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Acta Psychologica Oct 2023This study employs a qualitative research methodology to comprehensively investigate the psychological resilience of athletes impacted by the COVID-19 pandemic. Through...
This study employs a qualitative research methodology to comprehensively investigate the psychological resilience of athletes impacted by the COVID-19 pandemic. Through purposeful sampling, a diverse group of athletes representing various sports, competitive levels, and geographic locations was selected, ensuring a holistic exploration of their experiences. Data collection centered on in-depth interviews, utilizing a semi-structured approach guided by predetermined open-ended questions. Ethical standards were meticulously upheld, with informed consent obtained from all participants, and strict measures in place to safeguard their confidentiality and anonymity. Prior to data collection, pilot testing of interview questions was conducted to enhance their clarity and appropriateness. Subsequently, data analysis involved the meticulous transcription of field-notes and audio-recordings into protocols and transcripts, followed by systematic coding facilitated by qualitative data management software. To enhance research rigor, strategies including reflexivity, member-checking, and collaborative coding were embraced. This comprehensive methodology facilitated a deep and nuanced exploration of athletes' experiences, perceptions, and coping strategies during the pandemic, ultimately contributing valuable insights to the study of psychological resilience in sports. The findings shed light on the challenges athletes faced, the support systems and personal attributes that fostered resilience, and the role of well-being practices like mindfulness and self-care in enhancing psychological resilience. The implications of this research extend to proactive strategies for sports organizations and stakeholders, fostering a culture of resilience, and empowering athletes to thrive in the face of adversity, ultimately promoting their long-term psychological well-being.
Topics: Humans; Resilience, Psychological; Pandemics; COVID-19; Athletes; Adaptation, Psychological
PubMed: 37832493
DOI: 10.1016/j.actpsy.2023.104050 -
International Journal of Environmental... Aug 2023Cannabis is the main illicit psychoactive substance used in French childbearing women and very few data are available about adverse events (AEs) related to its use...
BACKGROUND
Cannabis is the main illicit psychoactive substance used in French childbearing women and very few data are available about adverse events (AEs) related to its use during pregnancy. The aim of this study was to evaluate the association between recreational cannabis use during pregnancy and adverse outcomes from a real-world clinical data warehouse.
METHODS
Data from the Poitiers University Hospital warehouse were analyzed between 1 January 2010 and 31 December 2019. Logistic regression models were used to evaluate associations between outcomes in three prenatal user groups: cannabis alone ± tobacco (C ± T) ( = 123), tobacco alone (T) ( = 191) and controls (CTRL) ( = 355).
RESULTS
Pregnant women in the C ± T group were younger (mean age: 25.5 ± 5.7 years), had lower pre-pregnancy body mass index (22.8 ± 5.5 kg/m), more psychiatric history (17.5%) and were more likely to benefit from universal free health-care coverage (18.2%) than those in the T and CTRL groups. Cannabis use increases the occurrence of voluntary interruption of pregnancy, at least one AE during pregnancy, at least one neonatal AE, the composite adverse pregnancy outcome over 28, prematurity and small for gestational age.
CONCLUSION
Given the trivialization of recreational cannabis use during pregnancy, there is an urgent need to communicate on AEs of cannabis use during pregnancy.
Topics: Infant, Newborn; Female; Humans; Pregnancy; Young Adult; Adult; Cannabis; Data Warehousing; Hallucinogens; Body Mass Index; Health Facilities
PubMed: 37681826
DOI: 10.3390/ijerph20176686 -
Allergology International : Official... Apr 2024In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse...
BACKGROUND
In clinical research on multifactorial diseases such as atopic dermatitis, data-driven medical research has become more widely used as means to clarify diverse pathological conditions and to realize precision medicine. However, modern clinical data, characterized as large-scale, multimodal, and multi-center, causes difficulties in data integration and management, which limits productivity in clinical data science.
METHODS
We designed a generic data management flow to collect, cleanse, and integrate data to handle different types of data generated at multiple institutions by 10 types of clinical studies. We developed MeDIA (Medical Data Integration Assistant), a software to browse the data in an integrated manner and extract subsets for analysis.
RESULTS
MeDIA integrates and visualizes data and information on research participants obtained from multiple studies. It then provides a sophisticated interface that supports data management and helps data scientists retrieve the data sets they need. Furthermore, the system promotes the use of unified terms such as identifiers or sampling dates to reduce the cost of pre-processing by data analysts. We also propose best practices in clinical data management flow, which we learned from the development and implementation of MeDIA.
CONCLUSIONS
The MeDIA system solves the problem of multimodal clinical data integration, from complex text data such as medical records to big data such as omics data from a large number of patients. The system and the proposed best practices can be applied not only to allergic diseases but also to other diseases to promote data-driven medical research.
Topics: Humans; Dermatitis, Atopic; Data Management; Biomedical Research; Precision Medicine
PubMed: 38102028
DOI: 10.1016/j.alit.2023.11.006 -
Cureus Jul 2023Cancer registration is crucial for any country's cancer surveillance and management program. However, there is a lack of systematic evidence on the operational... (Review)
Review
Cancer registration is crucial for any country's cancer surveillance and management program. However, there is a lack of systematic evidence on the operational feasibility of hospital-based cancer registries (HBCRs) in low- and middle-income countries (LMICs). We systematically reviewed and described the challenges and prospects of HBCRs in LMICs. We reported the study according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines. Electronic databases such as MEDLINE, EMBASE, Web of Science, ProQuest, and CINAHL were searched. Peer-reviewed studies published between January 1, 2000 and June 30, 2021 were included. We used thematic analysis to synthesize the findings discussing barriers and enablers of HBCRs. Thirteen studies were eligible for the analysis after eliminating duplicates, screening of title and abstract, and full-text review. The determinants, registry functionality, data management and abstraction, data security, data quality, organizational readiness, and perception of registry staff influence the implementation of HBCRs. In LMICs, many registries lacked functional documentation and data management systems due to a shortage of skilled professionals. However, in many instances, physicians and patients communicated via digital media, which helped obtain accurate data. The HBCR completeness rate was high in Ethiopia, China, Thailand, and Tanzania. Qualification and capacity building of the data managers was linked to the completeness and accuracy of the registry data, which led to sustainability. In addition, a few registries implemented new worksheets to enhance documentation. This review highlights the need for additional digitalization of the cancer registry to improve its functionality, completeness, follow-up, and output. Further, physicians and data managers require regular training to address cancer registry completeness and reduce errors.
PubMed: 37602029
DOI: 10.7759/cureus.42126 -
BMC Medical Informatics and Decision... Aug 2023The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide....
BACKGROUND
The recent advances in biotechnology and computer science have led to an ever-increasing availability of public biomedical data distributed in large databases worldwide. However, these data collections are far from being "standardized" so to be harmonized or even integrated, making it impossible to fully exploit the latest machine learning technologies for the analysis of data themselves. Hence, facing this huge flow of biomedical data is a challenging task for researchers and clinicians due to their complexity and high heterogeneity. This is the case of neurodegenerative diseases and the Alzheimer's Disease (AD) in whose context specialized data collections such as the one by the Alzheimer's Disease Neuroimaging Initiative (ADNI) are maintained.
METHODS
Ontologies are controlled vocabularies that allow the semantics of data and their relationships in a given domain to be represented. They are often exploited to aid knowledge and data management in healthcare research. Computational Ontologies are the result of the combination of data management systems and traditional ontologies. Our approach is i) to define a computational ontology representing a logic-based formal conceptual model of the ADNI data collection and ii) to provide a means for populating the ontology with the actual data in the Alzheimer Disease Neuroimaging Initiative (ADNI). These two components make it possible to semantically query the ADNI database in order to support data extraction in a more intuitive manner.
RESULTS
We developed: i) a detailed computational ontology for clinical multimodal datasets from the ADNI repository in order to simplify the access to these data; ii) a means for populating this ontology with the actual ADNI data. Such computational ontology immediately makes it possible to facilitate complex queries to the ADNI files, obtaining new diagnostic knowledge about Alzheimer's disease.
CONCLUSIONS
The proposed ontology will improve the access to the ADNI dataset, allowing queries to extract multivariate datasets to perform multidimensional and longitudinal statistical analyses. Moreover, the proposed ontology can be a candidate for supporting the design and implementation of new information systems for the collection and management of AD data and metadata, and for being a reference point for harmonizing or integrating data residing in different sources.
Topics: Humans; Alzheimer Disease; Semantics; Data Management
PubMed: 37553569
DOI: 10.1186/s12911-023-02211-6 -
Scientific Reports Sep 2023Large clinical trials often generate complex and large datasets which need to be presented frequently throughout the trial for interim analysis or to inform a data...
Large clinical trials often generate complex and large datasets which need to be presented frequently throughout the trial for interim analysis or to inform a data safety monitory board (DSMB). In addition, reliable and traceability are required to ensure reproducibility in pharmacometric data analysis. A reproducible pharmacometric analysis workflow was developed during a large clinical trial involving 1000 participants over one year testing Bacillus Calmette-Guérin (BCG) (re)vaccination in coronavirus disease 2019 (COVID-19) morbidity and mortality in frontline health care workers. The workflow was designed to review data iteratively during the trial, compile frequent reports to the DSMB, and prepare for rapid pharmacometric analysis. Clinical trial datasets (n = 41) were transferred iteratively throughout the trial for review. An RMarkdown based pharmacometric processing script was written to automatically generate reports for evaluation by the DSMB. Reports were compiled, reviewed, and sent to the DSMB on average three days after the data cut-off, reflecting the trial progress in real-time. The script was also utilized to prepare for the trial pharmacometric analyses. The same source data was used to create analysis datasets in NONMEM format and to support model script development. The primary endpoint analysis was completed three days after data lock and unblinding, and the secondary endpoint analyses two weeks later. The constructive collaboration between clinical, data management, and pharmacometric teams enabled this efficient, timely, and reproducible pharmacometrics workflow.
Topics: Humans; COVID-19; BCG Vaccine; Reproducibility of Results; Vaccination
PubMed: 37770596
DOI: 10.1038/s41598-023-43412-3 -
NCI Rectal-Anal Task Force consensus recommendations for design of clinical trials in rectal cancer.Journal of the National Cancer Institute Dec 2023The optimal management of locally advanced rectal cancer is rapidly evolving. The National Cancer Institute Rectal-Anal Task Force convened an expert panel to develop...
The optimal management of locally advanced rectal cancer is rapidly evolving. The National Cancer Institute Rectal-Anal Task Force convened an expert panel to develop consensus on the design of future clinical trials of patients with rectal cancer. A series of 82 questions and subquestions, which addressed radiation and neoadjuvant therapy, patient perceptions, rectal cancer populations of special interest, and unique design elements, were subject to iterative review using a Delphi analytical approach to define areas of consensus and those in which consensus is not established. The task force achieved consensus on several areas, including the following: 1) the use of total neoadjuvant therapy with long-course radiation therapy either before or after chemotherapy, as well as short-course radiation therapy followed by chemotherapy, as the control arm of clinical trials; 2) the need for greater emphasis on patient involvement in treatment choices within the context of trial design; 3) efforts to identify those patients likely, or unlikely, to benefit from nonoperative management or minimally invasive surgery; 4) investigation of the utility of circulating tumor DNA measurements for tailoring treatment and surveillance; and 5) the need for identification of appropriate end points and recognition of challenges of data management for patients who enter nonoperative management trial arms. Substantial agreement was reached on priorities affecting the design of future clinical trials in patients with locally advanced rectal cancer.
Topics: United States; Humans; Consensus; National Cancer Institute (U.S.); Rectal Neoplasms; Chemoradiotherapy; Neoadjuvant Therapy
PubMed: 37535679
DOI: 10.1093/jnci/djad143 -
The Journal of Privacy and... Dec 2023Sharing data produced through health research projects has been increasingly recognized as a way to advance science more rapidly by facilitating discovery and increasing...
Sharing data produced through health research projects has been increasingly recognized as a way to advance science more rapidly by facilitating discovery and increasing rigor and reproducibility. Much of the data collected from human subjects includes sufficient sociodemographic detail and/or covers sensitive topics, and thus requires restricted data management and sharing practices. Over the last two decades, scientific organizations, presidential memoranda, and other sources have all called for increasing opportunities to share data. Recognizing the value of shared data, the National Institutes of Health issued a new Data Management and Sharing Policy, effective January 25, 2023. Prior to this updated policy, in 2009, the National Institute on Drug Abuse recognized the value of sharing data and established an archive, the National Addiction and HIV Data Archive Program. This program focused on sharing data, often highly sensitive, generated from social and behavioral addiction research, including quantitative and qualitative assessments as well as biomarker and imaging data. NAHDAP has developed practices and curation standards to ensure datasets are improved and usable, and provides technical assistance for both data depositors and users. We share three key lessons learned working to disseminate sensitive data over the last 13 years, including (1) protecting the confidentiality of human subjects; (2) ensuring careful consideration of costs for archiving data requiring protection ; and (3) providing support to facilitate the discovery and use of the data.
PubMed: 38469321
DOI: 10.29012/jpc.853 -
Frontiers in Public Health 2023The SARS-CoV-2 pandemic represented a formidable scientific and technological challenge to public health due to its rapid spread and evolution. To meet these challenges...
INTRODUCTION
The SARS-CoV-2 pandemic represented a formidable scientific and technological challenge to public health due to its rapid spread and evolution. To meet these challenges and to characterize the virus over time, the State of California established the California SARS-CoV-2 Whole Genome Sequencing (WGS) Initiative, or "California COVIDNet". This initiative constituted an unprecedented multi-sector collaborative effort to achieve large-scale genomic surveillance of SARS-CoV-2 across California to monitor the spread of variants within the state, to detect new and emerging variants, and to characterize outbreaks in congregate, workplace, and other settings.
METHODS
California COVIDNet consists of 50 laboratory partners that include public health laboratories, private clinical diagnostic laboratories, and academic sequencing facilities as well as expert advisors, scientists, consultants, and contractors. Data management, sample sourcing and processing, and computational infrastructure were major challenges that had to be resolved in the midst of the pandemic chaos in order to conduct SARS-CoV-2 genomic surveillance. Data management, storage, and analytics needs were addressed with both conventional database applications and newer cloud-based data solutions, which also fulfilled computational requirements.
RESULTS
Representative and randomly selected samples were sourced from state-sponsored community testing sites. Since March of 2021, California COVIDNet partners have contributed more than 450,000 SARS-CoV-2 genomes sequenced from remnant samples from both molecular and antigen tests. Combined with genomes from CDC-contracted WGS labs, there are currently nearly 800,000 genomes from all 61 local health jurisdictions (LHJs) in California in the COVIDNet sequence database. More than 5% of all reported positive tests in the state have been sequenced, with similar rates of sequencing across 5 major geographic regions in the state.
DISCUSSION
Implementation of California COVIDNet revealed challenges and limitations in the public health system. These were overcome by engaging in novel partnerships that established a successful genomic surveillance program which provided valuable data to inform the COVID-19 public health response in California. Significantly, California COVIDNet has provided a foundational data framework and computational infrastructure needed to respond to future public health crises.
Topics: Humans; SARS-CoV-2; COVID-19; Genomics; California; Data Management
PubMed: 37937074
DOI: 10.3389/fpubh.2023.1249614 -
Healthcare (Basel, Switzerland) Sep 2023This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its...
This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.
PubMed: 37761680
DOI: 10.3390/healthcare11182483