Research on mobility in older adults to improve the fall risk screening in physiotherapy

Hintergrund Sturzprävention ist eine gesundheitspolitische Herausforderung in einer alternden Gesellschaft. Es ist für viele Bereiche der Versorgungsforschung von hohem Interesse, Prä-diktoren für Stürze zu identifizieren, um wiederum die Einleitung geeigneter Präventionsmaß-nahmen zu ermöglichen und die Versorgungsqualität zu verbessern. Die vorliegende Arbeit soll einen Beitrag zum Sturzrisikoscreening bei älteren Menschen in der Physiotherapie leisten. Methodik Drei Publikationen aus drei wissenschaftlichen Projekten wurden in die vorliegende Dissertation einbezogen. Methodisch folgen alle drei Ansätze einem quantitativen Verfahren. Zwei Aspekte der funktionellen Mobilität - das Treppensteigen und das Gehen in der Ebene - sowie ein psychischer Aspekt, die Sturzangst, wurden im Fokus der vorliegenden Dissertation betrachtet: Ziel des ersten Projektes war es, einen Beitrag zur Analyse von Gangmustern mittels moderner Sensortechnologie zu leisten. Hierfür wurde die grundsätzliche Eignung eines intelli-genten Fußbodensensors, des SensFloor® der Firma FutureShape GmbH, für den klinischen Be-reich der Ganganalyse kritisch überprüft. Junge, gesunde Proband*innen gingen wiederholt über den SensFloor®, um ein künstliches neuronales Netzwerk mit diesen Gangdaten zu trainie-ren. Ziel der zweiten Studie war es, die Treppensteigegeschwindigkeit in einer Kohorte älterer stationärer Patient*innen sowie einer Kohorte älterer Menschen ohne funktionelle Beeinträch-tigungen zu untersuchen. Hierfür stiegen die Studienteilnehmer*innen einen Treppenabsatz von 13 Stufen hinauf und wieder hinunter. In der dritten Studie wurden für den „Survey of Acti-vities and Fear of Falling in the Elderly“-Fragebogen Grenzwerte für die Einteilung in niedrige, moderate und hohe Sturzangst ermittelt. Grundlage waren die Daten aus einer Kohorte 98 älte-rer stationärer Patient*innen. Ergebnisse Die SensFloor-Technologie ist lernfähig und geeignet, um zwischen unterschiedli-chen Gangmodi zu differenzieren. Die Test-Retest-Analyse der Treppensteigegeschwindigkeit lieferte moderate bis exzellente Ergebnisse. Die Analyse des Sturzangstscores zeigte, dass die optimalen Grenzwerte zur Klassifikation niedriger, moderater und hoher Sturzangst bei 0,6 und 1,4 liegen. Schlussfolgerungen Mit der Anwendung der Sensfloor-Technologie, der Treppensteige-schwindigkeit in Stufen pro Sekunde sowie der Klassifikation der Sturzangst bietet die vorliegen-de Arbeit drei neue Ansätze, welche beim Sturzrisikoscreening sowohl im klinischen Setting als auch in der Forschung zukünftig eine stärkere Beachtung finden sollten.Background In an aging society fall prevention is a focal point in healthcare policy. It is of high in-terest to identify predictors of falls, in order to initiate appropriate preventive measures and to im-prove the quality of care. It is the purpose of this thesis to make a contribution to fall risk screening in the elderly in physical therapy. Methods Three publications resulting from three scientific projects were included in this disserta-tion. Methodologically, all three approaches follow a quantitative method. Two aspects of func-tional mobility - stair climbing and walking on level ground - as well as a psychological aspect, fear of falling, are in the focus of the present thesis. The aim of the first publication was the examination of gait patterns using modern sensor technology. For this purpose, the eligibility of an intelligent floor, the SensFloor® by the FutureShape company, was critically reviewed for the clinical field of gait analysis. Young healthy participants walked over the SensFloor® repeatedly in order to train an artificial neural network with this gait data. The aim of the second study was to investigate stair climbing speed in a cohort of older hospitalized patients and a cohort of older adults without func-tional impairments. For this purpose, the participants climbed up and down a flight of 13 steps. In the third study classification schemes for low, moderate, and high fear of falling were calculated using the “Survey of Activities and Fear of Falling in the Elderly“ (SAFE). For this, data from a cohort of 98 older hospitalized patients was analyzed. Results The SensFloor technology is capable of learning and able to differentiate various gait modes. Test-retest analysis of stair climbing speed provided moderate to excellent results. Analysis of the fear of falling score for classifying low, moderate, and high fear of falling resulted in optimal cut-off points with .6 and 1.4. Conclusions With the application of SensFloor technology, stair climbing speed in steps per second and classification of fear of falling, the present thesis offers three new approaches that should re-ceive more attention in fall risk screening. The results obtained should be considered in both the clinical setting and clinical research

App-Based Evaluation of Older People’s Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study

Background: Falls and the risk of falling in older people pose a high risk for losing independence. As the risk of falling progresses over time, it is often not adequately diagnosed due to the long intervals between contacts with health care professionals. This leads to the risk of falling being not properly detected until the first fall. App-based software able to screen fall risks of older adults and to monitor the progress and presence of fall risk factors could detect a developing fall risk at an early stage prior to the first fall. As smartphones become more common in the elderly population, this approach is easily available and feasible. Objective: The aim of the study is to evaluate the app Lindera Mobility Analysis (LIN). The reference standards determined the risk of falling and validated functional assessments of mobility. Methods: The LIN app was utilized in home- and community-dwelling older adults aged 65 years or more. The Berg Balance Scale (BBS), the Tinetti Test (TIN), and the Timed Up & Go Test (TUG) were used as reference standards. In addition to descriptive statistics, data correlation and the comparison of the mean difference of analog measures (reference standards) and digital measures were tested. Spearman rank correlation analysis was performed and Bland-Altman (B-A) plots drawn. Results: Data of 42 participants could be obtained (n=25, 59.5%, women). There was a significant correlation between the LIN app and the BBS (r=-0.587, P<.001), TUG (r=0.474, P=.002), and TIN (r=-0.464, P=.002). B-A plots showed only few data points outside the predefined limits of agreement (LOA) when combining functional tests and results of LIN. Conclusions: The digital app LIN has the potential to detect the risk of falling in older people. Further steps in establishing the validity of the LIN app should include its clinical applicability

Correction: Hoffmann et al. Detecting Walking Challenges in Gait Patterns Using a Capacitive Sensor Floor and Recurrent Neural Networks. Sensors 2021, 21, 1086

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