Low back pain (LBP) that radiates to the leg is not always related to a lesion or a disease of the nervous system (neuropathic pain): it might be nociceptive (referred) pain. Unfortunately, patients with low-back related leg pain are often given a variety of diagnoses (e.g. ‘sciatica’; ‘radicular pain’; pseudoradicular pain”). This terminology causes confusion and challenges clinical reasoning. It is essential for clinicians to understand and recognize predominant pain mechanisms. This paper describes pain mechanisms related to low back-related leg pain and helps differentiate these mechanisms in practice using clinical based scenarios. We illustrate this by using two clinical scenarios including patients with the same symptoms in terms of pain localization (i.e. low-back related leg pain) but with different underlying pain mechanisms (i.e. nociceptive versus neuropathic pain).

Teaching hypothetico-deductive clinical reasoning (CR) should be an essential part of the physiotherapy education system, but currently there are very few learning tools for teachers in the musculoskeletal discipline. The aim of this article was to describe and present the rules of a new game-based and structured didactic tool that can be used by teachers for ‘players’ (students and licensed clinicians) to learn systematic CR in musculoskeletal physiotherapy. Our tool is based on the ‘Happy Families’ card game, and we propose to use it as part of a classic musculoskeletal subjective examination-based hypothesis category framework and the International Classification of Functioning, Disability and Health model. It allows players to dynamically formulate hypotheses from clinical case studies. Each 'Family' of cards represents a hypothesis category. The game highlights the missing information and trains players to consider it in their CR. This game should efficiently structure all components of CR and is an interesting resource for all teachers. Its greatest strength is that it can be used with other category frameworks. Further studies are needed to assess the efficacy and efficiency of such a tool and to measure students’ actual progress in learning the CR.

Various noninvasive measurement devices can be used to assess cervical motion. The size, complexity, and cost of gold-standard systems make them not suited to clinical practice, and actually difficult to use outside a dedicated laboratory. Nowadays, ultra-low-cost inertial measurement units are available, but without any packaging or a user-friendly interface. The so-called DYSKIMOT is a home-designed, small-sized, motion sensor based on the latter technology, aiming at being used by clinicians in “real-life situations”. DYSKIMOT was compared with a gold-standard optoelectronic system (Elite). Our goal was to evaluate the DYSKIMOT accuracy in assessing fast head rotations kinematics. Kinematics was simultaneously recorded by systems during the execution of the DidRen Laser test and performed by 15 participants and nine patients. Kinematic variables were computed from the position, speed and acceleration time series. Two-way ANOVA, Passing–Bablok regressions, and dynamic time warping analysis showed good to excellent agreement between Elite and DYSKIMOT, both at the qualitative level of the time series shape and at the quantitative level of peculiar kinematical events’ measured values. In conclusion, DYSKIMOT sensor is as relevant as a gold-standard system to assess kinematical features during fast head rotations in participants and patients, demonstrating its usefulness in both clinical practice and research environments.

Sensorimotor control strategies during cervical axial rotation movements have been previously explored in narrow age ranges but never concurrently in Children and Seniors during a well-standardized task. However, the lifespan developmental approach provides a framework for research in human sensorimotor control of the head-neck complex. A cross-sectional design was used to investigate the influence of age on head-neck dynamic performance adopted by asymptomatic Children, Adults and Seniors using a standardized task (DidRen Laser test). Participants performed 5 cycles of left/right head-neck complex fast rotational movements toward 3 targets with 30° of angular separation. Dynamic performances were computed from total execution time of the test and kinematic variables derived from rotational motion of head measured by an optoelectronic system. Eighty-one participants, aged 8–85 yrs, were stratified in four groups: Children, Younger adults, Older adults and Seniors. Children were significantly slower than Younger (p<0.001) and Older adults (p<0.004) and Seniors slower than Younger adults (p<0.017) to perform the test. Children adopted a lower average speed compared to Younger (p<0.001) and Older adults (p<0.008). Children reached the peaks speed significantly later than Younger (p<0.004) and Older adults (p<0.04) and acceleration significantly later than Younger (p<0.001) and Older adults (p<0.013). From the peak acceleration, Children reached end of the cycle significantly slower than Younger (p<0.008) and Older adults (p<0.008). Children significantly differed from all other groups for rotational kinetic energy, with smaller values compared to Younger adults (p<0.001), Older adults (p<0.005) and Seniors (p<0.012). Variability was also significantly higher for Seniors and Children. In conclusion, age influences head-neck visually elicited rotational dynamics, especially in Children. These results suggest that age should be taken into account when establishing normative data and assessing dynamic head-neck sensorimotor control of patients with neck pain.

Background: The assessment of cervical spine kinematic axial rotation performance is of great importance in the context of the study of neck sensorimotor control. However, studies addressing the influence of the level of provocation of spinal pain and the potential benefit of passive manual therapy mobilizations in patients with acute-subacute non-specific neck pain are lacking. Methods: A non-randomized prospective clinical trial with an intervention design was conducted. We investigated: (1) the test-retest reliability of kinematic variables during a fast axial head rotation task standardized with the DidRen laser test device in 42 Healthy pain-free Control Participants (HCP) (24.3 years ±6.8); (2) the differences in kinematic variables between HCP and 38 patients with Acute-subacute Non-Specific neck Pain (ANSP) assigned to two different groups according to whether their pain was localized in the upper or lower spine (46.2 years ±16.3); and (3) the effect of passive manual therapy mobilizations on kinematic variables of the neck during fast axial head rotation. Results: (1) Intra-class correlation coefficients ranged from moderate (0.57 (0.06-0.80)) to excellent (0.96 (0.91-0.98)). (2) Kinematic performance during fast axial rotations of the head was significantly altered in ANSP compared to HCP (age-adjusted) for one variable: the time between peaks of acceleration and deceleration (p<0.019). No significant difference was observed between ANSP with upper vs lower spinal pain localization. (3) After the intervention, there was a significant effect on several kinematic variables, e.g., ANSP improved peak speed (p<0.007) and performance of the DidRen laser test (p<0.001), with effect sizes ranging from small to medium. Conclusion: (1) The DidRen laser test is reliable. (2) A significant reduction in time between acceleration and deceleration peaks was observed in ANSP compared to HCP, but with no significant effect of spinal pain location on kinematic variables was found. (3) We found that neck pain decreased after passive manual therapy mobilizations with improvements of several kinematic variables. Trial registration: Registration Number: NCT 04407637

Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients.