Factor Structure and Construct Validity of the Pain Resilience Scale Within Chinese Adult Chronic Musculoskeletal Pain Samples

You, Beibei; Jackson, Todd

doi:10.1080/00223891.2020.1801700

Cited by 9 publications

(32 citation statements)

References 40 publications

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“…Results of exploratory and confirmatory factor analyses on two samples of Chinese adults with chronic musculoskeletal pain (N = 551, N = 556) replicated this two factor PRS structure with the exception of two behavior perseverance items that failed to load with other perseverance items. Construct validity of these PRS facets is supported by their significant positive correlations with general resilience based on CDRS scores, pain self-efficacy, pain acceptance, activity engagement and cognitive coping as well as its negative correlations with pain catastrophizing, depression, pain disability and pain intensity (You & Jackson, 2020). In this sample the derived 10-item PRS had an alpha of α = .93.…”

Section: Methodsmentioning

confidence: 70%

“…Because operationalizations of resilience vary and consistent responses on different measures of the same construct reflect increased validity, higher versus lower resilience subgroup membership was identified on the basis of scoring, respectively, within the top or bottom 50% of score distributions on two different self-report resilience instruments, the Connor-Davidson Resilience Scale-Chinese (CD-RISC-C) (Wang et al, 2010) and Pain Resilience Scale-Chinese (PRS-C) (You & Jackson, 2020) rather than one resilience scale. Data from 40 participants were excluded because they did not score consistently (i.e., within the top or bottom 50% of responses) across both resilience indexes.…”

Section: Methodsmentioning

confidence: 99%

“…2.3.2 | Pain Resilience Scale-Chinese (PRS-C; You & Jackson, 2020) The 10-item PRS-C assesses positive affectivity and behavioral perseverance in the face of CP. Items are rated on a frequency scale from 0 = not at all to 4 = all the time.…”

Section: Methodsmentioning

confidence: 99%

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Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis

You

Wen

Jackson³

2021

Psychophysiology

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Studies have documented behavior differences between more versus less resilient adults with chronic pain (CP), but the presence and nature of underlying neurophysiological differences have received scant attention. In this study, we attempted to identify regions of interest (ROIs) in which resting state (Rs) brain activity discriminated more from less resilient CP subgroups based on multiple kernel learning (MKL). More and less resilient community-dwellers with chronic musculoskeletal pain (70 women, 39 men) engaged in structural and functional magnetic resonance imaging (MRI) scans, wherein MKL assessed Rs activity based on amplitude of low frequency fluctuations (ALFF), fractional amplitudes of low frequency fluctuations (fALFF), and regional homogeneity (ReHo) modalities to identify ROIs most salient for discriminating more versus less resilient subgroups. Compared to classification based on single modalities, multi-modal classification based on combined fALFF and ReHo features achieved a substantially higher classification accuracy rate (79%).Brain regions with the best discriminative power included those implicated in pain processing, reward, executive function, goal-directed action, emotion regulation and resilience to mood disorders though variation trends were not consistent between more and less resilient subgroups. Results revealed patterns of Rs activity that serve as possible biomarkers for resilience to chronic musculoskeletal pain.

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Section: Methodsmentioning

confidence: 70%

Section: Methodsmentioning

confidence: 99%

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Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis

You

Wen

Jackson³

2021

Psychophysiology

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“…Accurate measurement of resilience is vital to screen the population at-risk regarding mental health problems and maladaptive coping from others for further intervention (Duan et al, 2020). Given a large group of PWPD and the severe shortages of qualified specialists in China (You and Jackson, 2020), there is a need for a fully validated instrument to identify PWPD who are lack of resilience.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequent studies have yielded a four-factor model (Lamond et al, 2008) or a three-factor model (Yu and Zhang, 2007). Second, the CD-RISC measures trait-like capacities that are likely to generalize across circumstances, whereas it cannot capture the specific types of experience or source of adversity (You and Jackson, 2020). Third, the CD-RISC captures only the cognitive/individual aspect of resilience, but does not consider the social/interpersonal protective factor (Madewell and Ponce-Garcia, 2016).…”

Section: Introductionmentioning

confidence: 99%

Cross-Cultural Adaptation and Validation of the Physical Disability Resiliency Scale in a Sample of Chinese With Physical Disability

Duan

Xiong³

2020

Front. Psychol.

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This study adapted the Physical Disability Resilience Scale (PDRS) to Chinese conditions and evaluated the psychometric characteristics of the Chinese version in individuals with physical disability. A total of 438 individuals with physical disability were included in this study. The PDRS was translated to Chinese using a backward translation method. Construct validity, internal consistency reliability, and convergent validity were examined. Confirmatory factor analysis failed to replicate the original five-factor structure of the PDRS. After removing the Spirituality factor and an underperformed item (Item 22), exploratory factor analysis yielded four trait factors (i.e., Emotional and Cognitive Strategies, Physical Activity and Diet, Peer Support, and Support from Family and Friends) and a method-effect factor. A correlated trait–correlated method model that included the four trait factors and a method-effect factor reported better model fit than the four-factor model, which did not consider method effects. The four subscales of the revised PDRS showed adequate internal consistency. The convergent validity of the revised PDRS was established by the moderate-to-strong associations between its four subscales and theoretically related constructs. We conclude that the revised PDRS is a reliable and valid measure in assessing resilience among Chinese people with physical disability.

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Resting‐state brain activity as a biomarker of chronic pain impairment and a mediator of its association with pain resilience

You,

Wen,

Jackson

2024

Human Brain Mapping

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Past cross‐sectional chronic pain studies have revealed aberrant resting‐state brain activity in regions involved in pain processing and affect regulation. However, there is a paucity of longitudinal research examining links of resting‐state activity and pain resilience with changes in chronic pain outcomes over time. In this prospective study, we assessed the status of baseline (T1) resting‐state brain activity as a biomarker of later impairment from chronic pain and a mediator of the relation between pain resilience and impairment at follow‐up. One hundred forty‐two adults with chronic musculoskeletal pain completed a T1 assessment comprising a resting‐state functional magnetic resonance imaging scan based on regional homogeneity (ReHo) and self‐report measures of demographics, pain characteristics, psychological status, pain resilience, pain severity, and pain impairment. Subsequently, pain impairment was reassessed at a 6‐month follow‐up (T2). Hierarchical multiple regression and mediation analyses assessed relations of T1 ReHo and pain resilience scores with changes in pain impairment. Higher T1 ReHo values in the right caudate nucleus were associated with increased pain impairment at T2, after controlling for all other statistically significant self‐report measures. ReHo also partially mediated associations of T1 pain resilience dimensions with T2 pain impairment. T1 right caudate nucleus ReHo emerged as a possible biomarker of later impairment from chronic musculoskeletal pain and a neural mechanism that may help to explain why pain resilience is related to lower levels of later chronic pain impairment. Findings provide empirical foundations for prospective extensions that assess the status of ReHo activity and self‐reported pain resilience as markers for later impairment from chronic pain and targets for interventions to reduce impairment.Practitioner Points Resting‐state markers of impairment: Higher baseline (T1) regional homogeneity (ReHo) values, localized in the right caudate nucleus, were associated with exacerbations in impairment from chronic musculoskeletal pain at a 6‐month follow‐up, independent of T1 demographics, pain experiences, and psychological factors. Mediating role of ReHo values: ReHo values in the right caudate nucleus also mediated the relationship between baseline pain resilience levels and later pain impairment among participants. Therapeutic implications: Findings provide empirical foundations for research extensions that evaluate (1) the use of resting‐state activity in assessment to identify people at risk for later impairment from pain and (2) changes in resting‐state activity as biomarkers for the efficacy of treatments designed to improve resilience and reduce impairment among those in need.

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Factor Structure and Construct Validity of the Pain Resilience Scale Within Chinese Adult Chronic Musculoskeletal Pain Samples

Cited by 9 publications

References 40 publications

Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis

Identifying resting state differences salient for resilience to chronic pain based on machine learning multivariate pattern analysis

Cross-Cultural Adaptation and Validation of the Physical Disability Resiliency Scale in a Sample of Chinese With Physical Disability

Resting‐state brain activity as a biomarker of chronic pain impairment and a mediator of its association with pain resilience

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