Background: More than 50% of lower limb prosthesis (LLP) users report falling at least once a year, placing them at high risk for adverse health outcomes such as decreased mobility and diminished quality of life. Efforts to decrease falls in LLP users have traditionally focused on developing clinical tests to assess fall risk, designing prosthetic components to improve patient safety, and identifying risk factors to recognize potential fallers. Little attention has been directed toward recording, reporting, and characterizing the circumstances of falls in LLP users. Identifying the most common types of falls could help guide and prioritize clinical and research needs.
Objective: To characterize the frequency and circumstances of falls reported by unilateral LLP users.
Design: Secondary analysis of data from 2 cross-sectional studies.
Setting: Outpatient clinic and research laboratory.
Participants: Ambulatory unilateral transtibial and transfemoral LLP users (N = 66).
Intervention: None.
Outcome: A fall-type classification framework was developed based on biomechanical theory and published falls terminology. Self-reported falls and accompanying narrative descriptions of LLP users' falls in the previous 12 months were analyzed with the framework. Frequencies, estimated proportions, and estimated counts were compared across fall circumstances using 95% confidence intervals.
Results: Thirty-eight participants (57.6%) reported 90 falls during the previous year. All reported falls were successfully categorized using the proposed framework. Most falls occurred from disruptions to the base of support, intrinsic destabilizing factors, and a diverse set of fall patterns. Walking on level terrain was the most common activity at the time of a fall.
Conclusion: This secondary analysis showed that falls remain frequent in ambulatory LLP users and that clinicians and researchers might wish to prioritize falls owing to disruptions of the base of support that occur while walking. Additional research with a larger sample is required to confirm and expand these results.
Level Of Evidence: III.
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http://dx.doi.org/10.1016/j.pmrj.2018.08.385 | DOI Listing |
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July 2024
Department of Biomedical Engineering, University of North Dakota, 501 N. Columbia Road Stop 8380, Grand Forks, ND 58202, United States.
Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell.
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July 2024
Arkansas Integrative Metabolic Research Center, University of Arkansas, Fayetteville, AR.
This manuscript describes the development of a resources module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement.
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July 2024
Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo Street, Honolulu, HI 96813, United States.
This study describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement.
View Article and Find Full Text PDFBrief Bioinform
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Genomic Data Science Core, Center for Quantitative Biology (COBRE), Dartmouth College, 1 Medical Center Drive, Lebanon, NH 03766, United States.
This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning', https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial authored by National Institute of General Medical Sciences: NIGMS Sandbox: A Learning Platform toward Democratizing Cloud Computing for Biomedical Research at the beginning of this supplement.
View Article and Find Full Text PDFJ Appl Toxicol
October 2024
Deloitte Consulting LLP, Rosslyn, Virginia, USA.
Both nitric oxide (NO) and nitrogen dioxide (NO) gasses are toxic to humans but are commonly found in industrial settings such as semiconductor manufacturing sites. Due to the spontaneous oxidation of NO to NO under ambient conditions, individuals working with NO may in fact be exposed to both gasses in the case of an accidental release. Unfortunately, most safety materials provided to NO users do not address the potential for associated NO toxicity, and, until now, models developed to predict health consequences following a release of NO have not appropriately considered the oxidation kinetics nor the toxicity of both NO and NO in their assessments.
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