Exploratory Principal Component Analysis Research Articles

The disease recurrence perception scale for patients with inflammatory bowel disease: Instrument development and cross‐sectional validation study

AbstractDisease recurrence perception plays a key role in disease management and subsequent disease recurrence prevention. However, there are no specific tools for assessing disease recurrence perception in patients with inflammatory bowel disease (IBD) characterized by alternating remission and recurrence. To develop and validate an instrument for measuring disease recurrence perception of patients with IBD, the study was conducted in two steps: (1) instrument development and (2) psychometric tests. A total of 623 patients with IBD participated in the study. The common sense model of illness self‐regulation (CSM) was used as a framework for instrument development. The administered version contained 48 items intended to be relevant to at least one of the six dimensions of the model. Based on preliminary analyzes, 12 items were deleted leaving 36 items for more detailed psychometric and factor analyzes. The Cronbach's alpha coefficient of the total 36‐item instrument was 0.915. The content validity indexes at item and scale levels were satisfactory. The test‐retest reliability of the total instrument was 0.870. Exploratory principal components analysis (n = 278) was used to identify six components congruent with intended CSM constructs that accounted for 62.6% of total item variance. Confirmatory factor analysis (n = 345) found acceptable fit for the six factor measurement model (χ2/df = 1.999, GFI = 0.846, NFI = 0.855, IFI = 0.922, TLI = 0.910, CFI = 0.921, RMSEA = 0.054). Overall, the DRPSIBD demonstrated satisfactory reliability and validity to warrant further development as a measure of disease recurrence perception of patients with IBD.

Critical evaluation of energy dispersive X-ray fluorescence spectrometry for multielemental analysis of coffee samples: Sample preparation, quantification and chemometric approaches

In the present contribution, a critical evaluation of the use of EDXRF (Energy dispersive X-ray fluorescence spectrometry) for elemental composition determination (K, Ca, Mn, Fe, Cu, Zn, Sr and Rb) of coffee samples is presented. Firstly, several sample treatment procedures (pressed pellets and loose powder) and quantification strategies (fundamental parameters and empirical calibration) were evaluated.In addition to quality and nutritional purposes, information of elemental content in coffee samples can be also useful for coffee traceability and provenance studies. In view of that, a comparison of different tools such as explorative principal component analysis (PCA) and classification by partial least square regression (PLS-DA) were also tested as discriminating tools of different types of coffee samples (i.e., pure, instant and coffee with additives). Chemometric analysis was performed using coffee elemental concentrations determined by the developed EDXRF method and, for the first time, using raw EDXRF spectra obtained, without the need of a fitting and quantification procedure.

Belonging in Surgery: A validated instrument and single institutional pilot.

The aim of this study was to develop and validate an instrument to measure Belonging in Surgery among surgical residents. Belonging is the essential human need to maintain meaningful relationships and connections to one's community. Increased belongingness is associated with better well-being, job performance and motivation to learn. However, no tools exist to measure belonging among surgical trainees. A panel of experts adapted a belonging instrument for use among United States surgery residents. After administration of the 28-item instrument to residents at a single institution, a Cronbach's alpha was calculated to measure internal consistency, and exploratory principal component analyses (PCA) were performed. Multiple iterations of analyses with successively smaller item samples suggested the instrument could be shortened. The expert panel was reconvened to shorten the instrument. Descriptive statistics measured demographic factors associated with Belonging in Surgery. The overall response rate was 52% (114 responses). The Cronbach's alpha among the 28 items was 0.94 (95% CI: 0.93-0.96). The exploratory PCA and subsequent Promax rotation yielded one dominant component with an eigenvalue of 12.84 (70% of the variance). The expert panel narrowed the final instrument to 11 items with an overall Cronbach's alpha of 0.90 (95% CI: 0.86, 0.92). Belonging in Surgery was significantly associated with race (Black and Asian residents scoring lower than White residents), graduating with one's original intern cohort (residents who graduated with their original class scoring higher than those that did not), and inversely correlated with resident stress level. An instrument to measure Belonging in Surgery was validated among surgical residents. With this instrument, Belonging in Surgery becomes a construct that may be used to investigate surgeon performance and well-being.

Multimodal emotion recognition based on the fusion of vision, EEG, ECG, and EMG signals

This paper presents a novel approach for emotion recognition (ER) based on Electroencephalogram (EEG), Electromyogram (EMG), Electrocardiogram (ECG), and computer vision. The proposed system includes two different models for physiological signals and facial expressions deployed in a real-time embedded system. A custom dataset for EEG, ECG, EMG, and facial expression was collected from 10 participants using an Affective Video Response System. Time, frequency, and wavelet domain-specific features were extracted and optimized, based on their Visualizations from Exploratory Data Analysis (EDA) and Principal Component Analysis (PCA). Local Binary Patterns (LBP), Local Ternary Patterns (LTP), Histogram of Oriented Gradients (HOG), and Gabor descriptors were used for differentiating facial emotions. Classification models, namely decision tree, random forest, and optimized variants thereof, were trained using these features. The optimized Random Forest model achieved an accuracy of 84%, while the optimized Decision Tree achieved 76% for the physiological signal-based model. The facial emotion recognition (FER) model attained an accuracy of 84.6%, 74.3%, 67%, and 64.5% using K-Nearest Neighbors (KNN), Random Forest, Decision Tree, and XGBoost, respectively. Performance metrics, including Area Under Curve (AUC), F1 score, and Receiver Operating Characteristic Curve (ROC), were computed to evaluate the models. The outcome of both results, i.e., the fusion of bio-signals and facial emotion analysis, is given to a voting classifier to get the final emotion. A comprehensive report is generated using the Generative Pretrained Transformer (GPT) language model based on the resultant emotion, achieving an accuracy of 87.5%. The model was implemented and deployed on a Jetson Nano. The results show its relevance to ER. It has applications in enhancing prosthetic systems and other medical fields such as psychological therapy, rehabilitation, assisting individuals with neurological disorders, mental health monitoring, and biometric security.

Are remote health clinics primary health care focused? Validation of the Primary Health Care Engagement (PHCE) Scale for the Australian remote primary health care setting.

To test and validate a measure of primary health care (PHC) engagement in the Australian remote health context. PHC principles include quality improvement, community participation and orientation of health care, patient-centred continuity of care, accessibility, and interdisciplinary collaboration. Measuring the alignment of services with the principles of PHC provides a method of evaluating the quality of care in community settings. A two-stage design of initial content and face validity evaluation by a panel of experts and then pilot-testing the instrument via survey methods was conducted. Twelve experts from clinical, education, management and research roles within the remote health setting evaluated each item in the original instrument. Panel members evaluated the representativeness and clarity of each item for face and content validity. Qualitative responses were also collected and included suggestions for changes to item wording. The modified tool was pilot-tested with 47 remote area nurses. Internal consistency reliability of the Australian Primary Health Care Engagement scale was evaluated using Cronbach's alpha. Construct validity of the Australian scale was evaluated using exploratory factor analysis and principal component analysis. Modifications to suit the Australian context were made to 8 of the 28 original items. This modified instrument was pilot-tested with 47 complete responses. Overall, the scale showed high internal consistency reliability. The subscale constructs 'Quality improvement', 'Accessibility-availability' and 'population orientation' showed low levels of internal consistency reliability. However, the mean inter-item correlation was 0.31, 0.26 and 0.31, respectively, which are in the recommended range of 0.15 to 0.50 and indicate that the items are correlated and are measuring the same construct. The Australian PHCE scale is recommended as a tool for the evaluation of health services. Further testing on a larger sample may provide clarity over some items which may be open to interpretation.

Influencing factors of Six Sigma management in the construction of students mental health service system in the post-COVID-19 era

PurposeThis study aims to construct a mental health service system for middle school students in the post-COVID-19 era with the framework of Six Sigma DMAIC (define, measure, analyze, improve and control) and analyze the influencing factors of the mental health service system to study the implementation strategies of quality-oriented mental health services in middle schools.Design/methodology/approachThis study was conducted in Tianjin, China, from September to November 2022, and 350 middle school students from Tianjin Public Middle School were selected as subjects. A questionnaire survey was used to collect data. In this study, the Six Sigma DMAIC method, sensitivity analysis method, exploratory factor analysis and principal component analysis were used to analyze the mental health services provided to middle school students.FindingsBased on the Six Sigma DMAIC framework, this study indicates that the contribution rate of the mental health service process factor is the largest in the post-COVID-19 era. The mental health cultivation factor ranks second in terms of its contribution. Mental health quality and policy factors are also important in the construction of middle school students’ mental health service system. In addition, the study highlights the importance of parental involvement and social support in student mental health services during the post-COVID-19 era.Originality/valueTo the best of the authors’ knowledge, a study on middle school students’ mental health in the post-Covid-19 era has not yet been conducted. This study developed a quality-oriented mental health system and analyzed the influencing factors of mental health for middle school students based on data analysis and the Six Sigma DMAIC method.

Clustering of neuropsychiatric symptoms across time in Alzheimer’s disease and Mild Cognitive Impairment

AbstractBackgroundNeuropsychiatric symptoms are behavioural manifestations highly prevalent along the Alzheimer’s disease (AD) continuum, including at the stage of Mild Cognitive Impairment (MCI). Although various projects have investigated the factors that underpin these symptoms, the most stable clustering pattern is still matter for debate; furthermore, to our knowledge, no study has investigated, longitudinally, how clusters might change due to development of AD pathology. Therefore, our objective is to investigate neuropsychiatric clusters over time, in a large sample of MCI and AD dementia patients.MethodsSamples were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu), using data from the Neuropsychiatric Inventory (NPI), from the baseline assessment visit, followed up yearly until data recorded at month 72. The samples included MCI and AD dementia patients (based on ADNI’s inclusion criteria), presenting with at least one neuropsychiatric symptom at the time‐point selected. Using SPSS (Version 28), we performed a series of exploratory principal components analyses (PCA), Varimax rotation, and principal axis factor analyses (FA), comparing Promax and Direct Oblimin rotations.ResultsThe best‐fitting structure was interpreted at each time‐point, based on: Eigenvalues>1, items’ loadings>0.4, scree plot patterns, minimum 40% of variance explained by the model, and independence of factors. Results from both PCA and FA indicated that a unique structure could not be identified, as factors were not stable over time; however, some symptoms tended to load on the same factors across most measurements (i.e. delusions with hallucinations; agitation with depression, anxiety and irritability; to a lesser extent elation with disinhibition; apathy with aberrant motor behaviour, sleep disturbances and appetite disorder).ConclusionsThe available evidence reveals that factors underlying the neuropsychiatric symptoms in a sample of AD and MCI patients are not consistent across the time‐points. However, some symptoms tend to co‐occur across time, with implications, for example, that an investigation into one of those symptoms should consider the presence of the other; furthermore, as clear theoretically driven factors are not distinctively identified at every time‐point, this illustrates the potential importance of sample selection (e.g., disease stage, and/or heterogeneity) on studies of neuropsychiatric symptoms.

The Intuitive Eating Scale-2 Adapted for Mexican Pregnant Women: Psychometric Properties and Influence of Sociodemographic Variables.

A weight-inclusive approach to health involves the promotion of intuitive eating, i.e., the individual's ability to be aware of their physiological hunger and satiety cues to determine when and how much to eat, while paying attention to how certain foods affect their body. The second version of the Intuitive Eating Scale (IES-2) evaluates four interrelated traits of intuitive eating: Unconditional Permission to Eat (UPE), Eating for Physical rather than emotional Reasons (EPR), Reliance on internal Hunger/Satiety Cues (RHSC), and Body-Food Choice Congruence (BFCC). In this study, our aim was to evaluate the psychometric properties of a Mexican Spanish adaptation of the IES-2 for pregnant women and examine the relationship between intuitive eating traits and maternal sociodemographic characteristics. A sample of 514 pregnant women answered our IES-2 adaptation and a sociodemographic questionnaire. We determined the quality, validity, and reliability of our adaptation through descriptive measures, frequency distributions, intra-class correlations, and extreme answer group comparison for each item, eliminating those with weak technical properties. We then performed an exploratory principal component analysis and a confirmatory factor analysis. Last, we analyzed the association between intuitive eating and maternal sociodemographic and reproductive variables through correlation tests and multivariable linear regressions. Psychometric tests confirmed the validity and reliability of our IES-2 adaptation, which comprised 18 out of the 23 original items. Notably, both the exploratory and confirmatory factor analyses yielded not four but five factors, due to the EPR subscale splitting in two (the "emotional" and "physical" components of EPR). We attribute this novel finding to the emotional manifestations that naturally accompany pregnancy, which may incline pregnant women to base their eating behaviors more on the emotional than the physical component that would otherwise dominate their EPR trait. Further research is also needed about the UPE subscale during pregnancy, due to item removal and subtle changes in meaning. Finally, the influence of sociodemographic variables on the IES-2 score was extremely low, suggesting that other variables, possibly of a psychological nature, may have greater influence on a pregnant woman's intuitive eating.

Verification of the active pharmaceutical ingredient in tablets using a low-cost near-infrared spectrometer

The present study investigated the possibilities and limitations of using a low-cost NIR spectrometer for the verification of the presence of the declared active pharmaceutical ingredients (APIs) in tablet formulations, especially for medicine screening studies in low-resource settings. Spectra from 950 to 1650 nm were recorded for 170 pharmaceutical products representing 41 different APIs, API combinations or placebos. Most of the products, including 20 falsified medicines, had been collected in medicine quality studies in African countries. After exploratory principal component analysis, models were built using data-driven soft independent modelling of class analogy (DD-SIMCA), a one-class classifier algorithm, for tablet products of penicillin V, sulfamethoxazole/trimethoprim, ciprofloxacin, furosemide, metronidazole, metformin, hydrochlorothiazide, and doxycycline. Spectra of amoxicillin and amoxicillin/clavulanic acid tablets were combined into a single model. Models were tested using Procrustes cross-validation and by projection of spectra of tablets containing the same or different APIs. Tablets containing no or different APIs could be identified with 100 % specificity in all models. A separation of the spectra of amoxicillin and amoxicillin/clavulanic acid tablets was achieved by partial least squares discriminant analysis. 15 out of 19 external validation products (79 %) representing different brands of the same APIs were correctly identified as members of the target class; three of the four rejected samples showed an API mass percentage of the total tablet weight that was out of the range covered in the respective calibration set. Therefore, in future investigations larger and more representative spectral libraries are required for model building. Falsified medicines containing no API, incorrect APIs, or grossly incorrect amounts of the declared APIs could be readily identified.Variation between different NIR-S-G1 spectroscopic devices led to a loss of accuracy if spectra recorded with different devices were pooled. Therefore, piecewise direct standardization was applied for calibration transfer.The investigated method is a promising tool for medicine screening studies in low-resource settings.

Assessing Antecedents of Behavioral Intention to Use e-LMS: A Case From a Private Institution in the Northern Region of India

Students' and instructors' adoption of “e-learning management systems (e-LMSs)” is critical to their success in a “virtual learning environment”. Students can use “e-learning” to obtain instructional materials to supplement “traditional classroom” instruction. This study intends to highlight important factors of e-learning adoption in higher education institutes. The aim is to determine the factors forming positive intention towards using e-LMS based on “individual characteristics” (“Instructor Quality (IQ)”, “Course Quality (CQ)”, “Learning Value (LV)”, “Social Influence (SI)”, “Facilitating Condition (FC)”, “System Quality (SQ)”, “Perceived Usefulness (PU)”, and “Perceived Ease of Use (PEOU)”). The questionnaire-based survey based on 369 responses from LMS users. The proposed research model was tested using Structural Equation Modelling (SEM). Initially, Post Adoption Behavior was required to be investigated. The original measurement model contained eight independent constructs. A modified measurement model was created after the initial model was enhanced using exploratory factor and principal component analysis. The results of this study will help practitioners and policy makers in the effective development and implementation of e-Learning Management Systems in HEI's especially in developing countries like India. From the theoretical point of view, the result of this study will further enrich the IT adoption literature.

Latent Constructs of Economic Marginality Associated with Sexual Behavior, Healthcare Access and HIV Outcomes Among Transgender and Nonbinary People in Three U.S. Cities.

Transgender and nonbinary people (TNB) in the U.S. experience high HIV prevalence and diverse economic hardships. Yet a comprehensive understanding of how multiple, simultaneously occurring hardships-termed economic marginality-are together associated with healthcare and HIV outcomes is needed. Leveraging survey data from a sample of 330 TNB people in three U.S. cities, we conducted an exploratory mixed-source principal component analysis of latent factors of economic experience, then estimated their associations with sexual behavior, access to healthcare, HIV status, and HIV testing frequency. Two factors emerged: a traditional socioeconomic factor related to income, education, and employment (SES), and one related to housing precarity and (lack of) assets (Precarity). Higher Precarity scores were associated with sexual behavior, cost-based healthcare avoidance, discrimination-based healthcare avoidance, and more frequent HIV testing. Findings highlight the importance of understanding profiles of economic marginalization among trans and nonbinary people and can inform efforts to address upstream, structural factors shaping healthcare access and HIV outcomes in this key population.

The “why” behind generation Y amateur gamers' ongoing eSports gameplay intentions

PurposeTechnological advancements in games increased the popularity of online gaming. The rapid expansion of the eSports market may largely be attributed to the ever-increasing popularity amongst Generation Y amateur gamers. The primary objective of this study is to determine the factors influencing Generation Y amateur gamers' ongoing eSports gameplay intentions.Design/methodology/approachThis study used the extended unified theory of acceptance as the theoretical framework. Data analysis included exploratory principal component analysis, confirmatory factor analysis and path analysis.FindingsThe results of the confirmatory factor analysis suggest that Generation Y amateur gamers' ongoing eSports gameplay intentions is an eight-factor model that is reliable, valid and has acceptable model fit. The results of the path analysis indicate that habit, price-value, flow, effort expectancy and facilitating conditions have a statistically significant positive influence on amateur gamers' ongoing eSports gameplay intentions, whilst social influence and hedonic motivation have a negative but non-significant influence on those intentions.Research limitations/implicationsThe sample was formed using only amateur eSports gamers. In this regard, the opportunity exists to research professional eSports gamers. This study only focussed on Generation Y members between 18 and 36 years old. As a result, there is an opportunity for researchers to research the different generations of South African eSports gamers to determine whether there are any differences or similarities between generational segments.Practical implicationsThe results of this study clearly indicate that flow, together with habit are salient contributors to ongoing gameplay intentions amongst amateur eSports gamers in South Africa. A reasonable assumption that can be made here is that flow is also instrumental in encouraging habitual gaming, which increases the importance of flow in overall ongoing gameplay intentions. This suggests that R&D expenditure should be directed at enhancing user engagement by building increased levels of flow into eSports games.Social implicationseSports game developers can also achieve a desired state of flow by creating daily challenges that reward players when the players achieve specific objectives, which will encourage gamers to enter a state of flow when provided with challenges to complete. However, these in-game challenges should have a variety of levels regarding difficulty, ranging from beginner, intermediate and advanced levels so as not to exceed the effort expectancy of different groups of players. Game developers should provide regularly updated challenges to gamers to ensure that eSports games remain enjoyable and does not become predictable.Originality/valueGiven the nascence of research on eSports behaviour, the results of this study provide a novel addition to the knowledge pool, particularly in terms of amateur eSports behavioural intentions. Interestingly, hedonic motivation and social influence were non-significant negative predictors of Generation Y amateur gamers' ongoing eSports gameplay intentions. The recommendations provide various marketing strategies and opportunities for eSports business expansion.

A short screening tool identifying systemic barriers to distress screening in cancer care.

International guidelines on cancer treatment recommend screening for early detection and treatment of distress. However, screening rates are insufficient. In the present study, a survey was developed to assess perceived systemic barriers to distress screening. A three-step approach was used for the study. Based on qualitative content analysis of interviews and an expert panel, an initial survey with 53 questions on barriers to screening was designed. It was completed by 98 nurses in a large comprehensive cancer center in Switzerland. From this, a short version of the survey with 24 questions was derived using exploratory principal component analysis. This survey was completed by 150 nurses in four cancer centers in Switzerland. A confirmatory factor analysis was then performed on the shortened version, yielding a final set of 14 questions. The initial set of 53 questions was reduced to a set of 14 validated questions retaining 53% of the original variance. These 14 questions allow for an assessment within 2-3 min that identifies relevant barriers to distress screening from the perspective of those responsible for implementation of distress screening. Across several hospitals in Switzerland, the timing of the first distress screening, lack of capacity, patient and staff overload, and refusal of distressed patients to be referred to support services emerged as major problems. The validated 14 questions on barriers to screening cancer patients for distress enable clinicians and hospital administrators to quickly identify relevant issues and take action to improve screening programs.

Enhanced Differentiation of Wild and Feeding Civet Coffee Using Near-Infrared Spectroscopy with Various Sample Pretreatments and Chemometric Approaches

Civet coffee is the world’s most expensive and rarest coffee bean. Indonesia was the first country to be identified as the origin of civet coffee. First, it is produced spontaneously by collecting civet feces from coffee plantations near the forest. Due to limited stock, farmers began cultivating civets to obtain safe supplies of civet coffee. Based on this, civet coffee can be divided into two types: wild and fed. A combination of spectroscopy and chemometrics can be used to evaluate authenticity with high speed and precision. In this study, seven samples from different regions were analyzed using NIR Spectroscopy with various preparations: unroasted, roasted, unground, and ground. The spectroscopic data were combined with unsupervised exploratory methods (hierarchical cluster analysis (HCA) and principal component analysis (PCA)) and supervised classification methods (support vector machine (SVM) and random forest (RF)). The HCA results showed a trend between roasted and unroasted beans; meanwhile, the PCA showed a trend based on coffee bean regions. Combining the SVM with leave-one-out-cross-validation (LOOCV) successfully differentiated 57.14% in all sample groups (unground, ground, unroasted, unroasted–unground, and roasted–unground), 78.57% in roasted, 92.86% in roasted–ground, and 100% in unroasted–ground. However, using the Boruta filter, the accuracy increased to 89.29% for all samples, to 85.71% for unground and unroasted–unground, and 100% for roasted, unroasted–ground, and roasted–ground. Ultimately, RF successfully differentiated 100% of all grouped samples. In general, roasting and grinding the samples before analysis improved the accuracy of differentiating between wild and feeding civet coffee using NIR Spectroscopy.

Nuclear reactor at home? Public acceptance of small nuclear reactors in the neighborhood

Small Nuclear Reactors (SNR) can provide climate-neutral, stable electricity and heating if located in people’s neighborhoods close to people’s dwellings. The extensive use of SNR would reduce capacity requirements for energy transmission systems and increase the overall stability of energy grids. However, the public fear location of SNR close to their homes. This paper hypothesizes that the public acceptance of SNR in the neighborhood is contingent upon knowledge of technology, fear of nuclear energy (NE), trust in the government, the expected increase of future electricity needs and the expected ability of renewables to cover these needs, environmental and climate concerns, and media exposure. We rely upon representative survey data from the Czech Republic (N = 1,013, 51.2% female, aged 18–91, M ± SD: 47.7 ± 17.6; 19.6% with higher education). Methodologically we conduct exploratory Principal Component Analysis and a series of ordinal regressions. The results suggest that the knowledge of technology, trust in the government, the preference for NE expansion, and media exposure increased the acceptance of SNR, while fear of NE decreased SNR acceptance. The perceived replaceability of conventional energy sources with renewables decreased acceptance of SNR in most cases. Surprisingly, worries about climate change reduced the support for SNR. Women accept fewer SNRs located close to their residence compared to men. More educational effort is needed in the specifics of SNR technology and the environmental effects of SNR. Media proved to be an excellent way to start.

Anger Expressions of Bus Drivers and Passengers during Conflicts on the Bus

Conflicts often occur between bus drivers and passengers or among passengers, leading to dangerous situations while driving. However, tools to explore drivers’ and passengers’ forms of anger expression are lacking. This paper describes the development of a bus passenger anger expression inventory (BPAX) and a bus driver anger expression inventory (BDAX) based on a 402 passenger sample and a 414 driver sample. Exploratory principal component analysis revealed five factors in the BPAX: verbal aggressive expression, verbal positive expression, personal physical aggressive expression, adaptive/constructive expression, and displaced aggression. Similarly, six factors were identified in the BDAX: verbal positive expression, use of the vehicle to express anger, verbal aggressive expression, adaptive/constructive expression, personal physical aggressive expression, and displaced aggression. Overall, gender showed a difference only in aggressive expressions of passenger anger, not in drivers’ anger expressions. Older, less educated, and lower-income passengers preferred to express anger aggressively and rarely relieved conflicts in a positive verbal way. For driver groups, differences in age, anger level, and city grade were reflected in their forms of anger expression. The results of this paper are significant for strengthening driver safety training, improving safety facilities in buses, enhancing passenger education on civilized riding, and perfecting laws and regulations.

Developing a novel measure of psychological wellbeing for a community neurorehabilitation service

Psychological wellbeing during neurorehabilitation can be difficult to measure. This study aimed to develop a novel patient-rated wellbeing measure for patients attending a community neurorehabilitation service, and explore the relationships between scores on the new self-report measure, a mood questionnaire used in the service, and clinician wellbeing ratings. 178 healthy adults completed an initial wellbeing questionnaire comprised of 48 items. Exploratory principal component analysis was completed, and the final 11-item questionnaire was administered to 25 individuals undergoing community neurorehabilitation alongside the Hospital Anxiety and Depression Scale and Therapy Outcome Measures. Results suggested the novel Wellbeing in Neurorehabilitation Scale may be valid and reliable, with questions relating to social, emotional, and cognitive factors of wellbeing. Wellbeing ratings made by the clinicians correlated with patients self-reported wellbeing on the novel measure, although this was not a perfect relationship. The findings have implications for measuring psychological wellbeing during community neurorehabilitation.

The Psychometric Properties of Version 2 of the Canadian Nurse Informatics Competency Assessment Scale.

In 2020, we conducted a mixed methods study comprised of a cross-sectional survey in which we applied a modified version of the 21-item Canadian Nurse Informatics Competency Assessment Scale and one-on-one interviews to explore self-perceived nursing informatics competency and readiness for future digital health practice. A total of 221 senior-level students in BScN programs in western Canada participated. This article reports on results related to the factor structure and internal consistency reliability of the 26-item (version 2) of the Canadian Nurse Informatics Competency Assessment Scale. Exploratory principal component analysis with the varimax rotation revealed a four-component structure, explaining 55.10% of the variance. All items on the Canadian Nurse Informatics Competency Assessment Scale 2 had good loadings, except item 7, which did not load to any domain but was retained based on an evaluation of the α value and item relevance to nursing practice. A few items shifted to different domains. The overall reliability of the Canadian Nurse Informatics Competency Assessment Scale 2 was (α = .916) and its subscales: information and knowledge management (α = .814), professional and regulatory accountability (α = .741), and use of information and communication technology (α = .895). This study provided preliminary evidence for the factor structure and reliability of the Canadian Nurse Informatics Competency Assessment Scale 2 among nursing students. Further testing is recommended.

Association of the retail food environment, BMI, dietary patterns, and socioeconomic position in urban areas of Mexico.

The retail food environment is a key modifiable driver of food choice and the risk of non-communicable diseases (NCDs). This study aimed to assess the relationship between the density of food retailers, body mass index (BMI), dietary patterns, and socioeconomic position in Mexico. Cross-sectional dietary data, BMI and socioeconomic characteristics of adult participants came from the nationally representative 2012 National Health and Nutrition Survey in Mexico. Geographical and food outlet data were obtained from official statistics. Densities of food outlets per census tract area (CTA) were calculated. Dietary patterns were determined using exploratory factor analysis and principal component analysis. The association of food environment variables, socioeconomic position, BMI, and dietary patterns was assessed using two-level multilevel linear regression models. Three dietary patterns were identified-the healthy, the unhealthy and the carbohydrates-and-drinks dietary pattern. Lower availability of fruit and vegetable stores was associated with an unhealthier dietary pattern whilst a higher restaurant density was associated with a carbohydrates-and-drinks pattern. A graded and inverse association was observed for fruit and vegetable store density and socioeconomic position (SEP)-lower-income populations had a reduced availability of fruit and vegetable stores, compared with higher-income populations. A higher density of convenience stores was associated with a higher BMI when adjusting for unhealthy dietary patterns. Upper-income households were more likely to consume healthy dietary patterns and middle-upper-income households were less likely to consume unhealthy dietary patterns when exposed to high densities of fruit and vegetable stores. When exposed to a high concentration of convenience stores, lower and upper-lower-income households were more likely to consume unhealthy dietary patterns. Food environment and sociodemographic conditions within neighbourhoods may affect dietary behaviours. Food environment interventions and policies which improve access to healthy foods and restrict access to unhealthy foods may facilitate healthier diets and contribute to the prevention of NCDs.

Construct Validity and Applicability of a Team-Sport-Specific Change of Direction Test.

Cuts and changes of direction (COD) are frequent movements during games in team sports. Since those movements are seen as a key performance variable, COD assessments are included in performance diagnostics. However, some tests are criticized as they seem to be confounded by variables such as linear sprinting. Therefore, it is suggested that not only total COD time should be assessed, but also the athletes' COD movements should be examined more closely. For example, split times could be analyzed in tests with more than one COD like the Team-Sport-Specific COD (TSS-COD) test. We aimed to investigate the construct validity of the TSS-COD test, focusing on the homogeneity of the different test parts. We also tested how far sprint performance mapped onto COD performance. Test data were analyzed from 154 elite male and female volleyball and basketball athletes. A Fitlight© System was used to assess duration of the TSS-COD test. For the sprint tests, magnetic gates (Humotion GmbH) were used to measure sprint time. Explorative principal component analysis (PCA) was conducted including the test interval duration and the athletes' 5, 10, and 20 m sprint performance, to test the validity of the TSS-COD test. PCA results showed that the start interval formed a factor separate from the other COD sub-intervals. In addition, sprint performance was separated from all COD interval measures. The findings of the PCA were confirmed by split-half validation. Since sprint and COD performance represent independent performance domains within this analysis, we suggest the TSS-COD test to be a valid test to assess COD performance.