Evidence map›Paper›PMID 28462388›Full record

ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2017

Multidimensional assessment of challenging behaviors in advanced stages of dementia in nursing homes-The insideDEM framework.

Stefan Teipel, Christina Heine, Albert Hein, Frank Krüger, Andreas Kutschke, Sven Kernebeck, Margareta Halek, Sebastian Bader, Thomas Kirste

Abstract read
In one paragraph

Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

  1. Trial
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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

9 authors.

Stefan TeipelDepartment of Psychosomatic Medicine, University of Rostock, Rostock, Germany.
Christina HeineDepartment of Psychosomatic Medicine, University of Rostock, Rostock, Germany.
Albert HeinDepartment of Computer Science, University of Rostock, Rostock, Germany.
Frank KrügerDepartment of Computer Science, University of Rostock, Rostock, Germany.
Andreas KutschkeStädtische Seniorenheime Krefeld gGmbH, Krefeld, Germany.
Sven KernebeckDZNE, German Center for Neurodegenerative Diseases, Witten, Germany.
Margareta HalekDZNE, German Center for Neurodegenerative Diseases, Witten, Germany.
Sebastian BaderDepartment of Computer Science, University of Rostock, Rostock, Germany.
Thomas KirsteDepartment of Computer Science, University of Rostock, Rostock, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAssessment of challenging behaviors in dementia is important for intervention selection. Here, we describe the technical and experimental setup and the feasibility of long-term multidimensional behavior assessment of people with dementia living in nursing homes.

methodsWe conducted 4 weeks of multimodal sensor assessment together with real-time observation of 17 residents with moderate to very severe dementia in two nursing care units. Nursing staff received extensive training on device handling and measurement procedures. Behavior of a subsample of eight participants was further recorded by videotaping during 4 weeks during day hours. Sensors were mounted on the participants' wrist and ankle and measured motion, rotation, as well as surrounding loudness level, light level, and air pressure.

resultsParticipants were in moderate to severe stages of dementia. Almost 100% of participants exhibited relevant levels of challenging behaviors. Automated quality control detected 155 potential issues. But only 11% of the recordings have been influenced by noncompliance of the participants. Qualitative debriefing of staff members suggested that implementation of the technology and observation platform in the routine procedures of the nursing home units was feasible and identified a range of user- and hardware-related implementation and handling challenges. DISCUSSION: Our results indicate that high-quality behavior data from real-world environments can be made available for the development of intelligent assistive systems and that the problem of noncompliance seems to be manageable. Currently, we train machine-learning algorithms to detect episodes of challenging behaviors in the recorded sensor data.

Indexed as

Information and communication technologyNeuropsychiatric symptomsNursing careReal-world evidenceSensor-based assessmentVideo recording

Identifiers

PMID28462388
PMCPMC5403785

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Registered trials

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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.