Citation

Stuckenschneider T, Koschate J, Dunker E, Reeck N, Hackbarth M, Hellmers S, Kwiecien R, Lau S, Levke Brütt A, Hein A, Zieschang T. BMC Geriatr. 2022; 22(1): 594.

Copyright

(Copyright © 2022, Holtzbrinck Springer Nature Publishing Group - BMC)

DOI

10.1186/s12877-022-03261-7

PMID

35850739

PMCID

PMC9289928

Abstract

BACKGROUND: Falls are a leading cause for emergency department (ED) visits in older adults. As a fall is associated with a high risk of functional decline and further falls and many falls do not receive medical attention, the ED is ideal to initiate secondary prevention, an opportunity generally not taken. Data on trajectories to identify patients, who would profit the most form early intervention and to examine the impact of a fall event, are lacking. To tailor interventions to the individual's needs and preferences, and to address the whole scope of fall risks, we developed this longitudinal study using an extensive assessment battery including dynamic balance and aerobic fitness, but also sensor-based data. Additionally, participative research will contribute valuable qualitative data, and machine learning will be used to identify trips, slips, and falls in sensor data during daily life.

METHODS: This is a mixed-methods study consisting of four parts: (1) an observational prospective study, (2) a randomized controlled trial (RCT) to explore whether a diagnostic to measure reactive dynamic balance influences fall risk, (3) machine learning approaches and (4) a qualitative study to explore patients' and their caregivers' views. We will target a sample size of 450 adults of 60 years and older, who presented to the ED of the Klinikum Oldenburg after a fall and are not hospitalized. The participants will be followed up over 24 months (within four weeks after the ED, after 6, 12 and 24 months). We will assess functional abilities, fall risk factors, participation, quality of life, falls incidence, and physical activity using validated instruments, including sensor-data. Additionally, two thirds of the patients will undergo intensive testing in the gait laboratory and 72 participants will partake in focus group interviews.

DISCUSSION: The results of the SeFallED study will be used to identify risk factors with high predictive value for functional outcome after a sentinel fall. This will help to (1) establish a protocol adapted to the situation in the ED to identify patients at risk and (2) to initiate an appropriate care pathway, which will be developed based on the results of this study. TRIAL REGISTRATION: DRKS (Deutsches Register für klinische Studien, DRKS00025949 ). Prospectively registered on 4(th) November, 2021.

Language: en

Keywords

Machine learning; Cognitive impairment; Emergency department; Older adults; Falls prevention; Activities of daily living; Aerobic fitness; Dynamic balance; Patient involvement; Perturbation