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Relationship of sleep quality, smartphone dependence, and health-related behaviors in female junior college students
Open accessAbstract
INTRODUCTION: Smartphone usage has become commonplace and impact on sleep quality among adolescents. Adolescent girls have a greater tendency toward sleep quality problems. However, relationship of sleep quality, smartphone dependence, and health-related behaviors in female junior college students has not been studied. OBJECTIVES: This study had the two goals: to investigate the relationship between female college students' sleep quality, smartphone dependence, and health-related behaviors, and to identify predictors of sleep quality. METHODS: This study employed a cross-sectional research approach to gather 409 subjects at a junior college in southern Taiwan, and used a structured questionnaire to collect data. The questionnaire consisted of four parts: basic demographic data, the Pittsburgh Sleep Quality Index, assessment of smartphone dependence, and the Health Promoting Lifestyle Profile (HPLP). Logistic regression analysis was employed to check for any association between sleep quality and smartphone dependence or HPLP. RESULTS: Sleep quality was significantly associated with degree of smartphone dependence, total HPLP score, and scores on the four HPLP subscales of nutritional behavior, self-actualization, interpersonal support, and stress management behavior. The lower the subjects' degree of smartphone dependence was, the better their sleep quality was. Furthermore, the degree of smartphone dependence and total HPLP score were significant predictors of sleep quality. CONCLUSIONS: Smartphone dependence is associated with poor sleep quality among female college students. Improving health-related behaviors (nutritional behavior, self-actualization, interpersonal support, and stress management behavior) can also promote improvement in sleep quality.
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“This was one of the most frequently cited benefits of the challenge, which is not surprising given that numerous research studies have found negative correlations between smartphone usage and sleep.44-46”
From An Intervention Utilizing the Salience Principle to Reduce Pharmacy Students’ Psychological Attraction to Smartphones (2022) · cites this paper as [46]
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Metadata
- Authors
- Wang, P.-Y., Chen, K.-L., Yang, S.-Y., & Lin, P.-H.
- Affiliations
- Changhua Christian Hospital (TW)
- Asia University (TW)
- Corresponding author
- Yang, Shang-Yu
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2019-04-03
- Volume
- 14
- Issue
- 4
- Pages
- e0214769
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6447181
- OpenAlex
- W2927591937
- Cited by
- 118
- References
- 62
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 19 · 2023: 23 · 2022: 19 · 2021: 23 · 2020: 15 · 2019: 5
- FWCI
- 18.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Social Sciences › Sociology and Political Science › Impact of Technology on Adolescents
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Health and Lifestyle Studies
- Keywords
- sleep quality, smartphone addiction, health behavior, female university students, community college students, health-promoting lifestyle, nutritional behavior, stress management, interpersonal support, self-realization, predictors of sleep quality
- MeSH terms
- Smartphone, Healthy Lifestyle, Adolescent, Cross-Sectional Studies, Female, Humans, Surveys and Questionnaires, Sleep, Students, Taiwan, Universities, Health Behavior, Young Adult
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 ›
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