Delirium in Old Age, Dementia, Fall in Nursing Home, Frailty Syndrome, Incontinence Bowel, Incontinence, Urinary
Conditions
Keywords
3D sensors, falls detection, toileting algorithm
Brief summary
This is an observational study that intends to compare falls or fall-risk related alarms derived from a three-dimensional sensor system with the clinical reality definded by attending nurses.
Detailed description
Three dimensional sensor technology (3DS) is available for fall detection and fall prevention (e.g. unwanted getting up in persons with risks for frequent falls) in several institutions in Europe and Switzerland. 3DS are capable to analyze completely anonymized data and alert nurses towards a dangerous (fall) or potentially dangerous (getting out of bed) event during day- and nighttime. Multi-sensor technology has been applied to assess activities of daily living in persons cognitive problems living at home. To our knowledge, 3DS technology has not been examined as part of a structured clinical protocol. In addition, the combination of two digital technologies (3DS and a server based software) as an integrative platform could help to develop algorithms to analyze complex human activities such as using a toilet. Automated analyses of such complex activities have the potential to support nursing staff in the future.
Interventions
DEVICEThree dimensional sensor system designed for fall detection (Fearless)
Detection of falls in nursing home residents and acute geriatric hospital patiients
Sponsors
Geriatrische Klinik St. Gallen
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
75 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Informed consent given by person or caregiver Patients admitted * with or without cognitive decline for any reason * with acute and/or chronic conditions * after any kind of surgery
Exclusion criteria
1. For Falls assessment by 3D sensors: • Based on the multifactorial risk for falls there are no
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Falls detection rate | Through study completion, an average of 6 months | Number of falls detected by the system compared to falls detected by nursing staff members |
Other
| Measure | Time frame | Description |
|---|---|---|
| Assessment of toileting | Through study completion, an average of 6 months | 3d sensor system data will be used to develop an algorithm for a complex activity such as using a toilet. The senors results will be compared with a Nurse led 12 step observation protocol. Parts of the activity model include: (1) enter the room, (2) go to the toilet, (3) take off clothes, (4) sit on the toilet, (5) clean oneself, (6) stand up, (7) get dressed, (8) flush the toilet, (9) go to the sink, (10) wash hands, (11) dry hands, (12) leave the room, as well as emergencies. |
Countries
Switzerland
Contacts
Primary ContactThomas Münzer, MD
Outcome results
None listed