Temporal association between objectively measured smartphone usage, sleep quality and physical activity among Chinese adolescents and young adults

2020 ◽  
Author(s):  
Paul H. Lee ◽  
Andy C. Y. Tse ◽  
Cynthia S. T. Wu ◽  
Yim Wah Mak ◽  
Uichin Lee
Keyword(s):  
Physical Activity ◽  
Young Adults ◽  
Sleep Quality ◽  
Chinese Adolescents ◽  
Adolescents And Young Adults ◽  
Temporal Association ◽  
Objectively Measured

scholarly journals Objectively-Measured Smartphone Usage, Sleep Quality, and Physical Activity Among Chinese Adolescents and Young Adults

2020 ◽  
Author(s):  
Paul H Lee ◽  
Andy C. Y. Tse ◽  
Cynthia S. T. Wu ◽  
Yim Wah Mak ◽  
Uichin Lee
Keyword(s):  
Physical Activity ◽  
Young Adults ◽  
Secondary School ◽  
Sleep Quality ◽  
Secondary School Students ◽  
Adolescents And Young Adults ◽  
School Students ◽  
Sleeping Time ◽  
Hong Kong Adolescents ◽  
Objectively Measured

Abstract Objectives: We studied the association between objectively-measured smartphone usage and objectively-measured sleep quality and physical activity for seven consecutive days among Hong Kong adolescents and young adults aged 11–25 (n = 357, 67% female).Methods: We installed an app that tracked the subjects’ smartphone usage and had them wear an ActiGraph GT3X accelerometer on their wrist to measure their sleep quality and physical activity level. Smartphone usage data were successfully obtained from 187 participants (52.4%).Results: The participants on average spent 2 hours 46 minutes per day on their smartphone. Multilevel regression showed that, among secondary school students, one minute of daytime smartphone usage was associated with 0.12 minute decrease in total sleeping time that night (p = 0.042, 95% CI: -0.23, -0.007). One minute of bedtime smartphone usage was associated with 0.32 minute increase in wake after sleep onset that night (p = 0.04, 95% CI: 0.02, 0.62). One minute of smartphone usage during sleep was associated with sleep efficiency (β = 0.013%, p = 0.01, 95% CI: 0.003%, 0.023%) and WASO (β=-0.05, p = 0.04, 95% CI: -0.10, -0.005). One minute of daytime smartphone usage was associated with 7.15 steps increase in the number of steps (p = 0.02, 95% CI: 1.02, 13.28) among secondary school students and 3.52 steps increase in the number of steps (p = 0.03, 95% CI: 0.37, 6.66) among university students on the next day.Conclusion: Time spent on smartphone was associated with total sleeping time, the number of steps, and MVPA among Hong Kong adolescents and young adults.

scholarly journals Objectively-Assessed Physical Activity, Sedentary Behavior, Smartphone Use, and Sleep Patterns Pre- and during-COVID-19 Quarantine in Young Adults from Spain

2020 ◽  
Vol 12 (15) ◽  
pp. 5890 ◽  
Author(s):  
Borja Sañudo ◽  
Curtis Fennell ◽  
Antonio J. Sánchez-Oliver
Keyword(s):  
Physical Activity ◽  
Young Adults ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Vigorous Physical Activity ◽  
Sitting Time ◽  
Sleep Patterns ◽  
Lifestyle Modifications ◽  
Smartphone Use ◽  
Objectively Measured

This study assessed the effects of COVID-19 home confinement on physical activity, sedentary behavior, smartphone use, and sleep patterns. Data was collected in a sample of 20 young adults (mean age ± SD: 22.6 ± 3.4 years; 55% males) over seven days pre- and during the COVID-19 lockdown. Objective and subjective physical activity (Accelerometer and the International Physical Activity Questionnaire (IPAQ), respectively), the number of hours sitting (IPAQ), objectively-measured smartphone use (smartphone screen time applications), and objective and subjective sleep (accelerometer and the Pittsburgh Sleep Quality Index, respectively) were assessed. Results revealed significantly greater walking time and mean steps (p < 0.001, d = 1.223 to 1.605), and moderate and vigorous physical activity (p < 0.05, d = 0.568 to 0.616), in the pre- compared with the during-COVID-19 lockdown phase. Additionally, smartphone use (p = 0.009, d = 0.654), sitting time (p = 0.002, d = 1.120), and total sleep (p < 0.004, d = 0.666) were significantly greater in the during- compared with the pre-COVID-19 lockdown phase. Multiple regressions analyses showed associations between physical activity and sedentary behavior and sleep quality. The number of hours sitting per day and moderate-to-vigorous physical activity significantly predicted deep sleep (adj.R2 = 0.46). In conclusion, this study revealed that during the COVID-19 outbreak, behaviors changed, with participants spending less time engaging in physical activity, sitting more, spending more time using the smartphone, and sleeping more hours. These findings may be of importance to make recommendations, including lifestyle modifications during this time.

scholarly journals Associations of Objectively-Assessed Smartphone Use with Physical Activity, Sedentary Behavior, Mood, and Sleep Quality in Young Adults: A Cross-Sectional Study

2020 ◽  
Vol 17 (10) ◽  
pp. 3499 ◽  
Author(s):  
Moisés Grimaldi-Puyana ◽  
José María Fernández-Batanero ◽  
Curtis Fennell ◽  
Borja Sañudo
Keyword(s):  
Physical Activity ◽  
Young Adults ◽  
Sleep Quality ◽  
Sedentary Behavior ◽  
Mood State ◽  
Poor Sleep ◽  
Sleep Patterns ◽  
Smartphone Use ◽  
Low Levels ◽  
Objectively Measured

This study assesses the associations of objectively-measured smartphone time with physical activity, sedentary behavior, mood, and sleep patterns among young adults by collecting real-time data of the smartphone screen-state. The sample consisted of 306 college-aged students (mean age ± SD: 20.7 ± 1.4 years; 60% males). Over seven days of time, the following variables were measured in the participants: objectively-measured smartphone use (Your Hour and Screen Time applications), objective and subjective physical activity (GoogleFit and Apple Health applications, and the International Physical Activity Questionnaire (IPAQ), respectively), the number of hours sitting (IPAQ), mood (The Profile of Mood State (POMS)), and sleep (The Pittsburgh Sleep Quality Index (PSQI)). Multiple regressions analyses showed that the number of hours sitting per day, physical activity, and the POMS Global Score significantly predicted smartphone use (adj.R2 = 0.15). Further, participants with low levels of physical activity were more likely to increase the use of smartphones (OR = 2.981). Moreover, mood state (β = 0.185; 95% CI = 0.05, 0.32) and sleep quality (β = 0.076; 95% CI = −0.06, 0.21) predicted smartphone use, with those reporting poor quality of sleep (PSQI index >5) being more likely to use the smartphone (OR = 2.679). In conclusion, there is an association between objectively-measured smartphone use and physical activity, sedentary behavior, mood, and sleep patterns. Those participants with low levels of physical activity, high levels of sedentary behavior, poor mood state, and poor sleep quality were more likely to spend more time using their smartphones.

scholarly journals Obesity in Children and Adolescents during COVID-19 Pandemic

Children ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 135
Author(s):  
Androniki Stavridou ◽  
Evangelia Kapsali ◽  
Eleni Panagouli ◽  
Athanasios Thirios ◽  
Konstantinos Polychronis ◽  
...  
Keyword(s):  
Physical Activity ◽  
Young Adults ◽  
Health Care Providers ◽  
Eating Behaviors ◽  
Adolescents And Young Adults ◽  
Care Providers ◽  
Weight Changes ◽  
Sugary Drinks ◽  
Precautionary Measures ◽  
Obesity In Children

Background: The COVID-19 pandemic has led to special circumstances and changes to everyday life due to the worldwide measures that were imposed such as lockdowns. This review aims to evaluate obesity in children, adolescents and young adults during the COVID-19 pandemic. Methods: A literature search was conducted to evaluate pertinent studies up to 10 November 2020. Results: A total of 15 articles were eligible; 9 identified 17,028,111 children, adolescents and young adults from 5–25 years old, 5 pertained to studies with an age admixture (n = 20,521) and one study included parents with children 5–18 years old (n = 584). During the COVID-19 era, children, adolescents and young adults gained weight. Changes in dietary behaviors, increased food intake and unhealthy food choices including potatoes, meat and sugary drinks were noted during the ongoing COVID-19 pandemic. Food insecurity associated with financial reasons represents another concern. Moreover, as the restrictions imposed reduced movements out of the house, physical activity was limited, representing another risk factor for weight gain. Conclusions: COVID-19 restrictions disrupted the everyday routine of children, adolescents and young adults and elicited changes in their eating behaviors and physical activity. To protect them, health care providers should highlight the risk of obesity and provide prevention strategies, ensuring also parental participation. Worldwide policies, guidelines and precautionary measures should ideally be established.

scholarly journals Sleep problems among Chinese adolescents and young adults during the coronavirus-2019 pandemic

2020 ◽  
Vol 74 ◽  
pp. 39-47 ◽  
Author(s):  
Shuang-Jiang Zhou ◽  
Lei-Lei Wang ◽  
Rui Yang ◽  
Xing-Jie Yang ◽  
Li-Gang Zhang ◽  
...  
Keyword(s):  
Young Adults ◽  
Sleep Problems ◽  
Chinese Adolescents ◽  
Adolescents And Young Adults

A Longitudinal Study of Objectively Measured Built Environment as Determinant of Physical Activity in Young Adults: The European Youth Heart Study

2015 ◽  
Vol 12 (7) ◽  
pp. 909-914 ◽  
Author(s):  
Jasper Schipperijn ◽  
Mathias Ried-Larsen ◽  
Merete S. Nielsen ◽  
Anneli F. Holdt ◽  
Anders Grøntved ◽  
...  
Keyword(s):  
Physical Activity ◽  
Young Adults ◽  
Longitudinal Study ◽  
Built Environment ◽  
Cross Sectional ◽  
Environmental Indices ◽  
Domain Specific ◽  
Objectively Measured ◽  
European Youth

Background:This longitudinal study aimed to examine if a Movability Index (MI), based on objectively measured built environment characteristics, was a determinant for objectively measured physical activity (PA) among young adults.Methods:Data collected from 177 persons participating in the Danish part of the European Youth Hearth Study (EYHS) was used to examine the effect of the built environment on PA. A MI was developed using objectively measured built environment characteristics, and included residential density, recreational facilities, daily destinations and street connectivity.Results:Results showed a positive cross-sectional association between MI and PA. PA decreased from baseline to follow-up. MI increased, primarily due to participants relocating to larger cities. An increase in MI from baseline to follow-up was associated with a reduced decrease in PA for females.Conclusions:Our findings suggest that the built environment is a determinant for PA, especially for females. The found gender differences might suggest the need to develop gender specific environmental indices in future studies. The validity of the measures can be further improved by creating domain specific PA measures as well as domain specific environmental indices and this can potentially reveal more specific built environment determinants for PA.

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