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Identifying the Best Times for Cognitive Functioning Using New Methods: Matching University Times to Undergraduate Chronotypes

Overview of attention for article published in Frontiers in Human Neuroscience, April 2017
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • Among the highest-scoring outputs from this source (#21 of 7,760)
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
75 news outlets
blogs
6 blogs
twitter
82 X users
facebook
5 Facebook pages
googleplus
2 Google+ users
reddit
3 Redditors

Citations

dimensions_citation
33 Dimensions

Readers on

mendeley
135 Mendeley
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Title
Identifying the Best Times for Cognitive Functioning Using New Methods: Matching University Times to Undergraduate Chronotypes
Published in
Frontiers in Human Neuroscience, April 2017
DOI 10.3389/fnhum.2017.00188
Pubmed ID
Authors

M. D. R. Evans, Paul Kelley, Jonathan Kelley

Abstract

University days generally start at fixed times in the morning, often early morning, without regard to optimal functioning times for students with different chronotypes. Research has shown that later starting times are crucial to high school students' sleep, health, and performance. Shifting the focus to university, this study used two new approaches to determine ranges of start times that optimize cognitive functioning for undergraduates. The first is a survey-based, empirical model (SM), and the second a neuroscience-based, theoretical model (NM). The SM focused on students' self-reported chronotype and times they feel at their best. Using this approach, data from 190 mostly first and second year university students were collected and analyzed to determine optimal times when cognitive performance can be expected to be at its peak. The NM synthesized research in sleep, circadian neuroscience, sleep deprivation's impact on cognition, and practical considerations to create a generalized solution to determine the best learning hours. Strikingly the SM and NM results align with each other and confirm other recent research in indicating later start times. They add several important points: (1) They extend our understanding by showing that much later starting times (after 11 a.m. or 12 noon) are optimal; (2) Every single start time disadvantages one or more chronotypes; and (3) The best practical model may involve three alternative starting times with one afternoon shared session. The implications are briefly considered.

X Demographics

X Demographics

The data shown below were collected from the profiles of 82 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 135 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Brazil 1 <1%
Unknown 133 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 28 21%
Student > Master 18 13%
Student > Ph. D. Student 14 10%
Researcher 8 6%
Other 7 5%
Other 16 12%
Unknown 44 33%
Readers by discipline Count As %
Psychology 23 17%
Neuroscience 13 10%
Medicine and Dentistry 11 8%
Agricultural and Biological Sciences 8 6%
Biochemistry, Genetics and Molecular Biology 7 5%
Other 27 20%
Unknown 46 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 701. 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 22 April 2024.
All research outputs
#30,071
of 25,761,363 outputs
Outputs from Frontiers in Human Neuroscience
#21
of 7,760 outputs
Outputs of similar age
#561
of 325,279 outputs
Outputs of similar age from Frontiers in Human Neuroscience
#3
of 191 outputs
Altmetric has tracked 25,761,363 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,760 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has done particularly well, scoring higher than 99% 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 325,279 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 99% of its contemporaries.
We're also able to compare this research output to 191 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 98% of its contemporaries.