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- Breaking barriers: Artificial intelligence interpreting the interplay between mental illness and pain as defined by the International Association for the Study of PainPublication . Parolini, Franciele; Goethel, Márcio; Becker, Klaus; Fernandes, Cristofthe; Fernandes, Ricardo J.; Ervilha, Ulysses F.; Santos, Rubim; Vilas-Boas, João PauloLow back pain is one of the main causes of motor disabilities and psychological stress, with the painful process encompassing sensory and affective components. Noxious stimuli originate on the periphery; however, the stimuli are recombined in the brain and therefore processed differently due to the emotional environment. To better understand this process, our objective was to develop a mathematical representation of the International Association for the Study of Pain (IASP) model of pain, covering the multidimensional representation of this phenomenon. Data from the Oswestry disability index; the short form of the depression, anxiety, and stress scale; and pain catastrophizing daily questionnaires were collected through online completion, available from 8 June 2022, to 8 April 2023 (1021 cases). Using the information collected, an artificial neural network structure was trained (based on anomaly detection methods) to identify the patterns that emerge from the relationship between the variables. The developed model proved to be robust and able to show the patterns and the relationship between the variables, and it allowed for differentiating the groups with altered patterns in the context of low back pain. The distinct groups all behave according to the main finding that psychological and pain events are directly associated. We conclude that our proposal is effective as it is able to test and confirm the definition of the IASP for the study of pain. Here we show that the fiscal and mental dimensions of pain are directly associated, meaning that mental illness can be an enhancer of pain episodes and functionality.
- Precision and reliability of a dynamometer for trunk extension strength and steadiness assessmentPublication . Parolini, Franciele; Goethel, Márcio; Robalino, Johan; Becker, Klaus; Sousa, Manoela; Pulcineli, Barbara C.; Ervilha, Ulysses F.; Vilas-Boas, João Paulo; Santos, Rubim; Rubim Silva Santos, Manuel; Carvalho Santos Parolini, FrancieleLow back pain is a major cause of disability worldwide, often associated with deficits in trunk extensor strength control. Accurate assessment of trunk extension strength control is crucial for diagnosing impairments and monitoring interventions. This study evaluated the reliability of a dynamometry-based protocol for isometric trunk extension strength control assessment. Twenty-eight healthy volunteers (9 females, 19 males) completed two sessions, seven days apart. A single-point load cell system, encapsulated within a 3D-printed structure and connected to a Delsys system® at a sampling frequency of 2000 Hz, was used for data acquisition. Participants performed maximal voluntary contractions (MVC) and submaximal isometric contractions (SMVC) guided by trapezoidal visual feedback. Key outcome variables included peak force, mean force, and force steadiness. Calibration demonstrated high accuracy (R2 = 1) with a low root mean square error (0.55 N). Test–retest analysis showed excellent reliability for peak force (ICC = 0.81, SEM = 0.50, MDC = 1.39), mean force (ICC = 0.93, SEM = 0.17, MDC = 1.08), and steadiness (ICC = 0.87, SEM = 0.85, MDC = 2.36), with no significant intersession differences (p > 0.05). This study demonstrates the high reliability of using dynamometry to assess trunk extension strength during MVC and SMVC, endorsing the dynamometer as a tool for functional assessment and the development of personalized rehabilitation and training strategies.