Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Introduction: Toileting comprises multiple subtasks, and the difficulty of each is critical to determining the target and priority of intervention. The study aimed to examine the difficulty of subtasks that comprise toileting upon admission and the reacquisition of skills of subtasks during hospitalization.

Materials And Methods: This was a single-center prospective cohort study. We enrolled 101 consecutive stroke patients (mean age: 69.3 years) admitted to subacute rehabilitation wards. The independence in each of the 24 toileting subtasks was assessed using the Toileting Tasks Assessment Form (TTAF) every two or four weeks. The number of patients who were independent upon admission, as well as those who were not independent upon admission but became independent during hospitalization, was examined in each subtask.

Results: The most difficult subtask upon admission was "Lock the wheelchair brakes" (16.8% of patients were independent), followed by "Turn while standing (before urination/defecation)" (17.8%), "Pull the lower garments down" (18.0%), "Turn while standing (after urination/defecation)" (18.8%), "Pull the lower garments up and adjust them" (18.8%), and "Maintain a standing position (before urination/defecation)" (18.8%). The most difficult subtask for those who were not independent but became independent was "Dispose of incontinence pad/sanitary items" (19.3%), followed by "Press the nurse call button (after urination/defecation)" (28.3%), "Take the foot off the footrest and place it on the ground" (28.6%), and "Clean up after urination/defecation" (29.0%).

Conclusions: The difficult subtasks upon admission and those for reacquired skills were different. The most difficult subtasks upon admission were main tasks, and the difficult subtasks in reacquiring skills were preparatory tasks.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.jstrokecerebrovasdis.2023.107030DOI Listing

Publication Analysis

Top Keywords

difficult subtasks
12
subtasks
8
difficulty subtasks
8
cohort study
8
patients independent
8
independent admission
8
difficult subtask
8
"turn standing
8
standing urination/defecation"
8
"pull lower
8

Similar Publications

The problem of multiagent encirclement with multiobstacle collision avoidance (EMOCA) has been challenging since it is difficult to balance the tradeoff between surrounding a mobile target and avoiding obstacles simultaneously. To address the EMOCA problem, we proposed a novel policy-guided reinforcement learning (RL) method, namely, multiregulator-assisted RL for encirclement control (MRA-RLEC) which leverages the jump-start learning and curriculum learning (CL) mechanism to enhance training efficiency. MRA-RLEC divides the complex encirclement task into a sequence of subtasks, progressively increasing in difficulty.

View Article and Find Full Text PDF

Electromagnetic tomography (EMT), with the advantages of being non-contact, non-invasiveness, low cost, simple structure, and fast imaging speed, is a multi-functional tomography technique based on boundary measurement voltages to image the conductivity distribution within the sensing field. EMT is widely used in industrial and biomedical fields. Currently, there are few studies on the application of EMT in magnetic permeability materials, which makes it difficult to obtain high-quality reconstructed images due to its own properties that lead to obvious attenuation of electromagnetic waves during propagation, as well as the ill-posed and ill-conditioned characteristics of EMT.

View Article and Find Full Text PDF
Article Synopsis
  • 4D cone-beam computed tomography (CBCT) is important for adaptive radiation therapy in lung cancer, but low sampling data can cause image artifacts that complicate treatment.
  • Existing deep learning methods often need large labeled datasets to work effectively, which are hard to get in real-world situations, leading to difficulties in maintaining motion and recovering details.
  • The introduced Deep Prior Image Constrained Motion Compensation (DPI-MoCo) framework addresses these issues by separating the reconstruction process into artifact suppression and detail enhancement, achieving effective results without needing paired datasets and performing well in both simulations and clinical tests.
View Article and Find Full Text PDF

Sound Event Detection and Localization (SELD) is a comprehensive task that aims to solve the subtasks of Sound Event Detection (SED) and Sound Source Localization (SSL) simultaneously. The task of SELD lies in the need to solve both sound recognition and spatial localization problems, and different categories of sound events may overlap in time and space, making it more difficult for the model to distinguish between different events occurring at the same time and to locate the sound source. In this study, the Dual-conv Coordinate Attention Module (DCAM) combines dual convolutional blocks and Coordinate Attention, and based on this, the network architecture based on the two-stage strategy is improved to form the SELD-oriented Two-Stage Dual-conv Coordinate Attention Model (TDCAM) for SELD.

View Article and Find Full Text PDF

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter.

View Article and Find Full Text PDF