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.

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

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 number of smartphone users in the United States was forecast to continuously increase between 2024 and 2029 by in total 17.4 million users (+5.61 percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach 327.54 million users and therefore a new peak 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).Find more key insights for the number of smartphone users in countries like Mexico and Canada.

We will create a customized phones dataset tailored to your specific requirements. Data points may include brand names, model specifications, pricing information, release dates, market availability, feature sets, and other relevant metrics.

Utilize our phones datasets for a variety of applications to boost strategic planning and market analysis. Analyzing these datasets can help organizations grasp consumer preferences and technological trends within the mobile phone industry, allowing for more precise product development and marketing strategies. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing competitive benchmarking, identifying pricing trends, and optimizing product portfolios.

This dataset consists of aggregated statistics that enable to reproduce results for the paper: "Exposure to urban and rural contexts shapes smartphone usage behavior.", published on PNAS Nexus. The data contains smartphone usage for 454,018 individuals. More details about the data are included in the article. Each row represent one smartphone user, and includes the following information

[gender] self-reported gender [age-group] age-group (computed from self-reported age, 1=18-26, 2=27-36, 3=36-48, 4=48-66, 5=66+) [urbanization]: urbanization level around the home location (3=urban, 2=suburban, 1=rural) [GID_0]: country of residence [median_screen_time]: median daily smartphone usage (minutes) [median_n_apps]: median number of unique apps.

For each field x among the following, the value represents the fraction of usage in category x. Categories are assigned by the Android Play Store

[Books] [Browsing] [Business] [Camera/Album] [Communication] [Entertainment] [Game] [Health_and_Fitness] [Maps_and_Navigation] [Movie/TV] [Music] [News] [Other] [Productivity] [Shopping] [Social] [Tools] [Travel_and_Local] [Weather]

The global smartphone penetration in was forecast to continuously increase between 2024 and 2029 by in total 20.3 percentage points. After the fifteenth consecutive increasing year, the penetration is estimated to reach 74.98 percent and therefore a new peak in 2029. Notably, the smartphone penetration of was continuously increasing over the past years.The penetration rate refers to the share of the total population.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).Find more key insights for the smartphone penetration in countries like North America and the Americas.

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

Forecast: Mobile Data Usage per Smartphone Subscriber in the US 2022 - 2026 Discover more data with ReportLinker!

BackgroundThis study aimed to explore the adverse influences of mobile phone usage on pilots’ status, so as to improve flight safety.MethodsA questionnaire was designed, and a cluster random sampling method was adopted. Pilots of Shandong Airlines were investigated on the use of mobile phones. The data was analyzed by frequency statistics, linear regression and other statistical methods.ResultsA total of 340 questionnaires were distributed and 317 were returned, 315 of which were valid. The results showed that 239 pilots (75.87%) used mobile phones as the main means of entertainment in their leisure time. There was a significant negative correlation between age of pilots and playing mobile games (p

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

Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

Information Technology Usage and Penetration - Table 720-90006 : Persons aged 10 and over who had a mobile phone (including smartphone and non-smartphone) by sex and age group

Dataset from Info-communications Media Development Authority. For more information, visit https://data.gov.sg/datasets/d_5fb7ffda1ffd756151b1650d4c64363c/view

Source code, example screenprobe.csv data file, and README.txt for processing, visualising and analysing smartphone use data. csv2data.m converts ScreenProbe.csv to usable data, while barcode.m allows visualisations to be generated. descriptives.m generates descriptive statistics that can be used for quantitative analysis. Source code requires Matlab version 2014b or later, but does not require any specific toolboxes. (ZIP)

Raw Excel data showing survey results from participants in study to determine smartphone usage patterns.

Ziel dieser Studie war es, den Einfluss verschiedener Anreizsysteme auf die Bereitschaft zur Teilnahme an der passiven mobilen Datenerfassung unter deutschen Smartphone-Besitzern experimentell zu messen. Die Daten stammen aus einer Webumfrage unter deutschen Smartphone-Nutzern ab 18 Jahren, die aus einem deutschen, nicht wahrscheinlichen Online-Panel rekrutiert wurden. Im Dezember 2017 beantworteten 1.214 Teilnehmer einen Fragebogen zu den Themen Smartphone-Nutzung und -Fähigkeiten, Datenschutz und Sicherheit, allgemeine Einstellungen gegenüber der Umfrageforschung und Forschungseinrichtungen. Darüber hinaus enthielt der Fragebogen ein Experiment zur Bereitschaft, an der mobilen Datenerhebung unter verschiedenen Anreizbedingungen teilzunehmen. Themen: Besitz von Smartphone, Handy, Desktop- oder Laptop-Computer, Tablet-Computer und/oder E-Book-Reader; Art des Smartphones; Bereitschaft zur Teilnahme an der mobilen Datenerfassung unter verschiedenen Anreizbedingungen; Wahrscheinlichkeit des Herunterladens der App zur Teilnahme an dieser Forschungsstudie; Befragter möchte lieber an der Studie teilnehmen, wenn er 100 Euro erhalten könnte; Gesamtbetrag, den der Befragte für die Teilnahme an der Studie verdienen müsste (offene Antwort); Grund, warum der Befragte nicht an der Forschungsstudie teilnehmen würde; Bereitschaft zur Teilnahme an der Studie für einen Anreiz von insgesamt 60 Euro; Bereitschaft zur Aktivierung verschiedener Funktionen beim Herunterladen der App (Interaktionshistorie, Smartphone-Nutzung, Merkmale des sozialen Netzwerks, Netzqualitäts- und Standortinformationen, Aktivitätsdaten); vorherige Einladung zum Herunterladen der Forschungs-App; Herunterladen der Forschungs-App; Häufigkeit der Nutzung des Smartphones; Smartphone-Aktivitäten (Browsen, E-Mails, Fotografieren, Anzeigen/Post-Social-Media-Inhalte, Einkaufen, Online-Banking, Installieren von Apps, Verwenden von GPS-fähigen Apps, Verbinden über Bluethooth, Spielen, Streaming von Musik/Videos); Selbsteinschätzung der Kompetenz im Umgang mit dem Smartphone; Einstellung zu Umfragen und Teilnahme an Forschungsstudien (persönliches Interesse, Zeitverlust, Verkaufsgespräch, interessante Erfahrung, nützlich); Vertrauen in Institutionen zum Datenschutz (Marktforschungsunternehmen, Universitätsforscher, Regierungsbehörden wie das Statistische Bundesamt, Mobilfunkanbieter, App-Unternehmen, Kreditkartenunternehmen, Online-Händler und Social-Media-Plattformen); allgemeine Datenschutzbedenken; Gefühl der Datenschutzverletzung durch Banken und Kreditkartenunternehmen, Steuerbehörden, Regierungsbehörden, Marktforschung, soziale Netzwerke, Apps und Internetbrowser; Bedenken zur Datensicherheit bei Smartphone-Aktivitäten für Forschungszwecke (Online-Umfrage, Umfrage-Apps, Forschungs-Apps, SMS-Umfrage, Kamera, Aktivitätsdaten, GPS-Ortung, Bluetooth). Demographie: Geschlecht, Alter; Bundesland; höchster Schulabschluss; höchstes berufliches Bildungsniveau. Zusätzlich verkodet wurden: laufende Nummer; Dauer (Reaktionszeit in Sekunden); Gerätetyp, mit dem der Fragebogen ausgefüllt wurde. The goal of this study was to experimentally measure the influence of different incentive schemes on the willingness to participate in passive mobile data collection among German smartphone owners. The data come from a web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2017, 1,214 respondents completed a questionnaire on smartphone use and skills, privacy and security concerns, general attitudes towards survey research and research institutions. In addition, the questionnaire included an experiment on the willingness to participate in mobile data collection under different incentive conditions. Topics: Ownership of smartphone, cell phone, desktop or laptop computer, tablet computer, and/or e-book reader; type of smartphone; willingness to participate in mobile data collection under different incentive conditions; likelihood of downloading the app to particiapte in this research study; respondent would rather participate in the study if he could receive 100 euros; total amount to be earned for the respondent ot participate in the study (open answer); reason why the respondent wouldn´t participate in the research study; willlingness to participate in the study for an incentive of 60 euros in total; willingness to activate different functions when downloading the app (interaction history, smartphone usage, charateristics of the social network, network quality and location information, activity data); previous invitation for research app download; research app download; frequency of smartphone use; smartphone activities (browsing, e-mails, taking pictures, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, playing games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, government authorities such as the Federal Statistical Office, mobile service provider, app companies, credit card companies, online retailer, and social media platforms); general privacy concern; feeling of privacy violation by banks and credit card companies, tax authorities, government agencies, market research, social networks, apps, and internet browsers; concern regarding data security with smartphone activities for research purposes (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth). Demography: sex, age; federal state; highest level of school education; highest level of vocational education. Additionally coded was: running number; duration (response time in seconds); device type used to fill out the questionnaire.

The goal of this study is to measure willingness to participate in passive mobile data collection among German smartphone owners. The data come from a two-wave web survey among German smartphone users 18 years and older who were recruited from a German nonprobability online panel. In December 2016, 2,623 participants completed the Wave 1 questionnaire on smartphone use and skills, privacy and security concerns, and general attitudes towards survey research and research institutions. In January 2017, all respondents from Wave 1 were invited to participate in a second web survey which included vignettes that varied the levels of several dimensions of a hypothetical study using passive mobile data collection, and respondents were asked to rate their willingness to participate in such a study. A total of 1,957 respondents completed the Wave 2 questionnaire.

Wave 1

Topics: Ownership of smartphone, mobile phone, PC, tablet, and/or e-book reader; type of smartphone; frequency of smartphone use; smartphone activities (browsing, e-mails, taking photos, view/ post social media content, shopping, online banking, installing apps, using GPS-enabled apps, connecting via Bluethooth, play games, stream music/ videos); self-assessment of smartphone skills; attitude towards surveys and participaton at research studies (personal interest, waste of time, sales pitch, interesting experience, useful); trust in institutions regarding data privacy (market research companies, university researchers, statistical office, mobile service provider, app companies, credit card companies, online retailer, and social networks); concerns regarding the disclosure of personal data by the aforementioned institutions; general privacy concern; privacy violated by banks/ credit card companies, tax authorities, government agencies, market research companies, social networks, apps, internet browsers); concern regarding data security with smartphone activities for research (online survey, survey apps, research apps, SMS survey, camera, activity data, GPS location, Bluetooth); number of online surveys in which the respondent has participated in the last 30 days; Panel memberships other than that of mingle; previous participation in a study with downloading a research app to the smartphone (passive mobile data collection).

Wave 2

Topics: Willingness to participate in passive mobile data collection (using eight vignettes with different scenarios that varied the levels of several dimensions of a hypothetical study using passive mobile data collection. The research app collects the following data for research purposes: technical characteristics of the smartphone (e.g. phone brand, screen size), the currently used telephone network (e.g. signal strength), the current location (every 5 minutes), which apps are used and which websites are visited, number of incoming and outgoing calls and SMS messages on the smartphone); reason why the respondent wouldn´t (respectively would) participate in the research study used in the first scenario (open answer); recognition of differences between the eight scenarios; kind of recognized difference (open answer); remembered data the research app collects (recall); previous invitation for research app download; research app download.

Demography: sex; age; federal state; highest level of school education; highest level of vocational qualification.

Additionally coded was: running number; respondent ID; duration (response time in seconds); device type used to fill out the questionnaire; vignette text; vignette intro time; vignette time.

Global mobile data usage was estimated at over ******* petabytes in 2022, with forecasts placing 2027 usage at over ******* petabytes. Mobile handsets accounted for the majority of data use in 2022, followed by cellular internet of things (IoT) devices.

The article "A systematic review of the educational use of mobile phones in times of COVID-19" aims to review what research has delved into the educational use of mobile phones during the COVID-19 pandemic. To do this, 38 papers indexed in the Journal Citation Reports database between 2020 and 2021 were analyzed. These works were categorized into the following categories: the mobile phone as part of educational innovation, improvement of results and academic performance, positive attitude towards mobile phone use in education, and risks and/or barriers to mobile phone use. The conclusions show that most teaching innovation experiences focus more on the device than on the student. Beyond its innovative nature, the mobile phone became a tool to allow access and continuity of training during the pandemic, especially in post-compulsory and higher education.

This data set is composed of the table with the references used for the review.