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Meta-Analyses of the Effects of Habitual Running on Indices of Health in Physically Inactive Adults

Overview of attention for article published in Sports Medicine, July 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (96th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

1 news outlet
46 tweeters
3 Facebook pages
1 video uploader


51 Dimensions

Readers on

185 Mendeley
Meta-Analyses of the Effects of Habitual Running on Indices of Health in Physically Inactive Adults
Published in
Sports Medicine, July 2015
DOI 10.1007/s40279-015-0359-y
Pubmed ID

Luiz Carlos Hespanhol Junior, Julian David Pillay, Willem van Mechelen, Evert Verhagen


In order to implement running to promote physical activity, it is essential to quantify the extent to which running improves health. The aim was to summarise the literature on the effects of endurance running on biomedical indices of health in physically inactive adults. Electronic searches were conducted in October 2014 on PubMed, Embase, CINAHL, SPORTDiscus, PEDro, the Cochrane Library and LILACS, with no limits of date and language of publication. Randomised controlled trials (with a minimum of 8 weeks of running training) that included physically inactive but healthy adults (18-65 years) were selected. The studies needed to compare intervention (i.e. endurance running) and control (i.e. no intervention) groups. Two authors evaluated study eligibility, extracted data, and assessed risk of bias; a third author resolved any uncertainties. Random-effects meta-analyses were performed to summarise the estimates for length of training and sex. A dose-response analysis was performed with random-effects meta-regression in order to investigate the relationship between running characteristics and effect sizes. After screening 22,380 records, 49 articles were included, of which 35 were used to combine data on ten biomedical indices of health. On average the running programs were composed of 3.7 ± 0.9 sessions/week, 2.3 ± 1.0 h/week, 14.4 ± 5.4 km/week, at 60-90 % of the maximum heart rate, and lasted 21.5 ± 16.8 weeks. After 1 year of training, running was effective in reducing body mass by 3.3 kg [95 % confidence interval (CI) 4.1-2.5], body fat by 2.7 % (95 % CI 5.1-0.2), resting heart rate by 6.7 min(-1) (95 % CI 10.3-3.0) and triglycerides by 16.9 mg dl(-1) (95 % CI 28.1-5.6). Also, running significantly increased maximal oxygen uptake (VO2max) by 7.1 ml min(-1) kg(-1) (95 % CI 5.0-9.1) and high-density lipoprotein (HDL) cholesterol by 3.3 mg dl(-1) (95 % CI 1.2-5.4). No significant effect was found for lean body mass, body mass index, total cholesterol and low-density lipoprotein cholesterol after 1 year of training. In the dose-response analysis, larger effect sizes were found for longer length of training. It was only possible to combine the data of ten out the 161 outcome measures identified. Lack of information on training characteristics precluded a multivariate model in the dose-response analysis. Endurance running was effective in providing substantial beneficial effects on body mass, body fat, resting heart rate, VO2max, triglycerides and HDL cholesterol in physically inactive adults. The longer the length of training, the larger the achieved health benefits. Clinicians and health authorities can use this information to advise individuals to run, and to support policies towards investing in running programs.

Twitter Demographics

The data shown below were collected from the profiles of 46 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 185 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 3 2%
Brazil 2 1%
United Kingdom 1 <1%
United States 1 <1%
Chile 1 <1%
Unknown 177 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 35 19%
Student > Bachelor 34 18%
Student > Ph. D. Student 23 12%
Unspecified 21 11%
Student > Doctoral Student 14 8%
Other 58 31%
Readers by discipline Count As %
Sports and Recreations 44 24%
Medicine and Dentistry 43 23%
Unspecified 31 17%
Nursing and Health Professions 28 15%
Agricultural and Biological Sciences 9 5%
Other 30 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 45. 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 24 February 2019.
All research outputs
of 13,411,977 outputs
Outputs from Sports Medicine
of 2,185 outputs
Outputs of similar age
of 187,932 outputs
Outputs of similar age from Sports Medicine
of 22 outputs
Altmetric has tracked 13,411,977 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,185 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 31.9. This one has done well, scoring higher than 81% 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 187,932 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 96% of its contemporaries.
We're also able to compare this research output to 22 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 54% of its contemporaries.