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

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

The codebook, preregistration, and syntaxes can be found on the Open Science Framework (OSF) at https://osf.io/fzp7d/. For more information, please contact the authors via t.siebers@uva.nl or info@project-awesome.nl.

This data set contains all gather information of the MyMovez project, which investigated adolescents’ health behaviors (ie., nutrition, media use, and physical activity) and their social networks for three years. The first year (2016; data collection waves 1, 2, 3) and the second year (2017; wave 4) marked the first phase of the project in which the health behaviors of adolescents were monitored without intervening. The third year (waves 5, 6, 7) marked the second phase of the project in which four different types of interventions were tested to promote either water consumption or physical activity. A fifth group did not receive an intervention and is used as a control condition.

During the measurement periods, participants received the MyMovez Wearable Lab: a smartphone with a tailor-made research application and a wrist-worn accelerometer. The accelerometer (Fitbit Flex) measured the physical activity per minute and per day, and was water-resistant. The smartphone was equipped with a custom made research application by which daily questionnaires were administered. Beginning in wave 5, the app contained a social platform in which the participants could communicate with each other. The smartphone also connected to the accompanying accelerometer and other research smartphones via Bluetooth.

Among others, the most important measures in the project are:

• Questionnaire data: e.g. Food Frequency Questionnaires, Self-reported media exposure, measures related to the theory of planned behavior
• Physical activity measured by accelerometer.
• Sociometric nominations: Peers nominated classmates on certain questions
• Proximity networks inferred from the Bluetooth connections on the research phones (beacon data)
• Online communication data derived from the social platform (Social Buzz)
• Photo data (not shared in this repository)
• BMI measured by the researchers

For more information please see the accompanying overview, or the protocol paper of the project: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-018-5353-5

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.

YouTube, Instagram and Snapchat are the most popular online platforms among teens. Fully 95% of teens have access to a smartphone, and 45% say they are online 'almost constantly

this dataset has all you need to know about apps that are more popular among teens

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.

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.

Background and objectivesConcern exists regarding the potential negative consequences of smartphone addiction among adolescents. This study investigated the effect of use motivations and alexithymia on smartphone addiction among adolescents with two insecure attachment styles, namely, anxious and avoidant attachment. These attachment styles were regarded as mediating variables.MethodsSelf-report measures were used to assess use motivations, alexithymia, insecure attachment and smartphone addiction. Data were collected from 748 junior high school students (382 males and 366 females) in northeastern China. Structural equation modeling was used to test our hypothesis that use motivations and alexithymia are directly associated with smartphone addiction and also through the mediation of attachment insecurities.ResultsThe structural equation modeling results showed a strong and positive correlation between use motivation and smartphone addiction, with avoidant attachment mediating such a relationship. Meanwhile, the two components of alexithymia, difficulty identifying feelings and externally oriented thinking, positively predicted smartphone addiction, with avoidant attachment serving as a mediator of this effect. In addition, the mediation analysis results demonstrated that attachment anxiety mediated the connection between escape drive, extrinsically focused thought, and smartphone addiction.ConclusionFindings describe how attachment insecurities, smartphone use motivations, and alexithymia can interact with one another to predict smartphone addiction. Smartphone use motivation types and alexithymia symptoms should be taken into consideration when designing targeted intervention programs for smartphone addiction to address the different attachment needs of adolescents, which would be helpful to reduce their smartphone addiction behaviors.

The Chinese Kids Speech database (Lower Grade) contains the total recordings of 184 Chinese Kids speakers (98 males and 86 females), from 6 to 10 years’ old recorded in quiet rooms using smartphone. This database may be combined with the Chinese Kids Speech database (Upper Grade) also available in the ELRA Catalogue under reference ELRA-S0497.Number of speakers, utterances, duration and age are as follows :Number of speakers (Male/Female): 184 (98/86)Number of utterances (average): 237 utt/spkrTotal number of utterances: 43,667Age: from 6 to 10Total hours of data: 871,426 sentences were used. Recordings were made through smartphones and audio data stored in .wav files as sequences of 16KHz Mono, 16 bits, Linear PCM.Database・Audio data: WAV format, 16KHz, 16bit, mono (recorded with smartphone)・Transcription data: TSV format(tab-delimited), UTF-8 (without BOM) ), Line ending: LF・Size: 9.4GBAgeMaleFemaleTotal611617711819818294794736831011718Structure of database :├─ readme.txt├─ Chinese Kids Speech Database (Lower grade).pdfDescription document of the database├─ transcription(Lower).tsvTranscription└─ Low/directory of audio data └─ (1st/2nd/3rd)directory of version ID └─(0/1)directory of gender (0: male, 1: female) └─(audio_file)audio file (WAV format, 16KHz, 16bit, mono)Field information of “transcription(Lower).tsv” are as follows:Field numberContents0Script ID1Speaker ID2Audio file name3Transcription (in Chinese)File naming conventions of audio files are as follows:Field numberContentsDescriptionRemarks0Script IDFour digitsXXXX: four digits1Speaker IDThree digitsXXX: three digits2AgeTwo digitsFrom 06 to 103Gender0: male, 1: female4Utterance No.Three digitsSequential numbering starting from 001 within each speaker5Recording dateYYYYMMDDHHMM6Recording device nameRecording device nameEx. NTH-AN007OSOperating System info of recording deviceEx. android-118Durationduration in msecDuration of the actual spoken utteranceFiled separation character is “_”.For example, if the audio file name is “1318_373_09_1_010_202205041857_NTH-AN00_android-11_5480.wav “, this file has the following meaning:1318: script ID373: speaker ID09: age (nine years old)1: gender (female)010: utterance number202205041857: recording date (May 4, 2022, at 6:57 PM)NTH-AN00: recording device nameandroid-11: operating system info of recording device5480: duration of the actual spoken utterance (5,480 msec)

The Japanese Kids Speech database (Lower Grade) contains the total recordings of 179 Japanese Kids speakers (71 males and 108 females), from 6 to 9 years' old (first, second and third graders in elementary school), recorded in quiet rooms using smartphones. This database may be combined with the Japanese Kids Speech database (Upper Grade) also available in the ELRA Catalogue under reference ELRA-S0412.Number of speakers, utterances, duration and age are as follows :Number of speakers 179 (71 male/108 female)Number of utterances (average):325 utterances per speakerTotal number of utterances: 58,214Age: from 6 to 9 years' oldTotal hours of data: 116.61019 sentences were used. Recordings were made through smartphones and audio data stored in .wav files as sequences of 16KHz Mono, 16 bits, Linear PCM.Database:・Audio data: WAV format, 16KHz, 16bit, mono (recorded with smartphone)・Recording scripts: TSV format(tab-delimited), UTF-8 (without BOM)・Transcription data: TSV format(tab-delimited), UTF-8 (without BOM)・Size: 12.9GBNumber of speakers per age:6 years' old: 35 (17 male, 18 female)7 years' old: 58 (26 male, 32 female)8 years' old: 67 (22 male, 45 female)9 years' old: 19 (6 male, 13 female)Structure of database:├─ readme.txt├─ Japanese Kids Speech Database.pdfDescription document of the database├─ Transcription.tsvTranscription├─ scripts.tsvScript│└─ voices/directory of audio data └─ low/directory of lower grade └─(speaker_ID/)directory of speaker ID (six digits) └─(audio_file)audio file (WAV format, 16KHz, 16bit, mono)File naming conventions of audio files are as follows:Field number | Contents | Description | Remarks0 | Language ID | “JA” (fixed) | Japanese1 | Speaker ID | Six digit | 4XXXXX2 | Script ID | LXXXX | XXXX: four digits3 | Age | Two digits4 | Gender | M: male, F: femaleFiled separation character is “_”.For example, if the audio file name is “JA_400001_L0001_07_F.wav, this file has the following meaning:JA: Language ID (Japanese)400001: speaker IDL0001: script ID07: age (seven years old)F: gender (female)

Bivariate correlations among predictor and outcome variables for a general day.

effects of electronic media use may affect the adolescents both positively and negatively. One of the negative impacts is the occurrence of sleep disturbances.Since the Covid 19 pandemic, there has been an increase in the use of electronic media in families due to restrictions on activities outside the home. Objective:This study aimed to investigate the relationship between electronic device use and sleep disturbances in adolescents of a Senior High School students in Palembang.
Methods: This study is a cross-sectional conducted in January to February 2021 involving senior high school students aged 14 to 17 years as participants.The participants were given two questionnaires, a questionnaire to assess the electronic device use and a Sleep Disturbance Scale for Children (SDSC) questionnaire to assess sleep disturbances. Results: A final sample of 157 participants were obtained. The majority of the participants were aged ≥ 16 years old (56.7%). The majority of participants use smartphones (93%) with a median of media use of 10 hours a day. None of the participant’s characteristic variables showed statistically significant correlations (all p value were >0.05). Similarly, none of the electronic device use variables showed statistically significant correlations (all p value were >0.05)

Background and Aims: This present paper will review the existing evidence on the effects of excessive smartphone use on physical and mental health.Results: Comorbidity with depression, anxiety, OCD, ADHD and alcohol use disorder. Excessive smartphone use is associated with difficulties in cognitive-emotion regulation, impulsivity, impaired cognitive function, addiction to social networking, shyness and low self-esteem. Medical problems include sleep problems, reduced physical fitness, unhealthy eating habits, pain and migraines, reduced cognitive control and changes in the brain's gray matter volume.In Conclusion: Excessive smartphone use is associated with psychiatric, cognitive, emotional, medical and brain changes that should be considered by health and education professionals.

The Covid-19 pandemic and resultant disruptions to schooling presented significant challenges for many families. Well organised families have been shown to have a protective effect on adolescent wellbeing in periods of shock. At the onset of the Covid-19 pandemic, Asenze, a population-based cohort study, was conducting a third wave of data collection in peri-urban South Africa, examining risk and protective factors during adolescence. By March 2020, n = 272 adolescents and their caregivers (n = 241) in the cohort had been assessed when in-person data collection was halted by lockdown measures countrywide. During this cessation we undertook a brief telephonic qualitative sub-study to explore whether families enrolled in the cohort were able to cohabit cohesively and undertake distance learning during lockdown. A purposeful sample of 20 families (caregivers n = 20, adolescents n = 24) recently assessed in the Wave 3 of the main study, participated in semi-structured interviews. Quantitative data from Waves 1–3 of the main study was used to measure family function, adolescent cognitive function, and profile adolescent and caregivers. The quantitative and qualitative data were integrated to illustrate the dynamics of the participants’ lives before and during lockdown. We found that families classified as well-organized before lockdown, were more likely to report co-operation during lockdown. Adolescents who were self-motivated, had access to smartphones or the internet, and were supported by both family and educators, were well-placed to continue their education without much disruption. However, few schools instituted distance learning. Of the adolescents who were not assisted- some studied on their own or with peers, but others did no schoolwork, hindered by a lack of digital connectivity, and poor service delivery. The experience of adolescence and caregivers in the Asenze Cohort during lockdown highlight the importance of family functioning for adolescent wellbeing in crisis, as well as the need for access to health, mental health, and social services, communication upgrades, and enhancements to the education system during peaceful times, to make a difference to young lives in times of crisis.

Main purpose of smartphone use according to spent time using a smartphone.

The users’ experiences on internet and smartphone applications (N = 48).

Practices for preventing cardiovascular disease among adolescents by school type.

Multinomial logistic regression for the association between electronic media use with BMI.

Characteristics and pregnancy information among adolescent mothers (n = 48).