This dataset simulates anonymized mobile screen time and app usage data collected from Android/iOS users over a 3-month period (Jan–April 2024). It captures daily usage trends across various app categories including:

Productivity: Google Docs, Notion, Slack

Entertainment: YouTube, Netflix, TikTok

Social Media: Instagram, WhatsApp, Facebook

Utilities: Chrome, Gmail, Maps

For YouTube, additional engagement statistics such as views, likes, and comments are included to analyze video popularity and content consumption behavior.

The dataset enables exploration of:

Productivity vs. entertainment screen time patterns

Daily usage fluctuations

App-specific user engagement

Correlation between time spent and user interactions

YouTube content virality metrics

This is a great resource for:

EDA projects

Behavioral clustering

Dashboard development

Time series and anomaly detection

Building recommendation or focus-assistive apps

This dataset explores the relationship between digital device usage (screen time) and various mental health indicators among individuals. The data captures self-reported usage patterns of phones, laptops, tablets, and TVs, as well as daily habits, mood, stress levels, physical activity, and mental well-being scores. It aims to provide insights into how modern digital lifestyles affect mental health.

This dataset can be used for:

Predictive modeling

Behavioral clustering

Time-series simulation

Public health awareness

Wellness recommendation systems

How Does Daily Yoga Impact Screen Time Habits

A Study of Daily Screen Time Behavior

By Taylor L Bailey [source]

About this dataset

This dataset contains data on daily minutes of screen time between April 17th and May 14th. With this dataset, you can gain insights into daily phone usage habits and determine the effect that regular yoga practice has on reducing phone use. By recording the amount of time spent using different types of apps -- such as social media, reading, productivity and entertainment -- you can understand how phone habits have changed over time. Moreover, this dataset captures my attempt to do at least 10 minutes of yoga every day for a period of 15 days from April 29th to May 13th. Did this experiment successfully reduce my screen time overall? Dive in deep and find out!

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How to use the dataset

How to use this dataset

This dataset contains information on daily minutes of screen time habits, categorized by type of usage, as well as the effect of yoga on those habits. This is useful for gaining insights into an individual's screen time habits and its variability with respect to doing yoga.

To start with, there are a few key columns to check out: Date (to keep track of the days in view), Week Day (to identify which day it is precisely), Social Networking/Reading and Reference/Other/Productivity/Health and Fitness (to determine how much time was spent in each category) and Yoga (whether or not any yoga was done that day).

You may find it helpful to analyze the daily data over a certain duration by creating separate datasets grouped by weeks or months. Additionally, tallying each person's total minutes per week or per month can show changes over long-term periods. As you will notice right away in viewing this dataset, consistency is important; if someone were tracking their smartphone use regularly but only measured twice during a month period or skipped days without setting aside any reference points prior, then this particular experiment would be somewhat difficult to draw conclusions from. It would be especially impactful if specific factors such as sleep hygiene were tracked along with practice evolution such us advanced yoga sequences tried out over time alongside different approaches at making screens off-limits during mealtime - all items that could bring interesting insight into our relationship with technology devices when looking at screentime fluctuations before and after our mediations become part of our daily routine

Research Ideas

• Track the impact of daily yoga on overall and category-specific screen time.
• Explore the relationship between day of the week and overall or category-specific screen time.
• Investigate how long it takes to establish a healthy habit, such as decreased phone usage, by looking at changes in average daily screen time over the period of a month or two months before and after beginning yoga practice, adjusting for weekly period effect

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Screen Time Data.csv | Column name | Description | |:--------------------------|:------------------------------------------------------------------------------------------| | Date | The date of the data entry. (Date) | | Week Day | The day of the week of the data entry. (String) | | Social Networking | The amount of time spent on social networking. (Integer) | | Reading and Reference | The amount of time spent on reading and reference activities. (Integer) | | Other ...

Imagine waking up to the gentle buzz of your phone, checking the morning news, scrolling through messages, and booking your ride to work, all before even leaving your bed. This small routine speaks volumes about the place mobile phones hold in our lives today. By 2025, mobile phones aren’t just...

This dataset captures random yet realistic smartphone usage behavior of 50 users, including their daily screen time, app opens, primary app category, notifications received, and battery usage. It can be used for mobile analytics, user behavior research, productivity improvement studies, and predictive modeling.

The average time spent daily on a phone, not counting talking on the phone, has increased in recent years, reaching a total of * hours and ** minutes as of April 2022. This figure was expected to reach around * hours and ** minutes by 2024.

This statistic shows the average daily mobile data usage by network/provider in the United States in 2016. During the measured period, the average daily data used on T-Mobile networks amounted to ****** megabytes.

In 2022, the average data used per smartphone per month worldwide amounted to ** gigabytes (GB). The source forecasts that this will increase almost four times reaching ** GB per smartphone per month globally in 2028.

North America registered the highest mobile data consumption per connection in 2023, with the average connection consuming ** gigabytes per month. This figure is set to triple by 2030, driven by the adoption of data intensive activities such as 4K streaming.

The mobile_addiction_data.csv file is a synthetic yet realistic dataset designed to model global patterns of mobile phone usage and behavioral addiction. It includes data for 3,000 individuals across 10 countries, capturing 35 variables per user. These variables encompass a wide range of information, including demographics (such as age, gender, income, and education), daily smartphone behaviors (like screen time, app usage, phone unlocks), lifestyle habits (sleep duration, physical activity), and self-reported mental health indicators (stress, anxiety, depression). The dataset also includes user-reported addiction levels, the presence of screen-time control tools, and indicators of tech engagement like data usage and push notifications. This dataset is ideal for exploratory data analysis, behavioral research, and building machine learning models related to digital addiction, mental health, and mobile technology usage patterns.

We present the dataset for the article "Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being". The data were collected as part of the Smartphone Sensing Panel Study and comprise several dataset parts, as we replicated our analysis for two different 14-day measurement periods (A and B). At the macro level, we aggregated different measures of smartphone use (measured by mobile sensing) over 14 days and examined their associations with global survey-based measures of well-being (Flourishing, Satisfaction WIth Life, Positive Activation, Negative Activation, Valence; Dataset A: N = 236, Dataset B: N = 305). At the micro level, we aggregated various measures of smartphone use (measured via mobile sensing) over 60-minute windows before asking participants about their current mood using experience sampling questionnaires (Dataset A: N = 378, n = 5775; Dataset B: N = 534, n = 7287). In our supplementary analysis, we also aggregated the smartphone usage data for 15-minute windows to analyse social and non-social situations. Demographic variables (age, gender, education) that were not used for the data analyses were removed for privacy reasons, but can be provided upon request. The datasets are documented by a comprehensive accompanying codebook. Additional materials (e.g., preprocessing and analysis code) can also be found at https://osf.io/ckwge/ Further details on the variables provided and the associated study procedures can be found in the journal article: große Deters, F., & Schoedel, R. (2024). Keep on scrolling? Using intensive longitudinal smartphone sensing data to assess how everyday smartphone usage behaviors are related to well-being, Computers in Human Behavior, 150, 107977, https://doi.org/10.1016/j.chb.2023.107977

The percentage of respondents who report that they use a mobile phone daily. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies.

The four datasets 'phone', 'game', 'social', and 'video' are the processed datasets that are used as input files for the Mplus models (but then in .csv instead of .dat format). The dataset 'phone' contains all data related to the main analyses of daytime, pre-bedtime and post-bedtime smartphone use. The datasets 'game', 'social', and 'video' represent the data related to the exploratory analyses for game app, social media app, and video player app use, respectively. The dataset 'timeframes' contains information about respondents' bedtime and wake-up time, which is required to calculate the three timeframes (daytime, pre-bedtime, and post-bedtime).------------------The materials used, including the R and Mplus syntaxes (https://osf.io/tpj98/) and the preregistration of the current study (https://osf.io/kxw2h/) can be found on OSF. For more information, please contact the authors via t.siebers@uva.nl or info@project-awesome.nl.

This dataset provides a comprehensive analysis of mobile device usage patterns and user behavior classification. It contains 700 samples of user data, including metrics such as app usage time, screen-on time, battery drain, and data consumption. Each entry is categorized into one of five user behavior classes, ranging from light to extreme usage, allowing for insightful analysis and modeling.

Key Features: - User ID: Unique identifier for each user. - Device Model: Model of the user's smartphone. - Operating System: The OS of the device (iOS or Android). - App Usage Time: Daily time spent on mobile applications, measured in minutes. - Screen On Time: Average hours per day the screen is active. - Battery Drain: Daily battery consumption in mAh. - Number of Apps Installed: Total apps available on the device. - Data Usage: Daily mobile data consumption in megabytes. - Age: Age of the user. - Gender: Gender of the user (Male or Female). - User Behavior Class: Classification of user behavior based on usage patterns (1 to 5).

This dataset is ideal for researchers, data scientists, and analysts interested in understanding mobile user behavior and developing predictive models in the realm of mobile technology and applications. This Dataset was primarily designed to implement machine learning algorithms and is not a reliable source for a paper or article.

In 2023, Android users in Singapore spent an average of **** hours per day using their mobile devices. This represents an increase from the **** hours that users in the country spent on their devices in 2020.

The energy consumption of Android devices, measured via data collection from features, is a recurring theme in the literature. To evaluate the performance of such devices, databases are generated by collecting data from features while using the Android operating system. This is a database generated using Tucandeira Data Collector from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 10,331,114 records related to dynamic, background, list of applications, and static data. Device records were collected daily from ten distinct devices and stored in CSV files that were later organized to generate a database by cleaning and preprocessing the data that are publically available in the Mendeley Data Repository. The dataset formed an integral component of the SWPERFI RD&I Project, a research, development, and innovation initiative aimed at improving the performance and energy optimization of mobile devices. This project was undertaken at the Federal University of Amazonas.

As of the third quarter of 2024, the average daily time spent using the internet on mobile phones among people in Malaysia was around **** hours and ** minutes. By comparison, people spent around *** hours and ** minutes on social media every day. These numbers signified the importance of being present on the internet among Malaysians. Internet accessibility in Malaysia Presumably for convenience reasons, almost every internet user in Malaysia preferred accessing the internet on their smartphone. The upward trend of population coverage of 4G LTE mobile network in the country since 2016 may have contributed to this preference. Besides, Malaysia is one of the countries with the highest rates of mobile internet penetration in Asia. Main activities on the internet Malaysia has shown significant improvement in its internet infrastructure in recent years, which has allowed the internet users in the country to be more active online. The internet usage in Malaysia has mostly revolved around personal purposes, such as participating in social networks. As of January 2024, Malaysia recorded around ** million active social media users. A 2020 survey revealed that every internet user in Malaysia had about 9.6 social media accounts on average.

We present the first multi-year mobile sensing datasets. Our multi-year data collection studies span four years (10 weeks each year, from 2018 to 2021). The four datasets contain data collected from 705 person-years (497 unique participants) with diverse racial, ability, and immigrant backgrounds. Each year, participants would install a mobile app on their phones and wear a fitness tracker. The app and wearable device passively track multiple sensor streams in the background 24×7, including location, phone usage, calls, Bluetooth, physical activity, and sleep behavior. In addition, participants completed weekly short surveys and two comprehensive surveys on health behaviors and symptoms, social well-being, emotional states, mental health, and other metrics. Our dataset analysis indicates that our datasets capture a wide range of daily human routines, and reveal insights between daily behaviors and important well-being metrics (e.g., depression status). We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

As of February 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usage Currently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events and friends. Global impact of social media Social media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased polarization in politics, and heightened everyday distractions.