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Scientific Approach in Fall Prevention
Marie Kura, a member of the LINDERA Clinical R&D Team, presented our AI-based markerless motion tracking technology at the German Societies for Orthopedic and Trauma Surgery. The online event showcased LINDERA’s dedication to deliver safe and precise fall prevention technology accessible for everyone, everywhere.
LINDERA App: Advancing Fall Risk Assessment and Prevention
Central to the presentation was the LINDERA Mobility Analysis, a medically-certified product (MDD) for fall prevention. Deployed in over 500 care facilities, the app optimizes fall risk assessment through a combination of evidence-based questionnaires and AI-supported digital gait analysis enabling a quick and precise fall risk analysis1,2.
AI enhances gait analysis and makes cutting-edge medicine affordable
LINDERA has redefined traditional gait assessment methods using AI-based markerless motion tracking technology, allowing the real-time capture of gait parameters such as step height and gait speed through LINDERA Mobility Analysis App. With our scientific approach to fall prevention, LINDERA technology enables healthcare professionals in conducting precise gait analyses without the need for expensive laboratory equipment, making it accessible for everyone, everywhere.
Scientific Validation and Effectiveness
The (validity)1,2 and effectiveness of LINDERA Mobility Analysis app are supported by multiple independent studies:
- The fall risk score determined by the LINDERA Mobility Analysis App has a sensitivity of 93% to distinguish between fallers and non-fallers3.
- In a study with AOK Baden-Württemberg, it was found that the fall risk score in 16 elderly care facilities decreased by 17.8% after the implementation of the LINDERA Mobility Analysis App4.
- Moreover, the study participants reported reduced fear of falls and psychological strain after intervention with the LINDERA Mobility Analysis App4.
- A recently conducted cluster-randomized study demonstrates that the LINDERA Mobility Analysis App offers more efficient fall prevention than standard care in elder care facilities5.
Broad Applicability in Healthcare
LINDERA’s 3D markerless motion capture technology is versatile and can be applied to various conditions limiting mobility, such as lower limb fractures and neurological disorders. It is also suitable for use at home, providing accessibility for all patients in need of care independently of their location. Hence, we are currently preparing for the DiPA (Digitale Pflegeanwendungen) approval from the BfArM (Bundesinstitut für Arzneimittel und Medizinprodukte [Federal Institute for Drugs and Medical Devices]).
Feedback and Future Outlooks
The presentation was met with considerable interest, prompting numerous questions and demonstrating the potential of LINDERA’s technology within the orthopaedic and trauma surgery field.
We appreciate the society’s engagement, especially Prof. Dr. med. Dr. h.c. Joachim Grifka and Prof. Dr. med. Markus Huber-Lang for the opportunity provided to present our mission and discuss it.
References
1. Azhand, A., Rabe, S., Müller, S., Sattler, I., & Steinert, A. (2021). Algorithm Based on One Monocular Video Delivers Highly Valid and Reliable Gait Parameters. Nature Scientific Reports, 11 (1). https://doi.org/10.1038/s41598-021-93530-z
2. Strutz N, Brodowski H, Kiselev J, Heimann-Steinert A, Müller-Werdan UApp-Based Evaluation of Older People’s Fall Risk Using the mHealth App LINDERA Mobility Analysis: Exploratory Study. JMIR Aging 2022;5(3):e36872. https://doi.org/10.2196/36872
3. 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, (1). https://doi.org/10.2196/16131
4. 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 (DigiHT), Conference presentation. Manuscript in preparation for a peer-reviewed journal.
5. Alves, S., Kura, M., Zerth, J., Müller, S. (2023). When do (cluster-)randomized studies hit the mark, and when do they miss it? Intervention studies with mHealth in residential long-term care. 22. Deutscher Kongress für Versorgungsforschung, Conference presentation. Manuscript in preparation for a peer-reviewed journal.