Medical App Treatment of Non-Specific Low Back Pain in the 12-month Cluster-Randomized Controlled Trial Rise-uP: Where Clinical Superiority Meets Cost Savings

Abstract

Janosch A Priebe,1 Linda Kerkemeyer,2 Katharina K Haas,1 Katharina Achtert,2 Leida F Moreno Sanchez,1,3 Paul Stockert,1 Maximilian Spannagl,1 Julia Wendlinger,1 Reinhard Thoma,4 Siegfried Ulrich Jedamzik,3 Jan Reichmann,5 Sebastian Franke,6 Leonie Sundmacher,6 Volker E Amelung,2 Thomas R Toelle1 1Center of Interdisciplinary Pain Medicine, Department of Neurology, Klinikum Rechts der Isar, Technical University of Munich (TUM), Munich, Germany; 2Institute for Applied Health Services Research, Inav GmbH, Berlin, Germany; 3Bayerische TelemedAllianz, Ingolstadt, Baar-Ebenhausen, Germany; 4Pain Clinic, Algesiologikum Pain Center, Munich, Germany; 5StatConsult GmbH Magdeburg, Magdeburg, Germany; 6Department of Health Economics, Faculty of Sports and Health Sciences, Technical University of Munich (TUM), Munich, GermanyCorrespondence: Thomas R Toelle, Center of Interdisciplinary Pain Medicine, Department of Neurology, Klinikum rechts der Isar, Technical University of Munich (TUM), Ismaninger Str. 22, Munich, 81675, Germany, Tel [phone], Email [email protected]: Non-specific low back pain (NLBP) exerts a profound impact on global health and economics. In the era of Web 3.0, digital therapeutics offer the potential to improve NLBP management. The Rise-uP trial introduces a digitally anchored, general practitioner (GP)-focused back pain management approach with the Kaia back pain app as the key intervention. Here, we present the 12-months evaluation of the Rise-uP trial including clinical and economic outcomes, patient satisfaction and behavioral tracking analysis.Methods: The cluster-randomized controlled study (registration number: DRKS00015048) enrolled 1237 patients, with 930 receiving treatment according to the Rise-uP approach and 307 subjected to standard of care treatment. Assessments of pain, psychological state, functional capacity, and well-being (patient-reported outcome measures; PROMs) were collected at baseline, and at 3-, 6-, and 12-months follow-up intervals. Health insurance partners AOK, DAK, and BARMER provided individual healthcare cost data. An artificial intelligence (AI)-driven behavioral tracking analysis identified distinct app usage clusters that presented all with about the same clinical outcome. Patient satisfaction (patient-reported experience measures; PREMs) was captured at the end of the trial.Results: Intention-to-treat (ITT) analysis demonstrated that the Rise-uP group experienced significantly greater pain reduction at 12 months compared to the control group (IG: − 46% vs CG: − 24%; p < 0.001) with only the Rise-uP group achieving a pain reduction that was clinically meaningful. Improvements in all other PROMs were notably superior in patients of the Rise-uP group. The AI analysis of app usage discerned four usage clusters. Short- to long-term usage, all produced about the same level of pain reduction. Cost-effectiveness analysis indicated a substantial economic benefit for Rise-uP.Conclusion: The Rise-uP approach with a medical multimodal back pain app as the central element of digital treatment demonstrates both, clinical and economic superiority compared to standard of care in the management of NLBP.Keywords: digital medicine, medical apps, non-specific low back pain, multimodal pain therapy, healthcare costs, behavioral tracking analysis

Keywords

• digital medicine
• medical apps
• non-specific low back pain
• multimodal pain therapy
• healthcare costs
• behavioral tracking analysis