Accurate Measure Research Articles

Applying an osteopathic intervention to improve mild to moderate mental health symptoms: a mixed-methods feasibility randomised trial.

The increasing prevalence of mental health disorders in the United Kingdom necessitates the exploration of novel treatment modalities. This study aimed to assess the feasibility and acceptability of conducting a randomised controlled trial (RCT) evaluating the efficacy of four osteopathic interventions on psychophysiological and mental health outcomes. A mixed-methods feasibility study with an explanatory sequential design was implemented. The quantitative phase involved randomising 42 participants into four intervention groups: (1) high-velocity and articulation techniques (HVAT), (2) soft-tissue massage (STM), (3) craniosacral therapy (CST), and (4) a combination approach. Primary outcome measures encompassed recruitment rate, assessment duration, questionnaire completion, intervention attrition, and adverse events. Secondary outcomes included validated assessments of depression, anxiety, stress, psychological flexibility, heart rate variability (HRV), and interoception, administered pre- and post-intervention. Analysis of variance (ANOVA) was employed to evaluate pre-post intervention changes. The qualitative phase comprised semi-structured interviews analysed using thematic analysis. The study achieved a recruitment rate of 21 eligible participants per month, with 54.8% of respondents meeting eligibility criteria. All 33 participants who completed the study underwent interventions and assessments within the allocated one-hour timeframe, with full questionnaire completion. The attrition rate was 21%. No adverse events were reported. Qualitative analysis revealed positive participant experiences, with themes highlighting good practitioner communication, intervention accessibility, and increased bodily awareness. Some participants found the questionnaire battery burdensome. Exploratory quantitative analyses showed variations in effects across interventions for heart rate variability, interoceptive accuracy, and mental health measures, but these results should be interpreted cautiously due to the small sample size. This study provides evidence supporting the feasibility and acceptability of a larger-scale RCT investigating osteopathic interventions for individuals presenting with mild psychological symptoms. The preliminary findings suggest potential efficacy in improving mental health outcomes, warranting further investigation. Trial registration NCT05674071, registered 06/01/2023.

Examining the Accuracy of Self-Reported Antiretroviral Therapy Adherence Using a Visual Analog Scale Among Opioid-Dependent People Living with HIV.

Evidence indicates that regular assessment of antiretroviral therapy (ART) adherence is necessary to promote adherence and achieve viral suppression. Self-reported adherence using a visual analog scale (VAS) has been used extensively to measure ART adherence. However, less is known about the accuracy of the VAS for measuring ART adherence among opioid-dependent people living with HIV. In this study, we aimed to evaluate the accuracy of the VAS in measuring ART adherence in opioid-dependent people living with HIV who are enrolled in a methadone maintenance program (MMP). This study was conducted within a larger randomized controlled trial among opioid-dependent people living with HIV (N = 109) who were enrolled in an inner-city MMP. Self-reported VAS ART adherence data were compared to pharmacy refill data, which is a more objective measure. Self-reported VAS ratings and pharmacy refill behavior were significantly correlated at the 6-month post-intervention mark (r = 0.349, p = 0.006) but not at any other time point (i.e., pre-intervention, post-intervention, 3-months, and 9-months post-intervention). Based on the pharmacy refill data, participants did not consistently adhere to their ART medication but self-reported high adherence on the VAS. The self-report VAS may not be an accurate measure for assessing ART adherence among opioid-dependent persons living with HIV and enrolled in a MMP. Based on our findings, it may be more accurate to use pharmacy refill information or other objective measures to quantify ART adherence over extended periods for this patient population.

Arbitrary Waveform Voltage Measuring Converter for Wideband AC Voltmeter

Alternating current (AС) voltage measurement is one of the most common types of measurements in various fields of science and technology. To evaluate the AC voltage level, special voltmeters are used which allow recording of amplitude, average and/or root mean square (RMS) voltage values. Among these measuring instruments, voltmeters of mean square voltage are especially significant because RMS is a fundamental physical characteristic of an electrical signal and is a true measure of power. The wide distribution of non-sinusoidal signals necessitates the creation of voltmeters for direct measurement of RMS. The main component of such voltmeter is an AC voltage to direct current (DC) voltage measuring converter based on the root-mean-square value level (AC RMS-DC converter). An analysis of existing technical solutions shows that it is advisable to use a thermoelectric converter (TEC) for high-precision measurement of RMS voltage of an arbitrary shape with a spectrum in the frequency band from 20 Hz to 20–50 MHz. Such a AC RMS-DC converter must contain a differential semiconductor TEC and an input broadband voltage amplifier with low nonlinearity of the frequency response. The aim of the paper was to develop a measuring AC RMS-DC converter of arbitrary shape voltage in which special attention is paid to modernization of the TEC and reduction of the AC RMS-DC converter error using corection of the frequency response of the input amplifier and introduction automatic calibration of the output voltage. Features of semiconductor chips and design of TEC in the form of a microcircuit or microassembly, results of converter’s and TEC elements’ parameters measurements are presented. A significant influence of the frequency response of the input amplifier and the offset voltage of the TEC transistors on the AC RMS-DC converter error was noted. Modernization of the input amplifier and introduction of automatic calibration of the output voltage ensured an error in a sinusoidal signal converting of less than 1 % in the range from 20 Hz to 50 MHz.

SMART DENGUE CONTROL

This research presents a novel approach to combating dengue by integrating Biological Control methods with Artificial Intelligence (AI). The objective of the study is to reduce the Aedes aegypti mosquito population and decrease dengue transmission through sustainable strategies. A comprehensive research methodology was used, involving extensive literature review, case studies, and data analysis to identify the main factors driving the high incidence of dengue in Brazil. The research applied AI tools to analyze epidemiological, meteorological, and environmental data, allowing for early outbreak predictions and targeted interventions. AI-enhanced surveillance systems were also employed to monitor mosquito populations and identify breeding sites more accurately. These strategies were combined with biological control methods, such as using natural predators and genetically modified mosquitoes, to manage mosquito populations effectively. The study demonstrate that the integration of AI and biological control can significantly improve the efficiency, accuracy, and sustainability of dengue control measures. The study concludes that this data-driven approach, when adapted to local contexts, can reduce reliance on chemical pesticides and offer a more resilient framework for long-term dengue management.

Predicting radiation pneumonitis in lung cancer using machine learning and multimodal features: a systematic review and meta-analysis of diagnostic accuracy.

To evaluate the diagnostic accuracy of machine learning models incorporating multimodal features for predicting radiation pneumonitis in lung cancer through a systematic review and meta-analysis. Relevant studies were identified through a systematic search of PubMed, Web of Science, Embase, and the Cochrane Library from October 2003 to December 2023. Additional studies were located by reviewing bibliographies and relevant websites. Two independent researchers screened titles, abstracts, and full-text articles according to predefined inclusion and exclusion criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The primary outcomes, including combined sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC), were calculated using STATA MP-64 software(Stata Corporation LLC, College Station, USA) with a random-effects model. Meta-analysis was conducted to synthesize diagnostic accuracy measures, and analyses of heterogeneity and publication bias were performed. A total of 1,406 patients with primary lung cancer were included in this systematic review, drawing data from 9 studies. The pooled analysis revealed a sensitivity of 0.74 [0.58-0.85] and a specificity of 0.91 [0.87-0.95] for machine learning models in diagnosing radiation pneumonitis. The positive likelihood ratio (PLR) was 8.69 [5.21-14.50], the negative likelihood ratio (NLR) was 0.28 [0.16-0.49], and the diagnostic odds ratio (DOR) was 30.73 [11.96-78.97]. The area under the curve (AUC) was 0.93 [0.90-0.95], indicating excellent diagnostic performance. Meta-regression analysis identified that the number of machine learning models, year of publication, and study design contributed to heterogeneity among studies. No evidence of publication bias was found. Overall, machine learning models incorporating multimodal characteristics demonstrated 75% accuracy in predicting moderate to severe radiation pneumonitis. In conclusion, by integrating the current machine learning (ML) algorithm's ability in big data mining, a predictive model can be constructed by combining multi-modal features such as genetics, imaging, and cell factors. By selecting multiple machine learning algorithm frameworks and competing for the best combination model based on research goals, the reliability and accuracy of the radiation pneumonitis prediction model can be greatly improved. PROSPERO (CRD42024497599).

Locally purified density operators for noisy quantum circuits

Abstract Simulating open quantum systems is essential for exploring novel quantum phenomena and evaluating noisy quantum circuits. In this Letter, we address the problem of whether mixed states generated from noisy quantum circuits can be efficiently represented by locally purified density operators (LPDOs). We map an LPDO of N qubits to a pure state of size 2 × N defined on a ladder and introduce a unified method for managing virtual and Kraus bonds. We numerically simulate noisy random quantum circuits with depths up to d = 40 using fidelity and entanglement entropy as accuracy measures. LPDO representation proves to be effective in describing mixed states in both quantum and classical regions but encounters significant challenges at the quantum-classical critical point, limiting its applicability to the quantum region exclusively. In contrast, the matrix product operator (MPO) successfully characterizes the entanglement trend throughout the simulation, while truncation in MPOs breaks the positivity condition required for a physical density matrix. This work advances our understanding of efficient mixed-state representation in open quantum systems and provides insights into the entanglement structure of noisy quantum circuits.

Contribution of Smart Learning on Students’ Academic Competences in Private Universities in Rwanda

The integration of smart learning in teaching and learning process can be successful and increase Students’ academic competences as long as smart learning facilities are integrated. This study titled “contribution of smart learning on students’ academic competences in private universities in Rwanda” assessed the contribution of smart learning technologies on students’ academic performance and explored the relationship between smart learning and students’ academic competences in private universities in Rwanda. In order formulate research design, the study was guided by system and flipping classroom theory. The research adopted a descriptive research design. The targeted population of the study was 400 respondents who were sampled by using Yamane’s formula of sample calculation to become 200 composed by 7 Staff members, 23 lecturers and 170 students. The staff members were purposively chosen while students and lectures were simple randomly sampled. The data collection tools used in this study were Questionnaires and interview. In order to analyses data, the study adopted descriptive, quantitative and qualitative techniques. Through retesting piloted study, reliability was employed by using accurate valid measures. The results showed that the use of smart learning facilities has improved the students’ competences in campuses where 70.6 % strongly agree and 15.9 % answered agree. The study further confirmed that smart learning tools engage and motivate students’ learning process where 71.2 % responded strongly agree and 17.1% responded agree. The study concluded that there was a correlation between smart learning and students’ academic competences where Karl Pearson coefficient of correlation was 0.751, which means the smart learning, contribute to the students’ academic competences. The study recommended that through ministry of education government of Rwanda need to increase fund allocated in supporting private universities. Higher Education Council also need to increase its intensive monitoring and evaluation on the effective use of smart learning within campuses. Lecturers need allow all students to interact with computers and smart digital tools.

An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication

Semantic communication (SC) is a communication paradigm that has gained significant attention, as it offers a potential solution to move beyond Shannon’s formulation in bandwidth-limited communication channels by delivering the semantic meaning of the message rather than its exact form. In this paper, we propose an autoencoder-based SC system for transmitting images between two machines over error-prone channels to support emerging applications such as VIoT, XR, M2M, and M2H communications. The proposed autoencoder architecture, with a semantically modeled encoder and decoder, transmits image data as a reduced-dimension vector (latent vector) through an error-prone channel. The decoder then reconstructs the image to determine its M2M implications. The autoencoder is trained for different noise levels under various channel conditions, and both image quality and classification accuracy are used to evaluate the system’s efficacy. A CNN image classifier measures accuracy, as no image quality metric is available for SC yet. The simulation results show that all proposed autoencoders maintain high image quality and classification accuracy at high SNRs, while the autoencoder trained with zero noise underperforms other trained autoencoders at moderate SNRs. The results further indicate that all other proposed autoencoders trained under different noise levels are highly robust against channel impairments. We compare the proposed system against a comparable JPEG transmission system, and results reveal that the proposed system outperforms the JPEG system in compression efficiency by up to 50% and in received image quality with an image coding gain of up to 17 dB.

A Meta-Analysis of the Moderators in the Relationship Between Morphological Awareness and Chinese Reading Accuracy

ABSTRACT Purpose This study aimed to deepen the understanding of the relationship between morphological awareness and Chinese reading accuracy by exploring the factors that influence this connection through a meta-analysis. Method We conducted a meta-analysis using data from 84 articles, comprising 104 effect sizes and a sample of 14,348 children (mean ages = 3.76 to 13.79 years). The study examined potential moderators such as script type (traditional and simplified script), age, and the type of reading accuracy measure (word and character reading), as well as the interaction between these moderators on the abovementioned relationship. Results The analysis revealed that script type was a significant moderator in the relationship. The correlation between morphological awareness and word/character reading was stronger in traditional script than in simplified script. Age also had a significant moderating effect in the relationship, with the correlation increasing significantly with age. However, neither the type of reading accuracy measure nor the interaction between measure type and script type significantly moderated the relationship between MA and Chinese reading accuracy. Conclusion The moderation by script type suggests that the differences in morpheme orthography between the two scripts can affect the relationship between morphological awareness and reading accuracy. The moderator of age indicates that the influence of morphological awareness on reading accuracy may grow stronger as children get older. The lack of a moderating effect by the type of reading accuracy measure suggests that the correlation between morphological awareness and word/character reading is consistent across these measures.

Different view of the diagnostics test accuracy measures and optimal cut-off point selection procedure under tree or umbrella ordering

ABSTRACT In the realm of medical diagnostic testing, diagnostic tests can assume either binary forms, distinguishing between diseased and non-diseased states, or ordinal forms, categorizing states from non-diseased to various stages (1 to K). Another significant classification scheme for multi-class scenarios is tree or umbrella ordering, which entails several unordered sub-classes (subtypes) within a biomarker. Within tree or umbrella ordering, the classifier assesses whether the marker measurement for one class surpasses or falls below those for the other classes. Although Receiver Operating Characteristic (ROC) curves and summary measures have been adapted to accommodate tree and umbrella ordering, these approaches often yield cut-off points that generate highly sensitive tests for certain disease subtypes while compromising specificity for others. This may not be ideal for all diseases. Hence, in this investigation, we explore diverse measures of diagnostic test accuracy and optimal cut-off point selection procedures under tree or umbrella ordering to foster more specific tests. We present numerical examples and simulation studies and demonstrate the approach using real data on lung cancer.

A more accurate measure of endometrial cancer incidence rates in New Mexico: A hysterectomy-corrected analysis

A more accurate measure of endometrial cancer incidence rates in New Mexico: A hysterectomy-corrected analysis

Is L2 pronunciation affected by increased task complexity in pronunciation-unfocused speaking tasks?

Abstract This study examines the effects of task complexity on second language (L2) pronunciation accuracy and global pronunciation measures in pronunciation-unfocused tasks and assesses the relationship between acoustic and listener-based pronunciation measures. Eighty-two Catalan/Spanish learners of English performed simple and complex versions of a problem-solving monologic speaking task, for which the oral stops /p, t, k/ and vowel contrasts /iː/-/ɪ/ and /æ/-/ᴧ/ were embedded in the lexical items used to perform the task. Pronunciation accuracy was gauged through acoustic measurements of laryngeal timing (voice onset time), vowel contrastiveness and nativelikeness (Mahalanobis distances), and native speakers’ ratings of comprehensibility and accentedness. Results revealed detrimental effects of increased task complexity on the productions of oral stops and speech comprehensibility and accentedness; however, no consistent task complexity effects were found on vowel accuracy. The analysis also revealed an association between segmental accuracy and global dimensions of L2 speech.

Hierarchical Attention and Contextual Embedding for Robust Sarcasm Detection

Objective: The study aims to develop a technique for sarcasm detection in social media text to enrich the sentiment analysis. Methods: The methodology used in the present research is as follows: emotion-embedded vectors for capturing the emotional content of the text, dynamic contextual modulation for the adaption of the model to the given context, and Hierarchical attention mechanism for segmentation of the text at the different level of abstraction. Findings: Evaluation with the test set proved that the proposed model achieved accuracy rates 89% higher than the benchmark models. Including these several complicated methods helped achieve greater accuracy and F1 measures, contributing to sarcasm detection efficiency. Novelty: This approach ensures that vital sarcastic alerts are detected, providing a better text analysis and increasing sentiment analysis's accuracy and robustness. Keywords: Sarcasm detection, Sentiment analysis, Natural language processing, Deep learning, Opinion mining

Measuring fisheries outcomes from coastal wetland restoration: a review and meta‐analysis

Coastal wetlands, including saltmarshes, mangrove forests, and seagrass meadows, can support fisheries by providing habitat and exporting food and nutrients to adjacent or distant ecosystems. However, there is insufficient understanding about the extent to which wetland restoration can benefit fishery species. Here, we report qualitative and quantitative reviews of literature to evaluate the approaches used to measure or estimate fisheries outcomes of coastal wetland restoration and test for the effects of restoration on fisheries outcomes. Most studies focused on saltmarshes and mangroves, with fewer studies focussing on seagrasses. We found that effects of wetland restoration on fish populations have mainly been assessed for species that reside in coastal wetlands for at least part of their life cycle, while few studies have tested effects on fish populations via trophic subsidies. Moreover, few studies have directly assessed the effects of restoration on fisheries production. Instead, most studies have measured abundances of individual species at restored sites and compared those to undisturbed and/or disturbed areas. Restored areas contained similar fish densities to undisturbed or disturbed areas; but few studies compared restored areas to both undisturbed and disturbed areas at the same time. Overall, we did not find consistent and robust evidence for fisheries outcomes from wetland restoration projects because (1) there were not many studies, (2) the studies reported substantial variability in outcomes, and (3) the density of fish measured at a restoration site is not an accurate measure of the total fisheries benefit as adult fishes do not always reside in the restored habitat.

Using QALYs as an Outcome for Assessing Global Prediction Accuracy in Diabetes Simulation Models.

(1) To demonstrate the use of quality-adjusted life-years (QALYs) as an outcome measure for comparing performance between simulation models and identifying the most accurate model for economic evaluation and health technology assessment. QALYs relate directly to decision making and combine mortality and diverse clinical events into a single measure using evidence-based weights that reflect population preferences. (2) To explore the usefulness of Q2, the proportional reduction in error, as a model performance metric and compare it with other metrics: mean squared error (MSE), mean absolute error, bias (mean residual), and R2. We simulated all EXSCEL trial participants (N = 14,729) using the UK Prospective Diabetes Study Outcomes Model software versions 1 (UKPDS-OM1) and 2 (UKPDS-OM2). The EXSCEL trial compared once-weekly exenatide with placebo (median 3.2-y follow-up). Default UKPDS-OM2 utilities were used to estimate undiscounted QALYs over the trial period based on the observed events and survival. These were compared with the QALYs predicted by UKPDS-OM1/2 for the same period. UKPDS-OM2 predicted patients' QALYs more accurately than UKPDS-OM1 did (MSE: 0.210 v. 0.253; Q2: 0.822 v. 0.786). UKPDS-OM2 underestimated QALYs by an average of 0.127 versus 0.150 for UKPDS-OM1. UKPDS-OM2 predictions were more accurate for mortality, myocardial infarction, and stroke, whereas UKPDS-OM1 better predicted blindness and heart disease. Q2 facilitated comparisons between subgroups and (unlike R2) was lower for biased predictors. Q2 for QALYs was useful for comparing global prediction accuracy (across all clinical events) of diabetes models. It could be used for model registries, choosing between simulation models for economic evaluation and evaluating the impact of recalibration. Similar methods could be used in other disease areas. Diabetes simulation models are currently validated by examining their ability to predict the incidence of individual events (e.g., myocardial infarction, stroke, amputation) or composite events (e.g., first major adverse cardiovascular event).We introduce Q2, the proportional reduction in error, as a measure that may be useful for evaluating and comparing the prediction accuracy of econometric or simulation models.We propose using the Q2 or mean squared error for QALYs as global measures of model prediction accuracy when comparing diabetes models' performance for health technology assessment; these can be used to select the most accurate simulation model for economic evaluation and to evaluate the impact of model recalibration in diabetes or other conditions.

Executive functions and processing speed in covert cerebral small vessel disease.

Executive dysfunction and slowed processing speed are central cognitive impairments in cerebral small vessel disease (cSVD). It is unclear whether the subcomponents of executive functions become equally affected and whether computerized tests are more sensitive in detecting early cognitive changes over traditional tests. The associations of specific executive abilities (cognitive flexibility, inhibitory control, working memory) and processing speed with white matter hyperintensities (WMHs) and Instrumental Activities of Daily Living (IADL) were examined. In the Helsinki Small Vessel Disease Study, 152 older individuals without stroke or dementia were assessed with brain magnetic resonance imaging and comprehensive neuropsychological evaluation. WMH volumes were obtained with automated segmentation. Executive functions and processing speed measures included established paper-and-pencil tests and the computer-based Flexible Attention Test (FAT), Simon task and Sustained Attention to Response Task. White matter hyperintensity volume and IADL were associated with multiple cognitive measures across subdomains independently of demographic factors. The highest effect sizes were observed for FAT numbers and number-letter tasks (tablet modifications from the Trail Making Test), FAT visuospatial span, Simon task and semantic verbal fluency. Some of the widely used tests such as Stroop inhibition, phonemic fluency and digit span were not significantly associated with either WMHs or IADL. Processing speed and executive function subcomponents are broadly related to functional abilities and WMH severity in covert cSVD, but the strength of associations within subdomains is heavily dependent on the assessment method. Digital tests providing precise measures of reaction times and response accuracy seem to outperform many of the conventional paper-and-pencil tests.

Note on the radical inflation in the estimates of error variance in measurement models

This note discusses the radical technical inflation in error variance and the related standard error of test scores from both conceptual and empirical viewpoints. This technical inflation arises as a direct consequence of the technical underestimation of item-score correlation by the product–moment coefficient of correlation (PMC), which is embedded in the traditional estimators of reliability such as coefficients alpha, theta, omega, or rho (maximal reliability). Specifically, in educational settings where compilations usually include both easy and difficult items, the estimate by PMC may substantially deviate from the true association between an item and the score. Consequently, the use of traditional estimators of reliability leads to technically inflated estimates of standard errors, as the error variance related to these traditional measurement models is significantly inflated, resulting in deflated reliability estimates. In educational testing, employing deflation-corrected standard errors, calculated using deflation-corrected reliability estimators, would provide a more accurate measure of the test score’s true precision.

Assessing diagnostic accuracy: 18F-FDG PET-CT scans in low-grade infection detection among post-traumatic long bone non-unions; a literature review and clinical data

IntroductionThe diagnosis of low-grade infection in post-traumatic long bone non-unions poses challenges due to the absence of clinical signs. This study aimed to review the available literature on the diagnostic accuracy of imaging techniques for low-grade infections and assess the diagnostic accuracy of 18F-FDG PET-CT scans for low-grade infection in post-traumatic long bone non-unions. MethodsA mini-review was conducted using Pubmed in March 2024. A retrospective study was conducted including adult patients with a long bone non-union, suspected of infection. All patients underwent 18F-FDG PET-CT scans as the index test before surgical intervention, with peri‑operative cultures obtained during surgery serving as the reference standard. Quantitative analyses were performed on the standardized uptake value (SUV) measurements obtained from the 18F-FDG PET-CT scans. Diagnostic accuracy measures including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Receiver operating characteristic (ROC) curve analysis was employed to evaluate the discriminatory ability of SUV measurements. ResultsLiterature suggests that 18F-FDG PET-CT is the most accurate imaging technique to detect low-grade infections. The study included a total of 51 18F-FDG PET-CT scans and cultures from 50 patients with long bone non-unions. The diagnostic accuracy was found to be 0.67 (95 % CI 0.44–0.87). The PPV and NPV were calculated as 0.79 (95 % CI 0.53–1.00) and 0.52 (95 % CI 0.30–0.73), respectively. Quantitative analyses of SUV measurements demonstrated a low level of accuracy, with all area under the curve (AUC) values < 0.75 and ROC curves showing a trajectory fairly parallel to the diagonal line. Conclusion and discussionThe findings of this study indicate that in post-traumatic long bone non-unions, where a low-grade fracture-related infection (FRI) is suspected, the 18F-FDG PET-CT has a performance that is advantageous over other imaging techniques. A careful interpretation of the scan results is warranted, possibly including the quantitative analysis on tracer uptake as an adjunct. Nevertheless, the diagnostic accuracy in this condition is not as good as in early-onset FRI cases, and this should be taken into account when treating these challenging cases.

The role of interoception in lifestyle factors: a systematic review

Abstract Interoception, the perception of the internal state of the body, has been shown to play an important role in various conditions. A clear overview of the relationship between interoception and lifestyle factors has to the best of our knowledge not yet been presented. This systematic review aims to provide insight into the current state of evidence about the role of interoception in lifestyle factors. A protocol for this systematic review was registered in PROSPERO (CRD42023465957). A systematic search conducted in PubMed, Embase, and Web of Science databases included terms such as interoception (accuracy, attention, awareness, sensitivity, or sensibility), as well as lifestyle factors (alcohol consumption, cognitive leisure and relaxation activities, eating behavior, exercise, sleep, or smoking). Studies written in English, using qualitative data, including adults, using at least one measure of interoception, and using at least on measure of a lifestyle factor were included. The review included 68 studies, in which 47 measures of interoceptive accuracy and 34 measures of interoceptive attention were identified. Six studies quantified interoceptive awareness (e.g., the interaction between interoceptive accuracy and attention). 12 studies focused on alcohol consumption, 22 studies on cognitive leisure and relaxation activities, 19 studies on eating behavior, 9 studies on exercise, 4 studies on sleep, and 5 studies on smoking. Generally, cognitive leisure and relaxation activities, eating behavior, and fasting showed positive relations with interoception. Conflicting evidence on the relation between exercise and some interoceptive domains, and moderate evidence on the positive relation with other interoceptive domains was found. Greater sleep difficulties were related to enhanced interoception scores and alcohol consumption was negatively related with interoception, while results on the relation between interoception and smoking behavior showed varying results. Key messages • Some lifestyle factors positively influence interoception and vice versa, while for other factors the results are varying or conflicting. • The role of interoceptive skills in lifestyle factors is important for lifestyle and health management.

Aortic integral distensibility based on the auto-segmented dynamic motion of the aortic wall

Abstract Background Aortic stiffness is important in cardiovascular diseases and predicting outcomes. Echocardiography is a widely used noninvasive imaging modality for assessing aortic stiffness, but its accuracy can be limited by the low spatial resolution of ultrasound-based images and the subjectivity of manual measurements of aortic dimensions. Automated segmentation analysis can provide a more accurate and objective measure. Objective An automated algorithm and software have been developed for segmenting the aortic wall. A new measurement technique called "Aortic Integral Distensibility (AID)" has been formulated using this framework. This method overcomes limitations of traditional method focusing solely on the two positions of Aortic Distensibility (AD). Method In this particular investigation, 112 participants were subjected to echocardiography in order to quantify the dynamics of the aortic wall. An automated algorithm was devised to delineate the aortic wall by utilizing an M-mode echocardiography scan. The statistical analysis encompassed the measurement of AD and AID before and after the age of 65 group. This analysis was conducted considering various risk factors, including age, gender, weight, BMI, smoking, and blood pressure. Additionally, clinical parameters, specifically AIx @75 (Augmentation Index) and Framingham Risk Score (FRS), were evaluated as well. Inter-rater reliability was conducted between the traditional AD and the newly implemented AID. Results The results indicate that the group under 65 had more distensibility than the group over 65, and AID measurements were slightly higher than AD measurements. SBP showed a strong correlation with AID, rather than AD. AIx @75 and FRS had a negative relationship with distensibility. In the 65 years or older group, there was a significant negative correlation observed between AIx @75 and AID (r = -0.4, p = 0.012). Furthermore, a strong correlation was also observed between FRS and AID (r = -0.53, p = 0.002). Bland-Altman analysis demonstrated that the automated methodology had better reproducibility and less variability in distensibility measurements than the manual methodology. Conclusion The study suggests that AID could be valuable for assessing aortic stiffness. AID could help clinicians diagnose and manage cardiovascular diseases more accurately. The automatic approach provides reliable measurements for aortic distensibility and strain, which has clinical implications.Schematic Chart of the Study Process