Physical activity and pain catastrophizing predict temporal summation of mechanical pain

Awali, Abdulaziz; Nevsimal, R.; Hrdina, E.; Alsouhibani, A.; Bement, Marie Hoeger

doi:10.1016/j.jpain.2017.12.118

Cited by 1 publication

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“…TS and CPM have long been known to be affected by age and sex ( 30 – 38 ), but more recently they have also been linked to a number of other psychophysiological variables. For instance, TS is consistently associated with pain catastrophizing ( 39 – 42 ), whereas CPM is modulated by both catastrophizing ( 43 , 44 ) and expectations ( 45 ); additionally, a wide array of other psychological factors have been related to pain mechanisms, albeit in a method-dependent manner [for a review, see ( 46 )]. Certain physiological factors, such as blood pressure, also seem to correlate with TS/CPM ( 47 , 48 ) response, whereas others, such as activation of endocannabinoid and opioid systems, are known to directly contribute to pain inhibition ( 49 – 51 ).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Predictive Model of Pain Modulation Profile to Guide Chronic Pain Treatment: A Study Protocol

et al. 2021

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Introduction: Quantitative sensory testing is frequently used in research to assess endogenous pain modulation mechanisms, such as Temporal Summation (TS) and Conditioned Pain Modulation (CPM), reflecting excitatory and inhibitory mechanisms, respectively. Numerous studies found that a dysregulation of these mechanisms is associated with chronic pain conditions. In turn, such a patient's “profile” (increased TS and/or weakened CPM) could be used to recommend different pharmacological treatments. However, the procedure to evaluate these mechanisms is time-consuming and requires expensive equipment that is not available in the clinical setting. In this study, we aim to identify psychological, physiological and socio-demographic markers that could serve as proxies to allow healthcare professionals to identify these pain phenotypes in clinic, and consequently optimize pharmacological treatments.Method: We aim to recruit a healthy participant cohort (n = 360) and a chronic pain patient cohort (n = 108). Independent variables will include psychological questionnaires, pain measurements, physiological measures and sociodemographic characteristics. Dependent variables will include TS and CPM, which will be measured using quantitative sensory testing in a single session. We will evaluate one prediction model and two validation models (for healthy and chronic pain participants) using multiple regression analysis between TS/CPM and our independent variables. The significance thresholds will be set at p = 0.05, respectively.Perspectives: This study will allow us to develop a predictive model to compute the pain modulation profile of individual patients based on their biopsychosocial characteristics. The development of the predictive model is the first step toward the overarching goal of providing clinicians with a set of quick and cheap tests, easily applicable in clinical practice to orient pharmacological treatments.

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