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

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

The Social media, Smartphone use and Self-Harm (3S-YP) study is a prospective observational cohort study to investigate the associations between social media and smartphone use and self-harm in young people. Young people aged 13–25 years old from secondary mental health services were recruited and followed for up to 6 months. Data collected in the study includes questionnaire data and data extracted from electronic health records (EHR) and user generated data sources.

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

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).

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

Analyzing the screen time of a user helps smartphone companies give a review of all the activities of the user on their smartphone. It helps users understand if they were productive, creative, or wasted their time.

Here is the daily screen time data of a user collected and submitted by Ruchi Bhatia on Kaggle. Below are all the features in the data:

• Date: Date of the record
• Usage: Number of times the user used the smartphone in a day
• Notifications: Number of notifications the user received in a day
• Times opened: Number of times the app was opened by the user
• App: The app that was opened by the user

Analyze the screen time of the user to find relationships between the usage of the smartphone and factors like notifications and apps used by the user.

The dataset includes variables which were assessed in adolescent/mother and adolescent/father dyads, by a cross-sectional design. Gender was coded as 0 for males and 1 for females.

Nutritious free meals are available for children and teens 18 and younger at many locations throughout the nation throughout the summer while school is out of session. This mapping tool helps to find a site near you.

• 2024-12-09_FALAH_Vanuatu_socialMediaUse_Zenodo_anonymized.csv: csv file with anonymized information about the participants including their answers.
• FALAH_Vanuatu_Digital Landscape_Supplementary tables.pdf: pdf file with 3 supplementary tables.

Data anonymized from self-report questionnaires on owning a mobile phone, actions performed with a computer and/or a tablet and with a mobile phone and the use of digital media for health.

Participant characteristics: 13 to 20 years old students.

Number of participants: 197.

Year of the study: 2023.

Place of the study: Vanuatu.

The restricted non-anonymized dataset can also be found on https://doi.org/10.5281/zenodo.14323389" target="_blank" rel="noopener">Wattelez, G., Amon, K., Nedjar-Guerre, A., Forsyth, R., Peralta, L., Urvoy, M.-J., Caillaud, C., & Galy, O. (2024). Exploring the Digital Health Landscape: How adolescents living in urban and rural Vanuatu use online platforms to access health information (non-anonymized version) (1.0.0) [Data set]. Zenodo.

Baltimore City Child Health

An Exploration of 2010 Birth, Prenatal Visit, Lead Exposure and Teen Birth Rates

By City of Baltimore [source]

About this dataset

This Baltimore City Child and Family Health Indicators dataset provides us with crucial information that can support the health and well-being of Baltimore City residents. It contains 13 indicators such as low birth weight, prenatal visits, teen births, and more. This data is sourced from the Maryland Department of Health & Mental Hygiene (DHMH), Baltimore Substance Abuse Systems (BSAS), theBaltimore City Health Department, and the US Census Bureau. Through this data set we can gain a better understanding of how Baltimore City citizens’ health compares to other areas and how it has changed over time. By investigating this dataset we are given an opportunity to create potential strategies for providing better care for our community. With discoveries from these indicators, together as a city we can bring about lasting change in protecting public health within Baltimore

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How to use the dataset

This dataset provides valuable information about the health and wellbeing of children and families in Baltimore City in 2010. The data is organized by CSA (Census Statistical Area) and includes stats on term births, low birth weight births, prenatal visits, teen births, and lead testing. This dataset can be used to analyze trends in children's health over time as well as identify potential areas that need more attention or resources.

To use this dataset: - Read through the data dictionary to understand what each column represents.
- Choose which columns you would like to explore further.
- Filter or subset the data as you see fit then visualize it with graphs or maps to better understand how conditions vary across neighborhoods in Baltimore City.
- Consider comparing the data from this year with prior years if available for deeper analysis of changes over time.
- Look for correlations among columns that could help explain disparities between neighborhoods and create strategies for improving outcomes through policy interventions or other programs designed specifically for those areas needs

Research Ideas

• Mapping health disparities in high-risk areas to target public health interventions.
• Identifying neighborhoods in need of additional resources for prenatal care, infant care, and lead testing and create specific programs to address these needs.
• Creating an online dashboard that displays real time data on Baltimore City’s population health indicators such as birth weight, teenage pregnancies, and lead poisoning for the public to access easily

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: BNIA_Child_Fam_Health_2010.csv | Column name | Description | |:---------------|:----------------------------------------------------------| | the_geom | Geometry of the Census Statistical Area (CSA) (Geometry) | | CSA2010 | Census Statistical Area (CSA) (String) | | termbir10 | Total number of term births in 2010 (Integer) | | birthwt10 | Total number of low birth weight births in 2010 (Integer) | | prenatal10 | Total number of prenatal visits in 2010 (Integer) | | teenbir10 | Total number of teen births in 2010 (Integer) | | leadtest10 | Total number of lead tests conducted in 2010 (Integer) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Baltimore.

Appliances Energy Usage Data from Low-Energy Building

Capturing Temperature, Humidity, and Weather Variables

By UCI [source]

About this dataset

This dataset provides comprehensive experimental data on the energy usage of appliances in a low-energy building, with detailed information about temperature, humidity, and other weather conditions. Data was averaged over 10 minute periods from four and a half months' worth of measurements collected using a ZigBee wireless sensor network. Additional climate data from Chievres Airport in Belgium was merged into the dataset for further analysis. The data can be used to generate regression models that predict energy usage for various home appliances. Additionally, two random variables have been included in order to test different models and eliminate non-predictive attributes (parameters). This dataset is an invaluable resource for researchers looking to gain insights into how household energy usage changes with varying environmental factors such as temperature or humidity levels. By exploring this comprehensive dataset, researchers can gain valuable knowledge about how their findings might relate to real-world scenarios in low-energy buildings around the world!

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How to use the dataset

If you are looking to analyze energy usage and temperature in a low-energy building, then this dataset will be useful for you. The “Appliances Energy Usage Data from a Low-Energy Building” dataset contains data collected over 4.5 months at 10 minute intervals of energy use, temperature, humidity and other weather conditions within the building. Using this dataset, it is possible to generate regression models in order to better understand how different variables affect energy use within this project scope.

Research Ideas

• Develop an energy efficiency monitoring app with alerts that identifies and notifies users when their appliances become inefficient in order to reduce energy use and bills.
• Develop a predictive weather and energy forecasting tool that uses weather data from Chievres Airport along with appliance data to accurately forecast utility usage for businesses or homeowners in advance so that they can calculate future budgets more efficiently.
• Create an AI based smart home assistant that uses this dataset as foundations to better advise users on efficient ways of using their appliances while also taking into account environmental conditions such as humidity and temperature outside the building in order to recommend better settings for the household appliances according to external state of things as well individual households needs

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: energydata_complete.csv | Column name | Description | |:----------------|:------------------------------------------------------------| | date | Date of the data collection. (Date) | | Appliances | Energy usage of appliances in Wh. (Numeric) | | lights | Energy usage of lights in Wh. (Numeric) | | T1 | Temperature in kitchen area in Celsius. (Numeric) | | RH_1 | Humidity in kitchen area in percentage. (Numeric) | | T2 | Temperature in living room area in Celsius. (Numeric) | | RH_2 | Humidity in living room area in percentage. (Numeric) | | T3 | Temperature in laundry room area in Celsius. (Numeric) | | RH_3 | Humidity in laundry room area in percentage. (Numeric) | | T4 | Temperature in office room area in Celsius. (Numeric) | | RH_4 | Humidity in office room area in percentage. (Numeric) | | T5 | Temperature in bathroom area in Celsius. (Numeric) | | RH_5 | Humidity in bathroom area in percentage. (Numeric) | | T6 | Temperature in outdoor area in Celsius. (Numeric) | | RH_6 | Humidity in outdoor area in percentage. (Numeric) | | T7 | Temperature in ironing room area in Celsius. (Numeric) | | RH_7 | Humidity in ironing room area in percentage. (Numeric) | | T8 | Temperature in teenage bedroom 2 area in Celsius. (Numeric) | | RH_8 | Humidity in teenage bedroom 2 area in percentage. (Numeric) | | T9 ...

BMI  =  Body Mass Index; SOL  =  Sleep Onset Latency; NWKS  =  Number of Wakes; and ESS  =  Epworth Sleepiness Score [28]. Socioeconomic status was estimated using the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) derived from Socio-Economic Indexes for Areas (SEIFA) Australian census data [29]. Socioeconomic Index data was not available for 7 participants (n = 1177). Caffeine data are the median number of caffeinated drinks consumed in the past week (interquartile range).Demographics and Self-Reported Sleep Problems.

A team of researchers just dropped major truth bombs about American teens and their math game. A recently published study found US teens are the champions of hype when it comes to their own math skills. Let's dig in to sum science.

Gesture Recognition Data in Online Conference Scenes

Description

2,341 People Gesture Recognition Data in Meeting Scenes includes Asians, Caucasians, blacks, and browns, and the age is mainly young and middle-aged. It collects a variety of indoor office scenes, covering meeting rooms, coffee shops, libraries, bedrooms, etc. Each person collected 18 pictures and 2 videos. The pictures included 18 gestures such as clenching a fist with one hand and heart-to-heart with one hand, and the video included gestures such as clapping. For more details, please refer to the link: https://www.nexdata.ai/datasets/computervision/1292?source=Kaggle

Data size

2,341 people, each person collects 2 videos and 18 images

Race distribution

786 Asians, 1,002 Caucasians, 401 black, 152 brown people

Gender distribution

1,209 males, 1,132 females

Age distribution

from teenagers to the elderly, mainly young and middle-aged

Collection environment

indoor office scenes, such as meeting rooms, coffee shops, libraries, bedrooms, etc.

Collection diversity

different gestures data, different races, different age groups, different meeting scenes

Collection equipment

cellphone, using the cellphone to simulate the perspective of laptop camera in online conference scenes

Collection content

collecting the gestures data in online conference scenes

Data format

.mp4, .mov, .jpg

Accuracy rate

the accuracy exceeds 97% based on the accuracy of the actions; the accuracy of action naming is more than 97%

Licensing Information

Commercial License

BackgroundAdolescence and technological devices have a close relationship during this stage in which anxiety about using a cell phone increases when it is not available. The Nomophobia Scale (NMP-Q) is a measure that evaluates anxiety behaviors when being without a mobile phone.ObjectiveTo evaluate the psychometric properties of the Nomophobia Questionnaire (NMP-Q) in Peruvian adolescents.MethodsAn instrumental study was conducted and 900 adolescents of both sexes, between 12 and 17 years old, living in northern, rural, and eastern regions of Peru were evaluated. A confirmatory factor analysis was carried out to determine the structure and the structural invariance of the measures according to age was evaluated and the reliability was estimated by means of the Omega reliability coefficient.ResultsThe four--factor structure composed of 20 items was confirmed with optimal goodness-of-fit indices (CFI = 0.992; TLI = 0.991; SRMR = 0.053; RMSEA = 0.039). The MIMIC models reported invariance for age groups (ΔCFI <0.01, ΔRMSEA <0.015). The omega reliability coefficients ranged between 0.84 and 0.90.”ConclusionThe Peruvian version of the NMP-Q (20 items) has shown adequate psychometric properties to assess nomophobia in the adolescent population.

This dataset contains 11,113 people with gauze masks, each contributing 7 images, for a total of 77,791 images. The dataset covers multiple mask types, ages, races, light conditions and scenes. This data can be applied to computer vision tasks such as occluded face detection and recognition, masked face recognition and security systems.

Data size 11,113 people, 7 images per person Race distribution 9,655 Asian people, 951 black people, 42 brown people, 465 Caucasian people Gender distribution 6,055 males, 5,058 females

Age distribution ranging from teenager to the elderly, the middle-aged and young people are the majorities

Collecting environment including indoor and outdoor scenes

Data diversity multiple mask types, multiple ages, multiple races or nationalities, multiple light conditions and multiple collection scenes

Device cellphone

Data format .jpg, .jpeg

Accuracy the accuracy of labels of mask type, gender, race or nationality and age are more than 97%

Reversion of immune sensitization tests for Mycobacterium tuberculosis (M.tb) infection, such as interferon-gamma release assays or tuberculin skin test, has been reported in multiple studies. We hypothesised that QuantiFERON-TB Gold (QFT) reversion is associated with a decline of M.tb-specific functional T cell responses, and a distinct pattern of T cell and innate responses compared to persistent QFT+ and QFT- individuals. This dataset consists of meta data from the Adolescent Cohort Study (ACS) and innate and adaptive cellular responses that were measured in stimulated Peripheral blood mononuclear cells (PBMCs). PBMCs were collected at enrolment and at 6-monthly intervals during the 2-years of follow-up (termed months 0, 6, 12 and 18) when the QFT tests were performed to determine M.tb infection. Three groups of participants were defined based on their longitudinal QFT results: persistent QFT positives (QFT positive results > 0.35 IU/mL at four consecutive visits), QFT reverters (two QFT positive results, at least one > 0.7 IU/mL, followed by two QFT negative results, at least one < 0.2 IU/mL), and non-converters (QFT negative results < 0.2 at four consecutive visits). Overall, all three groups were matched by age, sex, ethnicity, school and known TB exposure. Five stimulations were used to induce M.tb-specific T cell responses of the adaptive arm, including M.tb-specific peptide pools spanning ESAT-6/CFP-10 or EspC, EspF and Rv2348c (collectively termed Esp), and M.tb-lysate; Staphylococcus Enterotoxin B (SEB), as a positive control; or the cells were left unstimulated as a negative control. The variables include a combination of 5 effector functions (IL2, TNF, IFNg, CD107, CD154) produced by CD4+ and CD8+ T cells upon stimulation. Combinations of the phenotypic markers CD45RA, CCR7, CD27, KLRG1, HLA-DR and CXCR3 were further measured on IFNg, IL2 or TNF producing T cells (total Th1).In the innate cells, effector responses were measured in unstimulated PBMC or after stimulation with M.tb-lysate or E. Coli (positive control). The variables in this dataset included a combination of 6 functional markers (Granzyme B, IL6, IL10, IL12, IFNg, TNF) produced by NK cells, B cells, monocytes, and DURT cells.This dataset was used in the manuscript Mycobacterium tuberculosis-specific T cell functional, memory and activation profiles in QuantiFERON-reverters are consistent with controlled infection. See the full manuscript for more details.

Facial Expression Data in Online Conference Scenes

Description

1,142 people facial expression data in online conference scenes, including Asian, Caucasian, black, and brown multitasking, mainly young and middle-aged, collected a variety of indoor office scenes, covering meeting rooms, coffee shops, libraries , bedroom, etc., each collector collected 7 kinds of expressions: normal, happy, surprised, sad, angry, disgusted, and fearful. For more details, please refer to the link: https://www.nexdata.ai/datasets/computervision/1281?source=Kaggle

Data size

1,142 people, each person collects 7 videos

Race distribution

153 Asians, 889 Caucasians, 66 blacks, 34 brown people

Gender distribution

535 males, 607 females

Age distribution

from teenagers to the elderly, mainly young and middle-aged

Collection environment

indoor office scenes, such as meeting rooms, coffee shops, libraries, bedrooms, etc.

Collection diversity

different facial expressions, different races, different age groups, different meeting scenes

Collection equipment

cellphone, using the cellphone to simulate the perspective of the laptop camera in online conference scenes

Collection content

collecting the expression data in online conference scenes

Data format

.mp4, .mov

Accuracy rate

the accuracy exceeds 97% based on the accuracy of the expressions; the accuracy of expression naming

Licensing Information

Commercial License