Application Of Scale Research Articles

Validation of the Portuguese MDS-UPDRS: Challenges to Obtain a Scale Applicable to Different Linguistic Cultures.

The MDS-UPDRS has been available in English since 2008, showing satisfactory clinimetric results and being proposed as the new official benchmark scale for Parkinson's disease (PD), being cited as a core instrument for PD in the National Institutes of Neurological Disorders and Stroke Common Data Elements program. For this reason, the MDS created guidelines for development of MDS-UPDRS official, clinimetrically validated translations. This study presents the formal process used to obtain the officially approved Portuguese version of the MDS-UPDRS. The study consisted of three phases: (1) Independent translation by Portuguese and Brazilian teams followed by a challenging consensus process that this article particularly emphasizes; (2) Cognitive pretest involving raters and patients from both Portugal and Brazil; (3) Validation test with a sample of 367 native Portuguese-speaking PD patients. The overall factor structure of the Portuguese version was consistent with the English version based on a comparative fit index ≥0.96 for all four parts of the MDS-UPDRS. This version can be designated as the official Portuguese version of the MDS-UPDRS.

Plasma and breast milk adipokines in women across the first year postpartum and their association with maternal depressive symptoms and infant neurodevelopment: Protocol for the APPLE prospective cohort study.

Adiponectin and leptin play important roles in the central nervous system. During the postpartum period, there is a need for a better understanding of the relationship between these cytokines and the neurological development of the infant, as well as their influence on preventing maternal depressive symptoms. To assess the correlation between adiponectin and leptin in maternal plasma and breast milk and their association with: infant neurodevelopment at 6 and 12 months of age; and maternal mental health over the first year postpartum. Prospective cohort study with four follow-up. Mothers and their newborns are recruited within the first 15 days postpartum (baseline). Follow-up visits occur at 2, 6, and 12 months postpartum. Visits include blood and breast milk collection, application of the Edinburgh Postnatal Depression Scale and Beck Depression Inventory to assess maternal mental health, application of the Bayley-III scale for infant developmental assessment, maternal and infant anthropometry and body composition, evaluation of reproductive history, mother-infant bonding, breastfeeding, consumption of ultra-processed foods, sleep quality, and socio-economic and demographic data. The research received funds in August 2022, and participant recruitment began in September 2022. The sample size will consist of 95 mother-child pairs. As of September 2023, 68 participants have been recruited. The project will provide insights into the association between adiponectin and leptin with postpartum depression and infant neurodevelopment, ultimately promoting improved care and quality of life for these groups. Additionally, it will provide data on the type of delivery, infant physical growth, maternal and infant body composition changes, sleep quality, consumption of ultra-processed foods, and maternal metabolic health, including vitamin D metabolites, oxidized polyunsaturated fatty acid metabolites, phospholipid species and triacylglycerols, which are of significant relevance to public health and, when interconnected, may yield important results and contribute to the existing literature. Name of the registry: Brazilian Clinical Trials Registry (ReBec). Registration number: RBR-9hcby8c.

79 Using artificial intelligence and machine learning to automate injury prevention surveillance data

Abstract Background The Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) is a pan-Canadian injury and poisoning surveillance system that tracks patients presenting to Canadian Emergency Departments (EDs). Like many surveillance systems, data is input in a manual fashion with humans screening all charts. Natural language processing algorithms using transformer models can analyze text from triage notes of previous CHIRPP-positive patients to train models to predict future CHIRPP patients. We sought to automate these functions using machine learning and artificial intelligence. Objectives The objective of this initiative is to automate detection of injured patients using artificial intelligence. Once charts are identified, a further summarization model will automatically generate a narrative of the events surrounding the injury, as required for the surveillance system. Design/Methods ED notes were obtained from an electronic health record for all patients visiting the ED from October 2018 to December 2021 (n =203626). All charts were manually reviewed to determine if patients met the criteria for CHIRPP. CHIRPP-positive patients made up 24.8% (n = 50499) of the total presentations. Patient charts were randomly split into train (70%) and validation (30%) datasets. Pre-trained distilbert-base-uncased model from Huggingface was utilized. The best model according to AUROC was selected for further development. For note summarization, we fine-tuned a multi-task model (t5-small) from the Huggingface repository. We used human summaries from the same dataset (October 2018-December 2021) as training data (49,078 note, summary pairs). A 70/30 train/validation split was performed. Results Our model captured 90% of all positive cases while reducing the total number of charts for review by 77%, 75% and 63% for train, validation and test sets respectively. See AUC curve (figure 1). Based on 2022 volumes this is a reduction to 24,027 from 64,938 charts. For note summarization, when we compared the cosine similarities of human summaries to actual note text vs t5-small summaries to note text we saw remarkable similarity implying high model performance. Conclusion Our model has shown that high volumes of charts that were previously screened manually can utilize natural language processing to automate the task with minimal sacrifices in terms of data capture and accuracy, dramatically reducing cost while improving speed to reporting. Our summarization model also showed potential for further automating this process. This application to an injury prevention program has wide scale applicability and scalability.

Краткая версия Шкалы аутентичности в отношениях: психометрическая проверка в российской культуре

<p style="text-align: justify;"><strong>Objective</strong>. Psychometric validation of the Russian-language short version of the Authenticity in Relationships Scale (AIRS), which allows you to assess the degree of authenticity in various contexts: partners, parents, friends and colleagues. <br><strong>Background.</strong> The basis of close relationships as an absolute value for different cultures is honest self-presentation, which contributes to a person’s psychological well-being and increases satisfaction with communication. Authentic relationships include unacceptability of deception and intimate risk taking. In authentic relationships, there should be no desire to produce and maintain ideas about oneself and one’s partner that do not correspond to reality. They also imply a desire for more open, close communication, i.e., revealing one’s real self to another person. To measure this quality, the Authenticity in Relationships Scale was developed in the USA and GB, which has not yet been subjected to systematic psychometric testing, although there are no analogues to this instrument in Russia, which determined the relevance of this study. <strong>Study design.</strong> Standard psychometric validation of the methodology was carried out for each of the four contexts: partners, parents, friends, colleagues. The Cronbach coefficient alpha was analyzed on the full sample of respondents; half of the respondents’ answers were selected for exploratory factor analysis, the other half for confirmatory factor analysis. <br><strong>Participants.</strong> The study involved 449 respondents aged 18 to 60 years (10% male, 90% female; average age – 27 years (min = 17, max = 60)). The study participants are Russian–speaking citizens of the Russian Federation, representatives of different professions. <br><strong>Measurements.</strong> The main method was a short version of the Authenticity in Relationships Scale, which included 10 statements for each context. Data were collected online using the 1ka.si service. Jamovi (v. 2.3.21.0) was used for data processing. <br><strong>Results.</strong> The scale is applicable only to the context of a romantic relationship. For other contexts, the original model has not been confirmed. <br><strong>Conclusions.</strong> The methodology can be applied in research and in psychological counseling to study the authenticity level of romantic relationships. The scale's applicability to other social contexts requires further testing.</p>

Linking Thermal Conductivity to Equations of State Using the Residual Entropy Scaling Theory.

In recent years, the application of the residual entropy scaling (RES) method for modeling transport properties has become increasingly prominent. Based on Yang et al. (Ind. Eng. Chem. Res. 2021, 60, 13052) in modeling the thermal conductivity of refrigerants, we present here an RES model that extends Yang et al.'s approach to a wider range of pure fluids and their mixtures. All fluids available in the REFPROP 10.0 software, i.e., those with reference equations of state (EoS), were studied. A total of 71,554 experimental data of 125 pure fluids and 16,702 experimental data of 164 mixtures were collected from approximately 647 references, mainly based on the NIST ThermoData Engine (TDE) database 10.1. As a result, over 68.2% (corresponding to the standard deviation of a normal distribution) of the well-screened experimental data agree with the developed RES model within 3.1% and 4.6% for pure fluids and mixtures, respectively. Comparative analysis against the various models implemented in the REFPROP 10.0 (one of the state-of-the-art software packages for thermophysical property calculations) reveals that our RES model demonstrates analogous statistical agreement with experimental data yet with much fewer parameters. Regarding the average absolute value of the relative deviation (AARD) from experimental values to model predictions, the developed RES model shows a smaller or equal AARD for 74 pure fluids out of 125 and 76 mixtures out of 164. Besides, a detailed examination of the impact of the critical enhancement term on mixture calculations was conducted. To use our model easily, a software package written in Python is provided in the Supporting Information.

Testing, revision and application of the social anxiety scale for Chinese social media users.

This study aims to modify the Social Anxiety Scale for Social Media Users (SAS-SMU) to make it more suitable for assessing social anxiety among Chinese social media users, taking into account the unique cultural nuances and social media usage patterns in China. Consequently, a systematic approach was adopted, involving three distinct studies. Study 1 involved translating the English scale into Chinese, conducting interviews with Chinese social media users, and subsequently revising the scale items based on the interview data to ensure cultural appropriateness. Study 2 employed an online survey to collect data and examine the reliability and construct validity of the revised scale, including a two-phase approach: an exploratory factor analysis (EFA) was conducted with 500 participants to identify the underlying factor structure, and a subsequent confirmatory factor analysis (CFA) with 408 participants was used to cross-validate the results. Thus far, this study has developed a social anxiety scale for Chinese mobile social media users (SAS-CMSMU) consisted of 11 items on three factors: Interaction Anxiety, Privacy Concern Anxiety, and Shared Content Anxiety. Study 3 (N = 1,006) applied the SAS-CMSMU to assess social anxiety among Wechat users. The results indicated that participants showed a high level of overall social anxiety (M = 3.646 > 3). Specifically, Privacy Anxiety was the most pronounced, followed by Shared Content Anxiety and Interaction Anxiety. Moreover, significant statistical differences in social anxiety levels were found among WeChat users across gender, age, education, income, and relationship status.

Psychometric Evaluation of the Personal Mastery Scale Among Italian Nurses: Assessing Personal Mastery in the Nursing Profession.

Background and Purpose: Personal mastery is essential in the nursing profession, as it directly impacts the quality of care and the well-being of the nurses themselves. However, there is a lack of psychometrically reliable tools to measure it among nurses. This study evaluates the applicability of the Personal Mastery Scale (PMS) among Italian nurses. Methods: A cross-sectional study surveyed Italian nurses using self-administered questionnaires, including the PMS. A total of 209 healthcare workers, primarily nurses, from various operational units participated in the study. The majority of respondents (84.2%) were nurses. The gender distribution was predominantly female (83.3%). Results: Statistical analyses, including factor analyses, assessed the scale's reliability and validity. The scale demonstrated adequate internal consistency (Cronbach's alpha = .670) and construct validity. Conclusions: The Italian version of the PMS is a reliable and valid tool for assessing personal mastery among nurses. Understanding personal mastery is crucial for promoting resilience and delivering quality care. Further research should explore its relationship with outcomes and intervention effectiveness.

The applicability of the Teachers' Inclusive Education Attitude Scale on Chinese preservice physical education teachers

The applicability of the Teachers' Inclusive Education Attitude Scale on Chinese preservice physical education teachers

Exploring Podcast And Videocast Consumption: A Construct Development And Validation Study Using TPB

Digital content consumption, particularly through podcasts and videocasts, is rapidly reshaping media engagement patterns globally. While existing research has explored general online behavior, there remains a gap in understanding the psychological drivers specific to digital audio and video content consumption, especially in emerging markets. This study aims developed and validated a Digital Content Consumption (DCC) scale within the Brazilian context, integrating it with the Theory of Planned Behavior (TPB) to enhance our understanding of consumption behavior. A cross-sectional survey was conducted among Brazilian consumers during thew month May of 2023. Participants were recruited through a convenience sample of adult internet users who had consumed podcasts or videocasts within the past month. The primary exposure was the consumption of digital audio and video content. The study employed Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling (SEM) to analyze the data and validate the DCC scale. The final sample comprised 223 participants, with a mean age of 32 years and an even gender distribution. The SEM analysis demonstrated a strong model fit for the DCB scale within the TPB framework. Attitudes emerged as a significant predictor of both intention and behavior in digital content consumption. Utilitarianism was found to mediate the relationship between attitudes and consumption. The DCCB scale showed high internal consistency and construct validity across different demographic subgroups. The development and validation of the DCC scale provide a robust tool for measuring digital content consumption behavior. This study extends the application of the Theory of Planned Behavior to the specific domain of podcast and videocast consumption, offering valuable insights for content creators and marketers. Future research should explore the scale's applicability in diverse cultural contexts and its potential for predicting long-term engagement patterns with longitudinal studies

Service quality in higher education: A literature review

In a time where the student experience increasingly mirrors a customer-centric approach, universities are tasked with fulfilling the role of comprehensive educational service providers. This paper presents a comprehensive literature review on service quality in higher education, focusing on various generic and industry-specific scales. The primary objective of this article is to analyse and compare prominent measurement scales such as SERVQUAL, SERVPERF, HEdPERF, UnivQual, and HiEduQual. Each scale is defined according to its original literature and provides a foundational understanding of their respective constructs and factors. To achieve a nuanced comparison, the paper examines these scales based on the number of factors extracted from studies and their characteristics. The analysis reveals a great diversity and specificity of factors in measuring service quality across different higher education contexts. Beyond that, the paper analyses a database of 45 selected peer-reviewed articles to understand the practical application of service quality scales in empirical research. This examination considers multiple dimensions such as the country of focus, the statistical procedures employed, and the research methods. These dimensions offer a rich comparative insight into the trends in service quality research within higher education. Additionally, the study delves into the number and names of factors identified in each article, mapping them against the established scales to uncover patterns and deviations in empirical findings. A notable aspect of the review is the exploration of gender differences in service quality perceptions, a critical yet often underexplored area. This gender-based analysis provides valuable insights into how male and female students may perceive service quality differently, thereby informing more nuanced and inclusive service quality enhancement strategies. The findings of this paper underscore the multifaceted nature of service quality in higher education and the importance of context-specific scales. By comparing different measurement approaches and analysing a broad spectrum of empirical studies, this review contributes to a deeper understanding of how service quality is conceptualised and measured in higher education. It also offers practical implications for researchers and practitioners aiming to enhance service quality and align it more closely with student expectations and experiences.

Are Parameterized Entrainment Rates as Scale‐Dependent as Those Estimated From Cloud Resolving Model Simulations?

AbstractEntrainment rates at varying horizontal grid spacing were diagnosed and analyzed based on Large Eddy Model (LES) and Cloud Resolving Model (CRM) simulations of shallow and deep convection. Results show the estimated entrainment rates increase with increasing resolution, and the growth rate is roughly power‐law related to resolution. However, all commonly used parameterizations except for the buoyancy sorting scheme fail to reproduce the monotonic increase of entrainment with resolution, but rather a slight decrease instead. For these schemes, it is suggested the power‐law fitting formula derived from LES/CRM simulations can be used as a scaling function for high‐resolution entrainment correction. Preliminary tests show that the application of entrainment scaling largely reduces the parcel buoyancy and thus the convective available potential energy, favoring the reduction of parameterized convection at high resolution.

Retrodiction of forest demography: Backward simulation with reverse matrix models

Abstract Reconstructing past ecological population dynamics and demographic events is crucial for understanding the dynamics of ecological processes, evaluating the impact of environmental changes and making informed conservation decisions. In forest ecosystems, retrodiction (i.e. the backward projection of ecological populations) plays a pivotal role in understanding historical forest carbon levels and the factors that have influenced their variation over time, because forest demography is a major determinant of the amount of carbon stored in forest ecosystems. The persistent lack of quantitative methods has been a significant obstacle in retrodicting forest demography, especially in applications of a broad geographical scale. While there is a wealth of models for predicting future forest conditions, models that can project these conditions backward in time are scarce. This study presents reverse matrix model (RMM), an innovative retrodiction modelling approach grounded in the principles of transition matrix models. RMM is designed to deduce past demographic characteristics of ecological populations using current data, making it one of the first models capable of projecting the fine‐scale dynamics of forest demography into the past. We assessed the retrodictive performance of RMM by fitting it to a dataset of a disturbed tropical rainforest in French Guiana in 2001–2023, then comparing the retrodictions to observations back to 1983 when the disturbance occurred. We further empirically evaluated the viability of retrodiction over a defined duration by inverting the density‐dependent matrix model by Lin et al. (1996), which predicts the dynamics of northern hardwoods in the United States. The case studies demonstrate significant potential for RMM application in various domains of forestry and conservation, including ecosystem management and conservation planning, global change impact assessment and biodiversity monitoring.

A Practical Approach for Developing Shared Decision-Making Knowledge, Skills, and Capability for Busy Health Care Practitioners.

There has been substantial development of shared decision-making (SDM) methods and approaches in the past few decades, but despite this, building capability and scaling application of SDM in clinical practice remains a challenge. Here the authors describe the development and initial experience with a new virtual Practical Approach continuing education program for busy practicing clinicians who care for people with complex, chronic, and costly conditions who are frequently faced with preference-sensitive decisions. This program was designed to provide plain language training in SDM for real-world clinical practice using an easy 4-step approach that does not require prior training or formal education in SDM theory or methods. The authors describe the development of the Practical Approach program using established evidence-based principles. The program was piloted in 4 different settings across 2 chronic conditions. Qualitative interviews of program participants were conducted to observe SDM attitudes and observed performance in repeated case-based simulation role-play exercises to assess knowledge and skills performance. The authors observed improved and more realistic SDM attitudes in qualitative interviews with program participants after exposure to the program compared to baseline, and they similarly observed improved knowledge and skills demonstrated in sequential simulations conducted as participants were exposed to the program. Post-program focus groups revealed that participants perceived the program to be feasible, acceptable, and useful. Initial experience with the Practical Approach program suggests that it may beneficially affect basic SDM knowledge, skills, and attitudes in busy practicing clinicians who are novices in SDM. It also has demonstrated initial feasibility, utility, and acceptability.

BUFIT: FINE-GRAINED DYNAMIC BURST FAULT INJECTION TOOL FOR EMBEDDED field programmable gate array TESTING

Fault injection (FI) is a well-known method to attack embedded systems, particularly advanced FPGAs and microcontrollers physically. The FPGA-based embedded system constitutes SRAM for configuration data storage. Multiple-bit upset is a main threat for FPGAs due to technology scaling and complex application bit files. Space environments additionally incur radiation threats to these devices. This paper proposes burst error modeling and a burst fault injection tool (BUFIT) to address these issues. BUFIT has been proposed with fine-grained and coarse-grained circuits. Built-in instrumented FPGA-based FI is proposed for effectively injecting MBUs in configuration memory with space adaptive rate for accurately estimating soft errors. Evaluation of proposed BUFIT on Kintex-7 target FPGA to various OR 1200-based workloads is given to analyze the speed up of the proposed technique. Results on the OR 1200 processor show that BUFIT is three and two orders of magnitude faster than existing DPR and SCFIT techniques. It uses only 0.4 % CLB overhead and has negligible impact on FFs of target SFPGAs.

Synthesis, performance and application of environmental protection scale inhibitorβ-CD-MA-SSS copolymer——in electric power industry

The issue of water scaling in the treatment of cooling water has become an imperative problem that should be solved urgently for the sake of safe operation in large-scale flexible DC converter stations. However, the coexistence of Ca2+ and Mg2+ in high content in this sort of pending raw water is quite hard to tackle. β-CD-MA-SSS, an eco-friendly ternary co-polymer antiscalant with good performance of inhibiting the formation of scale, was applied to address this conundrum in this paper. The efficacy of the antiscalant β-CD-MA-SSS was investigated in cooling water systems under the conditions of low hardness, low alkalinity, a temperature not exceeding 80 °C, and a pH range of 6–8. After optimization, this reagent dem onstrated an excellent scale inhibition rate up to 80% for both calcium carbonate and calcium-magnesium mixed scales. Scanning electron microscopy (SEM) analysis revealed a noticeable reduction in the size of CaCO3 particles when subjected to the action of β-CD-MA-SSS. It was postulated from FTIR analysis that nanoinpurity and chelation effects mainly contributed to the mixed scale inhibition. Further, the effect of β-CD-MA-SSS on the actual production wastewater was comprehensively evaluated from the technical, economic and environmental aspects.

The effect of cognitive intervention program on intelligence scores in preschool.

Dynamic assessment is an approach that aims to improve student performance through interventions. One of the important application areas of dynamic assessment is the assessment of intelligence. Within the scope of the study, after a Cognitive intervention program (CIP) was developed, its effects on intelligence scores were examined with a quasi-experimental research method. The CIP was prepared by adopting a dynamic assessment approach to improve the performance of students with expert support. This improvement would be provided by the clues and feedback given during the intervention within the scope of the CIP. The sample of the study included 173 students in the 5-6 age group (83 experimental group, 90 control group). The CIP developed by the researcher consisted of 54 worksheets and was applied to the experimental group for 9weeks. The implementation of the worksheets was supervised by classroom teachers. The digital application of the Anadolu-Sak Intelligence Scale (d-ASIS) and Raven's colored progressive matrices (RCPM) were applied to both the experimental and control groups as pretests and posttests. The increases in the intelligence scores of the experimental and control groups were analyzed by MANOVA. The analysis showed that the intelligence levels of the experimental group increased significantly more than the control group in terms of both d-ASIS and RCPM total gain scores (posttest to pretest). This result indicated that the CIP, which was developed by adopting a dynamic assessment approach, supported cognitive development.

A systematic review of post-stroke fatigue measurement scale based on COSMIN guidelines.

This study aimed to evaluate the methodological quality and measurement attribute quality of the post-stroke fatigue measurement scale, so as to provide some basis for the clinical application and promotion of related scales. The Chinese National Knowledge Infrastructure, the Wanfang Data Knowledge Service Platform, the China Science and Technology Journal Database, the Chinese Medical Journal Full-text Database, the Chinese Biology Medicine, PubMed, Embase, Medline, the Cochrane Library, the Web of Science, CINAHL, and PsycINFO databases were searched for literature on the post-stroke fatigue measurement scale up to June 2022. Literature screening and data extraction were carried out independently by two researchers, and in the case of disagreement, discussions were held with a third investigator to reach an agreement, and the COSMIN checklist and criteria were used to systematically evaluate the attributes of the measurement scale. A total of 17 studies were included, involving 10 post-stroke fatigue measurement scales. The content validity of FSS-7, FACIT-F, NRS-FRS, and MFI-20 was "not mentioned," and the remaining scales were "uncertain." In terms of construct validity, MFS was "adequate"; FSS-7, FACIT-F, and NRS-FRS were "not mentioned"; and the remaining scales were "uncertain." In terms of internal consistency, NRS-FRS was "not mentioned"; FSS and MFS were "adequate"; and the remaining scales were "uncertain." In terms of hypothesis testing, CIS and FACIT-F were "not mentioned," NRS-FRS was "adequate," and the remaining scales were "uncertain." The stability of FSS-7, CIS, FACIT-F, and MFI-20 was "not mentioned," and the remaining scales were "adequate." The cross-cultural validity of FSS-7 was "adequate," and the remaining scales were "not mentioned." All 10 scales were given a recommendation grade of "B". For the time being, the FSS can be recommended to measure post-stroke fatigue, but it still needs to be tested for more relevant measurement properties in order to gain more support from high-quality evidence. For a more comprehensive assessment of post-stroke fatigue, the FIS, FAS, and NFI-stroke should perhaps be considered, as the FSS is a one-dimensional scale that can only measure physical fatigue in patients; however, these scales also need to be tested for more relevant measurement properties to verify their clinical applicability.

Willingness to use clinical scales for menopause management among general practitioners

Objectives The use of digital healthcare technologies to enhance healthcare delivery has seen significant growth. However, a notable a notable research gap exists in the application of clinical scales for menopause management by general practitioners (GPs). This study aims to investigate willingness of GPs to use specific menopausal scale tools in the care of females for menopause management. Method An anonymous online survey was developed, which received responses from 348 French GPs in 2023. Multiple backward logistic regression was performed to identify the factors influencing the willingness to use a practical menopause management scale. Results In total, 87.93% of GPs are not familiar with the Greene Climacteric Scale and 90.52% are not familiar with the Menopause Quick 6 scale. In contrast, 90.52% would be interested in having access to such scales. The willingness to use a menopause management scale is associated with caring for menopausal females (odds ratio [OR] = 6.13, 95% confidence interval [CI] [2.08–18.08], p = 0.001), less experience (OR = 7.10, 95% CI [2.05–25.22], p = 0.002), the importance of health prevention in daily practice (comparing ‘very important’ to ‘not’, OR = 12.98, 95% CI [1.68–97.60], p = 0.004) and the use of a digital scale in daily practice for menopausal management (OR = 2.13, 95% CI [1.04–5.83], p = 0.014). Conclusion Future research is essential in representative population to confirm these findings in menopause management.

Effect of Topical Hemp (Cannabis sativa L.) Seed Oil on Knee Osteoarthritis: A Randomized Double-Blind Controlled Trial

Purpose, and DesignKnee osteoarthritis (OA) is one of the most common and debilitating diseases, especially in the elderly. Hemp seed oil is a plant product that has been used as a food or drug since ancient times because of its anti-inflammatory and analgesic properties. MethodsA double-blind, active, placebo-controlled trial was done to assess the efficacy of hemp seed oil on knee OA. Ninety patients were randomly allocated to three groups; hemp seed oil, diclofenac gel, and placebo via a blocked randomization method, and were asked to apply the topical treatment daily for 2 months. The study participant underwent assessments before, and four and 8 weeks after the intervention. Evaluation included measurements of the heel-to-thigh distance, utilization of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and application of visual analog scale (VAS). Data analysis was performed using SPSS.24 and the significance level was considered as p < .05. ResultsAll parameters, except heel-to-thigh distance, improved significantly in the hemp seed oil group compared to placebo group. Improvements in VAS and WOMAC parameters were not different comparing the hemp seed oil and diclofenac gel groups. Heel-to-thigh distance decreased significantly within all groups during the study. There were no significant differences in improvements in heel-to-thigh distance comparing the three groups. Conclusion, and Clinical ImplicationsHemp seed oil led to greater improvements in VAS pain score and WOMAC parameters, but not knee flexion range, compared to placebo. There were no differences in measured outcomes comparing hemp seed oil and diclofenac gel.

The application of Borg scale in cardiopulmonary resuscitation: An integrative review.

The study of human performance and perception of exertion constitutes a fundamental aspect for monitoring health implications and enhancing training outcomes such as cardiopulmonary resuscitation (CPR). It involves gaining insights into the varied responses and tolerance levels exhibited by individuals engaging in physical activities. To measure perception of exertion, many tools are available, including the Borg scale. In order to evaluate how the Borg scale is being used during CPR attempts, this integrative review was carried out between October/2020 and December/2023, with searches from PubMed, CINAHL, Web of Science, Embase, PsycINFO and VHL. Full publications relevant to the PICO strategy were included and letters, editorials, abstracts, and unpublished studies were excluded. In total, 34 articles were selected and categorised into three themes: a) CPR performed in different contexts; b) CPR performed in different cycles, positions, and techniques; c) CPR performed with additional technological resources. Because CPR performance is considered a strenuous physical activity, the Borg scale was used in each study to evaluate perception of exertion. The results identified that the Borg scale has been used during CPR in different contexts. It is a quick, low-cost, and easy-to-apply tool that provides important indicators that may affect CPR quality, such as perception of exertion, likely improving performance and potentially increasing the chances of survival.