The Role of Patient and Parental Resilience in Adolescents with Chronic Musculoskeletal Pain

Gmuca, Sabrina; Xiao, Rui; Urquhart, Audrey; Weiss, Pamela F.; Gillham, Jane E.; Ginsburg, Kenneth R.; Sherry, David D.; Gerber, Jeffrey S.

doi:10.1016/j.jpeds.2019.03.006

Cited by 18 publications

(17 citation statements)

References 77 publications

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“…Subsequent studies have supported the CDRS reliability and validity in Chinese samples with various chronic health concerns (Lei & Zhang, 2016;Wang et al, 2010). A single factor model of CDRS was also supported within a CP sample (Gmuca et al, 2019). Convergent validity of CDRS is supported by its significant positive correlations with pain acceptance and mindful attention awareness as well as its negative correlations with pain catastrophizing, anxiety and depression, pain-related impairment and pain intensity (Notario-Pacheco et al, 2014;Sharma et al, 2018).…”

Section: Methodsmentioning

confidence: 85%

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: 85%

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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“…The results of the studies also indicated that emotional, and behavioral problems were associated with lower resilience rates (Huang et al, 2019). Furthermore, in the sample of adolescents with chronic illness higher rates of resilience were associated with the lower scores of pain, physical disability, symptoms severity, and higher rates of energy, quality of life, related to health (Gmuca et al, 2019). The study conducted by Hildebrand et al (2019) showed significant links between social support and resilience in children and adolescents.…”

Section: Introductionmentioning

confidence: 85%

The Psychometric Properties of the Resilience Scale (RS-14) in Lithuanian Adolescents

Želvienė

Jovarauskaitė

Truskauskaitė‐Kunevičienė

2021

Front. Psychol.

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In the current study, we provided the evidence of satisfactory validity of the RS-14 scale in the Lithuanian adolescents’ sample (N = 1299; Mage = 14.24; SDage = 1.26), based on its internal structure, and relations to other variables. The results of the study indicated an acceptable model fit for a single-factor structure of the scale with a high internal consistency (McDonald’s omega = 0.89). We also confirmed the scalar measurement invariance across groups of adolescents in terms of their age (i.e., early and middle adolescence) and mental health profile as well as partial scalar gender invariance. Adolescents characterized by high levels of socio-emotional problems reported lower levels of resilience, in comparison to adolescents that reported low levels of socio-emotional problems. However, the data indicated that adolescents from emotional problems and behavioral problems groups cannot be differentiated with the RS-14 scale.

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“…A controlled 4-year, follow-up study on the impact of family environment on the long-term adjustment of patients with JPFS, indicated that adolescents from controlling family environments are at increased risk for poorer emotional functioning in early adulthood [ 109 ]. Another study indicated low levels of resilience to non-inflammatory chronic musculoskeletal pain syndrome in adolescent patients and their parents, with a negative effect on symptoms [ 110 ]. Therefore, behavioral and family interventions should foster independent coping among adolescents with JPFS and greater parental flexibility, to enhance successful long-term emotional functioning.…”

Section: Prognosismentioning

confidence: 99%

Juvenile primary fibromyalgia syndrome: A Review- Treatment and Prognosis

Coles

Uziel

2021

Pediatr Rheumatol

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Juvenile primary fibromyalgia syndrome (JPFS) is a chronic musculoskeletal pain syndrome affecting children and adolescents. In part one of this review, we discussed the epidemiology, etiology, pathogenesis, clinical manifestations and diagnosis of JPFS. Part two focuses on the treatment and prognosis of JPFS. Early intervention is important. The standard of care is multidisciplinary, combining various modalities—most importantly, exercise and cognitive behavioral therapy. Prognosis varies and symptoms may persist into adulthood.

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The Role of Patient and Parental Resilience in Adolescents with Chronic Musculoskeletal Pain

Cited by 18 publications

References 77 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

The Psychometric Properties of the Resilience Scale (RS-14) in Lithuanian Adolescents

Juvenile primary fibromyalgia syndrome: A Review- Treatment and Prognosis

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