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Objective: The main objectives of this review were, firstly, to study the effect of different physiotherapy interventions on BDNF levels, and, secondly, to analyze the influence of physiotherapy on pain levels to subsequently draw conclusions about its possible relationship with BDNF. Background: Based on the theory that neurotrophic factors such as BDNF play a fundamental role in the initiation and/or maintenance of hyperexcitability of central neurons in pain, it was hypothesized that the levels of this neurotrophic factor may be modified by the application of therapeutic interventions, favoring a reduction in pain intensity. Methods: A literature search of multiple electronic databases (Pubmed, PsycINFO, Medline (Ebsco), Scopus, WOS, Embase) was conducted to identify randomized control trials (RCTs) published without language restrictions up to and including March 2022. The search strategy was based on the combination of medical terms (Mesh) and keywords relating to the following concepts: "pain", "chronic pain", "brain derived neurotrophic factor", "BDNF", "physiotherapy", and "physical therapy". A total of seven papers were included. Results: There were two studies that showed statistically significant differences in pain intensity reduction and an increase in the BDNF levels that used therapies such as rTMS and EIMS in patients with chronic myofascial pain. However, the same conclusions cannot be drawn for the other physical therapies applied. Conclusions: rTMS and EIMS interventions achieved greater short-term reductions in pain intensity and increased BDNF over other types of interventions in chronic myofascial pain patients, as demonstrated by a moderate amount of evidence. In contrast, other types of physical therapy (PT) interventions did not appear to be more effective in decreasing pain intensity and increasing BDNF levels than placebo PT or minimal intervention, as a low amount of evidence was found. [ABSTRACT FROM AUTHOR]

Chronic neck pain is among the most common types of musculoskeletal pain. Manual therapy has been shown to have positive effects on this type of pain, but there are not yet many predictive models for determining how best to apply manual therapy to the different subtypes of neck pain. The aim of this study is to develop a predictive learning approach to determine which basal outcome could give a prognostic value (Global Rating of Change, GRoC scale) for Mulligan's mobilization technique and to identify the most important predictive factors for recovery in chronic neck pain subjects in four key areas: the number of treatments, time of treatment, reduction of pain, and range of motion (ROM) increase. A prospective cohort dataset of 80 participants with chronic neck pain diagnosed by their family doctor was analyzed. Logistic regression and machine learning modeling techniques (Generalized Boosted Models, Support Vector Machine, Kernel, Classsification and Decision Trees, Random Forest and Neural Networks) were each used to form a prognostic model for each of the nine outcomes obtained before and after intervention: disability—neck disability index (NDI), patient satisfaction (GRoC), quality of life (12-Item Short Form Survey, SF-12), State-Trait Anxiety Inventory (STAI), Beck Depression Inventory (BDI II), pain catastrophizing scale (ECD), kinesiophobia-Tampa scale of kinesiophobia (TSK-11), Pain Intensity Visual Analogue Scale (VAS), and cervical ROM. Pain descriptions from the subjects and pain body diagrams guided the physical examination. The most important predictive factors for recovery in chronic neck pain patients indicated that the more anxiety and the lower the ROM of lateroflexion, the higher the probability of success with the Mulligan concept treatment. [ABSTRACT FROM AUTHOR]