Sensorimotor performance in acute-subacute non-specific neck pain: a non-randomized prospective clinical trial with intervention

A Systematic Review of the Aerobic Exercise Program Variables for Patients with Non-Specific Neck Pain: Effectiveness and Clinical Applications

Studies have shown that aerobic exercise (AE) may improve symptoms related to non-specific neck pain (NNP); however, the variables of the exercise programs and the overall effectiveness of AE have not been evaluated in a systematic review. Therefore, this review aimed to describe and discuss the variables of the AE programs used in clinical trials for patients with NNP. Included studies were analyzed for the selected AE variables such as intensity, frequency, duration, delivery, supervision, and adherence. The PEDro scale was used to assess the methodological quality of the studies. From the literature search, six studies met the inclusion criteria and were evaluated. After reviewing all the included studies, it was found that a range of AE interventions were used such as cycling, brisk walking, aerobics, stationary bike, treadmill running, circuit training, and swimming. Further, the duration was between 30 and 45 min for each session, with or without progressive increases from week to week. The intervention periods ranged from 1 month to 6 months in duration. Most studies used AE three times per week. Furthermore, exercise intensity was measured with either subjective (BORG) or objective measures (heartrate reserve). Justification for the specified intensity and reporting of adverse events was reported only in two studies and differed between studies. Exercise interventions were poorly reported. This review showed that moderate-intensity AE undertaken three times per week, in patients with NNP, may be beneficial for pain and function; however, the development of reporting standards is essential for the successful replication of studies.

Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test

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.