Diminished functioning (e.g., physical disability and workrelated interference) and psychosocial interference (e.g., depressed mood, anxiety, pain-related fear, and limited social support) that often accompany chronic pain play a role in disrupted quality of life in individuals with pain. To date, existing research has primarily focused on risk and vulnerability factors related to the maintenance and exacerbation of pain. For example, negative psychological factors (e.g., negative affective states) have consistently been shown to facilitate pain and disability; depression and anxiety are highly comorbid with chronic pain and can significantly impact the pain experience, leading to greater pain severity, impaired functioning, and reduced quality of life (Bair et al., 2003; Lerman et al., 2015). In fact, evidence suggests that in older adults, depression can uniquely contribute to increased risk of developing disabling back pain (Reid et al., 2003). Similarly, reciprocal relationships between symptoms of anxiety and depression and greater pain interference have been demonstrated in the aging population (Arola et al., 2010).

Additionally, negative pain beliefs (e.g., pain catastrophizing and fear-avoidance) are known to adversely influence painrelated outcomes. Consistent evidence suggests that pain catastrophizing (pain-associated rumination, magnification, and helplessness) leads to enhanced pain and greater affective disturbance (Turner and Clancy, 1986; Sullivan et al., 2001). Likewise, individuals may develop a fear of pain and movement that facilitates avoidance of certain activities following a painful injury, when they view these activities as having the potential to cause re-injury and subsequent pain (Vlaeyen and Crombez, 1999; Vlaeyen and Linton, 2000; Crombez et al., 2012). These fear-avoidance beliefs can increase pain and functional impairment, such as physical deconditioning arising from limited mobility (Rainville et al., 2011; Wertli et al., 2014). Although informative, an emphasis on pathology/vulnerability does not capture the impact of additional contributors on the pain experience, including the potentially protective role of positive, adaptive factors on chronic pain.

While aging has been regarded as a period of loss, this view has been contrasted by mounting evidence that older adults have the capacity for resilience; evidenced by high levels of reported well-being, quality of life, and self-rated successful aging, despite worsening health and substantive physical challenges (i.e., pain) (MacLeod et al., 2016). Although there are competing approaches to the conceptualization and measurement of resilience, it has largely been characterized as a trajectory of positive adaptation in response to significant risk or adversity (Ong et al., 2009). Resilience has also been delineated as a trait-like construct, consisting of personality characteristics and stable psychosocial factors that contribute to adaptive functioning; however, it is argued that this definition lacks precision as it overlooks time-varying and contextually dependent aspects of resilient responding. Further, characterization of resilience as purely dispositional fails to account for the malleability of human functioning or the consideration of how resilience can be promoted through therapeutic intervention. More recent theoretical models have conceptualized resilience as a dynamic process, characterized as an interplay between traitbased resources (e.g., personality factors) and active mechanisms (e.g., cognitive and affective states) that influence adaptive coping responses to pain. This process, in turn, promotes sustainability in meaningful and valued activities, personal growth as a result of one's experience with chronic pain, and the capacity to recover or rebound from disruptions in physiological, emotional, or cognitive functioning (e.g., pain flare-up) (Sturgeon and Zautra, 2010, 2013).

Abundant literature has identified multiple psychological contributors to resilience.  Perceived social support also shows benefits in individuals with chronic pain, which may be particularly relevant for older adults as social engagement provides a means of coping with pain (Molton and Terrill, 2014). In fact, perceptions of support are associated with fewer depressive symptoms (Ferreira and Sherman, 2007; Lopez-Martinez et al., 2008; Lee et al., 2016; McKillop et al., 2017),

greater quality of life (Ethgen et al., 2004), lower pain intensity (Lopez-Martinez et al., 2008), and improvements in postsurgical (i.e., lower-limb amputation) functioning (Hanley et al., 2004).

Together with psychological and social functioning, numerous lifestyle and health factors also contribute to resilience. Tobacco usage is associated with a greater incidence and prevalence of pain (Goldberg et al., 2000; Shiri et al., 2010b), while multimorbidity has profound consequences on the occurrence (Schneider et al., 2007) and worsening of pain and physical functioning (Calders and Van Ginckel, 2018). Similarly, sleep has been posited as a key regulator of pain modulation, with effects on somatosensory sensitivity (Campbell et al., 2015; Schrimpf et al., 2015), pain severity (Gerhart et al., 2017), and interference (Kothari et al., 2015). Although sleep and pain are temporally related, sleep quality appears to have a more robust influence on pain symptomatology than vice versa (Finan et al., 2013; Gerhart et al., 2017), and may even serve as a risk factor for pain development and chronification (Gupta et al., 2007; Finan et al., 2013). Likewise, intervening on sleep may have salutary effects on pain, with recent evidence highlighting the influence that treatment-related sleep improvements have on pain intensity (de la Vega et al., 2019). Exercise as a therapeutic modality also confers many health benefits but can be especially potent for pain symptomatology. Increasing evidence suggests that sedentary behavior is inversely associated with functional performance (Lee et al., 2015), with greater physical activity predicting more optimal long-term outcomes in pain and disability (Pinto et al., 2014). Also, acute bouts of exercise yield analgesic effects on painevoked laboratory measures (Burrows et al., 2014), yet appear to be differentially influenced by physical activity behavior (e.g., sedentarism and level of physical activity) (Naugle et al., 2017; Ohlman et al., 2018). In turn, sedentary behavior may promote greater adiposity (i.e., body mass index and waist–hip ratio) which can be a risk factor for pain and functional disability (Fanuele et al., 2002; Shiri et al., 2010a; Walsh et al., 2018), presumably through a myriad of pathways such as increased joint loading, biochemical mediators, and mood disturbance (Okifuji and Hare, 2015). The association between pain and obesity is likely reciprocal, however, with chronic pain also potentiating risk for weight gain. Given that obesity is a potentially modifiable factor, some studies have highlighted the efficacy of weight loss interventions in reducing the incidence and severity of pain (Hughes et al., 2018; Dunlevy et al., 2019).

Taken together, there is a wealth of literature supporting the protective effects of psychological, social, and lifestyle factors in the experience of pain. However, much of research has examined these factors in isolation, with limited consideration of their additive contributions. Even more, while existing conceptualizations of resilience have varied widely, it has commonly been defined as a trait-based construct comprised primarily of psychological facets (Windle et al., 2011). Thus, prevailing approaches to the study of resilience may not fully capture the multidimensionality of the construct or how resilient functioning can be promoted through various systems. Extending our current conceptual models may carry important implications in terms of explicating the resources and mechanisms that promote adaptive pain outcomes. Only a modest literature has addressed the notion of multisystem resiliency. For instance, Agrigoroaei and Lachman (2011) found that a protective composite of psychosocial and behavioral factors (i.e., control beliefs, social support quality, and physical activity) predicted cognitive functioning, above and beyond the effects of sociodemographics, physical health, and cognitive activity engagement. Further, the combination of low-risk lifestyle factors (i.e., smoking, physical activity, adiposity, alcohol use, and diet) was more robustly associated with longer leukocyte telomere length (a marker of cellular aging) in women, as compared to the independent effects of each factor (Sun et al., 2012). Similarly, Johnson et al. (2019) found that a psychosocial and behavioral index of resilience [i.e., optimism, PA, negative affect (NA), active coping, perceived stress, social support, tobacco use, and waist–hip ratio] had a stronger association with telomere length in older adults with knee pain, relative to a composite comprised solely of psychological functioning measures. Overall, these findings provide compelling support for an integrative approach to studying resilience and underscore the importance of exploring these contributions in chronic pain.

The current study sought to address this gap in the literature by examining the association of multisystem resiliency with pain and psychological outcomes in a sample of older adults with cLBP. Given the dimensionality of resilience, several psychosocial resources (i.e., PA, hope, positive well-being, optimism, and social support) and health/lifestyle variables (i.e., waist–hip ratio, body mass index, physical health comorbidities, and smoking status) were considered for inclusion. These measures were selected as they represent modifiable factors with strong, empirical support for their impact on pain and health-related processes. Therefore, the primary aims were to: (1) empirically identify domains of resilience based upon psychological, social, and health-related factors and (2) using cluster analysis, explore whether resiliency phenotypes differ across measures of physical function, pain intensity, disability, and psychological functioning. It was hypothesized that: (1) homogenous subgroups would emerge from patterns of psychological, health, and social resiliency and (2) individuals with more resilient phenotypes (i.e., higher in protective resources) would exhibit higher physical function, lower self-reported pain and disability, and greater psychological functioning.

# MATERIALS AND METHODS

# Participants and Procedures

This was a cross-sectional study based on a secondary data analysis from the Adaptability and Resilience in Aging Adults (ARIAA) study, a project evaluating the effects of resilience mechanisms on pain modulatory capacity among individuals with cLBP. Sample size estimations were based upon previous pilot data (Bartley et al., 2019b) establishing that 60 participants would provide power of 0.80 at 0.05 (two-tailed) for detecting moderate to large effect sizes between measures of resilience and pain.

Older adults (ages 60+ years) with cLBP (N = 69) were recruited from the community via posted fliers, media

announcements, and word-of-mouth referral. All participants provided verbal and written informed consent. Participants were included if they reported at least mild LBP (≥2/10) occurring on at minimum half of the days during the preceding 3 months. Enrollment in the study was not limited to LBP (due to the presence of medical comorbidities in this population) as long as LBP was an individual's primary pain condition. Exclusion criteria were as follows: recent vertebral fracture; back surgery within the past 6 months; diagnosis of cauda equina syndrome; uncontrolled hypertension (≥150/90); severe cardiovascular disease (e.g., recent heart attack); neurological disease associated with somatosensory abnormalities (e.g., neuropathy, seizures, and Parkinson's disease); current major medical illness (e.g., metastatic or visceral disease); chronic opioid use; and systemic inflammatory disease (e.g., spondylarthropathies such as ankylosing spondylitis and systemic lupus erythematosus). Participants were provided up to \$100 compensation upon completion of the study.

The University of Florida Institutional Review Board approved all study procedures. Initially, participants were evaluated for study inclusion and exclusion through a brief telephone screen. The following sociodemographic and health data were obtained as part of the screening: self-reported sex, age, and a brief health history including the presence of major medical illnesses, recent back-related injuries or surgeries, and LBP symptoms. If eligible, participants attended two, 2–3.5-h appointments scheduled approximately 1 week apart. During Session 1, eligibility criteria were verified through a self-reported demographic and medical history assessment, and participants completed anthropometric tests, psychosocial questionnaires, and functional performance measures. During the time in between Sessions 1 and 2, participants completed several questionnaires at home. Sensory pain testing was conducted during Session 2 (data not reported), and additional psychosocial questionnaires were also completed at that visit.

# Measures

### Predictors of Multisystem Resilience

#### **Positive and negative affect schedule**

The 20-item Positive and Negative Affect Schedule (PANAS) was used to examine PA and NA (Watson et al., 1988). Respondents were presented with 10 positively valenced and 10 negatively valenced terms that are rated on a five-point scale ranging from 1 (very slightly or not at all) to 5 (extremely) resulting in scale scores for PA and NA, with higher scores indicating increased positive and NA, respectively (only PA scores were included in the current analysis). Reliability tests indicated high internal consistency of items on the PA scale (α = 0.90).

#### **Adult dispositional hope scale**

The Adult Dispositional Hope Scale (ADHS) is a 12-item questionnaire that includes eight statements measuring two aspects of hope: pathways (e.g., "There are lots of ways around a problem.") and agency (e.g., "I energetically pursue my goals."), as well as four "filler" statements that are not included in scoring (Snyder et al., 1991). Items are rated on a scale ranging from 1 (definitely false) to 8 (definitely true) and respondents select the number that best describes them for each statement. Higher scores indicate greater trait levels of hope. Reliability analyses from the current investigation revealed Cronbach's α for the ADHS = 0.92, indicating high internal consistency for this measure.

### **PROMIS positive affect and well-being scale**

The Patient-Reported Outcomes Measurement Information System (PROMIS) PA and Well-Being Scale was used to measure PA and overall sense of satisfaction with life (Salsman et al., 2013). This scale consists of 23 items rated on a 1 (never) to 5 (always) scale to indicate how often respondents experienced positive emotion and/or purpose/meaning in life (e.g., "[Lately], I had a sense of balance in my life."). Higher scores reflect greater PA and well-being (Cronbach's α = 0.97).

#### **Life-orientation test-revised**

Dispositional optimism was evaluated using the Life-Orientation Test-Revised (LOT-R), which consists of 10 items (including four unscored items and three reverse-scored items). Participants were asked to use a 5-point scale ranging from 0 (strongly disagree) to 4 (strongly agree) and rate the degree to which they agreed with the presented statements (e.g., "In uncertain times, I usually expect the best.") (Herzberg et al., 2006). Higher LOT-R scores indicate greater optimism. This measure demonstrated adequate reliability in the sample (α = 0.73).

#### **PROMIS support (emotional, instrumental, informational)**

To measure social functioning, the short forms of the PROMIS emotional (eight items; e.g., "I have someone who makes me feel appreciated."), instrumental (four items; e.g., "Do you have someone to take you to the doctor if you need it?"), and informational (four items; e.g., "I have someone to turn to for suggestions about how to deal with a problem.") support scales were administered (Hahn et al., 2014). Items are rated on a 1 (Never) to 5 (Always) scale for all three domains, with higher scores indicating greater social support. All three scales were found to have high internal consistency and were also highly reliable with each other: emotional (α = 0.97), instrumental (α = 0.96), informational (α = 0.96), all support measures combined (α = 0.97).

#### **Anthropometric tests: body composition**

During Session 1, participants' waist (5 cm above the navel) and hip circumferences (widest part of the hips) were calculated (in cm) using a measuring tape, with waist–hip ratio determined by dividing the waist circumference by the hip circumference. Body weight was measured to the nearest 0.1 kg using a digital scale (Healthometer) and height was assessed to the nearest centimeter using a wall stadiometer. Calculation of BMI was determined by weight in kilograms divided by height in meters squared.

#### **Health comorbidities**

To determine the presence of physical health comorbidities, participants completed a health status questionnaire whereby they were asked to place an "X" next to any current medical conditions (i.e., high blood pressure, heart disease, diabetes, asthma/breathing problems, kidney/renal disease, thyroid problem, neurological disorder, or other self-reported

health conditions). Medical diagnoses were placed into ICD-10 diagnostic categories for reporting purposes.

#### **Smoking status**

Current cigarette smoking status was assessed using the following question: "How would you describe your cigarette smoking?" Possible responses included: "never smoked," "used to smoke but have now quit," and "current smoker," and individuals were categorized as either current smokers (yes) or non-smokers (no).

### Study Outcomes

#### **Back performance scale**

Functional performance and movement-evoked pain (MEP) were measured using the Back Performance Scale (BPS). The BPS consists of a series of tasks (i.e., Sock Test, Pick-up Test, Rollup Test, Fingertip-to-Floor Test, and Lift Test) that are designed to measure functional capacity during completion of mobilityoriented activities that have been deemed to be particularly difficult for individuals with back pain (Magnussen et al., 2004; Strand, 2017). An evaluator assesses the degree to which these tasks are completed. Physical functioning scores range from 0 to 3 for each test (total scale score = 0–15), with increasing scores indicating greater difficulty with task performance. MEP was measured by asking participants to rate their current LBP from 0 (no pain) to 100 (most intense pain imaginable) immediately after completion of each of the five tasks on the BPS. MEP was determined from an average of the five pain ratings. Internal consistency was good for this measure (α = 0.83).

#### **PROMIS physical function**

To evaluate the general physical functioning, the short form of the PROMIS Physical Function measure was administered (Rose et al., 2008, 2014). This scale includes four questions (e.g., "Are you able to do chores such as vacuuming or yard work?") to examine the difficulty with which an individual is able to complete certain functional tasks. Ratings are made from 5 (without any difficulty) to 1 (unable to do) and lower scores indicate greater difficulty with task performance. This measure demonstrated high reliability among the sample (α = 0.85).

#### **PROMIS pain intensity**

The three-item PROMIS Pain Intensity short form measure was used to evaluate pain intensity over the past week (Cella et al., 2010). This scale asks respondents to report their average and worst pain during the past 7 days, as well as pain at the time of questionnaire completion by providing a 1 (no pain) to 5 (very severe) pain rating. The PROMIS Pain Intensity scale demonstrated good reliability (α = 0.81).

#### **Roland-morris disability questionnaire**

The Roland-Morris Disability Questionnaire (RMDQ) is a selfreport measure that assesses health status and disability related to LBP (Roland and Morris, 1983). The RMDQ is comprised of 24 statements such as "I stay at home most of the time because of my back" and "I only walk short distances because of my back." Respondents are instructed to indicate which of the statements describe their current experience. The number of endorsed items is summed to obtain a total score (more items endorsed = greater disability). Internal consistency was high for this measure (α = 0.87).

## **PROMIS depression scale**

The eight-item short form of the PROMIS Depression Scale was used to assess depressive symptoms (e.g., "I felt worthless.") (Pilkonis et al., 2011). Respondents rate the frequency of their experience of each symptom in the past 7 days from 1 (never) to 5 (always), with higher scores indicating a greater presence of depressive symptoms. The PROMIS Depression Scale demonstrated high reliability (α = 0.93).

#### **Brief resilience scale**

Trait resilience was examined using the Brief Resilience Scale (BRS), which is a six-item measure examining the ability to bounce back and recover from stressful events and challenges (e.g., "I tend to bounce back quickly after hard times.") (Smith et al., 2008). Responses are provided using a five-point scale (1 = strongly disagree, 5 = strongly agree), with total scores ranging from 6 to 30. Higher scores on the BRS indicate greater resilience (Cronbach's α = 0.84).

#### **World health organization quality of life-brief**

The World Health Organization Quality of Life-Brief (WHOQOL-BREF) is a 26-item questionnaire designed to measure quality of life across four domains over the past 2 weeks: physical health, psychological health, social relationships, and environment (Skevington et al., 2004). The first item of the WHOQOL-BREF (i.e., "How would you rate your quality of life?") was used to examine overall quality of life. This item is rated from 1 (very poor) to 5 (very good).

# Statistical Analysis

All analyses were conducted using SPSS 24 and significance level was set at p ≤ 0.05 (two-tailed). Means, standard deviations, and counts for demographic characteristics were calculated using descriptive statistics. Zero-order correlations were conducted between sociodemographic characteristics and outcome variables (i.e., physical function, MEP, pain intensity, back-related disability, depressive symptoms, general resilience, and quality of life). Demographic variables that were significantly related to outcome variables were controlled for in cluster analyses. The following 11 variables were entered into a PCA to characterize the dimensionality of each resilience measure: PA, dispositional hope, positive well-being, optimism, waist–hip ratio, body mass index, physical health comorbidities, smoking status, emotional support, instrumental support, and informational support. PCA with oblique rotation was used to allow for correlation between factors, with the recommendation that at least three items load on a factor and a difference of ≥0.20 was present between cross-loadings (Howard, 2016). Components with eigenvalues >1 were selected for further analysis and the scree plot was inspected to confirm the number of factors to be retained. Hierarchical cluster analysis employing Ward's clustering method with squared Euclidean distances as the similarity measures was conducted to identify subgroups of individuals that differed across empirically derived resilience domains. Agglomeration coefficients were examined to identify the cluster solution that

best represented the data, with the optimal number being chosen based upon the point at which the percentage change was the largest between the clusters (Milligan and Cooper, 1985). Chisquare analysis for categorical variables or analysis of variance (ANOVA) for continuous variables was employed to examine cluster group differences across demographic composition. Differences across physical function, pain, and psychological outcomes were assessed using multivariate ANOVA's, controlling for the effects of relevant sociodemographic characteristics. Significant findings on multivariate analyses were followed by Sidak-corrected post hoc comparisons. To obtain effect size estimates associated with F-tests, partial eta-squared (η 2 p ) was calculated (small = 0.01, medium = 0.06, and large = 0.14).

# RESULTS

# Participant Characteristics

Demographic characteristics (means and SDs) are reported in **Table 1**. Participants were mostly female (57%), White/Caucasian

TABLE 1 | Demographic and clinical characteristics.

(70%), had a college degree (50%), were married or partnered (52%), and were not employed (85%). Average age was 68 years (range: 60–93 years), duration of back pain was 16.4 years (range: 1–56 years), and participants reported back pain of moderate intensity during the initial session (M = 5.5, range = 2–10). Two of the 69 participants discontinued after the first session due to time constraints, and 7 participants who were initially eligible were excluded during their first appointment (n = 1 use of exclusion medications, n = 3 exclusionary medical condition, n = 3 not meeting pain duration criteria), thus leaving 60 participants. Based on ICD-10 classifications (World Health Organization, 2016), the following medical comorbidities/diseases were reported: circulatory and respiratory (n = 27, 45.0%), metabolic and endocrine (n = 14, 23.3%), genitourinary and renal (n = 4, 6.7%), digestive (n = 3, 5.0%), skin/subcutaneous tissue (n = 3, 5.0%), eye (n = 3, 5.0%), musculoskeletal (n = 1, 1.6%), nervous system (n = 1, 1.6%), infectious disease (n = 3, 5.0%), and sleep disorders (n = 2, 3.3%). Current smoking was reported among 16.7% (n = 10) of the sample.

<sup>∗</sup>Some data not reported. HS, high school.

\*p ≤ 0.05, \*\*p ≤ 0.01. Sex coded: 0 = male, 1 = female; Race coded: 0 = white, 1 = black/other; Marital Status coded: 0 = married/partnered, 1 = not married/partnered; Employment coded: 0 = employed, 1 = not employed. BPS, Back Performance Scale; PROMIS, Patient-Reported Outcomes Measurement Information System; RMDQ, Roland-Morris Disability Questionnaire; BRS, Brief Resilience Scale; WHOQOL, World Health Organization Quality of Life questionnaire.

# Zero-Order Correlations

To identify potential study covariates, zero-order correlations were analyzed across sociodemographic variables and study outcomes (**Table 2**). In general, age, sex, race, education, marital status, employment, income, and back pain duration were associated with physical function, pain, and psychological outcomes (all ps < 0.04). Hence, analyses assessing cluster group differences across study outcomes included these sociodemographic variables as statistical covariates.

# Principal Components Analysis

A PCA was conducted with all 11 items using oblique rotation (direct oblimin), resulting in a four-factor solution. However, on the basis of our item selection criteria (i.e., more than or equal to three items load on a factor), this solution was eliminated as it returned one component containing smoking status. This variable was therefore removed from the model. The resulting analysis revealed the presence of a three-factor solution with eigenvalues over Kaiser's criterion of 1, accounting for 72.4% of the variance in scores. The Kaiser–Meyer–Olkin measure verified the sampling adequacy for the analysis (Kaiser–Meyer– Olkin = 0.78; Bartlett's test of sphericity = <0.001) and all KMO values were above the acceptable limit of 0.50 (Field, 2013). Inspection of the scree plot confirmed inflexions that would justify retaining three factors. **Table 3** reports the factor loadings after rotation, with Component 1 representing positive, psychological factors (factor loadings 0.67–0.91), Component 2 denoting health-related functioning (factor loadings 0.60–0.78), and Component 3 reflecting social support (factor loadings 0.77– 0.86). The factor loadings from each domain were used in subsequent cluster analysis.

# Cluster Analysis Across Resilience Domains

The three composite domains were subjected to Cluster Analysis to identify empirically derived classifications based upon profiles of psychological, health, and social resiliency (**Figure 1** and **Supplementary Table 1**). For ease of interpretation, the health domain was reverse scored, such that lower scores reflected higher waist–hip ratio, body mass index, and health comorbidities. Four clusters were revealed and characterized by the following: (1) Cluster 1: High Resilience group (n = 25, 41.7%): high levels of psychological, health, and social support functioning; (2) Cluster 2: High Health/Low Psychosocial group (n = 13, 21.7%): optimal health-related functioning and low levels of psychosocial function; (3) Cluster 3: High Psychosocial/Low Health group (n = 15, 25.0%): poor health functioning, high psychological functioning, and moderate-to-high social support; and (4) Cluster 4: Low Resilience group (n = 7, 11.7%): low levels of functioning across psychological, social, and healthrelated factors. There were no sociodemographic differences across cluster groups, with the exception of the High Resilience group (Cluster 1) having the highest proportion of participants who were married or partnered (**Table 1**); thereby, consistent with previous research (Mun et al., 2019).

# Psychosocial Profiles Across Cluster Group

After adjusting for age, sex, race, education, marital status, employment, income, and back pain duration, significant differences across cluster membership emerged in functional performance, physical function, back-related functional disability, depression, general resilience, and quality of life

Bolded values were retained in each component.

(**Figures 2**, **3** and **Supplementary Table 2**). In particular, functional performance and functional disability due to LBP were poorest among the Low Resilience group, relative to individuals in the High Resilience and High Health/Low Psychosocial cluster groups (ps < 0.05). Post hoc comparisons were non-significant across cluster groups for self-reported physical function, although the difference between the High Health/Low Psychosocial and Low Resilience groups approached significance with a large effect size (η 2 <sup>p</sup> = 0.22). For psychological outcomes, individuals in the Low Resilience group reported the highest levels of depression (ps ≤ 0.001) and lowest quality of life (ps ≤ 0.04), relative to all other groups. Depression was also lower in the High Resilience group (p = 0.05), when compared to individuals with low psychosocial resources (Cluster 2). The High Resilience (p = 0.04) and High Psychosocial/Low Health (p = 0.02) clusters had greater general resilience than the High Health/Low Psychosocial group. In addition, while the Low Resilience group reported statistically lower levels of general resilience relative to the High Psychosocial/Low Health group (p = 0.04), these effects only approached significance (p = 0.07) when compared with the High Resilience group. No differences in MEP (p = 0.08) or self-reported pain intensity (p = 0.33) were detected across cluster groups.

# DISCUSSION

Although risk factors have been extensively studied in respect to pain, there is a burgeoning literature supporting the role of resilience mechanisms in promoting adaptive pain outcomes. Mounting evidence signifies that a multitude of psychological, social, and physical/biological factors confer resilience; however, much of the extant literature has focused on psychological resources. Moreover, resilience factors have predominantly been examined in isolation, thus overlooking their potentially synergistic and additive effects. Due to the exponential growth of older adults and global burden of chronic pain in this population, explicating the mechanisms that protect against pain and disability is of critical importance. While a modest literature has considered the cumulative effects of a broad range of personal resources (Agrigoroaei and Lachman, 2011; Sun et al., 2012; Johnson et al., 2019), we have extended previous research by capturing the multifaceted nature of resilience and exploring its influence on pain outcomes among older adults with cLBP.

Aligning with study hypotheses, we found evidence of phenotypic patterns of resilience based upon psychological, social, and health-related functioning. In particular, individuals with a higher array of protective factors exhibited more optimal outcomes in physical function, disability, and psychological processes (despite similar levels of pain), suggesting potentially important benefits of multiple adaptive resources. Overall, these findings signify that individuals with a more resilient phenotype may have a greater sense of coherence that allows them to mobilize resources to successfully navigate the ongoing challenges associated with pain. This would align with Antonovsky's salutogenic model of health (Antonovsky, 1996) which highlights the importance of coping strengths in fostering one's capacity for optimal health and well-being.

While a number of studies have classified patients according to negative psychological and lifestyle variables (Rabey et al.,

degree of protective resources had higher functional performance and self-reported physical function, as well as lower disability.  Higher scores on PROMIS function, better physical functioning; BPS, Back Performance Scale; ME, Movement-Evoked; PROMIS, Patient-Reported Outcomes Measurement Information System; RMDQ, Roland-Morris Disability Questionnaire.

FIGURE 3 | Psychological functioning across multisystem resilience profiles. Compared to Cluster 4 (Low Resilience group), individuals with more resilient phenotypes exhibited lower depressive symptoms, and higher general resilience and quality of life. PROMIS, Patient-Reported Outcomes Measurement Information System; BRS, Brief Resilience Scale; WHOQOL, World Health Organization Quality of Life questionnaire.

2016; Almeida et al., 2018), limited research has stratified subgroups according to sources of resilience. In fibromyalgia, Mun et al. (2019) found that individuals with a higher degree of personal resources (i.e., pain acceptance, resilience, social support, sleep quality) exhibited lower levels of morning pain and depressive symptoms, as well as afternoon pain interference (although effects varied according to level of depression). Similar findings were also observed among patients with chronic neurological/neuromuscular disease, as those with a more resilient profile reported lower interference from pain (Mun et al., 2019). Furthermore, our findings echo a previous study in knee osteoarthritis (Cruz-Almeida et al., 2013), whereby a subgroup characterized by high optimism and low NA exhibited the lowest degree of pain, disability, and somatosensory sensitivity.

Results also suggest that health and psychosocial factors are differentially expressed across older adults with LBP. In particular, when compared to individuals with low resilience (Cluster 4), those with a higher degree of protective resources exhibited lower depression and higher quality of life. However, the findings for general resilience were more robust among individuals with higher social support and positive, psychological function (Cluster 3), thus underscoring the protective nature of psychosocial resources in coping with stress and adversity. Likewise, more favorable outcomes in functional performance and disability were not only observed among individuals with higher overall resilience, but also among those with more adaptive health-related function. This is not entirely surprising as higher disease burden and adiposity may facilitate decrements in functional capacity through mechanisms linked to frailty, psychological comorbidities, physiological dysregulation, increased joint loading, cardiopulmonary reserve, and activity restriction, among others (Fried et al., 2004; Kalyani et al., 2010; Calders and Van Ginckel, 2018). What is more, the influence of these health factors is likely not independent; rather, their effects are interactive and systemic, impacting multiple homeostatic processes to potentiate downstream effects on disability and function (Chapman et al., 2008).

Importantly, our findings have important clinical value as the protective resources we examined are modifiable. For instance, a greater emphasis on enhancing social support and positive, psychological processes may be particularly advantageous for improving adaptive coping, and to some extent, attenuating depressive symptoms in Cluster 2 individuals. Interventions with empirical support for their efficacy (e.g., cognitive-behavioral therapy and spouse-assisted training) (Edwards et al., 2016) are likely to derive some benefit; however, therapies focusing on harnessing resilience through positive, psychological resources [e.g., positive activity interventions (PAIs)] have also shown promise in chronic pain populations (Hausmann et al., 2014, 2017; Muller et al., 2016; Peters et al., 2017). For individuals with poorer health-related functioning (Cluster 3), minimizing the severity of multimorbidity and reducing weight burden through diet and exercise promotion may mitigate functional decline and disability. Ultimately, the development of strategies for the prevention of obesity and medical comorbidities is a critical directive. Results also suggest that individuals with a low resilient profile would likely benefit from a multimodal approach that optimizes both psychosocial and health-related resources. In particular, combining psychotherapy with lifestyle modification may yield protective benefits in physical and emotional functioning. For individuals in Cluster 1 who appear to be adapting well despite the presence of cLBP, these treatments may be less justified. Surprisingly, while indices of pain intensity (i.e., PROMIS pain intensity, MEP) were lower among individuals with a greater degree of resources (Cluster 1), these effects failed to reach significance across cluster groups. Although it is conceivable that other unmeasured resources may have a more robust influence on pain severity, it is also possible our study was underpowered to detect pain-specific effects. On the basis of the effect sizes observed (η 2 <sup>p</sup> = 0.07 to 0.14), a power analysis revealed that a sample size of 72 to 150 participants would be adequate to detect significant effects in MEP and self-reported pain intensity, respectively. Thus, consideration of these findings in a larger sample is warranted.

Multiple resources shape the expression and development of resilience in chronic pain. While the measurement of resilience is inherently complex (Southwick et al., 2014) due to varying definitions and multiple methods by which to assess this construct, our current models lack precision and fail to account for the multifaceted nature of resilience. Indeed, recent theoretical literature (Liu et al., 2017) posits that resilience should be conceptualized from various levels of analysis that includes intraindividual (e.g., physiological, health behaviors), interpersonal (e.g., personality correlates, coping appraisals), and socio-ecological factors (e.g., socioeconomic status, group membership). While exploring the independent determinants that buffer against negative pain sequelae has clinical utility, recognition of resilience from a multidimensional perspective will likely provide a greater understanding of adaptive capacity. Expanding our current models of resilience and considering new approaches, both theoretically and statistically, in how resilience is conceptualized and assessed in the context of pain will be an important future direction.

# Strengths and Limitations

Several strengths of our study merit acknowledgment. To our knowledge, the current investigation is the first to examine resilience from a multidimensional perspective in older adults with cLBP. Participants were phenotyped according to several adaptive resources, offering a novel opportunity to explore how pain, disability, and psychological functioning differ across resiliency profiles. We used an empirical approach to characterize our resilience indices, which provides a statistical, data-driven, and robust method for classification of subgroups. A number of valid and reliable measures were also utilized across the assessment of psychological, social, and health-related functioning. Further, despite the small sample (N = 60), large effect sizes (ranging from η 2 <sup>p</sup> = 0.17 to 0.45) were observed for pain and psychological outcomes.

In spite of these strengths, a few limitations are worth noting in the interpretation of results. First, given the nature of

cluster analysis our findings should be considered exploratory. In addition, our relatively modest sample may have influenced our classification of individuals using cluster analysis, with few individuals categorized into particular profiles (e.g., only seven participants comprised the "Low Resilience" cluster). This may have impacted the external validity of the study, thereby compromising generalizability. Future studies with larger sample sizes are warranted to confirm these findings. In light of these limitations, results should be interpreted cautiously. Although we employed a robust, empirical approach to devise our resilience domains, smoking status was eliminated from the analyses due to its retention as an independent factor. Despite smoking being a relevant lifestyle behavior with tremendous health consequences, we have confidence that the omission of this variable did not alter our findings, as it only contributed a small amount of additional variance (4%) to our model. Furthermore, analyses revealed that smoking status did not differ across cluster groups (p = 0.18). Nevertheless, there are strengths and challenges to various statistical methods (e.g., factor analysis, z-scores, and median split approach) and future studies should consider the comparison of these approaches, as well as their clinical relevance. Of note, some cross-loadings (>0.30) were observed across psychological, health, and social support factors. Although this may be a limitation, this phenomenon is also anticipated given the natural correlation among these constructs (e.g., health-related constructs correlate with many other variables). Also, medical comorbidities were determined via participant self-report, which may not provide a complete representation of individuals' health histories. Medical records should be obtained for verification of health history in future endeavors. Related, because we excluded individuals experiencing major medical conditions, these results may not generalize to those who experience more severe health comorbidities.

Although multiple psychosocial and health constructs were used to derive subgroups, this did not reflect an exhaustive list of protective resources.  Moreover, there is a need to replicate these findings in a more diverse sample, especially given growing evidence of the impact of race/ethnicity on resilience and painrelated outcomes (Bartley et al., 2019a). Inclusion of these and other diversity variables will be important considerations for future studies. Employing additional measures of health/lifestyle factors (e.g., physical activity, sleep, diet, alcohol, and drug consumption), as well as other positive psychological (e.g., selfefficacy and sense of coherence) and social support-related indices (e.g., quantity versus quality of support) will be key to improving our understanding of resilience. Likewise, given the importance of external determinants of resilience, several other contextual and social/environmental factors, including socioeconomic status and access to healthcare, are critical areas to examine (Liu et al., 2017). And finally, it may be beneficial to use a dual-focus approach to closely examine both risk and resilience factors and how this interplay influences multisystem resiliency in respect to pain (perhaps even considering the "degree" of negative factors, such as the impact of lower NA, less catastrophic thinking, etc.). For example, personal resources (such as optimism or PA) may broaden an individual's coping repertoire by facilitating engagement in adaptive behaviors that mitigate the narrowing effects of pain catastrophizing (Fredrickson, 2001). This would align with predominant risk-resilience models that highlight the consideration of both vulnerability and protective mechanisms in understanding individual adaptation to pain (Sturgeon and Zautra, 2013; Goubert and Trompetter, 2017).

# CONCLUSION

In sum, our findings support the contribution of protective factors in the context of pain and suggest that examining resilience from a multisystem perspective may have significant clinical utility. Importantly, homogenous subgroups emerged from psychological, social, and health-related processes, with lower disability, better functional performance, and higher psychological functioning observed among individuals with a more resilient phenotype. Consideration of the multiple resources that harness resilience, including their additive effects, may improve our understanding of adaptive function among older adults with chronic pain and ultimately facilitate the development of more targeted clinical care.

# DATA AVAILABILITY

The datasets generated for this study are available on request to the corresponding author.

# ETHICS STATEMENT

The studies involving human participants were reviewed and approved by the University of Florida Institutional Review Board. The patients/participants provided their written informed consent to participate in this study.

# AUTHOR CONTRIBUTIONS

EB and SP contributed to the conception and design of the study, and wrote the first draft of the manuscript. EB performed the statistical analysis. All authors reviewed the text critically and approved the submitted version.

# FUNDING

This work was supported by the National Institutes of Health (NIH)/the National Institute on Aging (NIA) grant (K99AG052642) awarded to EB and the NIH/NIA grant (T32AG049673) provided to the University of Florida (SP). Dr. EB is currently funded by the NIH/NIA grant (R00AG052642).

# ACKNOWLEDGMENTS

fpsyg-10-01932 October 8, 2020 Time: 13:51 # 12

The authors would like to thank Ralisa Pop, Stephanie Hersman, Morgan Ingram, Jordan McGee, Kylie Broskus, Paige McKenzie, and Michelle Jacomino for their assistance with data collection.

# REFERENCES

# SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg. 2019.01932/full#supplementary-material

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Bartley, Palit, Fillingim and Robinson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Psychological Flexibility as a Resilience Factor in Individuals With Chronic Pain

Charlotte Gentili1,2† , Jenny Rickardsson1,2 \* † , Vendela Zetterqvist1,2,3, Laura E. Simons<sup>4</sup> , Mats Lekander<sup>2</sup> and Rikard K. Wicksell1,2

<sup>1</sup> Functional Area Medical Psychology, Functional Unit Behavior Medicine, Karolinska University Hospital, Stockholm, Sweden, <sup>2</sup> Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden, <sup>3</sup> Department of Neuroscience, Uppsala University, Uppsala, Sweden, <sup>4</sup> Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Palo Alto, CA, United States

#### Edited by:

John Andrew Sturgeon, University of Washington, United States

#### Reviewed by:

Joanna Eulalia Dudek, University of Social Sciences and Humanities, Poland Peter Maxwell Slepian, York University, Canada

#### \*Correspondence:

Jenny Rickardsson<EMAIL_ADDRESS>
†These authors have contributed equally to this work and share first authorship

#### Specialty section:

This article was submitted to Psychology for Clinical Settings, a section of the journal Frontiers in Psychology

Received: 20 May 2019 Accepted: 19 August 2019 Published: 03 September 2019

#### Citation:

Gentili C, Rickardsson J, Zetterqvist V, Simons LE, Lekander M and Wicksell RK (2019) Psychological Flexibility as a Resilience Factor in Individuals With Chronic Pain. Front. Psychol. 10:2016. doi: 10.3389/fpsyg.2019.02016 Resilience factors have been suggested as key mechanisms in the relation between symptoms and disability among individuals with chronic pain. However, there is a need to better operationalize resilience and to empirically evaluate its role and function. The present study examined psychological flexibility as a resilience factor in relation to symptoms and functioning among 252 adults with chronic pain applying for participation in a digital ACT-based self-help treatment. Participants completed measures of symptoms (pain intensity, and anxiety), functioning (pain interference and depression), as well as the hypothesized resilience factor psychological flexibility (measured as avoidance, value obstruction, and value progress). As expected, symptoms, functioning and resilience factors were significantly associated. Hierarchical linear regression analyses showed that psychological flexibility significantly contributed to the prediction of pain interference and depression when adjusting for age, pain and anxiety. Also, participants with low levels of psychological flexibility were more likely to be on sick leave. Furthermore, a series of multiple mediation analyses showed that psychological flexibility had a significant indirect effect on the relationship between symptoms and functioning. Avoidance was consistently shown to contribute to the indirect effect. Results support previous findings and suggest the importance of psychological flexibility as a resilience factor among individuals with chronic pain and anxiety.

#### Keywords: chronic pain, psychological flexibility, resilience, avoidance, values

# INTRODUCTION

Resilience – the ability to adapt and function well despite significant strain – has gained increasing attention in the field of chronic pain management. Turk et al. (2008) concluded that "Living with chronic pain requires considerable emotional resilience and tends to deplete emotional reserve." Patients often report "being stuck" or "putting life on hold" as a consequence of chronic pain, which corresponds with data showing that pain interference rather than pain intensity, predicts levels of functioning (Kemani et al., 2016). Why some patients continue to function well in day to day life after the onset of chronic pain, and others do not, is yet unclear (Reid et al., 2011; Hauser et al., 2014; Vervoort et al., 2014; Goubert and Trompetter, 2017).

Resilience is commonly defined as overcoming adversity or "effective functioning, despite the exposure to stressful circumstances, and/or internal distress" (Sturgeon and Zautra, 2013).

**48**

However, current definitions of resilience provide limited information on how a person recovers or maintain functioning during and following difficult life events. The concept of resilience sometimes reflects a deterministic view where resilience factors are relying on personality traits, biology, social support, past experiences, and/or innate properties such as sex. Resilience may alternatively, and in accordance with Goubert and Trompetter (2017), be conceptualized as a contextual behavioral factor, and defined as the ability to continuously engage in meaningful activities that promote current and future quality of life and health, in the presence of pain and distress (Goubert and Trompetter, 2017). This conceptualization of resilience is closely related to the construct psychological flexibility, defined as the ability to act in alignment with values and long-term goals in the presence of inner discomfort such as pain and distress and has been suggested as a key factor in maintaining or improving functioning among individuals with chronic pain (Hayes et al., 1999, 2006).

Lack of psychological flexibility, or psychological inflexibility, is commonly displayed as avoidance of stimuli and situations associated with pain, and related distress. From a learning perspective, avoidance is an operant under contextual control, meaning that a behavior is influenced by environmental factors (internal and external) preceding the behavior, and/or acting as consequences of that same behavior. Avoidance behaviors may be reinforced – for example resulting in short term reductions of pain or discomfort – and therefore sustained and used in similar contexts. Over time, such operant behaviors may become a default strategy in situations perceived as threatening, with increasing difficulties for the individual to respond differently (Vowles et al., 2007). Due to contextual factors, the behavior repertoire becomes increasingly narrow and less flexible. This behavior pattern is normally not associated with a corresponding decrease in pain or distress, but rather a life less stimulating and active. Avoidance may take many different forms, such as not engaging in social or physical activities, excessive opioid use (overt behaviors), thinking about situations associated with pain or refraining from planning future events (covert behaviors).

Value orientation is another key aspect of psychological flexibility, that can be defined as verbal guidelines that function to initiate and maintain behavior over time, also without the presence of obvious reinforcers (Hayes et al., 1999; Hayes, 2012). For example, clarifying the value ("being an attentive parent") associated with an operant ("playing in the park") can alter the context and thereby increase the likelihood that this behavior is initiated and/or maintained also in the context of potentially interfering pain. For chronic pain patients, value-based behaviors have been associated with higher levels of functioning and improved mood (Vowles et al., 2014) and adding a value aspect in an experimental intervention has been shown to elevate pain tolerance (Branstetter-Rost et al., 2009). Value orientation usually comprise components such as clarifying and engaging in valuebased behaviors, including effectively dealing with obstacles to value-based behaviors (Smout et al., 2014).

The aim of the present study was to examine the role and function of psychological flexibility, assessed as avoidance, value obstruction, and value progress as resilience factors in a sample of individuals with chronic pain. More specifically, analyses were conducted to (1) broadly characterize the relationship between symptoms, functioning and psychological flexibility, (2) assess the amount of variance in functioning (pain interference and depression) explained by psychological flexibility, (3) explore low vs. high psychological flexibility as a potential risk/resilience factor for self-reported sick leave and opioid use, and (4) examine the indirect effects of psychological flexibility in the relationship between symptoms and functioning.

# MATERIALS AND METHODS

# Data Collection

This study used a cross-sectional design, with data from baseline assessments for participants applying for internetdelivered Acceptance and Commitment Therapy for chronic pain (ClinicalTrials.gov identifier: NCT03105908 and NCT03344926). Participants were recruited via ads in newspapers and social media between January 30, 2017 and January 31, 2018. All questionnaires and demographic questions were completed online in a secure web platform. Participants provided written informed consent prior to enrollment in the study, the study was approved by the Regional Ethics Committee and followed the Helsinki declaration.

Eligibility criteria were pain duration over 6 months, age 18 years or older, ability to read and write in Swedish and completion of all assessments.

# Measurements

Demographic variables included age, sex and educational level.

#### Symptom Variables

Pain variables included pain intensity – current and average in the past week – measured with a numeric rating scale (NRS, 0 = no pain at all, 10 = worst pain imaginable), pain duration (selfreported in years), and pain location (self-reported descriptions of localization).

To account for some of the complexity of symptoms commonly co-occuring with chronic pain, the present study used anxiety as measured with Generalized Anxiety Disorder-7 (GAD-7) (Spitzer et al., 2006) as a proxy for distress symptoms such as strain, worry, and restlessness.

GAD-7 measures the frequency of anxiety symptoms during the last 2 weeks are scored on a four-point Likert scale (0 = Not at all, 3 = Every day). A total score of 10 was chosen as cutoff for anxiety problems, in accordance with guidelines (Spitzer et al., 2006). Internal consistency (Cronbach's Alpha) was 0.92 in the validation study (Spitzer et al., 2006), and 0.88 in the present dataset. GAD-7 is validated for chronic pain patients with migraine (Seo and Park, 2015a).

#### Functioning

Pain interference was measured with the pain interference index (PII), a brief self-report questionnaire assessing the influence of pain on behavior, or to what extent pain interferes with daily functioning (Kemani et al., 2016). Six items are rated on

a seven-point Likert scale (0 = Not at all, 6 = Completely). Internal consistency (Cronbach's Alpha) in the present sample was 0.85, which corresponds to the alpha from the original validation study, which was performed on a chronic pain sample (Kemani et al., 2016).

Level of depression was measured using the Patient Health Questionnaire 9 items (PHQ-9) (Kroenke et al., 2001). The frequency of depressive symptoms occurring during the past 2 weeks are rated on a four-point Likert scale from 0 = Not at all to 3 = Nearly every day. Internal consistency (Cronbach's Alpha) in the present sample was 0.81, and in the original validation studies Alpha varied from 0.86 to 0.89 (Kroenke et al., 2001). PHQ-9 has been validated for chronically ill patients (Wu, 2014) and migraine patients (Seo and Park, 2015b). PHQ-9 was used as a measure of functioning in this study, with the theoretical assumption that depressive disorder is a result of a series of behaviors, not merely the occurrence of symptoms.

Sick leave during the past 2 months was assessed using self-report and classified as temporary, permanent, or none.

Data on opioid use was collected via self-report. Participants listed all current medications, which then were classified according to the Anatomical Therapeutic Chemical classification system by an anesthesiologist. Participants with opioids in their list were coded as "currently taking opioids" and participants without opioids in their list were coded as "currently not taking opioids."

#### Psychological Flexibility

In the present study, two aspects of psychological flexibility were assessed: avoidance and values orientation.

Avoidance was measured using a subscale of Psychological Inflexibility in Pain Scale (PIPS) (Wicksell et al., 2008b). The avoidance subscale consists of 8 items, rated on a seven-point Likert scale from 1 = never true, to 7 = always true. The avoidance subscale has consistently been shown to be a robust and valid measure in chronic pain samples (Wicksell et al., 2010a; Barke et al., 2015). In the present sample internal consistency was 0.91, which corresponds with an alpha of 0.89 in the original validation study (Wicksell et al., 2008b).

Values orientation was assessed using the subscales value progress and value obstruction from the Valuing Questionnaire (VQ) (Smout et al., 2014). Items are rated on a seven-point Likert-scale ranging from 0 = not at all true, to 6 = completely true. Higher scores on the progress subscale indicate greater progress toward value-based behavior, while higher scores on the obstruction subscale indicate greater obstruction to value-based behavior. The two-factor solution has shown adequate model fit and strong internal consistency (Cronbach's alpha = 0.87) (Smout et al., 2014). In the present study Cronbach's Alpha was 0.83 for value progress and 0.76 for value obstruction. The Valuing Questionnaire has been validated for use with chronic pain samples (Carvalho et al., 2018).

Avoidance and value obstruction are both negatively valenced measures (measuring the occurrence of something negative) while value progress is positively valenced (measuring the presence of something positive). However, both are important aspects of the psychological flexibility model (McCracken and Morley, 2014).

# Participant Characteristics

The sample consists of adults with chronic pain applying for participation in a digital ACT-based self-help treatment. The sign-up-process for the clinical trial was initiated by 266 persons, of which 253 completed the assessment. One individual was excluded due to pain duration <6 months. Thus, data from 252 participants was used in the analyses.

Participants were predominantly female (81%, n = 204) and born in Sweden (90%, n = 226) with a mean age of 47.4 years (SD 11.5, range 18–70). Two thirds (66%, n = 166) of the sample had some level of university education (>12 years of education), nearly one third (30%, n = 75) had completed upper secondary school (12 years), and a few participants (4%, n = 11) had completed only compulsory school (9 years). Occupational status varied, with 31% (n = 79) working full-time, 25% (n = 62) parttime, 25% (n = 63) being on temporary sick leave, and 23% (n = 59) on permanent disability.

Pain duration was on average 18.2 years (SD 12.5, range 0.5–57), and the participants reported that last week's mean pain intensity was 6.6 (SD 1.7, range 1–10). Most individuals had multiple pain locations (88%, n = 222), and the most common pain locations were: back (75%, n = 188), neck (64%, n = 160), and lower extremities (64%, n = 160). Half of the sample experienced headaches (50%, n = 125) and 40% (n = 101) experienced generalized pain. Primary pain diagnoses were classified by an anesthesiologist as nociceptive (e.g., spinal disc hernia, and rheumatoid arthritis) for 37% (n = 93), as nociplastic (e.g., fibromyalgia and CRPS) for 17% (n = 44), neuropathic (e.g., trigeminal neuralgia and nerve damage) for 8% (n = 20), and headaches (e.g., migraine and Horton's) for 8% (n = 19). A fifth (19%, n = 47) had no diagnosis and 12% had mixed or unclassifiable pain diagnosis. Furthermore, more than half (54%, n = 157) scored above cut-off for depression, and one in four (25%, n = 64) above cut-off for anxiety.

## Statistical Analyses

Analyses were computed using SPSS version 25 and STATA version 15. The dataset was complete, and no imputation strategies were needed. In all analyses, statistical significance was set to a conservative level of p < 0.01, except in the criteria for inclusion of covariates where p < 0.05 was used.

To determine the adequate sample size for mediation and regression analyses, a power analysis was conducted using the G <sup>∗</sup>power software (Faul et al., 2009). Assuming a medium effect size (f 2 ) of 0.15, an alpha of 0.01, a power level of 0.80 and a total of 6 predictors, the power analysis suggested a minimum of 109 participants.

Initial analyses were conducted to broadly characterize the relationships between the variables using descriptive statistics and Pearson's product moment correlations (r) (Field, 2013).

A series of hierarchical linear regression analyses were performed to investigate the amount of variance explained by

psychological flexibility (avoidance, value obstruction, and value progress) in pain interference and depression. Demographic variables were entered as step 1, symptoms were added as step 2, and psychological flexibility as step 3. For each analysis, only variables having a significant bivariate correlation (p < 0.05) with the dependent variable were included.

The relationships between levels of psychological flexibility and the risk of sick leave and opioid use were analyzed using maximum likelihood logistic regression models. We estimated risks, or odds ratios (OR), for sick leave and opioid use (dependent variables) with each respective measure of psychological flexibility (avoidance, value obstruction, and value progress) as independent variables. The independent variables were categorized as low (first quartile), medium (second and third) and high (fourth) of the continuous distributions in line with recommendations from van Kuijk et al. (2019), as the association between predictors and outcomes was not linear and the assumption of non-additivity between different predictors and covariates was not met (van Kuijk et al., 2019). Age and pain intensity showed bivariate correlations with the dependent variables and were therefore used as covariates in these analyses.

A series of analyses of indirect effects using PROCESS for SPSS were conducted to evaluate the importance of psychological flexibility (avoidance, value obstruction, and value progress) for the relationships between predictors (pain and anxiety) and dependent variables (pain interference and depression). In all analyses the influence of age was adjusted for (covariate). Four models were analyzed, with each predictor and dependent variable, and with multiple mediators (PROCESS model #4). PROCESS is a bootstrapping method in which samples of the original size, drawn from the original data, are generated (Hayes and Rockwood, 2017). The total effect (c) is comprised of the direct effect (c 0 ) and the indirect effect (ab). Thus, the indirect effect represents the part of the relation between the predictor and the dependent variable that can be explained by the proposed mediator. The mean value for the ab product across the bootstrapped samples provided a point estimate of the indirect effect. Confidence intervals (CI) were derived from the obtained distribution of ab, using a 99% CI level representing a significance level of p > 0.01. If lower and upper bounds did not contain zero, the indirect effect was significant at the specified level. Each analysis was based on 5000 bootstrapped samples, as suggested by Preacher and Hayes (2008).

# RESULTS

Mean, standard deviations, and range for all self-report measures are reported in **Table 1**.

# Bivariate Correlations Between Symptoms, Functioning, and Psychological Flexibility

Strong positive correlations were found between avoidance and pain interference (r = 0.668), avoidance and depression (r = 0.514), value obstruction and depression (r = 0.522) as well as TABLE 1 | Self-report measures: Means, standard deviations, and range.

between anxiety and depression (r = 0.663). Pairwise correlations between all variables are shown in **Table 2**.

# Amount of Variance in Functioning Explained by Psychological Flexibility

Hierarchical regression analyses were conducted to evaluate the amount of variance explained by psychological flexibility in the two dependent variables pain interference and depression. Age had a significant positive bivariate correlation with sick-leave and a significant negative bivariate correlation with pain interference and depression and was therefore entered as step 1.

#### Pain Interference

Psychological flexibility explained a significant amount of variance in pain interference (r 2 change = 0.44, p < 0.0001), when adjusting for the influence of pain and anxiety (r <sup>2</sup> = 0.27, p < 0.0001). Of the psychological flexibility variables solely avoidance showed a significant – and positive – beta value (b 0.52, p < 0.0001).

#### Depression

In depression, psychological flexibility explained a significant amount of variance (r 2 change = 0.11, p < 0.0001) when adjusting for the influence of pain and anxiety. Avoidance had a significant positive beta coefficient and value progress had a significant negative beta coefficient in the model. Results from the hierarchical regression analyses are presented in **Table 3**.

# Odds for Sick Leave and Opioid Use in Individuals With Low vs. High Psychological Flexibility

Logistic regression analyses were conducted to evaluate the odds for sick leave and opioid use in individuals with low and high psychological flexibility.

#### Sick Leave

The odds of being on sick leave was four times lower in the low value progress group compared to the high value progress group (OR 0.25, p = 0.001). For avoidance, the high avoidance group had lower odds for being on sick leave (OR 5.23, p < 0.0001) compared to the low avoidance group. For different levels of value obstruction there were no significant differences in odds for sick leave.

#### TABLE 2 | Correlations between all variables.

fpsyg-10-02016 August 30, 2019 Time: 17:21 # 5

<sup>∗</sup>p < 0.05, ∗∗p < 0.01. <sup>1</sup>Spearman correlation used for categorical variables.

TABLE 3 | Hierarchical linear regressions: the influence of psychological flexibility on functioning.

Psy flex, psychological flexibility. ∗∗p < 0.01.

#### Opioid Use

For opioid use, no significant difference in odds between high and low avoidance, value obstruction, or value progress were found. Results from the logisitic regression analyses are presented in **Table 4**.

# The Indirect Effect of Psychological Flexibility in the Relationship Between Symptoms and Functioning

The bootstrap method (PROCESS) with n = 5000 bootstrap resamples and 99% bias-corrected and accelerated confidence intervals was used in a series of analyses conducted to evaluate the indirect effects of psychological flexibility (M1 = avoidance, M2 = progress, and M3 = obstruction) on the relationship between symptoms (average pain intensity and anxiety) and functioning (pain interference and depression).

In short, all four multiple indirect effect models, with pain intensity/anxiety (x) as predictors and pain interference/ depression (y) as dependent variables, showed a significant total indirect effect of psychological flexibility, i.e., the combined indirect effects of avoidance (M1), progress (M2), and obstruction (M3). Results from the

TABLE 4 | Odds ratios for sick leave and opioid use, respectively, for low, medium and high levels of psychological flexibility, with age and pain intensity as covariates.

∗∗p < 0.01.

TABLE 5 | Total, direct and indirect effect of symptoms on pain interference and depression using psychological flexibility as indirect effect.

All models adjusted for age. Psy flex, psychological flexibility. ∗∗p < 0.01.

analyses of indirect effects are summarized in **Table 5**. Detailed results for each model are presented below and in **Figures 1**–**4**.

#### Direct and Indirect Effect of Pain Intensity on Pain Interference

The full model showed a significant indirect effect of psychological flexibility on the relationship between pain intensity and pain interference. Among the individual psychological flexibility factors only avoidance had a significant indirect effect in the full model.

In the analyses of each respective path avoidance illustrated significant coefficients in both the a and b paths. Of the value factors, obstruction showed significant b path.

### Direct and Indirect Effect of Pain Intensity on Depression

A significant indirect effect was shown for the full model. Notably, the direct effect was not significant, which implies a strong indirect effect of psychological flexibility on the relation between pain intensity and depression. Again, avoidance individually showed a significant indirect effect in the full model. Furthermore, avoidance showed significance in both a and b paths, whereas value obstruction and value progress had significant b paths only.

#### Direct and Indirect Effect of Anxiety on Pain Interference

A significant indirect effect of psychological flexibility on pain interference was seen in the full model. The direct effect

(anxiety on pain interference) was not significant, implying a strong indirect effect of psychological flexibility. Avoidance had an individually significant indirect effect in the full model. The analysis of individual paths showed avoidance had significant a and b paths in the model, where value obstruction and value progress showed significant a paths only.

#### Direct and Indirect Effect of Anxiety on Depression

The full model showed a significant indirect effect of psychological flexibility on depression. Among the individual psychological flexibility factors, both avoidance and value progress individually had significant indirect effect. Avoidance

and value progress showed significant a and b paths while value obstruction only had significant a path.

# DISCUSSION

The purpose of the present study was to examine the role and function of psychological flexibility – assessed with avoidance, value obstruction and value progress – as a resilience factor in a sample of 252 persons with chronic pain. In general, psychological flexibility was shown to be a significant factor in the relationship between symptoms (pain intensity and anxiety) and functioning (pain interference and depression).

Avoidance was shown to be particularly important for the influence of symptoms on functioning, as illustrated by significant a- and b-paths across all four models of indirect effects. The two value components, obstruction and progress, were primarily relevant in the association between anxiety and functioning, which warrants further studies to explore the relative importance of different aspects of psychological flexibility.

The results from the present study support research indicating the relevance of psychological flexibility in explaining variance in functioning in individuals with chronic pain (McCracken and Vowles, 2007; Zetterqvist et al., 2017). Previous studies have also shown that psychological flexibility is an important change mechanism in exposure-based interventions (Vowles et al., 2008, 2014; Wicksell et al., 2010b; Trompetter et al., 2015).

The conceptualization of resilience as a key factor in the relation between pain and functioning is seen also in a Scottish population-based study illustrating that resilient persons (high pain intensity and low disability score) had a higher 10-year-survival than vulnerable persons (low pain intensity, high disability score) (Elliott et al., 2014). In that study, factors associated with higher resilience were being male, lower age, higher education, owning your own home, and absence of chronic illness. Although these factors are informative of risk for higher disability, they are not directly modifiable to increase resilience in individuals with chronic pain, and thereby elevating functioning. The authors underline the importance of identifying modifiable factors (Elliott et al., 2014). Similarly, in a recent cross-sectional study (Richardson and Jost, 2019) on development of depression and PTSD following early life trauma, the authors emphasize the importance of evaluating psychological flexibility rather than traits or personal attributes as it "seems to be more adaptable to change and is an opportunity for therapeutic intervention." This is also consistent with Goubert and Trompetter (2017) who emphasizes the importance of focusing on resilience factors that can be changed and used to improve the ability to "ward off, buffer against and recover from disability" for chronic pain patients. In the present study, resilience is conceptualized as a contextual behavioral factor, or a set of behaviors. This conceptualization is of particular clinical relevance, since (operant) behaviors are under contextual control, which implies they can be directly changed. Chronic pain has detrimental effects on functioning for many individuals. The results in the present study support the indirect effects of psychological flexibility – avoidance in particular – and suggest it as relevant target in treatment for chronic pain to improve functioning. This corresponds with previous research showing that avoidance is associated with functioning, and that addressing avoidance in exposure-based interventions can improve functioning (Wicksell et al., 2009; Vlaeyen et al., 2016; Bonnert et al., 2018; Hedman-Lagerlof et al., 2018). The results from the present study also supports the importance of values orientation, and the specific, or incremental, utility of interventions promoting value-oriented behaviors to improve resilience should be addressed in further research.

Furthermore, resilience is more than the absence of disability (Goubert and Trompetter, 2017), which corresponds with the conceptualization in the present study. Future research should further explore the construct of resilience by evaluating the importance of related variables among individuals with chronic pain and distress. For example, future research may benefit from using a longitudinal design to examine the mediating role of psychological flexibility as a resilience factor to allow for analyses of temporal relationships. Also, studies exploring the relationships between subprocesses of psychological flexibility, such as acceptance and present-moment-awareness, as well as

other constructs relevant to the concept of resilience in chronic pain, are warranted and should ideally use behavioral measures such as task performance, in combination with self-report questionnaires. Lastly, experimental studies evaluating the effects of specific interventions on resilience are needed.

While the present study examines the role and function of psychological flexibility as a resilience factor in relation to chronic pain, it is worth noting the transdiagnostic properties of the psychological flexibility model, particularly as there is a need for psychological interventions that better meet the needs of patients with comorbid psychiatric and medical conditions (Barlow et al., 2004; Evans et al., 2005; Merikangas et al., 2007; Löwe et al., 2008). Psychological flexibility is not limited to chronic pain but a psychological skill, or set of skills, that has broad applicability and goes beyond any single mental or physical health condition (Dindo et al., 2017). Psychological inflexibility has been suggested to underlie a wide array of problems, including mental health, behavioral and comorbid complications (Hayes et al., 2006; Kashdan and Rottenberg, 2010). Psychological inflexibility has also been associated with mood- and anxiety disorders (Spinhoven et al., 2016). Conversely, improvements in psychological flexibility has been found to predict improvements in depressive symptoms in patients with borderline personality disorder (Berking et al., 2009), improvements in depression and anxiety in patients at risk for vascular disease (Dindo et al., 2015), and improvements in diabetes self-care, blood glucose levels and diabetes-related acceptance in patients with diabetes (Gregg et al., 2007) to mention a few. Although more and larger studies are needed, the empirical support including the present findings, suggest that psychological flexibility is an interesting and important resilience factor across conditions. To address the transdiagnostic nature of psychological flexibility future research should address if the level and implications of psychological flexibility varies across subgroups of patients, for example diagnoses and comorbidities.

In acceptance and commitment therapy (ACT) psychological flexibility, rather than a reduction in symptoms (for example pain and anxiety), is the key therapeutic target. However, ACT is an exposure-based treatment and share several important aspects with other forms of exposure therapy, such as graded exposure based on the fear-avoidance model. Future research should further explore the unique contribution of ACT-specific components such as acceptance and values-orientation, as well as differences and similarities in change processes between exposure-based treatments.

A few limitations should be considered when interpreting the results from the present study. The use of a crosssectional design prevents any causal inferences. More research is needed to examine psychological flexibility as a resilience factor in longitudinal studies. Also, even though avoidance, value obstruction and value progress are variables relevant to resilience, other behavioral factors of potential importance that impact pain interference and depression such as sleep or social support, were not included. Furthermore, avoidance items from the Psychological Inflexibility in Pain Scale may have some conceptual overlap with pain interference (e.g., "I avoid scheduling activities because of my pain"). The levels of education as well as the proportion of women (81%) in the study sample is higher than the Swedish average, which may affect the generalizability of results. The sample is self-referred, which may imply limitations to the external validity. However, when compared with samples from a tertiary pain clinic the self-referred sample displays similar levels of pain, distress, and disability (Wicksell et al., 2008a, 2010b; Kemani et al., 2016).

# CONCLUSION

Psychological flexibility – in this study assessed as avoidance, value obstruction and value progress – plays a significant role as a resilience factor in the relationship between symptoms and functioning among individuals with chronic pain. Psychological flexibility has been successfully improved in previous clinical trials, and the present findings thus support the utility of this as an important target in treatment.

# DATA AVAILABILITY

The datasets generated for this study will not be made publicly available since the ethical permit does not allow sharing of data.

# ETHICS STATEMENT

Human Subject Research: This study was reviewed and approved by the Regional Board of Ethics, Stockholm, Sweden. All participants provided written informed consent prior to participation.

# AUTHOR CONTRIBUTIONS

CG and JR collected the data. CG, JR, and RW designed the study. JR and VZ prepared the data and performed the preliminary data analysis. CG, JR, and RW performed the final data analyses. CG, JR, and RW prepared the manuscript with valuable contributions from ML, LS, and VZ. All authors approved the final version of the manuscript.

# FUNDING

This work was supported by AFA Insurance as part of a larger research program on chronic pain. The funder had no influence on design, recruitment, analyses, or manuscript. RW was supported through the regional agreement on medical training and clinical research (ALF) between Stockholm City Council and Karolinska Institutet.

# ACKNOWLEDGMENTS

Thanks to all participants for their time and effort to contribute to this study. Thanks to Dr. Jan Persson for classifying medications and diagnoses, and Brjánn Ljótsson for providing the digital platform for data collection.

# REFERENCES

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with chronic pain following whiplash. Eur. J. Pain 14, 1059.e1–1059.e11. doi: 10.1016/j.ejpain.2010.05.001

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Gentili, Rickardsson, Zetterqvist, Simons, Lekander and Wicksell. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Pain-Specific Resilience in People Living With HIV and Chronic Pain: Beneficial Associations With Coping Strategies and Catastrophizing

Cesar E. Gonzalez<sup>1</sup>† , Jennifer I. Okunbor<sup>1</sup>† , Romy Parker<sup>2</sup> , Michael A. Owens<sup>1</sup> , Dyan M. White<sup>1</sup> , Jessica S. Merlin<sup>3</sup> and Burel R. Goodin<sup>1</sup> \*

<sup>1</sup> Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, United States, <sup>2</sup> Department of Anesthesia and Perioperative Medicine, University of Cape Town, Cape Town, South Africa, <sup>3</sup> Department of Medicine, Divisions of General Internal Medicine and Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, United States

Objective: Chronic pain is increasingly recognized as a common and disabling problem for people living with HIV (PLWH). In a recent systematic review of psychosocial factors associated with chronic pain in PLWH, it was reported that very few studies to date have examined protective psychological factors that might help mitigate chronic pain for PLWH. The current study examined pain-specific resilience in relation to clinical and experimental pain, as well as pain coping in PLWH and chronic pain. Pain-specific resilience specifically refers to the ability to maintain relatively stable, healthy levels of psychological and physical functioning in the face of ongoing and persistent pain.

Methods: A total of 85 PLWH (mean CD4 = 643; 13% detectable viral load ≥200; 99% on antiretroviral therapy) who met criteria for chronic pain (>3 consecutive month's duration) were enrolled. Medical records were reviewed to confirm clinical data. All participants provided sociodemographic information prior to completing the following validated measures: Pain Resilience Scale (PRS), Coping Strategies Questionnaire-Revised (CSQ-R), Center for Epidemiologic Studies – Depression Scale (CES-D), and the Brief Pain Inventory – Short Form (BPI-SF). They then completed a quantitative sensory testing battery designed to assess tolerance for painful heat and cold stimuli.

Results: In adjusted multiple regression models controlling for covariates, greater painspecific resilience was significantly associated with less pain interference (p = 0.022) on the BPI-SF, less pain catastrophizing (p = 0.002), greater use of distraction (p = 0.027) and coping self-statements (p = 0.039) on the CSQ-R, as well as significantly greater heat pain tolerance (p = 0.009). Finally, results of a parallel multiple mediation model demonstrated that the effect of pain-specific resilience on heat pain tolerance was indirectly transmitted through less pain catastrophizing (95% confidence interval:0.0042 to 0.0354), but not use of distraction (95% confidence interval: −0.0140 to 0.0137) or coping self-statements (95% confidence interval: −0.0075 to 0.0255).

Conclusion: The findings suggest that pain-specific resilience may promote adaptation and positive coping in PLWH and chronic pain.

Keywords: HIV-human immunodeficiency virus, chronic pain, resilience (psychological), coping, catastrophizing

#### Edited by:

Carmen Ramírez-Maestre, University of Málaga, Spain

#### Reviewed by:

Dimitri M. L. Van Ryckeghem, Maastricht University, Netherlands Rosa Esteve, University of Málaga, Spain

#### \*Correspondence:

Burel R. Goodin<EMAIL_ADDRESS><EMAIL_ADDRESS>
†These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Psychology for Clinical Settings, a section of the journal Frontiers in Psychology

Received: 08 May 2019 Accepted: 22 August 2019 Published: 06 September 2019

#### Citation:

Gonzalez CE, Okunbor JI, Parker R, Owens MA, White DM, Merlin JS and Goodin BR (2019) Pain-Specific Resilience in People Living With HIV and Chronic Pain: Beneficial Associations With Coping Strategies and Catastrophizing. Front. Psychol. 10:2046. doi: 10.3389/fpsyg.2019.02046

# INTRODUCTION

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Due to advancements in antiretroviral therapy (ART), people living with HIV (PLWH) who are connected to care and adhere to their medication regimens can achieve near normal life expectancies (Negin et al., 2012; O'Keefe et al., 2013). However, living longer with HIV is often accompanied by an increased likelihood of developing HIV-associated chronic health conditions (Pitts et al., 2005). One particularly important health condition that affects aging PLWH is chronic pain (Merlin et al., 2012). Estimates suggest that chronic pain may affect over half of all PLWH throughout their lifetimes (Parker et al., 2014). The experience of chronic pain in PLWH often comes at a high cost, such that it significantly and negatively impacts quality of life (Merlin et al., 2013, 2014). Furthermore, treatment of pain in this population can be difficult due to complicating factors including substance use and psychiatric illness (Tsao and Soto, 2009). Pharmacologic pain treatment options, including opioid medications, have limited efficacy for managing chronic pain for many PLWH (Bruce et al., 2017). Psychological approaches for chronic pain in PLWH have demonstrated initial promise (Merlin et al., 2018); however, the full potential of this treatment modality to yield positive outcomes remains underappreciated given the lack of sufficient research to date focused on psychological contributors to chronic pain in PLWH (Scott et al., 2018).

The extant literature addressing psychological contributors to chronic pain in PLWH has primarily focused on vulnerabilities and risk factors for poor outcomes. Scott et al. (2018) recently published a comprehensive review on this topic indicating that depression, psychological distress, post-traumatic stress and substance use were the psychological factors most associated with negative pain outcomes in PLWH. Only a small number of studies thus far have examined the role of protective psychological factors in relation to chronic pain for PLWH. For example, PLWH and chronic pain may possess lower optimism (Simmonds et al., 2005) and self-efficacy for disease management (Parker et al., 2017) and treatment adherence (Berg et al., 2009) relative to PLWH without chronic pain. In a study by Wadley et al. (2016), PLWH and chronic pain reported significantly lower levels of resilience compared to PLWH without chronic pain. However, in this same study resilience was not significantly associated with pain severity or interference for PLWH and chronic pain. Despite these equivocal findings, additional research investigating the association of resilience with chronic pain in PLWH appears warranted for two reasons. First, it is well documented that PLWH are often able to remain resilient despite the many hardships they often face (Dale et al., 2014; Emlet et al., 2017). Second, in non-HIV populations with chronic pain, high resilience has been associated with positive responses to pain, adaptive coping styles, and favorable health care and medication utilization patterns (Karoly and Ruehlman, 2006; Sturgeon and Zautra, 2010).

Resilience is broadly conceptualized as the ability to cope with a crisis or adversity while maintaining positive emotional and physical functioning (Joyce et al., 2018). Numerous measures have been developed to assess resilience as a general psychological construct, and indeed these measures have proven effective for predicting adaptation to chronic pain (Ong et al., 2010; Ramírez-Maestre et al., 2012). More recently, it has been suggested that a pain-specific measure of resilience is likely to be better suited for studies examining clinical and experimental pain experiences than a general measure of psychological resilience (Slepian et al., 2016). Pain-specific resilience specifically refers to the ability to maintain relatively stable, healthy levels of psychological and physical functioning in the face of ongoing and persistent pain (Ankawi et al., 2017). In the laboratory setting, high pain-specific resilience has been shown to be associated with less sensitivity to experimental pain stimuli during quantitative sensory testing (QST) (Slepian et al., 2016). Further, a series of clinical studies conducted with chronic pain samples found that high painspecific resilience was associated with better quality of life and lower pain intensity (Ankawi et al., 2017), as well as greater pain self-efficacy and acceptance (Slepian et al., 2018). It remains to be determined whether a measure of pain-specific resilience might also be associated with clinical and experimental pain in a sample of PLWH and chronic pain.

In studies conducted with non-HIV populations, highly resilient individuals with chronic pain have been shown to report greater positive emotions and less pain catastrophizing compared to their less resilient counterparts (Ong et al., 2010; Sturgeon and Zautra, 2013). Similarly, resilient individuals have been found to engage in more adaptive pain coping strategies, which promote efforts to control pain and to function at a high level in spite of pain (Sturgeon and Zautra, 2010).  Based upon the existing literature, it stands to reason that pain-specific resilience may be associated with less pain catastrophizing and greater engagement in adaptive pain coping strategies for PLWH and chronic pain, which in turn would be associated with decreased severity of clinical and experimental pain experiences.

The primary objective of the current study was to examine whether pain-specific resilience was associated with reports of clinical and experimental pain, as well as pain catastrophizing and coping strategies, in PLWH and chronic pain. Three distinct hypotheses were tested. (1) High pain-specific resilience would be significantly associated with lower clinical pain severity and interference, as well as greater tolerance for painful cold and heat stimuli during QST. (2) High pain-specific resilience would be significantly associated with less pain catastrophizing and greater use of pain coping strategies (e.g., distraction). (3) Pain catastrophizing and active pain coping strategies would significantly mediate the effect of pain-specific resilience on clinical and experimental pain.

# MATERIALS AND METHODS

# Study Design Overview

People living with HIV with chronic pain were recruited via posted flyers from a large, urban HIV clinic in Alabama,

United States. that provides comprehensive medical, social, and behavioral services to approximately 3,500 adults (≥18 years) living with HIV. Those interested in study participation were assessed for eligibility during an initial telephone screening. Medical records were then reviewed for each prospective participant to assist with eligibility determination. Eligible participants subsequently presented to the laboratory to complete a single study session. At the beginning of the study session resting blood pressure and core body temperature were recorded for each participant. Blood was then taken from each participant for determination of CD4+ count and viral load. Participants completed a QST battery designed to assess tolerance for thermal pain (heat and cold).  Sociodemographic information was collected from all participants, and this information included age, natal sex, ethnicity/race, educational attainment, and poverty status. Poverty status was determined through adjusting the recorded annual household income by number of occupants through guidelines put forth by the 2017 United States Department of Health and Human Services (U. S. Federal Poverty Guidelines, 2017).

# Medical Record Review

Medical record reviews were completed to ascertain rates of psychiatric diagnoses among participants, as well as determine whether participants were actively being prescribed antiretroviral therapy (ART). Medical record review also assisted with determining duration of chronic pain and whether participants were actively being prescribed analgesic medications that could affect reported pain and/or responses to QST, particularly opioids (Niesters et al., 2013). Lastly, medical record review was used to confirm participants' self-reported health history provided during telephone screening. Those PLWH and chronic pain whose medical records corroborated their self-reported health history, and who met study inclusion criteria, were deemed eligible for ongoing participation.

# Participants

A total of 91 PLWH and chronic pain were enrolled into this cross-sectional study. Six participants were disqualified from further participation due to the presence of uncontrolled hypertension, which was a contraindication for the completion of QST. This resulted in a final study sample size of 85 PLWH and chronic pain. Study procedures were approved by the local Institutional Review Board and carried out in accordance with guidelines for the ethical conduct of research. Written informed consent was obtained from each participant prior to the study, and the participants were compensated for their participation.

People living with HIV with chronic pain were included in this study if they reported chronic pain that had persisted for at least three consecutive months and was present on at least half the days in the past 6 months (Treede et al., 2015). Additional inclusion criteria were: age ≥18 years; no evidence of uncontrolled hypertension (i.e., resting blood pressure >150/95); no circulatory disorders (e.g., Raynaud's disease); no history of cardiac events, no history of stroke, seizures, or other neurological disorders, no history of metabolic disease, no history of cancer and related treatment, and not currently pregnant. Furthermore, participants were excluded from study participation if they demonstrated signs of acute infection (i.e., core body temperature >37.8◦C), reported any pain-alleviating surgery within the past year, or receipt of any pain intervention treatment within the past month (e.g., steroid injection).

# Measures

# Quantitative Sensory Testing

Previous studies have found that the relationship between resilience and responses to QST emerges during prolonged exposures to painful stimuli (Pulvers and Hood, 2013). For this reason, the QST battery in this study was designed to specifically assess tolerance for painful thermal stimuli (heat and cold). Heat pain tolerance (HPTo) refers to the maximum heat stimulus intensity (i.e., temperature, ◦C) a person is willing to tolerate before discontinuing due to pain. Similarly, cold pain tolerance (CPTo) refers to the maximum duration (i.e., time in seconds) a person is willing to tolerate a cold stimulus prior to discontinuing due to pain. Participants prescribed analgesics including opioids were not asked to abstain from these medications prior to the completion of QST given that temporary withdrawal could alter pain responses (Mao, 2006).

# Heat Pain Tolerance

HPTo was recorded as the temperature in Celsius at which the participant discontinued the heat stimulus. HPTo was assessed on participants' ventral forearm using a Medoc Thermal Sensory Analyzer-II (TSA) (Medoc Ltd., Ramat Yishai, Israel) with a 30 × 30-mm-diameter thermode in accordance with an ascending method of limits. From a baseline of 32◦C, probe temperature increased at a rate of 0.5◦C/s until participants responded by pressing a button on the patient response unit to indicate when they were no longer able to tolerate the pain. Three trials of HPTo were completed separately, and the position of the thermode was altered slightly between trials so that the site of stimulation did not overlap (though it remained on the ventral forearm). The average HPTo across all three trials was computed for use in statistical analysis.

# Cold Pain Tolerance

For the assessment of cold pain tolerance (CPTo), participants were asked to fully immerse their non-dominant hand up to the wrist in a cold pressor for a maximum of 300 s. The water temperature was maintained at 10◦C (±0.050C) by an ARTIC A25 refrigerated bath with an SC150 immersion circulator (Thermo Fisher Scientific, United States) that constantly circulated the water to prevent local warming around the submerged hand. The water temperature was selected based upon our previous work with other PLWH cohorts demonstrating that 10◦C was deemed moderately painful and resulted in the most normally distributed range of CPTo. Participants were asked to give pain intensity ratings on a 0 (no pain) to 100 (most intense pain possible) numeric rating scale at 30 and 60 s intervals. Participants were told that they could remove their hand from the cold pressor at any time if the pain became intolerable. The procedure lasted either the full 300 s or until the participant discontinued. Time of hand removal was recorded in seconds and included as an index of CPTo for statistical analysis.

#### Pain Severity and Interference

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The Brief Pain Inventory – Short Form (BPI-SF) is a multidimensional pain scale used to assess the severity of pain and its impact on daily functioning (Tan et al., 2004). The questionnaire is composed of four items asking about pain intensity (worst pain, least pain, average pain, and pain right now) over the past 24 h. There are also seven items that assess the degree to which pain interferes with functioning in the following domains: general activity, mood, walking ability, normal work, relations with other people, sleep, and enjoyment of life.  The pain severity score is the average of the four items asking about worst, least, average, and current pain. Each item is scored from 0 (no pain) to 10 (worst imaginable pain). The pain interference score is the average of the nine items addressing functional impairment. Each item is scored from 0 (does not interfere) to 10 (completely interferes). Higher scores suggest great pain severity and interference. Overall, the BPI-SF in this study had excellent internal consistency (Cronbach's α = 0.95).

#### Pain-Specific Resilience

The Pain Resilience Scale (PRS) is a 14-item assessment of resilience in the presence of intense or prolonged pain. The assessment has 2 subscales to measure specific domains of resilience: behavioral perseverance and cognitive/affective positivity (Slepian et al., 2016; Ankawi et al., 2017). The behavioral perseverance subscale examines an individual's ability to continue engaging in behaviors or activity when experiencing pain. The cognitive/affective positivity subscale examines an individual's ability to maintain positive thoughts and manage negative thoughts or emotions while in pain. Each item is scored from 0 (not at all) to 4 (all the time) to determine the degree to which individuals engage in resiliency resources. The total PRS score results from the summation of response to all 14 items. Higher scores are suggestive of greater pain related resilience. The PRS used in this study possessed excellent internal consistency (Cronbach's α = 0.94).

#### Pain Coping and Catastrophizing

The Coping Strategies Questionnaire-Revised (CSQ-R) is a 27-item assessment that was utilized to assess participants' use of cognitive strategies to cope with pain, as well as pain catastrophizing (Rosenstiel and Keefe, 1983). The CSQ-R includes the following subscales representing six cognitive domains: distraction (five-items), ignoring pain sensations (five-items), distancing oneself from pain (four-items), coping self-statements (four-items), praying/hoping (three-items), and catastrophizing (six-items). Each item is scored from 0 (never do that) to 6 (always do that) to indicate how frequently the strategy is engaged in response to pain. Each subscale is scored separately, and higher scores indicate greater engagement in that respective cognitive domain. The CSQ-R in this study had adequate internal consistency (Cronbach's α = 0.73).

#### Depression

Depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CES-D). This 20-item measure assesses the frequency of experiencing depressive symptoms over the past week (0 – never or rarely, to 3 – most of the time/all the time). Symptoms of depression measured by the CES-D include negative mood, guilt/worthlessness, helplessness/hopelessness, psychomotor retardation, loss of appetite, and sleep disturbance (Radloff, 1977). This measure has been shown to be reliable and valid in general populations, as well as HIV and chronic pain populations (Geisser et al., 1997; Natamba et al., 2014). Responses are summed (0–60), with higher scores indicating greater severity of depression. The CES-D measure used in the current study had good internal consistency (Cronbach's α=0.88).

### CD4 and Viral Load

Blood was collected from each participant at the beginning of the study session and sent to the local diagnostics laboratory for quantification of CD4 helper T-cell count and viral load. Absolute CD4 helper T-cell count was quantified as cells/microliter of blood, while viral load was quantified as viruses/microliter. Participants with 200 viruses/microliter of blood or greater were considered to be "detectable." CD4 and viral load reflect immune health and response to ART therapy, respectively. Each was included in this study to assess whether these aspects of HIV infection were associated with clinical and/or experimental pain.

# Data Organization and Analysis

All data were analyzed using SPSS, version 25 (IBM; Chicago, IL, United States). All participants provided complete demographic (e.g., sex, age) and QST data; however, a small portion of missing data existed for one or more key study variables such as pain-specific resilience and pain coping (≤5% of the total data comprising each measure). Data appeared to be missing at random. A simple data imputation method was completed using the macro for Hot Deck imputation (Myers, 2011). This data imputation method is well validated and accepted in the statistical community and resulted in complete study data for each of the 85 study participants.

Descriptive data for the sample are presented as percentages or as means and standard deviations. Differences across categorical variables were assessed using chi-square tests, while differences on continuous variables were assessed using analysis of variance (ANOVA). Zero order relationships among all study variables were assessed using Pearson correlations. To assess the unique relationships of pain-specific resilience with pain severity and interference, HPTo and CPTo, as well as pain coping and catastrophizing, a series of linear multiple regressions was completed controlling for selected covariates. The PROCESS macro (model 4) created and described by Hayes (2013) for obtaining 95% bootstrapped confidence interval with 5,000 resamples was utilized to test whether catastrophizing and/or any of the pain coping strategies significantly mediated the associations between pain-specific resilience and pain, including pain severity, pain interference, HPTo, and CPTo.

# RESULTS

# Participant Characteristics

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Descriptive characteristics for the 85 study participants are presented in **Table 1**. The mean age of the sample was 49 years. The study population was comprised of 67% men and 33% women. The majority of the study sample was non-Hispanic Black (74%), and lived below the poverty line (85%). The mean CD4 count was 643 cells/mm<sup>3</sup> , 13% had a detectable viral load, and 99% were actively prescribed antiretroviral therapy. Seventeen percent of the study sample were prescribed opioid medications for pain. The most frequently reported locations of chronic pain were low back/hips (46%), legs/feet (25%), widespread (2 + sites) (20%), arms/hands (6%), head (2%), and neck/shoulders (1%). Medical records indicated that 24% of the sample had a pain duration of >3 months but <1 year, 25% >1 year but <5 years, 23% >5 years but <10 years, and 28% >10 years. Average pain severity over the past 24 h was 5.8, while pain interference was 4.5 on the 0–10 numeric rating scale of the BPI-SF.

# Bivariate Associations and Selection of Covariates

Zero-order Pearson correlations among continuously measured variables are presented in **Table 2**. Greater pain-specific resilience was significantly correlated with less depressive symptoms (p = 0.011) and less pain catastrophizing (p < 0.001), greater use of distraction (p = 0.007) and coping self-statements (p = 0.013), less clinical pain severity (p = 0.042) and pain interference (p < 0.001), as well as greater HPTo (p = 0.014) and CPTo (p = 0.041). Greater depressive symptom severity was significantly correlated with increased pain interference (p < 0.001) and more pain catastrophizing (p < 0.001). Greater pain catastrophizing was significantly correlated with greater clinical pain severity (p = 0.034) and pain interference (p = 0.001), as well as diminished HPTo (p = 0.003) and CPTo (p = 0.031). Longer duration of pain was significantly correlated with greater clinical pain severity (p < 0.001) and pain interference (p = 0.034), as well as less pain-specific resilience (p = 0.038). Results from a series of one-way ANOVAs revealed that participants actively prescribed opioids tended to report greater pain interference (p = 0.069) than those not receiving opioid medication. A significant sex difference was observed for HPTo (p = 0.013), such that males demonstrated diminished HPTo compared to females Age, race, poverty status, and HIV clinical characteristics including CD4+ and detectable viral load, were not significantly associated with any of the key variables of interest.

Prior to completing the linear multiple regression analyses below, specific covariates were chosen based upon whether TABLE 1 | Participant characteristics (N = 85).

CES-D, Center for Epidemiologic Studies-Depression Scale; PRS, Pain-Specific Resilience Scale; CSQ-R, Coping Strategies Questionnaire-Revised; BPI-SF, Brief Pain Inventory-Short Form; HPTo, heat pain tolerance; CPTo, cold pain tolerance.

they demonstrated significant associations with key variables of interest including clinical pain severity and interference, HPTo and CPTo, as well as pain coping strategies and pain catastrophizing. Participants' sex, opioid medication prescription, depressive symptom severity, and chronic pain duration were included as covariates in all study models displayed in **Tables 3**–**5**. The reported answer to "Pain Right Now" on the BPI-SF was included as a covariate in all analyses (except for the

#### TABLE 2 | Zero-order pearson correlations.

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<sup>∗</sup>p < 0.05, ∗∗p < 0.01. PRS, Pain Resilience Scale; CES-D, Center for Epidemiological Studies Depression Scale; CSQ-R, Coping Strategies Questionnaire-Revised; BPI-SF, Brief Pain Inventory-Short Form; HPTo, Heat Pain Tolerance; CPTo, Cold Pain Tolerance.

TABLE 3 | Multiple regressions models demonstrating associations with pain interference and clinical pain severity.

<sup>a</sup>Coded Variable (1 = Males, 2 = Females); <sup>b</sup>Coded Variable (1 = Prescribed, 2 = Not Prescribed). <sup>∗</sup>p < 0.05; ∗∗p < 0.01. BPI-SF, Brief Pain Inventory-Short Form. CES-D, Center for Epidemiologic Studies Depression Scale.

<sup>a</sup>Coded Variable (1 = Males, 2 = Females); <sup>b</sup>Coded Variable (1 = Prescribed, 2 = Not Prescribed). <sup>∗</sup>p < 0.05; ∗∗p < 0.01. CSQ-R, Coping Strategies Questionnaire – Revised. CES-D, Center for Epidemiological Studies Depression Scale.

analysis of pain severity) to control for individual differences in clinical pain severity at the time of study participation.

# Associations With Clinical Pain Severity and Pain Interference

**Table 3** displays the results of two multiple regression models that assessed whether pain-specific resilience was uniquely and significantly associated with clinical pain severity and pain interference reported on the BPI-SF. On the left of **Table 3**, results revealed that the overall model accounted for a significant 52% of the variance in pain interference [F(6, 78) = 14.207, p < 0.001]. Greater pain-specific resilience was significantly associated with less pain interference in PLWH and chronic pain even after controlling for covariates (β = −0.199, p = 0.022). As seen on the right of **Table 3**, the overall model did account

TABLE 5 | Multiple regressions models demonstrating associations with heat pain tolerance (HPTo) and cold pain tolerance (CPTo).

<sup>a</sup>Coded Variable (1 = Males, 2 = Females); <sup>b</sup>Coded Variable (1 = Prescribed, 2 = Not Prescribed). <sup>∗</sup>p < 0.05; ∗∗p < 0.01. CES-D, Center for Epidemiologic Studies Depression Scale.

for a significant portion of variance in clinical pain severity [F(5, 79) = 4.320, p = 0.002]. However, pain-specific resilience was not significantly associated with clinical pain severity after controlling for covariates (β = −0.116, p = 0.283).

# Associations With Pain Coping Strategies and Pain Catastrophizing

A series of three additional multiple linear regressions were conducted to assess whether pain-specific resilience was significantly associated with pain catastrophizing, as well as the use of two pain coping strategies (distraction, coping selfstatements). Overall, the multiple regression model presented to the left in **Table 4** accounted for a significant 36% of the variance in pain catastrophizing [F(6, 78) = 7.143, p < 0.001]. Results revealed that greater pain-specific resilience was significantly associated with less catastrophizing about pain (β = −0.309, p = 0.002) controlling for covariates. The overall multiple regression model presented in the middle of **Table 4** accounted for a significant 13% of the variance in use of distraction [F(6, 78) = 2.329, p = 0.050]. Greater pain-specific resilience was found to be significantly associated with more frequent use of distraction as a pain coping technique (β = 0.257, p = 0.027). As demonstrated on the right side of **Table 4**, the overall multiple regression model accounted for a significant 18% of the variance in coping self-statements [F(6, 78) = 2.891, p = 0.013]. Pain-specific resilience was significantly associated with coping self-statements after controlling for covariates (β = 0.233, p = 0.039).

## Associations With HPTo and CPTo

Results of two multiple regression models examining associations with HPTo and CPTo are presented in **Table 5**. As shown on the left of **Table 5**, the overall model accounted for a significant 16% of the variance in HPTo [F(6, 78) = 2.426, p = 0.033]. Furthermore, results revealed that pain-specific resilience was significantly associated with HPTo, such that participants with greater pain-specific resilience demonstrated higher HPTo (β = 0.302, p = 0.009). On the right side of **Table 5** it can be seen that the overall model did not account for a significant portion of the variance in CPTo [F(6, 78) = 1.149, p = 0.342]. Although pain-specific resilience was not significantly associated with CPTo (β = 0.228, p = 0.056) after controlling for covariates, there was a trend toward significance. The observed power for the multiple regression model examining associations with CPTo was 0.497.

# Parallel Multiple Mediation

Whether pain catastrophizing, distraction, and/or coping selfstatements significantly mediated the effect of pain-specific resilience on HPTo was examined utilizing a parallel multiple mediation model with bias-corrected bootstrapped confidence intervals (**Figure 1**). Results indicated that this overall model accounted for a significant 23% of the variance in HPTo (R <sup>2</sup> = 0.229, p = 0.015). It was revealed that pain-specific resilience was indirectly related to HPTo (i.e., mediated) through catastrophizing (indirect effect = 0.0168, 95% CI:0.0042 to 0.0354), but not through distraction (indirect effect = 0.0001, 95% CI: −0.0140 to 0.0137) or coping self-statements (indirect effect = 0.0017, 95% CI: −0.0075 to 0.0255). More specifically, the higher HPTo shown by those with greater pain-specific resilience was partly accounted for by their less frequent engagement in pain catastrophizing. Additional parallel multiple mediation models were completed and demonstrated that neither catastrophizing, distraction, nor coping self-statements significantly mediated the effect of pain-specific resilience on CPTo or pain interference.

# DISCUSSION

Resilience to pain is a conceptually complex psychological phenomenon. The previous work of Sturgeon and Zautra (2010) has provided much needed clarity for this topic by addressing important resources and mechanisms that promote pathways to resilience for those with chronic pain. Qualities of an individual and his/her social world such as optimism, perseverance, high socioeconomic status, and a helpful social support network represent resilience resources that increase the likelihood of adaptive responses to chronic pain. Resilience mechanisms refer to the helpful thoughts, affects, and behaviors utilized by individuals with chronic pain when confronting adversity. Resilience resources promote the utilization of beneficial resilience mechanisms, and together these resources

and mechanisms interactively influence resilient responses to chronic pain. In this study, the pain-specific resilience measure (Slepian et al., 2016) is arguably an index of resilience mechanisms. This is because its two constituent subscales assess the ability to continue engaging in behaviors or activity when experiencing pain (behavioral perseverance subscale), as well as the ability to maintain positive thoughts and manage negative thoughts or emotions while in pain (cognitive/affective positivity subscale). In this regard, our study demonstrates that PLWH and chronic pain possess wide ranging pain-specific resilience mechanisms that confer either relative protection or vulnerability to the deleterious effects of chronic pain. Those with high pain resilience are perhaps best equipped to cope with chronic pain.

The goal of this study was to investigate the extent to which pain-specific resilience was associated with the following aspects of clinical and experimental pain in a sample of PLWH: (1) engagement in adaptive pain coping strategies, (2) pain catastrophizing, (3) pain interference, and (4) tolerance for painful stimuli delivered in a laboratory setting. As hypothesized, findings suggest that PLWH and chronic pain who demonstrate greater pain-specific resilience may be more likely to engage in adaptive pain coping strategies by specifically utilizing distraction techniques and coping self-statements, while concurrently refraining from catastrophizing about their chronic pain. Additionally, greater pain-specific resilience may mitigate the extent to which chronic pain interferes with daily living and the quality of life of PLWH. Similarly, our findings suggest that greater pain-specific resilience promotes the ability of PLWH and chronic pain to tolerate a painful heat stimulus, an effect which may be attributed to less engagement in pain catastrophizing. Our results are generally consistent with previous studies of chronic pain patients without HIV. For example, greater resilience was associated with better physical functioning and less pain interference in individuals with knee osteoarthritis (Wright et al., 2008). Furthermore, other positive psychological factors associated with resilience such as optimism are indirectly associated with less experimental pain sensitivity via decreased pain catastrophizing (Goodin et al., 2013; Pulvers and Hood, 2013).

Bivariate analyses initially revealed that pain-specific resilience was significantly correlated with diminished clinical pain severity and greater tolerance for a cold pain stimulus; however,

these associations were no longer statistically significant after adjustment for covariates in the multiple regression models. It appears that the adjusted multiple regression model examining the unique association between pain-specific resilience and CPTo may have lacked sufficient statistical power to detect a significant association. That the p-value was 0.056 and the observed power was 0.497 suggests that with a larger sample size of PLWH and chronic pain, it is very likely the association between greater pain-specific resilience and greater CPTo would have remained significant even after adjustment for covariates. However, future research with a larger sample of PLWH and chronic pain will be necessary to confirm this assertion. In the multiple regression model examining clinical pain severity, the strongest association was with duration of pain. Furthermore, PLWH and chronic pain with the longest pain duration (e.g., >10 years) also reported the lowest levels of pain-specific resilience. These findings suggest that PLWH and chronic pain who have been dealing with their pain for many years may be at greatest risk for poor pain outcomes due to a lack of pain-specific resilience mechanisms.

As a matter of clinical importance, a logical extension of our work would be to address the question of whether a tailored cognitive and behavioral intervention might promote resilience mechanisms, specifically for those PLWH with long duration of pain and who demonstrate low pain resilience. Previous intervention development efforts support the likelihood of this possibility. For example, Padesky and Mooney's (2012) fourstep, strengths-based cognitive-behavioral therapy model was designed to help individuals become more resilient by helping them identify and utilize their personal strengths in ways that promote self-efficacy, positive emotions, and better regulation of negative emotions in response to stress. This strengths-based approach to increasing resilience through cognitive-behavior therapy has not yet been applied to PLWH and chronic pain, to the best of our knowledge. Recent and ongoing work conducted by Merlin et al. (2018) suggests that a tailored and evidence-based behavioral intervention may facilitate adaptation to chronic pain in PLWH by promoting pain-specific resilience mechanisms. In their 12-session pain self-management intervention, "Skills TO Manage Pain (STOMP)," PLWH and chronic pain learn specific skills for coping with stress, building self-efficacy and worth, remaining appropriate engaged in valued activities of daily living. Acquisition of these resilience-building mechanisms is completed in group-based sessions that foster peer support around living with HIV and chronic pain, a resilience resource. Whether an ongoing clinical trial of STOMP will improve the chronic pain experiences of PLWH by promoting pain-specific resilience mechanisms has yet to be determined (clinicaltrials.gov NCT03692611). On balance, future psychological interventions that target pain-specific resilience may play an important role in determining whether PLWH effectively manage and cope with their chronic pain. Cognitive and behavioral-based psychological interventions designed to promote adaptive coping and resilience and specifically tailored for HIV populations may provide patients with the ability to ameliorate distress, reduce pain perception, and increase quality of life (Miller et al., 2019).

This study has several limitations that warrant consideration. First, the cross-sectional design of this study limits our ability to form conclusions regarding whether pain-specific resilience causally yields protective effects against chronic pain in PLWH. Similarly, this study could not address the question of whether pain-specific resilience prevents the worsening of pain interference for PLWH and chronic pain over time. Future longitudinal research will be necessary to better appreciate the mechanistic pathways and processes whereby pain-specific resilience yields its pain protective effects. Second, PLWH in this study completed study questionnaires after completion of the QST battery, not prior. Timing of questionnaire completion could potentially affect how participants respond. Third, our study was not designed with specific focus on possible determinants of pain-specific resilience. Although it appears that some PLWH and chronic pain in this study were especially pain resilient, we cannot meaningfully address how such painspecific resilience manifested. Future theoretical and applied research seems warranted in both HIV and non-HIV populations to better establish a framework for understanding how painspecific resilience develops, can be modified, and ultimately protects against the deleterious effects of chronic pain. Whether the previous pain resilience framework previously put forth by Sturgeon and Zautra (2010) applies specifically to PLWH would be a worthwhile investigation. Fourth, the vast majority of our study sample was non-Hispanic Black PLWH who lived below the poverty line. While these sociodemographic factors closely align with the population most affected by HIV in Alabama, and the HIV clinic from which study participants were recruited, the generalizability of our findings may be limited. Future studies should examine whether this study's findings can be replicated among larger, more diverse populations of PLWH and chronic pain. Findings from our study may prove to be even stronger in subsequent research conducted with PLWH and chronic pain who possess more and better resilience resources such as higher socioeconomic status and deeper social support networks among other. Lastly, we did not specifically assess behavioral domains of pain coping such as exercise and pursuit of hobbies. The CSQ-R measure incorporated in this study asks exclusively about the cognitive domains of pain coping (e.g., distraction, use of self-statements). Therefore, at this time it remains unclear the extent to which pain-specific resilience might be associated with greater utilization of behavioral pain coping strategies in PLWH and chronic pain. Despite these limitations, the results of this study contribute to stronger understanding of how pain-specific resilience might mitigate the deleterious effects of pain for PLWH and chronic pain.

The scant amount of research to date addressing psychological contributors to chronic pain in HIV has largely focused on factors such as pain catastrophizing and depression, which confer vulnerability to negative pain-related outcomes (Scott et al., 2018). More recent studies have begun to also address protective psychological factors that promote resilience and positively influence pain-related outcomes for PLWH (Penn et al., 2019). This study helps to address a gap in the current literature pertaining to the potential impact of positive psychological factors on the experience of chronic pain in PLWH. Individuals with a high degree of pain-specific resilience are generally able to maintain behavioral engagement and appropriately regulate