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(1) Background: Neck pain intensity, psychosocial factors, and physical function have been identified as potential predictors of neck disability. Machine learning algorithms have shown promise in classifying patients based on their neck disability status. So, the current study was conducted to identify predictors of neck disability in patients with neck pain based on clinical findings using machine learning algorithms. (2) Methods: Ninety participants with chronic neck pain took part in the study. Demographic characteristics in addition to neck pain intensity, the neck disability index, cervical spine contour, and surface electromyographic characteristics of the axioscapular muscles were measured. Participants were categorised into high disability and low disability groups based on the median value (22.2) of their neck disability index scores. Several regression and classification machine learning models were trained and assessed using a 10-fold cross-validation method; also, MANCOVA was used to compare between the two groups. (3) Results: The multilayer perceptron (MLP) revealed the highest adjusted R2 of 0.768, while linear discriminate analysis showed the highest receiver characteristic operator (ROC) area under the curve of 0.91. Pain intensity was the most important feature in both models with the highest effect size of 0.568 with p < 0.001. (4) Conclusions: The study findings provide valuable insights into pain as the most important predictor of neck disability in patients with cervical pain. Tailoring interventions based on pain can improve patient outcomes and potentially prevent or reduce neck disability.

This study investigated the impact of auditory stimuli on muscular activation patterns using wearable surface electromyography (EMG) sensors. Employing four key muscles (Sternocleidomastoid Muscle (SCM), Cervical Erector Muscle (CEM), Quadricep Muscles (QMs), and Tibialis Muscle (TM)) and time domain features, we differentiated the effects of four interventions: silence, music, positive reinforcement, and negative reinforcement. The results demonstrated distinct muscle responses to the interventions, with the SCM and CEM being the most sensitive to changes and the TM being the most active and stimulus dependent. Post hoc analyses revealed significant intervention-specific activations in the CEM and TM for specific time points and intervention pairs, suggesting dynamic modulation and time-dependent integration. Multi-feature analysis identified both statistical and Hjorth features as potent discriminators, reflecting diverse adaptations in muscle recruitment, activation intensity, control, and signal dynamics. These features hold promise as potential biomarkers for monitoring muscle function in various clinical and research applications. Finally, muscle-specific Random Forest classification achieved the highest accuracy and Area Under the ROC Curve for the TM, indicating its potential for differentiating interventions with high precision. This study paves the way for personalized neuroadaptive interventions in rehabilitation, sports science, ergonomics, and healthcare by exploiting the diverse and dynamic landscape of muscle responses to auditory stimuli.

Depressive mood states in healthy populations are prevalent but often under-reported. Biases exist in self-reporting of depression in otherwise healthy individuals. Gait and balance control can serve as objective markers for identifying those individuals, particularly in real-world settings. We utilized inertial measurement units (IMU) to measure gait and balance control. An exploratory, cross-sectional design was used to compare individuals who reported feeling depressed at the moment ( n = 49) with those who did not ( n = 84). The Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was employed to ensure internal validity. We recruited 133 participants aged between 18-36 years from the university community. Various instruments were used to evaluate participants' present depressive symptoms, sleep, gait, and balance. Gait and balance variables were used to detect depression, and participants were categorized into three groups: not depressed, mild depression, and moderate-high depression. Participant characteristics were analyzed using ANOVA and Kruskal-Wallis tests, and no significant differences were found in age, height, weight, BMI, and prior night's sleep between the three groups. Classification models were utilized for depression detection. The most accurate model incorporated both gait and balance variables, yielding an accuracy rate of 84.91% for identifying individuals with moderate-high depression compared to non-depressed individuals.

Objectives: To examine the impact of university closure and recourse to internet-based learning on the academic achievement of physical therapy students., Method: The retrospective study was conducted at Kafrelsheikh University, Egypt, and comprised academic grades of physical therapy students related to pre-coronavirus disease-2019 pandemic academic year 2019-2020 in group 1 and per-pandemic 2020-2021 session group 2. Data was analysed using SPSS 26., Results: Of the 8,190 students, 4,764(58.2%) were females and 3,426(41.8%) were males with age range 17-23 years who were studying at 5 academic levels. There were 3,732(45.6%) students in group 1 and 4,458(54.4%) in group 2. There were significant difference in the academic grades between the groups (p<0.005). There was greater improvement in the mean results of students' grades (p=0.001) and less variation between students in group 2 compared to group 1 (p<0.05)., Conclusions: Online learning was found to be an effective teaching method for physical therapy students during the coronavirus disease-2019 pandemic with respect to basic medical knowledge. However, its impact on clinical and practical skills of the students was not confirmed.

Objectives: To explore if clinical and epidemiological features of patients positive for coronavirus disease-2019 are affected by somatic work stress., Method: The retrospective study was conducted at Kafrelsheik University Hospital, Egypt, and comprised data of patients admitted between April 1, to June 6, 2020, with confirmed coronavirus disease-2019 infection. Health records of healthy subjects who had come to the hospital as part of their routine check-up were also included for comparison, and the researchers were blinded during the gathering and analysis phase. Demographic features, vitalsigns, infection severity,somatic workload of the patients'jobs at admission, and detailed discharge profile was noted. The relationship between clinical features and somatic work stress was evaluated. Data was analysed using SPSS 26., Results: Of the 1072 cases, 602(56.2%) were men and 470(43.8%) were women. The overall median age was 43 years (interquartile range: 29 years). The healthy group had 500 random subjects. There were significant differences in all vital signs between the patients and healthy controls (p<0.05). Among the patients, infection severity was higher in men, but it was notsignificant (p>0.05). The overall mortality was 69(6.4%); 46(4.3%) men and 23(2.2%) women. There was no significant association between gender and outcome (p>0.05). There were 816(76.11%) patients with low intensity physical workload pre-infection, 136(12.68%) moderate and 120(11.19%) high. Infection severity was significantly high in the low-intensity group (p<0.05). However, the fate of the patients was notsignificantly associated with their pre-infection work profile (p>0.05)., Conclusions: Coronavirus disease-2019 significantly affected patients'vitalsigns, and infection severity wassignificantly associated with physical work stress. However, mortality and pre-infection somatic workload were not associated.

The objective of this study was to determine whether vision-occluded progressive resistance training would increase upper-extremity movement performance using the one-repetition maximum (1-RM) bench press. Participants (n = 57) were recruited from a historically black college and university (HBCU), cross-matched by sex, age (±1 year), 1-RM (±2.27 kg), 1-RM/weight (±0.1), and 1-RM/lean mass ratio (±0.1), and randomly assigned to either the experimental group (vision occluded) or the control group. Participants performed resistance training for 6 weeks prior to beginning the study, and 1-RM was assessed the week prior to the beginning of the study. Weight and body composition were measured using a BOD POD. Of the 57 participants who started the study, 34 completed the study (Experimental = 16, Control = 18) and were reassessed the week after completing the 6-week-long training protocol. Using a combination of Mann−Whitney U and Wilcoxon signed-rank tests, we found that when accounting for changes in lean muscle mass, individuals who trained with their vision occluded reported significantly greater improvements in 1-RM strength compared to those who did not (p < 0.05). The findings from our study suggest that vision-occluded progressive resistance training increases upper-extremity performance when assessed using the bench press. These findings may have significant practical implications in both sports and rehabilitation, as these techniques may be used to enhance performance in athletes and/or improve rehabilitation effectiveness.

Literature suggests that anxiety affects gait and balance among young adults. However, previous studies using machine learning (ML) have only used gait to identify individuals who report feeling anxious. Therefore, the purpose of this study was to identify individuals who report feeling anxious at that time using a combination of gait and quiet balance ML. Using a cross-sectional design, participants (n = 88) completed the Profile of Mood Survey-Short Form (POMS-SF) to measure current feelings of anxiety and were then asked to complete a modified Clinical Test for Sensory Interaction in Balance (mCTSIB) and a two-minute walk around a 6 m track while wearing nine APDM mobility sensors. Results from our study finds that Random Forest classifiers had the highest median accuracy rate (75%) and the five top features for identifying anxious individuals were all gait parameters (turn angles, variance in neck, lumbar rotation, lumbar movement in the sagittal plane, and arm movement). Post-hoc analyses suggest that individuals who reported feeling anxious also walked using gait patterns most similar to older individuals who are fearful of falling. Additionally, we find that individuals who are anxious also had less postural stability when they had visual input; however, these individuals had less movement during postural sway when visual input was removed.