Personal Training Research Articles

Indicators of technostress, their association with burnout and the moderating role of support offers among nurses in German hospitals: a cross-sectional study

ObjectivesTo examine the level of indicators of technostress among nurses with and without a leadership position, the relationship between indicators of technostress and burnout and the moderating role of support...

Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions.

Diabetes mellitus is a prevalent chronic autoimmune disease with a high impact on global health, affecting millions of adults and resulting in significant morbidity and mortality. Achieving optimal blood glucose levels is crucial for diabetes management to prevent acute and long-term complications. Carbohydrate counting (CC) is widely used by patients with type 1 diabetes to adjust prandial insulin bolus doses based on estimated carbohydrate content, contributing to better glycemic control and improved quality of life. However, accurately estimating the carbohydrate content of meals remains challenging for patients, leading to errors in bolus insulin dosing. This review explores the current limitations and challenges in CC accuracy and emphasizes the importance of personalized educational programs to enhance patients' abilities in carbohydrate estimation. Existing tools for assessing patient learning outcomes in CC are discussed, highlighting the need for individualized approaches tailored to each patient's needs. A comprehensive review of the relevant literature was conducted to identify educational programs and assessment tools dedicated to training diabetes patients on carbohydrate counting. The research aims to provide insights into the benefits and limitations of existing tools and identifies future research directions to advance personalized CC training approaches. By adopting a personalized approach to CC education and assessment, healthcare professionals can empower patients to achieve better glycemic control and improve diabetes management. Moreover, this review identifies potential avenues for future research, paving the way for advancements in personalized CC training and assessment approaches and further enhancing diabetes management strategies.

The effects of five weeks of climbing training, on and off the wall, on climbing specific strength, performance, and training experience in female climbers-A randomized controlled trial.

Recent research has elucidated the effects of strength training on climbing performance. Although local muscular endurance training of the upper-limbs and finger flexors is frequently suggested, there is currently insufficient evidence to support its impact on climbing performance and climbing-specific strength. Furthermore, there is no evidence on climbers' experiences related to training and the likelihood of consistent engagement. In addition, the effects of more climbing-specific strength training on walls with built in lights and adjustable angles have yet to be examined. The low percentage of studies involving female subjects, additionally, demonstrates a significant gap in understanding the specific effects of strength training on women in the context of climbing. The aim of this study was thus to assess the effects of five-week on-, and off-the-wall climbing training on climbing performance, climbing-specific strength, and training experience. Thirty-one female lower-grade to advanced climbers were randomly assigned to either a control group, an off-the-wall training or an on-the-wall training group. Apart from the training regimen, all groups followed their usual climbing and bouldering routine. Subjects trained at least twice a week. Bouldering performance, and maximum strength and muscular endurance of the finger flexors and upper-limbs were assessed before and after the intervention. Furthermore, rate of perceived exertion and discomfort, exercise enjoyment, and exercise pleasure were assessed during the first and last training session, as well as after two and a half weeks of training. Intrinsic training motivation was assessed after the last training session. The results showed trends towards positive effects of off-the-wall training on climbing-specific strength, and on-the-wall training on climbing technique. Furthermore, our finding revealed high exercise enjoyment and intrinsic training motivation for both on- and off-the-wall training. Hence, lower-grade to advanced female climbers should rely on personal training preferences.

The privacy-explainability trade-off: unraveling the impacts of differential privacy and federated learning on attribution methods.

Since the advent of deep learning (DL), the field has witnessed a continuous stream of innovations. However, the translation of these advancements into practical applications has not kept pace, particularly in safety-critical domains where artificial intelligence (AI) must meet stringent regulatory and ethical standards. This is underscored by the ongoing research in eXplainable AI (XAI) and privacy-preserving machine learning (PPML), which seek to address some limitations associated with these opaque and data-intensive models. Despite brisk research activity in both fields, little attention has been paid to their interaction. This work is the first to thoroughly investigate the effects of privacy-preserving techniques on explanations generated by common XAI methods for DL models. A detailed experimental analysis is conducted to quantify the impact of private training on the explanations provided by DL models, applied to six image datasets and five time series datasets across various domains. The analysis comprises three privacy techniques, nine XAI methods, and seven model architectures. The findings suggest non-negligible changes in explanations through the implementation of privacy measures. Apart from reporting individual effects of PPML on XAI, the paper gives clear recommendations for the choice of techniques in real applications. By unveiling the interdependencies of these pivotal technologies, this research marks an initial step toward resolving the challenges that hinder the deployment of AI in safety-critical settings.

The humanization of medical education: publications review

The humanization of medical education is targeted at integration of humanitarian values and approaches into system of education of medical personnel to improve their professional and personal training. This process includes education in medical ethics, development of communication skills, stress management and implementation of humanitarian disciplines into the curriculum. The humanization contributes into formation of empathy, responsibility and professionalism in future physicians that helps to better understand and consider psychological, social and emotional needs of patients. The problems of including humanitarian sciences into medical education are associated with lack of systematic approach, adequate curricula and qualified lecturers. To optimize process, it is necessary to focus on education of ideals and beliefs, development of integrated curricula and enhancement of humanitarian component of education.

Performances of Large Language Models in Detecting Psychiatric Diagnoses from Chinese Electronic Medical Records: Comparisons between GPT-3.5, GPT-4, and GPT-4o

Objectives: As a type of artificial intelligence (AI), the large language model (LLM) is designed to understand and generate human-like fluent texts. Typical LLMs, e.g., GPT-3.5, GPT-4, and GPT-4o, interact with users through “prompts” and some internal parameters, like “temperature.” Currently, some AI models have been widely used in the field of psychiatry, but systemic reports examining the capacity and suitability of LLM in detecting psychiatry diagnoses are still lacking. In this study, we intended to explore the performances of different generations of LLMs with different levels of temperature in detecting mental illnesses from electronic medical records (EMRs). Methods: We collected 500 Chinese EMRs from one mental hospital in northern Taiwan, with the “current medical history” section as corpuses. We used the GPT-3.5-turbo-16K, GPT-4, and GPT-4o models provided by Microsoft’s Azure OpenAI service (www.portal.azure.com) to generate AI-based predictions (the probability) for the diagnoses of major depressive disorder (MDD), schizophrenia (SCZ), attention-deficit/hyperactivity disorder (ADHD), and autistic spectrum disorder (ASD). Clinic diagnoses made by qualified psychiatrists were treated as gold standards (target) of receiver operating characteristic curve analysis. Then, their area under the ROC curve (AUCs) were compared using the DeLong test. Results: Among 500 recruited Chinese EMRs in this study, 56.6% were primarily diagnosed with MDD, as well as 22.4% with SCZ, 11.2% with ADHD, and 9.2% with ASD. In general, our LLMs achieved AUCs of 0.84 to 0.98 for detecting four different diagnoses. There were no significant differences between versions, but newer versions (GPT-4o models with AUCs of 0.98–0.97 for SCZ, ADHD, and ASD) performed better than older versions (GPT-3.5 models with AUCs of 0.88–0.96) except for MDD (AUC of 0.95 for GPT-4 and AUC of 0.93 for GPT-4o). Although DeLong tests showed nonsignificant differences between the AUCs of models with different levels of temperature, models with zero temperatures generally represented the best performances in magnitudes. Conclusion: To the best of our knowledge, this study is the first to demonstrate that LLMs performed excellently in distinguishing some mental illnesses. Nevertheless, the diagnostic capabilities of LLMs differed from other diagnoses such as MDD. We hypothesize that this phenomenon may partially result from the complexity of symptomology and/or the content filtering rules of OpenAI. Therefore, more advanced models, e.g., GPT-5, or private training models, e.g., Llamma 3, with the relevance generative answering technique, are expected to answer our questions.

FedComm: Federated Learning as a Medium for Covert Communication

Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without revealing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">actual</i> private training data. To date, a substantial amount of research has investigated the security and privacy properties of FL, resulting in a plethora of innovative attack and defense strategies. This paper thoroughly investigates the communication capabilities of an FL scheme. In particular, we show that a party involved in the FL learning process can use FL as a covert communication medium to send an arbitrary message. We introduce FedComm, a novel covert-communication technique that enables robust sharing and transfer of targeted payloads within the FL framework. Our extensive theoretical and empirical evaluations show that FedComm provides a stealthy communication channel, with minimal disruptions to the training process. Our experiments show that FedComm successfully delivers 100% of a payload in the order of kilobits before the FL procedure converges. Our evaluation also shows that FedComm is independent of the application domain and the neural network architecture used by the underlying FL scheme.

Analisis Keselamatan dan Kesehatan Kerja dengan Metode JSA dan HAZOPS

This research was motivated by the problem of work accidents among workshop employees, including exposure to sparks, legs being hit by production items and iron fences, sore eyes and blistered hands. And welding employees do not use PPE when working. The aim of the research is to identify hazards and risks that can cause work accidents in CV. XYZ uses the Job Safety Analysis (JSA) method. Analyzing the level of risk by assessing likelihood (possibility) and consequence (severity) using the Hazard and Operability Study (HAZOPS) method at CV. XYZ. And provide recommendations for improvements to very high and high risk levels. The type of data in this research is primary data. Primary data obtained directly from the field, namely potential hazard data obtained by making direct observations at CV. XYZ. The results of the research have found that workers' attitudes towards work have a high level of risk and potential danger which is then classified into sources of danger that have extreme, high, medium and low risk. The proposed improvements given to overcome these dangers include providing personal protective equipment, conducting safety and health training for workers to get workers who are professional and understand the importance of occupational health and safety.

Effect of Feedback Styles in Robotic Fitness Coaching on Exercise Intensity

Effect of Feedback Styles in Robotic Fitness Coaching on Exercise Intensity

Applications of augmented reality (AR) in chemical engineering education: Virtual laboratory work demonstration to digital twin development

With advances in information technologies, virtual technologies have become a viable option for chemical engineering educators. Although traditional teaching methods remain effective, they have many limitations when presenting complex phenomena. Virtual technologies like Augmented Reality (AR) have shown great potential as an addition to traditional teaching methods. This study explored the practicality and potential applications of AR technologies in chemical engineering education. In this work, traditional 2D representations of CFD simulation results were replaced by AR experiences to visualise the flow pattern and assist the understanding of the working mechanism behind different devices. Other than CFD result integration, animations were also included in AR experiences for personalised training and inclusive laboratory work experience. AR-assisted training has shown good potential in reducing financial loss and equipment downtime due to improper handling. Besides delivering teaching content, the development process of a digital twin with an AR interface was used to demonstrate how virtual technologies can be involved in digitalisation in Industry 4.0. Beyond the technical contents, important design philosophies were also taught as part of the courses.

Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks

Membership inference (MI) attacks threaten user privacy through determining if a given data example has been used to train a target model. Existing MI defenses protect the membership privacy through preemptive exclusion of members techniques and knowledge distillation. Unfortunately, using either of these two defenses alone, the defense effect can still offers an unsatisfactory trade-off between membership privacy and utility.Given that the defense method that directly combines these two defenses is still very limited (e.g., the test accuracy of the target model is decreased by about 40% (in our experiments)), in this work, we propose a dual defense (DD) method that includes the preemptive exclusion of high-risk member samples module and the knowledge distillation module, which thwarts the access of the resulting models to the private training data twice to mitigate MI attacks. Our defense method can be divided into two steps: the preemptive exclusion of high-risk member samples (Step 1) and the knowledge distillation to obtain the protected student model (Step 2). We propose three types of exclusions: existing MI attacks-based exclusions, sample distances of members and nonmembers-based exclusions, and mutual information value-based exclusions, to preemptively exclude the high-risk member samples. During the knowledge distillation phase, we add ground-truth labeled data to the reference dataset to decrease the protected student model's dependency on soft labels, aiming to maintain or improve its test accuracy. Extensive evaluation shows that DD significantly outperforms state-of-the-art defenses and offers a better privacy-utility trade-off. For example, DD achieves ∼100% test accuracy improvement over the distillation for membership privacy (DMP) defense for ResNet50 trained on CIFAR100. DD simultaneously achieves the reductions in the attack effectiveness (e.g., the TPR@0.01%FPR of enhanced MI attacks decreased by 2.10% on the ImageNet dataset, the membership advantage (MA) of risk score-based attacks decreased by 56.30%) and improvements of the target models' test accuracies (e.g., by 42.80% on CIFAR100).

Association between social jetlag and weight and fat reduction in dieting

Objectives: Social jetlag (SJL), the discrepancy between an individual’s inherent circadian rhythm and external social schedule, is associated with obesity. This study aimed to investigate whether SJL also influences body weight and body fat loss during dieting. Methods: This was an observational study from 2015 to 2018 with participants who had joined an exercise and nutrition program at a private personal training gym. Data from 11,829 individuals provided by the gym along with their sleep logs were analyzed. Differences in change in body mass index (BMI) and body fat percentage (%body fat) were compared by the degree of SJL. Regression was conducted for the change in BMI and %body fat on SJL, adjusted for gender, age, engagement duration in the program, initial BMI, initial %body fat, chronotype, and dietary intakes. Results: The subjects comprised 3,696 men and 8,133 women with a mean age of 40.4 years. Greater SJL was associated with a lower efficacy of BMI and %body fat reduction. The change in BMI (+ 0.56 / hour: SJL) and %body fat (+ 1.40 / hour: SJL) was associated with SJL after adjusting for each variable including dietary intake. Conclusion: SJL was associated with the effect of exercise and nutrition instruction on BMI and body fat reduction, even after adjustment for covariates related to dietary intake. Maintaining consistent sleep–wake rhythms may be crucial for enhancing the efficacy of weight loss programs.

Differentially Private Fine-tuning of Language Models

We give simpler, sparser, and faster algorithms for differentially private fine-tuning of large-scale pre-trained language models, which achieve the state-of-the-art privacy versus utility tradeoffs on many standard NLP tasks. We propose a meta-framework for this problem, inspired by the recent success of highly parameter-efficient methods for fine-tuning. Our experiments show that differentially private adaptations of these approaches outperform previous private algorithms in three important dimensions: utility, privacy, and the computational and memory cost of private training. On many commonly studied datasets, the utility of private models approaches that of non-private models. For example, on the MNLI dataset we achieve an accuracy of $87.8\%$ using RoBERTa-Large and $83.5\%$ using RoBERTa-Base with a privacy budget of $\epsilon = 6.7$. In comparison, absent privacy constraints, RoBERTa-Large achieves an accuracy of $90.2\%$. Our findings are similar for natural language generation tasks. Privately fine-tuning with DART, GPT-2-Small, GPT-2-Medium, GPT-2-Large, and GPT-2-XL achieve BLEU scores of 38.5, 42.0, 43.1, and 43.8 respectively (privacy budget of $\epsilon = 6.8,\delta=$ 1e-5) whereas the non-private baseline is $48.1$. All our experiments suggest that larger models are better suited for private fine-tuning: while they are well known to achieve superior accuracy non-privately, we find that they also better maintain their accuracy when privacy is introduced.

Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey

The ultra-low latency requirements of 5G/6G applications and privacy constraints call for distributed machine learning systems to be deployed at the edge. With its simple yet effective approach, federated learning (FL) is a natural solution for massive user-owned devices in edge computing with distributed and private training data. FL methods based on FedAvg typically follow a naive star topology, ignoring the heterogeneity and hierarchy of the volatile edge computing architectures and topologies in reality. Several other network topologies exist and can address the limitations and bottlenecks of the star topology. This motivates us to survey network topology-related FL solutions. In this paper, we conduct a comprehensive survey of the existing FL works focusing on network topologies. After a brief overview of FL and edge computing networks, we discuss various edge network topologies as well as their advantages and disadvantages. Lastly, we discuss the remaining challenges and future works for applying FL to topology-specific edge networks.

미용전공자들을 위한 퍼스널컬러 이미지메이킹 교육프로그램 교수·학습안 개발

This study attempted to suggest basic training data for image making, which are needed to acquire personal color theories and technical aspects and improve job performances for the students majoring in cosmetology. For this, a training program was developed based on an ADDIE model (analysis, design, development, implementation, evaluation). First, 27 papers on image-making and personal color-related training programs were analyzed. Then, purposes, goals, modules, specific programs and a training method were designed. Specifically, a total of 7 modules were constructed as follows: Modules 1 handles the understanding of colors and tones while Module 2 covers the concept and types of personal color. Module 3 describes a personal color diagnosis system. In addition, Modules 4 and 5 explain color usage (makeup, hair color) and facial expression &amp; posture respectively while Modules 6 and 7 state manners &amp; communications and breaking inferiority complex &amp; vision setup each. Concerning teaching-learning methods, lectures and practices were designed. In terms of the length of lecture, Modules 1-6 were 2 hours each while Module 7 was 1 hour (13 hours in total). It is anticipated that it would be necessary to execute the training &amp; learning plan specially designed for the students majoring in cosmetology and develop a final training program after assessment and verification.

Information and cybersecurity audit: Statutory regulation and performance issues

Subject. This article focuses on the issues related to the audit of information systems and cybersecurity. Objectives. The article aims to determine the key areas of legal regulation of information security audit, taking into account the requirements of the time, technical capabilities and digital transformations taking place in society and the global space. Methods. For the study, we used a case study method, analysis, computational and graphical method, data systematization, and the ecosystem approach. Results. The article identifies gaps in the methodological framework and statutory regulation of the audit of information systems, substantiates the need for systematization and further development of the legislative framework for the audit of information security, and identifies the objects of information technologies that require special attention. It proposes to make certain changes to the Federal Law On Auditing regarding the introduction of information security checks of business entities into the list of other services, as well as develop standards for government auditing, which will unify this type of activity. Conclusions and Relevance. The number of cybercrimes is steadily growing, and this is due to both the widespread digitalization of ecosystems and the high level of technical and intellectual training of persons committing fraudulent actions. Ensuring the information security of economic entities and government agencies is impossible without conducting an audit that reduces the risk of cyber threats. The results of the study can be used to develop the regulatory framework for the audit of information systems, as well as for further scientific research and practical application.

Anaesthetic practices and attitudes to patient safety in a sample of Australian veterinary practices

ObjectiveTo survey anaesthetic practices and attitudes towards veterinary patient safety in Australia. Study designThis was a cross-sectional sample population from Australian veterinary practices, including first opinion small animal, mixed animal and referral small animal practices. The survey included practices’ anaesthetic management, monitoring equipment and topics regarding patient safety. ResultsResponses were obtained from 310/1700 (18%) veterinary practices, with 208 respondents from small animal practices (67%), 71 mixed animal practices (23%) and 31 referral small animal centres (10%). Overall, 61% of respondents reported always having a dedicated staff member monitoring the anaesthetic, who was most commonly a certified veterinary nurse (89%). In 22% of the practices, some of the staff monitoring an anaesthetic did not have any qualification. Completion of anaesthetic plans for each animal prior to an anaesthetic was reported by 24.5% of respondents and labelling of syringes was completed in 80% of practices. Pulse oximetry (98%) and temperature (88%) were the most common clinical variables monitored. The use of capnography (46%) or an electrocardiogram (48%) was also reported by the respondents. Emergency drugs, airway and ventilation equipment are available in 96%, 88% and 59% of practices, respectively. A defibrillator was available in 11% of practices with only 54% respondents being confident in using it. Of the respondents 60% were aware of anaesthesia safety interventions and clinical guidelines regarding patient safety during anaesthesia. Conclusions and clinical relevanceDespite practices showing a relative improvement over the years in the standard of anaesthesia care, most of them are still not meeting international guidelines. Areas where changes could lead to improved safety practices could include: a trained person dedicated to monitor anaesthesia, pain scoring evaluation in patients recovering from anaesthesia, an increased use of standardised handover checklists, record keeping and an increase in open discussion of adverse events by the veterinary team.

Bridging the Gap: Challenges and Solutions in Online Nephrology Education

The outbreak of COVID-19 has created an unprecedented situation that has accelerated online education in areas like nephrology, which are traditionally served by onsite training. Efforts to educate clinicians remotely provide convenience and flexibility but may leave many open questions when it comes to how well-trained professionals maybe after completing their programs online. This review paper has emphasized the deleterious effects of online nephrology education and identified a number of ways in which it can be problematic such as lack of hands-on training, less patient interface, lack of proper supervision, and inconsistent quality of programs. The ability to make a difference, however, is further restricted by technological barriers and resource constraints. Whilst these programs tend to focus on creating knowledgeable graduates, they often fail to provide the clinical exposure necessary for preparedness. Hybrid programs - which combine online courses with clinical training in person are required to deliver high-quality nephrology care.

PEMOCS: theory derivation of a concept for PErsonalized MOtor-Cognitive exergame training in chronic Stroke-a methodological paper with an application example.

Coping with residual cognitive and gait impairments is a prominent unmet need in community-dwelling chronic stroke survivors. Motor-cognitive exergames may be promising to address this unmet need. However, many studies have so far implemented motor-cognitive exergame interventions in an unstructured manner and suitable application protocols remain yet unclear. We, therefore, aimed to summarize existing literature on this topic, and developed a training concept for motor-cognitive exergame interventions in chronic stroke. The development of the training concept for personalized motor-cognitive exergame training for stroke (PEMOCS) followed Theory Derivation procedures. This comprised (1.1) a thorough (narrative) literature search on long-term stroke rehabilitation; (1.2) a wider literature search beyond the topic of interest to identify analogies, and to induce creativity; (2) the identification of parent theories; (3) the adoption of suitable content or structure of the main parent theory; and (4) the induction of modifications to adapt it to the new field of interest. We also considered several aspects of the "Framework for Developing and Evaluating Complex Interventions" by the Medical Research Council. Specifically, a feasibility study was conducted, and refining actions based on the findings were performed. A training concept for improving cognitive functions and gait in community-dwelling chronic stroke survivors should consider the principles for neuroplasticity, (motor) skill learning, and training. We suggest using a step-based exergame training for at least 12 weeks, 2-3 times a week for approximately 45 min. Gentile's Taxonomy for Motor Learning was identified as suitable fundament for the personalized progression and variability rules, and extended by a third cognitive dimension. Concepts and models from related fields inspired further additions and modifications to the concept. We propose the PEMOCS concept for improving cognitive functioning and gait in community-dwelling chronic stroke survivors, which serves as a guide for structuring and implementing motor-cognitive exergame interventions. Future research should focus on developing objective performance parameters that enable personalized progression independent of the chosen exergame type.

Lumbar and Cervical Concurrent Muscle Activity Analysis During Hamstring Length Testing to Design a Customized Treatment Plan: An Observational Cross-Sectional Study.

Hamstring length plays a significant role in a spectrum of clinical entities, from injury prevention and gait dysfunction to posture correction. Evidence suggests that the prevalence of hamstring tightness (HT)/reduced length is increasing. Despite the number of available tests and treatment protocols, HT is still a functional diagnosis. This study's primary goal is to evaluate concurrent muscle (CM) usage during these testing procedures to design a unique, customized treatment protocol. The study was conducted in two stages. In phase 1, Active Straight Leg Raise (ASLR), Active Total Knee Extension (ATKE), and Active Seated Total Knee Extension (ASTKE) were carried out. Next, two pressure gauges (PGs) were placed to align with the natural lumbar and cervical curvatures while testing ASLR and ATKE. After analyzing the results for pressure gauge placement, phase 2 data were collected for tests ASLR and ATKE with PG. The results of ASLR and ATKE, both with and without PG, indicate a high prevalence rate, whereas the results of ASTKE show no prevalence. Changes in the PG values indicate CM usage. Dichotomization revealed that participants with normal test scores (non-HT group) had increased usage of CM work. Positive and negative changes in PG indicate the involved CM group. In regular practice, most healthcare professionals and fitness trainers prefer ASTKE due to the ease of the testing procedure. Directing co-professionals on their choice of tests is challenging, whereas providing knowledge about CM use paves the way for creating customized treatment plans.