Chronic Pain

Chronic pelvic pain (CPP) has a complex etiology and a high degree of coprevalence with other chronic overlapping pain conditions (COPCs). Evidence suggests that central sensitization contributes to these conditions; however, the psychological risk and resilience factors influencing the relationship between COPCs and central sensitization have received limited attention. Therefore, the aim of this study was to examine whether positive and negative affect moderate the relationship between COPCs and central sensitization in individuals with CPP. Participants (N = 197) completed the Central Sensitization Inventory and Positive and Negative Affect Schedule and self-reported the presence of diagnosed COPCs. Results indicated that greater symptoms of central sensitization were associated with a greater number of COPCs (p < 0.01). Higher levels of positive affect were related to lower central sensitization (p < 0.01) and fewer COPCs (p < 0.05), while higher negative affect was associated with greater central sensitization (p < 0.01). After controlling for pelvic pain duration, a greater number of COPCs were associated with greater central sensitization, but only among individuals with lower positive affect (p = 0.01). No significant moderation effects were observed for negative affect (p = 0.13). The findings from this cross-sectional study suggest that lower positive affect may have an adverse effect on central sensitization in those with a greater number of COPCs. Individuals with CPP, especially those experiencing multiple chronic pain conditions, may benefit from interventions aimed at increasing positive affect.

BackgroundChronic low back pain (CLBP) is a major global health challenge. While nonpharmacological therapies are recommended, patient compliance is often hindered by kinesiophobia. Virtual reality (VR) offers an immersive, distraction-based approach, but the comparative effectiveness of different VR modalities remains unclear.ObjectiveThe aim of the study is to compare and rank the efficacy of different VR-based training modalities on pain intensity, disability, and kinesiophobia in patients with CLBP.MethodsSystematic searches were conducted in PubMed, Web of Science, Scopus, Embase, CINAHL, and the Cochrane Library from inception until June 2025. Randomized controlled trials (RCTs) assessing the effects of VR-based training on individuals with CLBP were selected. Primary outcomes were pain intensity, disability (Oswestry Disability Index), and kinesiophobia (Tampa Scale of Kinesiophobia). The Cochrane Risk of Bias 2 tool was used for quality assessment. Confidence in Network Meta-Analysis (CINeMA) framework was used to evaluate the credibility of cumulative evidence. A Bayesian network meta-analysis with standardized mean difference (SMD) as effect size was performed to synthesize evidence and rank interventions using surface under the cumulative ranking curve values. The GRADE (Grading of Recommendations Assessment, Development and Evaluation) framework was adapted to evaluate the quality of evidence.ResultsIn total, 25 RCTs with a total of 2610 participants were included in the analysis. For pain intensity, shooting games (SMD -4.40, 95% credible interval [CrI] -6.80 to -2.20) and VR-based equestrian training (SMD -2.00, 95% CrI -3.70 to -0.57) were significantly superior to all types of controls. Surface under the cumulative ranking curve indicated that shooting games had the highest probability (98%) of being the most effective intervention for pain relief. For disability, no intervention demonstrated statistically significant superiority. For kinesiophobia, shooting games (SMD -3.40, 95% CrI -5.60 to -1.10) significantly outperformed traditional exercise controls. The quality of evidence ranged from very low to moderate across outcomes.ConclusionsThis first network meta-analysis to compare and rank distinct VR modalities for CLBP offers several key innovations and contributions to the field. By moving beyond aggregate VR categorizations, we provide a granular, comparative ranking of specific, actionable VR interventions. Unlike previous reviews that treated VR as a homogeneous group or only compared it to sham, our network meta-analysis directly and indirectly compares 7 distinct VR modalities, revealing that not all VR is equally effective. Our findings suggest that shooting games have the potential to be the most effective VR therapy for relieving pain intensity and kinesiophobia, though evidence for disability remains limited. Unfortunately, due to heterogeneity and low-quality evidence, there is no evidence demonstrating significant improvement in specific outcomes for patients with CLBP. More RCTs are needed to provide robust clinical evidence.

BackgroundCertain diseases require rapid treatment to avoid long-term consequences for patients. However, they may be difficult to recognize, especially if the symptoms are ambiguous and compatible with multiple possible diagnoses. Completing all necessary examinations often takes time, thereby prolonging patient suffering. Data-driven approaches, such as single-label classification (SLC) and multi-label classification (MLC), can help accelerate the diagnostic process and improve accuracy.MethodsComparing SLC and MLC allows us to investigate whether disease recognition benefits from considering comorbidities. In this context, we aim to provide a conceptual framing of (differences between) the two approaches in model formulation, decision spaces and handling of class imbalance. To empirically assess their performance, we conduct a case study applying SLC and MLC to data from chronic pain patients.ResultsOur analysis yields an ambiguous picture of whether incorporating comorbidities improves disease recognition. The suitability of SLC and MLC is determined by multiple factors, notably the dependency structure among diseases and between diseases and covariates, as well as by data characteristics such as class imbalance. Consequently, it is of high importance to address each individual problem in an individual manner, especially when the implications can be significant, such as in patient referrals.ConclusionOur study highlights the importance of considering the specific characteristics of the data when selecting an appropriate classification approach for disease recognition and beyond. We illustrate how underlying assumptions, learning structures, and strategies for handling class imbalance shape predictive performance. Our results thus lead to the point that computational prediction tools should serve as decision support for treating physicians, but never replace their clinical judgment.