Ajuste fino de un modelo de producción vocal para estimar el estrés de impacto utilizando un método metaheurístico

Introducción: En modelos de producción vocal que emplean estructuras de resorte-masa-amortiguador, la precisión en la determinación de coeficientes de amortiguamiento que se asemejen a las características fisiológicas de las cuerdas vocales es crucial, teniendo en cuenta posibles variaciones en la representación de la viscoelasticidad. Objetivo: Este estudio tiene como objetivo realizar un ajuste paramétrico de un modelo de producción vocal basado en un sistema de resorte-masa-amortiguador que incorpora interacción con la presión subglótica, con el fin de modelar de manera precisa las fuerzas de colisión ejercidas por las cuerdas vocales durante la fonación. Método: Se utilizó un algoritmo de búsqueda metaheurística para la síntesis paramétrica. El algoritmo se aplicó a los coeficientes de elasticidad c1 y c2, así como a los coeficientes de amortiguamiento ε1 y ε2, que se correlacionan directamente con las matrices de masa del modelo. Esto facilita el ajuste de la composición de las cuerdas para lograr un comportamiento fisiológico deseado. Resultados: El comportamiento del sistema vocal para cada ciclo de simulación se comparó con un estándar predefinido en condiciones normales. El algoritmo determinó el punto final de la simulación evaluando las discrepancias entre características clave de las señales obtenidas y las deseadas. Conclusión: El ajuste paramétrico permitió la aproximación del comportamiento fisiológico de la producción vocal, proporcionando estimaciones de las fuerzas de impacto experimentadas por las cuerdas vocales durante la fonación.

Introduction: In vocal production models employing spring-mass-damper frameworks, precision in determining damping coefficients that align with physiological vocal fold characteristics is crucial, accounting for potential variations in the representation of viscosity-elasticity properties. Objective: This study aims to conduct a parametric fitting of a vocal production model based on a mass-spring-damper system incorporating subglottic pressure interaction, with the purpose of accurately modeling the collision forces exerted by vocal folds during phonation. Method: A metaheuristic search algorithm was employed for parametric synthesis. The algorithm was applied to elasticity coefficients c1 and c2, as well as damping coefficients ε1 and ε2, which directly correlate with the mass matrices of the model. This facilitates the adjustment of fold composition to achieve desired physiological behavior. Results: The vocal system's behavior for each simulation cycle was compared to a predefined standard under normal conditions. The algorithm determined the simulation endpoint by evaluating discrepancies between key features of the obtained signals and the desired ones. Conclusion: Parametric fitting enabled the approximation of physiological vocal production behavior, providing estimates of the impact forces experienced by vocal folds during phonation.

Recruitment strategies and retention rates for five National Dental PBRN studies.

Background: We describe a retrospective assessment of practitioner and patient recruitment strategies, patient retention strategies, and rates for five clinical studies conducted in the National Dental Practice-Based Research Network between 2012 and 2019, and practitioner and patient characteristics associated with retention. Methods: Similar recruitment strategies were adopted in the studies. The characteristics of the practitioners and patients are described. The proportion of patients who either attended a follow-up (FU) assessment or completed an online assessment was calculated. For studies with multiple FU visits or questionnaire assessments, rates for completing each FU were calculated, as were the rates for completing any and for completing all FU assessments. The associations of practitioner and patient characteristics with all clinic FU visits, and with the completion of all assessments for a study were ascertained. Results: Overall, 591 practitioners and 12,159 patients were included. FU rates by patients for any assessment varied from 91% to 96.5%, and rates for participating in all assessments ranged from 68% to 87%. The mean total number of patients each practitioner recruited was 21 (sd = 15); the mean number per study was 13 (sd = 7). For practitioners, practice type and patient enrollment were associated with greater clinic retention, while only race was associated with their patients completing post-visit online assessments. For patients, age was associated with clinic retention, while female gender, age, race, and education were all associated with greater completion of post-visit online assessments. Conclusion: The Network efficiently recruited practitioners and patients and achieved high patient retention rates for the five studies.

Headache Specialists' Perceptions of the Role of Health Psychologists in Headache Management: A Qualitative Study.

Background Since headache specialists cannot treat all the patients with headache disorders, multidisciplinary teams that include health psychologists are becoming more prevalent. Health psychologists mainly use a form of cognitive-behavioral therapy (CBT), along with biofeedback on occasion, to effectively address patients' pain and headache disorders. The Veterans Health Administration (VHA) is one setting that routinely includes a health psychologist with advanced training in pain disorders in their pain care to its veterans. The VHA has established Headache Centers of Excellence (HCoE) around the country to provide multidisciplinary treatment for patients with headache disorders, which enables headache specialists to regularly interact with health psychologists. Objective The study's objective is to evaluate headache specialists' views of health psychologists in the treatment of patients with headache disorders. Method Semi-structured interviews were conducted with headache specialists in academic-based healthcare settings, the community, and VHA HCoE sites. The interviews were audio-recorded and de-identified so they could be transcribed and analyzed using content matrix analysis. Results Four themes emerged: headache specialists desired to work with health psychologists and included them as members of multidisciplinary teams; valued health psychologists because they provided non-pharmacological treatments, such as CBT and biofeedback; preferred in-person communication with health psychologists; and used multiple titles when referring to health psychologists. Conclusion Headache specialists valued health psychologists as providers of behavioral and non-pharmacological treatments and considered them essential members of multidisciplinary teams. Headache specialists should strive to work with a headache psychologist, not just a general health psychologist. By committing to this, headache specialists can foster changes in the quality of care, resource allocation, and training experiences related to health psychologists.

A mixed-method evaluation of implementation determinants for chaplain intervention in a hospital setting.

Healthcare chaplains address broad social and emotional dimensions of care within a pluralistic religious landscape. Although the development and evaluation of chaplaincy interventions has advanced the field, little research has investigated factors influencing the implementation of new chaplain interventions. In this mixed-method study, we examined attitudes about evidence-based interventions held by chaplain residents (n = 39) at the outset of an ACPE-accredited residency program in the southeast United States. We also used semi-structured interviews (n = 9) to examine residents' attitudes, beliefs, and decision-making processes after they trained in the delivery of a novel manualized intervention, Compassion-Centered Spiritual Health (CCSH). Most residents reported favorable attitudes toward manualized approaches prior to training. Interviews revealed complex decision-making processes and highlighted personal motivations and challenges to learning and implementing CCSH. Implementation science can reveal factors related to motivation, intention, and training that may be optimized to improve the implementation of healthcare chaplaincy interventions.

Competitive Video Game Exposure Increases Aggression Through Impulsivity in Chinese Adolescents: Evidence From a Multi-Method Study.

It is widely known controversies about the results of violent video game increase aggression. However, the role of competitive video games, has received less research attention, and the underlying mechanisms of their influence are unknown. This study aimed to expand the existing literature by systematically exploring the effects of competitive video game exposure on adolescent aggression and the mediating role of impulsivity. In so doing, three types of studies (collectively N = 2919, mean age varied from 13.75 to 15.44 years, with a balanced gender) combining cross-sectional, experimental, and longitudinal approaches, were conducted. The findings consistently show that competitive video game exposure increased adolescents' aggression and impulsivity. Also, impulsivity mediated the correlation and long-term effect of competitive video game exposure on aggression. However, the experimental study did not confirm the short-term mediating effect of impulsivity, which may be related to the type of aggression measured in the study. The results indicate that competitive video game exposure is an important antecedent factor for adolescent aggression, and impulsivity is the key underlying mechanism.

U-Net enhanced real-time LED-based photoacoustic imaging.

Photoacoustic (PA) imaging is hybrid imaging modality with good optical contrast and spatial resolution. Portable, cost-effective, smaller footprint light emitting diodes (LEDs) are rapidly becoming important PA optical sources. However, the key challenge faced by the LED-based systems is the low light fluence that is generally compensated by high frame averaging, consequently reducing acquisition frame-rate. In this study, we present a simple deep learning U-Net framework that enhances the signal-to-noise ratio (SNR) and contrast of PA image obtained by averaging low number of frames. The SNR increased by approximately four-fold for both in-class in vitro phantoms (4.39 ± 2.55) and out-of-class in vivo models (4.27 ± 0.87). We also demonstrate the noise invariancy of the network and discuss the downsides (blurry outcome and failure to reduce the salt & pepper noise). Overall, the developed U-Net framework can provide a real-time image enhancement platform for clinically translatable low-cost and low-energy light source-based PA imaging systems.

The Emerging Role and Clinical Applications of Morphomics in Diagnostic Imaging.

Analytic morphomics refers to the accurate measurement of specific biological markers of human body composition in diagnostic medical imaging. The increasing prevalence of disease processes that alter body composition including obesity, cachexia, and sarcopenia has generated interest in specific targeted measurement of these metrics to possibly prevent or reduce negative health outcomes. Typical morphomic measurements include the area and density of muscle, bone, vascular calcification, visceral fat, and subcutaneous fat on a specific validated axial level in the patient's cross-sectional diagnostic imaging. A distinct advantage of these measurements is that they can be made retrospectively and opportunistically with pre-existing datasets. We provide a narrative review of the current state of art in morphomics, but also consider some potential future directions for this exciting field. Imaging based quantitative assessment of body composition has enormous potential across the breadth and scope of modern clinical practice. From risk stratification to treatment planning, and outcome assessment, all can be enhanced with the use of analytic morphomics. Moreover, it is likely that many new opportunities for personalized medicine will emerge as the field evolves. As radiologists, embracing analytic morphomics will enable us to contribute added value in the care of every patient.

Unlocking Patient Voices: Advancing Physical Therapist Practice with Discrete Choice Experiments.

As health care moves away from volume-based to value-based delivery, the role of the patient in determining value in health care is now paramount. Thus, health care should be aligned with what matters most to patients. Ascertaining patient preferences is therefore critical if we are to provide patients with care that is meaningful to them. However, preferences are difficult to measure and traditional methods of preference measurement in physical therapy face challenges when attempting to measure such. This perspective makes a case for greater use of the discrete choice experiment (DCE) in physical therapy as a research method to measure patient preferences. The DCE is a research method used to elicit preferences for services or goods. This article addresses the importance of eliciting patient preferences as part of person-centered care in the value-based space, the challenges faced in preference measurement in physical therapy, and how the discrete choice experiment can alleviate some of those challenges. It also provides examples of the discrete choice experiment in health care and suggests ways in which it can be effectively used in physical therapist practice to improve the delivery of meaningful rehabilitation services to patients. Implementing greater use of the DCE in physical therapy can improve person-centered physical therapist service delivery and inform policy development that creates cost-effective care which is meaningful to patients. It can further help to highlight the value of physical therapy to population health, and to policy makers as health care moves towards more value-based models.

Synthesis Strategies, Preparation Methods, and Applications of Chiral Metal-Organic Frameworks.

Chiral metal-organic framework (CMOFs) is a kind of material with great application value in recent years. Formed by the coordination of metal ions or metal clusters with organic ligands. It is widely used in chemistry, biology, medicine and materials science because of its ordered and adjustable pores, multi-dimensional network structure, large specific surface area and excellent adsorption properties. In this paper, the synthesis strategies and preparation methods of chiral metal-organic frameworks are reviewed. In addition, the applications of chiral metal-organic framework materials in enantiomer recognition and separation, circularly polarized luminescence and asymmetric catalysis are systematically summarized. Finally, the challenges and prospects of the development of chiral metal-organic frame materials are analyzed in detail.

Post-radiotherapy stage III/IV non-small cell lung cancer radiomics research: a systematic review and comparison of CLEAR and RQS frameworks.

BACKGROUND: Lung cancer, the second most common cancer, presents persistently dismal prognoses. Radiomics, a promising field, aims to provide novel imaging biomarkers to improve outcomes. However, clinical translation faces reproducibility challenges, despite efforts to address them with quality scoring tools. OBJECTIVE: This study had two objectives: 1) identify radiomics biomarkers in post-radiotherapy stage III/IV nonsmall cell lung cancer (NSCLC) patients, 2) evaluate research quality using the CLEAR (CheckList_for_EvaluAtion_of_Radiomics_research), RQS (Radiomics_Quality_Score) frameworks, and formulate an amalgamated CLEAR-RQS tool to enhance scientific rigor. MATERIALS AND METHODS: A systematic literature review (Jun-Aug 2023, MEDLINE/PubMed/SCOPUS) was conducted concerning stage III/IV NSCLC, radiotherapy, and radiomic features (RF). Extracted data included study design particulars, such as sample size, radiotherapy/CT technique, selected RFs, and endpoints. CLEAR and RQS were merged into a CLEAR-RQS checklist. Three readers appraised articles utilizing CLEAR, RQS, and CLEAR-RQS metrics. RESULTS: Out of 871 articles, 11 met the inclusion/exclusion criteria. The Median cohort size was 91 (range: 10-337) with 9 studies being single-center. No common RF were identified. The merged CLEAR-RQS checklist comprised 61 items. Most unreported items were within CLEAR's "methods" and "open-source," and within RQS's "phantom-calibration," "registry-enrolled prospective-trial-design," and "cost-effective-analysis" sections. No study scored above 50% on RQS. Median CLEAR scores were 55.74% (32.33/58 points), and for RQS, 17.59% (6.3/36 points). CLEAR-RQS article ranking fell between CLEAR and RQS and aligned with CLEAR. CONCLUSION: Radiomics research in post-radiotherapy stage III/IV NSCLC exhibits variability and frequently low-quality reporting. The formulated CLEAR-RQS checklist may facilitate education and holds promise for enhancing radiomics research quality. CLINICAL RELEVANCE STATEMENT: Current radiomics research in the field of stage III/IV postradiotherapy NSCLC is heterogenous, lacking reproducibility, with no identified imaging biomarker. Radiomics research quality assessment tools may enhance scientific rigor and thereby facilitate radiomics translation into clinical practice.

"Like not having an arm": a qualitative study of the impact of visitor restrictions on cancer care during the COVID-19 pandemic.

PURPOSE: Visitor restriction policies to prevent the spread of COVID-19 among patients and clinicians were widespread during the pandemic, resulting in the exclusion of caregivers at key points of cancer care and treatment decision-making. The aim of this study was to explore how visitor restrictions impacted cancer treatment decision-making and care from patient and physician perspectives. METHODS: Sixty-seven interviews, including 48 cancer patients and 19 cancer and palliative care physicians from four academic cancer centers in the USA between August 2020 and July 2021. RESULTS: Visitor restrictions that prevented caregivers from participating in clinic appointments and perioperative hospital care created challenges in cancer care that spanned three domains: practical, social, and informational. We identified eight themes that characterized challenges within the three domains across all three groups, and that these challenges had negative emotional and psychological consequences for both groups. Physicians perceived that patients' negative experiences due to lack of support through the physical presence of caregivers may have worsened patient outcomes. CONCLUSIONS: Our data demonstrate the tripartite structure of the therapeutic relationship in cancer care with caregivers providing critical support in the decision-making and care process to both patients and physicians. Caregiver absences led to practical, psychosocial, and informational burdens on both groups, and likely increased the risk of burnout among physicians. Our findings suggest that the quality of cancer care can be enhanced by engaging caregivers and promoting their physical presence during clinical encounters.

Quasi-integrability and nonlinear resonances in cold atoms under modulation.

Quantum dynamics of a collection of atoms subjected to phase modulation has been carefully revisited. We present an exact analysis of the evolution of a two-level system (represented by a spinor) under the action of a time-dependent matrix Hamiltonian. The dynamics is shown to evolve on two coupled potential energy surfaces (PESs): one of them is binding, while the other one is scattering type. The dynamics is shown to be quasi-integrable with nonlinear resonances. The bounded dynamics with intermittent scattering at random moments presents a scenario reminiscent of Anderson and dynamical localization. We believe that a careful analytical investigation of a multi-component system that is classically non-integrable is relevant to many other fields, including quantum computation with multi-qubit systems.

Bacterial diversity of baechu-kimchi with seafood based on culture-independent investigations.

Baechu-kimchi is a traditional Korean dish of fermented vegetables, in which kimchi cabbage is the major ingredient. Seafood is added to baechu-kimchi in coastal areas, giving this dish regional diversity. However, little is known about how the addition of seafood affects the bacterial diversity of kimchi. Therefore, in this study, the bacterial diversity of five varieties of baechu-kimchi with seafood and one variety of baechu-kimchi without seafood was analyzed using culture-independent techniques. In 81.7% of all kimchi analyzed, the predominant species were members of the phylum Firmicutes and the lactic acid bacteria, Latilactobacillus sakei, Leuconostoc mesenteroides, Pediococcus inopinatus, and Weissella koreensis. These organisms were similar to those identified in baechu-kimchi without the addition of seafood, which was used as a control group, and bacterial community of previously reported kimchi. Therefore, the results of this study confirmed that the addition of seafood did not significantly affect the bacterial community in baechu-kimchi.

Analytical approaches for assessing protein structure in protein-rich food: A comprehensive review.

This review focuses on changes in nutrition and functional properties of protein-rich foods, primarily attributed to alterations in protein structures. We provide a comprehensive overview and comparison of commonly used laboratory methods for protein structure identification, aiming to offer readers a convenient understanding of these techniques. The review covers a range of detection technologies employed in food protein analysis and conducts an extensive comparison to identify the most suitable method for various proteins. While these techniques offer distinct advantages for protein structure determination, the inherent complexity of food matrices presents ongoing challenges. Further research is necessary to develop and enhance more robust detection methods to improve accuracy in protein conformation and structure analysis.

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning.

Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing. However, traditional machine learning methods for calculating DTA often lack accuracy, posing a significant challenge in accurately predicting DTA. Fortunately, deep learning has emerged as a promising approach in computational biology, leading to the development of various deep learning-based methods for DTA prediction. To support researchers in developing novel and highly precision methods, we have provided a comprehensive review of recent advances in predicting DTA using deep learning. We firstly conducted a statistical analysis of commonly used public datasets, providing essential information and introducing the used fields of these datasets. We further explored the common representations of sequences and structures of drugs and targets. These analyses served as the foundation for constructing DTA prediction methods based on deep learning. Next, we focused on explaining how deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer, and Graph Neural Networks (GNNs), were effectively employed in specific DTA prediction methods. We highlighted the unique advantages and applications of these models in the context of DTA prediction. Finally, we conducted a performance analysis of multiple state-of-the-art methods for predicting DTA based on deep learning. The comprehensive review aimed to help researchers understand the shortcomings and advantages of existing methods, and further develop high-precision DTA prediction tool to promote the development of drug discovery.

Lessons learned from qualitative fieldwork in a multilingual setting.

Qualitative research conducted in a multilingual setting is an arduous, yet essential, endeavour. As part of my PhD research program, I set out to conduct qualitative process evaluation of a stroke trial in 11 languages in the Indian subcontinent. In this article, I reflect upon the challenges, oversights, and successes that I experienced in the hope of offering insight of use to fellow researchers conducting healthcare fieldwork in multicultural contexts where many languages are spoken. My account starts with a description of the setting's context and the necessity of conducting research in multiple languages. I elaborate on the planning of the study which included selection of the sample and preparation of relevant documents, including informed consent in patients' languages. Subsequent steps entailed submission and approval of requisite documents, setup and training of a research team, and conducting interviews using interpreters. During this process, I developed a hybrid technique for conducting interviews that reduced fatigue for both patient interviewees and interpreters while still yielding in-depth insights. Additionally, I discuss the benefits of engaging professional translators for performing translations. Finally, I introduce a stepwise approach to facilitate the thematic analysis of qualitative data. I believe this account will encourage and empower researchers to pave their own way while anticipating and preparing for potential obstacles when conducting research in similar settings.

Learning spiking neuronal networks with artificial neural networks: neural oscillations.

First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by a spiking neuronal network model. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.

Current Analytical Methods for the Sensitive Assay of New-Generation Ovarian Cancer Drugs in Pharmaceutical and Biological Samples.

Ovarian cancer, which affects the female reproductive organs, is one of the most common types of cancer. Since this type of cancer has a high mortality rate from gynaecological cancers, the scientific community shows great interest in studies on its treatment. Chemotherapy, radiotherapy, and surgical treatment methods are used in its treatment. In the absence of targeted treatments in these treatment methods, side effects occur in patients, and patients show resistance to the drug. In addition, the underlying causes of ovarian cancer are still not fully known. The scientific world thinks that genetic factors, environmental conditions, and consumed foods may cause this cancer. The most important factor in the treatment of ovarian cancer is early diagnosis. Therefore, the drugs used in the treatment of ovarian cancer are platinum-based anticancer drugs. In addition to these drugs, the most preferred treatment method recently is targeted treatment approaches using poly(adenosine diphosphate ribose) polymerase (PARP) inhibitors. In this review, studies on the sensitive analysis of the treatment methods of these new-generation drugs used in the treatment of ovarian cancer have been comprehensively examined. In addition, the basic features, structural aspects, and biological data of analytical methods used in treatments with new-generation drugs are explained. Analytical studies carried out in the literature in recent years aim to show future developments in how these new-generation drugs are used today and to guide future studies by comprehensively examining and explaining the structure-activity relationship, mechanism of action, toxicity, and pharmacokinetic studies. Finally, in this study, the methods used in the analysis of drugs used in the treatment of ovarian cancer and the studies conducted between 2015 and 2023 were discussed in detail.

Fostering Digital Life Skills Through Social Media With Adolescents in 6 German States: Protocol for an Accessibility Study According to the RE-AIM Framework.

BACKGROUND: Social media is essential in the lives of adolescents, with 97% of US teenagers engaging daily. While it facilitates communication, learning, and identity development, it also poses risks like harmful content exposure and psychological distress, particularly for adolescents in their critical developmental stage. Teaching digital life skills innovatively counters these risks, adapting traditional competencies such as decision-making, problem-solving, creative and critical thinking, communication, interpersonal skills, self-awareness, empathy, and emotional and stress management to digital challenges. OBJECTIVE: This study evaluates the accessibility of the "leduin" program, a novel intervention designed to impart digital life skills through Instagram. The program aims to leverage social media's educational potential, focusing on effective strategies to engage adolescents. Emphasizing accessibility is crucial, as it determines the program's overall impact. METHODS: The leduin program, developed through intervention mapping, applies behavior change techniques via social media for 9th and 10th graders. It is a 14-week spaced learning curriculum with daily sessions <5 minutes. Emphasizing the "reach" aspect of the reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) model, the recruitment targets diverse educational settings across 6 German states, aiming for inclusivity. Recruitment will involve schools, youth centers, and therapeutic facilities. The study seeks at least 128 participants, a calculated minimum to detect medium-sized effects in the quasi-experimental design and explore varying engagement levels and program responses. Data collection includes preintervention, postintervention, and 6-month follow-up surveys, using multilevel regression, latent growth models, and qualitative analysis to extensively assess reach and gain first insights on effectiveness, acceptance, implementation, and maintenance. The study aims to reveal key factors influencing program participation and interaction; a detailed analysis of engagement patterns will reveal the effectiveness of the recruitment strategies and barriers to participation. Additionally, initial indications of the program's impact on life skills, social media-related skills, health status, risk behaviors, and academic performance will be analyzed. RESULTS: Recruitment was planned from May 2023 until the beginning of the leduin program in October 2023. As of March 2024, we have recruited 283 participants. CONCLUSIONS: The leduin program stands as an innovative and essential initiative in adolescent health promotion, harnessing the power of social media to teach important digital life skills. This study highlights the critical role of accessibility in the success of social media interventions. Effective adolescent engagement strategies are imperative, as they dictate the overall impact of such interventions. The insights gained from this study will be instrumental in shaping future programs, laying groundwork for a subsequent, more comprehensive cluster-randomized controlled trial. The study's design acknowledges the limitations of the current quasi-experimental approach, including the anticipated sample size and the absence of a control group, and aims to provide a foundational understanding for future research in this field. TRIAL REGISTRATION: Deutsches Register Klinischer Studien DRKS00032308; https://drks.de/search/de/trial/DRKS00032308. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/51085.

Understanding quality of life's challenges in sarcoma patients: A mixed-methods study.

PURPOSE: Sarcoma, a highly aggressive form of cancer, profoundly affects patients both physically and psychologically. This study aimed to assess sarcoma patients' health-related quality of life and explore their experiences of living with the disease and their specific care requirements. METHODS: This research employed a convergent parallel mixed-methods design. Fifty-two sarcoma patients completed the European Portuguese version of the EORTC QLQ-C30 questionnaire, and seven of them participated in semi-structured interviews. Descriptive measures, Cronbach alpha for internal consistency, t-teste-Anova and stepwise regression were employed in the quantitative analysis. Qualitative data was analysed thematically. RESULTS: The most affected Quality of life (QoL) dimensions were role functioning and global health, areas of functioning that were also described as impacted in patients' experiences. Symptoms like insomnia, pain, and fatigue were frequently reported. Symptoms such as fatigue, pain, and financial difficulty explain QoL variance. No significant differences in QoL were found according to the different forms of treatment. Analysis of the interviews revealed three overarching themes: (1) disruption of daily life, (2) adaptation to life with sarcoma, and (3) the demand for more personalized care, allowing to gain a deeper understanding of living with sarcoma. CONCLUSION: The results underscore the significance of delivering care that enhances the quality of life for sarcoma patients, addressing both their physical and psychosocial needs, and providing personalized support throughout all stages of the disease.