirby, 2009; Kuang et al., 2018).

Depressive Symptoms

The Revised Children’s Anxiety and Depression Scale (RCADS; Chorpita et al., 2000) depression subscale was administered at each timepoint as a measure of depressive symptoms in adolescents. The depression subscale consists of 10 items scored on a 4-point scale that asks the participant to rate how often a participant experiences each item stem, such as “I feel like I don’t want to move.” The RCADS depression subscale has been shown to have satisfactory internal consistency in diverse community samples of adolescents (Esbjørn, et al., 2012; Kösters et al., 2015; Mathyssek et al., 2013). In the current study, the measure demonstrated adequate internal reliability across all waves (coefficient alphas ranged from 0.80 to 0.87).

Data Analytic Approach

Latent growth curve modeling was used to identify relations between DD and negative life events. We hypothesized that baseline rates of DD would predict increases in dependent (but not independent) negative life events over time. Further, we evaluated two exploratory and alternative models. The first examined DD in the context of the well-established depression-stress generation model, in which we evaluated whether baseline levels of DD were associated with depressive symptoms which, in turn, may predict the generation of negative life events. The second evaluated whether these relations held for independent life events (which would be contrary to stress generation hypotheses).

Latent growth curve modeling (LGCM) is a special case of structural equation modeling which allows for the examination of the shape of change in variables of interest over time. LGCM permits the examination of both inter- and intra-individual change over time, as well as the addition of predictors of variability in these trajectories. Latent baseline (i.e. intercept) and growth (i.e. slope) factors are estimated using manifest measures of each construct from T1 – T4. Analyses were conducted in Mplus 6.0 using full information maximum likelihood (ML), which uses all available data points in estimating the means and variances of the baseline and growth factors. For each LGCM, we first evaluated an unconditional intercept-only model, in which change over time is constrained to be zero. The fit of the model was examined using four key fit indices: the chi-square statistic, the Comparative Fit Index (CFI; Bentler, 1990), Tucker-Lewis Index (TLI; Tucker & Lewis, 1973), and the Root Mean Square Error of Approximation (RMSEA; Steiger, 1990). Using standard criteria, CFI and TLI values above 0.90 were deemed to represent good fit and 0.95 reflect excellent fit (Hu & Bentler, 1999). RMSEA values below 0.05 were considered close fit, while values below 0.10 represent marginal fit (Browne & Cudeck, 1993). Additional growth terms were then added when doing so significantly improved the fit of the model (as indexed by changes in χ2 values). The means and variances of each latent factor were then examined. A significant mean indicates that the factor is different from zero, while a significant variance suggests individual differences around this estimate.

Analyses were conducted in three stages. In the first stage, we examined an unconditional univariate growth curve of dependent life events, which allowed us to determine the shape of the change in negative life events over time. We then added DD and covariates (sex, age, race/ethnicity, SES, and baseline depressive symptoms) as predictors of both the baseline level and trajectory of dependent life events over time. Both sex (1 = girls, 0 = boys) and race/ethnicity (1 = White, 0 = non-White) were dummy coded. In the next phase of analyses, we evaluated a series of exploratory models examining whether DD played a role in the link between depression and stressful life events. We first tested an unconditional model of depressive symptoms. We then combined this model with the LGM of dependent life events into a parallel process model, which allowed for the examination of the influence of both depressive symptoms and dependent life events on changes in the other construct. Next, we examined whether the intercept of depressive symptoms mediated the relation between DD and stressful life events. We evaluated the significance of the indirect effect (i.e. baseline DD intercept of depressive symptoms slope of stressful life events) by examining a 95% confidence interval band around the estimated effect using bootstrapping procedures (as recommended by Preacher & Hayes, 2008). Our final phase of analyses evaluated an alternative model in which we examined whether DD is related to independent life events, using the same approach outlined above.

Results

Preliminary Analyses

Patterns of missing data were explored by examining whether missingness at any wave was correlated with baseline values of key study variables (e.g. dependent and independent life events, depressive symptoms, DD, and participant sex, age, race and SES). There was a small, but statistically significant, point biserial correlation between attendance (i.e. whether a participant completed the assessment) at Time 2 and dependent negative life events at Time 4 (r = 0.18), attendance at Time 3 and child age (r = −0.13), and attendance at Time 4 and child sex (r = 0.15). We then examined skew and kurtosis statistics for all dependent variables and found that each fell within acceptable ranges (≤ 3.00). Means, standard deviations, and correlations between all variables are reported in Table 1. Findings indicate that older youth reported a greater numbers of dependent negative life events at Time 1. White youth were more likely to be from a higher SES and both non-White and youth from lower SES reported higher (more problematic) rates of DD. Results also suggest that girls reported higher levels of depressive symptoms at Times 1, 2, and 4.

Table 1.

Means, Standard Deviations, and Bivariate Correlations between Key Study Variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 15
1. Child Age 1.00
2. Child Sex 0.15* 1.00
3. Child Ethnicity 0.01 <0.01 1.00
4. SES 0.09 0.11 0.45** 1.00
5. T1 DD −0.10 0.09 −0.25** −0.26** 1.00
6. T1 Dep Life Events 0.09 0.02 0.05 0.06 −0.04 1.00
7. T2 Dep Life Events 0.02 −0.11 −0.03 0.02 0.03 0.44** 1.00
8. T3 Dep Life Events 0.03 −0.04 −0.09 −0.12 0.09 0.46** 0.60** 1.00
9. T4 Dep Life Events 0.03 −0.05 −0.06 <0.01 0.13 0.52** 0.55** 0.76** 1.00
10. T1 Ind Life Events −0.06 −0.09 0.11 0.04 −0.09 0.43** 0.34** 0.25** 0.20* 1.00
11. T2 Ind Life Events −0.05 −0.13 0.03 −0.09 −0.10 0.15 0.47** 0.31** 0.15 0.59** 1.00
12. T3 Ind Life Events −0.07 −0.10 −0.01 −0.14 −0.01 0.25** 0.45** 0.38** 0.37** 0.57** 0.69** 1.00
13. T4 Ind Life Events 0.02 <0.00 0.05 −0.05 −0.07 0.21* 0.32** 0.35** 0.33** 0.58** 0.67** 0.70** 1.00
14. T1 RCADS −0.11 −0.15* 0.14 0.12 −0.18** 0.22** 0.16* 0.20* 0.17* 0.25** 0.23** 0.22** 0.14 1.00
15. T2 RCADS −0.04 −0.22** 0.12 0.09 −0.12 0.22** 0.24** 0.17 0.23** 0.22** 0.25** 0.24** 0.21** 0.69** 1.00
16. T3 RCADS −0.16 −0.11 0.19* 0.16 < −0.00 0.18* 023** 0.23** 0.23* 0.22** 0.25** 0.31** 0.23* 0.68** 0.69** 1.00
17. T4 RCADS 0.03 −0.16* 0.02 0.12 0.02 0.18* 0.26** 0.25** 0.23** 0.25** 0.30** 0.35** 0.24** 0.49** 0.51** 0.60* 1.00
M (SD) 15.02 (0.95) 0.56 (0.50) 0.49 (0.50) <0.01 (0.87) −4.43 (1.25) 1.41 (1.27) 1.72 (1.28) 2.01 (1.27) 2.04 (1.50) 5.26 (2.90) 5.41 (3.21) 5.90 (3.02) 6.10 (3.17) 6.00 (3.84) 6.06 (4.01) 6.18 (4.25) 6.58 (4.73)

Stage 1 Models: Evaluating the Impact of DD on Growth in Dependent Negative Life Events

We first examined an unconditional growth model of dependent negative life events. The intercept-only model yielded a poor fit to the data: χ2(df=8) = 81.86, p < 0.001; CFI = 0.68; TLI = 0.76; RMSEA = 0.20 (90% CI = 0.16 – 0.24). Adding a linear trend significantly improved model fit (Δχ2 = 70.51, Δdf = 3) and resulted in a good overall fit of the model to the data: χ2(df=5) = 11.35, p = 0.045; CFI = 0.97; TLI = 0.97; RMSEA = 0.07 (90% CI = 0.01 – 0.13). Both the mean (M = 1.45, SE = 0.08, p < 0.001) and the variance (Var. = 0.53, SE = 0.16, p = 0.001) of the intercept were significant, indicating that, at baseline, the number of dependent negative life events is significantly greater than zero and there are significant individual differences around this estimate. Only the mean of the slope (M = 0.23, SE = 0.04, p < 0.001) was significant, suggesting that dependent negative life events increase over time, on average, for the full sample (Fig. 1).

Fig. 1.

Fig. 1

Graph of Mean Levels of Dependent Life Events across Timepoints for Youth with Low, Medium, and High Levels of Delay Discounting (DD)

Next, we added our predictors (DD and depressive symptoms at baseline, and covariates child age, race/ethnicity, income group, and sex). This model continued to fit the data well: χ2(df=17) = 22.22, p = 0.176; CFI = 0.97; TLI = 0.95; RMSEA = 0.04 (90% CI = 0.00 – 0.09). Only depressive symptoms significantly predicted baseline levels of dependent stressful life events (est. = 0.08, p < 0.001), suggesting greater levels of baseline depressive symptoms were associated with more concurrent life events. In support of hypotheses, DD was the only significant predictor of the slope of dependent life events (est. = 0.07, p = 0.010), indicating that higher (more problematic) levels of discounting were associated with steeper growth in life events over time.

Stage 2 Exploratory Models: Examining the Impact of DD on Depressive Symptoms and Stress Generation

Next, we evaluated whether DD explained changes in depressive symptoms which, in turn, were responsible for increases in negative life events over time. An intercept-only model of depressive symptoms was examined: χ2(df=8) = 13.85, p < 0.086; CFI = 0.98; TLI = 0.98; RMSEA = 0.06 (90% CI = 0.00 – 0.11). Adding a linear trend significantly improved model fit (Δχ2 = 11.40, Δdf = 3) and resulted in a model with excellent overall fit: χ2(df=5) = 2.45, p = 0.784; CFI = 1.00; TLI = 1.00; RMSEA = 0.00 (90% CI = 0.00 – 0.06). The mean and variance of both the intercept (M = 5.95, SE = 0.26, p < 0.001; Var. = 12.08, SE = 1.56, p < 0.001) and slope (M = 0.20, SE = 0.10, p = 0.058; Var. = 0.66, SE = 0.28, p = 0.018) were significant. These estimates suggest that youth report non-zero levels of depressive symptoms at baseline, that these symptoms increase over time, and that there are significant individual differences around each of these factors.

The next set of analyses combined the two univariate growth curves into a single model in order to examine the interrelations between growth in dependent life events and depressive symptoms. The slope of each factor was regressed onto the intercept of the other factor, allowing for the estimation of the effect of initial levels of one construct predicting change over time in the other construct. The dual LGM fit the data well: χ2(df=36) = 27.35, p = 0.289; CFI = 0.99; TLI = 0.99; RMSEA = 0.03 (90% CI = 0.00 – 0.06). Results suggest that the intercepts of dependent life events and depressive symptoms were positively correlated (r = 0.34, p = 0.002), indicating that greater levels of baseline dependent life events were associated with greater initial levels of depressive symptoms. However, and inconsistent with the stress generation framework, neither intercept significantly predicted the slope of the other term.

Finally, we added predictor and control variables into the model (DD at baseline, child age, race/ethnicity, income group, and sex; see Fig. 2). The model continued to fit the data well: χ2(df=44) = 71.24, p = 0.006; CFI = 0.94; TLI = 0.91; RMSEA = 0.06 (90% CI = 0.00 – 0.09). Consistent with the previous model, DD continued to significantly predict the slope (but not intercept) of dependent negative life events, suggesting that higher levels of discounting were associated with greater increases in dependent negative life events over time. Only sex and was associated with the intercept of depressive symptoms, indicating that girls reported higher levels of depressive symptoms at baseline. Only DD was associated with the slope of depressive symptoms, suggesting that higher rates of initial DD predicted steeper increases in depressive symptoms over time (see Table 2).

Fig. 2.

Fig. 2

Exploratory Parallel Process Growth Model

Table 2.

Parameter Estimates for the Exploratory Parallel Process Growth Model

Std. Est 95% CI p
Predictors of Dependent Life Events
 Sex (male) → InterceptLife Events 0.06 −0.15 to 0.27 0.564
 Age → InterceptLife Events 0.02 −0.19 to 0.23 0.836
 Race/ethnicity (White) → InterceptLife Events −0.04 −0.27 to 0.19 0.734
 SES → InterceptLife Events 0.09 −0.14 to 0.32 0.453
 DD → InterceptLife Events −0.20 −0.41 to 0.01 0.069
 InterceptDepressive Symptoms ⇔ InterceptLife Events 0.44 0.24 to 0.64 < 0.001
 Sex (male) → SlopeLife Events −0.04 −0.32 to 0.25 0.804
 Age → SlopeLife Events 0.02 −0.26 to 0.29 0.913
 Race/ethnicity (White) → SlopeLife Events 0.02 −0.29 to 0.32 0.918
 SES → SlopeLife Events −0.11 −0.40 to 0.18 0.463
 DD → SlopeLife Events 0.36 0.09 to 0.63 0.013
 InterceptDepressive Symptoms → SlopeLife Events 0.26 −0.05 to 0.57 0.108
Predictors of Depressive Symptoms
 Sex (male) → InterceptDepressive Symptoms −0.20 −0.37 to −0.04 0.020
 Age → InterceptDepressive Symptom −0.15 −0.32 to 0.02 0.080
 Race/ethnicity (White) → InterceptDepressive Symptoms 0.03 −0.15 to 0.22 0.735
 SES → InterceptDepressive Symptoms 0.09 −0.10 to 0.28 0.341
 DD → InterceptDepressive Symptoms −0.13 −0.31 to 0.04 0.143
 SlopeLife Events ⇔ SlopeDepressive Symptoms 0.22 −0.39 to 0.82 0.510
 Sex (male) → SlopeDepressive Symptoms −0.13 −0.44 to 0.19 0.433
 Age → SlopeDepressive Symptoms 0.24 −0.07 to 0.54 0.127
 Race/ethnicity (White) → SlopeDepressive Symptoms 0.13 −0.21 to 0.47 0.451
 SES → SlopeDepressive Symptoms 0.18 −0.15 to 0.51 0.277
 DD → SlopeDepressive Symptoms 0.36 0.02 to 0.70 0.028
 InterceptLife Events → SlopeDepressive Symptoms 0.08 −0.36 to 0.51 0.732

We then looked at whether DD plays a role in the stress generation process by examining whether the intercept of depressive symptoms mediated the relation between DD and the trajectory of stressful life events. The indirect effect was not significant (ind. eff. = −0.01, SE = 0.01, 95% CI = −0.02 to 0.001), failing to indicate support for mediation.

Stage 3 Alternative Models: Evaluating the Impact of DD on Growth in Independent Life Events

Finally, we examined the relation between DD and independent negative life events. To do so, we first evaluated an unconditional univariate growth curve of independent negative life events. The intercept-only model provided a poor fit to the data: χ2(df=8) = 35.77, p < 0.001; CFI = 0.90; TLI = 0.92; RMSEA = 0.12 (90% CI = 0.08 – 0.17). Including a growth factor significantly improved the fit of the model (Δχ2 = 30.28, Δdf = 3) and yielded an excellent overall model fit: χ2(df=5) = 5.49, p = 0.360; CFI = 0.99; TLI = 0.99; RMSEA = 0.02 (90% CI = 0.00 – 0.10). Results indicate both the mean (M = 5.20, SE = 0.18, p < 0.001) and the variance (Var. = 4.70, SE = 0.84, p < 0.001) of the intercept were positive and significant, indicating that baseline levels of independent life events were greater than zero and there are significant individual differences around this estimate. Only the mean (M = 0.34, SE = 0.07, p < 0.001) of the slope factor (but not the variance) was significant, indicating a general increasing trajectory of independent life events for the full sample.

Next, we added exogenous predictors to the model (DD and depressive symptoms at baseline, child age race/ethnicity, income group, and sex). This model provided an adequate fit to the data: χ2(df=15) = 27.03, p = 0.029; CFI = 0.95; TLI = 0.91; RMSEA = 0.09 (90% CI = 0.03 – 0.14). In this model, only depressive symptoms at baseline were positively associated with the intercept of independent negative life events (est. = 0.16, p = 0.022). No other predictor was significantly associated with either the intercept or slope of the independent life events factor.

Discussion

Utilizing a stress-generation framework, the current study used longitudinal data to examine the impact of DD on changes in the number of behavior-dependent negative life events across adolescence. Consistent with the tenets of stress generation, the study also sought to test whether the associations between DD and negative life events were better explained by the role of depressive symptoms. The investigation yielded two important results. First, consistent with our hypotheses, DD assessed at baseline predicted steeper increases in (i.e. greater accumulation of) dependent, but not independent (circumstantial/fateful), negative life events over time. Notably, these results remained significant after controlling for baseline depressive symptoms. Second, results from our alternative model did not support the hypothesis that DD is part of a more complex model in which depressive symptoms are driving changes in dependent negative life events.

Although a significant portion of the stress generation literature has been exclusively dedicated to examining the role of interpersonal and dependent life stressors, poor interpersonal problem solving (Davila et al., 1995), and avoidant coping style (Holahan et al., 2005) on depression, the present analysis is the first attempt to examine intertemporal choice as a contributor to depressive symptoms and negative life events over time. These results confirm our initial hypothesis that the accumulation of stressful life events may be linked to individuals’ choices to act in ways in which they choose the smaller and gratifying reward in the short-term while disregarding the negative, future and long-term consequences of the same. For instance, it may be that youth who consistently choose an appetitive, but short-lived, reward such as drinking with friends rather than studying, may be more likely to experience an increasing number of stressful events related to these choices, such as a failing grade. Consistent with the stress generation perspective, individuals may act in ways that result in increased life stressors and create taxing environments, both of which may perpetuate depression. Thus, these findings are suggestive of the possibility that adolescents who tend to favor sooner, smaller rewards may not be able to take into account the possible consequences of those actions, which is likely to exacerbate negative life events.

Our results also support an emerging line of research suggesting that DD is associated with increases in rates of depressive symptoms across middle- and late-adolescence (e.g. Yoon et al., 2007), a critical period for the onset of depression in youth (i.e. Hankin et al., 1998). Importantly, however, results do not suggest that DD plays a role in the relation between depressive symptoms and subsequent negative life events within a stress generation framework. These suggest that DD is an important driver of stress generation over time independent of depressive symptoms, and reinforce the need for interventions to target DD with the goal of preventing and/or reducing both dependent negative life events and depressive symptoms during the adolescence. Results also point to the need for further examination of the complex relations between DD, trajectories of depressive symptoms and trajectories of negative life events, including consideration of other models such as the stress sensitization (Stroud et al., 2018) and “kindling” (Monroe & Harkness, 2005) hypotheses and their role in changes in life events and depression during adolescence.

Of clinical importance, interventions that have shown to be efficacious in reducing DD, including those which focus on increasing individuals’ capacity to imagine or visualize experiences that might occur in one’s future (i.e. Episodic Future Thinking; Schacter et al., 2017) or those which target working memory (Bickel et al., 2011; Felton et al., 2019), may help buffer adolescents’ experience of negative life events and depression. Future studies are needed to evaluate the efficacy of these approaches in reducing DD in adolescent samples (as they have primarily been evaluated in adult populations) and whether these reductions, in turn, impact the trajectory of negative life events and depressive symptoms during this developmental period.

This study had several significant strengths. For instance, the use of a longitudinal design with four distinct waves of data allowed us to draw conclusions about the predictive validity of each of the constructs of interest. Further, the focus on an adolescent, non-clinical sample, allows us to make inferences about stress generation in a normative population. The age group of the sample and inclusion of both boys and girls is beneficial given that previous research has focused mostly on women and midlife adults (Adrian & Hammen, 1993; Daley et al., 1997; Davila et al., 1997; Hammen, 1991). Therefore, the current study significantly extends the stress generation literature to adolescents, and points to the significant impact that DD may have early in one’s development that may contribute to stress generation. Finally, DD was measured using a pencil-and-paper behavioral measure, rather than self-report inventory, suggesting that it may be less influenced by participants wishing to be viewed in a positive light.

Notwithstanding these strengths, some limitations to the current study should be noted. First, children self-reported negative life events (dependent and independent) and depressive symptoms. Interview methods are considered to be the “gold standard” for assessing life events as they are deemed less influenced by reporter bias and future research corroborating the experiences measured in the current study with additional informants is needed to replicate these findings. Despite this, almost half of the extent literature examining stress generation in youth has utilized self-report measures and replicated similar results to studies using interview methods (Liu & Alloy, 2010). Moreover, research examining the relation between these self-report and interview methods have generally found acceptable correspondence across approaches and potentially a small advantage to checklist-style self-report measures in capturing overall stress levels (Duggal et al., 2000).

Second, we assessed symptoms of major depression but did not assess for actual clinical diagnoses of depression. Relatedly, because this research was conducted utilizing a community (non-clinical) sample, findings are not generalizable to youth suffering from clinical levels of psychopathology. Future research would benefit from replicating these findings among youth at specific risk for Major Depressive Disorder and other, clinically-relevant, externalizing disorders including Substance Use Disorder, Conduct Disorder, and Oppositional Defiant Disorder.

Third, parents of youth in the sample reported a relatively high annual family income. Given relations between DD and exposure to lower-resource, less stable, early environments (e.g. Martinez et al., 2022), future research should replicate these relations among youth with lower socio-economic status.

Finally, the DD task used in this investigation is comprised of hypothetical and money-related outcomes. While research suggests that hypothetical measures of discounting correlate strongly with choices made in the context of real rewards (Matusiewicz et al., 2013) most of this research has been conducted with adult samples; thus, it is unclear how performance on the current measure of DD may translate to other, real-world decisions among adolescents specifically.

Within the context of these limitations, this research contributes significantly to DD and its role in stress-generation perspectives, as well as to the broad developmental psychopathology literature. The current study emphasizes the transactional exchanges between youth and their environments and how these may contribute to negative outcomes across the lifespan. Although this study supported the influence of DD on negative life events, it did not identify the specific processes through which stress generation occurs. Future research needs to clarify the mechanisms that underlie these associations, which will be key to developing effective prevention and intervention efforts among adolescents. Moreover, additional research is needed to understand the bidirectional relations between negative life events and DD, including the relation between early, traumatic and unstable environments and the development of decision-making (e.g. Martinez et al., 2022). Within the context of the current research, however, findings suggest that one way to disturb the pathway of escalating dependent negative life events and depressive symptoms is to lessen the adverse influence of DD.

Funding

This project was supported in part by a grant from the National Institute on Drug Abuse (R01 DA18647) awarded to Carl W. Lejuez.

Footnotes

Ethics Approval All procedures performed in the study were in accordance with the ethical standards of the institution and with the 1964 Helsinki Declaration and its later amendments.

Informed Consent Informed consent and assent was obtained from all individual participants included in the study.

Conflicts of Interest The authors have no relevant financial or nonfinancial interests to disclose.

Data Availability

All data utilized in the current study will be made available upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All data utilized in the current study will be made available upon reasonable request.