Effect of sleep posture on neck muscle activity

Knowledge of spinal kinematics is essential for the diagnosis and management of spinal diseases. Distinguishing between physiological and pathological motion patterns can help diagnose these diseases, plan surgical interventions and improve relevant tools and software. During the last decades, numerous studies based on diverse methodologies attempted to elucidate spinal mobility in different planes of motion. The authors aimed to summarize and compare the evidence about cervical spine kinematics under healthy and degenerative conditions. This includes an illustrated description of the spectrum of physiological cervical spine kinematics, followed by a comparable presentation of kinematics of the degenerative cervical spine. Data was obtained through a systematic MEDLINE search including studies on angular/translational segmental motion contribution, range of motion, coupling and center of rotation. As far as the degenerative conditions are concerned, kinematic data regarding disc degeneration and spondylolisthesis were available. Although the majority of the studies identified repeating motion patterns for most motion planes, discrepancies associated with limited sample sizes and different imaging techniques and/or spine configurations, were noted. Among healthy/asymptomatic individuals, flexion extension (FE) and lateral bending (LB) are mainly facilitated by the subaxial cervical spine. C4–C5 and C5–C6 were the major FE contributors in the reported studies, exceeding the motion contribution of sub-adjacent segments. Axial rotation (AR) greatly depends on C1–C2. FE range of motion (ROM) is distributed between the atlantoaxial and subaxial segments, while AR ROM stems mainly from the former and LB ROM from the latter. In coupled motion rotation is quantitatively predominant over translation. Motion migrates caudally from C1–C2 and the center of rotation (COR) translocates anteriorly and superiorly for each successive subaxial segment. In degenerative settings, concurrent or subsequent lesions render the association between diseases and mobility alterations challenging. The affected segments seem to maintain translational and angular motion in early and moderate degeneration. However, the progression of degeneration restrains mobility, which seems to be maintained or compensated by adjacent non-affected segments. While the kinematics of the healthy cervical spine have been addressed by multiple studies, the entire nosological and kinematic spectrum of cervical spine degeneration is partially addressed. Large—scale in vivo studies can complement the existing evidence, cover the gaps and pave the way to technological and clinical breakthroughs.

Recognition of sleep patterns and posture has sparked interest in various clinical applications. Sleep postures can be monitored autonomously and constantly to provide useful information for decreasing health risks. Existing systems mostly use images to train the model to learn based on many sensors. For example, a camera, pressure sensor, and electrocardiogram. In this study, a model (named as SleepCon) was designed using deep learning, which will have the capability to train with any threshold image obtained from any sensor. This paper presented a system where data was obtained from a camera installed on the top of a mattress. The camera located the movement of the body posture on the mattress while the subject was lying down on the mattress. In doing so, CNN and other pre-processed steps took place to collect data and then analyze the data to recognize different sleep postures. This model was stored for use in real-time applications. The system can recognize the three major postures, i.e., left, right, and supine. A real-time application is also developed and operates the stored SleepCon model through an accompanying desktop application for detecting the posture live. The accuracy of classification was greater than 90%, while the actual application accuracy was 100% after carrying out the experiment on the SleepCon model. Doi: 10.28991/ESJ-2023-07-01-04 Full Text: PDF

Background: Musculoskeletal pain (MSP) is frequent complaint among college students, especially neck and shoulder pain (NSP). No study so far has demonstrated a correlation between the use of digital devices and the prevalence of NSP among college students in China. Therefore, a cross-sectional survey investigating this relationship among college students in Shanghai was urgently needed.Objective: The relationship between digital device usage, lifestyle, and the prevalence of self-reported NSP among college students in Shanghai, and neck muscle activity of different degrees of spinal curvature was investigated to provide suggestions for healthy spinal curvature lifestyle education. Methods: 6000 students were randomly enrolled, and a self-reported questionnaire was conducted to assess the prevalence and severity of NSP. The students’ demographic characteristics, habits of using digital devices, and lifestyles were recorded. c2 tests were used to compare the prevalence of NSP; univariable and multivariable logistic regression analyses were performed to identify potential risk factors for NSP. A musculoskeletal model of the standard human body was established with the AnyBody platform to test the neck muscle activity of different degrees of spinal curvature.Results: Among the 4848 college students (80.8%) who completed the survey, the prevalence of NSP was 39.1%, with more girls (45.0%) reporting NSP than boys (32.4%). The logistic regression indicated that gender, inappropriate posture and using digital devices for long time had a significant correlation with NSP. Neck muscle activity increased as spinal inclination increased based on the AnyBody platform.Conclusion: NSP seems to be a common condition among college students in Shanghai. Gender, inappropriate posture and using digital devices for long time are closely associated with NSP. The greater the anterior cervical and lumbar flexion, the higher the activity of cervical muscles. Healthy lifestyle education should be used to decrease the NSP among college students.