Professional Tool Research Articles

The Cathartic Value of Applied Theatre

As known in theatrical literature, one way for communities to heal their traumas is through Applied Theatre (AT). Presenting a case of a theatre workshop promoted and attended by several healthcare professionals of the main hospital in Bergamo, this article shows AT as a resource and tool for professionals dealing with trauma, grief, and loss. Bergamo was the epicentre of the first outbreak of the pandemic in Europe. COVID-19 hit the entire community, dramatically putting the hospital and the ER at the centre of the crisis. Several doctors and nurses organized a theatre workshop a few years later to heal from the trauma and present their story in a theatre play Silent Days, Sleepless Nights, for the city. The article contributes to the literature on Applied Theatre in the post-pandemic era by showing the cathartic power of theatre and its potential for healing from COVID-19 trauma. Returning to tragedy in the classics, AT should be understood as a tool for communities when they experience a shared condition of fragility. The catharsis in the communal sharing of experiences through the staging of the scenes and the construction of a common narrative horizon were the elements through which the meaning and overcoming of painful memories were substantiated.

Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8

Dental caries is a very common chronic disease that may lead to pain, infection, and tooth loss if its diagnosis at an early stage remains undetected. Traditional methods of tactile-visual examination and bitewing radiography, are subject to intrinsic variability due to factors such as examiner experience and image quality. This variability can result in inconsistent diagnoses. Thus, the present study aimed to develop a deep learning-based AI model using the YOLOv8 algorithm for improving interproximal caries detection in bitewing radiographs. In this retrospective study on 552 radiographs, a total of 1,506 images annotated at Tehran University of Medical Science were processed. The YOLOv8 model was trained and the results were evaluated in terms of precision, recall, and the F1 score, whereby it resulted in a precision of 96.03% for enamel caries and 80.06% for dentin caries, thus showing an overall precision of 84.83%, a recall of 79.77%, and an F1 score of 82.22%. This proves its reliability in reducing false negatives and improving diagnostic accuracy. YOLOv8 enhances interproximal caries detection, offering a reliable tool for dental professionals to improve diagnostic accuracy and clinical outcomes.

Larger models yield better results? Streamlined severity classification of ADHD-related concerns using BERT-based knowledge distillation.

This work focuses on the efficiency of the knowledge distillation approach in generating a lightweight yet powerful BERT-based model for natural language processing (NLP) applications. After the model creation, we applied the resulting model, LastBERT, to a real-world task-classifying severity levels of Attention Deficit Hyperactivity Disorder (ADHD)-related concerns from social media text data. Referring to LastBERT, a customized student BERT model, we significantly lowered model parameters from 110 million BERT base to 29 million-resulting in a model approximately 73.64% smaller. On the General Language Understanding Evaluation (GLUE) benchmark, comprising paraphrase identification, sentiment analysis, and text classification, the student model maintained strong performance across many tasks despite this reduction. The model was also used on a real-world ADHD dataset with an accuracy of 85%, F1 score of 85%, precision of 85%, and recall of 85%. When compared to DistilBERT (66 million parameters) and ClinicalBERT (110 million parameters), LastBERT demonstrated comparable performance, with DistilBERT slightly outperforming it at 87%, and ClinicalBERT achieving 86% across the same metrics. These findings highlight the LastBERT model's capacity to classify degrees of ADHD severity properly, so it offers a useful tool for mental health professionals to assess and comprehend material produced by users on social networking platforms. The study emphasizes the possibilities of knowledge distillation to produce effective models fit for use in resource-limited conditions, hence advancing NLP and mental health diagnosis. Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. Especially using readily available computational tools like Google Colab and Kaggle Notebooks. This study shows the accessibility and usefulness of advanced NLP methods in pragmatic world applications.

The Serbian version of the Pandemic-Related Pregnancy Stress Scale (PREPS-SRB)-A validation study.

Pregnancy is a sensitive period in a woman's life when psychological distress can have negative consequences for the mother and fetus. Prolonged and intensified symptoms of anxiety and depression caused by the COVID-19 pandemic increase the risk of maternal and fetal health complications. The Pandemic-Related Pregnancy Stress Scale (PREPS) is a thoroughly designed tool that helps determine and analyze stress among pregnant women during pandemics in three domains: Preparedness in childbirth, (2) Infection, and (3) Positive Appraisal. A cross-sectional study included 189 pregnant women attending a community health center, "Dr Simo Milošević," in Belgrade, Serbia, from January to February 2022. Pregnant women anonymously completed a questionnaire as part of the study. The mean scores for those three domains are as follows: Preparedness (2.4 ± 0.9), Infection stress (2.8 ± 1.1), and Positive Appraisal (3.7 ± 0.9). Internal consistency of the PREPS questionnaire for PREPS-Total (α = 0.867). An explanatory factor analysis of the PREPS showed that the Serbian version of the Pandemic-Related Pregnancy Stress Scale has good psychometric properties. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO) was found to be 0.860, indicating a high degree of sampling adequacy. Additionally, Bartlett's Test of Sphericity yielded a statistically significant result (χ2 = 1564.206, df = 105, p < 0.001). The CFA showed very good fit indices for the Serbian sample, confirming the factor structure of the original English version. The RMSEA value of 0.056 (0.036-0.075) and values for fit indices TLI (0.961) and CFI (0.974) were above the cut-off of ≥0.95, indicating an excellent fit. All standardized factor loadings were statistically significant and ranged from 0.50 to 0.85. The PREPS-SRB questionnaire serves as a valuable tool for Serbian healthcare professionals, allowing them to identify pregnant women experiencing significant stress related to the COVID-19 pandemic.

Exploration of different quantitative polymerase chain reaction-based genotyping methods to distinguish Apcmin/+ mice from wildtype mice.

Various molecular methods have been established for genotyping single-nucleotide variants (SNVs). However, despite the widespread availability of quantitative polymerase chain reaction (qPCR) instruments in biomedical laboratories, the lack of professional analytical tools impedes the application of qPCR in genotyping. Apcmin/+ mice, which harbour a germline Apc mutation (g.2549T>A) associated with multiple intestinal neoplasms, are extensively employed in colorectal cancer research. In this study, we used Apc as a model and assessed the feasibility of different qPCR-based methods for SNV genotyping, considering approaches with and without genotyping analytical tools. We initially employed allele-specific PCR followed by electrophoresis to determine the genotypes of Apc in tail tissues from potential Apcmin/+ mice, and this method served as the benchmark for evaluating the performance of qPCR-based methods. Dye-based qPCR and melting curve assays exhibited distinct dissociation patterns that differentiated between synthesised wildtype (TT) and heterozygous mutant (TA) DNA and between TT and TA genotype mice based on analysis of tissue samples. This discrimination ability of these assays was unaffected by the use of different intercalating dyes (SYBR Green I or EvaGreen). Dual-probe qPCR assays were developed to simultaneously detect mutant and wildtype alleles using differently labelled probes. The genotyping module and delta cycle threshold method were used to facilitate the analysis of results. The qPCR-based methods displayed 100% agreement with the standard genotyping outcomes. When the PCR-electrophoresis method was used, approximately 15% of the samples required re-examination to obtain conclusive results. In contrast, when the qPCR methods were used, success rates exceeding 99% were achieved with a single test. Additionally, all qPCR-based methods determined mouse genotypes by analysis of stool samples, highlighting the applicability of these methods for non-invasive genotyping. Loss of heterozygosity in the Apc gene in intestinal polyps was detected using the dual-probe assay with delta cycle threshold method. In summary, this study successfully implemented intercalator-based and probe-based qPCR methods, with and without professional analytical modules, for characterising Apc in tissue and stool samples. Furthermore, these methods can be extended to allow genotyping of other SNVs and can facilitate non-invasive genotyping of transgenic animals.

An analysis of dental board disciplinary action among general dentists in Texas.

Dental care professionals can gain valuable insights by examining infractions reported to the state board of dentistry, which can serve as a resource to enhance their professional practice. By reviewing various infractions (violations resulting in disciplinary action) reported by the state boards, dental care professionals can foster ethical awareness, uphold professional conduct, manage risks effectively, prioritize patient-centered care, engage in continuous professional development, and promote peer accountability. This study analyzes state dental board disciplinary action data in Texas to identify common infractions among practicing general dentists and to leverage this information as an educational tool for dental care professionals. An electronic search of disciplinary actions was conducted, using the Texas State Board of Dental Examiners database. For this study, 1,056 disciplinary action reports were reviewed, and disciplinary infractions were classified into 5 main categories based on the nature of the infractions related to diagnosis, treatment, renewals, recordkeeping, and ethics and professionalism. After reviewing 1,056 disciplinary actions, the results showed that most infractions were due to inadequate recordkeeping (39%). The rest of the infractions included inadequate treatment (23%), lack of ethics and professionalism (23%), improper diagnosis (9%), and renewal issues (6%). Dental disciplinary records offer valuable insights into common instances of misconduct within dental practices. It is essential to approach this information with a critical and reflective mindset, aiming to enhance clinical practice by learning from the common mistakes made by other clinicians. Understanding the frequency and nature of infractions may promote a deeper awareness and sense of direction regarding where additional focus on ethics and practice management is needed.

Potential benefits of social media in educating healthcare professionals and students on medical imaging with ionizing radiation: A scoping review.

Advancements in medical imaging with ionizing radiation have significantly transformed the field and enhanced the education and training of medical professionals. A notable development in this educational landscape is the use of social media, which engages millions of users worldwide. This scoping review aims to explore the potential of social media as an educational tool for healthcare professionals and students in medical imaging with ionizing radiation, highlighting its benefits and disadvantages. In March 2024, research was conducted on three databases: PubMed, Scopus, and Web of Science. The research equation combined the terms "Social Media," "Education," and "Medical Imaging." Data extraction was based on PRISMA-ScR guidelines for scoping reviews. Seventeen articles published between 2017 and 2023 were included in this review, all of which were descriptive cross-sectional studies and primarily focused on popular platforms such as Twitter, Facebook, and YouTube. The key advantages of social media use for medical imaging education were accessibility, ease of use, and broad reach, while the main limitation was the potential for misinformation. While there is clear potential to integrate social media into medical imaging education, more must be done to understand their effectiveness. This ongoing process requires the active engagement of all stakeholders. While promising, further research is needed to validate social media as an educational tool. Successfully integrating social media into medical imaging education, thus enhancing learning, communication, and collaboration between healthcare professionals, requires a strong commitment to managing accurate and reliable content sources on social media.

ConciliaMed: an interactive mobile and web tool to reconcile chronic medications of patients undergoing elective surgery

ObjectiveThe last decade has seen exponential growth in electronic health tools. However, only a limited number of electronic medication reconciliation tools have been developed and implemented in healthcare settings. Here,...

In vivo bone regeneration performance of hydroxypropyl methylcellulose hydrogel-based composite bone cements in ovariectomized and ovary-intact rats: a preliminary investigation

The objective of this study is to fabricate and develop hydroxypropyl methylcellulose (HPMC) hydrogel (HG)-based composite bone cements with incorporation of hydroxyapatite (HA), beta-tricalcium phosphate (β-TCP), and with/without polymethylmethacrylate (PMMA) for vertebroplasty. For animal study, twenty female Wister rats (250–300 g, 12 weeks of age) were divided into four groups including a two non-ovariectomy (NOVX) groups and two ovariectomy (OVX)-induced osteoporosis groups. Two prepared biocomposites including HG/β-TCP/HA and HG/β-TCP/HA/PMMA were injected into the tibial defects of both OVX and NOVX rats for evaluating in vivo osteogenesis after 12 weeks. Micro-computed tomography and histological analysis using hematoxylin and eosin (H&E) and Masson's trichrome stains of the two composite cements implanted into the tibial defects of OVX and NOVX rats revealed enhanced bone regeneration potential. However, no statistically significant differences were noted among the groups based on new bone formation, demonstrating that the injected composite cements showed similar osteogenesis effects in both OVX and NOVX rats. These findings suggest that the newly developed composite bone cement composed of HG, β-TCP, HA and/or PMMA may be a promising and professional tool for treating osteoporotic and non-osteoporotic vertebral fractures.

Human upper limb kinematics using a novel algorithm in post-stroke patients.

Assessing the kinematics of the upper limbs is crucial for rehabilitation treatment, especially for stroke survivors. Nowadays, researchers use computer vision-based algorithms for Human motion analysis. However, specific challenges include less accuracy, increased computational complexity and a limited number of anatomical key points. This study aims to develop a novel algorithm using the MediaPipe framework to estimate five specific upper limb movements in stroke survivors. A single mobile camera recorded the movements on their affected side in a study involving 10 hemiplegic patients. The algorithm was then utilized to calculate the angles associated with each movement, and its accuracy was validated against standard goniometer readings, showing a mean bias within an acceptable range. Additionally, a Bland-Altman analysis demonstrated a 95% limit of agreement between the algorithm's results and those of the Goniometer, indicating reliable performance. The MediaPipe framework provides several advantages over other methods like OpenPose and PoseNet, such as several anatomical key points, improved precision and reduced execution time. This algorithm facilitates efficient measurement of upper limb movement angles in stroke survivors and allows for straightforward tracking of mobility improvements. Such innovative technology is a valuable tool for healthcare professionals assessing upper limb kinematics in rehabilitation settings.

Cataract Eye Disease Diagnosis Using the Random Forest Method

This study developed a machine learning-based classification model using the Random Forest algorithm to detect cataract risk based on 11 variables: age, gender, family history, lens opacity, visual acuity reduction, light sensitivity, color changes, double vision, intraocular pressure, slit-lamp results, and visual acuity. Feature importance analysis revealed that lens opacity and visual acuity variables contributed most significantly to cataract risk prediction, followed by intraocular pressure and visual acuity reduction. The system was designed using Google Colab for model training and Streamlit as an interactive interface, enabling real-time predictions with intuitive result visualization. After optimization using Grid Search, the model achieved an accuracy of 92.0%, precision of 95.0%, sensitivity of 90.0%, F1 Score of 92.4%, and specificity of 98.0%. This system is expected to serve as an effective supporting tool for medical professionals in the early diagnosis of cataracts.

P0605 Nutritional Therapy for IBD: a new non-profit organization that offers trusted, evidence-based information and novel resources on diet and nutritional therapies in IBD

Abstract Background There is a well-described need for credible resources on the role of diet in IBD, with many Internet and social media sources found to be unbalanced and misaligned with the evidence1,2. Nutritional Therapy for IBD (https://www.nutritionaltherapyforibd.org/) was founded in 2019 to enhance the well-being and health outcomes of children and adults with IBD by optimizing medical care through adjunctive evidence-based nutritional therapy. An overview of the characteristics of this organization’s online resources, web traffic, and user impressions are described. Methods Website performance metrics were collected via Google Analytics from Oct 31, 2023, to Oct 31, 2024, and an online survey was conducted for two weeks in Oct 24. Results The website fosters training and dissemination of knowledge of the role of diet and nutritional therapies in IBD and includes four main sections: dietary options for IBD, research database, information for patients and families, and tools for healthcare professionals. Over the study period, there were 453,103 visits and 151,588 active users from 10 countries, with an average of 3 visits per user. The top-engaged countries were the United States, United Kingdom, Canada, South Africa, and Australia (Figure 1). The most visited website sections included 1) Recipes, 2) The Crohn’s Disease Exclusion Diet, and 3) The low FODMAP diet for non-inflammatory symptoms (Figure 2). Of 81 users completing the survey, 80.2% were female, 35.8% were patients with IBD, 29.5% were healthcare professionals, and 13.6% were family members. Among healthcare professionals, 30.3% were registered dietitians, 21.2% IBD-specialized registered dietitians, and 12.1% IBD gastroenterologists. 91.4% of responders indicated the content was useful, with high scientific rigor (82.7%), and stated the content influenced how they viewed nutrition as part of IBD care (74.1%). The most valuable resources were research summaries (80.0%), resources for implementing nutritional therapies in daily life (73.8%), and detailed information on specific nutritional therapies (68.8%). Conclusion Internet users are clearly interested in and find it useful to have access to evidence-based information about dietary interventions for IBD. Scientific rigor combined with accessible language are distinctive features of Nutritional Therapy for IBD, developed to help healthcare professionals use the best available evidence to inform their practice and help patients make informed choices about nutrition.

A graph neural network model application in point cloud structure for prolonged sitting detection system based on smartphone sensor data

AbstractThe prolonged sitting inherent in modern work and study environments poses significant health risks, necessitating effective monitoring solutions. Traditional human activity recognition systems often fall short in these contexts owing to their reliance on structured data, which may fail to capture the complexity of human movements or accommodate the often incomplete or unstructured nature of healthcare data. To address this gap, our study introduces a novel application of graph neural networks (GNNs) for detecting prolonged sitting periods using point cloud data from smartphone sensors. Unlike conventional methods, our GNN model excels at processing the unordered, three‐dimensional structure of sensor data, enabling more accurate classification of sedentary activities. The effectiveness of our approach is demonstrated by its superior ability to identify sitting, standing, and walking activities—critical for assessing health risks associated with prolonged sitting. By providing real‐time activity recognition, our model offers a promising tool for healthcare professionals to mitigate the adverse effects of sedentary behavior.

ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios.

Supporting professional development for early career pre-tenure nursing faculty using Narrative Reflective Process

Background: Early career, pre-tenure nursing faculty face significant challenges as they navigate the demands of academia, particularly during their first year. While there is growing research about pre-tenure faculty needs, specific insights into early career nursing faculty, especially in Canada, is limited. It is also not known how professional development strategies such as Schwind’s Narrative Reflective Process (NRP) could facilitate integration into academia and support professional growth. The purpose of this article is to explore the application of NRP as a professional development tool for nursing faculty during their first year of a tenure-track position.Methods: At a large urban school of nursing in Canada, four pre-tenure nursing faculty members in their first year of appointment engaged in a series of collaborative, professional development exercises guided by a senior faculty member using NRP. A composite reflection was analyzed using qualitative content analysis.Results: Six themes emerged from the faculty members’ experiences using NRP: needing time to reflect, learning process, creative process, sharing experiences and finding our unique path, experiencing emotions, and looking forward.Discussion: This article highlights the experiences of NRP on early career nursing faculty. Findings reveal the need for dedicated reflection time to foster emotional engagement. Creative activities deepened insights into faculty identities, while sharing experiences fostered community and collaboration. Faculty expressed enthusiasm for applying NRP in future educational and research contexts, recognizing its value for ongoing professional development. Conclusion: NRP is a promising tool to promote professional development in early career tenure-track nursing faculty in academia.

Turning nursing students’ mistakes into resources for learning in simulation-based training: facilitators’ assumptions about providing feedback in debriefing

BackgroundThe aim of this study was to investigate how facilitators approach and use nursing students’ mistakes in simulation-based training as learning resources in the simulation debriefing phase. Facilitators are responsible for raising students’ awareness of their performances during the debriefing and facilitating reflections on their performances, including satisfactory behaviours and performance gaps. Research on facilitators’ work during debriefing has highlighted various challenges, such as providing a safe and constructive climate among novice students while simultaneously teaching them the correct procedures, methods, and knowledge of caring practices to become professional nurses. There is a lack of research on how facilitators approach, handle, and use students’ mistakes as a learning resource. Thus, this study investigated facilitators’ assumptions about providing feedback to nursing students when they made mistakes during simulation-based trainingMethodIndividual semi-structured interviews were conducted with nine experienced facilitators from three universities in Norway. Data were analyzed following the principles of thematic analysis (TA).ResultsFacilitators made varying assumptions about the simulations and debriefings as learning processes. These differences were evident in their accounts of how feedback was provided to students when they made mistakes during the simulation-based training.ConclusionFacilitators’ statements about their practices reflect assumptions about how they make simulation activities a resource for meaningful learning, including how to use students’ mistakes as learning opportunities during debriefing discussions. Consequently, these assumptions regarding learning provide valuable insights into the ambiguous and complex praxis of using simulation-based training as a professional educational tool.

Yoga AI - Integrating artificial intelligence with yoga and therapy for personalized healthcare

Yoga AI combines the ancient wisdom of yoga with modern artificial intelligence (AI) to revolutionize healthcare. This innovative approach leverages AI algorithms to analyze individual health data, providing personalized yoga recommendations for optimal wellness. By integrating yoga therapy with AI-driven insights, Yoga AI enhances treatment outcomes, improves patient engagement, and streamlines clinical workflows. Our research demonstrates the efficacy of Yoga AI in managing chronic diseases, reducing stress, and promoting overall well-being. As a cutting-edge tool for healthcare professionals and individuals alike, Yoga AI redefines the future of integrative medicine.

Is AI image generation a creative or optimize tool in product design process?—The usage of AI image generation tools for vehicle shape as an example

This study explores the shape thinking processes and decision-making factors of designers when using AI image generation tools for conceptualizing the shapes of two-wheeled vehicles through four design tasks. Eight designers were invited to create hand-drawn sketches based on a specific aesthetic direction (technological geometry), followed by a shape divergence exercise using two AI graphics tools, Stable Diffusion and Vizcom, to generate images from text prompts. After selecting the designs closest to their original concepts and their favorite designs, the designers used an iPad to explore different shape directions (technological biology) for partial shape modifications. Finally, retrospective interviews were conducted to understand whether there were differences in designers’ thinking process regarding the use of various AI tools for shape conceptualization, as well as their focal points regarding design modification and shape thinking at different stages of the process. The research findings indicate that current AI tools are more suitable for shape divergence. If designers wish to achieve shape convergence, they need to be more familiar with the various settings of AI image generation tools and understand which prompts significantly influence specific shape characteristics. Designers’ perceptions of shape modification primarily revolve around: 1. Outline contours, 2. Parting lines, 3. Variations in surface curvature, and 4. The resulting features (light and shadow effects). Furthermore, it is recommended that future AI image generation tools, if developed as professional assistive tools for product design, should provide two modes—shape divergence and convergence—focusing on both the main shape and details. Additionally, it is suggested that developing AI-3D technologies should address the four key aspects of shape manipulation presented in this study, offering adjustments for overall appearance and detailing the contour lines of parts, including the manipulation of surface curvature and shape positioning.

Establishing Reliability and Validity for a Professional Development Tool for Graduate Health Care Programs

Establishing Reliability and Validity for a Professional Development Tool for Graduate Health Care Programs

A transmural intensive dietetic care-pathway for optimal protein intake and physical functioning in malnourished older patients: A protocol for the randomised ProIntens trial

Background Disease-related malnutrition is common among hospitalised and recently discharged older adults. Consequences of malnutrition are physical limitations, negative health outcomes, decreased quality of life and increased healthcare costs. Dietetic care can counteract the effects of malnutrition by increasing protein and energy intake. The ProIntens study aims to evaluate the impact of an intensive dietetic care pathway on dietary protein intake and physical functioning in older adults during hospitalisation and after discharge. Process and economic evaluations will be performed. Methods The ProIntens study is a multicentre two-armed parallel individually randomised trial in five hospitals in the Amsterdam region. In total, 250 hospitalised older adults, aged ≥55 years and at risk of malnutrition will be randomised to receive regular care (control) or intensive dietetic care (intervention). The intervention consists of intensive personalised care which involves transmural guidance by trained dietitians, supportive materials on protein intake in a social context and a mobilization program during hospitalisation until three months after discharge. Controls will receive regular care. The primary outcome will be the between-groups difference in the mean change on the Short Physical Performance Battery three months after discharge. Secondary outcomes include dietary protein intake, body composition, physical activity, activities of daily living, quality of life and healthcare costs. Discussion The ProIntens trial will study the effects and costs of an intensive dietetic treatment on recovery outcomes in hospitalised older adults at risk of malnutrition. The intervention will result in practical tools for care professionals which aim to improve malnutrition treatment for older patients. Trial registration Landelijk Trial Register (NL8041; NL72069.029.19); registered 2019-09-23; covering all WHO Trial Registration Data Set items.