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Mendeley readers
Attention Score in Context
| Title |
Sensors vs. experts - A performance comparison of sensor-based fall risk assessment vs. conventional assessment in a sample of geriatric patients
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, June 2011
|
| DOI | 10.1186/1472-6947-11-48 |
| Pubmed ID | |
| Authors |
Michael Marschollek, Anja Rehwald, Klaus-Hendrik Wolf, Matthias Gietzelt, Gerhard Nemitz, Hubertus Meyer zu Schwabedissen, Mareike Schulze |
| Abstract |
Fall events contribute significantly to mortality, morbidity and costs in our ageing population. In order to identify persons at risk and to target preventive measures, many scores and assessment tools have been developed. These often require expertise and are costly to implement. Recent research investigates the use of wearable inertial sensors to provide objective data on motion features which can be used to assess individual fall risk automatically. So far it is unknown how well this new method performs in comparison with conventional fall risk assessment tools. The aim of our research is to compare the predictive performance of our new sensor-based method with conventional and established methods, based on prospective data. |
Mendeley readers
The data shown below were compiled from readership statistics for 151 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 2% |
| Israel | 1 | <1% |
| Germany | 1 | <1% |
| Unknown | 146 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 17% |
| Student > Master | 19 | 13% |
| Researcher | 18 | 12% |
| Student > Bachelor | 16 | 11% |
| Professor > Associate Professor | 8 | 5% |
| Other | 26 | 17% |
| Unknown | 38 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 29 | 19% |
| Engineering | 19 | 13% |
| Nursing and Health Professions | 15 | 10% |
| Computer Science | 13 | 9% |
| Neuroscience | 8 | 5% |
| Other | 22 | 15% |
| Unknown | 45 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 27 November 2012.
All research outputs
#5,847,828
of 22,687,320 outputs
Outputs from BMC Medical Informatics and Decision Making
#515
of 1,979 outputs
Outputs of similar age
#32,761
of 115,606 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#7
of 19 outputs
Altmetric has tracked 22,687,320 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,979 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 74% 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 115,606 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 19 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.