Real World Longitudinal iOS App Usage Study at Scale

Dohyun Kim; Joshua Gluck; Malcolm Hall; Yuvraj Agarwal

Real World Longitudinal iOS App Usage Study at Scale

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arxiv 1912.12526 v1 pith:YYIA6AUW submitted 2019-12-28 cs.HC

Dohyun Kim , Joshua Gluck , Malcolm Hall , Yuvraj Agarwal This is my paper · ORCID

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keywords usageappschangeslongitudinalmobiletimeusersdevices

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Given the importance of understanding the interaction between mobile devices and their users, app usage patterns have been studied in various contexts. However, prior work has not fully investigated longitudinal changes to app usage behavior. In this paper, we present a longitudinal, large-scale study of mobile app usage based on a dataset collected from 162,006 iPhones and iPads over 4 years. We explore multiple dimensions of app usage pattern proving useful insights on how app usage changes over time. Our key findings include (i) app usage pattern changes over time both at the individual app level and the app category level (i.e. proportion of time a user spends using an app), (ii) users keep a small set of apps frequently launched (90% of iPhone users launch roughly 14-18 apps weekly), (iii) a small number of apps remain popular while some specific kinds of apps (e.g. Games) have a shorter life cycle compared to other apps of different categories. Finally, we discuss our findings and their implications, for example, a short-term study as an attempt to understand the general needs of mobile devices may not achieve useful results for the long term.

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MIDiff: Tackling Sparsity and Imbalance in Mobile Usage Generation via Multivariate-Imaging Diffusion
cs.LG 2026-07 conditional novelty 6.0 of 10

A diffusion model trained on C-GASF images of mobile usage generates synthetic user traces that match real trace statistics far better than prior time-series generative baselines.

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