Background: Depression and anxiety are leading causes of disability worldwide but often
remain undetected and untreated. Smartphone and wearable devices may offer a unique …

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 …

Recent industrial and academic research has focused on data-driven analytics with
smartphones by collecting user interaction, context, and device systems data through …

Mobile communication differs from other forms of mediated communication in terms of
connectedness, dynamics, omnipresence, and interactivity. Consequently, it can be difficult …

Background: Smartphones are increasingly used in health research. They provide a
continuous connection between participants and researchers to monitor long-term health …

Background Machine learning models often use passively recorded sensor data streams as
inputs to train machine learning models that predict outcomes captured through ecological …

In recent years, the emergence of the COVID-19 pandemic has led to new viral variants,
such as Omicron. These variants are more harmful and impose more restrictions on people's …

As the capabilities of smart sensing and mobile technologies continue to evolve and
expand, storing diverse sensor data on smartphones and cloud servers becomes …

The frequency and duration of social contact, represented by conversation, is positively
correlated with our physical and mental health. Therefore, a method that automatically …

Adaptive mobile device-based health interventions often use machine learning models
trained on non-mobile device data, such as social media text, due to the difficulty and high …