Rehabilitative ultrasound imaging (RUSI) is used by physical therapists as a feedback tool for measuring changes in muscle morphology during therapeutic interventions such as motor control exercises (MCE). However, a structured overview of its efficacy is lacking. We aimed to systematically review the efficacy of RUSI for improving MCE programs compared with no feedback and other feedback methods. MEDLINE, PubMed, SCOPUS and Web of Science databases were searched for studies evaluating efficacy data of RUSI to improve muscular morphology, quality, and/or function of skeletal muscles and MCE success. Eleven studies analyzing RUSI feedback during MCE were included. Most studies showed acceptable methodological quality. Seven studies assessed abdominal wall muscles, one assessed pelvic floor muscles, one serratus anterior muscle, and two lumbar multifidi. Eight studies involved healthy subjects and three studies clinical populations. Eight studies assessed muscle thickness and pressure differences during MCE, two assessed the number of trials needed to successfully perform MCE, three assessed the retain success, seven assessed the muscle activity with electromyography and one assessed clinical severity outcomes. Visual RUSI feedback seems to be more effective than tactile and/or verbal biofeedback for improving MCE performance and retention success, but no differences with pressure unit biofeedback were found.

Since manual palpation is a subjective procedure for identifying and differentiate Myofascial Trigger Points -MTrPs-, the use of Shear Wave Elastography -SWE- as an objective alternative is increasing. This study aimed to analyze pain pressure thresholds -PPTs- and SWE differences between active MTrPs, latent MTrPs and control points located in the upper trapezius to analyze the association of SWE features with clinical severity indicators (e.g., pain extension area, PPTs, neck pain and neck disability). An observational study was conducted to calculate the correlation and to analyze the differences of sociodemographic, clinical and SWE features on 34 asymptomatic subjects with latent MTrPs and 19 patients with neck pain and active MTrPs. Significant PPT differences between active with latent MTrPs (p < 0.001) and control points (p < 0.001) were found, but no differences between latent MTrPs and control points (p > 0.05). No stiffness differences were found between active MTrPs with latent MTrPs or control points (p > 0.05). However, significant control point stiffness differences between-samples were found (p < 0.05). SWE showed no significant correlation with clinical severity indicators (p > 0.05). No stiffness differences between active and latent MTrPs were found. Neck pain patients showed increased control point stiffness compared with asymptomatic subjects. SWE showed no association with clinical severity indicators.

The aim of this study was to assess potential associations between sensory, cognitive, health-related, and physical variables in women with fibromyalgia syndrome (FMS) using a network analysis for better understanding the complexity of psycho-biological mechanisms. Demographic, clinical, pressure pain threshold (PPT), health-related, physical, and psychological/cognitive variables were collected in 126 women with FMS. A network analysis was conducted to quantify the adjusted correlations between the modeled variables and to assess the centrality indices (i.e., the degree of connection with other symptoms in the network and the importance in the system modeled as a network. This model showed several local associations between the variables. Multiple positive correlations between PPTs were observed, being the strongest weight between PPTs over the knee and tibialis anterior ($\rho :$0.28). Catastrophism was associated with higher hypervigilance ($\rho$: 0.23) and lower health-related EuroQol-5D ($\rho$: −0.24). The most central variables were PPT over the tibialis anterior (the highest strength centrality), hand grip (the highest harmonic centrality) and Time Up and Go (the highest betweenness centrality). This study, applying network analysis to understand the complex mechanisms of women with FMS, supports a model where sensory-related, psychological/cognitive, health-related, and physical variables are connected. Implications of the current findings, e.g., developing treatments targeting these mechanisms, are discussed.

This study aimed to describe a network including demographic, sensory-related, psychological/cognitive and other variables in individuals with post-COVID pain after hospitalization. Demographic (i.e., age, height, weight, months with symptoms), sensory-related (Central Sensitization Inventory -CSI-, Self-Report Leeds Assessment of Neuropathic Symptoms -S-LANSS-, PainDETECT), psychological/cognitive (Hospital Anxiety and Depression Scale -HADS-A/HADS-D-, Pain Catastrophizing Scale -PCS-, Tampa Scale for Kinesiophobia -TSK-11-) and other (sleep quality and health-related quality of life -EQ/5D/5L) variables were collected in 146 COVID-19 survivors with post-COVID pain. A network analysis was conducted to quantify the adjusted correlations between the modelled variables, and to assess their centrality indices (i.e., the connectivity with other symptoms in the network and the importance in the system modelled as network). The network revealed associations between sensory-related and psychological/cognitive variables. PainDETECT was associated with S-LANSS ($\rho$: 0.388) and CSI ($\rho$: 0.207). Further, CSI was associated with HADS-A ($\rho$: 0.269), TSK-11 ($\rho$: 0.165) and female gender ($\rho$: 0.413). As expected, HADS-A was associated with HADS-D ($\rho$: 0.598) and TSK-11 with PCS ($\rho$: 0.405). The only negative association was between sleep quality and EQ-5D-5L ($\rho$: −0.162). Gender was the node showing the highest strength, closeness, and betweenness centralities. In addition, CSI was the node with the second highest closeness and betweenness centralities, whereas HADS-D was the node with the second highest strength centrality. This is the first study applying a network analysis for phenotyping post-COVID pain. Our findings support a model where sensitization-associated symptoms, neuropathic phenotype, and psychological aspects are connected, reflecting post-COVID pain as a nociplastic pain condition. In addition, post-COVID pain is gender dependent since female sex plays a relevant role. Clinical implications of current findings, e.g., developing treatments targeting these mechanisms, are discussed.

We aimed to explore a path model identified using a structural equation model (SEM) which best explains the multivariate contributions of sensitization, sensitivity, and emotional variables to clinical severity in women with FMS. Pain features, the Central Sensitization Inventory (CSI), painDETECT, S-LANSS, the Hospital Anxiety and Depression Scale (HADS), the Pittsburgh Sleep Quality Index (PSQI), the Pain Catastrophizing Scale (PCS), the Pain Vigilance and Awareness Questionnaire (PVAQ), the 11-item Tampa Scale for Kinesiophobia (TSK-11), and pressure pain thresholds (PPTs) were collected from 113 women with FMS. Four latent variables were created: severity (clinical pain features), sensitivity (PPTs), sensitization (S-LANSS, CSI, painDETECT), and distress (HADS-A, HADS-D, PCS, PVAQ, TSK-11). Data fit for the measurement model were considered excellent (RMSEA = 0.043, CFI = 0.966, SRMR = 0.067, and NNFI = 0.960). Distress had a significant relationship with the mediators of sleep (β = 0.452, p = 0.031) and sensitization (β = 0.618, p = 0.001). The only mediator with a significant effect (β = 1.113, p < 0.001) on severity was sensitization. A significant indirect effect of sensitization (β = 0.687, p = 0.001) that explained the relationship between distress and severity was also identified. The proposed model suggests that distress and sensitization, together with poor sleep, have a complex mediating effect on severity in women with FMS. The identified path model can be leveraged in clinical trials investigating treatment approaches for FMS.

Muscle fatigue is defined as a reversible decline in performance after intensive use, which largely recovers after a resting period. Surface electromyography (EMG), ultrasound imaging (US) and dynamometry are used to assess muscle activity, muscle morphology and isometric force capacity. This study aimed to assess the convergent validity between these three methods for assessing muscle fatigue during a manual prehension maximal voluntary isometric contraction (MVIC). A diagnostic accuracy study was conducted, enrolling 50 healthy participants for the measurement of simultaneous changes in muscle thickness, muscle activity and isometric force using EMG, US and a hand dynamometer, respectively, during a 15 s MVIC. An adjustment line and its variance (R²) were calculated. Muscle activity and thickness were comparable between genders (p > 0.05). However, men exhibited lower force holding capacity (p < 0.05). No side-to-side or dominance differences were found for any variable. Significant correlations were found for the EMG slope with US (r = 0.359; p < 0.01) and dynamometry (r = 0.305; p < 0.01) slopes and between dynamometry and US slopes (r = 0.227; p < 0.05). The sample of this study was characterized by comparable muscle activity and muscle thickness change between genders. In addition, fatigue slopes were not associated with demography or anthropometry. Our findings showed fair convergent associations between these methods, providing synergistic muscle fatigue information.