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Objectives: This study aimed to elucidate the mechanisms of chiropractic care using resting electroencephalography (EEG), somatosensory evoked potentials (SEPs), clinical health assessments (Fitbit), and Patient-reported Outcomes Measurement Information System (PROMIS-29). Methods: Seventy-six people with chronic low back pain (mean age ± SD: 45 ± 11 years, 33 female) were randomised into control (n = 38) and chiropractic (n = 38) groups. EEG and SEPs were collected pre and post the first intervention and post 4 weeks of intervention. PROMIS-29 was measured pre and post 4 weeks. Fitbit data were recorded continuously. Results: Spectral analysis of resting EEG showed a significant increase in Theta, Alpha and Beta, and a significant decrease in Delta power in the chiropractic group post intervention. Source localisation revealed a significant increase in Alpha activity within the Default Mode Network (DMN) post intervention and post 4 weeks. A significant decrease in N30 SEP peak amplitude post intervention and post 4 weeks was found in the chiropractic group. Source localisation demonstrated significant changes in Alpha and Beta power within the DMN post-intervention and post 4 weeks. Significant improvements in light sleep stage were observed in the chiropractic group along with enhanced overall quality of life post 4 weeks, including significant reductions in anxiety, depression, fatigue, and pain. Conclusions: These findings indicate that many health benefits of chiropractic care are due to altered brain activity. [ABSTRACT FROM AUTHOR]

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 restingstate 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). [ABSTRACT FROM AUTHOR]

This study examines the performance of various machine learning (ML) models in predicting Interstitial Glucose (IG) levels using data from wrist-worn wearable sensors. The insights from these predictions can aid in understanding metabolic syndromes and disease states. A public dataset comprising information from the Empatica E4 smart watch, the Dexcom Continuous Glucose Monitor (CGM) measuring IG, and a food log was utilized. The raw data were processed into features, which were then used to train different ML models. This study evaluates the performance of decision tree (DT), support vector machine (SVM), Random Forest (RF), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), lasso cross-validation (LassoCV), Ridge, Elastic Net, and XGBoost models. For classification, IG labels were categorized into high, standard, and low, and the performance of the ML models was assessed using accuracy (40–78%), precision (41–78%), recall (39–77%), F1-score (0.31–0.77), and receiver operating characteristic (ROC) curves. Regression models predicting IG values were evaluated based on R-squared values (−7.84–0.84), mean absolute error (5.54–60.84 mg/dL), root mean square error (9.04–68.07 mg/dL), and visual methods like residual and QQ plots. To assess whether the differences between models were statistically significant, the Friedman test was carried out and was interpreted using the Nemenyi post hoc test. Tree-based models, particularly RF and DT, demonstrated superior accuracy for classification tasks in comparison to other models. For regression, the RF model achieved the lowest RMSE of 9.04 mg/dL with an R-squared value of 0.84, while the GNB model performed the worst, with an RMSE of 68.07 mg/dL. A SHAP analysis identified time from midnight as the most significant predictor. Partial dependence plots revealed complex feature interactions in the RF model, contrasting with the simpler interactions captured by LDA. [ABSTRACT FROM AUTHOR]

Despite most episodes of low back pain (LBP) being short-lasting, some transition into persistent long-lasting problems. Hence, the need for a deeper understanding of the physiological mechanisms of this is pertinent. Therefore, the aims of the present study are (1) to map pain-induced changes in brain activity and blood gene expression associated with persistent LBP, and (2) to explore whether these brain and gene expression signatures show promise as predictive biomarkers for the development of persistent LBP. The participants will be allocated into three different pain groups (no pain, mild short-lasting, or moderate long-term). One in-person visit, where two blood samples will be collected and sent for RNA sequencing, along with resting 64-channel electro-encephalography measurements before, during, and after a cold pressor test, will be conducted. Thereafter, follow-up questionnaires will be distributed at 2 weeks, 3 months, and 6 months. Recruitment will start during the second quarter of 2024, with expected completion by the last quarter of 2024. The results are expected to provide insight into the relationship between central nervous system activity, gene expression profiles, and LBP. If successful, this study has the potential to provide physiological indicators that are sensitive to the transition from mild, short-term LBP to more problematic, long-term LBP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This document is an editorial introducing a special issue of the journal Sensors focused on signal processing for Brain-Computer Interfaces (BCIs). BCIs have the potential to improve quality of life by processing brain signals to control external devices. The editorial explains the three main steps of signal processing in BCIs: noise removal, feature extraction, and classification. The special issue includes articles on various signal processing techniques, such as power spectral density analysis, deep learning, and functional near-infrared spectroscopy (fNIRS). The articles cover topics such as neurophysiology, home control, rehabilitation, and neuropsychiatry. The editorial expresses appreciation for the authors' contributions and hopes that readers will find inspiration and learn from the research articles. [Extracted from the article]

The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications. [ABSTRACT FROM AUTHOR]

Background: The contingent negative variation (CNV) is a negative shift in electroencephalography (EEG) related to the planning and execution of an externally cued movement task. The CNV has the potential to be applied within stroke rehabilitation; however, there is insufficient knowledge about the CNV characteristics under movement conditions relevant to rehabilitation. This study explores the CNV characteristics during a functional movement task (versus a simple movement task) and when using an audio-visual cue that has been previously evaluated for its usability in stroke rehabilitation (versus a simple visual cue). Methods: Thirty healthy participants performed five randomized movement tasks: simple ankle dorsiflexion with a visual cue (1), audio-visual cue (2), and auditory-only cue (3), and sit-to-stand with a visual (4) and audio-visual cue (5). Fifty repetitions of each movement were performed while continuous EEG was recorded. The band-passed and Laplacian-filtered (Cz) EEG was averaged for each condition and the peak negativity (PN) latency and amplitude were identified. Results: PN latency was significantly later during sit-to-stand with the audio-visual cue versus the visual cue (p = 0.027). PN amplitude was significantly larger during sit-to-stand versus ankle dorsiflexion, with both visual and audio-visual cues (p < 0.0001). Conclusion: The CNV changes under more complex movement conditions. Assumptions about the MRCP from simple laboratory recordings should not be generalized to the rehabilitation setting. [ABSTRACT FROM AUTHOR]

Electronic medical records (EMRs) help in identifying disease archetypes and progression. A very important part of EMRs is the presence of time domain data because these help with identifying trends and monitoring changes through time. Most time-series data come from wearable devices monitoring real-time health trends. This review focuses on the time-series data needed to construct complete EMRs by identifying paradigms that fall within the scope of the application of artificial intelligence (AI) based on the principles of translational medicine. (1) Background: The question addressed in this study is: What are the taxonomies present in the field of the application of machine learning on EMRs? (2) Methods: Scopus, Web of Science, and PubMed were searched for relevant records. The records were then filtered based on a PRISMA review process. The taxonomies were then identified after reviewing the selected documents; (3) Results: A total of five main topics were identified, and the subheadings are discussed in this review; (4) Conclusions: Each aspect of the medical data pipeline needs constant collaboration and update for the proposed solutions to be useful and adaptable in real-world scenarios. [ABSTRACT FROM AUTHOR]

Prostheses control using electromyography signals have shown promising aspects in various fields including rehabilitation sciences and assistive technology controlled devices. Pattern recognition and machine learning methods have been observed to play a significant role in evaluating features and classifying different limb motions for enhanced prosthetic executions. This paper proposes feature extraction and evaluation method using intramuscular electromyography (iEMG) signals at different arm positions and hand postures based on the RES Index value statistical criterion method. Sixteen-time domain features were selected for the study at two main circumstances; fixed arm position (FAP) and fixed hand posture (FHP). Eight healthy male participants (30.62 ± 3.87 years) were asked to execute five motion classes including hand grip, hand open, rest, hand extension, and hand flexion at four different arm positions that comprise of 0°, 45°, 90°, and 135°. The classification process is accomplished via the application of the k-nearest neighbor (KNN) classifier. Then RES index was calculated to investigate the optimal features based on the proposed statistical criterion method. From the RES Index, we concluded that Variance (VAR) is the best feature while WAMP, Zero Crossing (ZC), and Slope Sign Change (SSC) are the worst ones in FAP conditions. On the contrary, we concluded that Average Amplitude Change (AAC) is the best feature while WAMP and Simple Square Integral (SSI) resulted in least RES Index values for FHP conditions. The proposed study has possible iEMG based applications such as assistive control devices, robotics. Also, working with the frequency domain features encapsulates the future scope of the study. Graphical abstract [ABSTRACT FROM AUTHOR]

Certain blood biomarkers are associated with neural protection and neural plasticity in healthy people and individuals with prior brain injury. To date, no studies have evaluated the effects chiropractic care on serum brain-derived neurotrophic factor (BDNF), insulin-like growth factor-II (IGF-II) and glial cell-derived neurotrophic factor (GDNF) in people with stroke. This manuscript reports pre-specified, exploratory, secondary outcomes from a previously completed parallel group randomized controlled trial. We evaluated differences between four weeks of chiropractic spinal adjustments combined with the usual physical therapy (chiro + PT) and sham chiropractic with physical therapy (sham + PT) on resting serum BDNF, IGF-II and GDNF in 63 adults with chronic stroke. Blood samples were assessed at baseline, four weeks (post-intervention), and eight weeks (follow-up). Data were analyzed using a linear multivariate mixed effects model. Within both groups there was a significant decrease in the mean log-concentration of BDNF and IGF-II at each follow-up, and significant increase log-concentration of GDNF at eight-weeks' follow-up. However, no significant between-group differences in any of the blood biomarkers at each time-point were found. Further research is required to explore which factors influence changes in serum BDNF, IGF-II and GDNF following chiropractic spinal adjustments and physical therapy. [ABSTRACT FROM AUTHOR]

Balance disorders are caused by several factors related to functionality deficits in one or multiple sensory systems such as vision, vestibular, and somatosensory systems. Patients usually have difficulty explaining their dizziness, often using ambiguous words to describe their symptoms. A common practice by clinicians is to objectively evaluate the patient's dizziness by applying the Sensory Organization Test (SOT), which measures the contribution of each sensory system (vestibular, visual, somatosensory). The SOT protocol can record up to 2000 measurements in 20 s to generate the Equilibrium Score (EQS) with its five load sensors. EQS is an indicator that reflects how well a patient can maintain balance. However, its calculation only considers two instances from these 2000 measurements that reflect the maximum anterior and posterior sway angle during the test performance; therefore, there is an opportunity to perform further analysis. This article aims to use the Centre of Pressure (COP) time series generated by the SOT and describes a methodology to pre-process and reduce the dimensionality of this raw data and use it as an input for machine learning algorithms to diagnose patients with balance disorder impairments. After applying this methodology to data from 475 patients, the logistic regression model (LR) produced the highest f1-score with 76.47%, and the support vector machine (SVM) performed almost as well, with an f1-score of 76.19%. [ABSTRACT FROM AUTHOR]