AHFE Open AccessSafe Patient Handling and Mobility Programs for Overweight and Obese Patients: A Cross-sectional Survey
Abstract
Healthcare providers face numerous challenges in lifting and mobilizing overweight and obese patients, which often lead to musculoskeletal disorders (MSDs). To address this, hospitals implement safe patient handling and mobility (SPHM) programs, including mechanical lift equipment, policies, and training. This study surveyed 134 healthcare workers in five Veterans Administration Medical Centers who regularly used SPHM programs. According to findings, handling bariatric patients frequently correlated with higher chronic back pain risk. Injuries occurred when not using powered equipment. Improvements like sufficient time with equipment and clear policies reduced injury likelihood. Equipment was crucial in preventing musculoskeletal injuries and pain. Findings emphasize using powered equipment and updating SPHM programs based on worker feedback for better patient handling practices.
Keywords: Safe Patient Handling, Overexertion, back pain, Musculoskeletal Disorders
DOI: 10.54941/ahfe1004381
Cite this paper
More from this volume
- Automatic Classification of Infant Sleeping Postures Using an Infrared Camera
- Analysis of Stair-Ascent Activities with Handrail Use in Daily Living Space and Motion Features using RGBD Camera
- Body Movement Support System for Prevent Disability and Promote Progress
- Shaping a device for Anti-viral disinfection and checking health of people moving in public space
- Transforming the homecare offering scene: How the technology plays a role
- Improving Comfort of Shoulder and Back Health in Children's School Bags: Examining Damper Shoulder Straps and Ergonomic Factors
- Tiny Titans: Acceptance of In-Vivo Capsule and Micro Robots in Healthcare Innovation
- Early Characterization of Stroke Using Video Analysis and Machine Learning
- Upper trapezius muscle activity pattern at work and associated neck pain - Study protocol for analyses of a pooled EMG data set
- Use of predictive models based on biomedical signals and motion measurements for predicting extremity kinematics
- Feature Selection for Machine Learning-Based Core Body Temperature Estimation Using Hand-Measurable Biological Information
- The Effect of Automated Agents on Individual Performance Under Induced Stress