Are We All Screenagers? Challenging Assumptions with Hard Data
By: Zoey Sun, Sophia Xu, Jessica Ye, Kailani Hoang, and Janelle Ahkit
INTRODUCTION
In this digital age, smartphones have become an integral part of daily life, yet they are often linked to adverse effects on mental health and sleep patterns. Younger individuals, in particular, are frequently accused of excessive phone usage, perpetuating the stereotype that their dependence on devices is unhealthy. However, this article presents a data-driven analysis that challenges these assumptions.
By examining datasets on mobile device usage, user behavior, and its potential impacts on mental health and sleep, we reveal surprising trends that defy common narratives. Through visualizations, we explore how phone usage varies across age groups, its true correlation with mental well-being, and whether sleep deprivation is universally attributable to screen time. These insights aim to foster a more nuanced understanding of modern phone usage, debun me king myths while encouraging informed conversations about its societal implications.
Assessing Social Media Usage by Age Group
Social media usage has become an integral part of daily life, but how does it vary across different age groups? Many assume that younger individuals dominate social media platforms, but could this be an oversimplification? A closer look at the data showcased in the following boxplot reveals surprising similarities in social media usage across five age groups, prompting a reevaluation of common stereotypes.
The above boxplot showcases social media usage hours across five age groups: 18–25, 25–35, 35–45, 45–55, and 55–65. The plot reveals strikingly similar patterns across all age groups. Median social media usage remains consistent at approximately 2–3 hours per day, regardless of age. Additionally, the IQR for each group spans roughly 1–4 hours, showing most individuals in all age groups fall within the same range of usage.
The data challenges the stereotype that younger individuals dominate social media usage disproportionately. Instead, it suggests social media usage is a shared behavior across all age groups, reflecting a universal trend rather than a generational divide. While slight differences in the medians and variability exist, they are not pronounced enough to support the idea that one group is significantly more engaged than others. This finding has broader societal implications. It highlights the need to approach conversations about the impact of social media — on mental health, sleep, and overall well-being — through an inclusive lens. Policies, awareness campaigns, and interventions should target all age groups rather than singling out younger users. By understanding that social media is a shared experience across generations, we can foster more balanced and informed discussions about its role in modern life.
An important factor to consider when discussing screen time is the specific purpose for which individuals are using their phones. Different generations may argue that their device usage is driven by more important or productive activities. The chart below breaks down the types of apps each generation is using their phones for.
The visualization explores the relationship between age groups and what kinds of apps users are spending their time on: social media apps, productivity apps, and gaming apps. Across the different age groups, there looks to be very similar patterns of screen time breakdown, with a pretty even distribution of app usage across the 5 age groups.
The stacked bar chart reveals that not only is average screen time even across the age groups, but also that average app usage across age groups is relatively consistent, with very little variation in the hours spent on social media apps, productivity apps, and gaming apps. These results challenge the common beliefs that younger generations are using their phones more for entertainment, while older generations use their phones for more productive activities. While there are slight variations in app usages across the age groups, they are not significant enough to draw a conclusion that a certain age group spends more time on social media compared to productivity apps than another. The general pattern of this chart shows that the way people use their phones is relatively similar, regardless of their age. These results point towards more investigation and research into how these apps have been implemented into daily life for all ages, which could explain the lack of significant differences between the age groups.
Technology Usage vs. Sleep Hours
Similarly, when we examine the relationship between overall technology usage and sleep patterns, we encounter another set of surprising results that question established beliefs.
This heatmap explores the relationship between technology usage and sleep hours, dividing each variable into defined ranges to uncover patterns in their correlation. Regions of purple suggest that individuals in those ranges might experience strongly aligned outcomes, indicating scenarios where technology usage seems to reveal a positive correlation to a specific amount of sleep.
Interestingly, the visualization challenges the widely held belief that screen time is consistently and strongly detrimental to sleep. The overall correlation appears minimal, with most regions of the heatmap showing weak relationships (orange and pink), regardless of the level of technology usage or sleep hours.
Notably, even in areas of high technology usage (e.g., over 7.5 hours), the correlations remain largely neutral, with only small pockets of negative association. Similarly, lower technology usage does not consistently align with improved sleep, as these bins show equally weak or inconsistent correlations. These findings suggest that the connection between screen time and sleep quality may not be as straightforward as assumed and that the influence of other factors which impact sleep, such as stress, mental health, physical activity, and individual differences should be taken in consideration. This raises important questions about the broader narrative surrounding technology and sleep — is it possible that the impact of technology on sleep has become an old wives’ tale?
Social Media vs Productivity App Usage
To investigate technology usage further, we can isolate the two main variables relevant to our analysis: social media and productivity app hours. By examining the relationship between these two variables, we can gain insights into how social media usage might impact productivity.
Based on the scatterplot above, there seems to be no strong correlation between social media usage hours and productivity app usage hours. The graph displays that ‘Social Media Usage’ and ‘Productivity App Usage’ have a weak negative correlation of -0.076, indicating a very slight tendency for users who spend more time on social media to spend less time on productivity apps. With this specific data, it challenges the idea that spending time on social media directly takes away from productivity.
As shown, the data points are mostly evenly distributed across the plot, indicating that individuals who spend varying amounts of time on social media also exhibit a wide range of productivity app usage, with no clear pattern or trend. This suggests that the two behaviors might be independent, or other factors could be influencing the relationship, such as personal priorities. The lack of clustering around a certain area or a noticeable linear pattern implies that increased social media usage does not directly lead to either an increase or decrease in productivity app usage.
Additionally, the scatterplot shows that most of the data points lie within the range of 0–5 hours for both social media and productivity app usage, which might reflect the typical daily usage behavior of the population sampled. The even spread of points indicates variability in user behavior, with some individuals balancing time between both categories while others may favor one over the other. Overall, this distribution highlights that social media usage does not directly take away from productivity, as individuals can balance their time across both activities without a clear negative impact on their use of productivity apps.
Screen Time Distribution by Mental Health Status
Another commonly discussed topic in the digital age is the potential impact of screen time on mental health. With increasing reliance on screens for work, social interactions, and entertainment, concerns have emerged about whether excessive screen time may contribute to negative mental health outcomes.
This visualization explores the relationship between screen time and mental health, presenting the daily screen time distributions for four mental health statuses — “Poor,” “Fair,” “Good,” and “Excellent” — using Kernel Density Estimation (KDE) plots.
Each subplot highlights the patterns of screen time within a specific group, revealing similar distributions across categories. Most individuals, regardless of mental health status, report screen time concentrated between 3 and 12 hours per day. While the overall shapes of the curves align closely, minor variations exist. For example, individuals with “Excellent” mental health exhibit a slight peak around 2.5 hours, while the “Poor” mental health group has a peak around 7.
The peaks in each curve represent the most common screen time durations, while the width reflects variability within each group. The relatively wide curves for all groups indicate significant variation in screen time, suggesting that screen time habits are not highly uniform within any mental health category. This broad overlap highlights the complexity of isolating mental health effects solely based on screen time duration.
To directly compare the different categories, this stacked density plot overlays the screen time distributions from all mental health groups into a single visualization.
The overlapping curves across all groups confirm that the distributions are similar across all mental groups. This indicates minimal variation in daily screen time habits between mental health statuses, reinforcing the notion that screen time duration alone may not strongly correlate with mental health outcomes.
While the visualization provides clarity in comparing distributions, it also emphasizes the need to explore other factors — such as the quality of screen time or individual behaviors — that could provide deeper insights into the relationship between screen time and mental well-being.
CONCLUSION
So, are we all “screenagers”? The data suggests that the answer is a resounding yes — but not in the way you might think. Far from being a habit confined to the younger generations, our visualizations reveal that phone usage is a universal part of modern life, spanning all ages and demographics. The common narratives about social media addiction, sleep deprivation, and mental health impacts from screen time deserve a closer, more nuanced look. The reality is far more complex than blaming screens alone, and scrolling responsibly is key.
