Literature DB >> 20083427

Development and alarm threshold evaluation of a side rail integrated sensor technology for the prevention of falls.

Johannes Hilbe1, Eva Schulc, Barbara Linder, Christa Them.   

Abstract

OBJECTIVE: Patient falls constitute a serious problem both for the persons fallen and for the institutions involved. Bed-exit alarm systems are used to reduce patient falls. Existing bed-exit alarm systems have several disadvantages depending on the technology used. As in "Evaluation of Bed-Exit Alarms" stated, restless, light weighted, uncooperative, incontinent and confused patients require different systems. The aim of this work is to present the research and development process of the integrated, universally applicable BUCINATOR bed-exit-alarm system.
METHODS: The use-case technique was applied to capture the functional requirements for the development of the new integrated bed-exit alarm system. An experimental study was carried out to collect data regarding preliminary sensitivity and specificity for alarm set-off.
RESULTS: Major identified requirements for an optimized bed-exit alarm system were usability, wide range usage, low costs, hygiene factors, integration into nursing beds and nurse call systems and an adequate alarm/false alarm ratio with early alarm trigger functionality. On the basis of the criteria mentioned above, a sensor system was developed, comprising tubes with an air-filled passageway attached on the top of side rails. These tubes are coupled via lines to transducers which trigger an alarm when a predetermined level of pressure is reached. Both the preliminary sensitivity (96.0%) and the specificity (>or=95.5%) of the trigger level indicate a satisfactory alarm/false alarm ratio which is now to be evaluated in a clinical trial.
CONCLUSIONS: After experimental testing, BUCINATOR shows great potential to be a reliable bed-exit alarm system. In general, bed-exit alarm systems with extended features could play a major role in ambient assisted living technologies. LIMITATIONS: Besides the theoretical evaluation, it will be imperative to perform more tests and to gather more data about the effect on fall rates and resulting injuries. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

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Mesh:

Year:  2010        PMID: 20083427     DOI: 10.1016/j.ijmedinf.2009.12.004

Source DB:  PubMed          Journal:  Int J Med Inform        ISSN: 1386-5056            Impact factor:   4.046


  9 in total

1.  Hospital Personnel Attitude Toward Utilization of Depth Imaging Technology in Hospital Rooms.

Authors:  Rebbecca Fenton; Mihail Popescu; David Moxley
Journal:  Mo Med       Date:  2016 Jan-Feb

2.  Night-Time Monitoring System (eNightLog) for Elderly Wandering Behavior.

Authors:  James Chung-Wai Cheung; Eric Wing-Cheong Tam; Alex Hing-Yin Mak; Tim Tin-Chun Chan; Will Po-Yan Lai; Yong-Ping Zheng
Journal:  Sensors (Basel)       Date:  2021-01-20       Impact factor: 3.576

3.  Nurses' Perception of the Bed Alarm System in Acute-Care Hospitals.

Authors:  Ayaka Okumoto; Chiharu Miyata; Satoko Yoneyama; Ayae Kinoshita
Journal:  SAGE Open Nurs       Date:  2020-04-05

4.  A Night-Time Monitoring System (eNightLog) to Prevent Elderly Wandering in Hostels: A Three-Month Field Study.

Authors:  James Chung-Wai Cheung; Eric Wing-Cheung Tam; Alex Hing-Yin Mak; Tim Tin-Chun Chan; Yong-Ping Zheng
Journal:  Int J Environ Res Public Health       Date:  2022-02-13       Impact factor: 3.390

5.  Tablet-based strength-balance training to motivate and improve adherence to exercise in independently living older people: a phase II preclinical exploratory trial.

Authors:  Patrícia Silveira; Rolf van de Langenberg; Eva van Het Reve; Florian Daniel; Fabio Casati; Eling D de Bruin
Journal:  J Med Internet Res       Date:  2013-08-12       Impact factor: 5.428

6.  Effectiveness of a Batteryless and Wireless Wearable Sensor System for Identifying Bed and Chair Exits in Healthy Older People.

Authors:  Roberto Luis Shinmoto Torres; Renuka Visvanathan; Stephen Hoskins; Anton van den Hengel; Damith C Ranasinghe
Journal:  Sensors (Basel)       Date:  2016-04-15       Impact factor: 3.576

Review 7.  Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Authors:  Ramesh Rajagopalan; Irene Litvan; Tzyy-Ping Jung
Journal:  Sensors (Basel)       Date:  2017-11-01       Impact factor: 3.576

8.  A battery-less and wireless wearable sensor system for identifying bed and chair exits in a pilot trial in hospitalized older people.

Authors:  Roberto L Shinmoto Torres; Renuka Visvanathan; Derek Abbott; Keith D Hill; Damith C Ranasinghe
Journal:  PLoS One       Date:  2017-10-09       Impact factor: 3.240

Review 9.  Pathway of Trends and Technologies in Fall Detection: A Systematic Review.

Authors:  Rohit Tanwar; Neha Nandal; Mazdak Zamani; Azizah Abdul Manaf
Journal:  Healthcare (Basel)       Date:  2022-01-17
  9 in total

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