Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Feb 1.
Published in final edited form as: J Subst Use Addict Treat. 2024 Nov 9;169:209573. doi: 10.1016/j.josat.2024.209573

The Phenotype of Recovery XII: A Reinforcer Pathology Perspective on Associations between Delay Discounting and Pain Catastrophizing in Substance Use Disorder Recovery

Candice L Dwyer 1,2, William H Craft 1, Yu-Hua Yeh 3, Daniel AR Cabral 1, Liqa N Athamneh 1, Allison N Tegge 4, Jeffrey S Stein 1, Warren K Bickel 1
PMCID: PMC11801409  NIHMSID: NIHMS2036054  PMID: 39522768

Abstract

Background:

Substance use disorder (SUD) and pain are highly comorbid conditions and several pain indices (e.g., pain intensity) are associated with an increased risk of relapse. However, the cognitive-emotional experience of pain (i.e., pain catastrophizing) is understudied in SUD recovery. Further, how the association between pain catastrophizing and delay discounting - a posited biomarker of addiction, impacts multidimensional aspects of SUD recovery, including remission and quality of life (QOL), has yet to be examined.

Methods:

Individuals (n=170) in SUD recovery reporting pain were asked about their chronic pain status, completed the Brief Pain Inventory, the Pain Catastrophizing Scale, an Adjusting Amount Delay Discounting Task, and the World Health Organization QOL-BREF scale. Univariate logistic and linear regressions examined associations between delay discounting and several pain indices with remission and QOL. Mediation analyses were investigated whether pain catastrophizing mediates the relationship between delay discounting and 1) sustained remission and 2) QOL.

Results:

Significant negative associations were found between delay discounting (p<.001) and pain catastrophizing (p=.001) with sustained remission. Pain catastrophizing significantly mediated the relationship between delay discounting and physical QOL (p=.044), psychological QOL (p=.009), social (p=.018), and environmental QOL (p=.014). Pain catastrophizing did not mediate the relationship between DD and sustained remission.

Conclusion:

Individuals with greater DD exhibited greater pain catastrophizing, contributing to poorer QOL in SUD recovery. Our findings support that a Reinforcer Pathology framework is useful to understanding the cognitive-emotional experience of pain within the context of SUD recovery. Interventions that target both delay discounting and maladaptive cognitive and emotional responses to pain may lessen the negative impact of pain on SUD recovery and improve SUD outcomes.

Keywords: pain catastrophizing, delay discounting, quality of life, substance use disorder, recovery

Introduction

Substance use disorders (SUD) and physical pain are highly comorbid conditions, with over 70% of individuals with SUD also experiencing pain that interferes with functioning (Jennings et al., 2022; Rosenblum et al., 2003; Witkiewitz & Vowles, 2018; Zale et al., 2015). Research on physical dimensions of pain show that pain severity and pain interference are associated with SUD relapse (Griffin et al., 2016; Jakubczyk et al., 2016; Potter et al., 2015; Witkiewitz et al., 2015), while findings on the association between chronic pain and relapse are more mixed (Dhingra et al. 2015; Fox et al. 2012; Potter et al. 2015). One understudied pain index among individuals in SUD recovery is the cognitive/emotional experience of pain. Pain catastrophizing, the propensity to magnify the threat value of pain or the inability to inhibit thoughts regarding pain, and feelings of helplessness about pain (Quartana et al., 2009), is related to poor health outcomes (Slawek et al., 2021; Tran et al., 2015; Wong et al., 2014). Recovery from SUD is a multidimensional process involving not only reductions in substance use but also improvements in psychosocial functioning, including quality of life (QOL; National Institute on Drug Abuse, 2017; NIAAA Recovery Research Definitions, n.d.; Witkiewitz et al., 2020). A more nuanced understanding of how pain catastrophizing impacts multidimensional aspects of SUD recovery may help improve SUD prognoses.

Broadly, pain catastrophizing is related to reduced pain-treatment response including lower response to analgesics (Mankovsky et al., 2012), worse pain severity (Craner et al., 2016; Feinstein et al., 2017; Wertli et al., 2014), pain interference (Craner et al., 2016), mental health symptomatology, particularly depression and suicidality (Craner et al., 2016; Noyman-Veksler et al., 2017; Richardson et al., 2009), and poorer QOL (Chung et al., 2012; Hayashi et al., 2019; Wong et al., 2014). Pain-related anxiety, a feature of pain catastrophizing, is also associated with opioid misuse (Martel et al., 2013) and cigarette smoking relapse (LaRowe et al., 2017). Moreover, one investigation of patients with chronic pain undergoing SUD treatment found that pain catastrophizing was related to greater pain interference, substance craving, as well as worse anxiety and mood symptoms (Kneeland et al., 2019). Mechanistically, pain catastrophizing is associated with over-attending to affective and sensory pain stimuli, an attention bias that hinders the ability to inhibit pain-related thoughts (Quartana et al., 2009; Van Damme et al., 2002, 2004). Importantly, pain-associated negative emotions may exert stronger cognitive effects than pain itself, with a series of studies providing evidence that pain-related fear was more disabling than pain duration, pain intensity, or negative affect (Crombez et al., 1999). One SUD framework that may inform our understanding of pain catastrophizing in SUD is the Reinforcer Pathology theory.

Reinforcer Pathology is a behavioral economics-guided theory of decision-making that delineates how individuals place subjective value on the future to guide behavior (Bickel et al., 2017, 2019, 2020; Bickel & Athamneh, 2020). Specifically, the temporal distance individuals’ factor into their decision-making (i.e., the temporal window) determines the subjective value of a reinforcer (Bickel & Athamneh, 2020). The temporal window is indexed by delay discounting - a preference for smaller, immediate rewards over larger, delayed rewards - also referred to as an immediacy bias. In this, individuals with an immediacy bias confer greater value on unhealthy immediate, intense, and reliable reinforcers (e.g., substances) while undervaluing temporally extended prosocial reinforcers (e.g., improved health in SUD recovery).

Greater delay discounting is a proposed biomarker of addiction (Bickel et al., 2014) that is robustly associated with SUD, including substance use initiation (Audrain-McGovern et al., 2009) and worse SUD severity and worse treatment outcomes (Albein-Urios et al., 2012; Exum et al., 2023; Johnson et al., 2007). Conversely, lower DD is associated with an increased probability of SUD remission (Athamneh et al., 2023; Athamneh et al., 2022; Craft et al., 2022; Dwyer et al., 2023), more time in recovery (Craft et al., 2021), and better QOL (Athamneh et al., 2022; Rubenis et al., 2018). Relationships between delay discounting and pain have also been characterized, with higher discounting rates associated with higher pain severity (Craft et al., 2021; Tompkins et al., 2016; Wakaizumi et al., 2019), and recently, pain-related anxiety (Białaszek et al., 2023) and pain catastrophizing (Mistretta et al., 2022).

The purpose of this study was to understand if the Reinforcer Pathology framework can be useful in understanding how the cognitive-emotional experience of pain affects recovery outcomes. Specifically, we examined the relationship between delay discounting, pain catastrophizing, and multidimensional SUD recovery outcomes including remission and QOL. In the context of pain, individuals with an immediacy bias may attend to their physical pain more and with a higher emotional valence (i.e., ruminating, magnifying, and feeling helpless) engendering a negative state that is related to poorer recovery outcomes such as reduced QOL and likelihood of remission. Thus, we hypothesized that greater discounting would be associated with greater pain catastrophizing as well as a reduced likelihood of remission and poorer QOL among individuals in SUD recovery.

Materials & Methods

2.1. Participants

The study recruited participants (N=283) from the International Quit and Recovery Registry (IQRR; https://www.quitandrecovery.org/) online community of more than 10,000 individuals who self-identify as being in recovery from SUD. The IQRR offers monthly assessments investigating recovery-related factors. Participants are awarded an online badge and 400–1000 points for each assessment completed, and points can be redeemed for monetary compensation (100 points = $1.00). For further details regarding the IQRR see (Athamneh et al., 2020; Craft et al., 2021; Dwyer et al., 2023). Inclusion criteria for the present study included being 18 years or older, having a lifetime history of at least one SUD (as defined by The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]; American Psychiatric Association, 2013), and self-identifying as being in recovery from substance use. Initiating recovery was defined by a participant endorsing that they have taken steps toward actively changing substance use behaviors and making efforts to reduce or cease substance use. Individuals were excluded (n=50) if they reported that they had never experienced physical pain (other than everyday aches and pains such as minor headaches) at some point in their lives. The data analyzed in this study was collected between March and May of 2020. The Institutional Review Board at Virginia Tech approved this study. Participation was voluntary and consent was implied through the submission of the survey.

2.2. Measures

The Adjusting Amount Delay Discounting Task (Du et al., 2002).

Participants successively chose between an immediately available, but smaller hypothetical monetary reward ($500 initial), and a delayed, larger reward ($1000) across seven randomized delays (1 day, 1 week, 1 month, 3 months, 1 year, 5 years, and 25 years). Within each delay, the smaller reward was adjusted based on the response pattern across the six trials until reaching an indifference amount. The indifference amount reflects the point at which the immediate, smaller, and delayed, larger rewards are subjectively equal in value. Subsequently, the study used Mazur’s hyperbolic discounting function (Mazur, 1987)

SV=A1+kD

to fit the discounting curve to the indifference point for each delay. SV reflects the immediate reward’s subjective value (i.e., indifference amount), A is the objective, full-magnitude value, D is the time delay to the reward receipt, and k is an index of the discounting function (higher k values represent higher delayed rewards discounting). The study natural log transformed (ln(k)) All k values to stabilize the variance and normalize the data.

The Brief Pain Inventory - Short Form (BPI; Cleeland & Ryan, 1991; Mendoza et al., 2006).

The BPI is a validated self-report questionnaire that assesses the presence and severity of pain. All participants were asked (yes/no) if they had “ever experienced pain (other than everyday aches and pains such as minor headaches) at some point in their lives”. Individuals who responded “no” were not eligible to participate (see inclusion criteria). Respondents are asked on an 11-point scale to rate their current and average pain as well as their past 24-hour worst and least pain. A pain intensity score is calculated as the average across these four responses (Cleeland & Ryan, 1991). Respondents also report how much their pain interferes with their ability to function over the past 24 hours across seven areas (i.e., general activity, mood, walking ability, normal work, interpersonal relations, sleep, and enjoyment of life). Past-day pain interference is the average score across these seven areas. For both pain indices, the higher the scores, the greater the pain severity.

Chronic Pain.

Chronic pain status was defined based on the duration respondents experienced pain (International Association for the Study of Pain Task Force on Taxonomy, 1994; Treede et al., 2015). To assess chronic pain status, individuals were asked “How long have you experienced this pain?” (less than 1 month ago; at least 1 month but less than 3 months; at least 3 months but less than 6 months; at least 6 months but less than 1 year; 1 year or more). Individuals who endorsed experiencing pain for at least three months were considered to have chronic pain (Treede et al., 2015). All other individuals were considered to have non-chronic pain.

Pain Catastrophizing Scale (PCS; Sullivan et al., 1995).

The PCS is a validated 13-item self-report questionnaire that assesses pain-related catastrophic thinking. Respondents were asked to indicate on a 5-point Likert scale how often (0=”not at all”, 4=”all the time”) they have thoughts and feelings related to three domains (rumination, magnification, and helplessness) when they experience pain. Items related to rumination include statements such as, “I can’t stop thinking about how much it hurts”; magnification items include statements such as, “I become afraid that the pain will get worse”; and finally, helplessness-related items include statements such as, “there is nothing I can do to reduce the intensity of my pain.” In addition to the three subscales, the measure yields a total score (range: 0–52). In the present study, the total score was used to represent overall pain catastrophizing.

SUD History and Remission Status (American Psychiatric Association, 2013)

Participants were asked to report (yes/no) which substances (i.e., alcohol, cannabis, cocaine, dissociative anesthetics, hallucinogens, inhalants, nicotine, opioids and prescription pain relievers, stimulants, and/or tranquilizers) they had used problematically in their lifetime. For each substance the participants endorsed using problematically, they were asked about the 11 DSM-5 symptoms. Participants who endorsed at least 2 symptoms were considered to have a lifetime history of SUD for that substance. If an individual met criteria for 2 or more lifetime SUDs they were also coded as an individual with poly-SUD. Individuals were considered to be in remission if they endorsed no symptoms other than craving in the past 12 months (i.e., sustained remission) across all substances in which they met the lifetime SUD criteria.

World Health Organization Quality of Life Assessment

(WHOQOL-BREF; “Development of the World Health Organization WHOQOL-BREF Quality of Life Assessment. The WHOQOL Group,” 1998). The WHOQOL-BREF is a 26-item validated self-report scale that captures a respondent’s subjective QOL across four domains. The physical health domain includes questions related to activities of daily living, pain/discomfort, energy, fatigue, and mobility. The psychological domain contains questions about emotions, thinking, motivation, concentration, self-esteem, and spirituality. The social relationship domain includes questions about social support, personal relationships, and sexual activity. Finally, the environmental domain includes questions related to home environment, finances, access to health care, safety, and transportation. Higher scores on each domain reflect a higher subjective QOL. Each domain was considered independently, as recommended in the guidelines of the WHOQOL user manual (Division of Mental Health and Prevention of Substance Abuse World Health Organization, 1998).

2.3. Statistical Analysis

Two data cleaning procedures were implemented for quality control. Individuals who did not provide complete data (n=4) and individuals who provided non-systematic discounting data (Johnson & Bickel, 2008; n=59) were excluded, leaving a final analytical sample of 170 participants. Sample demographics, as well as delay discounting, all pain indices (i.e., pain catastrophizing, chronic pain status, pain intensity, and pain interference), remission status, and QOL domains, are summarized using frequency and percentage or mean and standard deviation.

The study conducted univariate logistic regressions with delay discounting, the pain indices, demographics, and polysubstance-SUD (poly-SUD) status as the explanatory measures and sustained remission status as the outcome measure. Univariate linear regressions were conducted with delay discounting, all pain indices, demographics, and poly-SUD status as the explanatory measures, and each QOL domain (i.e., physical, psychological, social, and environmental) as the outcome measures.

Hayes’ method (Hayes, 2017) examined whether pain catastrophizing (M) mediated the relationship between delay discounting (X) and 1) sustained remission (Y1), 2) physical QOL (Y2), 3) psychological QOL (Y3), 4) social QOL (Y4), and 5) environmental QOL (Y5). Indirect effects were evaluated using a bootstrapping approach, with 10,000 bootstrap random samples to determine 99% confidence intervals (given multiple comparisons). All results were reported as standardized estimates. For each dependent measure, we performed an exhaustive search of the model space using multivariable logistic (Y1) and linear (Y2 - Y5) regression with chronic pain status, pain intensity, pain interference, demographics, and poly-SUD status as independent variables. The variables included in the model with the lowest Bayesian Information Criterion (BIC) were included as covariates for each mediation model. The study conducted all data analyses using R version 4.2.1 (R Core Team, 2022). Mediation analyses were performed using the lavaan package (Rosseel, 2012).

Results

Table 1 summarizes sample characteristics, including demographic data, delay discounting, all pain variables, and QOL, stratified by remission status. Overall, 50.6% of the sample was in remission (n=86). Further, the sample was, on average, 44.44 years old, 51.2% women, 86.5% white, and 94.7% not Hispanic or Latino. DSM-5 lifetime SUD and sustained remission prevalences are shown in Table 2. Supplemental Table 1 displays associations between delay discounting and pain indices.

Table 1.

Sample demographics and characteristics (n = 170).

Mean (SD) / Frequency (%) p-value
Remission (n=86) Non-Remission (n=84)
Sample demographics
Agea 48.58 (16.58) 39.99 (12.91) <.001
Sex - Femaleb 50 (59.5) 37 (43.0) .046
Raceb .270
 American Indian or Alaskan Native 2 (2.4) 1 (1.2)
 Asian 0 (0.0) 1 (1.2)
 Black or African American 3 (3.6) 11 (12.8)
 Native Hawaiian or Other Pacific Islander 1 (1.2) 1 (1.2)
 White 76 (90.5) 71 (82.6)
 Other 2 (2.4) 1 (1.2)
Ethnicity - not Hispanic or Latino b 81 (96.4) 80 (93.0) .516
Education (years) a 14.88 (5.54) 14.35 (4.47) .491
Income b .037
 Less than $10,000 12 (14.3) 27 (31.4)
 $10,000–$29,999 23 (27.4) 24 (27.9)
 $30,000–$49,999 20 (23.8) 10 (11.6)
 $50,000–$79,999 15 (17.9) 9 (10.5)
 $80,000-$119,999 7 (8.3) 11 (12.8)
 $120,000+ 7 (8.3) 5 (5.8)
Poly-SUD (lifetime) b 49 (58.3) 48 (55.8) .860
Sample characteristics
Delay discounting ratea −6.28 (2.42) −4.57 (3.54) <.001
Pain catastrophizinga 16.86 (9.73) 22.24 (11.53) .001
Pain intensitya 3.31 (2.00) 3.26 (1.93) .882
Pain status - chronicb 57 (67.9) 66 (76.7) .261
Pain interference (past day) a 3.38 (2.49) 3.49 (2.40) .772
QOL - Physicala 25.19 (5.24) 23.26 (5.49) .020
QOL - Psychologicala 21.07 (4.38) 18.03 (4.86) <.001
QOL - Sociala 10.10 (2.25) 8.98 (2.76) .004
QOL - Environmentala 30.65 (6.08) 27.36 (5.98) <.001
Open in a new tab
a

Mean (SD)

b

Frequency (%)

Table 2.

Frequency of lifetime DSM-5 SUD and sustained remission.

Lifetime SUD Historya Sustained Remission Statusb
Alcohol 113 (66.5) 79 (69.9)
Cannabis 74 (43.5) 59 (79.7)
Cocaine 62 (36.5) 50 (80.6)
Dissociatives 18 (10.6) 14 (77.8)
Hallucinogens 29 (17.1) 26 (89.7)
Inhalants 21 (12.4) 20 (95.2)
Opioids (non-prescription) 39 (22.9) 30 (81.1)
Prescription Opioids 46 (27.1) 39 (84.8)
Stimulants 52 (30.6) 40 (76.9)
Tobacco 100 (58.8) 54 (54.0)
Tranquilizers 38 (22.4) 31 (81.6)
Open in a new tab
a

= Percentage of overall sample (n = 170)

b

= Percentage of individuals who met lifetime DSM-5 criteria

Univariate logistic regressions examined the relationship between sustained remission status and delay discounting, all pain variables, pain catastrophizing, and demographic characteristics (Table 3). Significant negative associations were found between sustained remission and delay discounting (OR=0.958, 95% CI [0.936, 0.980], p<.001) as well as pain catastrophizing (OR=0.989, 95% CI [0.982, 0.995], p=.001). Individuals who were older (OR=1.01, 95% CI [1.00, 1.04], p<.001), female (OR=1.18, 95% CI [1.02, 1.37], p=.032), or had higher income (OR=1.02, 95% CI [1.00, 1.04], p=.013) were more likely to be in sustained remission while being Black or African American (OR=0.739, 95% CI [0.562, 0.971], p=.032) was associated with less likelihood of being in remission.

Table 3.

Univariate logistic regression associations with sustained remission status.

Odds Ratio (CI) p-value
Delay discounting 0.958 (0.936, 0.980) <.001
Pain catastrophizing 0.989 (0.982, 0.995) .001
Pain intensity 1.00 (0.965, 1.04) .882
Pain interference 0.995 (0.965, 1.03) .772
Pain status - chronic 1.12 (0.944, 1.32) .197
Poly-SUD 1.03 (0.881, 1.195) .742
Age 1.01 (1.00, 1.04) <.001
Sex - female 1.18 (1.02, 1.37) .032
Race (reference is white) - -
 American Indian or Alaskan Native 1.16 (0.656, 2.06) .608
 Asian 0.596 (0.223, 1.59) .304
 Black or African American 0.739 (0.562, 0.971) .032
 Native Hawaiian or Other Pacific Islander 0.983 (0.490, 1.97) .962
 Other 1.16 (0.656, 2.06) .608
Ethnicity - not Hispanic or Latino 1.19 (0.846, 1.66) .324
Education 1.01 (0.990, 1.02) .491
Income 1.02 (1.00, 1.04) .013
Open in a new tab

Univariate linear regressions examined the relationship between each QOL domain and delay discounting, the pain variables, as well as the demographic characteristics (Table 4). For physical QOL, significant negative associations were found for pain status (i.e., having chronic pain; F(1,168)=17.59, p<.001), pain catastrophizing (F(1,168)=47.67, p<.001), pain interference (F(1,168)=50.11, p<.001), pain intensity (F(1,168)=28.48, p<.001), and delay discounting (F(1,168)=15.22, p<.001). Significant positive associations were found for age (F(1,168)=18.81, p<.001), income (F(1,168)=44.29, p<.001), years of education (F(1,168)=12.54, p<.001), and poly-SUD status (F(1,168)=7.84, p=.006). For psychological QOL, significant negative associations were found for pain catastrophizing (F(1,168)=54.17, p<.001), pain interference (F(1,168)=7.54, p=.007), and delay discounting (F(1,168)=18.74, p<.001). Significant positive associations were found for age (F(1,168)=32.81, p<.001), income (F(1,168)=15.92, p<.001), and years of education (F(1,168)=8.11, p=.005). For social QOL, a significant negative association was found for pain catastrophizing (F(1,168)=31.41, p<.001), pain interference (F(1,168)=4.46, p=.036), and delay discounting (F(1,168)=4.25, p=.041) while a significant positive association was found for age (F(1,168)=7.66, p=.006) and income (F(1,168)=12.30, p<.001). For environmental QOL, significant negative associations were found for pain interference (F(1,168)=12.97, p <.001), pain intensity (F(1,168)=6.44, p=.012), pain catastrophizing (F(1,168)=41.32, p<.001), and delay discounting (F(1,168)=38.40, p<.001). Significant positive associations were found between poly-SUD (F(1,168)=19.08, p<.001), being female (F(1,168)=48.49, p<.001), income (F(1,168)=38.32, p<.001), and years of education (F(1,168)=14.39, p<.001).

Table 4.

Univariate linear regression associations of delay discounting rate (ln(k)), pain variables, and demographics with quality of life.

Physical p-value Psychological p-value Social p-value Environmental p-value
Delay discounting F (1,168) = 15.22 <.001 F (1,168) = 18.74 <.001 F (1,168) = 4.25 .041 F (1,168) = 38.40 <.001
Pain catastrophizing F (1,168) = 47.67 <.001 F (1,168) = 54.17 <.001 F (1,168) = 35.41 <.001 F (1,168) = 41.32 <.001
Pain intensity F (1,168) = 28.48 <.001 F (1,168) = 2.15 .144 F (1,168) = 3.45 .065 F (1,168) = 6.44 .012
Pain interference F (1,168) = 50.11 <.001 F (1,168) = 7.54 .007 F (1,168) = 4.46 .036 F (1,168) = 12.97 <.001
Pain status F (1,168) = 17.59 <.001 F (1,168) = 3.13 .079 F (1,168) = 1.02 .315 F (1,168) = 2.99 .086
Poly-SUD F (1,168) = 7.84 .006 F (1,168) = 2.57 .111 F (1,168) = 1.40 .238 F (1,168) = 19.08 <.001
Age F (1,168) = 18.81 <.001 F (1,168) = 32.81 <.001 F (1,168) = 7.66 .006 F (1,168) = 6.50 .012
Sex F (1,168) = 0.842 .360 F (1,168) = 0.088 .767 F (1,168) = 0.350 .555 F (1,168) = 48.49 <.001
Race F (1,168) = 0.976 .434 F (1,168) = 0.530 .753 F (1,168) = 0.209 .958 F (1,168) = 1.14 .340
Ethnicity F (1,168) = 0.258 .612 F (1,168) = 0.016 .899 F (1,168) = 1.20 .274 F (1,168) = 0.004 .952
Education F (1,168) = 12.54 <.001 F (1,168) = 8.11 .005 F (1,168) = 0.787 .376 F (1,168) = 14.39 <.001
Income F (1,168) = 44.29 <.001 F (1,168) = 15.92 <.001 F (1,168) = 12.30 <.001 F (1,168) = 38.32 <.001
Open in a new tab

Multivariate linear regression with model selection identified covariates of interest for the mediation analysis, using the variables from the model with the lowest BIC. For sustained remission status the model with the lowest BIC included age (p=.002). For physical QOL the optimal model included pain status (p=.004), pain interference (p<.001), and income (p<.001). For psychological QOL the optimal model included age (p<.001). For social QOL the optimal model included income (p <.001). Finally, for environmental QOL the optimal model included age (p=.002).

We examined the mediating effect of pain catastrophizing (M) on the relationship between delay discounting (X) and: sustained remission (Y1), physical QOL (Y2), psychological QOL (Y3), social QOL (Y4), and environmental QOL (Y5). After controlling for the relevant covariates, pain catastrophizing significantly mediated the relationship between delay discounting and physical QOL (ꞵ=−0.051, 99% CI: −0.183, −0.018, p=.044), psychological QOL (ꞵ=−0.082, 99% CI: −0.231, −0.043, p=.009), social (ꞵ=−0.077, 99% CI: −0.121, −0.019, p=.018), and environmental QOL (ꞵ=−0.062, 99% CI: −0.232, −0.040, p=.014). Pain catastrophizing did not mediate the relationship between delay discounting and sustained remission (p>.05). The significant findings are displayed in Figure 1A–1D.

Figure 1.

Figure 1

Figure 1

Open in a new tab

Discussion

This study investigated associations between delay discounting, pain catastrophizing, sustained SUD remission, and QOL among individuals in SUD recovery. Pain catastrophizing significantly mediated the relationship between delay discounting and physical QOL, psychological QOL, social, and environmental QOL, with significant indirect effects accounting for 30.5%, 31.2%, 77%, and 17.0% of the total effects, respectively. Further, while pain catastrophizing did not mediate the relationship between delay discounting and sustained remission, lower delay discounting and pain catastrophizing were significantly associated with an increased likelihood of sustained remission. To our knowledge, this is the first study to report significant associations between delay discounting, pain catastrophizing, and QOL among individuals in recovery from SUD.

Our study aligns with previous research showing that delay discounting is associated with QOL (Athamneh et al., 2022; Einarsson, 2018; Rubenis et al., 2018) and SUD remission and SUD symptom severity (Athamneh et al., 2023; Athamneh et al., 2022; Craft et al., 2021; Dwyer et al., 2023; García-Pérez et al., 2020; Kräplin et al., 2020). Discounting rates identified in the present study among those in remission (MD=−6.28, SD=2.42) and not in remission (M=−4.57, SD=3.54) are similar to other investigations. For example, Dwyer et al., (2023) found that individuals in early remission and not in early remission had mean discounting rates of −6.52 and −4.16, respectively. These findings also align with a broader literature showing that individuals in remission have lower delay discounting rates compared to those not in remission from opioid use disorder (Craft et al., 2022) and that ex-smokers exhibit lower discounting rates compared to current smokers (Bickel et al., 1999). Taken together, the findings of this study support that delay discounting is a behavioral marker of addiction and recovery outcomes (Athamneh et al., 2022; Bickel et al., 2014).

Greater pain catastrophizing has been robustly shown to be related to poor QOL. Specifically, pain catastrophizing leads to pain hypervigilance which together negatively impacts physical and mental QOL (Wong et al., 2014). Further, greater pain catastrophizing is associated with decreased QOL in chronic pain patients (Lamé et al., 2005; Merlijn et al., 2006; Tran et al., 2015), medical conditions such as endometriosis (McPeak et al., 2018), hip osteoarthritis (Hayashi et al., 2019), chronic rhinosinusitis (Kazi et al., 2021), fibromyalgia (Galvez-Sánchez et al., 2020; Libby & Glenwick, 2010), and mental health disorders and symptomatology (Chung et al., 2012; Slawek et al., 2021). For example, Slawek et al., (2021) conducted a latent class analysis of pain catastrophizing, anxiety, depression, posttraumatic stress disorder (PTSD), and attention-deficit/hyperactivity disorder (ADHD) on a sample of patients on opioid treatments for pain. The authors found that the group characterized by high pain catastrophizing/high mental health symptoms had the poorest QOL compared to groups lower in pain catastrophizing. Our findings extend this body of research, identifying pain catastrophizing as a barrier to SUD recovery. While research on the relationship between pain catastrophizing and delay discounting is relatively limited, a prior investigation comparing discounting of monetary gains and losses among adults with and without chronic pain noted that while differences in discounting among pain groups were not observed, pain catastrophizing exerted a moderating effect. Specifically, individuals with chronic pain and high pain catastrophizing discounted monetary losses at a greater rate relative to individuals without chronic pain and low pain catastrophizing (Mistretta et al., 2022). Mistretta and colleagues’ findings suggest that pain status/pain intensity coupled with pain catastrophizing drive effects of reward processing and punishment, rather than pain status or intensity alone.

Importantly, converging neural findings in both chronic pain as well as delay discounting highlight overlapping patterns of aberrant neural activation. As individuals transition from acute to chronic pain, neural activation shifts from primarily somatosensory regions to limbic areas (Apkarian et al., 2013). Conversely, individuals who prefer smaller, immediate reward exhibit hyperactivation of limbic and paralimbic regions (McClure et al., 2004), providing a neurobiological basis for the behavioral economic findings we reported. The present findings in context of the broader literature demonstrate the potential utility of applying a Reinforcer Pathology framework to understanding the cognitive-emotional experience of pain in SUD recovery. Specifically, individuals in SUD recovery who have an immediacy bias (i.e., greater delay discounting) may render short-term events (e.g., pain) more salient. As such, individuals with higher delay discounting may be more vulnerable to over-attending to their immediate environment and resultantly, experiencing pain with a greater emotional valence. The present findings highlight the potential therapeutic utility of intervening on both delay discounting and pain catastrophizing in SUD recovery.

Our findings underscore the potential value of interventions targeting both delay discounting and pain catastrophizing in individuals with lifetime SUDs and pain. Several studies have demonstrated reductions in delay discounting and substance use following SUD treatment, including contingency management (García-Pérez et al., 2020) and episodic future thinking (EFT; Athamneh et al., 2022; Ruhi-Williams et al., 2022; Snider et al., 2016, 2021; Stein et al., 2018). EFT involves mentally projecting oneself into the future and vividly imagining positive and meaningful experiences (Patel & Amlung, 2020; Peters & Büchel, 2010; Ruhi-Williams et al., 2022), thereby shifting attention towards distal, prosocial outcomes and making longer-term benefits of recovery more salient and rewarding. Notably, one study investigated EFT as a treatment for pain and found that EFT not only reduced pain severity in a rate-dependent manner (i.e., individuals reporting the worst pain experienced the greatest reductions in pain posttreatment), reductions in delay discounting mediated reductions in pain severity (Craft et al., 2020). In this, investigations of whether EFT can ameliorate pain catastrophizing is a promising avenue for future research. Moreover, we identified that individuals who are younger, male, and lower income reported reduced rates of remission. Thus, these demographic groups who are also experiencing pain in SUD recovery may particularly benefit from treatments targeting delay discounting and pain catastrophizing to improve remission rates. Overall, this field is still nascent and would benefit from further investigation on the interaction between delay discounting and pain catastrophizing, with a particular emphasis on understanding potential moderating effects of chronic pain etiology and substance use type (e.g., opioid vs cannabis).

4.1. Limitations

Several limitations of the study should be noted. First, our results were based on a cross-sectional assessment and did not establish causality. Future longitudinal investigations are necessary to determine the validity of our mediation model. Such research would contribute to a better understanding of the complex dynamics between pain, psychological factors, and QOL as well as hold direct clinical importance for developing effective interventions. Second, while this study assessed the mediating role of overall pain catastrophizing, we did not investigate potential effects of pain catastrophizing subscales (i.e., rumination, magnification, and helplessness). Future investigation of these components will help further refine understanding of catastrophizing in the context of recovery. Third, our mediation models accounted for a relatively low amount of the indirect effect; thus, a significant portion of the variance is unexplained. Fourth, most participants in our sample were self-selected, middle-aged, and white; therefore, the generalizability of the findings may be limited. Fifth, ~20% (n=59) of individuals were excluded from our analysis because they provided non-systematic delay discounting data. However, previous investigations have found that excluding similar proportions of data due to non-systematic delay discounting results in improved data quality (Craft, Tegge, Freitas-Lemos, et al., 2022; Yeh et al., 2022). Further understanding other factors associated with non-systematic discounting and the extent to which random responding on discounting tasks correlates with responding on other measures is important. Sixth, our group has published extensive research on delay discounting using the IQRR as a crowdsourcing platform. Therefore, some overlap in our samples may exist and findings regarding associations between delay discounting and SUD remission as well as quality of life could potentially be confirmatory. However, this is a larger issue for online research - for example, while Amazon Mechanical Turk has advertised a participant pool of ~500,000 participants, researchers have reported that the pool available at any time is closer to ~7,000 (Stewart et al. 2012). Lastly, this study was conducted during the early part of the COVID-19 pandemic and we did not account for potential impact of COVID-19 on substance use and quality of life outcomes.

Conclusion

This study found that among individuals in SUD recovery reporting pain, 1) pain catastrophizing mediated the relationship between delay discounting and QOL such that individuals with a greater immediacy bias reported higher pain catastrophizing and poorer QOL and 2) greater delay discounting and pain catastrophizing were associated with a decreased likelihood of sustained SUD remission. The findings suggest that interventions that target both delay discounting and maladaptive cognitive and emotional responses to pain may lessen the negative impact of pain on SUD recovery and improve several SUD recovery outcomes.

Supplementary Material

1

Highlights.

  • Delay discounting (DD) and pain are associated with SUD outcomes

  • Associations between DD and the psychological experience of pain is understudied

  • Pain catastrophizing mediated the relationship between DD and QOL

  • Lower pain catastrophizing and delay discounting were associated with SUD remission

  • A Reinforcer Pathology framework of pain catastrophizing may inform SUD treatment

Funding:

This study was supported by NIAAA R01AA027381 and the Fralin Biomedical Research Institute at VTC. The funders had no role other than financial support.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Albein-Urios N, Martinez-González JM, Lozano O, Clark L, & Verdejo-García A. (2012). Comparison of impulsivity and working memory in cocaine addiction and pathological gambling: Implications for cocaine-induced neurotoxicity. Drug and Alcohol Dependence, 126(1–2), 1–6. 10.1016/j.drugalcdep.2012.03.008 [DOI] [PubMed] [Google Scholar]
  2. American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders, 5th Edition: DSM-5 (5th ed.). American Psychiatric Association. 10.1176/appi.books.9780890425596 [DOI] [Google Scholar]
  3. Apkarian AV, Baliki MN, & Farmer MA (2013). Predicting transition to chronic pain. Current Opinion in Neurology, 26(4), 360–367. 10.1097/WCO.0b013e32836336ad [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Athamneh LN, Brown J, Stein JS, Gatchalian KM, LaConte SM, & Bickel WK (2022). Future thinking to decrease real-world drinking in alcohol use disorder: Repairing reinforcer pathology in a randomized proof-of-concept trial. Experimental and Clinical Psychopharmacology, 30(3), 326–337. 10.1037/pha0000460 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Athamneh LN, Freitas Lemos R, Basso JC, Tomlinson DC, Craft WH, Stein MD, & Bickel WK (2022). The phenotype of recovery II: The association between delay discounting, self-reported quality of life, and remission status among individuals in recovery from substance use disorders. Experimental and Clinical Psychopharmacology, 30(1), 59–72. 10.1037/pha0000389 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Athamneh LN, King MJ, Craft WH, Freitas-Lemos R, Tomlinson DC, Yeh Y-H, & Bickel WK (2023). The Associations between Remission Status, Discounting Rates, and Recovery from Substance Use Disorders. Substance Use & Misuse, 58(2), 275–282. 10.1080/10826084.2022.2161312 [DOI] [PubMed] [Google Scholar]
  7. Audrain-McGovern J, Rodriguez D, Epstein LH, Cuevas J, Rodgers K, & Wileyto EP (2009). Does delay discounting play an etiological role in smoking or is it a consequence of smoking? Drug and Alcohol Dependence, 103(3), 99–106. 10.1016/j.drugalcdep.2008.12.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Białaszek W, Mizak S, Ostaszewski P, & Bąbel P. (2023). The role of chronic pain and pain anxiety in delay discounting of pain and monetary losses. Scientific Reports, 13(1), 19169. 10.1038/s41598-023-46378-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Bickel WK, & Athamneh LN (2020). A Reinforcer Pathology perspective on relapse. Journal of the Experimental Analysis of Behavior, 113(1), 48–56. 10.1002/jeab.564 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bickel WK, Athamneh LN, Snider SE, Craft WH, DeHart WB, Kaplan BA, & Basso JC (2020). Reinforcer Pathology: Implications for Substance Abuse Intervention. In de Wit H. & Jentsch JD (Eds.), Recent Advances in Research on Impulsivity and Impulsive Behaviors (pp. 139–162). Springer International Publishing. 10.1007/7854_2020_145 [DOI] [PubMed] [Google Scholar]
  11. Bickel WK, Koffarnus MN, Moody L, & Wilson AG (2014). The behavioral- and neuro-economic process of temporal discounting: A candidate behavioral marker of addiction. Neuropharmacology, 76 Pt B, 518–527. 10.1016/j.neuropharm.2013.06.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Bickel WK, Odum AL, & Madden GJ (1999). Impulsivity and cigarette smoking: delay discounting in current, never, and ex-smokers. Psychopharmacology, 146(4), 447–454. 10.1007/pl00005490 [DOI] [PubMed] [Google Scholar]
  13. Bickel WK, Snider SE, & Mellis AM (2019). Using an Experimental Medicine Approach to Identify Novel Determinants of Addiction. Perspectives on Behavior Science, 42(3), 385–396. 10.1007/s40614-019-00215-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Bickel WK, Stein JS, Moody LN, Snider SE, Mellis AM, & Quisenberry AJ (2017). Toward Narrative Theory: Interventions for Reinforcer Pathology in Health Behavior. Nebraska Symposium on Motivation. Nebraska Symposium on Motivation, 64, 227–267. 10.1007/978-3-319-51721-6_8 [DOI] [PubMed] [Google Scholar]
  15. Chung K-F, Tso K-C, Yeung W-F, & Li W-H (2012). Quality of life in major depressive disorder: the role of pain and pain catastrophizing cognition. Comprehensive Psychiatry, 53(4), 387–395. 10.1016/j.comppsych.2011.05.005 [DOI] [PubMed] [Google Scholar]
  16. Cleeland CS, & Ryan K. (1991). The brief pain inventory. Pain Research Group, 20, 143–147. https://oml.eular.org/sysModules/obxOml/docs/ID_363/bpi.pdf [Google Scholar]
  17. Craft WH, Tegge AN, Athamneh LN, Tomlinson DC, Freitas-Lemos R, & Bickel WK (2021). The phenotype of recovery VII: Delay discounting mediates the relationship between time in recovery and recovery progress. Journal of Substance Abuse Treatment, 108665. 10.1016/j.jsat.2021.108665 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Craft WH, Tegge AN, & Bickel WK (2020). Episodic future thinking reduces chronic pain severity: A proof of concept study. Drug and Alcohol Dependence, 215, 108250. 10.1016/j.drugalcdep.2020.108250 [DOI] [PubMed] [Google Scholar]
  19. Craft WH, Tegge AN, & Bickel WK (2021). Chronic pain and COVID-19: The association of delay discounting with perceived stress and pain severity. Experimental and Clinical Psychopharmacology. 10.1037/pha0000479 [DOI] [PubMed] [Google Scholar]
  20. Craft WH, Tegge AN, Freitas-Lemos R, Tomlinson DC, & Bickel WK (2022). Are poor quality data just random responses?: A crowdsourced study of delay discounting in alcohol use disorder. Experimental and Clinical Psychopharmacology. 10.1037/pha0000549 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Craft WH, Tegge AN, Keith DR, Shin H, Williams J, Athamneh LN, Stein JS, Chilcoat HD, Le Moigne A, DeVeaugh-Geiss A, & Bickel WK (2022). Recovery from opioid use disorder: A 4-year post-clinical trial outcomes study. Drug and Alcohol Dependence, 234, 109389. 10.1016/j.drugalcdep.2022.109389 [DOI] [PubMed] [Google Scholar]
  22. Craner JR, Sperry JA, & Evans MM (2016). The Relationship Between Pain Catastrophizing and Outcomes of a 3-Week Comprehensive Pain Rehabilitation Program. Pain Medicine, 17(11), 2026–2035. 10.1093/pm/pnw070 [DOI] [PubMed] [Google Scholar]
  23. Crombez G, Vlaeyen JW, Heuts PH, & Lysens R. (1999). Pain-related fear is more disabling than pain itself: evidence on the role of pain-related fear in chronic back pain disability. Pain, 80(1–2), 329–339. 10.1016/s0304-3959(98)00229-2 [DOI] [PubMed] [Google Scholar]
  24. Development of the World Health Organization WHOQOL-BREF quality of life assessment. The WHOQOL Group. (1998). Psychological Medicine, 28(3), 551–558. 10.1017/s0033291798006667 [DOI] [PubMed] [Google Scholar]
  25. Division of Mental Health and Prevention of Substance Abuse World Health Organization. (1998). WHOQOL User Manual. World Health Organization. https://www.who.int/publications/i/item/WHO-HIS-HSI-Rev.2012.03 [Google Scholar]
  26. Du W, Green L, & Myerson J. (2002). Cross-Cultural Comparisons of Discounting Delayed and Probabilistic Rewards. The Psychological Record, 52(4), 479–492. 10.1007/BF03395199 [DOI] [Google Scholar]
  27. Dwyer CL, Tegge AN, Craft WH, Tomlinson DC, Athamneh LN, & Bickel WK (2023). The Phenotype of Recovery X: Associations between delay discounting, regulatory flexibility, and remission from substance use disorder. Journal of Substance Use and Addiction Treatment, 209122. 10.1016/j.josat.2023.209122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Einarsson SB (2018). Impulsive buying and delay of gratification among adults with ADHD. Skemman.is https://skemman.is/handle/1946/31265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Exum AC, Sutton CA, Bellitti JS, Yi R, & Fazzino TL (2023). Delay discounting and substance use treatment outcomes: A systematic review focused on treatment outcomes and discounting methodology. Journal of Substance Use and Addiction Treatment, 149, 209037. 10.1016/j.josat.2023.209037 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Feinstein AB, Sturgeon JA, Darnall BD, Dunn AL, Rico T, Kao MC, & Bhandari RP (2017). The Effect of Pain Catastrophizing on Outcomes: A Developmental Perspective Across Children, Adolescents, and Young Adults With Chronic Pain. The Journal of Pain: Official Journal of the American Pain Society, 18(2), 144–154. 10.1016/j.jpain.2016.10.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Galvez-Sánchez CM, Montoro CI, Duschek S, & Del Paso GAR (2020). Pain catastrophizing mediates the negative influence of pain and trait-anxiety on health-related quality of life in fibromyalgia. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 29(7), 1871–1881. 10.1007/s11136-020-02457-x [DOI] [PubMed] [Google Scholar]
  32. García-Pérez Á, Vallejo-Seco G, Weidberg S, González-Roz A, & Secades-Villa R. (2020). Long-term changes in delay discounting following a smoking cessation treatment for patients with depression. Drug and Alcohol Dependence, 212, 108007. 10.1016/j.drugalcdep.2020.108007 [DOI] [PubMed] [Google Scholar]
  33. Griffin ML, McDermott KA, McHugh RK, Fitzmaurice GM, Jamison RN, & Weiss RD (2016). Longitudinal association between pain severity and subsequent opioid use in prescription opioid dependent patients with chronic pain. Drug and Alcohol Dependence, 163, 216–221. 10.1016/j.drugalcdep.2016.04.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hayashi K, Morishima T, Ikemoto T, Miyagawa H, Okamoto T, Ushida T, & Deie M. (2019). Pain Catastrophizing Is Independently Associated with Quality of Life in Patients with Severe Hip Osteoarthritis. Pain Medicine, 20(11), 2220–2227. 10.1093/pm/pny265 [DOI] [PubMed] [Google Scholar]
  35. Hayes AF (2017). Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition: A Regression-Based Approach. Guilford Publications. https://play.google.com/store/books/details?id=6uk7DwAAQBAJ [Google Scholar]
  36. International Association for the Study of Pain Task Force on Taxonomy. (1994). Classification of Chronic Pain: Descriptions of Chronic Pain Syndromes and Definitions of Pain Terms (Merskey H And Bogduk(Ed.); 2nd Edition). IASP Press. http://www.iasppain.org/Education/content.aspx?ItemNumber=1698 [Google Scholar]
  37. Jakubczyk A, Ilgen MA, Kopera M, Krasowska A, Klimkiewicz A, Bohnert A, Blow FC, Brower KJ, & Wojnar M. (2016). Reductions in physical pain predict lower risk of relapse following alcohol treatment. Drug and Alcohol Dependence, 158, 167–171. 10.1016/j.drugalcdep.2015.11.020 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Jennings MV, Lee H, Rocha DB, Bianchi SB, Coombes BJ, Crist RC, Faucon AB, Hu Y, Kember RL, Mallard TT, Niarchou M, Poulsen MN, Straub P, Urman RD, Walsh CG, Davis LK, Smoller JW, Troiani V, & Sanchez-Roige S. (2022). Identifying High-Risk Comorbidities Associated with Opioid Use Patterns Using Electronic Health Record Prescription Data. Complex Psychiatry, 8(1–2), 47–55. 10.1159/000525313 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Johnson MW, Bickel WK, & Baker F. (2007). Moderate drug use and delay discounting: a comparison of heavy, light, and never smokers. Experimental and Clinical Psychopharmacology, 15(2), 187–194. 10.1037/1064-1297.15.2.187 [DOI] [PubMed] [Google Scholar]
  40. Kazi A, West E, Rahman S, Kim S, Sima A, & Schuman TA (2021). Pain Catastrophizing and Quality of Life in Adults With Chronic Rhinosinusitis. The Laryngoscope, 131(9), 1939–1945. 10.1002/lary.29405 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kneeland ET, Griffin ML, Taghian N, Weiss RD, & McHugh RK (2019). Associations between pain catastrophizing and clinical characteristics in adults with substance use disorders and co-occurring chronic pain. The American Journal of Drug and Alcohol Abuse, 45(5), 488–494. 10.1080/00952990.2019.1581793 [DOI] [PubMed] [Google Scholar]
  42. Kräplin A, Höfler M, Pooseh S, Wolff M, Krönke K-M, Goschke T, Bühringer G, & Smolka MN (2020). Impulsive decision-making predicts the course of substance-related and addictive disorders. Psychopharmacology, 237(9), 2709–2724. 10.1007/s00213-020-05567-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lamé IE, Peters ML, Vlaeyen JWS, Kleef M. v., & Patijn J. (2005). Quality of life in chronic pain is more associated with beliefs about pain, than with pain intensity. European Journal of Pain, 9(1), 15–24. 10.1016/j.ejpain.2004.02.006 [DOI] [PubMed] [Google Scholar]
  44. LaRowe LR, Langdon KJ, Zvolensky MJ, Zale EL, & Ditre JW (2017). Pain-related anxiety as a predictor of early lapse and relapse to cigarette smoking. Experimental and Clinical Psychopharmacology, 25(4), 255–264. 10.1037/pha0000127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Libby CJ, & Glenwick DS (2010). Protective and exacerbating factors in children and adolescents with fibromyalgia. Rehabilitation Psychology, 55(2), 151–158. 10.1037/a0019518 [DOI] [PubMed] [Google Scholar]
  46. Mankovsky T, Lynch M, Clark A, Sawynok J, & Sullivan MJ (2012). Pain catastrophizing predicts poor response to topical analgesics in patients with neuropathic pain. Pain Research & Management: The Journal of the Canadian Pain Society = Journal de La Societe Canadienne Pour Le Traitement de La Douleur, 17(1), 10–14. 10.1155/2012/970423 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Martel MO, Wasan AD, Jamison RN, & Edwards RR (2013). Catastrophic thinking and increased risk for prescription opioid misuse in patients with chronic pain. Drug and Alcohol Dependence, 132(1–2), 335–341. 10.1016/j.drugalcdep.2013.02.034 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mazur JE (1987). An adjusting procedure for studying delayed reinforcement. The Effect of Delay and of Intervening Events on Reinforcement Value, 344, 55–73. https://psycnet.apa.org/fulltext/1986-98701-003.pdf [Google Scholar]
  49. McClure SM, Laibson DI, Loewenstein G, & Cohen JD (2004). Separate neural systems value immediate and delayed monetary rewards. Science, 306(5695), 503–507. 10.1126/science.1100907 [DOI] [PubMed] [Google Scholar]
  50. McPeak AE, Allaire C, Williams C, Albert A, Lisonkova S, & Yong PJ (2018). Pain Catastrophizing and Pain Health-Related Quality-of-Life in Endometriosis. The Clinical Journal of Pain, 34(4), 349–356. 10.1097/AJP.0000000000000539 [DOI] [PubMed] [Google Scholar]
  51. Mendoza T, Mayne T, Rublee D, & Cleeland C. (2006). Reliability and validity of a modified Brief Pain Inventory short form in patients with osteoarthritis. European Journal of Pain, 10(4), 353–361. 10.1016/j.ejpain.2005.06.002 [DOI] [PubMed] [Google Scholar]
  52. Merlijn VPBM, Hunfeld JAM, van der Wouden JC, Hazebroek-Kampschreur AAJM, Passchier J, & Koes BW (2006). Factors related to the quality of life in adolescents with chronic pain. The Clinical Journal of Pain, 22(3), 306–315. 10.1097/01.ajp.0000177509.93523.68 [DOI] [PubMed] [Google Scholar]
  53. Mistretta EG, Craft WH, Davis MC, McClure SM, & Bickel WK (2022). Delay discounting in adults with and without chronic pain: Differentiation across commodity and sign. Experimental and Clinical Psychopharmacology. 10.1037/pha0000619 [DOI] [PubMed] [Google Scholar]
  54. National Institute on Drug Abuse. (2017, June 15). Recovery. National Institute on Drug Abuse. https://nida.nih.gov/research-topics/recovery
  55. NIAAA Recovery Research Definitions. (n.d.). Retrieved October 25, 2023, from https://www.niaaa.nih.gov/research/niaaa-recovery-from-alcohol-use-disorder/definitions
  56. Noyman-Veksler G, Lerman SF, Joiner TE, Brill S, Rudich Z, Shalev H, & Shahar G. (2017). Role of Pain-Based Catastrophizing in Pain, Disability, Distress, and Suicidal Ideation. Psychiatry, 80(2), 155–170. 10.1080/00332747.2016.1230984 [DOI] [PubMed] [Google Scholar]
  57. Patel H, & Amlung M. (2020). Acute and extended exposure to episodic future thinking in a treatment seeking addiction sample: A pilot study. Journal of Substance Abuse Treatment, 116, 108046. 10.1016/j.jsat.2020.108046 [DOI] [PubMed] [Google Scholar]
  58. Peters J, & Büchel C. (2010). Episodic future thinking reduces reward delay discounting through an enhancement of prefrontal-mediotemporal interactions. Neuron, 66(1), 138–148. 10.1016/j.neuron.2010.03.026 [DOI] [PubMed] [Google Scholar]
  59. Potter JS, Dreifuss JA, Marino EN, Provost SE, Dodd DR, Rice LS, Fitzmaurice GM, Griffin ML, & Weiss RD (2015). The multi-site prescription opioid addiction treatment study: 18-month outcomes. Journal of Substance Abuse Treatment, 48(1), 62–69. 10.1016/j.jsat.2014.07.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  60. Quartana PJ, Campbell CM, & Edwards RR (2009). Pain catastrophizing: a critical review. Expert Review of Neurotherapeutics, 9(5), 745–758. 10.1586/ern.09.34 [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. R Core Team. (2022). R: A Language and Environment for Statistical Computing. https://www.R-project.org/
  62. Richardson EJ, Ness TJ, Doleys DM, Baños JH, Cianfrini L, & Richards JS (2009). Depressive symptoms and pain evaluations among persons with chronic pain: catastrophizing, but not pain acceptance, shows significant effects. Pain, 147(1–3), 147–152. 10.1016/j.pain.2009.08.030 [DOI] [PubMed] [Google Scholar]
  63. Rosenblum A, Joseph H, Fong C, Kipnis S, Cleland C, & Portenoy RK (2003). Prevalence and characteristics of chronic pain among chemically dependent patients in methadone maintenance and residential treatment facilities. JAMA: The Journal of the American Medical Association, 289(18), 2370–2378. 10.1001/jama.289.18.2370 [DOI] [PubMed] [Google Scholar]
  64. Rosseel Y. (2012). lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48, 1–36. 10.18637/jss.v048.i02 [DOI] [Google Scholar]
  65. Rubenis AJ, Fitzpatrick RE, Lubman DI, & Verdejo-Garcia A. (2018). Impulsivity predicts poorer improvement in quality of life during early treatment for people with methamphetamine dependence. Addiction, 113(4), 668–676. 10.1111/add.14058 [DOI] [PubMed] [Google Scholar]
  66. Ruhi-Williams P, King MJ, Stein JS, & Bickel WK (2022). Episodic Future Thinking about Smoking-Related Illness: A Preliminary Investigation of Effects on Delay Discounting, Cigarette Craving, and Cigarette Demand. International Journal of Environmental Research and Public Health, 19(12). 10.3390/ijerph19127136 [DOI] [PMC free article] [PubMed] [Google Scholar]
  67. Slawek DE, Syed M, Cunningham CO, Zhang C, Ross J, Herman M, Sohler N, Minami H, Levin FR, Arnsten JH, & Starrels JL (2021). Pain catastrophizing and mental health phenotypes in adults with refractory chronic pain: A latent class analysis. Journal of Psychiatric Research, 145, 102–110. 10.1016/j.jpsychires.2021.12.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Snider SE, LaConte SM, & Bickel WK (2016). Episodic Future Thinking: Expansion of the Temporal Window in Individuals with Alcohol Dependence. Alcoholism, Clinical and Experimental Research, 40(7), 1558–1566. 10.1111/acer.13112 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Snider SE, Turner JK, McClure SM, & Bickel WK (2021). Reinforcer pathology in cocaine use disorder: Temporal window determines cocaine valuation. Drug and Alcohol Dependence, 225, 108795. 10.1016/j.drugalcdep.2021.108795 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Stein JS, Tegge AN, Turner JK, & Bickel WK (2018). Episodic future thinking reduces delay discounting and cigarette demand: an investigation of the good-subject effect. Journal of Behavioral Medicine, 41(2), 269–276. 10.1007/s10865-017-9908-1 [DOI] [PubMed] [Google Scholar]
  71. Sullivan MJL, Bishop SR, & Pivik J. (1995). The Pain Catastrophizing Scale: Development and validation. Psychological Assessment, 7(4), 524–532. 10.1037/1040-3590.7.4.524 [DOI] [Google Scholar]
  72. Tompkins DA, Johnson PS, Smith MT, Strain EC, Edwards RR, & Johnson MW (2016). Temporal preference in individuals reporting chronic pain: discounting of delayed pain-related and monetary outcomes. Pain, 157(8), 1724–1732. 10.1097/j.pain.0000000000000576 [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Tran ST, Jastrowski Mano KE, Hainsworth KR, Medrano GR, Anderson Khan K, Weisman SJ, & Davies WH (2015). Distinct Influences of Anxiety and Pain Catastrophizing on Functional Outcomes in Children and Adolescents With Chronic Pain. Journal of Pediatric Psychology, 40(8), 744–755. 10.1093/jpepsy/jsv029 [DOI] [PubMed] [Google Scholar]
  74. Treede R-D, Rief W, Barke A, Aziz Q, Bennett MI, Benoliel R, Cohen M, Evers S, Finnerup NB, First MB, Giamberardino MA, Kaasa S, Kosek E, Lavand’homme P, Nicholas M, Perrot S, Scholz J, Schug S, Smith BH, … Wang S-J (2015). A classification of chronic pain for ICD-11. Pain, 156(6), 1003–1007. 10.1097/j.pain.0000000000000160 [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Van Damme S, Crombez G, & Eccleston C. (2002). Retarded disengagement from pain cues: the effects of pain catastrophizing and pain expectancy. Pain, 100(1–2), 111–118. 10.1016/s0304-3959(02)00290-7 [DOI] [PubMed] [Google Scholar]
  76. Van Damme S, Crombez G, & Eccleston C. (2004). Disengagement from pain: the role of catastrophic thinking about pain. Pain, 107(1–2), 70–76. 10.1016/j.pain.2003.09.023 [DOI] [PubMed] [Google Scholar]
  77. Wakaizumi K, Jabakhanji R, Ihara N, Kosugi S, Terasawa Y, Morisaki H, Ogaki M, & Baliki MN (2019). Altered functional connectivity associated with time discounting in chronic pain. Scientific Reports, 9(1), 8154. 10.1038/s41598-019-44497-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  78. Wertli MM, Eugster R, Held U, Steurer J, Kofmehl R, & Weiser S. (2014). Catastrophizing-a prognostic factor for outcome in patients with low back pain: a systematic review. The Spine Journal: Official Journal of the North American Spine Society, 14(11), 2639–2657. 10.1016/j.spinee.2014.03.003 [DOI] [PubMed] [Google Scholar]
  79. Witkiewitz K, Montes KS, Schwebel FJ, & Tucker JA (2020). What Is Recovery? Alcohol Research: Current Reviews, 40(3), 01. 10.35946/arcr.v40.3.01 [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Witkiewitz K, & Vowles KE (2018). Alcohol and Opioid Use, Co-Use, and Chronic Pain in the Context of the Opioid Epidemic: A Critical Review. Alcoholism, Clinical and Experimental Research, 42(3), 478–488. 10.1111/acer.13594 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Witkiewitz K, Vowles KE, McCallion E, Frohe T, Kirouac M, & Maisto SA (2015). Pain as a predictor of heavy drinking and any drinking lapses in the COMBINE study and the UK Alcohol Treatment Trial. Addiction, 110(8), 1262–1271. 10.1111/add.12964 [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Wong WS, Lam HMJ, Chow YF, Chen PP, Lim HS, Wong S, & Fielding R. (2014). The effects of anxiety sensitivity, pain hypervigilance, and pain catastrophizing on quality of life outcomes of patients with chronic pain: a preliminary, cross-sectional analysis. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 23(8), 2333–2341. 10.1007/s11136-014-0683-y [DOI] [PubMed] [Google Scholar]
  83. Yeh Y-H, Freitas-Lemos R, Craft WH, & Bickel WK (2022). The relationship between nonsystematic delay discounting and low-quality survey responses in a sample of smokers: ROC curve analysis. Experimental and Clinical Psychopharmacology. 10.1037/pha0000584 [DOI] [PMC free article] [PubMed] [Google Scholar]
  84. Zale EL, Maisto SA, & Ditre JW (2015). Interrelations between pain and alcohol: An integrative review. Clinical Psychology Review, 37, 57–71. 10.1016/j.cpr.2015.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

1
NIHMS2036054-supplement-1.docx (14.1KB, docx)

RESOURCES