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Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep

Overview of attention for article published in PLOS ONE, November 2016
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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 (99th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

news
114 news outlets
blogs
14 blogs
twitter
192 X users
facebook
19 Facebook pages
wikipedia
4 Wikipedia pages
googleplus
4 Google+ users
reddit
1 Redditor

Readers on

mendeley
639 Mendeley
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Title
Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep
Published in
PLOS ONE, November 2016
DOI 10.1371/journal.pone.0165331
Pubmed ID
Authors

Matthew A. Christensen, Laura Bettencourt, Leanne Kaye, Sai T. Moturu, Kaylin T. Nguyen, Jeffrey E. Olgin, Mark J. Pletcher, Gregory M. Marcus

Abstract

Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality. The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep. We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index-PSQI) were obtained by survey. Linear regression was used to obtain effect estimates. Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency. These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.

X Demographics

X Demographics

The data shown below were collected from the profiles of 192 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 639 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
France 1 <1%
Unknown 637 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 120 19%
Student > Master 76 12%
Student > Ph. D. Student 46 7%
Researcher 34 5%
Student > Doctoral Student 27 4%
Other 93 15%
Unknown 243 38%
Readers by discipline Count As %
Medicine and Dentistry 78 12%
Psychology 73 11%
Nursing and Health Professions 51 8%
Social Sciences 28 4%
Computer Science 19 3%
Other 105 16%
Unknown 285 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1111. 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 14 February 2024.
All research outputs
#13,569
of 25,498,750 outputs
Outputs from PLOS ONE
#192
of 222,328 outputs
Outputs of similar age
#212
of 319,409 outputs
Outputs of similar age from PLOS ONE
#3
of 4,007 outputs
Altmetric has tracked 25,498,750 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 222,328 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.8. 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 319,409 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 4,007 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.