MGait : Model-Based Gait Analysis Using Wearable Bend and Inertial Sensors

Movement disorders, such as Parkinson’s disease, affect more than 10 million people worldwide. Gait analysis is a critical step in the diagnosis and rehabilitation of these disorders. Specifically, step and stride lengths provide valuable insights into the gait quality and rehabilitation process. However, traditional approaches for estimating step length are not suitable for continuous daily monitoring since they rely on special mats and clinical environments. To address this limitation, this article presents a novel and practical step-length estimation technique using low-power wearable bend and inertial sensors. Experimental results show that the proposed model estimates step length with 5.49% mean absolute percentage error and provides accurate real-time feedback to the user.

Time-Specific Associations of Wearable, Sensor-Based Cardiovascular and Behavioral Readouts with Disease Phenotypes in the Outpatient Setting of the Chronic Renal Insufficiency Cohort (CRIC)

Patients with chronic kidney disease (CKD) are at risk of developing cardiovascular disease. To facilitate out-of-clinic evaluation, we piloted wearable device-based analysis of heart rate variability and behavioral readouts in patients with CKD participating in the Chronic Renal Insufficiency Cohort and (n=49) controls. Time-specific partitioning of HRV readouts indicate higher parasympathetic nervous activity during the night (mean RR at night 14.4+/-1.9 ms versus 12.8+/-2.1 ms during active hours; n=47, ANOVA q=0.001). The alpha2 long-term fluctuations in the detrended fluctuation analysis, a parameter predictive of cardiovascular mortality, significantly differentiated between diabetic and non-diabetic patients (prominent at night with 0.58+/-0.2 versus 0.45+/-0.12, respectively, adj. p=0.004). Both diabetic and nondiabetic CKD patients showed loss of rhythmic organization compared to controls, with diabetic CKD patients exhibiting deconsolidation of peak phases between their activity and SDNN (standard deviation of interbeat intervals) rhythms (mean phase difference CKD 8.3h, CKD/T2DM 4h, controls 6.8h). This work provides a roadmap toward deriving actionable clinical insights from the data collected by wearable devices outside of highly controlled clinical environments.

Proposal for a Standard Architecture for the Integration of Clinical Information Systems in a Complex Hospital Environment

The evolution of technology in clinical environments increases the level of precision in patient care, as well as optimizes the management of healthcare centers. However, the need to have information systems that are more sophisticated and require interoperability between them means that a great deal of effort has to be made to assume the maintenance and scalability of the systems. Therefore, a proposal for a standard information model for the integration of clinical systems in a healthcare environment is presented. In order to elaborate the model, an analysis of the functional needs of the different clinical areas of a clinical environment is made based on the information systems that make up the system and application map. An evaluation of the technical requirements and the technological solutions that can satisfy these requirements is also carried out, delving into the different technical alternatives that allow the exchange of information. From the analysis carried out, an integration model capable of covering the needs that arise in clinical environments with a high level of complexity is obtained, also allowing the continuous evolution of the systems that make up the model, along with the incorporation of new systems. Although the model presented may fully cover the expectations raised, the rapid evolution in terms of both functional needs and technical aspects makes it necessary to continuously monitor and evaluate the model, in order to adapt it to the needs that arise.

Residents’ transformational changes through self-regulated, experiential learning for professionalism

Purpose: Postgraduate trainees (‘residents”) are required to convey professional behaviours as they navigate complex clinical environments. However, little is known about experiential learning for professionalism. Thus, we asked residents about professionalism challenges within the clinical learning environment: 1) how challenges were identified, 2) what supported successfully addressing challenges and 3) the impact of addressing challenges to further inform resident education. Method: From 2015-2016, twenty-five residents across specialties and multiple university affiliated teaching hospitals participated in appreciative inquiry informed audio-taped semi-structured interviews. Transcripts were categorized deductively for the 2015 CanMEDS Professional Role element addressed (commitment to patients, society, the profession, and physician health). A pragmatic research paradigm focussed descriptive data analysis on actions and outcomes. Results: Residents actively identify opportunities for experiential learning of professionalism within the clinical workplace– addressing conflicting priorities with interprofessional clinicians to ensure excellent patient care, providing informal feedback regarding peers’ and other healthcare clinicians’ professionalism lapses and by gaining self-awareness and maintaining wellness. There were no descriptions of commitment to society. Values, relationships, and reflection supported professional behaviours. Many described transformative personal and professional growth as an outcome of addressing professionalism challenges. Conclusions: Residents self-regulated experiential learning for professionalism often results in transformational changes personally and professionally. Elucidation of how residents successfully navigate power dynamics and conflict to provide excellent patient care and feedback for professional regulatory behaviour will support professionalism education. An interprofessional research lens will be valuable to explore how best to incorporate commitment to society within clinical environments.

Removing Ableist Barriers in Nursing Education: Clinical Essential Requirements

The current approach to clinical placement training for nurses excludes students with disabilities. The purpose of this article is to introduce a four-step model for nursing programs to identify clinical essential requirements – specific skills and competencies students must gain during placement. Engaging this four-step model will allow educators to identify how essential requirements can be achieved in a variety of ways, and thus can involve accommodations. It will also allow for the identification of which essential requirements cannot be accommodated and must be demonstrated in a prescribed manner due to impacting the nature or integrity of the task. Analyzing clinical essential requirements using this framework will create a consistent and defensible method to determine the flexibility or inflexibility of clinical tasks. The framework provided requires a collaborative process including key experts, nursing students and nurses with disabilities to comprehensively address the challenges clinical environments pose to inclusiveness.

Clinical placement models for undergraduate health professions students: a scoping review

Background Clinical learning is fundamental to undergraduate health professions students. There are several calls for the transformation of health professions education, which have direct implications on clinical learning. Clinical placement models provide structure to clinical learning. Therefore, this scoping review could contribute to supporting curriculum transformation to enhance learning in the clinical environments for undergraduate health professions students. Objectives This scoping review identified the characteristics of research evidence related to mapping the purpose, methodologies used, outcomes, and specific recommendations associated with clinical placement models in undergraduate health professions education. Design A scoping review method was used in this study. A search string developed from the title of the review was used to search online databases to identify research published between January 2000 and March 2020. Results Forty-eight articles reporting on ten clinical placement models were included in this review. The majority of these articles originated from Australia and predominantly report on nursing. The aims of these articles aligned with the evaluation of the implementation of a clinical placement model. Seven categories of outcomes of the clinical placement models are reported namely, relationships, influence, environment, facilitation, inputs, knowledge scores, and student perceptions. Conclusions As clinical learning is fundamental to undergraduate health professions education, clinical placement models should prioritise the development of competence among undergraduate students. Insights into outcomes reported in literature could guide educators in fostering optimal learning in students who may then be able to influence community health outcomes positively.

The influence of nursing students’ perfectionism tendency and perception of instructor caring on incivility experienced by nursing students

Purpose: The purpose of this study was to identify the influences of nursing students perfectionism tendencies and their perception of instructor caring on incivility experienced by nursing students during clinical practice.Methods: A descriptive correlational study was conducted. The participants were 244 nursing students from five universities in B city. Data were analyzed using an independent t-test, ANOVA, Pearson’s correlation coefficient, Scheffé test and a stepwise regression analysis.Results: The mean score for incivility in nursing students was 2.61 out of 5 points. The explanatory power of the model for incivility was in nursing students 52.8% of the variance in training in student’s university hospital (β=-.15, p=.002), total period of clinical practice (β=.17, p<.001), confidence through caring (β=-.23, p<.001), respectful sharing (β=-.15, p=.005), supportive learning climate (β=-.15, p=.005), self-oriented perfectionism (β=.14, p=.004), and socially prescribed perfectionism (β=.18, p<.001).Conclusion: The research results suggest that instructor caring is an important factor in regard to the incivility of nursing students. Organizational efforts and institutional devices will be needed to improve the incivility in clinical environments. By communicating with students and showing them respect, clinical nurses will help nursing students cope with incivility and recognize the clinical practice education environment positively.

Deep Learning Recurrent Neural Network for Concussion Classification in Adolescents Using Raw Electroencephalography Signals: Toward a Minimal Number of Sensors

Artificial neural networks (ANNs) are showing increasing promise as decision support tools in medicine and particularly in neuroscience and neuroimaging. Recently, there has been increasing work on using neural networks to classify individuals with concussion using electroencephalography (EEG) data. However, to date the need for research grade equipment has limited the applications to clinical environments. We recently developed a deep learning long short-term memory (LSTM) based recurrent neural network to classify concussion using raw, resting state data using 64 EEG channels and achieved high accuracy in classifying concussion. Here, we report on our efforts to develop a clinically practical system using a minimal subset of EEG sensors. EEG data from 23 athletes who had suffered a sport-related concussion and 35 non-concussed, control athletes were used for this study. We tested and ranked each of the original 64 channels based on its contribution toward the concussion classification performed by the original LSTM network. The top scoring channels were used to train and test a network with the same architecture as the previously trained network. We found that with only six of the top scoring channels the classifier identified concussions with an accuracy of 94%. These results show that it is possible to classify concussion using raw, resting state data from a small number of EEG sensors, constituting a first step toward developing portable, easy to use EEG systems that can be used in a clinical setting.

CsrA Coordinates Compatible Solute Synthesis in Acinetobacter baumannii and Facilitates Growth in Human Urine

The opportunistic human pathogen Acinetobacter baumannii has become one of the leading causes of nosocomial infections around the world due to the increasing prevalence of multidrug-resistant strains and their optimal adaptation to clinical environments and the human host. Recently, it was found that CsrA, a global mRNA binding posttranscriptional regulator, plays a role in osmotic stress adaptation, virulence, and growth on amino acids of A. baumannii AB09-003 and 17961.

Mammalian meat allergy emerges after tick bite: the alpha-gal syndrome

Alpha-gal syndrome (AGS) is a hypersensitivity reaction to mammalian meat that develops after tick bite exposure. AGS was first described in 2009 and testing for the allergy has become available in the last decade. We report the case of a 56-year-old farmer with a history of frequent lone star tick bites who presented with a 7-year history of diffuse urticaria occurring hours after eating red meat. AGS is likely underdiagnosed because of the unusual presentation of the allergy, historic lack of available testing, and deficiency of physician knowledge about the condition. Recognition of AGS is important both to help alleviate symptom burden and to avoid iatrogenesis. Patients with AGS should not receive products containing mammalian products, such as cat-gut suture, porcine-derived heart valves, and bovine-derived vaccines. Patients with AGS may present in a variety of clinical environments and physicians of all kinds should be able to recognise the symptoms.