Individual Categories Research Articles

FC41: A guide to developing individualized behavioral care plans for expressions in advanced neurocognitive disorders (ANCD) and based on the meaning ascribed to them

Expressions in ANCD are viewed as a mode of communication and ascribing meaning for their presence the key step to developing individualized behavioral care plans. Existing dementia care philosophies (PIECES™, GPA™, DementiaAbility and Teepa Snow Dementia Workshops) offer general directions but fail assist in ascribing meaning for the individual or clustering of the constellation of, varied phenotypic presentation of expressions in ANCD. The first step was to establish a biopsychosocial model for the generation of expressions; as all existing models were dichotomized along biological and psychosocial lines. The next step was developing a classification for the various phenotypic manifestations of expressions. This involved collecting ‘alike’ symptoms into individual categories and giving each category title which adequately represented the symptoms therein. Formation of behavioral categories was justified using existing validated psychological theories. Five psychological theories used to justify existence of behavioral categories: Information Processing Theories, Motivational Theories, Emotional Regulation Theories, Developmental Theories and Theories on Compliance and Aggression. These theories were able to ascribe meaning to individual behavioral categories and derived from their lived experiences: their Personhood. The ascribed meaning offers a framework for developing an individualized behavioral care plan for the expressions represented in each behavioral category. This framework, titled, LuBAIR™ Paradigm was structured in the form of an evaluative framework, both quantitative and qualitative, and presented as full day workshops, between 2017 to 2019, to frontline staff trained in existing dementia care philosophies. 92% of the attendees affirmed this paradigm’s ability to offer better assistance in “behavior care planning”. Qualitative thematic analysis: An innovative, simplified, enhanced, and comprehensive approach to understanding the reasons for the occurrence and recognition and labeling of behaviors in PwNCD. This approach offers increased insight into the understanding of “meaning” of behaviors in PwNCD. The Journal of Aging and Social Change: Volume 12, Issue 1, 2021.

Investigating level descriptors and their impact on validity

This article presents a validation study which investigates the relationship between mastery level descriptors and item difficulty in the National Tests of English (NTE) in Norway. The aim is to establish the extent to which the descriptors indicate item difficulty and thus support the argument that the mastery levels are a reflection of the framework of the NTE. This argument strength has direct implications for validity and for the defensibility of moving from criterion-based descriptors to norm-based national data. The study involved a panel of 10 raters assigning level descriptors from 7 content categories to 80 test items, giving a total of 5,600 individual judgements. These judgements are compared to the real test scores of around 46,000 pupils to establish if the level descriptors assigned by raters can predict pupil performance on the tests. The results show a strong correlation between rater judgements and real test scores, meaning that the descriptors offer an indication of item difficulty. However, some individual descriptor categories contain deviations from the expected order. We conclude that, while the level descriptors reflect the test’s framework, and thus support the validity argument, the argument could be strengthened with the revision of some individual descriptors.

Evaluation of mining landscape changes with development landscape metrics in the Ostrava-Karviná Mining District (Czech Republic).

The study presents an analysis of changes in the landscape of the Ostrava-Karviná Mining District (in the Czech Republic) covering the period of more than 170years. In the area of interest affected by underground coal mining, both areas affected by changes and land cover preserving areas were identified in the study. A detailed assessment of the landscape changes was enabled by using landscape metrics and indices, namely the development index and total landscape change index. The underlying data were obtained from maps of stable cadastre (from the year 1836) and aerial images of the years 1947, 1971, and 2009. Visual photointerpretation of aerial images and interpretation of the maps of stable cadastre made it possible to create land cover maps according to CORINE Land Cover categories. Obtained information on the representation of individual land cover categories were used to identify and to analyze changes in the landscape affected by hard coal mining.

How Stable Do Multiligament Knee Reconstructions Become?: An Objective Stability and PROMs Study.

Multiligament injuries (MLKI) of the knee mainly result from high-velocity impacts. This study was done to analyse multilgament knee reconstructions done in a single setting at a minimum 12month follow-up with objective gait, squat, stability and fluoroscopic examination along with PROMs and compare the individual KD categories. Also, the study analyses the difference between < 30- and > 30-year-olds as well as those who had sporting injuries versus RTAs. All cruciate ligaments were reconstructed arthroscopically. Collateral ligaments were reconstructed using autografts and fixed with interference screws. All were kept non-weight-bearing for 6weeks on a brace. ROM exercises were begun at the earliest. Gait analysis, stability, balance, squat analysis and fluoroscopic analysis were used to determine the objective outcomes during follow-ups. PROMs were analysed pre- and postoperatively to document the functional activity of the patient. The analysis of the various parameters with objective measurements of the clinical, functional and patient-reported measures was done to assess the outcome in multiligament knee injury patients. ACL with MCL was the most common presenting multiligament-involved presentation, with KD3 being the most common pattern of injury. The parameters for contact time, step length, ROM at knee and hip on the normal and operated limbs were near normal and same at a minimum 1-year follow-up with a good stability index. The PROMs improved significantly postop. There was no difference in the results of sporting versus non-sporting injuries, those < 30 and > 30years and between the individual KD groups. The results showed statistically significant improvement in stability and PROMs at follow-up in multiligament reconstructions, but no difference comparing individual KD groups, < 30 and > 30years age and those injured by RTA s and sporting injuries.

Leveraging Pre-trained Deep Learning Models for Remote Sensing Image Classification: A Case Study with ResNet50 and EfficientNet

The procedure of categorizing images from remote sensing is also another application of machine learning not just ground-based platforms (for instance satellites), aerial platforms become platforms sometimes in aviation either. They erase the counterparts that were based on individual categories and are portrayed on a specific part of the image. Geospatial Supply of gravel mainly is used for producing railway track, road and concrete surface. Data by analyzing their buildup, dams, bridges, extraordinary open spaces, reservoirs and canals. It targets to be specific and exact as possible in a different specific area of the land. Aspects of the enlarged portrait or distinctions weaved into the completed arts. This might have aspects such as mapping of the trees, plants, rivers, cities, farms and woodlands, and other items. Geospatial image classification is necessary for the identification and real-time analysis of different hazards and unrests. Provide numerous applications, including waste management, water resources, air quality, and traffic control in the urban contexts. Planning, monitoring the environment, land cover, mapping, as well as post-disaster recovery. Management team, traffic control, and situation assessments. In the past, human experts situated in a selected area classified geographical images by means of manual processing. One that involved the allocation of too much time. As this is one of the two broad categories, how to get rid of it is consequently. Applying machine learning and deep learning methods we analyze and interpret the data in order to reduce the time required to provide feedback which allows the system to reach a higher accuracy. The procedure will also be more reliable and the outcome will hopefully be more efficient CNNs are one of the deep learning subclasses in which the network learns and improves without the need for human intervention. It extracts features from images. They are main for the performance and metrics to help the organization to decide on whether they have accomplished their goals, using visual imagery.

Evaluation of hydrothermal carbonization of biomass residues for bioenergy: A life-cycle based comparison against incumbent technologies

Hydrothermal carbonization of biowaste with energy recovery was evaluated as a biowaste treatment technology using attributional life cycle assessment carried out in line with ISO 14044. Results show that important differences were observed for individual impact categories. Hydrothermal carbonization outperformed all other incumbent technologies in three impact categories and performed on par with anaerobic digestion and composting in the climate change category, where impact scores across three different wet biowaste streams ranged from -0.014 to -0.032 kg CO2 equivalents per kg of wet biowaste treated. However, it performed the worst in those environmental impacts which address resource depletion, including mineral, fossil and renewable resource depletion. This stresses the need to include all impact categories when evaluating environmental performance of biowaste treatment technologies. Differences in the ranking between different midpoint categories corroborate earlier studies suggesting that the general waste hierarchy might not necessarily apply to hydrothermal carbonization of biowaste.

Combining unsupervised constraints on weakly supervised semantic segmentation of skin cancer

Precise segmentation for skin cancer lesions at different stages is conducive to early detection and further treatment. Considering the huge cost of obtaining pixel-perfect annotations for this task, segmentation using less expensive image-level labels has become a research direction. Most image-level label weakly supervised segmentation uses class activation mapping (CAM) methods. A common consequence of this method is incomplete foreground segmentation, insufficient segmentation, or false negatives. At the same time, when performing weakly supervised segmentation of skin cancer lesions, ulcers, redness, and swelling may appear near the segmented areas of individual disease categories. This co-occurrence problem affects the model's accuracy in segmenting class-related tissue boundaries to a certain extent. The above two issues are determined by the loosely constrained nature of image-level labels that penalize the entire image space. Therefore, providing pixel-level constraints for weak supervision of image-level labels is the key to improving performance. To solve the above problems, this paper proposes a joint unsupervised constraint-assisted weakly supervised segmentation model (UCA-WSS). The weakly supervised part of the model adopts a dual-branch adversarial erasure mechanism to generate higher-quality CAM. The unsupervised part uses contrastive learning and clustering algorithms to generate foreground labels and fine boundary labels to assist segmentation and solve common co-occurrence problems in weakly supervised skin cancer lesion segmentation through unsupervised constraints. The model proposed in the article is evaluated comparatively with other related models on some public dermatology data sets. Experimental results show that our model performs better on the skin cancer segmentation task than other weakly supervised segmentation models, showing the potential of combining unsupervised constraint methods on weakly supervised segmentation.

Modeling the acceptance of vegetarian diets to promote sustainable food systems.

With increasing concerns about health, animal welfare, and the environment, changes in dietary patterns are emerging, as evidenced by the gradual shift toward plant-rich diets. To appropriately plan vegetarian meals with high consumer satisfaction that would help promote this dietary pattern and ultimately improve the sustainability of food systems, meal acceptability testing would be crucial. The present work aims to investigate the influence of individual food components' acceptability on the overall meal acceptability in vegetarian diets. Over four taste-testing periods, 94 panelists of US nationality recruited from Cornell University staff (47 males and 47 females; 30-50 years of age) were asked to rate the acceptability of 41 vegetarian meals containing various food category items, including a main dish, side dish, appetizer, salad, soup, bread, dessert, beverage, and a control food. Each meal included six menu items to ensure the tested foods resembled a typical meal. Consequently, some meals lacked one or more of the nine total food categories. Participants were asked to rate the acceptability of the six meal items and the overall meal acceptability on a 9-point hedonic scale. Multiple imputation was used to fill in any missing data, and multiple linear regression analysis was then performed. Through this study, a model that predicts the overall meal acceptability of vegetarian diets based on individual meal category components was developed with an R-squared value of .7033, suggesting that around 70.33% of the variation in overall meal acceptability could be explained by the nine food category predictors. The findings also suggested that the most influential food item on overall meal acceptability was the main dish followed by the side dish. For every unit increase in the acceptability score of the main dish on a 9-point hedonic scale, the overall meal acceptability increased by 0.324 points. The positive findings would be beneficial to food service providers in designing a vegetarian meal with high meal acceptability. By focusing on optimizing the acceptability of these two key categories, menu planners can effectively elevate customers' satisfaction of their meals.

Evaluating effects of silvicultural treatments on forest canopy structure outcomes

Traditional forest management can homogenize forests, and management strategies that can restore complexity and promote adaptive capacity to global change are needed. However, effects of different silvicultural practices on complexity of forest structural components, such as the canopy, are not well understood mechanistically. We conducted a coupled field and simulation-modeling study to evaluate: 1) how near-term effects of silviculture on canopy structure and complexity differ across treatment types, and 2) how outcomes of common silvicultural treatments compare to a spectrum of randomly implemented removals. Thirteen different silvicultural treatments replicated across 12 study plots were simulated within 3D models derived from terrestrial lidar data. Treatment types often differed in their multi-dimensional structural outcomes, including vertical heterogeneity and canopy structural complexity. Moderate intensity thinning treatments that preferentially removed smaller trees increased near-term canopy structural complexity, while diameter-limit cutting often reduced complexity. Silvicultural treatments collectively produced a wide range of residual canopy conditions; however, variability among structural outcomes within individual treatment types or categories was limited relative to the range of possible outcomes from random removals. Most treatments induced shifts in canopy structure outside the spectrum of random removal, suggesting ample space for adapting management to promote forest heterogeneity.

Update on PI-RADS Version 2.1 Diagnostic Performance Benchmarks for Prostate MRI: Systematic Review and Meta-Analysis.

Background Prostate MRI for the detection of clinically significant prostate cancer (csPCa) is standardized by the Prostate Imaging Reporting and Data System (PI-RADS), currently in version 2.1. A systematic review and meta-analysis infrastructure with a 12-month update cycle was established to evaluate the diagnostic performance of PI-RADS over time. Purpose To provide estimates of diagnostic accuracy and cancer detection rates (CDRs) of PI-RADS version 2.1 categories for prostate MRI, which is required for further evidence-based patient management. Materials and Methods A systematic search of PubMed, Embase, Cochrane Library, and multiple trial registers (English-language studies published from March 1, 2019, to August 30, 2022) was performed. Studies that reported data on diagnostic accuracy or CDRs of PI-RADS version 2.1 with csPCa as the primary outcome were included. For the meta-analysis, pooled estimates for sensitivity, specificity, and CDRs were derived from extracted data at the lesion level and patient level. Sensitivity and specificity for PI-RADS greater than or equal to 3 and PI-RADS greater than or equal to 4 considered as test positive were investigated. In addition to individual PI-RADS categories 1-5, subgroup analyses of subcategories (ie, 2+1, 3+0) were performed. Results A total of 70 studies (11 686 lesions, 13 330 patients) were included. At the patient level, with PI-RADS greater than or equal to 3 considered positive, meta-analysis found a 96% summary sensitivity (95% CI: 95, 98) and 43% specificity (95% CI: 33, 54), with an area under the summary receiver operating characteristic (SROC) curve of 0.86 (95% CI: 0.75, 0.93). For PI-RADS greater than or equal to 4, meta-analysis found an 89% sensitivity (95% CI: 85, 92) and 66% specificity (95% CI: 58, 74), with an area under the SROC curve of 0.89 (95% CI: 0.85, 0.92). CDRs were as follows: PI-RADS 1, 6%; PI-RADS 2, 5%; PI-RADS 3, 19%; PI-RADS 4, 54%; and PI-RADS 5, 84%. The CDR was 12% (95% CI: 7, 19) for transition zone 2+1 lesions and 19% (95% CI: 12, 29) for 3+0 lesions (P = .12). Conclusion Estimates of diagnostic accuracy and CDRs for PI-RADS version 2.1 categories are provided for quality benchmarking and to guide further evidence-based patient management. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tammisetti and Jacobs in this issue.

Do capital controls absorb global financial shocks? Evidence from emerging market economies

ABSTRACT This paper examines the role of capital controls as an effective policy instrument to insulate Emerging Market Economies (EMEs) from global financial shocks. Using state-dependent Local Projection method on a sample of emerging market economies, we find that economies with strict inflow controls are able to moderate the effects of global financial shock on GDP, real housing prices and private credit. We also find that monetary policy responds countercyclically to global financial shock in strict capital inflow controls state, which provides support for the use of inflow controls as a macroeconomic stabilising policy tool. Further, the results suggest that individual asset category inflow controls significantly contribute to overall effectiveness of capital inflow controls to act as shock absorbers. The findings suggests that the policy makers in EMEs may consider capital controls as an effective policy tool to increase the resilience of their economies against global financial shocks.

Editorial

Dear Readers, It gives me great pleasure to announce the seventh regular issue of 2024. In this issue, 5 papers by 12 authors from 6 countries - Brazil, Ecuador, Germany, Iraq, Spain, Turkey - cover various topical aspects of computer science. In a continuous effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. As always, I would like to thank all the authors for their sound research and the editorial board and guest reviewers for their extremely valuable review effort and suggestions for improvement. I also want to thank the readers for their interest in our articles, which is reflected in the consistently high number of user accesses and PDF downloads. These contributions, together with the generous support of the consortium members, maintain the quality of our journal. In the seventh regular issue, I am very pleased to present the following 5 accepted articles: Vinicius Bischoff and Kleinner Farias from Brazil report their study on a controlled experiment with 22 participants (15 students, 7 professionals) in which the correctness and the effort required for the integration of feature models were investigated. Abdulkadir Buldu, Kaplan Kaplan and Melih Kuncan from Turkey present their research on an assistive system based on EEG data and Continuous Wavelet Transform (CWT), which aims to reduce the life-threatening risk for epilepsy patients. In a collaboration between researchers from Ecuador and Spain, Darwin Alulema, Maximiliano Paredes-Velasco and Ricardo de Arriba Lasso report in their manuscript on the LESCA system, which performs adaptive content feedback through scaffolding and supports the development of high-level competencies. In another joint research between colleagues from Iraq and Germany, Anwar Mira and Olaf Hellwich present their approach to improve recognition capabilities by optimizing deep learning features for hand gesture image recognition. Specifically, they propose to enhance features of well-trained DNNs using an improved radial basis function (RBF) neural network, targeting recognition within individual gesture categories. And last but not least, Rasim &amp;Ccedil;ekik and Mahmut Kaya from Turkey propose in their research a new performance metric to evaluate filter feature selection methods in text classification.&amp;nbsp; Enjoy Reading! Best regards,&amp;nbsp; Christian G&amp;uuml;tl, Managing Editor&amp;nbsp;

An Embedded Neural Network Approach for Reinforcing Deep Learning: Advancing Hand Gesture Recognition

Deep neural networks (DNNs) can face limitations during training for recognition, motivating this study to improve recognition capabilities by optimizing deep learning features for hand gesture image recognition. We propose a novel approach that enhances features from well-trained DNNs using an improved radial basis function (RBF) neural network, targeting recognition within individual gesture categories. We achieve this by clustering images with a self-organizing map (SOM) network to identify optimal centers for RBF training. Our enhanced SOM, employing the Hassanat distance metric, outperforms the traditional K-Means method across a comparative analysis of various distance functions and the expanded number of cluster centers, accurately identifying hand gestures in images. Our training pipeline learns from hand gesture videos and static images, addressing the growing need for machines to interact with gestures. Despite challenges posed by gesture videos, such as sensitivity to hand pose sequences within a single gesture category and overlapping hand poses due to the high similarities and repetitions, our pipeline achieved significant enhancement without requiring time-related training data. We also improve the recognition of static hand pose images within the same category. Our work advances DNNs by integrating deep learning features and incorporating SOM for RBF training.

The Assessment of Green Poverty Reduction Strategies in Ecologically Fragile Areas: A Case Study of Southern Xinjiang in China

Green poverty reduction is a strategic choice for China to bring ecological benefits as well as economic and social benefits. This study examines three typical models of green poverty reduction strategies in Southern Xinjiang, which is an ecologically fragile region. The data for calculating the comprehensive benefits of the three models were derived from satellite remote sensing data, regional forestry bureau statistics, and survey data from 2021. The economic benefits are calculated to measure the net profit of a certain type of cover such as the supply of timber, forest products, and crops. The ecological benefits are calculated to measure the improvement in water resource regulation, soil conservation, carbon sequestration, windbreak and sand fixation, biodiversity conservation, and landscape recreation. The social benefits include providing employment opportunities and government subsidy. The comprehensive benefits are a weighted average over individual benefit categories. We found that the comprehensive benefits of the composite forest model, the drought-resistant crop model, and the industrial transformation model are CNY 288 million, CNY 50 million, and CNY 545 million, respectively. The composite forest model and the industrial transformation model have greater ecological benefits, while the drought-resistant crop model has greater economic benefits.

Assessing the quality of conceptual knowledge through dynamic constructions

In this contribution, we address the gap that has appeared in mathematics education research and practice with the emergence of dynamic geometry environments and build on the opportunities these environments offer to school geometry. In our qualitative empirical study, we investigate how to elaborate on the general model of conceptual knowledge to make it applicable to dynamic geometry tasks, specifically to tasks including dynamic geometric constructions. We present a design of dynamic constructions of quadrilaterals that comply with Euclidean constructions, derive an assessment instrument based on them, and study what information the instrument can provide about the quality of students’ conceptual knowledge. We present the results in the form of an assessment framework consisting of an example of the assessment instrument and an ordered system of qualitative categories serving as an assessment codebook for interpreting students’ responses in terms of the quality of conceptual knowledge. To clarify the relations between the assessment framework and the general model of conceptual knowledge, we establish a system of subdimensions of conceptual knowledge that indicates how conceptual knowledge can be understood in the context of dynamic geometric constructions and identifies the conceptual knowledge needed to achieve individual categories of the assessment framework.

Longitudinal associations of an exposome score with serum metabolites from childhood to adolescence

Environmental and lifestyle factors, including air pollution, impaired diet, and low physical activity, have been associated with cardiometabolic risk factors in childhood and adolescence. However, environmental and lifestyle exposures do not exert their physiological effects in isolation. This study investigated associations between an exposome score to measure the impact of multiple exposures, including diet, physical activity, sleep duration, air pollution, and socioeconomic status, and serum metabolites measured using LC-MS and NMR, compared to the individual components of the score. A general population of 504 children aged 6–9 years at baseline was followed up for eight years. Data were analysed with linear mixed-effects models using the R software. The exposome score was associated with 31 metabolites, of which 12 metabolites were not associated with any individual exposure category. These findings highlight the value of a composite score to predict metabolic changes associated with multiple environmental and lifestyle exposures since childhood.

Self-reported food liking and wanting: A factor analytic study of ratings across 49 consecutive days

Reward responses to food are thought to play an important role in highly palatable food overconsumption. In animal models, food reward responses can be decoupled into unique “liking” (in the moment enjoyment) and “wanting” (motivation/craving) components. However, research on liking and wanting has been hampered by uncertainty regarding whether liking and wanting can be reliably separated in humans. We used factor analysis to test whether ratings of liking and wanting could be empirically separated in women assessed across 49 consecutive days. Female participants (N = 688; ages 15–30) from the Michigan State University Twin Registry reported liking and wanting of foods consumed that day, and wanting of foods not consumed that day, separately for sweets (e.g., cookies), fast food (e.g., French fries), carbohydrates (e.g., bread), and whole foods (fruit, plain chicken) each evening for 49 consecutive days. We examined both average levels and daily levels of liking/wanting across the 49-day period that captured individual differences in liking/wanting over time. Across both types of analyses, liking and wanting for foods that were eaten formed a single factor rather than separate, dissociable factors, while wanting of foods not eaten formed an independent factor. At the daily level, a liking/wanting factor emerged for each individual food category (e.g., liking/wanting sweets), whereas in average analyses, a single factor emerged that collapsed across all food types (i.e., liking/wanting of all foods). Results suggest individuals have difficulty distinguishing between liking and wanting of foods they have eaten on that day but may be able to more reliably separate wanting of foods they have not consumed.

Even a journey can be a destination: Exploring the spatial patterns of motorcycle traffic in the Czech Republic based on official and crowdsourced data

Not only in geography is motorcycle transport still a very under-researched phenomenon. Although there has been a recent growth in academic interest in motorcycle transport, there are still some key questions that remain unanswered. Empirical studies analysing motorcycle transport are surprisingly also based mainly on observations in the Global South, with very little research in the economically developed countries of the Global North. This paper aims to examine the spatial patterns of motorcycle transport in the Czech Republic. For the analysis, we use official data on average motorcycle intensities on the road network (i), and unofficial data crowdsourced from individual GPS navigations and social networks (ii). We found major inconsistencies between the two data sources. The paper’s main contribution is to demonstrate the great potential of crowdsourced data to identify important aspects of the spatial organisation of motorcycle traffic. Based on these data, we found substantial differences in the intensity of motorcycle traffic on the road network (different use of individual categories of communications according to both types of data), the popularity of individual motorcycle routes (higher popularity of scenic roads in GPS data), and the different lengths of individual trips during weekdays and weekends.

Impact of Marital Status „Single“ on Behavior and Well-Being of an Employee or a Student

The behavior of employees, their attitudes and interest in work are the subject of countless studies and investigations. Many look at the issue of employee motivation and stimulation from the point of view of position, education, age or gender. However, within the available findings, there is no knowledge regarding the status of employees, although the very fact of whether an employee lives alone or with someone else presupposes different behavior. The aim of this paper was to find out whether the status “single” has an impact on behavior, performance, and job satisfaction of an employee. As part of the search for information on this issue, we came across interesting findings of many authors who devoted themselves not only to this issue, but also investigated the issues of multicultural values, leadership, management in practice, education and other topics related to the influence of various factors on the performance and work of individuals. We tried to apply his survey, previously carried out in the USA and Canada to the territory of Europe/European Union, while taking into account multiculturalism and gender differences, since students participating in the Erasmus program took part in the survey too. We based our research on the fact that the shares and development of individual categories are the same in both regions.

A diet low in fermentable oligo-, di-, monosaccharides and polyols improves abdominal and overall symptoms in persons with all subtypes of irritable bowel syndrome.

A diet low in fermentable oligo-, di-, monosaccharides and polyols (LFD) improves symptoms in patients with irritable bowel syndrome (IBS). Previous studies have focused on patients with IBS and diarrhea (IBS-D). It is unclear whether LFD is effective for IBS with constipation (IBS-C) or IBS with mixed bowel habits (IBS-M). This open-label, real-world study evaluates the relative effectiveness of the LFD among IBS subtypes. This study analyzes data from a service that provides low-FODMAP meals to individuals with IBS. Participants met with a registered dietitian and completed the IBS symptom severity survey (IBS-SSS) before and after undergoing a 2-4-week period of FODMAP restriction. The primary endpoint was the proportion of participants with ≥50-point decrease in IBS-SSS between the three IBS subtypes. After FODMAP restriction, 90% of participants with IBS-D, 75% with IBS-C, and 84% with IBS-M met the primary endpoint (p = 0.045). Similar improvement was seen for a 100-point decrease, but the difference between IBS subtypes was not significant (p = 0.46). After FODMAP restriction, all groups had statistically significant improvement in total IBS-SSS as well as individual symptom categories. Improvement in IBS-SSS subcategories was similar among the groups except for the categories of bloating severity (IBS-M had greatest improvement) and bowel movement satisfaction (IBS-C had less improvement). Though the proportion of responders was highest for IBS-D and lowest for IBS-C, the LFD led to robust improvement in overall symptoms in all IBS subtypes. Key individual symptoms also showed significant improvements in all IBS subtypes.