| Title |
Novel algorithm for a smartphone-based 6-minute walk test application: algorithm, application development, and evaluation
|
|---|---|
| Published in |
Journal of NeuroEngineering and Rehabilitation, January 2015
|
| DOI | 10.1186/s12984-015-0013-9 |
| Pubmed ID | |
| Authors |
Nicole A Capela, Edward D Lemaire, Natalie Baddour |
| Abstract |
The 6-minute walk test (6MWT: the maximum distance walked in 6 minutes) is used by rehabilitation professionals as a measure of exercise capacity. Today's smartphones contain hardware that can be used for wearable sensor applications and mobile data analysis. A smartphone application can run the 6MWT and provide typically unavailable biomechanical information about how the person moves during the test. |
X Demographics
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 5 | 50% |
| France | 2 | 20% |
| United States | 1 | 10% |
| India | 1 | 10% |
| Unknown | 1 | 10% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 7 | 70% |
| Scientists | 2 | 20% |
| Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 191 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 1% |
| Portugal | 1 | <1% |
| Australia | 1 | <1% |
| Colombia | 1 | <1% |
| Canada | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 184 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 35 | 18% |
| Researcher | 33 | 17% |
| Student > Bachelor | 23 | 12% |
| Student > Ph. D. Student | 22 | 12% |
| Other | 11 | 6% |
| Other | 35 | 18% |
| Unknown | 32 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 36 | 19% |
| Engineering | 32 | 17% |
| Computer Science | 22 | 12% |
| Nursing and Health Professions | 16 | 8% |
| Agricultural and Biological Sciences | 8 | 4% |
| Other | 31 | 16% |
| Unknown | 46 | 24% |
Attention Score in Context
This research output has an Altmetric Attention Score of 17. 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 29 September 2015.
All research outputs
#1,920,766
of 23,342,092 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#77
of 1,306 outputs
Outputs of similar age
#27,955
of 355,827 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#1
of 29 outputs
Altmetric has tracked 23,342,092 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,306 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. This one has done particularly well, scoring higher than 94% 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 355,827 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.