scholarly journals How objectively measured Twitter and Instagram use relate to self‐reported personality and tendencies toward Internet/Smartphone Use Disorder

2021 ◽  
Author(s):  
Jessica Peterka‐Bonetta ◽  
Cornelia Sindermann ◽  
Jon D. Elhai ◽  
Christian Montag
Keyword(s):  
Smartphone Use ◽  
Objectively Measured

scholarly journals Problematic Smartphone Use in a Large Nationally Representative Sample: Age, Reporting Biases, and Technology Concerns

2021 ◽  
Author(s):  
Sharon Horwood ◽  
Jeromy Anglim ◽  
Sumudu R. Mallawaarachchi
Keyword(s):  
Screen Time ◽  
Representative Sample ◽  
Female Age ◽  
Problematic Smartphone Use ◽  
Age Related ◽  
Generational Changes ◽  
Smartphone Use ◽  
Nationally Representative ◽  
Objectively Measured ◽  
Sample Age

This study utilized data from a nationally representative sample of Australian adults (n =1164; 50.7% female; age M = 44.9 years, SD = 16.3) to examine the relationships between age, technology concerns, self-rated and objective amount of smartphone use, and problematic smartphone use. Participants completed measures of problematic smartphone use and technology concern, while amount of smartphone use was self-rated and objectively measured using smartphone screen time reporting tools (Screen Time for iOS and Digital Wellbeing for Android). Amount of self-rated and objective smartphone use declined linearly with age. In contrast, problematic smartphone use was relatively high and stable in young adults before rapidly declining around age 40. People were reasonably good at estimating their amount of smartphone use (r = .73), although they did tend to underestimate usage. Technology concern was high across all ages, but unrelated to amount of usage and problematic smartphone usage. Age related differences are interpreted in terms of a combination of developmental and generational changes. Results also suggest that amount of use is an important but not complete cause of problematic smartphone use.

Instagram use frequency is associated with problematic smartphone use, but not with depression and anxiety symptom severity

2020 ◽  
Vol 8 (3) ◽  
pp. 400-418 ◽  
Author(s):  
Dmitri Rozgonjuk ◽  
Patrik Pruunsild ◽  
Kadi Jürimäe ◽  
Rosiine-Johanna Schwarz ◽  
Jaan Aru
Keyword(s):  
Indirect Effects ◽  
Online Survey ◽  
Anxiety Symptom ◽  
Demographic Variables ◽  
Bivariate Analysis ◽  
Depression And Anxiety ◽  
Problematic Smartphone Use ◽  
Younger Age ◽  
Smartphone Use ◽  
Objectively Measured

Studies have demonstrated that social media use, as well as problematic smartphone use (PSU), are associated with psychopathology variables, such as depression and anxiety. However, it has not been studied how Instagram use frequency is associated with depression, anxiety, and PSU. The aim of this study was to investigate whether Instagram use frequency is related to these psychopathology variables. Three hundred and five active Instagram users ( Mage = 23.61, SDage = 5.33; 82.2% female) comprised the effective sample in this study. They responded to an online survey that included questionnaires regarding their Instagram and smartphone use, as well as about experiencing depression and anxiety symptoms. We also retrieved objectively measured Instagram use data. The results showed that although Instagram use frequency, depression, and anxiety were associated with PSU in bivariate analysis, Instagram use frequency did not have indirect effects in the relations between psychopathology variables and PSU. Furthermore, while younger age and female sex predicted Instagram use frequency, these socio-demographic variables did not predict PSU. According to our findings Instagram use frequency contributes to PSU, but it is not related to depression and anxiety.

Objectively-measured digital technology use does not predict within- or between-person increases in psychological distress among young adults

2021 ◽  
Author(s):  
Craig Jeffrey Robb Sewall ◽  
Aidan G.C. Wright
Keyword(s):  
Social Media ◽  
Young Adults ◽  
Psychological Distress ◽  
Statistical Analysis ◽  
Social Isolation ◽  
Digital Technology ◽  
Technology Use ◽  
Smartphone Use ◽  
Lagged Effects ◽  
Objectively Measured

Despite a plethora of research, the link between digital technology use (i.e., smartphones and social media) and psychological distress among young adults remains inconclusive. The relia-bility and validity of findings in this area are typically undermined by common methodological limitations related to measurement, study design, and statistical analysis. Addressing these limitations, we examined the prospective, within-person associations between three aspects of objectively-measured digital technology use (smartphone use duration, smartphone use frequency, and social media use duration) and three aspects of psychological distress (depression, anxiety, and social isolation) among a sample of young adults (N = 384). We found that the digital technology use -> psychological distress within-person lagged effects, as well as vice versa, were very small (Bs ≤ .10) and non-significant. This study is the first to examine the pro-spective association between objectively-measured digital technology use and psychological distress—providing much-needed clarification into this highly relevant area of research.

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 Fatigue, boredom, and objectively-measured smartphone use at work

2020 ◽  
Author(s):  
Jonas Dora ◽  
Madelon van Hooff ◽  
Sabine Geurts ◽  
Michiel A. J. Kompier ◽  
Erik Bijleveld
Keyword(s):  
Real Life ◽  
Current Level ◽  
Future Research ◽  
Smartphone Use ◽  
Objectively Measured

Nowadays, many people take short breaks with their smartphone at work. The decision whetherto continue working or to take a smartphone break is a so-called labor vs leisure decision. Motivational models predict that people are more likely to switch from labor (work) to leisure (smartphone) the more fatigue or boredom they experience. In turn, fatigue and boredom are expected to decrease after the smartphone was used. However, it is not yet clear how smartphone use at work relates to fatigue and boredom. In this study, we tested these relationships in both directions. Participants (N = 83) reported their current level of fatigue and boredom every hour at work while an application continuously logged their smartphone use. Results indicate that participants were more likely to interact with their smartphone the more fatigued or bored they were, but that they did not use it for longer when more fatigued or bored. Surprisingly, participants reported increased fatigue and boredom after having used the smartphone (more). While future research is necessary, our results a) provide real-life evidence for the notion that fatigue and boredom trigger task disengagement and b) suggest that taking a short break with the smartphone may have phenomenological costs.

scholarly journals Discrepancies between Self-Reports and Behavior: Fear of Missing Out (FoMO), Self-Reported Problematic Smartphone Use Severity, and Objectively Measured Smartphone Use

2021 ◽  
Vol 2 (2) ◽  
pp. 3-10
Author(s):  
Dmitri Rozgonjuk ◽  
Jon D. Elhai ◽  
Onur Sapci ◽  
Christian Montag
Keyword(s):  
Repeated Measures ◽  
Strong Predictor ◽  
Published Data ◽  
Data Sets ◽  
Problematic Smartphone Use ◽  
Fear Of Missing Out ◽  
Scale Scores ◽  
Smartphone Use ◽  
Objectively Measured ◽  
And Behavior

Fear of Missing Out (FoMO) is associated with self-reported problematic smartphone use (PSU) severity, but there is little investigation that includes objectively measured smartphone use. The aim of the current study was to provide insights into this domain. We combined the partially published data from two previous U.S.-based studies with college student samples that tracked smartphone use data with a different focus from the current study. Both data sets included socio-demographic measures, FoMO and PSU scale scores, and data for objectively measured screentime and frequency of screen unlocks over a week, amounting up to more than a thousand observations. FoMO had a strong correlation with self-reported PSU severity; however, FoMO was not associated with objectively measured smartphone use variables. FoMO did not predict behavioral smarthpone use over a week in multilevel modeling for repeated measures. Even though FoMO is a strong predictor of self-reported PSU severity, it does not predict objectively measured smartphone use.

scholarly journals The Association Between Well-Being and Objectively Measured Versus Self-Reported Smartphone Time

2021 ◽  
Author(s):  
Lisa C. Walsh ◽  
Karynna Okabe-Miyamoto ◽  
Annie Regan ◽  
Jean Twenge ◽  
Sonja Lyubomirsky
Keyword(s):  
Social Media ◽  
Screen Time ◽  
Media Use ◽  
Well Being ◽  
Time Function ◽  
Self Report ◽  
Correlational Research ◽  
Self Reports ◽  
Smartphone Use ◽  
Objectively Measured

Recent correlational research links smartphone and social media use to lower well-being among Gen Z youth, yet other work suggests that the effects are small and unnoteworthy. However, these findings rely heavily on self-report. How accurate is self-reported smartphone time and are objectively measured screen activities associated with lower well-being than nonscreen activities? Finally, are some smartphone uses “better” for well-being than others? We addressed these questions by examining correlations among psychosocial well-being and smartphone time in 414 Gen Z participants. Although objective smartphone use (i.e., assessed via Apple’s Screen Time function) and self-reports were correlated at r=.55, most participants were unable to accurately estimate their smartphone time. Furthermore, the more they used their smartphones—whether assessed objectively or via self-report—the less happy they were (rs=–.14 to .17). However, some apps were associated with more well-being (e.g., Camera, News, Snapchat) and others with less (e.g., Facebook, Reddit, Tinder, Twitter).

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.

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