Investigating the effects of chiropractic care on resting-state EEG of MCI patients.

Introduction: Mild cognitive impairment (MCI) is a stage between health and dementia, with various symptoms including memory, language, and visuospatial impairment. Chiropractic, a manual therapy that seeks to improve the function of the body and spine, has been shown to affect sensorimotor processing, multimodal sensory processing, and mental processing tasks. Methods: In this paper, the effect of chiropractic intervention on Electroencephalogram (EEG) signals in patients with mild cognitive impairment was investigated. EEG signals from two groups of patients with mild cognitive impairment (n = 13 people in each group) were recorded pre- and post-control and chiropractic intervention. A comparison of relative power was done with the support vector machine (SVM) method and non-parametric cluster-based permutation test showing the two groups could be separately identified with high accuracy. Results: The highest accuracy was obtained in beta2 (25-35 Hz) and theta (4-8 Hz) bands. A comparison of different brain areas with the SVM method showed that the intervention had a greater effect on frontal areas. Also, interhemispheric coherence in all regions increased significantly after the intervention. The results of the Wilcoxon test showed that intrahemispheric coherence changes in frontal-occipital, frontal-temporal and right temporal-occipital regions were significantly different in two groups. Discussion: Comparison of the results obtained from chiropractic intervention and previous studies shows that chiropractic intervention can have a positive effect on MCI disease and using this method may slow down the progression of mild cognitive impairment to Alzheimer's disease.

Factors influencing imaging clinical decision-making in low back pain management. A scoping review.

BACKGROUND: The use of diagnostic imaging in low back pain (LBP) management is often inappropriate, despite recommendations from clinical practice guidelines. There is a limited understanding of factors that influence the imaging clinical decision-making (CDM) process. AIM: Explore the literature on factors influencing imaging CDM for people with LBP and consider how these findings could be used to reduce inappropriate use of imaging in LBP management. DESIGN: Scoping review. METHOD: This review followed the Preferred Reporting Items for Systematic Review extension for scoping reviews. A digital search was conducted in Medline, the Cumulative Index of Nursing and Allied Health Literature, Scopus, and the Cochrane Central Register of Controlled Trials for eligible studies published between January 2010-2023. Data reporting influences on imaging CDM were extracted. Data were then analysed through an inductive process to group the influencing factors into categories. RESULTS: After screening, 35 studies (5 qualitative and 30 quantitative) were included in the review, which reported factors influencing imaging CDM. Three categories were developed: clinical features (such as red flags, pain, and neurological deficit), non-modifiable factors (such as age, sex, and ethnicity) and modifiable factors (such as beliefs about consequences and clinical practice). Most studies reported non-modifiable factors. CONCLUSIONS: The results of this scoping review challenge the perception that imaging CDM is purely based on clinical history and objective findings. There is a complex interplay between clinical features, patient and clinician characteristics, beliefs, and environment. These findings should be considered when designing strategies to address inappropriate imaging behaviour.

The effects of chiropractic adjustment on inattention, hyperactivity, and impulsivity in children with attention deficit hyperactivity disorder: a pilot RCT.

Background: Attention deficit hyperactivity disorder (ADHD) is a neurobiological disorder characterized by inattention, hyperactivity, and impulsivity. We hypothesized that chiropractic adjustments could improve these symptoms by enhancing prefrontal cortex function. This pilot study aimed to explore the feasibility and efficacy of 4 weeks of chiropractic adjustment on inattention, hyperactivity, and impulsivity in children with ADHD. Methods: 67 children with ADHD were randomly allocated to receive either chiropractic adjustments plus usual care (Chiro+UC) or sham chiropractic plus usual care (Sham+UC). The Vanderbilt ADHD Diagnostic Teacher Rating Scale (VADTRS), Swanson, Nolan and Pelham Teacher and Parents Rating Scale (SNAP-IV), and ADHD Rating Scale-IV were used to assess outcomes at baseline, 4 weeks, and 8 weeks. Feasibility measures such as recruitment, retention, blinding, safety, and adherence were recorded. Linear mixed regression models were used for data analysis. Results: 56 participants (mean age ± SD: 10.70 ± 3.93 years) were included in the analysis. Both the Chiro+UC and Sham+UC groups showed significant improvements in total and subscale ADHD scores at 4 weeks and 8 weeks. However, there were no significant differences between the two groups. Conclusion: This pilot study demonstrated that it was feasible to examine the effects of chiropractic adjustment when added to usual care on ADHD outcomes in children. While both groups showed improvements, the lack of significant between-group differences requires caution in interpretation due to the small sample size. Further research with larger samples and longer follow-up periods is needed to conclusively evaluate the effects of chiropractic adjustments on ADHD in children.

The Effects of Chiropractic Spinal Adjustment on EEG in Adults with Alzheimer's and Parkinson's Disease: A Pilot Randomised Cross-over Trial.

OBJECTIVES: In this study, we explored the effects of chiropractic spinal adjustments on resting-state electroencephalography (EEG) recordings and early somatosensory evoked potentials (SEPs) in Alzheimer's and Parkinson's disease. METHODS: In this randomized cross-over study, 14 adults with Alzheimer's disease (average age 67 ± 6 years, 2 females:12 males) and 14 adults with Parkinson's disease (average age 62 ± 11 years, 1 female:13 males) participated. The participants underwent chiropractic spinal adjustments and a control (sham) intervention in a randomized order, with a minimum of one week between each intervention. EEG was recorded before and after each intervention, both during rest and stimulation of the right median nerve. The power-spectra was calculated for resting-state EEG, and the amplitude of the N30 peak was assessed for the SEPs. The source localization was performed on the power-spectra of resting-state EEG and the N30 SEP peak. RESULTS: Chiropractic spinal adjustment significantly reduced the N30 peak in individuals with Alzheimer's by 15% (p = 0.027). While other outcomes did not reach significance, resting-state EEG showed an increase in absolute power in all frequency bands after chiropractic spinal adjustments in individuals with Alzheimer's and Parkinson's disease. The findings revealed a notable enhancement in connectivity within the Default Mode Network (DMN) at the alpha, beta, and theta frequency bands among individuals undergoing chiropractic adjustments. CONCLUSIONS: We found that it is feasible to record EEG/SEP in individuals with Alzheimer's and Parkinson's disease. Additionally, a single session of chiropractic spinal adjustment reduced the somatosensory evoked N30 potential and enhancement in connectivity within the DMN at the alpha, beta, and theta frequency bands in individuals with Alzheimer's disease. Future studies may require a larger sample size to estimate the effects of chiropractic spinal adjustment on brain activity. Given the preliminary nature of our findings, caution is warranted when considering the clinical implications. CLINICAL TRIAL REGISTRATION: The study was registered by the Australian New Zealand Clinical Trials Registry (registration number ACTRN12618001217291 and 12618001218280).

Editorial: Signal Processing for Brain-Computer Interfaces-Special Issue.

With the astounding ability to capture a wealth of brain signals, Brain-Computer Interfaces (BCIs) have the potential to revolutionize humans' quality of life [...].

A tensor decomposition scheme for EEG-based diagnosis of mild cognitive impairment.

Mild Cognitive Impairment (MCI) is the primary stage of acute Alzheimer's disease, and early detection is crucial for the person and those around him. It is difficult to recognize since this mild stage does not have clear clinical signs, and its symptoms are between normal aging and severe dementia. Here, we propose a tensor decomposition-based scheme for automatically diagnosing MCI using Electroencephalogram (EEG) signals. A new projection is proposed, which preserves the spatial information of the electrodes to construct a data tensor. Then, using parallel factor analysis (PARAFAC) tensor decomposition, the features are extracted, and a support vector machine (SVM) is used to discriminate MCI from normal subjects. The proposed scheme was tested on two different datasets. The results showed that the tensor-based method outperformed conventional methods in diagnosing MCI with an average classification accuracy of 93.96% and 78.65% for the first and second datasets, respectively. Therefore, it seems that maintaining the spatial topology of the signals plays a vital role in the processing of EEG signals.

Optimizing the performance of convolutional neural network for enhanced gesture recognition using sEMG.

Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value < 0.05). In comparison to manual (12.76 ± 4.66), grid (0.10 ± 0.03), and random (0.12 ± 0.05) search hyperparameters optimisation strategies, the proposed optimisation technique resulted in a mean classification error rate (CER) of 0.08 ± 0.03 across all subjects. In addition, a generalised CNN architecture with an optimal set of hyperparameters is proposed. When tested separately on all individuals, the single generalised CNN architecture produced an overall CER of 0.09 ± 0.03. This study's significance lies in its contribution to the field of EMG signal processing by demonstrating the superiority of the overlap segmentation technique, optimizing CNN hyperparameters through Bayesian optimization, and offering practical insights for improving prosthetic control and human-computer interfaces.

A randomized controlled trial comparing different sites of high-velocity low amplitude thrust on sensorimotor integration parameters.

Increasing evidence suggests that a high-velocity, low-amplitude (HVLA) thrust directed at a dysfunctional vertebral segment in people with subclinical spinal pain alters various neurophysiological measures, including somatosensory evoked potentials (SEPs). We hypothesized that an HVLA thrust applied to a clinician chosen vertebral segment based on clinical indicators of vertebral dysfunction, in short, segment considered as "relevant" would significantly reduce the N30 amplitude compared to an HVLA thrust applied to a predetermined vertebral segment not based on clinical indicators of vertebral dysfunction or segment considered as "non-relevant". In this double-blinded, active-controlled, parallel-design study, 96 adults with recurrent mild neck pain, ache, or stiffness were randomly allocated to receiving a single thrust directed at either a segment considered as "relevant" or a segment considered as "non-relevant" in their upper cervical spine. SEPs of median nerve stimulation were recorded before and immediately after a single HVLA application delivered using an adjusting instrument (Activator). A linear mixed model was used to assess changes in the N30 amplitude. A significant interaction between the site of thrust delivery and session was found (F1,840 = 9.89, p < 0.002). Pairwise comparisons showed a significant immediate decrease in the N30 complex amplitude after the application of HVLA thrust to a segment considered "relevant" (- 16.76 ± 28.32%, p = 0.005). In contrast, no significant change was observed in the group that received HVLA thrust over a segment considered "non-relevant" (p = 0.757). Cervical HVLA thrust applied to the segment considered as "relevant" altered sensorimotor parameters, while cervical HVLA thrust over the segment considered as "non-relevant" did not. This finding supports the hypothesis that spinal site targeting of HVLA interventions is important when measuring neurophysiological responses. Further studies are needed to explore the potential clinical relevance of these findings.

The validity of smartphone-based spatiotemporal gait measurements during walking with and without head turns: Comparison with the GAITRite® system.

Smartphone accelerometry has potential to provide clinicians with specialized gait analysis not available in most clinical settings. The Gait&Balance Application (G&B App) uses smartphone accelerometry to assess spatiotemporal gait parameters under two conditions: walking looking straight ahead and walking with horizontal head turns. This study investigated the validity of G&B App gait parameters compared with the GAITRite® pressure-sensitive walkway. Healthy young and older adults (age range 21-85 years) attended a single session where a smartphone was secured over the lumbosacral junction. Data were collected concurrently with the app and GAITRite® systems as participants completed the two walking conditions. Spatiotemporal gait parameters for 54 participants were determined from both systems and agreement evaluated with partial Pearson's correlation coefficients and limits of agreement. The results demonstrated moderate to excellent validity for G&B App measures of step time (rp 0.97, 95 % CI [0.96, 0.98]), walking speed (rp 0.83 [0.78, 0.87]), and step length (rp 0.74, [0.66, 0.80]) when walking looking straight ahead, and results were comparable with head turns. The validity of walking speed and step length measures was influenced by sex and height. G&B App measures of step length variability, step time variability, step length asymmetry, and step time asymmetry had poor validity. The G&B App has potential to provide valid measures of unilateral and bilateral step time, unilateral and bilateral step length, and walking speed, under two walking conditions in healthy young and older adults. Further research should validate this tool in clinical conditions and optimise the algorithm for demographic characteristics.

A Brain Computer Interface Neuromodulatory Device for Stroke Rehabilitation: Iterative User-Centered Design Approach.

BACKGROUND: Rehabilitation technologies for people with stroke are rapidly evolving. These technologies have the potential to support higher volumes of rehabilitation to improve outcomes for people with stroke. Despite growing evidence of their efficacy, there is a lack of uptake and sustained use in stroke rehabilitation and a call for user-centered design approaches during technology design and development. This study focuses on a novel rehabilitation technology called exciteBCI, a complex neuromodulatory wearable technology in the prototype stage that augments locomotor rehabilitation for people with stroke. The exciteBCI consists of a brain computer interface, a muscle electrical stimulator, and a mobile app. OBJECTIVE: This study presents the evaluation phase of an iterative user-centered design approach supported by a qualitative descriptive methodology that sought to (1) explore users' perspectives and experiences of exciteBCI and how well it fits with rehabilitation, and (2) facilitate modifications to exciteBCI design features. METHODS: The iterative usability evaluation of exciteBCI was conducted in 2 phases. Phase 1 consisted of 3 sprint cycles consisting of single usability sessions with people with stroke (n=4) and physiotherapists (n=4). During their interactions with exciteBCI, participants used a "think-aloud" approach, followed by a semistructured interview. At the end of each sprint cycle, device requirements were gathered and the device was modified in preparation for the next cycle. Phase 2 focused on a "near-live" approach in which 2 people with stroke and 1 physiotherapist participated in a 3-week program of rehabilitation augmented by exciteBCI (n=3). Participants completed a semistructured interview at the end of the program. Data were analyzed from both phases using conventional content analysis. RESULTS: Overall, participants perceived and experienced exciteBCI positively, while providing guidance for iterative changes. Five interrelated themes were identified from the data: (1) "This is rehab" illustrated that participants viewed exciteBCI as having a good fit with rehabilitation practice; (2) "Getting the most out of rehab" highlighted that exciteBCI was perceived as a means to enhance rehabilitation through increased engagement and challenge; (3) "It is a tool not a therapist," revealed views that the technology could either enhance or disrupt the therapeutic relationship; and (4) "Weighing up the benefits versus the burden" and (5) "Don't make me look different" emphasized important design considerations related to device set-up, use, and social acceptability. CONCLUSIONS: This study offers several important findings that can inform the design and implementation of rehabilitation technologies. These include (1) the design of rehabilitation technology should support the therapeutic relationship between the patient and therapist, (2) social acceptability is a design priority in rehabilitation technology but its importance varies depending on the use context, and (3) there is value in using design research methods that support understanding usability in the context of sustained use.

Can water-based EEG caps record robust movement-related cortical potentials (MRCPs) for single and multiple joint movements?

Movement-related cortical potentials (MRCPs) have been used extensively in the literature to develop rehabilitation interventions for people with neurological conditions. In this pilot study, we recorded and extracted MRCPs using a water-based cap to determine whether water-based caps are effective. Five participants took part in the study, where their EEG was recorded during single-joint (dorsiflexion) and multiple-joint (sit-to-stand) lower limb movements. We were able to see clear MRCPs for both movement types with an average peak negativity (PN) latency of +22ms for dorsiflexion and +218ms for sit-to-stand. Similarly, the PN amplitude of -14.89µV was recorded for dorsiflexion and -43.54µV for sit-to-stand. These values were comparable to the values reported in studies using gel-based caps. Based on these results, water-based caps can be an effective way to produce robust MRCPs, which can have many advantages over gel-based caps.Clinical Relevance- The study provides clinicians with a more viable method of collecting EEGs and extracting MRCPs, thus allowing them to design more robust interventions for people with neurological disorders.

Assessing Gait & Balance in Adults with Mild Balance Impairment: G&B App Reliability and Validity.

Smartphone applications (apps) that utilize embedded inertial sensors have the potential to provide valid and reliable estimations of different balance and gait parameters in older adults with mild balance impairment. This study aimed to assess the reliability, validity, and sensitivity of the Gait&Balance smartphone application (G&B App) for measuring gait and balance in a sample of middle- to older-aged adults with mild balance impairment in Pakistan. Community-dwelling adults over 50 years of age (N = 83, 50 female, range 50-75 years) with a Berg Balance Scale (BBS) score between 46/56 and 54/56 were included in the study. Data collection involved securing a smartphone to the participant's lumbosacral spine. Participants performed six standardized balance tasks, including four quiet stance tasks and two gait tasks (walking looking straight ahead and walking with head turns). The G&B App collected accelerometry data during these tasks, and the tasks were repeated twice to assess test-retest reliability. The tasks in quiet stance were also recorded with a force plate, a gold-standard technology for measuring postural sway. Additionally, participants completed three clinical measures, the BBS, the Functional Reach Test (FRT), and the Timed Up and Go Test (TUG). Test-retest reliability within the same session was determined using intraclass correlation coefficients (ICCs) and the standard error of measurement (SEM). Validity was evaluated by correlating the G&B App outcomes against both the force plate data and the clinical measures using Pearson's product-moment correlation coefficients. To assess the G&B App's sensitivity to differences in balance across tasks and repetitions, one-way repeated measures analyses of variance (ANOVAs) were conducted. During quiet stance, the app demonstrated moderate reliability for steadiness on firm (ICC = 0.72) and compliant surfaces (ICC = 0.75) with eyes closed. For gait tasks, the G&B App indicated moderate to excellent reliability when walking looking straight ahead for gait symmetry (ICC = 0.65), walking speed (ICC = 0.93), step length (ICC = 0.94), and step time (ICC = 0.84). The TUG correlated with app measures under both gait conditions for walking speed (r -0.70 and 0.67), step length (r -0.56 and -0.58), and step time (r 0.58 and 0.50). The BBS correlated with app measures of walking speed under both gait conditions (r 0.55 and 0.51) and step length when walking with head turns (r = 0.53). Force plate measures of total distance wandered showed adequate to excellent correlations with G&B App measures of steadiness. Notably, G&B App measures of walking speed, gait symmetry, step length, and step time, were sensitive to detecting differences in performance between standard walking and the more difficult task of walking with head turns. This study demonstrates the G&B App's potential as a reliable and valid tool for assessing some gait and balance parameters in middle-to-older age adults, with promise for application in low-income countries like Pakistan. The app's accessibility and accuracy could enhance healthcare services and support preventive measures related to fall risk.

A Cross-Day Analysis of EMG Features, Classifiers, and Regressors for Swallowing Events Detection and Fluid Intake Volume Estimation.

Dehydration is a common problem among older adults. It can seriously affect their health and wellbeing and sometimes leads to death, given the diminution of thirst sensation as we age. It is, therefore, essential to keep older adults properly hydrated by monitoring their fluid intake and estimating how much they drink. This paper aims to investigate the effect of surface electromyography (sEMG) features on the detection of drinking events and estimation of the amount of water swallowed per sip. Eleven individuals took part in the study, with data collected over two days. We investigated the best combination of a pool of twenty-six time and frequency domain sEMG features using five classifiers and seven regressors. Results revealed an average F-score over two days of 77.5±1.35% in distinguishing the drinking events from non-drinking events using three global features and 85.5±1.00% using three subject-specific features. The average volume estimation RMSE was 6.83±0.14 mL using one single global feature and 6.34±0.12 mL using a single subject-specific feature. These promising results validate and encourage the potential use of sEMG as an essential factor for monitoring and estimating the amount of fluid intake.

New Zealanders with low back pain seeking health care: a retrospective descriptive analysis of Accident Compensation Corporation-funded low back pain healthcare service usage.

Introduction Most New Zealanders experience low back pain (LBP) at least once throughout their lifetime and many seek help from the large range of health providers in primary care. Accident Compensation Corporation (ACC) funds a significant proportion of those claims, but which services are they funding and what are the costs? Method This was a retrospective audit and descriptive analysis of ACC-funded, non-public hospital healthcare service use by people with LBP in New Zealand (NZ). Outcome measures were the healthcare services accessed by people with ACC-funded LBP,the claims (all occurrences for a service that has generated a payment/year), single contact (with a service), and costs (NZ$) for services between 2009 and 2020. Results The number of claims for services were 129 000 for physiotherapy, 105 000 for general practitioner and 59 000 for radiology services. Per single contact, elective surgery and radiology services were the most expensive. During 2009-2020, there were 3.3 million ACC claims for LBP with a total cost of NZ$4 billion. Over this time, there was an increase in claims, costs and single contacts. Costs decreased slightly during 2010 due to changes in healthcare funding and in 2020 due to the COVID-19 pandemic. Discussion Consumers have considerable choice in where they access health care for ACC-funded LBP services. This study shows the services they use most frequently and the cost to NZ for those services. These data can inform service planning for ACC-funded LBP health care in NZ.

Validation of the filling factor index to study the filling process of the sEMG signal in the quadriceps.

INTRODUCTION: The EMG filling factor is an index to quantify the degree to which an EMG signal has been filled. Here, we tested the validity of such index to analyse the EMG filling process as contraction force was slowly increased. METHODS: Surface EMG signals were recorded from the quadriceps muscles of healthy subjects as force was gradually increased from 0 to 40% MVC. The sEMG filling process was analyzed by measuring the EMG filling factor (calculated from the non-central moments of the rectified sEMG). RESULTS: (1) As force was gradually increased, one or two prominent abrupt jumps in sEMG amplitude appeared between 0 and 10% of MVC force in all the vastus lateralis and medialis. (2) The jumps in amplitude were originated when a few large-amplitude MUPs, clearly standing out from previous activity, appeared in the sEMG signal. (3) Every time an abrupt jump in sEMG amplitude occurred, a new stage of sEMG filling was initiated. (4) The sEMG was almost completely filled at 2-12% MVC. (5) The filling factor decreased significantly upon the occurrence of an sEMG amplitude jump, and increased as additional MUPs were added to the sEMG signal. (6) The filling factor curve was highly repeatable across repetitions. CONCLUSIONS: It has been validated that the filling factor is a useful, reliable tool to analyse the sEMG filling process. As force was gradually increased in the vastus muscles, the sEMG filling process occurred in one or two stages due to the presence of abrupt jumps in sEMG amplitude.

Non-Specific Low Back Pain: An Inductive Exploratory Analysis through Factor Analysis and Deep Learning for Better Clustering.

Non-specific low back pain (NSLBP) is a significant and pervasive public health issue in contemporary society. Despite the widespread prevalence of NSLBP, our understanding of its underlying causes, as well as our capacity to provide effective treatments, remains limited due to the high diversity in the population that does not respond to generic treatments. Clustering the NSLBP population based on shared characteristics offers a potential solution for developing personalized interventions. However, the complexity of NSLBP and the reliance on subjective categorical data in previous attempts present challenges in achieving reliable and clinically meaningful clusters. This study aims to explore the influence and importance of objective, continuous variables related to NSLBP and how to use these variables effectively to facilitate the clustering of NSLBP patients into meaningful subgroups. Data were acquired from 46 subjects who performed six simple movement tasks (back extension, back flexion, lateral trunk flexion right, lateral trunk flexion left, trunk rotation right, and trunk rotation left) at two different speeds (maximum and preferred). High-density electromyography (HD EMG) data from the lower back region were acquired, jointly with motion capture data, using passive reflective markers on the subject's body and clusters of markers on the subject's spine. An exploratory analysis was conducted using a deep neural network and factor analysis. Based on selected variables, various models were trained to classify individuals as healthy or having NSLBP in order to assess the importance of different variables. The models were trained using different subsets of data, including all variables, only anthropometric data (e.g., age, BMI, height, weight, and sex), only biomechanical data (e.g., shoulder and lower back movement), only neuromuscular data (e.g., HD EMG activity), or only balance-related data. The models achieved high accuracy in categorizing individuals as healthy or having NSLBP (full model: 93.30%, anthropometric model: 94.40%, biomechanical model: 84.47%, neuromuscular model: 88.07%, and balance model: 74.73%). Factor analysis revealed that individuals with NSLBP exhibited different movement patterns to healthy individuals, characterized by slower and more rigid movements. Anthropometric variables (age, sex, and BMI) were significantly correlated with NSLBP components. In conclusion, different data types, such as body measurements, movement patterns, and neuromuscular activity, can provide valuable information for identifying individuals with NSLBP. To gain a comprehensive understanding of NSLBP, it is crucial to investigate the main domains influencing its prognosis as a cohesive unit rather than studying them in isolation. Simplifying the conditions for acquiring dynamic data is recommended to reduce data complexity, and using back flexion and trunk rotation as effective options should be further explored.

Scoping out noisy galvanic vestibular stimulation: a review of the parameters used to improve postural control.

Objective: Noisy galvanic vestibular stimulation (nGVS) has been used to facilitate vestibular function and improve gait and balance in people with poor postural control. The aim of this scoping review is to collate, summarize and report on the nGVS parameters that have been used to augment postural control. Method: A systematic scoping review was conducted up to December 2022. Data were extracted and synthesized from 31 eligible studies. Key nGVS parameters were identified, and the importance of these parameters and their influence on postural control evaluated. Results: A range of nGVS parameters have been used to augment postural control, including; noise waveform, amplitude, frequency band, duration of stimulation, method of amplitude optimization, size and composition of electrodes and the electrode skin interface. Conclusion: Systematic evaluation of the individual parameters that can be manipulated in the nGVS waveform identified that a broad array of settings have been utilized in each parameter across the studies. Choices made around the electrode and electrode-skin interface, as well as the amplitude, frequency band, duration and timing of the waveform are likely to influence the efficacy of nGVS. The ability to draw robust conclusions about the selection of optimal nGVS parameters to improve postural control, is hindered by a lack of studies that directly compare parameter settings or consider the variability in individuals' response to nGVS. We propose a guideline for the accurate reporting of nGVS parameters, as a first step toward establishing standardized stimulation protocols.

Electrocardiography Assessment of Sympatico-Vagal Balance during Resting and Pain Using the Texas Instruments ADS1299.

Sympatico-vagal balance is essential for regulating cardiac electrophysiology and plays an important role in arrhythmogenic conditions. Various noninvasive methods, including electrocardiography (ECG), have been used for clinical assessment of the sympatico-vagal balance. This study aimed to use a custom-designed wearable device to record ECG and ECG-based cardiac function biomarkers to assess sympatico-vagal balance during tonic pain in healthy controls. Nineteen healthy volunteers were included for the ECG measurements using the custom-designed amplifier based on the Texas Instruments ADS1299. The ECG-based biomarkers of the sympatico-vagal balance, (including heart rate variability, deceleration capacity of the heart rate, and periodic repolarization dynamic), were calculated and compared between resting and pain conditions (tonic pain). The custom-designed device provided technically satisfactory ECG recordings. During exposure to tonic pain, the periodic repolarization dynamics increased significantly (p = 0.02), indicating enhancement of sympathetic nervous activity. This study showed that custom-designed wearable devices can potentially be useful in healthcare as a new telemetry technology. The ECG-based novel biomarkers, including periodic repolarization dynamic and deceleration capacity of heart rate, can be used to identify the cold pressor-induced activation of sympathetic and parasympathetic systems, making it useful for future studies on pain-evoked biomarkers.