Evidence map›Paper›PMID 27956058›Full record

Observational studyGeriatric nursing (New York, N.Y.)

US and Dutch nurse experiences with fall prevention technology within nursing home environment and workflow: A qualitative study.

Ann E Vandenberg, Bert-Jan van Beijnum, Vera G P Overdevest, Elizabeth Capezuti, Theodore M Johnson

Registry-linked trialAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Geriatric nursing (New York, N.Y.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01774708 (Predict Near Future Initiation of Bed Exit to Prompt Effective Intervention to Avoid Nighttime Falls With Pattern-recognition Algorithms Using Unobtrusive Monitoring of Movement and Vital Signs), which is not on this map. Cited by 12 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT01774708 terminatednot on this map

Predict Near Future Initiation of Bed Exit to Prompt Effective Intervention to Avoid Nighttime Falls With Pattern-recognition Algorithms Using Unobtrusive Monitoring of Movement and Vital Signs

TypeobservationalSponsorAtlanta VA Medical CenterRan2012 to 2014Enrolled5ConditionsSleepArmsPressure sensitive pad
3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Application of Machine Learning Methods in Nursing Home Research.International journal of environmental research and public health · 2020
    Article
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  10. Article
  11. Article
  12. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ann E VandenbergEmory University, Department of Medicine, Division of General Medicine and Geriatrics, USA. Electronic address: avanden@emory.edu.
Bert-Jan van BeijnumUniversity of Twente, Faculty of Electrical Engineering, Mathematics, and Computer Science EMCS, Biomedical Signals and Systems, Netherlands.
Vera G P OverdevestUniversity of Twente, Faculty of Electrical Engineering, Mathematics, and Computer Science EMCS, Biomedical Signals and Systems, Netherlands.
Elizabeth CapezutiHunter College of CUNY, Hunter-Bellevue School of Nursing, USA.
Theodore M JohnsonEmory University, Department of Medicine, Division of General Medicine and Geriatrics, USA; Birmingham/Atlanta VA Geriatric Research Education and Clinical Center, USA.

Funding

Predict Near Future Bed Exit for Effective Intervention to Avoid Nighttime FallsR43AG042237 · NIA · CDIC, INC. · PI WHALEN, THOMAS HENRY · 2012 to 2012
$150k
NIA NIH HHS R43 AG042237
6 · The paper itself

Abstract

Falls remain a major geriatric problem, and the search for new solutions continues. We investigated how existing fall prevention technology was experienced within nursing home nurses' environment and workflow. Our NIH-funded study in an American nursing home was followed by a cultural learning exchange with a Dutch nursing home. We constructed two case reports from interview and observational data and compared the magnitude of falls, safety cultures, and technology characteristics and effectiveness. Falls were a high-magnitude problem at the US site, with a collectively vigilant safety culture attending to non-directional audible alarms; falls were a low-magnitude problem at the NL site which employed customizable, infrared sensors that directed text alerts to assigned staff members' mobile devices in patient-centered care culture. Across cases, 1) a coordinated communication system was essential in facilitating effective fall prevention alert response, and 2) nursing home safety culture is tightly associated with the chosen technological system.

Indexed as

Nursing HomesSafety ManagementWorkflowAccidental FallsBiomedical TechnologyFemaleHumansMaleNetherlandsOrganizational Case StudiesQualitative ResearchUnited StatesAlarm fatigueBed exit alarmsFall preventionLong-term careMonitoring technologyPosition monitorsPressure sensor mats

Identifiers

PMID27956058
PMCPMC6546295

What OpenQuestion holds

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Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.