Background: Registered dietitian nutritionists (RDNs)-referred to as registered dietitians in Japan-contribute to disease management, prevention of complications, and improvement in quality of life through individualized nutritional guidance. However, these techniques often rely on individual experience, leading to variations in quality. The nutrition care process provides a standardized framework for nutritional care, but the specific techniques used in clinical practice and their interrelationships remain unclear. Interpretive structural modeling (ISM) is a method that visualizes and hierarchically organizes interrelationships among multiple elements, making it useful for structuring complex practical skills. Therefore, clarifying the structure of nutritional guidance techniques may support the standardization of practice and the development of educational frameworks.
Objective: This study aimed to identify the elements influencing nutritional guidance techniques in clinical practice, clarify their hierarchical structure using ISM, and explore their potential applicability to the education of registered dietitians.
Methods: Three experienced RDNs participated in an expert panel. Elements influencing nutritional guidance techniques were identified through structured brainstorming and consensus-building sessions. The extracted elements were analyzed using ISM to generate a reachability matrix and derive a hierarchical structure that visualized the interrelationships among the elements.
Results: A total of 14 elements were identified and organized into a 6-level hierarchical structure. The upper levels included nutrition care process-related elements, with the "nutritional intervention plan" positioned at the top, whereas the lower levels consisted of foundational elements such as "clinical knowledge" and "understanding of patient background."
Conclusions: This study identified 14 elements influencing nutritional guidance techniques in clinical practice and systematically visualized their interrelationships as a 6-level hierarchy using ISM. The resulting model provides an initial framework that may inform the development of clinical education curricula and competency evaluation frameworks for RDNs, and it could contribute to the advancement of standardized approaches in nutritional guidance education.
Background: Aspiration causes or aggravates a variety of respiratory diseases. Subjective bedside evaluations of aspiration are limited by poor inter- and intra-rater reliability, while gold standard diagnostic tests for aspiration, such as video fluoroscopic swallow study (VFSS) and fiberoptic endoscopic evaluation of swallowing (FEES), are cumbersome or invasive and healthcare resource intensive.
Objective: To develop and validate a novel machine learning algorithm that can analyze simple vowel phonations, to aid in predicting aspiration risk.
Methods: Recorded [i] phonations during routine nasal endoscopy from 163 unique patients were retrospectively analyzed for acoustic features including pitch, jitter, shimmer, harmonic to noise ratio (HNR), and others. Supervised machine learning (ML) was performed on the vowel phonations of those at high-risk for aspiration versus those at low-risk for aspiration. Ground truth of aspiration risk classification for model development was established using VFSS. The performance of the ML model was tested on an independent, external cohort of patient voice samples. The performance of trained Speech Language Pathologists (SLPs) to categorize high versus low-risk aspirators by listening to phonations was compared against the ML model.
Results: Mean ML risk score for those with the ground truth of high versus low aspiration risk was 0.530+ 0.310 vs 0.243+0.249, which was a significant difference (0.287, 95% CI: 0.192-0.381) p<0.001. In the development cohort, the model showed an area under the curve (AUC) for the Receiver Operator Characteristic (ROC) of 0.76 (0.67-0.84) with specificity of 0.76 and F1 score of 0.63. The performance of the model in an external testing cohort was comparable, with AUC of 0.70 (0.52-0.88) with a specificity of 0.81, and F1 score of 0.67. The ML model had comparable accuracy, sensitivity, specificity, negative and positive predictive values compared to trained SLPs in classifying aspiration risk by evaluating vowel phonations.
Conclusions: Otolaryngology (ENT) patients at high risk for aspiration have quantifiable voice characteristics that significantly differ from those who are at a low risk for aspiration, as detected by a ML model trained to analyze sustained phonation and tested on an independent cohort.
Clinicaltrial:
Background: Kazakhstan has lacked validated tools to comprehensively assess physicians' perceptions, usability, and perceived effectiveness of telemedicine services. International frameworks such as the Telehealth Usability Questionnaire (TUQ) and the Model for Assessment of Telemedicine (MAST) have not previously been adapted to the national clinical and organizational context.
Objective: This study aims to develop and validate TUQ-MAST-KZ, a Kazakhstan-adapted questionnaire integrating components of the TUQ and MAST models to assess physicians' perceptions, usability, and effectiveness of telemedicine services.
Methods: A multiphase study was conducted, including literature review, questionnaire development, linguistic and cultural adaptation, expert content validity assessment, and pilot testing. An online survey (Google Forms) was administered to 156 physicians representing different regions and levels of health care delivery in Kazakhstan. Internal consistency (Cronbach α) and content validity indices were calculated. Additional evaluations covered clarity, structure, and practical applicability.
Results: The final TUQ-MAST-KZ instrument contains 27 items capturing technological, clinical, organizational, and behavioral dimensions of telemedicine use. The scale demonstrated high content validity (scale-level content validity index=0.94). Internal consistency was excellent, with an overall Cronbach α of 0.924. Respondents reported that the questionnaire was clearly structured, easy to complete, and relevant to clinical practice. Organizational items identified key barriers to telemedicine adoption, including limited infrastructure, insufficient managerial support, and the need for additional training.
Conclusions: TUQ-MAST-KZ is a valid, reliable, and practice-oriented instrument for assessing physicians' perceptions of telemedicine services in Kazakhstan. It can support digital health monitoring, implementation analysis, educational planning, and policy development. Future studies should evaluate its applicability across broader samples and diverse clinical specialties.
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