61 Medizin und Gesundheit
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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: Recent shoulder injury prevention programs have utilized resistance exercises combined with different forms of instability, with the goal of eliciting functional adaptations and thereby reducing the risk of injury. However, it is still unknown how an unstable weight mass (UWM) affects the muscular activity of the shoulder stabilizers. Aim of the study was to assess neuromuscular activity of dynamic shoulder stabilizers under four conditions of stable and UWM during three shoulder exercises. It was hypothesized that a combined condition of weight with UWM would elicit greater activation due to the increased stabilization demand.
Methods: Sixteen participants (7 m/9 f) were included in this cross-sectional study and prepared with an EMG-setup for the: Mm. upper/lower trapezius (U.TA/L.TA), lateral deltoid (DE), latissimus dorsi (LD), serratus anterior (SA) and pectoralis major (PE). A maximal voluntary isometric contraction test (MVIC; 5 s.) was performed on an isokinetic dynamometer. Next, internal/external rotation (In/Ex), abduction/adduction (Ab/Ad) and diagonal flexion/extension (F/E) exercises (5 reps.) were performed with four custom-made-pipes representing different exercise conditions. First, the empty-pipe (P; 0.5 kg) and then, randomly ordered, water-filled-pipe (PW; 1 kg), weight-pipe (PG; 4.5 kg) and weight + water-filled-pipe (PWG; 4.5 kg), while EMG was recorded. Raw root-mean-square values (RMS) were normalized to MVIC (%MVIC). Differences between conditions for RMS%MVIC, scapular stabilizer (SR: U.TA/L.TA; U.TA/SA) and contraction (CR: concentric/eccentric) ratios were analyzed (paired t-test; p ≤ 0.05; Bonferroni adjusted α = 0.008).
Results: PWG showed significantly greater muscle activity for all exercises and all muscles except for PE compared to P and PW. Condition PG elicited muscular activity comparable to PWG (p > 0.008) with significantly lower activation of L.TA and SA in the In/Ex rotation. The SR ratio was significantly higher in PWG compared to P and PW. No significant differences were found for the CR ratio in all exercises and for all muscles.
Conclusion: Higher weight generated greater muscle activation whereas an UWM raised the neuromuscular activity, increasing the stabilization demands. Especially in the In/Ex rotation, an UWM increased the RMS%MVIC and SR ratio. This might improve training effects in shoulder prevention and rehabilitation programs.
Introduction: The use of social marketing strategies to induce the promotion of cognitive health has received little attention in research. The objective of this scoping review is twofold: (i) to identify the social marketing strategies that have been used in recent years to initiate and maintain health-promoting behaviour; (ii) to advance research in this area to inform policy and practice on how to best make use of these strategies to promote cognitive health.
Methods and analysis: We will use the five-stage methodological framework of Arksey and O'Malley. Articles in English published since 2010 will be searched in electronic databases (the Cochrane Library, DoPHER, the International Bibliography of the Social Sciences, PsycInfo, PubMed, ScienceDirect, Scopus). Quantitative and qualitative study designs as well as reviews will be considered. We will include those articles that report the design, implementation, outcomes and evaluation of programmes and interventions concerning social marketing and/or health promotion and/or promotion of cognitive health. Grey literature will not be searched. Two independent reviewers will assess in detail the abstracts and full text of selected citations against the inclusion criteria. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses flowchart for Scoping Reviews will be used to illustrate the process of article selection. We will use a data extraction form, present the results through narrative synthesis and discuss them in relation to the scoping review research questions.
Ethics and dissemination: Ethics approval is not required for conducting this scoping review. The results of the review will be the first step to advance a conceptual framework, which contributes to the development of interventions targeting the promotion of cognitive health. The results will be published in a peer-reviewed scientific journal. They will also be disseminated to key stakeholders in the field of the promotion of cognitive health.
Background: In recent years, the volume of medical knowledge and health data has increased rapidly. For example, the increased availability of electronic health records (EHRs) provides accurate, up-to-date, and complete information about patients at the point of care and enables medical staff to have quick access to patient records for more coordinated and efficient care. With this increase in knowledge, the complexity of accurate, evidence-based medicine tends to grow all the time. Health care workers must deal with an increasing amount of data and documentation. Meanwhile, relevant patient data are frequently overshadowed by a layer of less relevant data, causing medical staff to often miss important values or abnormal trends and their importance to the progression of the patient’s case.
Objective: The goal of this work is to analyze the current laboratory results for patients in the intensive care unit (ICU) and classify which of these lab values could be abnormal the next time the test is done. Detecting near-future abnormalities can be useful to support clinicians in their decision-making process in the ICU by drawing their attention to the important values and focus on future lab testing, saving them both time and money. Additionally, it will give doctors more time to spend with patients, rather than skimming through a long list of lab values.
Methods: We used Structured Query Language to extract 25 lab values for mechanically ventilated patients in the ICU from the MIMIC-III and eICU data sets. Additionally, we applied time-windowed sampling and holding, and a support vector machine to fill in the missing values in the sparse time series, as well as the Tukey range to detect and delete anomalies. Then, we used the data to train 4 deep learning models for time series classification, as well as a gradient boosting–based algorithm and compared their performance on both data sets.
Results: The models tested in this work (deep neural networks and gradient boosting), combined with the preprocessing pipeline, achieved an accuracy of at least 80% on the multilabel classification task. Moreover, the model based on the multiple convolutional neural network outperformed the other algorithms on both data sets, with the accuracy exceeding 89%.
Conclusions: In this work, we show that using machine learning and deep neural networks to predict near-future abnormalities in lab values can achieve satisfactory results. Our system was trained, validated, and tested on 2 well-known data sets to ensure that our system bridged the reality gap as much as possible. Finally, the model can be used in combination with our preprocessing pipeline on real-life EHRs to improve patients’ diagnosis and treatment.
Static (one-legged stance) and dynamic (star excursion balance) postural control tests were performed by 14 adolescent athletes with and 17 without back pain to determine reproducibility. The total displacement, mediolateral and anterior-posterior displacements of the centre of pressure in mm for the static, and the normalized and composite reach distances for the dynamic tests were analysed. Intraclass correlation coefficients, 95% confidence intervals, and a Bland-Altman analysis were calculated for reproducibility. Intraclass correlation coefficients for subjects with (0.54 to 0.65), (0.61 to 0.69) and without (0.45 to 0.49), (0.52 to 0.60) back pain were obtained on the static test for right and left legs, respectively. Likewise, (0.79 to 0.88), (0.75 to 0.93) for subjects with and (0.61 to 0.82), (0.60 to 0.85) for those without back pain were obtained on the dynamic test for the right and left legs, respectively. Systematic bias was not observed between test and retest of subjects on both static and dynamic tests. The one-legged stance and star excursion balance tests have fair to excellent reliabilities on measures of postural control in adolescent athletes with and without back pain. They can be used as measures of postural control in adolescent athletes with and without back pain.
Background: Stratified care has the potential to be efficient in addressing the physical and psychosocial components of low back pain (LBP) and optimise treatment outcomes essential in low-income countries. This study aimed to investigate the perceptions of physiotherapists and patients in Nigeria towards stratified care for the treatment of LBP, exploring barriers and enablers to implementation.
Methods: A qualitative design with semistructured individual telephone interviews for physiotherapists and patients with LBP comprising research evidence and information on stratified care was adopted. Preceding the interviews, patients completed the Subgroups for Targeted Treatment tool. The interviews were recorded, transcribed and analysed following grounded theory methodology.
Results: Twelve physiotherapists and 13 patients with LBP participated in the study (11 female, mean age 42.8 (SD 11.47) years). Seven key categories emerged: recognising the need for change, acceptance of innovation, resistance to change, adapting practice, patient’s learning journey, trusting the therapist and needing conviction. Physiotherapists perceived stratified care to be a familiar approach based on their background training. The prevalent treatment tradition and the patient expectations were seen as major barriers to implementation of stratified care by the physiotherapists. Patients see themselves as more informed than therapists realise, yet they need conviction through communication and education to cooperate with their therapist using this approach. Viable facilitators were also identified as patients’ trust in the physiotherapist and adaptations in terms of training and modification of the approach to enhance its use.
Conclusion: Key barriers identified are the patients’ treatment expectations and physiotherapists’ adherence to the tradition of practice. Physiotherapists might facilitate implementation of the stratified care by communication, hierarchical implementation and utilisation of patients’ trust. Possibilities to develop a consensus on key strategies to overcome barriers and on utilisation of facilitators should be tested in future research.
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.