Objective and Subjective iPhone Use in Relation to App usage, Risk of Addiction and Device Use Deprivation

Description

The dataset contains information from 100 adult iPhone users who provided objective smartphone usage data recorded via the Apple iOS Screen Time feature over eight consecutive days, alongside self-report questionnaire data. Variables include demographic information (age and gender), perceived smartphone use volume, and objectively measured total and average daily smartphone use. The dataset further includes time spent in 12 predefined application usage categories as classified by Screen Time, as well as aggregated hedonic and utilitarian use indices derived from these categories. Self-report measures comprise the Smartphone Application-Based Addiction Scale (SABAS) and the Smartphone Deprivation Inventory, including both total scores and individual component scores. In addition, the dataset contains component-level indicators of smartphone addiction reflecting salience, mood modification, tolerance, withdrawal, conflict, and relapse. Together, these variables enable integrated analyses of objective smartphone behaviour, subjective use perceptions, application-level usage patterns, and psychological correlates related to addiction risk and deprivation.

Steps to reproduce

To reproduce this study, recruit at least 100 iPhone users aged 18 or older through social media platforms using convenience sampling, after obtaining institutional ethical approval. Collect informed consent electronically and gather demographic information including age and gender. Ask participants to rate their perceived iPhone use volume on a 4-point scale (little, average, a lot, too much). Instruct participants to capture and submit screenshots of their Apple iOS Screen Time data covering eight consecutive days, ensuring they provide usage data for all application categories. Extract total usage time and categorize applications into 12 standard iOS categories, then aggregate these into hedonic use (Social Networking, Entertainment, Games, Creativity, Shopping & Food, Travel & Navigation) and utilitarian use (Productivity & Finance, Information & Reading, Education, Health & Fitness, Utilities) indices. Administer the validated Smartphone Application-Based Addiction Scale (6 items, 6-point Likert scale) to assess addiction risk based on Griffiths' components model. Administer the Smartphone Deprivation Inventory (9 items, 7-point Likert scale) to measure withdrawal symptoms during smartphone unavailability. Calculate descriptive statistics, Pearson correlations between all variables, and create heatmaps to visualize component-level associations. Conduct hierarchical multiple regression with deprivation, hedonic use, and perceived use in the first block, followed by age and gender in the second block, to predict addiction risk while checking assumptions of normality, linearity, homoscedasticity, and multicollinearity.

Institutions

• Eötvös Loránd University

  Budapest, Budapest

Categories

Licence