Smartphone app usage analysis: datasets, methods, and applications
As smartphones have become indispensable personal devices, the number of smartphone
users has increased dramatically over the last decade. These personal devices, which are …
users has increased dramatically over the last decade. These personal devices, which are …
[HTML][HTML] Predicting depression from smartphone behavioral markers using machine learning methods, hyperparameter optimization, and feature importance analysis …
K Opoku Asare, Y Terhorst, J Vega… - JMIR mHealth and …, 2021 - mhealth.jmir.org
Background Depression is a prevalent mental health challenge. Current depression
assessment methods using self-reported and clinician-administered questionnaires have …
assessment methods using self-reported and clinician-administered questionnaires have …
[HTML][HTML] Mood ratings and digital biomarkers from smartphone and wearable data differentiates and predicts depression status: A longitudinal data analysis
Depression is a prevalent mental disorder. Current clinical and self-reported assessment
methods of depression are laborious and incur recall bias. Their sporadic nature often …
methods of depression are laborious and incur recall bias. Their sporadic nature often …
Putting human behavior predictability in context
Various studies have investigated the predictability of different aspects of human behavior
such as mobility patterns, social interactions, and shopping and online behaviors. However …
such as mobility patterns, social interactions, and shopping and online behaviors. However …
Show me your smartphone… and then I will show you your brain structure and brain function
Research in the field of digital phenotyping and mobile sensing has seen a tremendous rise
in interest over the last few years. The psychological and psychiatric sciences were early …
in interest over the last few years. The psychological and psychiatric sciences were early …
Predicting Big Five personality traits from smartphone data: a meta‐analysis on the potential of digital phenotyping
Objective Since the first study linking recorded smartphone variables to self‐reported
personality in 2011, many additional studies have been published investigating this …
personality in 2011, many additional studies have been published investigating this …
You are how you use apps: user profiling based on spatiotemporal app usage behavior
Mobile apps have become an indispensable part of people's daily lives. Users determine
what apps to use and when and where to use them based on their tastes, interests, and …
what apps to use and when and where to use them based on their tastes, interests, and …
Artificial intelligence and identity: the rise of the statistical individual
JC Bjerring, J Busch - AI & SOCIETY, 2024 - Springer
Algorithms are used across a wide range of societal sectors such as banking, administration,
and healthcare to make predictions that impact on our lives. While the predictions can be …
and healthcare to make predictions that impact on our lives. While the predictions can be …
Examining the empirical links between digital social pressure, personality, psychological distress, social support, users' residential living conditions, and smartphone …
Based on the recent scientific literature on the social ecology of smartphone addiction, we
have examined the empirical relationship between social digital pressure (SDP) and …
have examined the empirical relationship between social digital pressure (SDP) and …
DDHCN: Dual decoder Hyperformer convolutional network for Downstream-Adaptable user representation learning on app usage
F Zeng, Y Li, J Xiao, D Yang - Expert Systems with Applications, 2024 - Elsevier
In mobile scenarios, there is a need for general user representations to solve multiple target
tasks. However, there are some challenges in the related research (eg, difficulty in learning …
tasks. However, there are some challenges in the related research (eg, difficulty in learning …