PROJECT SUMMARY/ABSTRACT This study is part of the NIH’s Helping to End Addiction Long-term (HEAL) initiative to speed scientific solutions to understand the basis of pain and enhance clinical pain management. For many individuals working in industries such as construction, manufacturing, logistics, healthcare, and agriculture, a typical workday involves exposure to frequent overexertion activities (repetitive bending, twisting, lifting, and sustained flexion postures) that increase the risk of back injury. Overexertion is the most common event leading to musculoskeletal disorders (MSDs)—over 65% of MSDs are associated with overexertion, and one-third of all MSDs resulting in days away from work are accounted for by disabling occupational chronic low back pain (cLBP). A majority of back injuries are considered non-specific, as an exact nociceptive cause is often impossible to identify. Among biophysical risk factors, studies have shown that cumulative and peak loads on the back are predictors of low back pain. Lifting is a dynamic and highly variable activity and is the most commonly reported event leading to low back pain, accounting for nearly one-third of all back pain emergency room visits. Lifting with awkward poses (asymmetrical lifting, large horizontal or vertical load distances), at a high frequency, or performing multiple repetitions can result in an increased risk of injury due to increased peak and cumulative mechanical loading of the back and fatigue effects. A primary factor contributing to acute or recurrent back injury is overexertion via excessive peak and cumulative forces on the back, and that primary factors involved in the progression of acute low back injury to cLBP include maladaptive motor control strategies, muscle hyperactivity, reduced movement variability, and the development of fear cognitions. This project will focus on the development of robotic apparel with integrated biofeedback components can reduce exertion, encourage safe, varied movement strategies, and promote recovery. Robotic apparel will be capable of providing supportive forces to the back and hip joints when needed via adaptive control algorithms that respond to dynamic movements and become fully transparent when assistance is no longer needed. This technology can may be able to be used to prevent cLBP in individuals who are exposed to overexertion; in this way, robotic apparel will supplement ergonomic training implemented in many manual materials handling occupations to encourage proper lifting strategies in the workplace. Robotic apparel will also provide a new tool to physical therapists and the clinical community to enhance rehabilitation programs by assisting in the safe progression back to normal activity, enabling people to get back to work sooner while encouraging non-maladaptive movement strategies. This research will undertake a staged approach and be based on a human-in-the-loop development process that evaluates component and system function...