Related papers: Loneliness Forecasting Using Multi-modal Wearable …
Loneliness is a critical mental health issue among university students, yet traditional monitoring methods rely primarily on retrospective self-reports and often lack real-time behavioral context. This study explores the use of passive…
Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants…
Loneliness is a growing health concern as it can lead to depression and other associated mental health problems for people who experience feelings of loneliness over prolonged periods of time. Utilizing passive sensing methods that use…
Background: Loneliness among students is increasing across the world, with potential consequences for mental health and academic success. To address this growing problem, accurate methods of detection are needed to identify loneliness and…
Loneliness has reached epidemic proportions globally, posing serious risks to mental and physical health. As social media platforms increasingly mediate social interaction, understanding their relationship with loneliness has become urgent.…
Monitoring and understanding affective states are important aspects of healthy functioning and treatment of mood-based disorders. Recent advancements of ubiquitous wearable technologies have increased the reliability of such tools in…
Loneliness and depression are interrelated mental health issues affecting students well-being. Using passive sensing data provides a novel approach to examine the granular behavioural indicators differentiating loneliness and depression,…
Loneliness does not only have emotional implications on a person but also on his/her well-being. The study of loneliness has been challenging and largely inconclusive in findings because of the several factors that might correlate to the…
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults. However, tools to effectively monitor and manage anxiety are lacking, and comparatively limited research has been applied to addressing…
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility,…
This study provides evidence that personality can be reliably predicted from activity data collected through mobile phone sensors. Employing a set of well informed indicators calculable from accelerometer records and movement patterns, we…
We present an intelligent wearable system to monitor and predict mood states of elderly people during their daily life activities. Our system is composed of a wristband to record different physiological activities together with a mobile app…
Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states. Compared to the traditional self-assessment questionnaires, the passive data collected from mobile phones is easier to…
Companion chatbots offer a potential solution to the growing epidemic of loneliness, but their impact on users' psychosocial well-being remains poorly understood, raising critical ethical questions about their deployment and design. This…
The global mental health crisis is a pressing concern, with college students particularly vulnerable to rising mental health disorders. The widespread use of smartphones among young adults, while offering numerous benefits, has also been…
Personalized longitudinal disease assessment is central to quickly diagnosing, appropriately managing, and optimally adapting the therapeutic strategy of multiple sclerosis (MS). It is also important for identifying the idiosyncratic…
Smartphone sensing offers an unobtrusive and scalable way to track daily behaviors linked to mental health, capturing changes in sleep, mobility, and phone use that often precede symptoms of stress, anxiety, or depression. While most prior…
Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of wearable technologies, such as fitness trackers and smartwatches, has made it possible to…
We introduce a system to measure individual happiness based on interpreting body sensors on smartwatches. In our prototype system we use a Pebble smartwatch to track activity, heartrate, light level, and GPS coordinates, and extend it with…
Despite the advent of wearable devices and the proliferation of smartphones, there still is no ideal platform that can continuously sense and precisely collect all available contextual information. Ideally, mobile sensing data collection…