We continuously develop our digital solutions. For this, we work closely with scientific partners and conduct ongoing studies. We publish the results in peer-reviewed journals.
Motamedi, S., Pommer, & W., Baumgart, A. (2025). App-Based Fall Risk Assessment in Elderly Ambulatory Care: Distinguishing Fallers from Non-Fallers in a Retrospective Case-Control Study. In Preparation.
Alves, S., Teme, S., Motamedi, S., Kura, M., Weber, S., Zeichen, J., Pommer, W., Baumgart, A. (2024).Evaluating the prognostic and clinical validity of the Fall Risk Score derived from an AI-based mHealth application for fall prevention: a retrospective real-world data analysis. JMIR Aging 2024;7:e55681.https://aging.jmir.org/2024/1/e55681
Azhand, A., Rabe, S., Müller, S., Sattler, I., & Heimann-Steinert, A. (2021).Algorithm based on one monocular video delivers highly valid and reliable gait parameters. Scientific reports, 11(1), 14065. https://doi.org/10.1038/s41598-021-93530-z
Rabe, S., Azhand, A., Pommer, W., Müller, S., & Steinert, A. (2020).Descriptive Evaluation and Accuracy of a Mobile App to Assess Fall Risk in Seniors: Retrospective Case-Control Study. JMIR aging, 3(1), e16131. https://doi.org/10.2196/16131
Publications by External Researchers
Pfannstiel, M. (2025). Künstliche Intelligenz im Einsatz für die erfolgreiche Patientenreise. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-48573-3
Raeder, K., Strutz, N., Kuntz, S., Müller-Werdan, U., & Lahmann, N. A. (2025). Using 3D Skeleton Tracking for Gait Analysis in Older Adults Compared to a Kinect®-Based Mobility Analysis System. Under Review.
Müller, S. & Zerth, J. (2024). LINDERA DiPA – Multicenter, controlled, prospective, single-arm, intra-individual study to stabilize or improve the risk of falls, measured by the LINDERA mobility analysis app in outpatient care. Under Review.
Strutz, N., Brodowski, H., Kiselev, J., Heimann-Steinert, A., & Müller-Werdan, U. (2022). App-Based Evaluation of Older People's Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study. JMIR aging, 5(3), e36872. https://doi.org/10.2196/36872
Stamm, O., & Heimann-Steinert, A. (2020). Accuracy of Monocular Two-Dimensional Pose Estimation Compared Witha Reference Standard for Kinematic Multiview Analysis: Validation Study. JMIR mHealth and uHealth, 8(12), e19608. https://doi.org/10.2196/19608
Conference Posters and Presentations
Alves, S., Temme, S., Motamedi, S., Kura, M., Weber, S., Zeichen, J., Pommer, W., & Baumgart, A. (2025). Mit KI Stürze in der Pflege verlässlich vorhersehen: Das erste Ampel-System – 100% mit Real-World-Daten. Clusterkonferenz 2025: Zukunft der Pflege, conference presentation, March 2025.
Alves, S., Kura, M., Paolino, C., Heinrichs, D., Zerth, J., Müller, S. (2024).Efficacy of mHealth Tools in a Multifactorial Approach to Fall Prevention Among Elderly Care Patients: A Cluster-Randomized Controlled Trial. EU Falls Fest 2024, conference poster, April 2024.
Alves, S., Kura, M., Zerth, J., Müller, S. (2023).When do (cluster-)randomized studies hit the mark, and when do they miss it? Intervention studieswith mHealth in residentiallong-term care. Deutscher Kongress für Versorgungsforschung Berlin 2023,conferenceposter & presentation, October 2023.
Dahms, R., Vorwerg-Gall, S., Heeger, S., Ettrich, M., Heimann-Steinert, A. (2023).AI-based digital healthcare application to manage fall risks in residents of nursing homes in Germany: clinical and resident-reported outcomes. The International Conference on Digital Health and Telemedicine 2023, conference presentation, October 2023.
