Percentage of smartphone users by selected smartphone use habits in a typical day.

The number of smartphone users in the Philippines was forecast to increase between 2024 and 2029 by in total 5.6 million users (+7.29 percent). This overall increase does not happen continuously, notably not in 2026, 2027, 2028 and 2029. The smartphone user base is estimated to amount to 82.33 million users in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

This dataset has been artificially generated to mimic real-world user interactions within a mobile application. It contains 100,000 rows of data, each row of which represents a single event or action performed by a synthetic user. The dataset was designed to capture many of the attributes commonly tracked by app analytics platforms, such as device details, network information, user demographics, session data, and event-level interactions.

Key Features Included

User & Session Metadata

User ID: A unique integer identifier for each synthetic user. Session ID: Randomly generated session identifiers (e.g., S-123456), capturing the concept of user sessions. IP Address: Fake IP addresses generated via Faker to simulate different network origins. Timestamp: Randomized timestamps (within the last 30 days) indicating when each interaction occurred. Session Duration: An approximate measure (in seconds) of how long a user remained active. Device & Technical Details

Device OS & OS Version: Simulated operating systems (Android/iOS) with plausible version numbers. Device Model: Common phone models (e.g., “Samsung Galaxy S22,” “iPhone 14 Pro,” etc.). Screen Resolution: Typical screen resolutions found in smartphones (e.g., “1080x1920”). Network Type: Indicates whether the user was on Wi-Fi, 5G, 4G, or 3G. Location & Locale

Location Country & City: Random global locations generated using Faker. App Language: Represents the user’s app language setting (e.g., “en,” “es,” “fr,” etc.). User Properties

Battery Level: The phone’s battery level as a percentage (0–100). Memory Usage (MB): Approximate memory consumption at the time of the event. Subscription Status: Boolean flag indicating if the user is subscribed to a premium service. User Age: Random integer ranging from teenagers to seniors (13–80). Phone Number: Fake phone numbers generated via Faker. Push Enabled: Boolean flag indicating if the user has push notifications turned on. Event-Level Interactions

Event Type: The action taken by the user (e.g., “click,” “view,” “scroll,” “like,” “share,” etc.). Event Target: The UI element or screen component interacted with (e.g., “home_page_banner,” “search_bar,” “notification_popup”). Event Value: A numeric field indicating additional context for the event (e.g., intensity, count, rating). App Version: Simulated version identifier for the mobile application (e.g., “4.2.8”). Data Quality & “Noise” To better approximate real-world data, 1% of all fields have been intentionally “corrupted” or altered:

Typos and Misspellings: Random single-character edits, e.g., “Andro1d” instead of “Android.” Missing Values: Some cells might be blank (None) to reflect dropped or unrecorded data. Random String Injections: Occasional random alphanumeric strings inserted where they don’t belong. These intentional discrepancies can help data scientists practice data cleaning, outlier detection, and data wrangling techniques.

Usage & Applications

Data Cleaning & Preprocessing: Ideal for practicing how to handle missing values, inconsistent data, and noise in a realistic scenario. Analytics & Visualization: Demonstrate user interaction funnels, session durations, usage by device/OS, etc. Machine Learning & Modeling: Suitable for building classification or clustering models (e.g., user segmentation, event classification). Simulation for Feature Engineering: Experiment with deriving new features (e.g., session frequency, average battery drain, etc.).

Important Notes & Disclaimer

Synthetic Data: All entries (users, device info, IPs, phone numbers, etc.) are artificially generated and do not correspond to real individuals. Privacy & Compliance: Since no real personal data is present, there are no direct privacy concerns. However, always handle synthetic data ethically.

ObjectivesObesity is an increasing public health problem affecting young people. The causes of obesity are multi-factorial among Chinese youth including lack of physical activity and poor eating habits. The use of an internet curriculum and cell phone reminders and texting may be an innovative means of increasing follow up and compliance with obese teens. The objectives of this study were to determine the feasibility of using an adapted internet curriculum and existing nutritional program along with cell phone follow up for obese Chinese teens.Design and MethodsThis was a randomized controlled study involving obese teens receiving care at a paediatric obesity clinic of a tertiary care hospital in Hong Kong. Forty-eight subjects aged 12 to 18 years were randomized into three groups. The control group received usual care visits with a physician in the obesity clinic every three months. The first intervention (IT) group received usual care visits every three months plus a 12-week internet-based curriculum with cell phone calls/texts reminders. The second intervention group received usual care visits every three months plus four nutritional counselling sessions.ResultsThe use of the internet-based curriculum was shown to be feasible as evidenced by the high recruitment rate, internet log-in rate, compliance with completing the curriculum and responses to phone reminders. No significant differences in weight were found between IT, sLMP and control groups.ConclusionAn internet-based curriculum with cell phone reminders as a supplement to usual care of obesity is feasible. Further study is required to determine whether an internet plus text intervention can be both an effective and a cost-effective adjunct to changing weight in obese youth.Trial RegistrationChinese Clinical Trial Registry ChiCTR-TRC-12002624

This project examined the role of technology use in teen dating violence and abuse, and bullying. The goal of the project was to expand knowledge about the types of abuse experiences youth have, the extent of victimization and perpetration via technology and new media (e.g., social networking sites, texting on cellular phones), and how the experience of such cyber abuse within teen dating relationships or through bullying relates to other life factors. This project carried out a multi-state study of teen dating violence and abuse, and bullying, the main component of which included a survey of youth from ten schools in five school districts in New Jersey, New York, and Pennsylvania, gathering information from 5,647 youth about their experiences. The study employed a cross-sectional, survey research design, collecting data via a paper-pencil survey. The survey targeted all youth who attended school on a single day and achieved an 84 percent response rate.

The ‘Predicting And Preventing Child Neglect In Teen Mothers’ project was designed to assess the impact of varying degrees and types of neglect and poor parenting on children’s development during the first 3 years of life, including changes in intelligence and behavior, language, social and emotional well-being, physical growth, and health status. This study included a broad array of assessments related to the construct of childhood neglect, and can be used to test the developmental associations among parenting characteristics, parenting behaviors and attitudes, and child development in multiple domains. Six hundred and eighty-two expectant mothers were recruited during pregnancy through primary care facilities in the communities of Birmingham, AL, Kansas City, KS, South Bend, IN, and Washington, D.C. Three different groups of first-time mothers were included in the sample: adolescents (n=396), low-ed adults (less than 2 years formal education beyond high school; n=169), and hi-ed adults (at least 2 years of formal education; n=117). The mothers’ ages at child birth ranged from 14.68 to 36.28, with an average of 17.49 for the adolescents, 25.48 for the low-ed adults, and 27.88 for the hi-ed adults. Approximately 65% of the sample were African-American, 19% were White/Non-Hispanic, 15% were Hispanic, 1% were multi-racial, and .5% were of an other race. The adolescent and low-ed adult samples were closely matched on race/ethnicity. Mothers were interviewed in their last trimester of pregnancy as well as when their children were 4, 6, 8, 12, 18, 24, 30, and 36-months old. Interviews at the prenatal, 6, 12, 24, and 36-month visits primarily focused on risks for poor parenting, such as maternal depression (Beck II), parenting stress (Parenting Stress Index – Short Form), and lack of social support; parenting beliefs and practices; as well as other demographic information. The 4, 8, 18, and 30-month visits occurred in the home and included both interviews and observations of parenting practices (Home Observation for Measurement of the Environment, Supplement to the HOME for Impoverished Families, and Landry Naturalistic Observation). After each of the home visits, mothers were given a cellular phone and interviewed multiple times concerning their daily parenting practices (Parent-Child Activities Interview). At the 12, 24, and 36-months visits, the children were also tested for intellectual (Bayley II) and language abilities (Pre-School Language Scales – IV), rated on their behavior by both their mother (Infant Toddler Social and Emotional Assessment) and child tester (Bayely Behavioral Rating Scale II), and their height and weight were measured. Upon completing each assessment after the child’s birth, the interviewers also rated the child’s environment for risks of physical neglect. This study represents one of the first-ever prospective broad-based, multi-site investigations of child neglect among a diverse sample of adolescent mothers and will help to establish a foundation for future preventive interventions to reduce the incidence and impact of neglect and abuse on child development. This data set provides a broad range of risk and protective factors to better map the multiple and fluctuating social ecologies and life circumstances of teen mothers and their young children. This dataset contains data from pre-natal to 36-months. Please note: attachment codes, Parent-Child Activity interviews, short cell phone interviews are NOT included in this data collection.

Investigators: John G. BorkowskiUniversity of Notre Dame Notre Dame, INJudy CartaUniversity of Kansas Kansas City, KSSteven F. WarrenUniversity of Kansas Lawrence, KSSharon L. RameyGeorgetown University NW Washington, DCCraig RameyGeorgetown University NW Washington, DCKristi GuestUniversity of Alabama - Birmingham Birmingham, ALBette KeltnerGeorgetown University NW Washington, DCRobin G. LanziGeorgetown University NW Washington, DCLorraine KlermanBrandeis University Wa