BACKGROUND

Recovery is essential for high-intensity intermittent sports athletes to achieve optimal performance. Heart rate variability (HRV) serves as a marker of the autonomic nervous system, which also measures the parasympathetic regulation that facilitates recovery. Neurofeedback (NBF) intervention, combined with deep breathing and mental imagery, presented positive results in facilitating parasympathetic reactivation. However, limited studies exist in investigating the influence of the NBF intervention on HRV parameters and recovery, specifically in high-intensity intermittent sports athletes.

This pilot study aims to investigate the effects and influence of neurobiofeedback intervention on recovery via the use of HRV of UAAP Collegiate Basketball and Football Athletes.

The research will be done with a Quasi-experimental onegroup pretest-posttest study design.

Participants will undergo a neurobiofeedback intervention following neuromuscular and metabolic training. Data is collected with a Polar H10 HRM Chest Strap connected to an Elite HRV monitoring application and will be analyzed by Kubios HRV software.

Descriptive statistics will be computed for participant characteristics. Kolmogorov-Smirnov test (p >0.05) will assess normality. Two-way repeated-measures ANOVAs will examine NBF effects across exercise types, with Bonferroni-corrected pairwise comparisons and trend analysis for the main effects and non-significant but clinically relevant patterns. All analyses will be done using SPSS v25.

It is expected that the neurobiofeedback intervention will have an effect and influence by eliciting a lower LF/HF ratio and SD1/SD2, suggesting a facilitated reactivation of the parasympathetic nervous system, promoting recovery after undergoing neuromuscular or metabolic training.

INTRODUCTION

Burnout is becoming more common among healthcare professionals, notably during the COVID-19 pandemic. It can result in lower performance and effectiveness at work as well as employment withdrawal, all of which affects the standard of healthcare services provided.

In order to ascertain the effectiveness of mindfulness meditation-based interventions (MMBIs) in reducing burnout among healthcare workers, a systematic review and meta-analysis was done.

Two investigators searched records in CENTRAL, PubMed/MEDLINE, Google Scholar, Preprints, Grey Literature, and cross-referencing to acquire articles using search terms related to “mindfulness meditation”, “healthcare workers”, and “burnout”. Inclusion criteria included randomized controlled trials (RCTs) and nonrandomized controlled trials (NRTs) that assessed the effectiveness of MMBIs on burnout as measured by the Maslach Burnout Inventory (MBI) among healthcare workers in the hospital setting. Study selection, data extraction, risk of bias assessment were done by the investigators independently. Analysis was done using RevMan 5 software, forest plots were generated, and subgroup analyses were done.

Of 25,453 identified records, 28 studies were included. The studies were rated with low to unclear selection bias and high risk of performance bias. MMBIs were associated with significant reduction on the emotional exhaustion, depersonalization and personal accomplishment subscales with pooled mean differences of -2.60 (95% CI = -3.64, -1.55), -0.51 (95% CI = -0.77, -0.26), and 0.82 (95% CI = 0.24, 1.39), respectively. On subgroup analyses, the types of MMBI implemented had no influence in the intervention effect noted on all subscales among RCTs but had significant influence among NRTs. Reduction of burnout was noted to be higher in nurses compared with physicians and mixed healthcare workers. Overall quality of evidence for RCTs was low to moderate and very low to low for NRTs.

The results suggest that MMBIs can reduce the burnout symptoms of healthcare workers. To address the high risk of bias of included studies and improve quality of evidence, future research should be done with high-quality RCTs.

Nano-ayurvedic medicine is an emerging field in which nanoparticles are functionalized with active principles of potent ayurvedic herbs to enhance their efficacy and target-specific delivery. Scientific advances in the past couple of decades have revealed the molecular mechanisms behind the anticancer potential of several ayurvedic herbs, attributed chiefly to their secondary metabolites including polyphenols and other active substances. With the advancement of nanotechnology, it has been established that size-, shape-, and surface-chemistry-optimized nanoparticles can be utilized as synergizing carriers for these phytochemicals. Nano-ayurvedic medicine utilizes herbs that are commonly used in Ayurveda to functionalize different nanoparticles and thereby enhance their potency and target specificity. Studies have shown that the active phytochemicals of such herbs can be coated onto the nanoparticles of different metals, such as gold, and that they work more efficiently than the free herbal extract, for example, in inhibiting cancer cell proliferation. Recently, an Ayurveda, Yoga & Naturopathy, Unani, Siddha and Homeopathy (AYUSH)-based clinical trial in humans indicated the anticancer potential of such formulations. Nano-ayurvedic medicine is emerging as a potential treatment option for hyperproliferative diseases.
Humans ; Medicine, Ayurvedic ; Homeopathy ; Naturopathy ; Yoga ; Neoplasms/drug therapy*

OBJECTIVE: To evaluate the feasibility and safety of Liuzijue exercise (LE) for the clinical effect in patients after cardiac surgery. METHODS: Totally 120 patients who underwent cardiac surgery and were admitted to the Cardiothoracic Intensive Care Unit of Nanjing Drum Tower Hospital between July and Oclober, 2022 were allocated to the LE group, the conventional respiratory training (CRT) group, and the control group by a random number table at a ratio of 1:1:1; 40 patients in each group. All patients received routine treatment and cardiac rehabilitation. LE group and CRT group respectively performed LE and CRT once a day for 30 min for 7 days. Control group did not receive specialized respiratory training. The forced vital capacity, forced expiratory volume in 1 s, peak inspiratory flow rate, peak expiratory flow rate, maximum inspiratory pressure, maximum expiratory pressure, modified Barthel index (MBI), and Hamilton Rating Scale for Anxiety (HAM-A) were evaluated before, after 3 and 7 days of intervention. In addition, the postoperative length of hospital stay (LOS) and the adverse events that occurred during the intervention period were compared. RESULTS: A total of 107 patients completed the study, 120 patients were included in the analysis. After 3 days of intervention, the pulmonary function, respiratory muscle strength, MBI and HAM-A of all 3 groups improved compared with that before the intervention (P<0.05 or P<0.01). Compared with the control group, pulmonary function and respiratory muscle strength were significantly improved in the CRT and LE groups (P<0.05 or P<0.01). MBI and HAM-A were significantly improved in the LE group compared with the control and CRT groups (P<0.05 or P<0.01). On the 7th day after intervention, the difference was still statistically significant (P<0.01), and was significantly different from that on the 3rd day (P<0.05 or P<0.01). In addition, on the 7th day of intervention, the pulmonary function and respiratory muscle strength in the LE group were significantly improved compared with those in the CRT group (P<0.01). MBI and HAM-A were significantly improved in the CRT group compared with the control group (P<0.01). There were no significant differences in postoperative LOS among the 3 groups (P>0.05). No training-related adverse events occurred during the intervention period. CONCLUSIONS LE is safe and feasible for improving pulmonary function, respiratory muscle strength, the ability to complete activities of daily living and for relieving anxiety of patients after cardiac surgery (Registration No. ChiCTR2200062964).
Humans ; Activities of Daily Living ; Breathing Exercises ; Cardiac Surgical Procedures/adverse effects* ; Respiratory Muscles ; Muscle Strength/physiology*

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.
Humans ; Time Factors ; Brain ; Electroencephalography ; Imagery, Psychotherapy ; Neural Networks, Computer

Background: A daily habit of yogic practice or walking, along with an oral hypoglycemic agent (OHA) could be beneficial for better control of type 2 diabetes mellitus (T2DM). We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs) to find out the efficiency of yoga or walking on glycemic control in T2DM. Methodology: The present systematic review and meta-analysis were completed according to the PRISMA guidelines. The risk of bias in included studies was evaluated, by using the revised Cochrane risk-of-bias tool for randomized trials. Meta-analysis was implemented using RevMan software. Forest plots were used to illustrate the study findings and meta-analysis results. Results: Sixteen studies were included in this systematic review, where 1820 participants were allocated to one of the following interventions: yoga, walking, and without any regular exercise (control group). Participants were between 17–75 years of age. Compared to the control group, the yoga group had a significant reduction in fasting blood glucose (FBG) by 31.98 mg/dL (95% CI,–47.93 to –16.03), postprandial blood glucose (PPBG) by 25.59 mg/dL (95% CI, –44.00 to –7.18], glycosylated hemoglobin (HbAlc) by 0.73% (95% CI, –1.24 to -0.22), fasting insulin by 7.19 μIU/mL (95% CI, –12.10 to –2.28), and homeostatic model assessment for insulin resistance (HOMA-IR) by 3.87 (95% CI, –8.40 to -0.66). Compared to the control group, the walking group had a significant reduction in FBG by 12.37 mg/dL (95% CI, –20.06 to –4.68) and HbA1c by 0.35% (95% CI, –0.70 to –0.01). Compared to the walking group, the yoga group had a significant reduction in FBG by 12.07 mg/dL (95% CI, –24.34 to – 0.20), HbA1c by 0.20% (95% CI, –0.37 to –0.04), fasting insulin by 10.06 μIU/mL (95% CI, –23.84 to 3.71) and HOMA-IR by 5.97 (95% CI, –16.92 to 4.99). Conclusions Yoga or walking with OHA has positive effects on glycemic control. For the management of T2DM, yoga has relatively more significant effects on glycemic control than walking.
Yoga ; Walking ; Diabetes Mellitus, Type 2 ; Glycemic Control ; Insulin Resistance

This is a report to share the experience of managing a patient planned for surgical wound procedure under anaesthesia with medical hypnosis as a tool for sedation and pain relief with a wrist block to supplement the analgesic effect. This was a patient who presented with severe preoperative anxiety and pain concerning her surgical procedure. She developed an allergy to some analgesia from her first surgical procedure. Adequate motivational interviewing techniques with the patient enabled issues about her anxiety to be explored and helped to ease the patient's anxiety. The surgical procedure was successfully done with hypnosis and regional anaesthesia. The patient's response towards pain and anxiety was documented based on Numerical Reporting Scale. The patient's wound care in the ward and clinic proved to be less painful and more comfortable for the patient with her self-hypnosis skill. Empowering the patient with medical hypnosis during the procedure is an option that should be explored.
Hypnosis

The effective classification of multi-task motor imagery electroencephalogram (EEG) is helpful to achieve accurate multi-dimensional human-computer interaction, and the high frequency domain specificity between subjects can improve the classification accuracy and robustness. Therefore, this paper proposed a multi-task EEG signal classification method based on adaptive time-frequency common spatial pattern (CSP) combined with convolutional neural network (CNN). The characteristics of subjects' personalized rhythm were extracted by adaptive spectrum awareness, and the spatial characteristics were calculated by using the one-versus-rest CSP, and then the composite time-domain characteristics were characterized to construct the spatial-temporal frequency multi-level fusion features. Finally, the CNN was used to perform high-precision and high-robust four-task classification. The algorithm in this paper was verified by the self-test dataset containing 10 subjects (33 ± 3 years old, inexperienced) and the dataset of the 4th 2018 Brain-Computer Interface Competition (BCI competition Ⅳ-2a). The average accuracy of the proposed algorithm for the four-task classification reached 93.96% and 84.04%, respectively. Compared with other advanced algorithms, the average classification accuracy of the proposed algorithm was significantly improved, and the accuracy range error between subjects was significantly reduced in the public dataset. The results show that the proposed algorithm has good performance in multi-task classification, and can effectively improve the classification accuracy and robustness.
Humans ; Adult ; Imagination ; Neural Networks, Computer ; Imagery, Psychotherapy/methods* ; Electroencephalography/methods* ; Algorithms ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted

Aiming at the problem of low recognition accuracy of motor imagery electroencephalogram signal due to individual differences of subjects, an individual adaptive feature representation method of motor imagery electroencephalogram signal is proposed in this paper. Firstly, based on the individual differences and signal characteristics in different frequency bands, an adaptive channel selection method based on expansive relevant features with label F (ReliefF) was proposed. By extracting five time-frequency domain observation features of each frequency band signal, ReliefF algorithm was employed to evaluate the effectiveness of the frequency band signal in each channel, and then the corresponding signal channel was selected for each frequency band. Secondly, a feature representation method of common space pattern (CSP) based on fast correlation-based filter (FCBF) was proposed (CSP-FCBF). The features of electroencephalogram signal were extracted by CSP, and the best feature sets were obtained by using FCBF to optimize the features, so as to realize the effective state representation of motor imagery electroencephalogram signal. Finally, support vector machine (SVM) was adopted as a classifier to realize identification. Experimental results show that the proposed method in this research can effectively represent the states of motor imagery electroencephalogram signal, with an average identification accuracy of (83.0±5.5)% for four types of states, which is 6.6% higher than the traditional CSP feature representation method. The research results obtained in the feature representation of motor imagery electroencephalogram signal lay the foundation for the realization of adaptive electroencephalogram signal decoding and its application.
Humans ; Imagination ; Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Electroencephalography/methods* ; Imagery, Psychotherapy ; Algorithms

Brain-computer interaction (BCI) is a transformative human-computer interaction, which aims to bypass the peripheral nerve and muscle system and directly convert the perception, imagery or thinking activities of cranial nerves into actions for further improving the quality of human life. Magnetoencephalogram (MEG) measures the magnetic field generated by the electrical activity of neurons. It has the unique advantages of non-contact measurement, high temporal and spatial resolution, and convenient preparation. It is a new BCI driving signal. MEG-BCI research has important brain science significance and potential application value. So far, few documents have elaborated the key technical issues involved in MEG-BCI. Therefore, this paper focuses on the key technologies of MEG-BCI, and details the signal acquisition technology involved in the practical MEG-BCI system, the design of the MEG-BCI experimental paradigm, the MEG signal analysis and decoding key technology, MEG-BCI neurofeedback technology and its intelligent method. Finally, this paper also discusses the existing problems and future development trends of MEG-BCI. It is hoped that this paper will provide more useful ideas for MEG-BCI innovation research.
Brain/physiology* ; Brain-Computer Interfaces ; Electroencephalography ; Humans ; Imagery, Psychotherapy ; Magnetoencephalography ; Technology