Get Back, a person-centred digital programme targeting physical activity for patients undergoing spinal stenosis surgery : A study protocol of a randomized feasibility study

BACKGROUND: Spinal stenosis is the most common reason for elective spine surgery, and the cardinal symptom is leg pain and discomfort when walking. Patients with spinal stenosis have a decreased level of physical activity and thereby an increased risk of poor health. Get Back is a person-centred digital programme that strives to support patients being physically active after surgery. The aim is to explore if Get Back, in its present format (referred to as Get Backfeasibility), is feasible and contributes to detectable change in variables related to intervention content. METHODS: Thirty patients planned for decompression surgery due to central lumbar spinal stenosis who present with low physical activity, pain catastrophizing or fear of movement, will be included in a randomized feasibility study. All patients will be randomly allocated to either Get Backfeasibility or usual physical therapy. Get Backfeasibility aims to increase the patient's physical activity level by combining a person-centred and cognitive behavioural approach. It comprises 10 video and telephone sessions led by a physical therapist over 12 weeks (pre/postoperatively). Outcomes are treatment fidelity (treatment dose, adherence, and content), process feasibility (recruitment, intervention use, and acceptability of measurements and intervention), and variables related to the intervention content (steps per day, physical activity level, pain catastrophizing, fear of movement, and general self-efficacy). Treatment fidelity and feasibility data will be assessed during the full study period (12 weeks). Physical activity, physical capacity, and patient-reported outcomes will be assessed digitally at baseline (2 weeks preoperatively) and 11-12 weeks postoperatively. Variables related to the intervention content will be monitored weekly through a digital application. Feasibility data will be analysed descriptively and inferentially using a nonparametric approach, data from repeated measures will be display