Objectives: To ascertain whether a mobile patient lift facilitates early mobilization in ventilated ICU patients.
Design: A single-center, open-label, randomized controlled trial.
Setting: An academic ICU in Tokyo.
Patients: Eighty patients were admitted to ICU and expected ventilation for at least 48 hours.
Interventions: In the intervention group, in addition to the rehabilitation protocol received by the control group, patients were assisted in sitting, standing, transfers, and walking using the mobile patient lift.
Measurements And Main Results: The intervention group predominantly stood faster than the control group (1.0 vs. 3.0 d, p < 0.01). The Intervention group also had significantly higher Functional Status Score-ICU scores at ICU discharge. However, the Medical Research Council score and Barthel index at discharge, length of ICU stay, and number of ventilator-free days did not differ between the two groups.
Conclusions: The use of mobile patient lifts facilitates the earlier standing of patients on ventilators. This may contribute to patients improved physical function in the ICU.
Trial Registration: The study protocol was registered with the University Hospital Medical Information Network (UMIN) under the registration number UMIN000044965. Registered July 30, 2021.
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http://dx.doi.org/10.1097/CCM.0000000000006219 | DOI Listing |
JAMIA Open
February 2025
Division of Nursing, Midwifery and Social Work, The University of Manchester, Manchester M13 9PL, United Kingdom.
Objectives: There is no guidance to support the reporting of systematic reviews of mobile health (mhealth) apps (app reviews), so authors attempt to use/modify the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). There is a need for reporting guidance, building on PRISMA where appropriate, tailored to app reviews. The objectives were to describe the reporting quality of published mHealth app reviews, identify the need for, and develop potential candidate items for a reporting guideline.
View Article and Find Full Text PDFEJIFCC
December 2024
National Reference Laboratory, Abu Dhabi, UAE.
Background: An increasing number of wearable medical devices are being used for personal monitoring and professional health care purposes. These mobile health devices collect a variety of biometric and health data but do not routinely connect to a patient's electronic health record (EHR) or electronic medical record (EMR) for access by a patient's health care team.
Methods: The International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MHBLM) developed consensus recommendations for consideration when interfacing mobile health devices to an EHR/EMR.
Contemp Clin Trials Commun
February 2025
Healthcare Delivery Research, MedStar Health Research Institute, Washington, DC, USA.
Background: Black individuals with cancer have a higher prevalence of comorbidities and a worse cancer prognosis than other racial groups in the US. As part of a quality improvement project, we aimed to demonstrate feasibility of self-monitoring and community health worker (CHW) support among managing comorbidities for Black individuals with breast or prostate cancer.
Methods: In a single arm, pre-post study, we enrolled patients with diabetes and/or hypertension who identified as Black and were diagnosed with 1) stage 0-IV breast cancer, or 2) prostate cancer and on long-term androgen-deprivation therapy.
JMIR Form Res
January 2025
Hamamatsu University School of Medicine, Hamamatsu City, Chuo-ku, Japan.
Background: One method for noninvasive and simple urinary microalbumin testing is urine test strips. However, when visually assessing urine test strips, accurate assessment may be difficult due to environmental influences-such as lighting color and intensity-and the physical and psychological influences of the assessor. These complicate the formation of an objective assessment.
View Article and Find Full Text PDFBMC Oral Health
January 2025
Pediatric Dentistry Department, Faculty of Dentistry, Başkent University, 06490, Ankara, Turkey.
Background: Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. The aim of this study was to classify single premolar agenesis, multiple premolar agenesis, and without tooth agenesis using various artificial intelligence approaches.
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