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The Associations between Daytime Physical Activity, While-in-Bed Smartphone Use, Sleep Delay, and Sleep Quality: A 24-h Investigation among Chinese College Students

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  • Hongxing Chen

    (Department of Physical Education, Nanjing Institute of Technology, Nanjing 211167, China)

  • Guodong Zhang

    (Institute of Sports Science, College of Physical Education, Southwest University, Chongqing 400715, China)

  • Zhenhuan Wang

    (Institute for Health and Sport (iHeS), Victoria University, Melbourne 3011, Australia)

  • Siyuan Feng

    (Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI 53706, USA)

  • Hansen Li

    (Institute of Sports Science, College of Physical Education, Southwest University, Chongqing 400715, China)

Abstract

Bedtime smartphone use is an emerging issue that threatens the sleep health of children and young adults. Physical activity can have numerous health benefits, including reducing problematic or addictive behavior. However, the role of daily physical activity in reducing bedtime smartphone use is understudied. Hence, we conducted a one-day cross-sectional on the weekend (21–22 May 2021) to investigate the associations between daytime physical activity, bedtime smartphone use, and sleep quality. A total of 828 college students were recruited in two colleges. Their daytime physical activity indices were captured, including self-reported physical activity duration, intensity, volume, and smartphone-monitored walking steps. The participants reported whether they used smartphone while lying in bed (before sleep) and whether they delayed sleep due to smartphone use. Their while-in-bed screen time (duration) and subsequent sleep quality were also measured with self-report and a numeric rating scale, respectively. The results suggested that daytime physical activity duration was associated with lower chances of while-in-bed smartphone use (OR = 0.907, p = 0.019) and smartphone-related sleep delay (OR = 0.932, p = 0.014). However, no significant association was found between physical activity indices and while-in-bed screen time or sleep quality. These findings may contribute to understanding the reciprocal relationship between physical activity and smartphone use and highlighting the potential of controlling problematic bedtime smartphone use through daily physical activity. Future research is warranted to examine the associations with extra objective measures.

Suggested Citation

  • Hongxing Chen & Guodong Zhang & Zhenhuan Wang & Siyuan Feng & Hansen Li, 2022. "The Associations between Daytime Physical Activity, While-in-Bed Smartphone Use, Sleep Delay, and Sleep Quality: A 24-h Investigation among Chinese College Students," IJERPH, MDPI, vol. 19(15), pages 1-10, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9693-:d:881856
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    References listed on IDEAS

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    2. Moisés Grimaldi-Puyana & José María Fernández-Batanero & Curtis Fennell & Borja Sañudo, 2020. "Associations of Objectively-Assessed Smartphone Use with Physical Activity, Sedentary Behavior, Mood, and Sleep Quality in Young Adults: A Cross-Sectional Study," IJERPH, MDPI, vol. 17(10), pages 1-12, May.
    3. Xing Zhang & Siyuan Feng & Rui Peng & Hansen Li, 2022. "Using Structural Equation Modeling to Examine Pathways between Physical Activity and Sleep Quality among Chinese TikTok Users," IJERPH, MDPI, vol. 19(9), pages 1-13, April.
    4. Paula Magalhães & Vânia Cruz & Sara Teixeira & Sónia Fuentes & Pedro Rosário, 2020. "An Exploratory Study on Sleep Procrastination: Bedtime vs. While-in-Bed Procrastination," IJERPH, MDPI, vol. 17(16), pages 1-14, August.
    5. Shijie Liu & Tao Xiao & Lin Yang & Paul D. Loprinzi, 2019. "Exercise as an Alternative Approach for Treating Smartphone Addiction: A Systematic Review and Meta-Analysis of Random Controlled Trials," IJERPH, MDPI, vol. 16(20), pages 1-16, October.
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    Cited by:

    1. Diego A. Bonilla & Isabel A. Sánchez-Rojas & Darío Mendoza-Romero & Yurany Moreno & Jana Kočí & Luis M. Gómez-Miranda & Daniel Rojas-Valverde & Jorge L. Petro & Richard B. Kreider, 2022. "Profiling Physical Fitness of Physical Education Majors Using Unsupervised Machine Learning," IJERPH, MDPI, vol. 20(1), pages 1-13, December.

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