Startseite Resilience as a protective factor in face of pain symptomatology, disability and psychological outcomes in adult chronic pain populations: a scoping review
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Resilience as a protective factor in face of pain symptomatology, disability and psychological outcomes in adult chronic pain populations: a scoping review

  • Zanna Chng , Jerry Jay Yeo EMAIL logo und Ashutosh Joshi
Veröffentlicht/Copyright: 11. August 2022
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Scandinavian Journal of Pain
Aus der Zeitschrift Scandinavian Journal of Pain Band 23 Heft 2

Abstract

Objectives

Patients suffering from chronic pain experience significant disability and disease burden. Resilience has been understood to be a protective factor in face of adversity, eventually contributing to positive outcomes. As such, the current review sought to summarize the existing literature focusing on the roles of resilience in relation to pain phenomenology, pain outcomes (including function and mental health), amongst relevant clinical correlates in a bid to promote holistic management of debilitating chronic pain conditions from a resilience-oriented psychotherapeutic approach as an adjunct to pharmacological treatment.

Methods

A scoping review was conducted on empirical studies surrounding the theme of resilience in adult chronic pain populations published before 9th May 2021. The following main inclusion criteria was applied; (a) adults diagnosed with chronic pain disorders, (b) use of quantifiable pain measures, (c) use of quantifiable resilience measures. A total of 32 studies were then selected for the review.

Results

First, higher levels of resilience were associated with a reduced likelihood of experiencing any chronic pain, fewer pain sites, better psychological response towards nociception and reduced need for analgesia. Second, higher levels of resilience correlated with better daily and physical function, quality of life, psychosocial functioning and lower likelihood of co-morbid mental health disorders. Third, resilience was an intermediary variable in the pathways from pain phenomenology leading to pain interference, depression and post-traumatic growth.

Conclusions

The findings were contextualized using pain-disability and resilience frameworks (The Pain and Disability Drivers Model, O’Leary’s Resilience models) with suggestions to enhance resilience and contextual factors in the holistic management of adult chronic pain conditions. Future research should examine the differences in resilience between pain types as well as evaluate the efficacy of streamlined resilience-oriented interventions.

Keywords: chronic pain; disability; function; psychological distress; resilience

Introduction

Pain is a complex phenomenon that has been understood to be a personal experience influenced by biological, psychological and social factors [1]. Pain and pain-related distresses has been found to be one of the leading causes of disability and disease burden globally [2]. As such, protective factors have been widely studied in recent decades as researchers sought to establish their relevance in the context of chronic pain. One such protective factor is the construct of resilience which consists of two distinct facets; the presence of significant adversity and a subsequent positive adaptation response [3, 4]. Skrove et al. [5] described resilience as the factors, processes and mechanisms contributing to positive outcomes despite the presence of risk factors, such as having poor individual, familial or social resources. Patients suffering from chronic pain conditions certainly face significant adversity in the forms of physical disability [6, 7], psychological distress, job loss and social isolation [8] amidst many day-to-day struggles. Pain-related phenomenology encompasses a wide range of possible outcomes such as pain intensity, pain generalization (spreading of pain from chronic regional to multi-localized pain) [9] and pain interference (degree of pain-related functional impairment) [10]. Subsequent psychological adaptation processes to physical painful stimuli include that of pain acceptance (adaptive response to pain-related experiences without attempting to control them) [7], pain catastrophizing (tendency to amplify threat of pain stimulus, accompanied by inability to inhibit pain-related thoughts) [11], pain self-efficacy, use of active coping strategies, etc. Synthesizing current evidence of how individual resiliency profiles relate to pain symptomatology, functional outcomes, psychological outcomes and other clinical correlates would proffer greater insights into the ways in which resilience acts as a protective factor with regards to chronic pain outcomes. We hope that our findings can serve as a foundation to enhance the holistic management of debilitating chronic pain conditions from a resilience-based psychotherapeutic approach as an adjunct to pharmacological treatment.

One earlier systematic review focused on psychological factors as a whole in a more specific chronic pain population. Martinez-Calderon et al. [12] reviewed 27 articles and observed a heterogeneity of findings including the relationships between self-efficacy, resilience and expectations of recovery with pain intensity and disability amongst patients with chronic shoulder pain. Several previous topical reviews have also explored the understanding of resilience in the contexts of chronic headache patients [13], paediatric chronic pain populations [14] and resilience-enhancing interventions for patients with chronic pain [15]. Having identified the need to focus on resilience as a protective factor in a broader spectrum of adult chronic pain conditions, our review aims to systematically summarize the extant data focusing on the interrelation between resiliency factors and pain correlates including function and psychopathology, in view of the potential roles for resilience in the prognostication and holistic management of adult patients plagued by chronic pain. We also aimed to identify the gaps in existing literature and suggest directions for future research.

Methodology

Literature search

This review was carried out using a staged scoping process as depicted in Figure 1 [16, 17]. The theme of the review was designed in accordance with the Arskey and O’Malley Population, Concept and Context (PCC) framework [16]; (a) Population: adult chronic pain patients, (b) Concept: pain-related clinical correlates including functional and psychological outcomes, (c) Context: individual resiliency profiles. A systematic search was then performed using digital databases such as Pubmed/Medline, PsychINFO, Cochrane library and ScienceDirect up till 9th May 2021 to source for relevant empirical studies surrounding the theme of resilience and chronic pain. Keywords used for the search included “Resilien*”, “Chronic pain”, “Function”, “Psychological distress”, “Predict* or Marker* or Correlate*”, either alone or as a combination of search terms. Author ZC screened the abstracts of potentially useful articles based on the search performed using keywords and the inclusion criteria which was then systematically presented in a table to ensure that included studies met the relevant inclusion criterion and did not fulfil any exclusion criteria. From this, authors JJY and AJ then read the full texts of shortlisted studies and evaluated them independently to ensure relevancy before thorough discussion to reach a shared consensus. The full reports of selected studies were then reviewed and summarised by authors ZC and JJY, which also involved going through their references for additional relevant material which satisfied the inclusion criteria below. The preferred reporting items for systematic reviews (PRISMA) flowchart for this review is shown in Figure 2 [18]. The quality of included studies was assessed using the Newcastle-Ottawa Scale for cross-sectional and cohort studies [19] in Table 1 by author JJY, whereby each Asterik represents one point on the rating scale. Ratings provided for each study was subject to careful verification by author ZC to ensure better accuracy.

Figure 1: Staged scoping process.
Figure 1:

Staged scoping process.

Figure 2: PRISMA flowchart of search process.
Figure 2:

PRISMA flowchart of search process.

Table 1:

Newcastle–Ottawa scale assessment of included studies.

Study Selection Comparability Outcomes/Exposure Total
Cross-sectional Representativeness of sample Sample size Non-respondents Ascertainment of exposure Assessment of outcome Statistical test –
Giannantoni et al. [11] * * ** * * – ******
Haney et al. [47] * * ** * * – ******
Tanner et al. [29] * * ** ** * * – ********
You et al. [22] * * ** ** * * – ********
Liesto et al. [39] * * * ** * * – *******
Ahmed et al. [42] * * ** * * – ******
Bartley et al. [38] * * ** ** * * – ********
Gonzalez et al. [10] * * * ** ** * * – *********
Häuser et al. [30] * * * ** ** * * – *********
Mun et al. [20] * * ** ** * * – ********
Musich et al. [34] * * ** ** * * – ********
Wettstein et al. [23] * * ** ** * * – ********
Arewasikporn et al. [44] * * * ** * * – *******
Arewasikporn et al. [31] * * * * * * – ******
Chen et al. [41] * * ** * * * – *******
de Souza et al. [28] * ** ** * * – *******
Hemington et al. [33] * * ** ** * * – ********
Sharma et al. [45] * * ** * * * – *******
Bauer et al. [24] * * * ** ** * * – *********
Wadley et al. [21] * * ** * * – ******
Costello et al. [32] * * ** ** * * – ********
Ruiz-Párraga et al. [7] * * ** * * * – *******
Min et al. [27] * * ** * * * – *******
Newton-John et al. [26] * * ** * * – ******
Ramirez-Maestre et al. [35] * * ** * – *****
Ruiz-Párraga et al. [36] * * ** * * – ******
Viniol et al. [37] * * ** * * – ******
Ramírez-Maestre et al. [40] * * ** * * – ******
Viggers et al. [46] * * ** * * – ******
Study Selection Comparability Outcomes/Exposure Total
Cohort Representativeness of exposed cohort Selection of non-exposed cohort Ascertainment of exposure Outcome was not present at start of study Assessment of outcome Length of follow-up Adequacy of follow-up
Jegan et al. [43] * * * * * * * *******
Driver et al. [25] * * * * ** * * ********
Viniol et al. [9] * * * * ** * * ********

Inclusion/exclusion criteria

Empirical studies included met the following criteria: (a) Population must include patients diagnosed with chronic pain disorders (greater than 3 months), (b) Population studied must only include adults older than 18-years-old, (c) Pain must be quantifiably measured using a pain scale, such as numerical rating scales, visual analogue scales (VAS), etc. (d) Resilience outcomes must be measured by validated resilience scales such as the Connor-Davidson Resilience Scale (CD-RISC), 25-item Resilience Scale (RS-25), Brief Resilience Scale (BRS), etc. (e) Studies must be published in English.

Studies were excluded based on the following: (a) Samples including caregivers only, (b) Study designs such as intervention studies, reviews, study protocols, construct validity, case reports, or dissertations.

Data extraction

For each included study, authors ZC and JJY extracted variables including sample characteristics, pain measures used, resilience scales used, as well as key findings related to resilience and compiled them into Table 2 for ease of comparison and critical evaluation. The extracted data was subject to independent scrutinization by authors JJY and AJ to ensure relevancy of salient findings as well as to discuss the commonly identified themes suitable for the review.

Table 2:

Summary of main findings from included studies.

Authors Design Demographics (diagnosis, mean age, sex) Measure of pain Measure of resilience Key findings (focusing on resilience) Summary
Giannantoni et al. [11] Cross-sectional Urologic chronic pelvic pain=48 (Pelvic pain=12, Widespread pain=36) NRS-11, VAS RS-14

  1. Poor resilience found in patients with pelvic pain and widespread pain

  1. Urologic chronic pelvic pain a/w ↓resilience
Age=47.3 ± 9.5 PCS

  1. RS-14 higher in patients with pelvic pain than those with widespread pain (50.2 ± 12.5 vs. 40.2 ± 10.2)

  1. ↓Resilience a/w ↑pain, ↑pain catastrophizing and ↑depression
M=45.5%

  1. Lower RS-14 scores seen in those with higher pelvic and non-pelvic NRS, VAS, PCS, DASS-21

  1. ↓Resilience a/w female gender, single and unemployed

  1. Lower RS-14 scores seen in women, single and non-working participants
Haney et al. [47] Cross-sectional Combat-related extremity vascular injury=81 GCPS CD-RISC

RSES-resilient coping

  1. Effects of high pain intensity were significant in the multivariate regression analysis of SMFA components (increase bother index and dysfunction index by 19.8 and 16.0 points respectively compared to low pain intensity group)

  1. ↑Pain a/w ↓MSK function (bother, dysfunction) and poorer mental health
Age=28.4 ± 8.6

  1. Higher RSES-resilient coping score correlated with lower SMFA dysfunction

  1. Resilient coping a/w ↓MSK dysfunction
M=97.5%

  1. MCS positively correlated with higher RSES-resilient coping and CD-RISC scores

  1. ↑Resilient coping and resilience a/w better mental health
Tanner et al. [29] Cross-sectional Chronic MSK pain=166 (Knee pain=135) SF-MPQ-2, GCPS Resilience index

  1. Inverse association between resilience index and clinical knee pain as measured by the SF-MPQ-2 total score and GCPS knee disability score after controlling for covariates

  1. ↑resilience a/w ↓incidence of pain and ↓disability
Age=57.97 ± 8.2

  1. Association between high levels of resilience and lower knee pain intensity did not remain statistically significant after adjusting for covariates (p=0.068)

  1. Resilience not a/w pain intensity
M=33.7%

  1. Resilience index was inversely related to the anxiety, depression, and sleep­related impairment measures in adjusted models

  1. ↑Resilience a/w ↓anxiety, ↓depression and ↓sleep-related impairment

  1. Greater right/ left amygdala (pain-related subcortical structure) asymmetry on structural MRI was positively a/w resilience

  1. Neural correlates: ↑resilience a/w greater right/ left amygdala asymmetry
You et al. [22] Cross-sectional Chronic MSK pain=118 GCPS PCS-Chinese CD-RISC-10-Chinese PRS

  1. Sample divided into higher vs. lower resilience group with more men in the higher resilience group.

  1. ↑Resilience a/w male gender
Age=51.22 ± 16.85

  1. Higher resilience group had fewer people who used prescription analgesics

  1. ↑Resilience a/w ↓pain severity, ↓pain catastrophising, ↓analgesia use and ↓depression
M=36.4%

  1. The higher resilience subgroup demonstrated lower average GCPS, PCS and CES-D

  1. Neural correlates: ↑resilience a/w greater regional GMV

  1. Higher resilience group showed greater regional GMV in 4 identified clusters (bilateral precuneus, left SPL, left IPL, orbital part of the right MFG extending to the medial right SFG, bilateral median cingulate and paracingulate gyri).
Liesto et al. [39] Cross-sectional Breast cancer=160 BPI RS-14

  1. No significant association between resilience and pain severity

  1. Resilience is not a/w pain severity
Age=61.8 ± 7.7

  1. Pain interference negatively correlated with resilience

  1. ↑Resilience a/w ↓pain interference (moderated by anxiety)
M=0%

  1. Negative relationship between resilience and relative pain interference was stronger among participants with low anxiety than those with high anxiety
Ahmed et al. [42] Cross-sectional Total=180 (Back pain=139, Neck pain=41) PSEQ-2 BRS

  1. BRS strongly negatively correlated with NDI (r=0.61, p<0.0001)

  1. ↑Resilience a/w ↓functional disability and ↑pain-efficacy
Age=53 ± 17 ODI, NDI

  1. BRS moderately negatively correlated with ODI (r=0.34, p<0.0001).
M=46.1%

  1. BRS showed moderate positive correlation with PSEQ-2 (r=0.36, p<.0001)
Bartley et al. [38] Cross-sectional Back pain=60 PROMIS pain intensity BRS

  1. More resilient individuals (Cluster 1) exhibited less disability, higher QOL, better psychosocial functioning and greater functional performance when compared to individuals with lower degree of personal resources (Cluster 4)

  1. ↑Resilience a/w ↓disability, ↑QOL, ↑psychosocial functioning and ↑functional performance
Age=68.1 ± 7.0

  1. No significant cluster differences in terms of movement evoked pain (p=0.08) or pain-intensity (p=0.33) was detected

  1. Resilience not a/w pain intensity
M=43%
Gonzalez et al. [10] Cross-sectional HIV with chronic pain=85 BPI-SF PRS

  1. Greater pain-specific resilience significantly a/w less pain interference even after controlling for covariates

  1. ↑Resilience a/w ↓pain interference, ↓pain catastrophizing, ↑use of pain coping strategies
Age=49 ± 4.07 CSQ-Revised

  1. However, pain-specific resilience was not a/w pain severity after controlling for covariates (p=0.283)

  1. Resilience not a/w pain severity
M=67%

  1. Multiple regression models revealed that greater pain-specific resilience was significantly a/w less catastrophizing about pain and more frequent use of pain coping strategies; distraction and coping self-statements
Häuser et al. [30] Cross-sectional Total=2425 (non-disabling chronic non-cancer pain=463, disabling chronic non-cancer pain=230, no pain=1732) Age=50.8 M=46.5% GCPS BRCS

  1. Comparing participants with any chronic non-cancer pain and disabling chronic non-cancer pain with participants with no pain, the statistically significant negative association between resilience and all stages of chronic non-cancer pain was no longer detectable in multivariate analysis

  1. No protective role of resilience for any chronic non-cancer pain
Mun et al. [20] Cross-sectional Study 1 Fibromyalgia= 220 Age=51.2 ± 11.02 M=11% Study 1 Morning pain intensity NRS, Afternoon pain interference NRS Study 1 BRS

  1. In both studies, the High Personal Resource group (high level of resilience) reported the highest level of annual household income

  1. ↑Resilience a/w high annual household income and having a romantic partner
Study 2 Total=483 (Muscular dystrophy=15%, multiple sclerosis=26%, post-polio syndrome=27%, spinal cord injury=32%) Age=55.88 ± 12.61 M=38% Study 2 Pain NRS, PROMIS pain interference Study 2 CD-RISC-10

  1. For individuals with fibromyalgia, romantic partner status significantly varied across subgroups, whereby individuals in the Low Personal Resource group (low level of resilience) reported lowest romantic partner status

  1. ↓Resilience a/w ↑pain interference

  1. In study 2, all subgroups were significantly different with respect to their levels of pain interference at baseline over and above various covariates, with Low Personal Resource group reporting the highest level of pain interference at baseline
Musich et al. [34] Cross-sectional Back pain/ Osteoarthritis/ Rheumatoid arthritis=4161 PEG scale BRS

  1. High resilience attenuated moderate and severe pain severity by 40-60% and was relatively more protective than social networks

  1. ↑Resilience had protective effects against pain severity and pain interference
Mean age not provided Pain related survey

  1. High resilience attenuated moderate and severe pain interference by 50-60%
M=32%

  1. Positive resources (especially medium and diverse social networks) had greater effect on pain interference than severity
Wettstein et al. [23] Cross-sectional Chronic lower back pain=239 West Haven-Yale MPI RS-11

  1. Mean age of the low-well-being cluster (lowest resilience) was significantly lower than the mean age of high/ moderate well-being groups

  1. ↑Resilience a/w older age, male gender and married status
Age=58.06 ± 10.97

  1. Highest proportion of women (84.4%) in the low-well-being-cluster and the lowest proportion (58.8%) in the high-well-being group

  1. ↑Resilience a/w ↓pain intensity and ↓subjective disability
M=28%

  1. Higher proportion of married individuals in the high-well-being cluster (84.3%) compared to the low-well-being group (57.1%)

  1. More pain sites a/w ↓resilience

  1. High-well-being group reported lowest pain intensity scores and lowest subjective disability, however, group differences regarding objective disability outcomes were not significant

  1. Mean number of pain locations was highest in the low-well-being cluster and the lowest in the high-well-being cluster
Arewasikporn et al. [44] Cross-sectional Multiple sclerosis=455 Age=61.0 ± 10.1 M=17.6% NRS-11 PROMIS-29 Health Profile CD-RISC-10 Broaden and-Build Model:

  1. ↑Resilience a/w positive affect, ↓pain interference and ↓depression

  1. Positive affect positively associated with resilience

  1. Resilience negatively associated with pain interference and depressive symptoms

  1. Resilience significantly mediated the associations between pain intensity with both pain interference and depressive symptoms
Stress and Coping Model:

  1. Resilience did not moderate the associations between pain intensity with both pain interference or depressive symptoms
Arewasikporn et al. [31] Cross-sectional Multiple sclerosis=163 Age=52.1 ± 10.1 M=12.9% NRS-11 CD-RISC-10 Pain Model:

  1. ↓Resilience a/w negative affect

  1. Negative affect a/w more depression, less resilience, and marginally more pain interference

  1. ↓Resilience a/w ↑pain intensity (via negative affect)

  1. Positive affect a/w less depression and more resilience, but it was not related to pain interference

  1. Negative affect mediated the relationship between pain intensity and resilience (indirect effect)
Chen et al. [41] Cross-sectional Chronic Back Pain=307 Age=52.6 ± 17.0 M=38.45 GCPS CD-RISC-10

  1. High resilience levels were related to elevations in primary appraisals of pain as a challenge and higher scores on pain self-efficacy beliefs

  1. ↑Resilience a/w pain challenge appraisals, ↑pain self-efficacy, ↓pain catastrophizing and ↓overall dysfunction
PAI-SF-C, PSEQ, CSQ-Catastrophizing subscale, MPI-Screening Chinese-Affective Distress subscale

  1. Participants reporting higher resilience and pain self-efficacy had lower scores on the pain catastrophizing measure

  1. Elevations in resilience and pain self-efficacy as well as reductions in pain catastrophizing were related to lower overall dysfunction
de Souza et al. [28] Cross-sectional HIV=49 (neuropathic pain=27, nociceptive pain=10, pain free=12) Brazilian-PCP:S Brazilian-PCS RS-25

  1. Females with HIV chronic neuropathic pain had significantly lower resilience compared to the pain free group

  1. ↓Resilience a/w neuropathic pain
Mean age not provided M=0%

  1. PCS was moderately a/w depressive symptoms while resilience weakly correlated with depressive symptoms

  1. ↓Resilience weakly a/w ↑depression
Hemington et al. [33] Cross-sectional Total=102 NRS RS-25

  1. Significant negative relationship between resilience and both clinical pain scores and BASDAI disease activity scores in ankylosing spondylitis patients

  1. ↓Resilience a/w ↑pain and ↑disease activity
Ankylosing Spondylitis=5

Age=36.7 ± 10.8 Healthy Control=51 Age=31.1 ± 9.1 M=100%

  1. fMRI data showed Cross-network FC between the DMN and the sensorimotor network was abnormally high in patients with high pain scores on the day of the study. In this group, the typically negative (among healthy control) relationship between resilience and within-DMN connectivity tends to be disrupted

  1. Neural correlates: ↑pain disrupts negative relationship between resilience and within-DMN connectivity
Sharma et al. [45] Cross-sectional Chronic MSK Pain=143 NRS-11 CD-RISC

  1. Regression analysis showed that both pain intensity and income moderated the association between resilience and physical function

  1. Resilience a/w physical function (moderated by pain intensity and income)
Age=47.06 ± 14.45

  1. Statistically significant moderating effect of income on the association between resilience and depression

  1. ↓Resilience a/w ↑depression (stronger in ↓income group)
M=35%

  1. A stronger negative association between resilience and depression was found for participants reporting lower income relative to those reporting higher income
Jegan et al. [43] Cohort Total=423 (chronic lower back pain=320, chronic widespread pain=103) GCPS RS-11

  1. At baseline, resilience was negatively a/w disability, somatization and depression

  1. ↓Resilience a/w ↑disability, ↑somatization and ↑depression
Age=56.6 ± 14.1

  1. Resilience showed positive correlation with coping

  1. ↑Resilience a/w ↑coping
M=42.3%

  1. Resilience negatively correlated with disability at 1-year follow-up; however, this association was small and multiple regression showed that resilience did not predict disability in any of the subgroups

  1. Resilience did not predict disability at 1yr follow-up
Bauer et al. [24] Cross-sectional Total=724 (No pain=219, Chronic local pain=416, Chronic widespread pain=89) Pain questionnaire RS-5

  1. Resilience score tended to be lower in participants with greater pain extent

  1. Greater pain extent a/w ↓resilience
Age=77.6±6.1

  1. Resilience scores did not differ across age groups in men, but decreased significantly with age in women

  1. ↓Resilience a/w older age (only in women)
M=49.6%

  1. Resilience significantly moderated the effect of chronic widespread pain in participants with low or average depression scores

  1. Resilience moderated the relationship between chronic widespread pain and depression

  1. Resilience score was negatively a/w depression, this protective effect was particularly strong in the chronic widespread pain group
Driver et al. [25] Cohort Spinal cord injury=31 (cervical=13, thoracic=18) VAS CD-RISC-10

  1. At baseline (inpatient), only age was significantly associated with resilience

  1. ↑Resilience a/w older age
Age=38.96±12.22

  1. At 3-month follow-up, negative associations were found between inpatient resilience and both depression and pain

  1. ↑Resilience predicts ↓pain and ↓depression
M=64.5%

  1. Regression analysis showed that only self-efficacy was a significant predictor of inpatient resilience while depression predicted resilience at follow-up
Wadley et al. [21] Cross-sectional HIV=197 BPI RS-25

  1. Participants in pain had lower resilience scale scores than those not in pain

  1. Pain a/w ↓resilience
Chronic pain=99 Age=44±10 M=34% EQ-5D CD-RISC

  1. In the chronic pain group, resilience was not found to correlate with pain intensity or subjective ratings of functional interference

  1. Resilience not a/w pain intensity
No chronic pain=98 Age=40 ± 10 M=22%

  1. Resilience correlated with ratings of overall perception of health status on the EQ-5D VAS scale in the whole sample but was not found to moderate the relationship between pain and QOL in the chronic pain group

  1. ↑Resilience a/w ↑HRQOL

  1. Actigraphy showed that resilience scores did not correlate with median activity in the whole sample while pain was also not associated with level of activity
Costello et al. [32] Cross-sectional Total=152 (chronic pain=73) BPI CSQ-24 CD-RISC-10

  1. Higher resilience scores negatively correlated with pain severity and pain interference

  1. ↑Resilience a/w ↓pain severity and ↓pain interference
Mean age not provided

  1. Resilience negatively correlated with depression, anxiety and pain catastrophizing
M=83.6%

  1. Social support was positively correlated with resilience

  1. Depression emerged as the only significant predictor of pain severity and pain interference
Ruiz-Párraga et al. [7] Cross-sectional Chronic Back Pain=229 Age=45.53 ± 11.89 M=28.8% NRS-11, INDEX CPAQ, RMDQ. RS-25

  1. Structural equation analysis showed resilience yielded two statistically path coefficients

  1. ↑Resilience a/w ↑pain acceptance and ↓trauma-related symptoms

  1. First, to pain acceptance, higher levels of resilience related to higher levels of pain acceptance

  1. Indirect effect of resilience on pain intensity, pain disability and emotional distress (via pain acceptance/ trauma-related symptoms)

  1. Second, to trauma-related symptoms, higher levels of resilience related to lower levels of trauma-related symptoms

  1. Higher levels of pain acceptance were related to lower levels of pain intensity, pain disability and emotional distress

  1. Trauma-related symptoms yielded a statistically significant path coefficient to pain adjustment, whereby individuals with more of these symptoms reported higher levels of pain intensity, pain disability and emotional distress
Viniol et al. [9] Cohort Chronic lower back pain=423 GCPS RS-11

  1. The 1-year incidence for onset of chronic widespread pain among patients with chronic lower back pain was 23.8%

  1. No protective effect of resilience on pain generalization
Age=56.56 ± 14.07

  1. Regression analysis identified female sex, longer duration of back pain and high rate of psychosomatic symptoms to be significantly a/w onset of chronic widespread pain among patients with chronic lower back pain, protective factors (such as resilience and coping resources) had no significant influence
M=42.32%
Min et al. [27] Cross-sectional Spinal cord injury=37 (chronic pain=89.2%) Age=41.5 ± 10.8 M=78.4% EQ-5D (subscale) CD-RISC

  1. Employed individuals had significantly higher resilience than unemployed individuals

  1. ↑Resilience a/w employed status

  1. Post hoc analyses showed that individuals with extreme pain showed significantly higher depression scores than those with no and moderate pain

  1. Hierarchical regression model of depression showed after entering resilience, the influence of pain on depression was decreased

  1. Hierarchical regression model of post-traumatic growth showed that pain severity did not remain as a significant predictor after entering resilience
Newton-John et al. [26] Cross-sectional Chronic pain=101 (back pain=69%) NRS-11 PSEQ, RMDQ BRS

  1. BRS showed significant positive a/w pain self-efficacy and social support

  1. ↑Resilience a/w ↑pain self-efficacy, social support and employment
Age=43 ± 10.96

  1. BRS showed significant negative relationships with pain intensity, fear of movement/ re-injury and pain-related disability

  1. ↑Resilience a/w ↓pain intensity, ↓fear avoidance and ↓pain-related disability
M=56%

  1. Point-biserial correlation showed a significant positive relationship between greater resilience with a greater likelihood of attending work

  1. Resilience does not predict depression/ disability

  1. Regression analyses showed that resilience did not add significantly to the prediction of depression scores and disability scores
Ramirez-Maestre et al. [35] Cross-sectional Chronic spinal pain=400 Age=46.22 ± 13.21 M=47.5% NRS-11 CPAQ, VPMI, PASS RS-25

  1. Structural equation model showed that resilience had a positive signification with confrontation (pain acceptance, coping strategies) and was equal in both men and women.

  1. ↑Resilience a/w ↑confrontation

  1. Confrontation had a significant association with negative mood, functional status, and pain intensity and was equal between men and women.

  1. Indirect effect of resilience on mood, functional status and pain intensity (via confrontation)
Ruiz-Párraga et al. [36] Cross-sectional Chronic MSK pain=346 NRS-11 RMDQ, CPAQ RS-25

  1. Participants were divided into three groups, non-trauma exposed (n=117), trauma-exposed (n=119) and trauma-exposed with Posttraumatic Stress Symptoms (n=110)

  1. Resilience predicts pain intensity and disability
Mean age not provided

  1. Participants in the Trauma-exposed with Posttraumatic Stress Symptoms group had lower mean scores for resilience and pain acceptance
M=29.5%

  1. Moderated regressions showed resilience significantly predicted pain intensity and disability
Viniol et al. [37] Cross-sectional Chronic lower back pain =634 German Pain Questionnaire GCPS RS-11

  1. The sample was divided into 3 clusters

 –↓Resilience a/w ↑pain severity, more pain sites, and ↑pain-related disability
Age=56.30 ± 13.95

  1. Patients from cluster 2 (middle-aged patients with high mental distress and poor coping resources) had the most pronounced pain (highest rate of high pain severity, higher number of pain areas and pain-related disability)
M=38.8%

  1. They had more psychological distress in terms of anxiety, depression and somatization

  1. They had the lowest resilience scores compared to the other 2 clusters
Ramírez-Maestre et al. [40] Cross-sectional Chronic spinal pain=299 Age=44.18 ± 12.17 M=46.2% NRS-11 RS-25

  1. Resilience yielded significant path coefficients to pain acceptance and active coping, whereby individuals characterized by higher levels of resilience reported higher levels of pain acceptance and higher use of active coping strategies

  1. ↑Resilience a/w ↑pain acceptance and ↑active coping
CPAQ, VPMI

  1. Resilience also had statistically significant effects on anxiety and depression, whereby patients with higher levels of resilience reported lower levels of both variables

  1. ↑Resilience a/w ↓anxiety and ↓depression
Viggers et al. [46] Cross-sectional Chronic pain=87 (back pain=54%, arthritis=33%) MPQ

CSQ		 CD-RISC

  1. When resilience was added in the final model, only pain rating and pain catastrophizing were a/w physical HRQOL

  1. ↑Resilience a/w ↑HRQOL, ↓depression and ↓anxiety
Mean age not provided

  1. However, results were significant for mental HRQOL, whereby mental HRQOL was higher for participants who reported higher resilience
M=35.6%

  1. Resilience significantly a/w depression, where those with higher resilience reported less depression, this relationship was stronger than the relationship between pain severity and depression

  1. Resilience significantly a/w anxiety (those who scored high on resilience reported less anxiety), the strength of the relationship was not as large as for depression

  1. BASDAI, bath ankylosing spondylitis disease activity index; BPI, body pain inventory pain interference; BPI-SF, body pain inventory pain interference-short form; BRCS, brief resilience coping scale; BRS, brief resilience scale; CD-RISC, connor-davidson resilience scale; CES-D, center for epidemiologic studies depression scale; CSQ, coping strategies questionnaire; DASS-21, 21-item depression anxiety and stress scale; DMN, default mode network; DOI, duration of illness; EQ-5D, euroqol-5 dimensions questionnaire; EVI, extremity vascular injury; FC, functional connectivity; FMRI, functional magnetic resonance imaging; GCPS, the graded chronic pain scale; GMV, grey matter volume; HIV, human immunodeficiency virus; HRQOL, health-related quality of life; INDEX, composed pain intensity index; IPL, inferior parietal lobule; MCS, mental component score; MFG, middle frontal gyrus; MPI, multidimensional pain inventory; MPQ; McGill questionnaire; MRI, magnetic resonance imaging; MSK, musculoskeletal; neck disability index; NDI, neck disability index; NRS, numerical rating scale; ODI, oswestry disability index; PAI-SF-C, pain appraisal inventory-short-form-challenge; PASS, pain anxiety symptom scale; PCP:S, profile of chronic pain: screen; PCS, pain catastrophizing scale; PEG, pain, enjoyment of life and general activity; PHQ-9, patient health questionnaire-9 depression scale; PROMIS, patient-reported outcomes measurement information; PRS, the pain resilience scale; PSEQ, pain self-efficacy questionnaire; QOL, quality of life; RMDQ, Roland-Morris disability questionnaire; RS, resilience scale; RSES, response to stressful experiences scale; SFG, superior frontal gyrus; SEM, structural equation modelling; SF-MPQ-2, The revised short-form McGill pain questionnaire; SMFA, short musculoskeletal function assessment questionnaire; SPL, superior parietal lobule; VAS, visual analogue scale; VPMI, the vanderbilt pain management inventory.

Results

Included studies

A total of 32 observational studies were included in this review with the relevant key findings summarized in Table 2. A large majority of studies (29 out of 32, 90.6%) adopted a cross-sectional design while the rest were cohort studies (3 out of 32, 9.4%) with follow-up periods ranging from 3-months to 1-year. There was a wide range of sample sizes, ranging from 31 to 4,161 participants with 56.3% (n=18) of study populations consisting of fewer than 200 participants. There was also a breadth of medical diagnoses giving rise to chronic pain, the most common of which was musculoskeletal-related disorders (such as chronic lower back pain; n=16, 50%) followed by neurological disorders (such as multiple sclerosis, spinal cord injury; n=7, 21.9%). The remaining study populations included but were not limited to Human Immunodeficiency Virus (HIV) positive adults, breast cancer patients, military veterans who sustained combat-related extremity vascular injury, etc. In terms of resilience measures employed, 13 studies used the Resilience Scale (RS) (25-item=6, 14-item=2, 11-item=4, 5-item=1), 13 studies used the Connor-Davidson Resilience Scale (CD-RISC) (25-item=6, 10-item=7), five studies used the Brief Resilience Scale (BRS), two studies used The Pain Resilience Scale (PRS), the Resilience index and the Brief Resilience Coping Scale (BRCS) were utilized by one study each. Out of the included studies, three studies made use of more than one resilience measures in their analysis [20], [21], [22]. Table 3 encompasses a brief overview of the above tools used to measure resilience including author(s), score ranges, internal consistency and test-retest reliability where available.

Table 3:

Summary of resilience measures.

Tool Author(s) Test Items Score Ranges Internal Consistency Test-retest Reliability
Resilience scale (RS) Wagnild and Young (1993) 25-item 25–175 α=0.91 r=0.67–0.84
14-item 14–98
11-item 11–77
5-item 5–35
Connor-Davidson resilience scale (CD-RISC) Connor and Davison (2003) 25-item 0–100 α=0.89 ICC=0.87
10-item 0–40
Brief resilience scale (BRS) Smith et al. (2008) 6-item 6–30 α=0.80–0.91 ICC=0.62–0.69
Pain resilience scale (PRS) Slepain et al. (2016) 14-item 0–56 α=0.93 ICC=0.80
Resilience index Johnson et al. (2019) 8-item 0–8 Not assessed Not assessed
Brief resilience coping scale (BRCS) Sinclair and Wallston (2004) 4-item 4–20 α=0.69 r=0.71

  1. ICC, intraclass correlation coefficient.

  2. References

  3. Campbell-Sills L, Stein MB. Psychometric analysis and refinement of the Connor-davidson Resilience Scale (CD-RISC): Validation of a 10-item measure of resilience. J Trauma Stress 2007;20(6):1019–28. https://doi.org/10.1002/jts.20271.

  4. Connor KM, Davidson JR. Development of a new resilience scale: the Connor-Davidson resilience scale (CD-RISC). Depression and anxiety 2003;18(2):76–82. https://doi.org/10.1002/da.10113.

  5. Johnson AJ, Terry E, Bartley EJ, Garvan C, Cruz-Almeida Y, Goodin B, Glover TL, Staud R, Bradley LA, Fillingim RB and Sibille KT. Resilience factors may buffer cellular aging in individuals with and without chronic knee pain. Mol Pain 2019;15:1744806919842962. https://doi.org/10.1177/1744806919842962.

  6. Sinclair VG, Wallston KA. The development and psychometric evaluation of the brief resilient coping Scale. Assess 2004;11(1):94–101. https://doi.org/10.1177/1073191103258144.

  7. Slepian PM, Ankawi B, Himawan LK and France CR. Development and initial validation of the pain resilience scale. J Pain 2016;17(4):462–72. https://doi.org/10.1016/j.jpain.2015.12.010.

  8. Smith BW, Dalen J, Wiggins K, Tooley E, Christopher P, & Bernard J. The brief resilience scale: assessing the ability to bounce back. Int J Behav Med 2008;15(3):194–200. https://doi.org/10.1080/10705500802222972.

  9. Wagnild GM, and Young HM. Development and psychometric evaluation of the resilience scale. J Nur Meas 1993;1(2):165–78.

With regards to the quality assessment, publications selected for the review were evaluated using the Newcastle-Ottawa Scale as shown in Table 1 [19]. Out of 29 cross-sectional studies, 11 studies scored eight to nine points out of a maximum of ten, 17 studies scored six to seven points while only one study scored 5 points which may indicate a significant risk of bias. As for the three cohort studies, two studies scored eight points out of a maximum of nine while the remaining study scored a rating of seven.

Socio-demographical factors

The included studies suggest that resilience profiles may differ between individuals with specific socio-demographical characteristics such as gender [11, 22, 23], age [23], [24], [25], romantic relations [11, 20, 23], social support [26], employment status [11, 26, 27], and household income [20]. Males experiencing chronic pain were found to have higher levels of resilience compared to their female counterparts [11, 22, 23]. The literature surrounding age was less consistent. Wettstein et al. [23] and Driver et al. [25] observed in their samples that older participants scored higher on resilience scales when compared to relatively younger participants. Per contra, the opposite association was observed by Bauer et al. [24] whereby younger participants had higher resilience scores, although this only reached significance amongst females. It was also noted that higher levels of resilience positively correlated with having a romantic partner [11, 20, 23], strong social support networks [26], being employed [11, 26, 27], as well as higher household income [20].

Resilience and pain correlates

The findings surrounding pain outcomes appear to be rather controversial from this review. A number of included studies found that higher levels of resilience predicted a lower incidence of chronic pain [11, 21, 25, 28, 29]. However, Häuser et al. [30] concluded in their sample of 2,425 participants that resilience was not a significant protective factor for the development of chronic pain (pain persisting beyond expected tissue healing time which is arbitrarily set as 3 months) based on comparative analyses between the chronic non-cancer pain subgroup vs. participants who did not have chronic pain. The negative association between resilience and pain intensity was demonstrated in 12 of the included studies [7, 11, 22, 23, 26, 31], [32], [33], [34], [35], [36], [37]. On the other hand, five other studies seem to refute this, arriving at the conclusion that resiliency profiles had no significant correlations with pain intensity as measured on numerical or visual analogue pain rating scales (such as Brief Pain Inventory, PROMIS pain intensity scale, etc.) [10, 21, 29, 38, 39]. In terms of pain distribution, higher resilience levels were associated with a lower incidence of reporting any chronic pain [23, 24, 37] but had no protective effects against pain generalization [9].

In the domain of psychological adaptation in the face of chronic pain conditions, patients with higher resilience scores were found to have higher pain acceptance [7, 35, 40] and lower tendencies for pain catastrophizing [10, 11, 22, 32, 41]. Higher levels of resilience also correlated with higher pain self-efficacy [26, 41, 42], increased likelihood of appraising pain as a challenge [41] and increased use of active coping strategies in an attempt to control pain percept [10, 35, 40, 43]. You et al. [22] highlighted the potential role of resilience in reducing analgesic requirements in their sample of 118 patients with chronic musculoskeletal pain whereby the higher resilience subgroup had fewer participants who needed to use non-opioid prescription analgesics for pain control, although the exact medications taken by study participants were not further elaborated on.

Resilience and functional outcomes

A large component of the morbidity associated with chronic pain conditions manifests itself in terms of limitations in daily functioning. That being said, individuals who scored higher on resilience scales experienced less pain interference [10, 20, 32, 34, 39, 44], less pain-related disability [7, 26, 36, 37] and less overall disability [23, 29, 38, 42, 43]. The latter being contradicted by the findings from two included studies that suggest a limited role of resilience in predicting disability status. Although Jegan et al. [43] observed in their cohort of chronic lower back pain and chronic widespread pain patients that resilience negatively associated with disability at baseline, it had no predictive effect at 1-year follow-up. In a similar vein, regression analyses performed by Newton-John et al. [26] showed that resilience levels did not add significantly to the prediction of disability scores.

Resilience also positively correlated with physical function [45] and overall functional performance [38]. Ramírez-Maestre and Esteve [35], found that resilience had a more indirect effect on functional status through confrontation which encompasses pain acceptance and use of coping strategies. Furthermore, higher resilience levels corresponded with better quality of life [21, 38, 46].

Resilience and psychological outcomes

Pertaining to psychological well-being amongst adult chronic pain populations, individuals reporting higher pain scores were found to suffer from higher degrees of emotional distress [7] and poor overall mental health status [47]. These undesirable outcomes may be mitigated in part by the protective factor that is resilience, whereby individuals who possessed stronger resiliency profiles were noted to have better overall mental health [47] and psychosocial functioning [38]. In particular, higher levels of resilience was associated with lower rates of depression [11, 22, 25, 28, 29, 32, 40, 43], [44], [45], [46] as well as lower levels of anxiety [29, 32, 37, 40, 46]. Of note, the predictive effect of resilience for depression was found to be insignificant in the study by Newton-John et al. [26].

In terms of affect, Arewasikporn et al. [31, 44] noted that multiple sclerosis patients with higher scores on the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) were more likely to report positive affect. Additionally, Ramírez-Maestre and Esteve [35], found an indirect effect of resilience on mood via confrontation in their sample of 400 patients with chronic spinal pain.

With regards to other domains, higher levels of resilience were associated with lesser somatization [37, 43], less sleep-related impairment [29] and lesser fear avoidance [26]. Moreover, amongst 229 participants with post-traumatic chronic back pain, resilience had an inverse correlation with trauma-related psychopathology [7].

Mediational or moderating effects

In terms of mediational role, the Broaden-and-Build model proposed by Arewasikporn et al. [44] showed that trait resilience mediated the relationships between pain intensity and both pain interference as well as depressive symptoms.

In terms of mitigating negative outcomes, Bauer et al. [24] observed that resilience significantly moderated the effect of chronic widespread pain in participants with low or average depression scores on the Geriatric Depression Scale (GDS-15). In a similar light, Min et al. [27] noted in their sample of community-dwelling individuals with spinal cord injury that resilience was able to mitigate the negative effects related to pain severity, contributing to reduced depression and greater post-traumatic growth.

Neural correlates

Amongst sparse studies examining neuroimaging correlates of resilience, Tanner et al. [29] found on structural magnetic resonance imaging (MRI) that larger right amygdala volumes when compared to the left amygdala positively correlated with resilience scores, whereby the observed difference is not significantly associated with handedness. A Voxel based morphology study by You et al. [22] demonstrated larger gray matter volumes amongst chronic musculoskeletal pain patients with better resiliency profiles in contrast to those with lower levels of resilience in the bilateral precuneus, left superior and inferior parietal lobules, orbital right middle frontal gyrus and medial right superior frontal gyrus, bilateral median cingulate and paracingulate gyri regions. With regards to the modality of functional-MRI, Hemington et al. [33] noted that the typically negative relationship between resilience and within-DMN (default mode network) connectivity in health controls tended to be disrupted in ankylosing spondylitis patients reporting higher pain scores on the day of the study.

Discussion

A few key findings prevailed based on the systematic synthesis of current evidence available pertaining to resilience in adult chronic pain populations. First, higher levels of resilience were associated with a reduced likelihood of experiencing any chronic pain, fewer number of pain sites, improved psychological adaptation towards pain percept (such as higher pain acceptance, lesser pain catastrophizing, higher pain self-efficacy, challenge appraisals, use of active coping strategies) as well as decreased analgesic requirements. Second, higher levels of resilience were associated with lesser disability (such as lesser pain interference/pain-related disability, improved physical functional performance), higher quality of life, better psychosocial functioning and lower rates of negative mental health outcomes (such as depression, anxiety, somatization, sleep-related impairment, fear avoidance, post-traumatic stress reactions). Third, resilience also had indirect effects on pathways from pain phenomenology leading to pain interference, depressive symptoms and post-traumatic growth.

The abovementioned findings can potentially be better understood using The Pain and Disability Drivers Model [48] which is a proposed theoretical framework constituting of five domains, namely, nociceptive drivers, nervous system dysfunction drivers, comorbidities drivers, cognitive-emotional drivers, and contextual drivers. These domains synergistically contribute towards pain experience and associated disability. Resilience is a modifiable dynamic construct [49] that is able to influence various elements encompassed by The Pain and Disability Drivers Model. For instance, this review demonstrates the protective effects of resilience against nociceptive pain drivers including pain distribution, pain interference and functional performance. Similarly, resilience was seen to mitigate co-morbidity drivers particularly in the domain of mental health disorders. With regards to cognitive-emotional drivers, there was also an interplay between resiliency profiles and a myriad of pain beliefs such as pain acceptance, pain catastrophizing, self-efficacy and recruitment of coping resources. Pertaining to contextual drivers, individuals who reported having romantic partners, strong social support networks and employment possessed higher levels of resilience which correlates with better psychosocial functioning. The bidirectional relationship between social support and resilience has been noted in previous studies, whereby individuals with high social supports tend to have better mental health and resilience levels, which in turn gives rise to positive emotions that help form and maintain strong social supports [50], [51], [52].

The key findings from this review can be further conceptualized using O’Leary’s [53], three resilience models. Within the Compensatory Model, it was proposed that resilient individuals possess certain traits that confer protection against adversity. Such traits include adopting active approaches to solve life problems and having tendencies towards perceiving experiences positively [54]. This is consistent with our review findings whereby patients with higher resilience scores were noted to utilize active coping strategies and were more inclined towards pain acceptance, self-efficacy as well as more frequent appraisals of pain as a challenge. The inverse correlation between resilience and pain intensity found in 12 of the included articles may potentially be explained by the Challenge Model which states that non-excessive stressors (such as nociception) can become enhancers of successful adaptation. Whilst it is possible that the participants of the five studies that refuted this association experienced overwhelming pain percept which did not correspond with resilience levels, definitive conclusions are limited given the use of non-uniform measures of pain severity. With reference to the Protective Factor Model, protective factors have been theorised to mitigate exposure to risk, thereby negating negative outcomes. This ties in with the moderating and mediating roles of resilience captured by this review whereby sequelae of chronic pain conditions such as pain interference, depression and post-traumatic growth were significantly modulated by individual resiliency profiles.

In view of the recurring themes summated in this review, supported with pain-disability and resilience frameworks, there is certainly a strong case for clinicians to adopt a comprehensive biopsychosocial approach with resilience as a therapeutic target in the management of chronic pain populations. In accordance with the O’Leary [53], resilience models, strengthening resiliency factors will protect against the adversity that presents in the form of pain phenomenology, directly influencing pain-related outcomes such as disability, functional performance, quality of life, depression, anxiety, etc. In addition, these effects can be further potentiated via the indirect mechanisms of resilience through the modulation of risk exposure. Bearing in mind The Pain and Disability Drivers Model, resilience should be enhanced alongside contextual factors including social support (such as family, caregivers, peers) to maximally disrupt the various drivers of disability.

Limitations and future directions

Several limitations surfaced from this review for which future directions can be taken. Firstly, there are likely to be discrepancies between how resilience influences outcomes in differing specific chronic pain populations. Our review has taken on a broader approach to identify common pathways and protective effects of resilience across various of chronic pain conditions that have fulfilled our inclusion criteria. Further meta-analytic subgroup comparisons would certainly help to elucidate the differential impact of resiliency factors on disease outcomes between specific chronic pain diagnoses but this is unfortunately beyond the scope of our review.

Second, there is a lack of studies examining the differences in resilience correlates between groups experiencing different types of pain (such as nociceptive, neuropathic, nociplastic) using comparable or adjusted samples. The current review adopted a more general approach towards chronic pain. Although the findings relating to resilience were fairly consistent across different chronic pain diagnoses, future research should delve deeper into the subtleties between patients experiencing different categories of pain. Delineation of subgroup-specific elements will proffer greater insight into the design of more targeted resilience-based interventions as well as differential prognostication.

Thirdly, a large proportion of studies included in our review were of cross-sectional design. As such, future studies of prospective cohorts should be performed in order to establish causality from resilience to pain-correlates, disability and mental health outcomes. As mentioned earlier, resilience has been understood to exist as a dynamic construct, hence, prospective work will also aid in the understanding of how resiliency factors change over time in adult chronic pain populations. Additionally, given the relatively heterogenous data especially with regards to the association between resilience and pain severity, meta-analytic research would be useful in addressing these conflicting findings.

Last but not least, there is a paucity of studies evaluating resilience-oriented interventions in the context of adult chronic pain populations. Majority of the existing studies have focused on multi-positive activity programs for chronic pain management without specifically targeting resilience traits such as optimism, state positive affect, extraversion, etc. This is an area that has also been highlighted in a previous literature review [15]. Considering the promising findings of resilience in influencing positive clinical outcomes in chronic pain diseases, future interventional studies should seek to refine and evaluate the efficacy of resilience-oriented therapies.

Conclusions

In conclusion, within the summarized extant data, we found favourable associations between higher levels of resilience and pain phenomenology, psychological adaptation to pain, overall function and mental health status either as a direct or indirect contributing factor. Viewed within the context of pain-disability and resilience frameworks (The Pain and Disability Drivers Model, O’Leary’s Resilience models) with future research warranted, suggestions were made to enhance resilience and contextual factors in the management of adult chronic pain conditions from a holistic angle.

Corresponding author: Jerry Jay Yeo, MBBS Program, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, 119228, Singapore, Singapore, Phone: +65 8113 8665, E-mail:

  1. Research funding: No funding received for the research, authorship, and/or publication of this article.

  2. Author contributions: All authors were involved in the conception of the theme for the review. Author ZC conducted the literature search based on the inclusion criteria. Studies meeting the selection criteria were evaluated independently by authors JJY and AJ to ensure relevancy and remove duplication. Author JJY performed the quality assessment for included studies. Authors ZC and JJY then extracted the relevant data into Table 2 which was subject to independent scrutinization by authors JJY and AJ. Any disagreements were resolved upon reaching a shared consensus. Authors ZC and JJY wrote the first draft of the manuscript and all authors contributed to and have given final approval of the manuscript version to be published and agreed to be accountable for aspects of the work.

  3. Competing interests: All authors declare that they have no conflicts of interest.

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Received: 2021-10-19
Accepted: 2022-06-28
Published Online: 2022-08-11
Published in Print: 2023-04-25

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial Comment
  3. Chronic pain and health inequalities: why we need to act
  4. Systematic Reviews
  5. Resilience as a protective factor in face of pain symptomatology, disability and psychological outcomes in adult chronic pain populations: a scoping review
  6. Is intravenous magnesium sulphate a suitable adjuvant in postoperative pain management? – A critical and systematic review of methodology in randomized controlled trials
  7. Topical Review
  8. Pain assessment 3 × 3: a clinical reasoning framework for healthcare professionals
  9. Clinical Pain Researches
  10. The treatment lottery of chronic back pain? A case series at a multidisciplinary pain centre
  11. Parameters of anger as related to sensory-affective components of pain
  12. Loneliness in patients with somatic symptom disorder
  13. The development and measurement properties of the Dutch version of the fear-avoidance components scale (FACS-D) in persons with chronic musculoskeletal pain
  14. Observational Studies
  15. Can interoceptive sensitivity provide information on the difference in the perceptual mechanisms of recurrent and chronic pain? Part I. A retrospective clinical study related to multidimensional pain assessment
  16. Distress intolerance and pain catastrophizing as mediating variables in PTSD and chronic noncancer pain comorbidity
  17. Stress-induced headache in the general working population is moderated by the NRCAM rs2300043 genotype
  18. Does poor sleep quality lead to increased low back pain the following day?
  19. “I had already tried that before going to the doctor” – exploring adolescents’ with knee pain perspectives on ‘wait and see’ as a management strategy in primary care; a study with brief semi-structured qualitative interviews
  20. Problematic opioid use among osteoarthritis patients with chronic post-operative pain after joint replacement: analyses from the BISCUITS study
  21. Worst pain intensity and opioid intake during the early postoperative period were not associated with moderate-severe pain 12 months after total knee arthroplasty – a longitudinal study
  22. Original Experimentals
  23. How gender affects the decoding of facial expressions of pain
  24. A simple, bed-side tool to assess evoked pressure pain intensity
  25. Effects of psychosocial stress and performance feedback on pain processing and its correlation with subjective and neuroendocrine parameters
  26. Participatory research: a Priority Setting Partnership for chronic musculoskeletal pain in Denmark
  27. Educational Case Report
  28. Hypophosphatasia as a plausible cause of vitamin B6 associated mouth pain: a case-report
  29. Short Communications
  30. Pain “chronification”: what is the problem with this model?
  31. Korsakoff syndrome and altered pain perception: a search of underlying neural mechanisms
https://doi.org/10.1515/sjpain-2021-0190
Schlagwörter für diesen Artikel
chronic pain; disability; function; psychological distress; resilience

Artikel in diesem Heft

  1. Frontmatter
  2. Editorial Comment
  3. Chronic pain and health inequalities: why we need to act
  4. Systematic Reviews
  5. Resilience as a protective factor in face of pain symptomatology, disability and psychological outcomes in adult chronic pain populations: a scoping review
  6. Is intravenous magnesium sulphate a suitable adjuvant in postoperative pain management? – A critical and systematic review of methodology in randomized controlled trials
  7. Topical Review
  8. Pain assessment 3 × 3: a clinical reasoning framework for healthcare professionals
  9. Clinical Pain Researches
  10. The treatment lottery of chronic back pain? A case series at a multidisciplinary pain centre
  11. Parameters of anger as related to sensory-affective components of pain
  12. Loneliness in patients with somatic symptom disorder
  13. The development and measurement properties of the Dutch version of the fear-avoidance components scale (FACS-D) in persons with chronic musculoskeletal pain
  14. Observational Studies
  15. Can interoceptive sensitivity provide information on the difference in the perceptual mechanisms of recurrent and chronic pain? Part I. A retrospective clinical study related to multidimensional pain assessment
  16. Distress intolerance and pain catastrophizing as mediating variables in PTSD and chronic noncancer pain comorbidity
  17. Stress-induced headache in the general working population is moderated by the NRCAM rs2300043 genotype
  18. Does poor sleep quality lead to increased low back pain the following day?
  19. “I had already tried that before going to the doctor” – exploring adolescents’ with knee pain perspectives on ‘wait and see’ as a management strategy in primary care; a study with brief semi-structured qualitative interviews
  20. Problematic opioid use among osteoarthritis patients with chronic post-operative pain after joint replacement: analyses from the BISCUITS study
  21. Worst pain intensity and opioid intake during the early postoperative period were not associated with moderate-severe pain 12 months after total knee arthroplasty – a longitudinal study
  22. Original Experimentals
  23. How gender affects the decoding of facial expressions of pain
  24. A simple, bed-side tool to assess evoked pressure pain intensity
  25. Effects of psychosocial stress and performance feedback on pain processing and its correlation with subjective and neuroendocrine parameters
  26. Participatory research: a Priority Setting Partnership for chronic musculoskeletal pain in Denmark
  27. Educational Case Report
  28. Hypophosphatasia as a plausible cause of vitamin B6 associated mouth pain: a case-report
  29. Short Communications
  30. Pain “chronification”: what is the problem with this model?
  31. Korsakoff syndrome and altered pain perception: a search of underlying neural mechanisms
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