Purpose: To describe the barriers and facilitators of the assistive technology service delivery process (AT-SDP), based on the perspectives of assistive technology service professionals (ATPs) and assistive technology (AT) service users.
Methods: We conducted semi-structured interviews with nine AT users and eight ATPs in South Korea. The data were analyzed using a constant comparative approach based on the grounded theory.
Results: AT users and ATPs identified common barriers in the assessment, matching, and implementation of the AT-SDP. In the assessment process, the preparation of detailed selection criteria was suggested for assistive technology devices (ATDs). Insufficient linkages on assessments among institutions providing AT services was a reported barrier, and standardized evaluation tools were suggested to address this issue. In the matching process, to meet users' needs, versatility in the characteristics or type of ATD was highlighted. In the implementation process, participants emphasized the need to shorten the time required during the delivery process. Along with these facilitators, institutional support, including staffing securement, the establishment of AT centres, and funding policies were recommended to facilitate the AT-SDP.
Conclusions: Our results highlight the importance of government support and considering realistic funding levels to overcome the barriers reported by AT users and ATPs.
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http://dx.doi.org/10.1080/17483107.2023.2174606 | DOI Listing |
Appl Sci (Basel)
June 2024
Department of Biomechanics and Center for Research in Human Movement Variability, University of Nebraska at Omaha, Omaha, NE 68182, USA.
Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2-5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series.
View Article and Find Full Text PDFBr J Ophthalmol
January 2025
Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
Background/aims: Large language models (LLMs) have substantial potential to enhance the efficiency of academic research. The accuracy and performance of LLMs in a systematic review, a core part of evidence building, has yet to be studied in detail.
Methods: We introduced two LLM-based approaches of systematic review: an LLM-enabled fully automated approach (LLM-FA) utilising three different GPT-4 plugins (Consensus GPT, Scholar GPT and GPT web browsing modes) and an LLM-facilitated semi-automated approach (LLM-SA) using GPT4's Application Programming Interface (API).
JMIR Res Protoc
January 2025
Department of Physical Medicine and Rehabilitation, University of Alabama at Birmingham, Birmingham, AL, United States.
Background: Wheelchair users live predominantly sedentary lifestyles and have a substantially higher risk for cardiometabolic disease and mortality compared to people without disabilities. Exercise training has been found to be effective in improving cardiometabolic health (CMH) outcomes among people without disabilities, but research on wheelchair users is limited and of poor quality.
Objective: The primary aim of this study is to examine the immediate and sustained effects of a 24-week, telehealth, movement-to-music cardiovascular (M2M-C) exercise program on core indicators of CMH among adult wheelchair users compared to an active control group.
Assist Technol
January 2025
Maynooth University, Ireland.
Wearable Technol
December 2024
Sensory Motor Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland.
Cable-driven exosuits have the potential to support individuals with motor disabilities across the continuum of care. When supporting a limb with a cable, force sensors are often used to measure tension. However, force sensors add cost, complexity, and distal components.
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