RISK PROFILING IN CHRONIC LOW BACK PAIN: NEUROPHYSIOLOGICAL AND NEUROMUSCULAR DIFFERENCES ACROSS LEVELS OF RISK OF POOR PAIN PROGNOSIS—A CROSS-SECTIONAL STUDY
Description
this study aimed to compare neurophysiological and neuromuscular outcomes across different risk strata of poor prognosis for pain in participants with CLBP. The study hypothesis predicted that psychosocial factors, implicit in the risk of poor pain prognosis, would exert a progressively negative effect on neurophysiological and neuromuscular outcomes in participants with CLBP. Participants with CLBP were classified into low-, intermediate-, and high-risk groups based on risk strata for poor pain prognosis. Neurophysiological outcomes were assessed using heart rate variability (HRV) recorded with a qualified heart rate monitor. The rate of force development (at 100 and 200 milliseconds), maximal isometric strength, and proprioceptive accuracy were assessed using a portable traction dynamometer as neuromuscular outcomes. We performed inferential statistical analyses alongside the respective effect sizes (metrics expressing clinical magnitude). Statistical analyses were performed using the free software Jamovi (version 2.4.11). An alpha level of 0.05 was used. Given the study’s cross-sectional design, comparisons related to sample characterization variables were conducted using generalized linear models (GzLM). These models are based on maximum likelihood estimation and use the wald χ-square test (Wald χ²) to assess the effect of variables within the model. The risk strata for poor pain prognosis (LR, MR, and HR) were included as a factor in the analyses. Effect size (ES) estimates were included in the inferential analyses using Hedges’ g. The effect sizes were interpreted according to the following thresholds: null (<0.10), very small (0.10–0.19), moderate (0.20–0.79), large (0.80–1.19), very large (1.20–1.99), and huge (>2.0). Higher ES values indicate greater clinical relevance, reflecting larger differences between the means of each comparison pair .
Files
Steps to reproduce
Participants were stratified according to the risk of poor pain prognosis using the STarT Back Screening Tool (SBST) and allocated into risk strata (low, medium, and high). When the total SBST score was ≤ 3, participants were classified as having a low risk of poor pain prognosis. For scores > 3, items 4–9 were analyzed separately. If the score for these items was ≤ 3, participants were classified as having a medium risk of poor prognosis; if the score ranged from 4 to 6, the participants were allocated to the high-risk stratum for poor pain prognosis. The variables used to characterize the sample included age, body mass, height, pain duration since onset, average pain intensity over the past 4 weeks, pain intensity at the time of screening, and fear of movement (kinesiophobia). Anthropometric data and pain history information were recorded using a standardized assessment form. Pain with mechanical characteristics was defined as pain that was causally related to movement. Kinesiophobia was assessed using the Tampa Scale for Kinesiophobia (TSK). The HRV parameters were assessed in the time and frequency domains. The standard deviation of normal RR intervals (SDNN), the root mean square of successive differences between adjacent RR intervals (RMSSD), and the percentage of consecutive RR intervals differing by more than 50 ms (pNN50) were analyzed in the time domain. The low-frequency component (LF), predominantly influenced by sympathetic ANS activity, the high-frequency component (HF), reflecting parasympathetic ANS activity, and the ratio between these components (LF/HF), indicative of autonomic balance, were analyzed in the frequency domain. HRV data were collected using a Polar H10 heart rate monitor connected to the Elite HRV mobile application. Recordings were obtained while the participants were at rest in the supine position for 10 minutes. Data collection was conducted in an environment with controlled temperature, lighting, and low ambient noise. Neuromuscular outcomes included the rate of force development at 100 and 200 milliseconds (RFD100 and RFD200), maximal isometric force (MIF), and proprioceptive acuity. Assessments were performed using a portable traction dynamometer (Dinabang, Movi, Montevideo, Uruguay). The dynamometer was equipped with an inertial sensor and a digital interface via the Dinabang smartphone application, which is compatible with Android and iOS operating systems. This system captures force recordings and transmits data to the application via Bluetooth technology at a sampling frequency of 200 Hz. All recorded data, including the date and time of acquisition, were stored within each participant’s profile. Force data were expressed in Newtons (N).
Institutions
- Universidade Estadual do Oeste do Parana