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Novel sensing technology in fall risk assessment in older adults: a systematic review

Overview of attention for article published in BMC Geriatrics, January 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (63rd percentile)

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13 X users

Citations

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134 Dimensions

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323 Mendeley
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Title
Novel sensing technology in fall risk assessment in older adults: a systematic review
Published in
BMC Geriatrics, January 2018
DOI 10.1186/s12877-018-0706-6
Pubmed ID
Authors

Ruopeng Sun, Jacob J. Sosnoff

Abstract

Falls are a major health problem for older adults with significant physical and psychological consequences. A first step of successful fall prevention is to identify those at risk of falling. Recent advancement in sensing technology offers the possibility of objective, low-cost and easy-to-implement fall risk assessment. The objective of this systematic review is to assess the current state of sensing technology on providing objective fall risk assessment in older adults. A systematic review was conducted in accordance to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA). Twenty-two studies out of 855 articles were systematically identified and included in this review. Pertinent methodological features (sensing technique, assessment activities, outcome variables, and fall discrimination/prediction models) were extracted from each article. Four major sensing technologies (inertial sensors, video/depth camera, pressure sensing platform and laser sensing) were reported to provide accurate fall risk diagnostic in older adults. Steady state walking, static/dynamic balance, and functional mobility were used as the assessment activity. A diverse range of diagnostic accuracy across studies (47.9% - 100%) were reported, due to variation in measured kinematic/kinetic parameters and modelling techniques. A wide range of sensor technologies have been utilized in fall risk assessment in older adults. Overall, these devices have the potential to provide an accurate, inexpensive, and easy-to-implement fall risk assessment. However, the variation in measured parameters, assessment tools, sensor sites, movement tasks, and modelling techniques, precludes a firm conclusion on their ability to predict future falls. Future work is needed to determine a clinical meaningful and easy to interpret fall risk diagnosis utilizing sensing technology. Additionally, the gap between functional evaluation and user experience to technology should be addressed.

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The data shown below were collected from the profiles of 13 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 323 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 323 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 48 15%
Researcher 41 13%
Student > Master 36 11%
Student > Bachelor 35 11%
Student > Doctoral Student 13 4%
Other 42 13%
Unknown 108 33%
Readers by discipline Count As %
Engineering 48 15%
Nursing and Health Professions 39 12%
Medicine and Dentistry 37 11%
Computer Science 17 5%
Sports and Recreations 11 3%
Other 43 13%
Unknown 128 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 February 2018.
All research outputs
#3,272,497
of 23,372,952 outputs
Outputs from BMC Geriatrics
#857
of 3,240 outputs
Outputs of similar age
#76,045
of 444,132 outputs
Outputs of similar age from BMC Geriatrics
#27
of 72 outputs
Altmetric has tracked 23,372,952 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,240 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.6. This one has gotten more attention than average, scoring higher than 73% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 444,132 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 72 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.