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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.
A particular form of social pain is invalidation. Therefore, this study (a) investigates whether patients with chronic low back pain experience invalidation, (b) if it has an influence on their pain, and (c) explores whether various social sources (e.g. partner and work) influence physical pain differentially. A total of 92 patients completed questionnaires, and for analysis, Pearson’s correlation coefficients and hierarchical linear regression analyses were conducted. They indicated a significant association between discounting and disability due to pain (respective β = .29, p > .05). Especially, discounting by partner was linked to higher disability (β = .28, p > .05).
Die Lifespan-Forschung untersucht die Entwicklung von Individuen über den gesamten Lebenslauf. Die medizinische Rehabilitation hat nach geltendem Sozialrecht die Aufgabe, chronische Krankheiten abzuwenden, zu beseitigen, zu mindern, auszugleichen, eine Verschlimmerung zu verhüten und Negativfolgen für die Lebensführung zu reduzieren. Dies erfordert in wissenschaftlicher wie in praxisbezogener Hinsicht die Entwicklung einer Lebensspannenperspektive als Voraussetzung für die Klassifikation und Diagnostik chronischer Erkrankungen, die Beschreibung von verlaufsbeeinflussenden Faktoren, kritischen Lebensphasen und Critical Incidents (kritische Verlaufszeitpunkte), die Durchführung von prophylaktischen Maßnahmen, die Entwicklung von Assessmentverfahren zur Erfassung und Bewertung von Verläufen oder Vorbehandlungen, die Auswahl und Priorisierung von Interventionen, eine Behandlungs- und Behandlerkoordination auf der Zeitachse, die Präzisierung der Aufgabenstellung für spezialisierte Rehabilitationsmaßnahmen, wie beispielsweise Rehabilitationskliniken, und als Grundlage für die Sozialmedizin. Aufgrund der Vielfalt der individuellen Risikokonstellationen, Krankheitsverläufe und Behandlungssituationen über die Lebensspanne hinweg, bedarf es in der medizinischen Rehabilitation in besonderer Weise einer personalisierten Medizin, die zugleich rehabilitationsförderliche und -behindernde Umweltfaktoren im Rehabilitationsverlauf berücksichtigt.
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: 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. 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.