Time Spent on Floor After Falls of Frailty People Overnight
NCT ID: NCT03116386
Last Updated: 2018-04-10
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
WITHDRAWN
NA
INTERVENTIONAL
2017-01-20
2019-05-31
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
The hypothesis of the study is that the use of a bed-raising detection system linked with the activation of a lighting environment and a caregivers alert system (Etolya-F® gerontechnology device, Anaxi Technology Company) would reduce intervention time in this population, thus limiting the time spent on floor and its physical and psychological consequences.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Early Fall Risk Detection and Fall Prevention Among Inpatients With Delirium
NCT05391334
Impact of an Automatic Alert Device on the Occurrence of Nocturnal Falls in Nursing Home Residents
NCT05964972
The Nightlight Falls Prevention Study
NCT05973448
Digital Intelligent Assistant for Nursing Application
NCT04393272
Assessment of an Automated Telesurveillance System on the Incidence of Serious Falls in Nursing Homes
NCT01551121
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
The most frequent reason for admitting in long term care facilities is the worsening of health status of elderly, often triggered by a fall. Elderly living in long term care facilities have frequently several comorbidities; the first ones are Alzheimer and related diseases. The proportion of such very dependent institutionalized people has risen for the last recent years and they represent a population at very high risk of falling. In an epidemiological analysis of more than 70,000 falls from residents of Bavarian nursing homes, the prevalence of fall was estimated at 1.49 falls for women and 2.18 for men. Those results didn't take into account the fact that people could fall more than once a day. In Alzheimer people (or people with related diseases) who lived in long term care facilities, the incidence of falls was even highest with 2.7 falls per resident per year.
The consequences of falls are not only physical injuries (wounds, fractures); they are frequently associated with psychological repercussions as loss of self-confidence, fear of new falls, reduction of abilities of moving which lead into declining of daily activities and loss of autonomy.
The incapacity of getting up alone is reported by more than a third of patients who have fallen, even if the fall is not complicated by a fracture. The length of time people stay on floor is directly link to the ability of the elderly person to give an alarm and to the presence or not of someone else to help him/her to get up. Patients who live in long term care facilities have limited functional capabilities not compatible with an operational use of active alarm systems.
In long term care facilities, 30-40% of falls occur between 8pm and 8am. Falls occurring at night seem to be associated with more severe injuries. Staff are less numerous at night with only 3 to 4 caregivers for 100 people.
To the best of the knowledge of the investigators, delay intervention time after a fall occurring at night has never been studied. Based on the investigators' experience, elderly people can only be discovered and helped when caregivers find them on floor on the occasion of a planned surveillance visit. These visits are carried out every 2 to 4 hours at night.
Automated alarms are used to alert staff to situations where there is a high risk of falling: an attempt to lift an armchair from a person who cannot stand or to detect the night-time rise of a high-risk people with the use of various sensors (pressure sensors connected to the mattress or environmental sensors).
In the context of staff reduced at night for the supervision of dependent elderly, automated risk alert systems could also have a positive impact on the organization of night care by better targeting monitoring. Residents' sleep could be less affected with use of automatic alert system than by systematic monitoring visits. One study shows an improvement in the humor of residents after the use of such a system.
The hypothesis of the study is that the use of a bed-raising detection system linked with the activation of a lighting environment and a personnel alert system (Etolya-F® gerontechnology device, Anaxi Technology Company) would reduce intervention time in this population, thus limiting the time spent on floor and its physical and psychological consequences.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
NON_RANDOMIZED
SEQUENTIAL
DEVICE_FEASIBILITY
NONE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
run-in period
In order to improve the precision of data, the run-in period is dedicated to sensitize the caregivers about the importance of
* reporting all the falls occurring during the night
* tracking in each resident's file, all informations about the estimate length of time spent on floor after a fall occurring during the night
* and also reporting every other events occuring at night as wandering. All the beds will progressively equipped with the Etolya-F ® devices but the Etolya-F ® ddevices will stay off.
run-in period
observational time i.e. baseline situation
control period
We expect 30 falls will occurr at night during this 6 months period. Etolya-F ® devices will be installed on the bed of all participant residents but with limited fonctionnalities i.e. only the length of absence in the bed will be recorded (difference between time of detection of the beginning of absence in the bed and time where the resident will be found by the caregivers out of his bed).
Control period
neither activation of any lighting environment when the resident gets up from his bed nor alert if the resident did not return to bed after 15 minutes Etolya-F ® devices will only permit detection and recording of the moment of the elderlly will leave his/her bed and recording of the moment the elderly will be found by caregivers
Etolya-F ® devices
We also expect 30 falls will occur at night during this 6-month period. Etolya-F ® devices will be used with all their functionalities i.e. permit detection of absence in the bed, activation of a lighting environment when the resident gets up from his bed, transmission of alert to caregivers through the centralized system of sick call if the resident do not return to bed after 15 minutes and recording the time when caregivers will find the resident out of bed, distinguishing between a fall and a night wandering in the room or corridors without a fall
Etolya-F ® devices
Etolya-F ® devices will permit detection of absence in the bed, activation of a lighting environment when the resident gets up from his bed, transmission of alert to caregivers through the centralized system of sick call if the resident do not return to bed after 15 minutes and recording the time when caregivers will find the resident out of bed, distinguishing between a fall and a night wandering in the room or corridors without a fall
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
run-in period
observational time i.e. baseline situation
Control period
neither activation of any lighting environment when the resident gets up from his bed nor alert if the resident did not return to bed after 15 minutes Etolya-F ® devices will only permit detection and recording of the moment of the elderlly will leave his/her bed and recording of the moment the elderly will be found by caregivers
Etolya-F ® devices
Etolya-F ® devices will permit detection of absence in the bed, activation of a lighting environment when the resident gets up from his bed, transmission of alert to caregivers through the centralized system of sick call if the resident do not return to bed after 15 minutes and recording the time when caregivers will find the resident out of bed, distinguishing between a fall and a night wandering in the room or corridors without a fall
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* non opposed to participate to the study or whose his/her legal representative is not opposed to the participation of the resident to the study
Exclusion Criteria
18 Years
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
Centre Hospitalier Annecy Genevois
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Dr Matthieu DEBRAY, MD
Role: STUDY_DIRECTOR
CH Annecy Genevois
Dr Nathalie RUEL, MD
Role: PRINCIPAL_INVESTIGATOR
CH Annecy Genevois
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
Résidence St François CH ANNECY-GENEVOIS
Annecy, , France
Countries
Review the countries where the study has at least one active or historical site.
References
Explore related publications, articles, or registry entries linked to this study.
Rapp K, Becker C, Cameron ID, Konig HH, Buchele G. Epidemiology of falls in residential aged care: analysis of more than 70,000 falls from residents of bavarian nursing homes. J Am Med Dir Assoc. 2012 Feb;13(2):187.e1-6. doi: 10.1016/j.jamda.2011.06.011. Epub 2011 Aug 4.
Pellfolk T, Gustafsson T, Gustafson Y, Karlsson S. Risk factors for falls among residents with dementia living in group dwellings. Int Psychogeriatr. 2009 Feb;21(1):187-94. doi: 10.1017/S1041610208007837. Epub 2008 Oct 6.
Jensen J, Lundin-Olsson L, Nyberg L, Gustafson Y. Falls among frail older people in residential care. Scand J Public Health. 2002;30(1):54-61.
Vu MQ, Weintraub N, Rubenstein LZ. Falls in the nursing home: are they preventable? J Am Med Dir Assoc. 2006 Mar;7(3 Suppl):S53-8, 52. doi: 10.1016/j.jamda.2005.12.016.
Lach HW, Parsons JL. Impact of fear of falling in long term care: an integrative review. J Am Med Dir Assoc. 2013 Aug;14(8):573-7. doi: 10.1016/j.jamda.2013.02.019. Epub 2013 Apr 16.
Fleming J, Brayne C; Cambridge City over-75s Cohort (CC75C) study collaboration. Inability to get up after falling, subsequent time on floor, and summoning help: prospective cohort study in people over 90. BMJ. 2008 Nov 17;337:a2227. doi: 10.1136/bmj.a2227.
Bergland A, Laake K. Concurrent and predictive validity of "getting up from lying on the floor". Aging Clin Exp Res. 2005 Jun;17(3):181-5. doi: 10.1007/BF03324594.
Lester P, Haq M, Vadnerkar A, Feuerman M. Falls in the nursing home setting: does time matter? J Am Med Dir Assoc. 2008 Nov;9(9):684-6. doi: 10.1016/j.jamda.2008.06.007. Epub 2008 Sep 25.
Pelissier C, Vohito M, Fort E, Sellier B, Agard JP, Fontana L, Charbotel B. Risk factors for work-related stress and subjective hardship in health-care staff in nursing homes for the elderly: A cross-sectional study. J Occup Health. 2015;57(3):285-96. doi: 10.1539/joh.14-0090-OA. Epub 2015 Apr 10.
Capezuti E, Brush BL, Lane S, Rabinowitz HU, Secic M. Bed-exit alarm effectiveness. Arch Gerontol Geriatr. 2009 Jul-Aug;49(1):27-31. doi: 10.1016/j.archger.2008.04.007. Epub 2008 Jun 3.
Banerjee S, Steenkeste F, Couturier P, Debray M, Franco A. Telesurveillance of elderly patients by use of passive infra-red sensors in a 'smart' room. J Telemed Telecare. 2003;9(1):23-9. doi: 10.1258/135763303321159657.
Lipsitz LA, Tchalla AE, Iloputaife I, Gagnon M, Dole K, Su ZZ, Klickstein L. Evaluation of an Automated Falls Detection Device in Nursing Home Residents. J Am Geriatr Soc. 2016 Feb;64(2):365-8. doi: 10.1111/jgs.13708. Epub 2016 Jan 19.
Tinetti ME, Williams CS. Falls, injuries due to falls, and the risk of admission to a nursing home. N Engl J Med. 1997 Oct 30;337(18):1279-84. doi: 10.1056/NEJM199710303371806.
Parker MJ, Gillespie WJ, Gillespie LD. Effectiveness of hip protectors for preventing hip fractures in elderly people: systematic review. BMJ. 2006 Mar 11;332(7541):571-4. doi: 10.1136/bmj.38753.375324.7C. Epub 2006 Mar 2.
Related Links
Access external resources that provide additional context or updates about the study.
New technologies likely to improve gerontological practices and the daily life of elderly patients and their families
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
2016-A01799-42
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.