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.

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.

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

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total *** billion users (+***** percent). After the fifteenth consecutive increasing year, the smartphone user base is estimated to reach *** billion 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 *** 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 the Americas and Asia.

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

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion 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 Australia & Oceania and Asia.

The number of smartphone users in Ireland was forecast to continuously increase between 2024 and 2029 by in total 0.3 million users (+6.15 percent). After the seventh consecutive increasing year, the smartphone user base is estimated to reach 5.22 million users and therefore a new peak in 2029. 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 information concerning Serbia and Sweden.

The number of smartphone users in the United Kingdom was forecast to continuously increase between 2024 and 2029 by in total *** million users (+**** percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach ***** 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 *** 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 information concerning Denmark and Latvia.

People_with_smartphone

## Overview

People_with_smartphone is a dataset for object detection tasks - it contains Person With Phone annotations for 1,010 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

We are publishing a walking activity dataset including inertial and positioning information from 19 volunteers, including reference distance measured using a trundle wheel. The dataset includes a total of 96.7 Km walked by the volunteers, split into 203 separate tracks. The trundle wheel is of two types: it is either an analogue trundle wheel, which provides the total amount of meters walked in a single track, or it is a sensorized trundle wheel, which measures every revolution of the wheel, therefore recording a continuous incremental distance.
Each track has data from the accelerometer and gyroscope embedded in the phones, location information from the Global Navigation Satellite System (GNSS), and the step count obtained by the device. The dataset can be used to implement walking distance estimation algorithms and to explore data quality in the context of walking activity and physical capacity tests, fitness, and pedestrian navigation. Methods The proposed dataset is a collection of walks where participants used their own smartphones to capture inertial and positioning information. The participants involved in the data collection come from two sites. The first site is the Oxford University Hospitals NHS Foundation Trust, United Kingdom, where 10 participants (7 affected by cardiovascular diseases and 3 healthy individuals) performed unsupervised 6MWTs in an outdoor environment of their choice (ethical approval obtained by the UK National Health Service Health Research Authority protocol reference numbers: 17/WM/0355). All participants involved provided informed consent. The second site is at Malm ̈o University, in Sweden, where a group of 9 healthy researchers collected data. This dataset can be used by researchers to develop distance estimation algorithms and how data quality impacts the estimation.

All walks were performed by holding a smartphone in one hand, with an app collecting inertial data, the GNSS signal, and the step counting. On the other free hand, participants held a trundle wheel to obtain the ground truth distance. Two different trundle wheels were used: an analogue trundle wheel that allowed the registration of a total single value of walked distance, and a sensorized trundle wheel which collected timestamps and distance at every 1-meter revolution, resulting in continuous incremental distance information. The latter configuration is innovative and allows the use of temporal windows of the IMU data as input to machine learning algorithms to estimate walked distance. In the case of data collected by researchers, if the walks were done simultaneously and at a close distance from each other, only one person used the trundle wheel, and the reference distance was associated with all walks that were collected at the same time.The walked paths are of variable length, duration, and shape. Participants were instructed to walk paths of increasing curvature, from straight to rounded. Irregular paths are particularly useful in determining limitations in the accuracy of walked distance algorithms. Two smartphone applications were developed for collecting the information of interest from the participants' devices, both available for Android and iOS operating systems. The first is a web-application that retrieves inertial data (acceleration, rotation rate, orientation) while connecting to the sensorized trundle wheel to record incremental reference distance [1]. The second app is the Timed Walk app [2], which guides the user in performing a walking test by signalling when to start and when to stop the walk while collecting both inertial and positioning data. All participants in the UK used the Timed Walk app.

The data collected during the walk is from the Inertial Measurement Unit (IMU) of the phone and, when available, the Global Navigation Satellite System (GNSS). In addition, the step count information is retrieved by the sensors embedded in each participant’s smartphone. With the dataset, we provide a descriptive table with the characteristics of each recording, including brand and model of the smartphone, duration, reference total distance, types of signals included and additionally scoring some relevant parameters related to the quality of the various signals. The path curvature is one of the most relevant parameters. Previous literature from our team, in fact, confirmed the negative impact of curved-shaped paths with the use of multiple distance estimation algorithms [3]. We visually inspected the walked paths and clustered them in three groups, a) straight path, i.e. no turns wider than 90 degrees, b) gently curved path, i.e. between one and five turns wider than 90 degrees, and c) curved path, i.e. more than five turns wider than 90 degrees. Other features relevant to the quality of collected signals are the total amount of time above a threshold (0.05s and 6s) where, respectively, inertial and GNSS data were missing due to technical issues or due to the app going in the background thus losing access to the sensors, sampling frequency of different data streams, average walking speed and the smartphone position. The start of each walk is set as 0 ms, thus not reporting time-related information. Walks locations collected in the UK are anonymized using the following approach: the first position is fixed to a central location of the city of Oxford (latitude: 51.7520, longitude: -1.2577) and all other positions are reassigned by applying a translation along the longitudinal and latitudinal axes which maintains the original distance and angle between samples. This way, the exact geographical location is lost, but the path shape and distances between samples are maintained. The difference between consecutive points “as the crow flies” and path curvature was numerically and visually inspected to obtain the same results as the original walks. Computations were made possible by using the Haversine Python library.

Multiple datasets are available regarding walking activity recognition among other daily living tasks. However, few studies are published with datasets that focus on the distance for both indoor and outdoor environments and that provide relevant ground truth information for it. Yan et al. [4] introduced an inertial walking dataset within indoor scenarios using a smartphone placed in 4 positions (on the leg, in a bag, in the hand, and on the body) by six healthy participants. The reference measurement used in this study is a Visual Odometry System embedded in a smartphone that has to be worn at the chest level, using a strap to hold it. While interesting and detailed, this dataset lacks GNSS data, which is likely to be used in outdoor scenarios, and the reference used for localization also suffers from accuracy issues, especially outdoors. Vezovcnik et al. [5] analysed estimation models for step length and provided an open-source dataset for a total of 22 km of only inertial walking data from 15 healthy adults. While relevant, their dataset focuses on steps rather than total distance and was acquired on a treadmill, which limits the validity in real-world scenarios. Kang et al. [6] proposed a way to estimate travelled distance by using an Android app that uses outdoor walking patterns to match them in indoor contexts for each participant. They collect data outdoors by including both inertial and positioning information and they use average values of speed obtained by the GPS data as reference labels. Afterwards, they use deep learning models to estimate walked distance obtaining high performances. Their results share that 3% to 11% of the data for each participant was discarded due to low quality. Unfortunately, the name of the used app is not reported and the paper does not mention if the dataset can be made available.

This dataset is heterogeneous under multiple aspects. It includes a majority of healthy participants, therefore, it is not possible to generalize the outcomes from this dataset to all walking styles or physical conditions. The dataset is heterogeneous also from a technical perspective, given the difference in devices, acquired data, and used smartphone apps (i.e. some tests lack IMU or GNSS, sampling frequency in iPhone was particularly low). We suggest selecting the appropriate track based on desired characteristics to obtain reliable and consistent outcomes.

This dataset allows researchers to develop algorithms to compute walked distance and to explore data quality and reliability in the context of the walking activity. This dataset was initiated to investigate the digitalization of the 6MWT, however, the collected information can also be useful for other physical capacity tests that involve walking (distance- or duration-based), or for other purposes such as fitness, and pedestrian navigation.

The article related to this dataset will be published in the proceedings of the IEEE MetroXRAINE 2024 conference, held in St. Albans, UK, 21-23 October.

This research is partially funded by the Swedish Knowledge Foundation and the Internet of Things and People research center through the Synergy project Intelligent and Trustworthy IoT Systems.

China is leading the ranking by number of smartphone users, recording ****** million users. Following closely behind is India with ****** million users, while Seychelles is trailing the ranking with **** million users, resulting in a difference of ****** million users to the ranking leader, China. 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).

Luxembourg number dataset is a popular platform for cell phone number lists. Many companies in Luxembourg use our phone number library for promotions. Our services have many advantages. Firstly, you will receive our products within 24 hours after confirming your order and payment. Secondly, our phone number list works on all devices, like smartphones, computers, and tablets. Thirdly, our packages are affordable and fit every budget. Moreover, our Luxembourg number dataset also has a filter option. This allows you to find specific numbers based on your needs. You will also receive a free updated telemarketing list six months after purchase. Our database complies with GDPR and provides over 95% accuracy. If there are any errors, we will fix them for free. This ensures you have accurate and current phone numbers, improving your telemarketing efforts. Luxembourg phone data helps you easily contact people or businesses in Luxembourg. Our system is user-friendly and saves time. It also provides additional details like location, age, and gender. We offer a “Do Not Call” list to avoid legal issues in SMS marketing. You can get both a call list and an SMS marketing list in one package. Also, List to Data helps businesses find the right telephone numbers quickly, which makes the process even easier. In addition, our Luxembourg phone data contains both B2B and B2C phone numbers, which support the growth of your business. You can get our customer-friendly after-sales service. We also provide excellent customer service 24/7. If you have any questions or problems, please call us anytime. We are always here to assist you in any situation. Luxembourg phone number list is a valuable tool. It helps you connect with people in Luxembourg. The list includes phone numbers that help companies reach new customers. With name, age, and contact information, it is perfect for marketing. So, use it for promotions, updates, or feedback. This phone number list is available at a reasonable price. So, buy this mobile phone number list at a low price and get huge benefits. Moreover, our Luxembourg phone number list offers good value for your money. Since they update and ensure its accuracy, it helps you get the best results. Moreover, telemarketing saves money and grows your brand. Our cell phone list increases sales. Therefore, you will get great returns on marketing.

Smartphones have become crucial in people's everyday lives, including in the medical field. However, as people become close to their smartphones, this leads easily to overuse. Overuse leads to fatigue due to lack of sleep, depressive symptoms, and social relationship failure, and in the case of adolescents, it hinders academic achievement. Self-control solutions are needed, and effective tools can be developed through behavioral analysis. Therefore, the aim of this study was to investigate the determinants of users' intentions to use m-Health for smartphone overuse interventions. A research model was based on TAM and UTAUT, which were modified to be applied to the case of smartphone overuse. The studied population consisted of 400 randomly selected smartphone users aged from 19 to 60 years in South Korea. Structural equation modeling was conducted between variables to test the hypotheses using a 95% confidence interval. Perceived ease of use had a very strong direct positive association with perceived usefulness, and perceived usefulness had a very strong direct positive association with behavioral intention to use. Resistance to change had a direct positive association with behavioral intention to use and, lastly, social norm had a very strong direct positive association with behavioral intention to use. The findings that perceived ease of use influenced perceived usefulness, that perceived usefulness influenced behavioral intention to use, and social norm influenced behavioral intention to use were in accordance with prior related research. Other results that were not consistent with previous research imply that these are unique behavioral findings regarding smartphone overuse. This research identifies the critical factors that need to be considered when implementing systems or solutions in the future for tackling the issue of smartphone overuse.

About

The COTIDIANA Dataset is a holistic, multimodal, and multidimensional dataset that captures three dimensions in which patients are frequently impacted by Rheumatic and Musculoskeletal Diseases (RMDs), namely, (a) mobility and physical activity, due to joint stiffness, fatigue, or pain; (b) finger dexterity, due to finger joint stiffness or pain; or (c) mental health (anxiety/depression level), due to the functional impairments or pain.

We release this dataset to facilitate research in rheumatology, while contributing to the characterisation of RMD patients using smartphone-based sensor and log data.

We gathered smartphone and self-reported data from 31 patients with RMDs and 28 age-matched controls, including (i) inertial sensors, (ii) keyboard metrics, (iii) communication logs, and (iv) reference tests/scales. We provide both raw and (pre-)processed dataset versions, to enable researchers or developers to use their own methods or benefit from the computed variables. Additional materials containing (a) illustrations, (b) visualization charts, and (c) variable descriptions can be consulted through this link.

Citing

When using this dataset, please cite P. Matias, R. Araújo, R. Graça, A. R. Henriques, D. Belo, M. Valada, N. N. Lotfi, E. Frazão Mateus, H. Radner, A. M. Rodrigues, P. Studenic, F. Nunes (2024) COTIDIANA Dataset – Smartphone-Collected Data on the Mobility, Finger Dexterity, and Mental Health of People With Rheumatic and Musculoskeletal Diseases, in IEEE Journal of Biomedical and Health Informatics, vol. 28, no. 11, pp. 6538-6547, DOI: 10.1109/JBHI.2024.3456069.

Data structure

The data is organised by participant and includes:

• Inertial Sensor Data, retrieved from accelerometer, gyroscope, and magnetometer sensors collected during three distinct walking exercises (Timed Up and Go, Daily Living Activity, and Simple Walk);

• Keyboard Dynamic Metrics, collecting 38 raw variables related with the keyboard typing performance while writing 10 sentences (e.g., number of errors, words-per-minute);

• Communication Logs, e.g., with weekly averages of number of calls and SMS sent or received;

• Validated Clinical Questionnaires, such as general Health (EQ-5D-5L), Multidimensional Health Assessment Questionnaire (MDHAQ), Hospital Anxiety and Depression Scale (HADS);

• Validated Functional Tests, including time to perform the Timed Up and Go (TUG) and Moberg Pick-Up Test (fine motor skills);

• Characterization Questionnaire, containing sociodemographic and clinical information.

cotidiana_dataset
├── info
│ ├── codebook.xlsx
│ ├── missings_report.csv
├── processed
│ ├── com_calls
│ │ └── features.csv
│ ├── com_sms
│ │ └── features.csv
│ ├── full
│ │ └── cotidiana_dataset.csv
│ ├── hd_kst
│ │ └── features.csv
│ ├── hd_mpu
│ │ └── features.csv
│ ├── mob_dla
│ │ └── features.csv
│ ├── mob_sw
│ │ └── features.csv
│ ├── mob_tug
│ │ └── features.csv
│ ├── quest
│ └── features.csv
├── raw
│ ├── com_calls
│ │ └── p[0-58]
│ │ └── calls_log.csv
│ ├── com_sms
│ │ └── p[0-58]
│ │ └── sms_log.csv
│ ├── hd_kst
│ │ └── p[0-58]
│ │ ├── imu
│ │ │ ├── Accelerometer_s[0-9].csv
│ │ │ ├── Gyroscope_s[0-9].csv
│ │ │ └── Magnetometer_s[0-9].csv
│ │ └── keyboard
│ │ └── kb_metrics.csv
│ ├── hd_mpu
│ │ └── p[0-58]
│ │ └── mpu_time.csv
│ ├── mob_dla
│ │ └── p[0-58]
│ │ ├── bag
│ │ │ ├── Accelerometer.csv
│ │ │ ├── Gyroscope.csv
│ │ │ ├── Magnetometer.csv
│ │ │ └── Annotation.csv
│ │ └── pocket
│ │ ├── Accelerometer.csv
│ │ ├── Gyroscope.csv
│ │ ├── Magnetometer.csv
│ │ └── Annotation.csv
│ ├── mob_sw
│ │ └── p[0-58]
│ │ ├── ann
│ │ │ └── walk_ann.csv
│ │ ├── bag
│ │ │ ├── Accelerometer.csv
│ │ │ ├── Gyroscope.csv
│ │ │ ├── Magnetometer.csv
│ │ │ └── Annotation.csv
│ │ └── pocket
│ │ ├── Accelerometer.csv
│ │ ├── Gyroscope.csv
│ │ ├── Magnetometer.csv
│ │ └── Annotation.csv
│ ├── mob_tug
│ │ └── p[0-58]
│ │ ├── bag
│ │ │ ├── Accelerometer.csv
│ │ │ ├── Gyroscope.csv
│ │ │ ├── Magnetometer.csv
│ │ │ └── Annotation.csv
│ │ └── pocket
│ │ ├── Accelerometer.csv
│ │ ├── Gyroscope.csv
│ │ ├── Magnetometer.csv
│ │ └── Annotation.csv
│ ├── quest
│ └── features.csv

The Arabic Sign Language (ASL) 20-Words Dataset v1 was carefully designed to reflect natural conditions, aiming to capture realistic signing environments and circumstances. Recognizing that nearly everyone has access to a smartphone with a camera as of 2020, the dataset was specifically recorded using mobile phones, aligning with how people commonly record videos in daily life. This approach ensures the dataset is grounded in real-world conditions, enhancing its applicability for practical use cases.

Each video in this dataset was recorded directly on the authors' smartphones, without any form of stabilization—neither hardware nor software. As a result, the videos vary in resolution and were captured across diverse locations, places, and backgrounds. This variability introduces natural noise and conditions, supporting the development of robust deep learning models capable of generalizing across environments.

In total, the dataset comprises 8,467 videos of 20 sign language words, contributed by 72 volunteers aged between 20 and 24. Each volunteer performed each sign a minimum of five times, resulting in approximately 100 videos per participant. This repetition standardizes the data and ensures each sign is adequately represented across different performers. The dataset’s mean video count per sign is 423.35, with a standard deviation of 18.58, highlighting the balance and consistency achieved across the signs.

For reference, Table 2 (in the research article) provides the count of videos for each sign, while Figure 2 (in the research article) offers a visual summary of the statistics for each word in the dataset. Additionally, sample frames from each word are displayed in Figure 3 (in the research article), giving a glimpse of the visual content captured.

For in-depth insights into the methodology and the dataset's creation, see the research paper: Balaha, M.M., El-Kady, S., Balaha, H.M., et al. (2023). "A vision-based deep learning approach for independent-users Arabic sign language interpretation". Multimedia Tools and Applications, 82, 6807–6826. https://doi.org/10.1007/s11042-022-13423-9

Please consider citing the following if you use this dataset:

@misc{balaha_asl_2024_db,
 title={ASL 20-Words Dataset v1},
 url={https://www.kaggle.com/dsv/9783691},
 DOI={10.34740/KAGGLE/DSV/9783691},
 publisher={Kaggle},
 author={Mostafa Magdy Balaha and Sara El-Kady and Hossam Magdy Balaha and Mohamed Salama and Eslam Emad and Muhammed Hassan and Mahmoud M. Saafan},
 year={2024}
}

@article{balaha2023vision,
 title={A vision-based deep learning approach for independent-users Arabic sign language interpretation},
 author={Balaha, Mostafa Magdy and El-Kady, Sara and Balaha, Hossam Magdy and Salama, Mohamed and Emad, Eslam and Hassan, Muhammed and Saafan, Mahmoud M},
 journal={Multimedia Tools and Applications},
 volume={82},
 number={5},
 pages={6807--6826},
 year={2023},
 publisher={Springer}
}

This dataset is available under the CC BY-NC-SA 4.0 license, which allows for sharing and adaptation under conditions of non-commercial use, proper attribution, and distribution under the same license.

For further inquiries or information: https://hossambalaha.github.io/.

The smartphone helps workers balance the demands of their professional and personal lives but can also be a distraction, affecting productivity, wellbeing, and work-life balance. Drawing from insights on the impact of physical environments on object engagement, this study examines how the distance between the smartphone and the user influences interactions in work contexts. Participants (N = 22) engaged in two 5h knowledge work sessions on the computer, with the smartphone placed outside their immediate reach during one session. Results show that limited smartphone accessibility led to reduced smartphone use, but participants shifted non-work activities to the computer and the time they spent on work and leisure activities overall remained unchanged. These findings suggest that discussions on smartphone disruptiveness in work contexts should consider the specific activities performed, challenging narratives of ‘smartphone addiction’ and ‘smartphone overuse’ as the cause of increased disruptions and lowered work productivity.

This database automatically captures metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Number of individuals in terms of security when using a smartphone in the last 3 months for private purposes, by activity status, Slovenia, 2020”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

This database automatically captures metadata, the source of which is the GOVERNMENT OF THE REPUBLIC OF SLOVENIA STATISTICAL USE OF THE REPUBLIC OF SLOVENIA and corresponding to the source database entitled “Safety in smartphone usage in the last 12 months for private purposes by individuals, by activity status, Slovenia, 2018”.

Actual data are available in Px-Axis format (.px). With additional links, you can access the source portal page for viewing and selecting data, as well as the PX-Win program, which can be downloaded free of charge. Both allow you to select data for display, change the format of the printout, and store it in different formats, as well as view and print tables of unlimited size, as well as some basic statistical analyses and graphics.

Apple is one of the most influential and recognisable brands in the world, responsible for the rise of the smartphone with the iPhone. Valued at over $2 trillion in 2021, it is also the most valuable...

This dataset contains transcribed fieldnotes from a “methodological assemblage” and technological prototype connecting autoethnography to the practices of self-research in personal science. As an experimental process of personal data gathering, the author used a low-tech device for the active registration of events and their perception, in a case study on disengaging from his smartphone.

The dataset contains 267 data points (timestamp and intensity/duration of perceived events) with alternative classification and subclassification categories through thematic analysis, as well as 112 "on spot" fieldnotes and 112 related retrospective interpretation notes (with corresponding references where indicated) from a focused autoethnography intervention, tracking the authors' experience of living without constant access to a smartphone for a month (during May 2021).

For more info on the process and the main results of this personal experiment, here's the derived publication: Senabre Hidalgo, E., & Greshake Tzovaras, B. (2023). “One button in my pocket instead of the smartphone”: A methodological assemblage connecting self-research and autoethnography in a digital disengagement study. Methodological Innovations. https://doi.org/10.1177/20597991231161093

As described in the paper, this dataset contains a classification of events based on two complementary approaches:

Initial categories (May 2021): Positive - Negative - Reflection

Retrospective categories and subcategories (November 2021):

Response to habit:

Basic impulse: looking for smartphone in pocket / “If I had it now…”

Abstinence reaction: attempts to reconnect to “the cloud”

Fear of missing out (FOMO) after long periods of time

Disruption in basic routine: perception of altered habits

Social context:

Feelings of embarrassment: in relation to others due to feature phone

Observing others: people using smartphones around me

Meta level: how to share process afterwards (in community, academically)

User experience (UX) practicalities:

Moving around solo: orientation when commuting without GPS

Parasitic use of other smartphones (in case of urgent need)

Capturing things on the spot (pictures, information, feelings)

Local stored culture (music and reading away from screens)

Communication problems: dysfunctions in everyday coordination

Reconnection need: whenever ending up using the smartphone again

Self-reinforcing / awareness:

Succeeding in intended detox: satisfaction for achievement

Full attention to moment / stimulus: mindfulness and concentration

Relevant changes in routine: positive reinforcement by new habit