Cited by 18Impact of Technology on AdolescentsSleep and related disordersSleep and Work-Related Fatigue

Effect of smartphone overuse on sleep problems in medical students

Open access
Boonluksiri, Pairoj·2018·The Asia Pacific Scholar

Abstract

Background: Smartphones are used worldwide. Consequently, it does seem to be having an impact on health-related problems \nif overused. However, it is uncertain whether it is associated with sleep problems or poor learning. \nObjective: To determine the association between smartphone overuse and sleep problems in medical students as primary outcome and poor learning as secondary outcome. \nMethods: A cross-sectional study was conducted in 89 students having their own smartphones, at Hatyai Medical Education \nCentre, Thailand. The habits of using smartphone were obtained. Smartphone overuse during bedtime was defined as using longer \nthan 1 hour according to Smartphone Addiction Scale (SAS). The primary outcome was napping in a classroom that was defined \nas a problem if it happened more than 20% of the time attending class. Sleep problems using Pittsburgh Sleep Quality Index \n(PSQI) and Epworth Sleepiness Scale (ESS) were obtained by self-assessment. Learning outcome measured by grade point \naverage was the secondary outcome. Multivariable analysis was performed for the association between smartphone overuse and \nsleep problems. \nResults: Of all students, 77.5% had sleep problems and 43.6% had napped in the classroom. No personal characteristics, daily \nlife behaviours, and physical environments were associated with sleep problems. 70.8% of all students found to over use \nsmartphones during bedtime. The Facebook website was the most popular. Smartphone overuse was significantly associated with poor sleep quality (odds ratio= 3.46) and napping in the classroom (odds ratio=4.09) but not grade point average. \nConclusion: Smartphone overuse during bedtime in medical students is associated with sleep problems but not learning \nachievement.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.29060/taps.2018-3-2/oa1039

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Boonluksiri, P.
Corresponding author
Boonluksiri, Pairoj
Venue
The Asia Pacific Scholar
Published
2018-05-02
Volume
3
Issue
2
Pages
25-28
ISSN
  • 2424-9270 (ISSN-L)
  • 2424-9335
OpenAlex
W2800333277
Cited by
18
References
11
Citations by year
2026: 1 · 2025: 2 · 2024: 7 · 2023: 3 · 2022: 3 · 2021: 1 · 2019: 1
FWCI
1.14
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • The Asia Pacific Scholar (publishedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and Work-Related Fatigue
Keywords
smartphone overuse, sleep disorders, medical students, sleep quality, daytime napping, academic performance, smartphone addiction, bedtime screen use, Facebook use

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review