61 Medizin und Gesundheit
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Background: Core-specific sensorimotor exercises are proven to enhance neuromuscular activity of the trunk, improve athletic performance and prevent back pain. However, the dose-response relationship and, therefore, the dose required to improve trunk function is still under debate. The purpose of the present trial will be to compare four different intervention strategies of sensorimotor exercises that will result in improved trunk function.
Methods/design: A single-blind, four-armed, randomized controlled trial with a 3-week (home-based) intervention phase and two measurement days pre and post intervention (M1/M2) is designed. Experimental procedures on both measurement days will include evaluation of maximum isokinetic and isometric trunk strength (extension/flexion, rotation) including perturbations, as well as neuromuscular trunk activity while performing strength testing. The primary outcome is trunk strength (peak torque). Neuromuscular activity (amplitude, latencies as a response to perturbation) serves as secondary outcome.
The control group will perform a standardized exercise program of four sensorimotor exercises (three sets of 10 repetitions) in each of six training sessions (30 min duration) over 3 weeks. The intervention groups’ programs differ in the number of exercises, sets per exercise and, therefore, overall training amount (group I: six sessions, three exercises, two sets; group II: six sessions, two exercises, two sets; group III: six sessions, one exercise, three sets). The intervention programs of groups I, II and III include additional perturbations for all exercises to increase both the difficulty and the efficacy of the exercises performed. Statistical analysis will be performed after examining the underlying assumptions for parametric and non-parametric testing.
Discussion: The results of the study will be clinically relevant, not only for researchers but also for (sports) therapists, physicians, coaches, athletes and the general population who have the aim of improving trunk function.
Disconnection in a left-hemispheric temporo-parietal network impairs multiplication fact retrieval
(2023)
Arithmetic fact retrieval has been suggested to recruit a left-lateralized network comprising perisylvian language areas, parietal areas such as the angular gyrus (AG), and non-neocortical structures such as the hippocampus. However, the underlying white matter connectivity of these areas has not been evaluated systematically so far. Using simple multiplication problems, we evaluated how disconnections in parietal brain areas affected arithmetic fact retrieval following stroke. We derived disconnectivity measures by jointly considering data from n = 73 patients with acute unilateral lesions in either hemisphere and a white-matter tractography atlas (HCP-842) using the Lesion Quantification Toolbox (LQT). Whole-brain voxel-based analysis indicated a left-hemispheric cluster of white matter fibers connecting the AG and superior temporal areas to be associated with a fact retrieval deficit. Subsequent analyses of direct gray-to-gray matter disconnections revealed that disconnections of additional left-hemispheric areas (e.g., between the superior temporal gyrus and parietal areas) were significantly associated with the observed fact retrieval deficit. Results imply that disconnections of parietal areas (i.e., the AG) with language-related areas (i.e., superior and middle temporal gyri) seem specifically detrimental to arithmetic fact retrieval. This suggests that arithmetic fact retrieval recruits a widespread left-hemispheric network and emphasizes the relevance of white matter connectivity for number processing.
The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient “data fingerprint” of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO2) and ideal body weight-adjusted tidal volume (Vt). The observed outcome was in-hospital or 90-day mortality. VentAI reached a significantly increased estimated performance return of 83.3 (primary dataset) and 84.1 (secondary dataset) compared to physicians’ standard clinical care (51.1). The number of recommended action changes per mechanically ventilated patient constantly exceeded those of the clinicians. VentAI chose 202.9% more frequently ventilation regimes with lower Vt (5–7.5 mL/kg), but 50.8% less for regimes with higher Vt (7.5–10 mL/kg). VentAI recommended 29.3% more frequently PEEP levels of 5–7 cm H2O and 53.6% more frequently PEEP levels of 7–9 cmH2O. VentAI avoided high (>55%) FiO2 values (59.8% decrease), while preferring the range of 50–55% (140.3% increase). In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.
Background: Stratified care approach involving use of the STarT-Back tool to optimise care for patients with low back pain is gaining widespread attention in western countries. However, adoption and implementation of this approach in low-and-middle-income countries will be restricted by context-specific factors that need to be addressed. This study aimed to develop with physiotherapists, tailored intervention strategies for the implementation of stratified care for patients with low back pain.
Methods: A two-round web-based Delphi survey was conducted among purposively sampled physiotherapists with a minimum of three years of clinical experience, with post-graduation certification or specialists. Thirty statements on barriers and enablers for implementation were extracted from the qualitative phase. Statements were rated by a Delphi panel with additional open-ended feedback. After each Delphi round, participants received feedback which informed their subsequent responses. Additional qualitative feedback were analysed using qualitative content analysis. The criteria for consensus and stability were pre-determined using percentage agreement (≥ 75%), median value (≥ 4), Inter-quartile range (≤ 1), and Wilcoxon matched-pairs test respectively.
Results: Participants in the first round were 139 and 125 of them completed the study, yielding a response rate of 90%. Participants were aged 35.2 (SD6.6) years, and 55 (39.6%) were female. Consensus was achieved in 25/30 statements. Wilcoxon’s test showed stability in responses after the 5 statements failed to reach consensus: ‘translate the STarT-Back Tool to pidgin language’ 71% (p = 0.76), ‘begin implementation with government hospitals’ 63% (p = 0.11), ‘share knowledge with traditional bone setters’ 35% (p = 0.67), ‘get second opinion on clinician’s advice’ 63% (p = 0.24) and ‘carry out online consultations’ 65% (p = 0.41). Four statements strengthened by additional qualitative data achieved the highest consensus: ‘patient education’ (96%), ‘quality improvement appraisals’ (96%), ‘undergraduate training on psychosocial care’ (96%) and ‘patient-clinician communication’ (95%).
Conclusion: There was concordance of opinion that patients should be educated to correct misplaced expectations and proper time for communication is vital to implementation. This communication should be learned at undergraduate level, and for already qualified clinicians, quality improvement appraisals are key to sustained and effective care. These recommendations provide a framework for future research on monitored implementation of stratified care in middle-income countries.
Decoding the cellular network interaction of neurons and glial cells are important in the development of new therapies for diseases of the central nervous system (CNS). Electrophysiological in vivo studies in mice will help to understand the highly complex network. In this paper, the optimization of epidural liquid crystal polymer (LCP) electrodes for different platinum electroplating parameters are presented and compared. Constant current and pulsed current electroplating varied in strength and duration was used to decrease the electrode impedance and to increase the charge storage capacity (CSCc). In best cases, both methods generated similar results with an impedance reduction of about 99%. However, electroplating with pulsed currents was less parameter-dependent than the electroplating with constant current. The use of ultrasound was essential to generate platinum coatings without plating defects. Electrode model parameters extracted from the electrode impedance reflected the increase in surface porosity due to the electroplating processes.
Deep brain stimulation (DBS) is an established therapy for movement disorders such as in Parkinson's disease (PD) and essential tremor (ET). Adjusting the stimulation parameters, however, is a labour-intensive process and often requires several patient visits. Physicians prefer objective tools to improve (or maintain) the performance in DBS. Wearable motion sensors (WMS) are able to detect some manifestations of pathological signs, such as tremor in PD. However, the interpretation of sensor data is often highly technical and methods to visualise tremor data of patients undergoing DBS in a clinical setting are lacking. This work aims to visualise the dynamics of tremor responses to DBS parameter changes with WMS while patients performing clinical hand movements. To this end, we attended DBS programming sessions of two patients with the aim to visualise certain aspects of the clinical examination. PD tremor and ET were effectively quantified by acceleration amplitude and frequency. Tremor dynamics were analysed and visualised based on setpoints, movement transitions and stability aspects. These methods have not yet been employed and examples demonstrate how tremor dynamics can be visualised with simple analysis techniques. We therefore provide a base for future research work on visualisation tools in order to assist clinicians who frequently encounter patients for DBS therapy. This could lead to benefits in terms of enhanced evaluation of treatment efficacy in the future.
Background: Deficiency in musculoskeletal imaging (MI) education will pose a great challenge to physiotherapists in clinical decision making in this era of first-contact physiotherapy practices in many developed and developing countries. This study evaluated the nature and the level of MI training received by physiotherapists who graduate from Nigerian universities.
Methods: An online version of the previously validated Physiotherapist Musculoskeletal Imaging Profiling Questionnaire (PMIPQ) was administered to all eligible physiotherapists identified through the database of the Medical Rehabilitation Therapist Board of Nigeria. Data were obtained on demographics, nature, and level of training on MI procedures using the PMIPQ. Logistic regression, Friedman’s analysis of variance (ANOVA) and Kruskal-Wallis tests were used for the statistical analysis of collected data.
Results: The results (n = 400) showed that only 10.0% of the respondents had a stand-alone entry-level course in MI, 92.8% did not have any MI placement during their clinical internship, and 67.3% had never attended a MI workshop. There was a significant difference in the level of training received across MI procedures [χ2 (15) = 1285.899; p = 0.001]. However, there was no significant difference in the level of MI training across institutions of entry-level programme (p = 0.36). The study participants with transitional Doctor of Physiotherapy education were better trained in MI than their counterparts with a bachelor’s degree only (p = 0.047).
Conclusions: Most physiotherapy programmes in Nigeria did not include a specific MI module; imaging instructions were mainly provided through clinical science courses. The overall self-reported level of MI training among the respondents was deficient. It is recommended that stand-alone MI education should be introduced in the early part of the entry-level physiotherapy curriculum.
Background: Improving movement control might be a promising treatment goal during chronic non-specific low back pain (CLBP) rehabilitation. The objective of the study is to evaluate the effect of a single bout of game-based real-time feedback intervention on trunk movement in patients with CLBP.
Methods: Thirteen CLBP patients (8female;41 ± 16 years;173 ± 10 cm;78 ± 22 kg) were included in this randomized cross-over pilot trial. During one laboratory session (2 h), participants performed three identical measurements on trunk movement all including: first, maximum angle of lateral flexion was assessed. Secondly, a target trunk lateral flexion (angle: 20°) was performed. Main outcome was maximum angle ([°]; MA). Secondary outcomes were deviation [°] from the target angle (angle reproduction; AR) and MA of the secondary movement planes (rotation; extension/flexion) during lateral flexion. The outcomes were assessed by an optical 3D-motion-capture-system (2-segment-trunk-model). The measurements were separated by 12-min of intervention and/or resting (randomly). The intervention involved a sensor-based trunk exergame (guiding an avatar through virtual worlds). After carryover effect-analysis, pre-to-post intervention data were pooled between the two sequences followed by analyses of variances (paired t-test).
Results: No significant change from pre to post intervention for MA or AR for any segment occurred for the main movement plane, lateral flexion (p > .05). The upper trunk segment showed a significant decrease of the MA for trunk extension/flexion from pre to post intervention ((4.4° ± 4.4° (95% CI 7.06–1.75)/3.5° ± 1.29° (95% CI 6.22–0.80); p = 0.02, d = 0.20).
Conclusions: A single bout of game-based real-time feedback intervention lead to changes in the secondary movement planes indicating reduced evasive motion during trunk movement.
Background: High numbers of consumable medical materials (eg, sterile needles and swabs) are used during the daily routine of intensive care units (ICUs) worldwide. Although medical consumables largely contribute to total ICU hospital expenditure, many hospitals do not track the individual use of materials. Current tracking solutions meeting the specific requirements of the medical environment, like barcodes or radio frequency identification, require specialized material preparation and high infrastructure investment. This impedes the accurate prediction of consumption, leads to high storage maintenance costs caused by large inventories, and hinders scientific work due to inaccurate documentation. Thus, new cost-effective and contactless methods for object detection are urgently needed.
Objective: The goal of this work was to develop and evaluate a contactless visual recognition system for tracking medical consumable materials in ICUs using a deep learning approach on a distributed client-server architecture.
Methods: We developed Consumabot, a novel client-server optical recognition system for medical consumables, based on the convolutional neural network model MobileNet implemented in Tensorflow. The software was designed to run on single-board computer platforms as a detection unit. The system was trained to recognize 20 different materials in the ICU, while 100 sample images of each consumable material were provided. We assessed the top-1 recognition rates in the context of different real-world ICU settings: materials presented to the system without visual obstruction, 50% covered materials, and scenarios of multiple items. We further performed an analysis of variance with repeated measures to quantify the effect of adverse real-world circumstances.
Results: Consumabot reached a >99% reliability of recognition after about 60 steps of training and 150 steps of validation. A desirable low cross entropy of <0.03 was reached for the training set after about 100 iteration steps and after 170 steps for the validation set. The system showed a high top-1 mean recognition accuracy in a real-world scenario of 0.85 (SD 0.11) for objects presented to the system without visual obstruction. Recognition accuracy was lower, but still acceptable, in scenarios where the objects were 50% covered (P<.001; mean recognition accuracy 0.71; SD 0.13) or multiple objects of the target group were present (P=.01; mean recognition accuracy 0.78; SD 0.11), compared to a nonobstructed view. The approach met the criteria of absence of explicit labeling (eg, barcodes, radio frequency labeling) while maintaining a high standard for quality and hygiene with minimal consumption of resources (eg, cost, time, training, and computational power).
Conclusions: Using a convolutional neural network architecture, Consumabot consistently achieved good results in the classification of consumables and thus is a feasible way to recognize and register medical consumables directly to a hospital’s electronic health record. The system shows limitations when the materials are partially covered, therefore identifying characteristics of the consumables are not presented to the system. Further development of the assessment in different medical circumstances is needed.