Recently, the International Association for the Study of Pain (IASP) released clinical criteria and a grading system for nociplastic pain affecting the musculoskeletal system. These criteria replaced the 2014 clinical criteria for predominant central sensitization (CS) pain and accounted for clinicians’ need to identify (early) and correctly classify patients having chronic pain according to the pain phenotype. Still, clinicians and researchers can become confused by the multitude of terms and the variety of clinical criteria available. Therefore, this paper aims at (1) providing an overview of what preceded the IASP criteria for nociplastic pain (‘the past’); (2) explaining the new IASP criteria for nociplastic pain in comparison with the 2014 clinical criteria for predominant CS pain (‘the present’); and (3) highlighting key areas for future implementation and research work in this area (‘the future’). It is explained that the 2021 IASP clinical criteria for nociplastic pain are in line with the 2014 clinical criteria for predominant CS pain but are more robust, comprehensive, better developed and hold more potential. Therefore, the 2021 IASP clinical criteria for nociplastic pain are important steps towards precision pain medicine, yet studies examining the clinimetric and psychometric properties of the criteria are urgently needed.

Chronic pain is the most prevalent disease worldwide, leading to substantial disability and socioeconomic burden. Therefore, it can be regarded as a public health disease and major challenge to scientists, clinicians and affected individuals. Behavioral lifestyle factors, such as, physical (in)activity, stress, poor sleep and an unhealthy diet are increasingly recognized as perpetuating factors for chronic pain. Yet, current management options for patients with chronic pain often do not address lifestyle factors in a personalized multimodal fashion. This state-of-the-art clinical perspective aims to address this gap by discussing how clinicians can simultaneously incorporate various lifestyle factors into a personalized multimodal lifestyle intervention for individuals with chronic pain. To do so the available evidence on (multimodal) lifestyle interventions targeting physical (in)activity, stress, sleep and nutritional factors, specifically, was reviewed and synthetized from a clinical point of view. First, advise is provided on how to design a personalized multimodal lifestyle approach for a specific patient. Subsequently, best-evidence recommendations on how to integrate physical (in)activity, stress, sleep and nutritional factors as treatment targets into a personalized multimodal lifestyle approach are outlined. Evidence supporting such a personalized multimodal lifestyle approach is growing, but further studies are needed.

Nociplastic pain syndromes include particular fibromyalgia, irritable bowel syndrome, headache, complex regional pain syndrome, and idiopathic orofacial pain. Several mechanisms have been proposed to account for nociplastic pain including central sensitisation, alterations of pain modulatory controls, epigenetic changes, and peripheral mechanisms. Importantly, nociplastic pain might also be present in patients with cancer pain, particularly those with pain related to complications of cancer treatment. Increased awareness of nociplastic pain associated with cancer should have important implications for monitoring and managing such patients.

This review discusses chronic pain, multiple modifiable lifestyle factors, such as stress, insomnia, diet, obesity, smoking, alcohol consumption and physical activity, and the relationship between these lifestyle factors and pain after cancer. Chronic pain is known to be a common consequence of cancer treatments, which considerably impacts cancer survivors’ quality of life when it remains untreated. Improvements in lifestyle behaviour are known to reduce mortality, comorbid conditions (i.e., cardiovascular diseases, other cancer, and recurrence) and cancer-related side-effects (i.e., fatigue and psychological issues). An inadequate stress response plays an important role in dysregulating the body’s autonomic, endocrine, and immune responses, creating a problematic back loop with pain. Next, given the high vulnerability of cancer survivors to insomnia, addressing and treating those sleep problems should be another target in pain management due to its capacity to increase hyperalgesia. Furthermore, adherence to a healthy diet holds great anti-inflammatory potential for relieving pain after cancer. Additionally, a healthy diet might go hand in hand with weight reduction in the case of obesity. Consuming alcohol and smoking have an acute analgesic effect in the short-term, with evidence lacking in the long-term. However, this acute effect is outweighed by other harms on cancer survivors’ general health. Last, informing patients about the benefits of an active lifestyle and reducing a sedentary lifestyle after cancer treatment must be emphasised when considering the proven benefits of physical activity in this population. A multimodal approach addressing all relevant lifestyle factors together seems appropriate for managing comorbid conditions, side-effects, and chronic pain after cancer. Further research is needed to evaluate whether modifiable lifestyle factors have a beneficial influence on chronic pain among cancer survivors.

The Central Sensitization Inventory (CSI) measurement properties in patients having nonspecific, noncancer pain are well-established. However, studies examining the reliability and validity of either the CSI or the Central Sensitization Inventory short-form version (CSI-9) in breast cancer survivors (BCS) are scarce. The purpose was to evaluate convergent validity and internal consistency of the CSI and CSI-9. Additionally, the relevance of a new cluster calculator using the CSI was explored. The cross-sectional multi-center study included 65 BCS and 37 healthy volunteers. Patients filled out multiple questionnaires assessing pain, number of painful areas, anxiety, depression and quality of life. The relevance of a cluster calculator was explored by known-group comparisons and boxplot description. All hypotheses were formulated before data analysis. The majority of hypotheses on the correlations between the CSI or CSI-9 and other health outcomes were confirmed (22 out of 27). The CSI and CSI-9 have excellent (α = 0.92) and good (α = 0.86) internal consistency, respectively. The CSI cluster calculator might be an interesting tool to use to have a patient’s overall condition snapshot. Generally, the study findings support the construct validity and internal consistency of the CSI, which underline the use of this self-reported instrument in BCS. The CSI-9 shows promising results, but should be further evaluated.