ArticleBMC geriatrics2022
Sentinel fall presenting to the emergency department (SeFallED) - protocol of a complex study including long-term observation of functional trajectories after a fall, exploration of specific fall risk factors, and patients' views on falls prevention.
Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- A randomized control trial comparing Falls Reduction for Elderly Emergency Department (FREED) interventions and usual care.BMC emergency medicine · 2025Trial
- Words and Language Matter: Improving Older Person's Healthcare Outcomes Through Use of Age-Positive Language.Emergency medicine Australasia : EMA · 2026Review
- Predictors of Hospital Admission Following Emergency Department Presentation for Falls Among Older Adults: A Retrospective Analysis.Australasian journal on ageing · 2026Article
- Association of depressive symptoms, gait and dynamic balance with risk of falling in older adults discharged from the emergency department after a fall: a prospective observational study.BMC geriatrics · 2026Observational
- Article
- Tailored interventions to prevent functional decline after a Sentinel Fall presenting to the Emergency Department (iSeFallED) - protocol for a pragmatic mixed-methods implementation study.BMC geriatrics · 2026Article
- Neighbourhood-level age-friendliness in urban environments: associations with functional independence and physical activity in older adults after a fall.Frontiers in public health · 2026Article
- Emergency department visits due to severe falls: comparing patient self- reports and general practitioner records: A cross-sectional study.BMC geriatrics · 2025Article
- Research in Partnership With Older People-Involvement in Conducting and Analysing Focus Groups.Health expectations : an international journal of public participation in health care and health policy · 2025Observational
- How socioeconomic status shapes health outcomes following severe falls: a cross-sectional analysis.BMC geriatrics · 2025Article
- A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.BMC medical informatics and decision making · 2025Review
- Physical activity six months after a severe fall - moderating factors in older individuals.BMC geriatrics · 2025Article
- A novel score for predicting falls in community-dwelling older people: a derivation and validation study.BMC geriatrics · 2024Article
- Reduced adaptability to balance perturbations in older adults with probable cognitive impairment after a severe fall.PloS one · 2024Article
- Towards comparable quality-assured Azure Kinect body tracking results in a study setting-Influence of light.PloS one · 2024Article
- Recruiting patients for falls prevention in the emergency department - worth the challenge.BMC geriatrics · 2023Observational
- [Reactive dynamic balance in the geriatric setting : Possibilities for evaluation and quantification in functionally heterogeneous persons].Zeitschrift fur Gerontologie und Geriatrie · 2023Review
- Comparison of machine learning approaches for near-fall-detection with motion sensors.Frontiers in digital health · 2023Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFalls 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.
methodsThis 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 registrationDRKS (Deutsches Register für klinische Studien, DRKS00025949 ). Prospectively registered on 4
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