Project Summary/Abstract For older adults, prolonged hospitalization can lead to a devastating loss of mobility and independence. Each year, 12 million adults over the age of 65 are hospitalized, and 30% are discharged to a post-acute care facility. One of the risks of hospitalization is bed rest, which is associated with a number of hospital-acquired complications, including falls, delirium, venous thrombosis and skin breakdown. Hospital mobility programs attempt to ambulate patients up to three times daily, but this work is generally assigned to nurses, who have many competing and often more pressing tasks. Consequently, ambulating patients is the most frequently overlooked nursing duty. This problem has been exacerbated by the COVID-19 pandemic and the resulting nursing shortage. Small studies have examined the benefits of mobility technicians (MTs), whose sole job is to safely ambulate patients. These studies have demonstrated that MTs can increase steps taken, but they are too small to prove the impact of MTs on other outcomes, such as whether patients have in-hospital complications or whether they can go home instead of to a post-acute care facility. Hospitals are hesitant to adopt MT programs because they perceive them to be expensive and unproven. We propose to conduct a large randomized trial to test the impact of MTs on short and intermediate term outcomes for 3000 patients aged 65 years and older at 5 hospitals in 2 health systems. Patients will be randomized to receive supervised ambulation up to 3 times daily with a MT or to receive usual care. All participants will wear an accelerometer on their wrist to track their movement throughout the hospital stay. The study has 3 aims. First, we will compare the mobility of patients at discharge (or 10 days) to assess the impact of the MTs on this outcome. We are particularly interested in whether the use of MTs will increase the proportion of patients who can go home vs. post-acute care, and whether the improvements in mobility are sustained at 30 days. Second, we will use predictive modeling to identify which patients are most likely to benefit from this intervention. Third, we will assess the impact of the intervention on overall costs associated with the episode of care, including inpatient costs and the 30 days after discharge. This information will be important to convince health systems to adopt this approach.