Wired Minds: How Screen Time Affects Our Sanity

In the digital age, our lives are increasingly interwoven with screens and smart devices. While technology offers countless benefits, from global connectivity to instant information access, it also raises important questions about its psychological effects. Prolonged exposure to screens, especially without balance or moderation, has become a growing concern in relation to mental health.

This project explores the correlation between technology usage, particularly daily screen time, and mental health outcomes such as stress, anxiety, depression, sleep quality, and productivity. The goal is to uncover how our digital behaviors might influence our emotional and psychological well-being.

This project is both a personal and collective inquiry into how technology is shaping our minds. Whether you're a student, professional, parent, or policymaker, the insights derived from this analysis can help:

Encourage more mindful digital habits
Inform workplace or educational policies on screen time
Support ongoing efforts to promote digital wellness and mental health awareness

Data Source

Kaggle

About the Dataset

The dataset, sourced from Kaggle, contains 10,000 records across 14 variables. It captures a wide range of behavioral and psychological indicators, including:

Daily screen time
Total technology usage hours
Reported stress levels
Sleep quality
Productivity metrics
Demographics such as age and gender

These features provide a rich foundation for analyzing patterns and drawing meaningful insights into the relationship between digital habits and mental health.

Tools and Approach

To clean, visualize, and analyze the data, I used the following tools:

Power Query: to clean and transform the data, ensuring accuracy and structure.
Power BI: to create interactive dashboards, uncover trends, and visually communicate findings.

Exploratory Data Analysis

EDA involved exploring the mental health dataset to answer key questions, such as:

How does screen time correlate with stress levels?
What is the relationship between screen time and sleep duration?
Which activity, social media, gaming, or general tech use, has the strongest link to poor mental health?
Do gamers sleep less or report higher stress than non-gamers?
How does social media usage affect reported stress levels?
Do individuals without a support system experience higher stress or worse mental health?
Does online support usage improve mental health?
How do stress levels compare between those with access to a support system vs. online-only support?
Do individuals who sleep more tend to have better mental health outcomes?
Is regular physical activity associated with lower stress, even with high screen time?
How does a negative work environment impact mental health or stress levels?
Are younger individuals more affected by screen time in terms of mental health?
Is there a gender difference in how screen time relates to mental health or stress?
What combination of high screen time, low support, and poor sleep predicts the highest mental health risk?

Key Insights & Findings

Findings from Demographics & Activity Overview

The total number of participants was 10,000, with ages 23-27, having the highest number of participants (~1080).
The average age is approximately 41.52 years, indicating that the data skews toward a more mature demographic.
Participation significantly drops in the 63–65 age range (only ~645), possibly indicating lower digital engagement in older populations.
Participants spend an average of 6.47 hours daily on technology and 7.98 hours on screens overall. The overlap indicates heavy multi-platform or prolonged device use.
Females spend slightly more time on gaming and technology usage than males, and males, on the other hand, spend slightly more time than females on social media usage.

This section explores how sleep, physical activity, screen time, and work environment relate to stress and mental health.
Averagely participants sleep for 6.50 hours and engage in physical activity for 5 hours.
While physical activity doesn’t drastically change sleep duration, low stress levels are associated with slightly more sleep overall.
Averagely participants with poor mental health sleep slightly more than excellent mental health.
Better work environments likely encourage or allow more physical activity, possibly due to flexible schedules, better morale, or wellness initiatives.
Sleep hours slightly decline in mid-life (especially 33–42), often prime working/parenting years, corresponding with higher stress potential, but rise again after age 52.
Moderate screen time may not be harmful and could reflect balanced lifestyles in mentally healthy individuals.
Age and gender affect sleep patterns, which in turn relate to stress management.

Participants who have access to and use a support system have slightly lower stress levels than those who do not.
Participants without access to a support system and who use online support experience lower stress levels than those who don't have access to or don't use online support.
The data suggests that a positive work environment does not always correlate with positive mental health, and vice versa. This implies that while the work environment may influence mental health, it is not the sole determining factor. Individual resilience, external life factors, and mental health support systems also play a critical role.
Sleep is a dominant factor in stress levels because individuals who sleep around 5 hours consistently show higher stress.
A positive work environment alone does not offset the stress caused by sleep deprivation.
Surprisingly, a neutral work environment is associated with the highest stress in the sleep-deprived group, suggesting that unclear or unengaging work conditions might exacerbate stress when people are already physically strained.

The filters applied on this page are synced on all the other pages.
24.84% of participants reported poor mental health, while 33.30% of participants reported experiencing stress.
Surprisingly, participants with good mental health status report the highest average stress level (2.01), slightly higher than those with Poor (2.00) and Excellent/Fair mental health (1.99). This could suggest that the subjective perception of mental health doesn’t always align directly with stress levels.
While access to online support systems is often promoted as a mental health resource, the data suggests that usage alone may not significantly impact reported mental health outcomes. Further analysis into the quality, consistency, and timing of support usage is needed to assess its effectiveness.
The correlation matrix reveals very weak linear relationships between lifestyle factors and stress levels. This indicates that stress may be driven by more complex interactions or external influences beyond these individual behaviors.

Conclusion

Are our digital habits silently shaping our state of mind? While digital habits do appear to silently shape our mental well-being, their effects are entangled with other lifestyle factors such as sleep, work environment, and personal support systems. Future wellness strategies should prioritize balance, promote healthy digital consumption, and integrate personalized interventions that account for the broader context of individuals’ lives.