Loneliness detection using technology

Over the last decade, especially during the COVID-19 pandemic, loneliness has emerged as a global issue. Although loneliness is a common issue that most people experience at some stage in their lives, it can have harmful effects on both physical and mental health if this condition becomes chronic. To avoid the long-term effects, detecting it in the early stages is critical, which would lead to timely intervention and treatment. Modern smartphones and wearables are equipped with a wide range of sensors that can provide a vast amount of information on people's daily lives and behaviours in real time, which can help detect early signs of loneliness. Researchers collect extensive data on a user's daily life and behavioural patterns, including social activities, mobility patterns, communication, and activity data through sensors like accelerometers, heart rate sensors, microphones, and GPS and Bluetooth connectivity acting as proximity sensors. All these capabilities of smartphones and wearables have made them a powerful tool for monitoring users' health and well-being passively. Many studies have used passive sensing for loneliness detection in recent years, but these methods have severe limitations. The main issues are related to relying on generic models, and neglecting the dynamic, multifaceted nature of loneliness. This thesis made several contributions to address these critical gaps in the literature. First, we presented an approach for personalized loneliness detection through the behavioural grouping of passive sensing data. We used unsupervised machine learning for this and then trained customized models for each identified group that outperformed generic models for loneliness detection. Building upon this work, we addressed its limitation of static approach for loneliness detection. So, we proposed an approach for dynamic loneliness detection that can adjust to users' changing behaviours. We also analyzed how users' behaviours changed throughout the study period and how these changes are linked with their loneliness levels. This contribution helps detect loneliness in its early stages and highlights the importance of recognizing individual differences in how loneliness manifests. Previous research on loneliness with passive sensing data often analyzed loneliness as a single concept. This thesis explored the different types of loneliness and their behavioural manifestations. Mainly, we investigated how behavioural patterns can distinguish between social and emotional loneliness and what is the predictive power of these digital biomarkers to detect the specific type of loneliness in order to develop more targeted interventions. Research shows that loneliness and depression often co-occur, but their behavioural overlap wasn't well understood in terms of behavioural patterns extracted through passive sensing. We investigated the complex relationship between these two conditions and identified both overlapping and distinct behavioural markers captured through passive sensing along with the predictive power of these markers. This work helps us understand how they interact with each other, and this could lead to improved detection and intervention strategies for individuals experiencing these conditions.

Grantor dc:publisher

University College Cork

Year dc:date.issued

2024

Author dc:creator

• Qirtas, Malik Muhammad

Advisors dc:contributor.advisor

• Pesch, Dirk H J
• Bantry White, Eleanor

• Loneliness
• Passive sensing
• Sensors
• Mental health

Statement dc:rights

• © 2024, Malik Muhammad Qirtas.

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10468/16955

OAI identifier oai:identifier

oai:cora.ucc.ie:10468/16955