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The genesis of chronic pain is explained by a biopsychosocial model. It hypothesizes an interdependency between environmental and genetic factors provoking aberrant long-term changes in biological and psychological regulatory systems. Physiological effects of psychological and physical stressors may play a crucial role in these maladaptive processes. Specifically, long-term demands on the stress response system may moderate central pain processing and influence descending serotonergic and noradrenergic signals from the brainstem, regulating nociceptive processing at the spinal level. However, the underlying mechanisms of this pathophysiological interplay still remain unclear. This paper aims to shed light on possible pathways between physical (exercise) and psychological stress and the potential neurobiological consequences in the genesis and treatment of chronic pain, highlighting evolving concepts and promising research directions in the treatment of chronic pain. Two treatment forms (exercise and mindfulness-based stress reduction as exemplary therapies), their interaction, and the dose-response will be discussed in more detail, which might pave the way to a better understanding of alterations in the pain matrix and help to develop future prevention and therapeutic concepts
Objective: To investigate associations between socioeconomic status (SES) indicators (education, job position, income, multidimensional index) and the genesis of chronic low back pain (CLBP).
Design: Longitudinal field study (baseline and 6-month follow-up).
Setting: Four medical clinics across Germany.
Participants: 352 people were included according to the following criteria: (1) between 18 and 65 years of age, (2) intermittent pain and (3) an understanding of the study and the ability to answer a questionnaire without help. Exclusion criteria were: (1) pregnancy, (2) inability to stand upright, (3) inability to give sick leave information, (4) signs of serious spinal pathology, (5) acute pain in the past 7 days or (6) an incomplete SES indicators questionnaire.
Outcome measures: Subjective intensity and disability of CLBP.
Results Analysis: showed that job position was the best single predictor of CLBP intensity, followed by a multidimensional index. Education and income had no significant association with intensity. Subjective disability was best predicted by job position, succeeded by the multidimensional index and education, while income again had no significant association.
Conclusion: The results showed that SES indicators have different strong associations with the genesis of CLBP and should therefore not be used interchangeably. Job position was found to be the single most important indicator. These results could be helpful in the planning of back pain care programmes, but in general, more research on the relationship between SES and health outcomes is needed.
Background
In health research, indicators of socioeconomic status (SES) are often used interchangeably and often lack theoretical foundation. This makes it difficult to compare results from different studies and to explore the relationship between SES and health outcomes. To aid researchers in choosing appropriate indicators of SES, this article proposes and tests a theory-based selection of SES indicators using chronic back pain as a health outcome.
Methods
Strength of relationship predictions were made using Brunner & Marmot’s model of ‘social determinants of health’. Subsequently, a longitudinal study was conducted with 66 patients receiving in-patient treatment for chronic back pain. Sociodemographic variables, four SES indicators (education, job position, income, multidimensional index) and back pain intensity and disability were obtained at baseline. Both pain dimensions were assessed again 6 months later. Using linear regression, the predictive strength of each SES indicator on pain intensity and disability was estimated and compared to the theory based prediction.
Results
Chronic back pain intensity was best predicted by the multidimensional index (beta = 0.31, p < 0.05), followed by job position (beta = 0.29, p < 0.05) and education (beta = −0.29, p < 0.05); whereas, income exerted no significant influence. Back pain disability was predicted strongest by education (beta = −0.30, p < 0.05) and job position (beta = 0.29, p < 0.05). Here, multidimensional index and income had no significant influence.
Conclusions
The choice of SES indicators influences predictive power on both back pain dimensions, suggesting SES predictors cannot be used interchangeably. Therefore, researchers should carefully consider prior to each study which SES indicator to use. The introduced framework can be valuable in supporting this decision because it allows for a stable prediction of SES indicator influence and their hierarchy on a specific health outcomes.
Background Low back pain (LBP) is a common pain syndrome in athletes, responsible for 28% of missed training days/year. Psychosocial factors contribute to chronic pain development. This study aims to investigate the transferability of psychosocial screening tools developed in the general population to athletes and to define athlete-specific thresholds.
Methods Data from a prospective multicentre study on LBP were collected at baseline and 1-year follow-up (n=52 athletes, n=289 recreational athletes and n=246 non-athletes). Pain was assessed using the Chronic Pain Grade questionnaire. The psychosocial Risk Stratification Index (RSI) was used to obtain prognostic information regarding the risk of chronic LBP (CLBP). Individual psychosocial risk profile was gained with the Risk Prevention Index – Social (RPI-S). Differences between groups were calculated using general linear models and planned contrasts. Discrimination thresholds for athletes were defined with receiver operating characteristics (ROC) curves.
Results Athletes and recreational athletes showed significantly lower psychosocial risk profiles and prognostic risk for CLBP than non-athletes. ROC curves suggested discrimination thresholds for athletes were different compared with non-athletes. Both screenings demonstrated very good sensitivity (RSI=100%; RPI-S: 75%–100%) and specificity (RSI: 76%–93%; RPI-S: 71%–93%). RSI revealed two risk classes for pain intensity (area under the curve (AUC) 0.92(95% CI 0.85 to 1.0)) and pain disability (AUC 0.88(95% CI 0.71 to 1.0)).
Conclusions Both screening tools can be used for athletes. Athlete-specific thresholds will improve physicians’ decision making and allow stratified treatment and prevention.