Digital Intelligent Assistant for Nursing Application

• Conditions: Frailty Syndrome; Dementia; Fall in Nursing Home; Incontinence, Urinary; Incontinence Bowel; Delirium in Old Age

• Registration Number: NCT04393272
• Lead Sponsor: Geriatrische Klinik St. Gallen

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.

Study Timeline

• Study First Submitted: 2020-05-11
• Study First Submitted QC: 2020-05-13
• Study First Posted: 2020-05-19
• Study Start: 2021-05-01
• Status Verified: 2022-08
• Last Update Submitted: 2022-08-24
• Last Update Posted: 2022-08-25
• Primary Completion: 2023-04-30
• Study Completion: 2023-07-31

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 25

Inclusion Criteria

Not provided

Exclusion Criteria

1. For Falls assessment by 3D sensors:

   • Based on the multifactorial risk for falls there are no exclusion criteria for falls assessment

2. For the development of toileting algorithm:

   • severe urine or fecal incontinence
   • permanent indwelling urinary catheter
   • permanent urinary or bowel stoma

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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

Trial Locations

Locations (1)

Geriatrische Klinik; 🇨🇭 Sankt Gallen, SG, Switzerland