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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Developing Prediction Equations and a Mobile Phone Application to Identify Infants at Risk of Obesity
|
|---|---|
| Published in |
PLOS ONE, August 2013
|
| DOI | 10.1371/journal.pone.0071183 |
| Pubmed ID | |
| Authors |
Gillian Santorelli, Emily S. Petherick, John Wright, Brad Wilson, Haider Samiei, Noël Cameron, William Johnson |
| Abstract |
Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 115 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 21 | 18% |
| Student > Master | 17 | 15% |
| Student > Ph. D. Student | 13 | 11% |
| Researcher | 10 | 9% |
| Lecturer | 6 | 5% |
| Other | 18 | 16% |
| Unknown | 30 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 24 | 21% |
| Nursing and Health Professions | 13 | 11% |
| Psychology | 10 | 9% |
| Computer Science | 8 | 7% |
| Agricultural and Biological Sciences | 6 | 5% |
| Other | 20 | 17% |
| Unknown | 34 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 23 April 2019.
All research outputs
#18,812,604
of 23,314,015 outputs
Outputs from PLOS ONE
#159,411
of 199,281 outputs
Outputs of similar age
#149,322
of 198,773 outputs
Outputs of similar age from PLOS ONE
#3,648
of 4,834 outputs
Altmetric has tracked 23,314,015 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 199,281 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.3. This one is in the 10th percentile – i.e., 10% of its peers scored the same or lower than it.
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 198,773 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4,834 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.