\chapter{Introduction}\label{chapter:introduction}
\section{Overview}
Loneliness is a global problem that affects people of all backgrounds. Recent studies, like the European Loneliness Survey, highlight just how common loneliness has become and show its status as a global concern \cite{eu_loneliness_survey}. The implications of loneliness extend far beyond the subjective experience of isolation. It is increasingly recognized for its serious physical and mental health consequences \cite{henriksen2019loneliness}. Loneliness is not just about feeling alone; it can be detrimental to one's health if it becomes chronic. Loneliness has been associated with a higher likelihood of developing other mental health and physical health issues such as cardiovascular diseases and stroke \cite{henriksen2019loneliness}. Research has indicated that an absence of healthy social networks may contribute to an increased risk of these issues. Additionally, loneliness can disrupt hormones and compromise the body's immune system, making it more difficult to maintain good health \cite{uchino1999social}. It can also affect stress levels, blood pressure, and sleep quality \cite{cacioppo2000lonely}, all of which can contribute to more severe health problems or even reduce one's lifespan. Moreover, chronic loneliness can increase mental health problems such as severe depression, suicidal thoughts, difficulty sleeping, and a general feeling of sadness \cite{cacioppo2006loneliness}. The social and economic costs associated with loneliness are substantial. It results in a significant burden on healthcare systems due to increased demand for mental health services and physical health complications arising from prolonged loneliness \cite{mihalopoulos2020economic}. The resulting effect on productivity and societal well-being further highlights the need for effective strategies to address this challenge. Loneliness is a big problem that got even worse during the COVID-19 pandemic. We need to understand loneliness better in order to develop effective and precise detection methods, which would be a step toward mitigating the negative health and societal impacts of this growing problem.

\section{What is Loneliness?}
Humans inherently seek social connections, and maintaining positive social interactions is essential for our well-being \cite{baumeister2017need}. Perlman defined loneliness as a state of emotional suffering resulting from a discrepancy between the desired and actual social interactions \cite{perlman1981toward}. Loneliness is not just limited to being physically alone; one might experience feelings of loneliness even in the presence of others. Unfortunately, loneliness sometimes bears a social stigma which indicates that an individual is unable to establish meaningful relationships \cite{rokach2012loneliness}. It has long been a worldwide problem, and it became even more noticeable during the COVID-19 pandemic. Before the pandemic, surveys showed that approximately one in four individuals reported feeling lonely. During the pandemic, that increased to almost two in every five people feeling lonely \cite{schroyen2023prevalence}. This shows just how much a big crisis can affect how lonely people feel all over the world.

It is important to understand that loneliness and being alone are not the same thing. Being alone is simply a physical state, while loneliness is a complex psychological experience. One can be alone and not feel lonely, and the opposite is also true: one can feel extremely lonely even when with others. This highlights that loneliness originates from the subjective quality of our social connections, not just their quantity. Furthermore, it is important to distinguish loneliness from social isolation. Social isolation describes a lack of social contact and interaction. Someone might be socially isolated without feeling lonely, or they might feel lonely even while maintaining a social network.

To further understand the nuances of loneliness, it is essential to distinguish between different types, which we will be referring to in this thesis (Chapter 6). 

\begin{itemize}
  \item Social Loneliness: This refers to the perceived lack of a social network, such as belonging to a group, having friends, or feeling like a part of a community \cite{russell1984social}. Individuals experiencing social loneliness often feel disconnected from others and lack a sense of belonging.
  \item Emotional Loneliness: This happens when an individual feels the absence of close, intimate, meaningful relationships \cite{vaux1988social}. Even those with a broad social network can experience emotional loneliness if they lack deep personal connections.
  \item Dispositional Loneliness: This refers to a stable personality trait where an individual consistently experiences feelings of loneliness, regardless of their external social environment or actual level of social interaction. It is often linked to factors such as low self-esteem, social anxiety, and negative self-perception \cite{rius2012dispositional}.
  \end{itemize}

\section{The Global Impact of Loneliness: A Growing Health Crisis}
Loneliness has become a global issue that manifests across various demographics and geographical regions. This section provides a detailed overview of the global prevalence of loneliness and discusses its widespread nature.

The prevalence of loneliness is not limited to specific age groups, genders, or socio-economic classes. According to the European Loneliness Survey 2023, around 13\% of respondents reported feeling lonely most or all of the time, while 36\% reported feeling lonely at least some of the time \cite{eu_loneliness_survey}. In a 2021 survey, younger adults, especially those aged between 18 and 34 years, reported higher levels of loneliness compared to the older age groups, with 16.8\% feeling lonely "most of the time" or "always" \cite{baarck2021loneliness}. The COVID-19 pandemic has further increased the issue of loneliness, particularly among young adults and women \cite{al2023prevalence}. During the pandemic, loneliness prevalence rates surged, with up to 20\% of respondents in certain studies reporting feelings of loneliness \cite{al2023prevalence}. Younger generations, specifically adolescents and young adults, report higher levels of loneliness compared to older adults. Some studies reported that 20–48\% of adolescents and young adults experience severe levels of loneliness. However with older populations, more than 60\% report never feeling lonely, and fewer than 10\% report severe loneliness \cite{beam2020psychological}.

Furthermore, socio-economic factors play a significant role in the experience of loneliness. Income level, employment status, and educational history are important factors that could have a direct relationship with loneliness levels. Studies suggested that lower income and education levels, as well as unemployment are associated with increased chances of loneliness \cite{hawkley2020comparing}. The prevalence rates of loneliness also vary considerably across Europe, with countries like the Netherlands reporting as low as 6\% and others such as Bulgaria, Hungary, and Croatia experiencing rates over 16\%. This variation shows the complexity of cultural, economic, and social factors in loneliness experiences. Socio-economic issues have a broader impact that goes beyond individual circumstances. A study reported that country-level income inequality correlates with the prevalence of loneliness, particularly among older adults \cite{tapia2022income}. This suggests that broader socio-economic dynamics, including income distribution and relative deprivation, can substantially impact the experience of loneliness. This extensive prevalence shows the importance of understanding loneliness in its various aspects and contexts to address and mitigate its impact worldwide effectively.

The impact of loneliness on health and mental well-being is significant and multifaceted. Many studies highlight its close relationship with both physical and mental health issues which are given below.

\begin{itemize}
  \item Physical Health Risks: Studies have found a significant association between loneliness and many physical health issues. Loneliness has been linked with cardiovascular diseases, inflammation, and even an increased risk of functional decline and death \cite{park2020effect}.
  \item Mental Health Implications: Loneliness significantly impacts mental health. It is associated with higher rates of depression, anxiety, and poorer quality of life \cite{loades2020rapid,hards2022loneliness}. The impact is higher in older adults, where loneliness is a known risk factor for dementia and other health issues \cite{sundstrom2020loneliness,van2022longitudinal}. Similarly, the association between loneliness and various forms of anxiety, including social and generalized anxiety, shows moderate to strong connections \cite{mchugh2020mediating}. Individuals experiencing loneliness have significantly higher odds of suffering from anxiety disorders. Longitudinal studies reinforce this, which identify loneliness as a predictive factor for developing anxiety disorders \cite{maes2019loneliness}. This evidence shows the role of loneliness as a risk factor for mental health conditions and highlights the urgent need for early detection and intervention. Studies across various age groups have consistently shown a strong correlation between loneliness and the severity of depressive symptoms \cite{isik2021relationship,qirtas2023relationship,kilincc2020relationship,wiwatkunupakarn2022social}.
  \item Impact on Immune Function and Sleep: Loneliness can affect immune function and increase susceptibility to getting infected more quickly \cite{zilioli2021endocrine}. It is also associated with disrupted sleep, which results in affecting immune function, glucose regulation, and cardiovascular risk \cite{griffin2020loneliness,wakefield2020longitudinal}. Other sleep issues like insomnia are also commonly found in lonely individuals, which can further increase health issues \cite{mclay2021loneliness}.
\end{itemize}

\section{The Challenge of Early Loneliness Detection and The Importance of Addressing Loneliness in Younger Populations}
Monitoring early indicators of loneliness can enable us to intervene timely in order to help those who are suffering from this issue. Traditionally, clinicians rely on in-person consultations and patient self-disclosure to identify these signs, which are not always the most reliable methods and come with several challenges. First, there is a risk of underreporting, where individuals might not fully disclose their true feelings. It could be due to several reasons, such as a desire to appear socially content or, in some cases, to exaggerate their actual feelings to gain sympathy. Additionally, self-reports may not always be accurate. People might lack awareness of their own loneliness or find it difficult to express their emotions correctly. Misinterpretations of questionnaire language can also lead to incorrect responses. Another major challenge is recall bias. These traditional methods often capture feelings from a specific time frame, which may not accurately represent an individual's experience at other times. Memory biases can also alter the reporting of past events or emotions which can lead to further inaccuracies. These challenges highlight the need for more objective and continuous monitoring methods. The adoption of advanced technologies in smartphones and wearable devices has opened up new possibilities for addressing these issues.

Early detection of loneliness is critical for the success of interventions. Timely detection can lead to more effective ways to manage loneliness. This can prevent the conversion of loneliness into more severe mental health issues. However, early loneliness detection comes with significant challenges, particularly in populations that may not readily express or recognize their loneliness. One of the primary challenges in early detection is the subjective nature of loneliness. Loneliness is a personal experience that varies greatly among individuals, which makes it difficult to establish universal criteria for its detection. This subjective nature of loneliness can lead to underreporting, mainly in groups or cultures where a stigma has been associated with loneliness for different reasons. Another challenge is the lack of standardized screening tools for loneliness. While there are scales and questionnaires designed to measure loneliness, such as the University of California, Los Angeles (UCLA) Loneliness Scale \cite{russell1980revised}, their usage is not common in clinical or community settings \cite{national2020social}. This lack of standardized screening means that loneliness is often overlooked in routine health assessments, mainly in primary care settings where such identification could be most beneficial.

Some specific populations are at higher risk of loneliness, and at the same time they could be more challenging to reach. For example, older adults, especially those in long-term care facilities may experience loneliness but lack the means or willingness to express it \cite{ozkan2018effects}. Similarly, individuals with chronic illnesses are at increased risk of social isolation and loneliness and they may not have enough opportunities to communicate their feelings with others \cite{rokach2006loneliness}. Additionally, individuals with disabilities face unique challenges that can increase their vulnerability to loneliness. This includes physical barriers to social participation, stigma, and limited access to support networks \cite{gomez2023loneliness}. It also significantly affects marginalized groups, such as the LGBTQ+ community. Research has consistently shown that LGBTQ+ individuals experience higher rates of loneliness compared to their heterosexual and cisgender counterparts \cite{blankenau2023queer,perone2020social}. Similarly, the younger population despite being highly connected through digital platforms, may not recognize or admit their feelings of loneliness. This population is often masked by superficial online interactions that do not have any deeper meanings in their lives \cite{sundqvist2021adolescents}. Younger people and college students are also a challenging demographic for the early detection of loneliness. The complexity is in the unique social dynamics, psychological development stages, and the recent impact of global crises, such as the COVID-19 pandemic on this age group \cite{Lipson2021DepressionAnxiety}.

The prevalence of depression, anxiety, and loneliness among college students has reached high levels, and it is worsened by different factors like the pandemic, political unrest, racism, and inequality. An extensive survey of around 33,000 college students found that a majority of two-thirds had difficulties with loneliness and a sense of isolation, showing the significant impact of these combined stress factors \cite{Lipson2021DepressionAnxiety}. The COVID-19 pandemic significantly affected students' social lives and relationships, which highlights the impact of social crises \cite{bu2020lonely}.

The early detection of loneliness in younger populations is important but at the same time very challenging due to the unique stressors and transitional phases associated with this population. This population group is increasingly at risk of loneliness. Multiple factors like life transitions, academic pressures, and evolving social dynamics contribute to this increased vulnerability \cite{arslan2021loneliness}. The pervasive use of technology among young people makes technological interventions more relevant and effective for this group. Their familiarity and comfort with digital devices give us a unique opportunity to reach them where they are most accessible. The normalization of health monitoring in the young population, particularly through wearable devices and smartphone apps gives a good scope for passive sensing and early detection of loneliness indicators.

\section{Passive Sensing for Loneliness Detection}
The use of passive sensing methods is increasingly recognized as an important tool in the early and timely detection of different conditions including loneliness. Passive sensing technology, which includes the use of smartphones, wearables, and environmental sensors provides a non-intrusive way to monitor behavioural and physiological indicators that may be indicative of loneliness signs.

One of the primary advantages of passive sensing is its ability to collect continuous and objective data unobtrusively without the user's active involvement. This can track patterns in communication, social interactions, physical activity, and even sleep, which are often disruptive in individuals experiencing loneliness \cite{fernandes2022survey}. For example, changes in communication patterns, such as reduced frequency of calls or messages, could be indicative of social withdrawal which is a common symptom of loneliness. Similarly, the changes in usual sleep patterns, which we can detect through wearable devices are also indicative of some issue with mental well-being. Integrating passive sensing data with machine learning algorithms can lead to the early detection of signs of loneliness. These algorithms can analyze complex datasets to identify patterns that may not be immediately apparent to individuals or healthcare providers. For instance, a decrease in social activity or a change in daily routine detected through passive sensing can trigger alerts and prompt early intervention.

Moreover, technology can assist in bridging the gap in loneliness interventions. This is especially useful for populations that are hard to reach or reluctant to seek help due to the associated stigma with loneliness. Digital platforms and telehealth services can provide access to support and resources for individuals who might not otherwise engage with traditional healthcare systems. This is especially relevant for younger populations who are more used to technology and for older adults who may face mobility or accessibility issues. Studies exploring technological solutions for detecting loneliness highlight the potential of these tools in improving intervention strategies. For example, research into smartphone app usage patterns has shown promise in identifying loneliness indicators which shows that digital behaviour can be a valuable source of information for detecting loneliness \cite{cornet2018systematic}. Recent developments in smart technology, especially smartphones and fitness trackers, have significantly improved health monitoring capabilities \cite{mehdi2020smartphone}.

Smartphone sensors like accelerometers and others can be used to distinguish between stationary and active users. In addition, the GPS sensor can be used to track social patterns, including the frequency and locations of visits, which may provide details about the level of social activity or isolation. Smartphones also have text and call logs, which can provide data about the frequency of our interpersonal interactions. This may serve as a significant indicator of our social connections. Bluetooth and WIFI logs can help us understand the proximity and distinct encounters with others. They can also be indicative of where a person spends the majority of their time based on the logs of the networks these sensors are connected to.

The type and usage of mobile apps are also important factors. For example, higher use of social media apps may indicate our intention to connect with people, but an increased duration spent on gaming applications may imply our preference for solitude. Similarly, the frequency with which one opens their phone may provide information about digital usage behaviours. Researchers can develop a more comprehensive understanding of human activities and behaviours by integrating the data collected from various sensors. All this information can be helpful with different analyses, and we can detect early loneliness signs which would result in timely intervention.

Similarly, fitness bands provide continuous physiological data. They measure step count, which is an effective measure of daily activity levels. These levels can be indicative of behavioural trends that are often linked to feelings of loneliness. Fitness bands also keep track of our heart rate. If the heart rate is different from usual, it could be an indicator of stress or feelings of strong emotions which could be linked with loneliness. Furthermore, detailed sleep pattern analysis from these wearables can provide behavioural markers of overall well-being.

An important part of validating data collected from passive sensors is using a reliable way to measure what we are actually trying to detect (loneliness). This is where clinical scales come in. Clinical scales are standardized tools developed by researchers to measure specific psychological condition or behavioural traits. These scales help provide a ground truth which is a reliable baseline measure that can be compared against the data collected from sensors. By using both passive data collection and clinical scales, researchers can check if what they are seeing in the sensor data actually corresponds to real feelings of loneliness that people report. The University of California, Los Angeles (UCLA) Loneliness Scale \cite{russell1980revised}, serves as the standard for assessing the subjective experience of loneliness. It is the most widely used scale in the general population and clinical settings for assessing the frequency and severity of subjective loneliness in an individual. This self-report questionnaire consists of a set of questions that respondents evaluate based on the frequency with which they experience certain feelings. The validity and reliability of this scale have undergone rigorous testing in various demographics and healthcare settings, and it has been established as an important tool for measuring loneliness by researchers.

In studies using passive sensing for loneliness detection, the UCLA scale is administered at various stages throughout the data collection process. The responses serve as labels that establish a connection between behavioural patterns obtained from sensors and self-reported feelings of loneliness. This direct input from participants about their perceived loneliness provides a benchmark against which the efficacy of machine learning models for loneliness prediction can be measured since these responses act as labels while training models. It ensures that the algorithm's interpretation of data aligns with the actual loneliness feelings of the users, which will improve the predictive power of the model. Through the periodic usage of the UCLA scale, researchers can also track changes over time. This provides information about the dynamic nature of loneliness and the impact of various interventions on managing loneliness.

Passive sensing has been extensively investigated in the context of various mental and physical health conditions. Many studies have focused on a broad range of conditions, including depression, stress, mood disorders, schizophrenia, sleep disturbances, cardiac health, and other chronic conditions \cite{doryab2019identifying,fulford2021smartphone,kadiyono2019finding,stankovic2021association,sheikh2021wearable,de2022digital,appelhans2006heart}. These studies have used a wide range of features extracted from sensor data, such as physical activity, sleep patterns, communication logs (call and SMS), and social interactions (via GPS or Bluetooth data), in combination with machine learning algorithms to predict different conditions. For example, \cite{doryab2019identifying} applied these features to detect depression, while \cite{kadiyono2019finding} focused on detecting stress through the same features. These features are also relevant to loneliness detection but require different interpretive frameworks. For example, while high phone usage is usually associated with stress and depressive symptoms in previous studies, in the context of loneliness, it might reflect an attempt to seek social connection through digital means. Similarly, reduced physical activity may indicate withdrawal from social interactions which is a key behavioural marker of loneliness. In this thesis, we build upon these established features but focus on how they relate specifically to loneliness. By analyzing features like call and SMS logs, Bluetooth and WIFI proximity data, and location changes, we aim to detect social withdrawal or lack of social connectivity, which are key indicators of loneliness. While these features have been successfully applied to detect different mental health conditions, their contextual relevance to loneliness in a personalised manner has been underexplored. This thesis aims to fill that gap by applying these features to a more personalized and dynamic approach for loneliness detection.

While a number of studies have explored the potential of passive sensing for loneliness detection, there has been an absence of systematic reviews or surveys that synthesize the existing research on this specific topic. To bridge this gap, we performed a pioneering scoping review dedicated exclusively to the application of passive sensing in the context of loneliness and social isolation detection. This review is the first in the field to provide an in-depth look at the current capabilities of these technologies for loneliness detection and highlight the limitations that need to be addressed in future work.

The existing research shows multiple approaches that combine machine learning algorithms with passive sensing data to detect indicators of loneliness in diverse populations. These methods range from the analysis of communication logs, capturing call and text message data, to the application of machine learning techniques on sensor data that tracks physical activity and social interactions through accelerometers and GPS. Ambient sensors within smart home environments have been used to monitor routine domestic activities, while wearable devices have been used to collect physiological data for further analysis. Machine learning methods, especially those using predictive analytics and pattern recognition, have played an important role in analyzing this data. This body of work collectively validates the efficacy of passive sensing and machine learning as promising tools for the early detection of loneliness. However, this comes with a number of limitations and potential issues that set a direction for researchers to improve these technological approaches further.

This brief section provided the groundwork for the detailed exploration of passive sensing for loneliness detection in Chapter 2, where we will look into the methodology, findings, and implications of our scoping review with a comprehensive discussion of our research contributions.

\subsection{Limitations of Existing Research}

Existing studies in the field have made significant contributions to using passive sensing for loneliness detection. However, these approaches come with several limitations. Our work aims to address some of these limitations and improve upon existing methods. Below, we present the primary challenges that need to be addressed:

\subsubsection{Privacy and Ethical Concerns}

One of the key challenges is the issue of privacy. Most of the studies used a traditional machine learning model training process in which all data from multiple participants is collected on a single server, which trains the model on the whole data set at once. The collection of this sensitive data in a single location (central server) presents an important risk to the privacy of users, as it could expose them to unauthorized access and data breaches. When studies gather all their data in one place, it can make people worried about their privacy, especially with the sensitive topic of mental health. People might not want to be part of research that could expose their personal information. While it is important to address this issue, another important distinction should be made between the training and inference phases of machine learning. The goal is to avoid centralizing data during the model training process but once models are trained, they can be applied locally on personal devices for inference. It is really important to fix this data centralisation problem as when people feel sure that their private information is safe, they are more likely to use these health-tracking tools.

\subsubsection{Generalized Models and Lack of Personalization}
Existing methods for detecting loneliness with passive sensing often rely on generic models trained on the entire dataset. Given the subjective nature of loneliness, behavioural patterns can differ among people in relation to their feelings of loneliness. These generic models may not accurately capture the nuances of individual differences in behavioural patterns and this can lead to less precise detection. By identifying smaller groups of individuals with similar behavioural patterns, we can develop more targeted models that better reflect these individual differences. This approach can help us to uncover how loneliness manifests in specific groups, which can be overlooked when analyzing the entire population as a whole. Furthermore, monitoring the evolution of these behavioural groups over time can provide important information about how loneliness changes and adapts to individual experiences and circumstances which can ultimately lead to more timely and effective interventions.

\subsubsection{Static Approaches and Lack of Real-Time Monitoring}
Most studies in the domain of loneliness detection focus on a static analysis of passive sensing data, often collecting data over a fixed period and then analyzing it retrospectively. This limitation restricts the capacity of such systems to quickly detect and intervene in instances of loneliness that may arise suddenly as a consequence of life events. This highlights the need for a methodology that not only monitors but also adjusts to the evolving behavioural trends linked to loneliness. Such an approach has the capacity to provide more timely and personalized interventions that may stop the conversion of intermittent loneliness into a chronic condition like depression.

\subsubsection{Multidimensional Nature of Loneliness}

There is no existing work on examining the various dimensions of loneliness through the lens of passive sensing data. As we discussed earlier, loneliness is a multifaceted phenomenon that manifests in multiple forms. One of them is emotional loneliness, which is caused by the absence of a close emotional attachment, and social loneliness, which can be linked to perceived deficiencies in one's social network \cite{stephens2022older}. Acknowledging and analyzing the different facets of loneliness is important for a deeper understanding of the specific type of loneliness an individual is experiencing. This understanding is a key step towards developing personalized interventions that directly address the particular aspects and nuances of loneliness.

\subsubsection{Relationship Between Loneliness and Depressions}
Understanding the link between loneliness and depression is critical because they often occur together and can each make the other worse \cite{wolska2023associations}. No study has yet explored how loneliness relates to depression and vice versa by using passive sensing and behavioural data. Since chronic loneliness can develop into more severe mental health issues, it’s important to broaden our exploration of how behavioural data can be used to explore the relationship between loneliness and depression. By doing so, we can develop better strategies to help those who suffer from both depression and loneliness.

The following subsection presents the main contributions that are aimed at addressing some of the above issues and limitations.

% Need for a literature review in this area
% Advances and use of passive sensing in loneliness detection
% Privacy issue
% Subjective behavioural differences issue/Personalisation
% Continous monitoring for early detection 
% Nuances of loneliness - social emotional loneliness
% Loneliness and depression

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

\section{Thesis Contributions}

The key contributions of this thesis are summarized below:

\begin{enumerate}
  \item The first contribution of this thesis presents a comprehensive scoping review. This scoping review aimed to identify and synthesize recent studies that used passive sensing techniques to extract patterns in daily routines and behaviours that could indicate loneliness or social isolation. Moreover, we performed a critical examination of these studies in various aspects like demographics, privacy considerations, and validation methodologies. As a result, this review provides us with a solid foundation for research by identifying the gaps in the existing literature related to passive sensing-based methods for loneliness detection, and based on those identified gaps, we have worked on subsequent studies.
%  \item The next study contributes to the growing body of knowledge in privacy-preserving mental health monitoring by being the first to explore and validate the use of federated learning (FL) for loneliness detection within a student population using passive sensing data. By using federated learning, this work eliminates the need to transmit sensitive user data to a central server, which results in improving privacy. Despite the slightly superior performance of centralized models, federated learning shows significant promise for loneliness detection through passive sensing, balancing the trade-off between user privacy and model performance. This contribution represents a crucial step forward in creating more secure and privacy-conscious passive sensing solutions for mental health monitoring.
  \item The next contribution is about the development of personalized methods for loneliness detection to address the limitation presented in the section 1.5.1.2. To do so, we used clustering methods to identify similar behavioural subgroups in passive sensing data that have emerged over time and then developed personalized loneliness detection models for each subgroup to improve performance. A key feature of this work is the weekly analysis of the behavioural patterns of each identified group. Our results show that models tailored to student groups with similar behaviours are more effective at detecting loneliness than the one-size-fits-all approach, which highlights the importance of personalized detection systems.
  \item Expanding upon the groundwork established in the last contribution, we introduced a personalized and dynamic approach for detecting loneliness to address the limitation presented in the section 1.5.1.3. The objective of this work is to have a dynamic loneliness detection method that adjusts to the user's evolving behaviours and continually improves its predictive capabilities with the integration of fresh data. We used incremental clustering to identify behavioural subgroups dynamically while, in parallel, applying incremental classification to detect loneliness in each subgroup. We also presented the evolution of weekly subgroups based on their behavioural patterns and loneliness levels. This helps in continuous monitoring and detecting behavioural changes that could indicate the emergence of loneliness.
  \item The next contribution looked into the nuanced aspects of loneliness to address the limitation presented in the section 1.5.1.4. We used passive sensing data to explore social and emotional loneliness and its behavioural manifestations. This study investigates whether digital behavioural markers can differentiate between socially and emotionally lonely students, the predictive power of these markers in classifying socially and emotionally lonely students, and which markers are most important for these predictive models. This contribution improves the precision of loneliness detection and also provides a detailed understanding of its varied facets, which would lead to the development of interventions that are more closely aligned with the individual's specific experiences of loneliness.
  \item The next contribution explores the complex relationship between loneliness and depression to address the limitation presented in the section 1.5.1.5. This study rigorously investigates how behavioural features captured by passive sensing correlate with and differ between loneliness and depression among students. It addresses important research questions to identify the common and unique behavioural characteristics of loneliness and depression through the lens of passive sensing. This helps us understand how these conditions influence each other and what specific behavioural features act as mediators within their interplay. Additionally, it evaluates the efficacy of using behavioural features alongside loneliness and depression scales to accurately classify individuals experiencing these conditions. This contribution provides perspectives on the behavioural indicators of loneliness and depression through passive sensing data.
\end{enumerate}

\section{Publications}
There are 6 deliverables of this PhD. Four have been published, one has been accepted for publication, and one is currently being prepared for submission.

\begin{enumerate}
  \item \textbf{Qirtas, Malik Muhammad}, Evi Zafeiridi, Dirk Pesch, and Eleanor Bantry White. "Loneliness and social isolation detection using passive sensing techniques: scoping review." JMIR mHealth and uHealth 10, no. 4 (2022): e34638.
  \item \textbf{Qirtas, Malik Muhammad}, Dirk Pesch, Evi Zafeiridi, and Eleanor Bantry White. "Privacy-preserving loneliness detection: A federated learning approach." In 2022 IEEE International Conference on Digital Health (ICDH), pp. 157-162. IEEE, 2022.
  \item \textbf{Qirtas, Malik Muhammad}, Eleanor Bantry White, Evi Zafeiridi, and Dirk Pesch. "Personalising Loneliness Detection through Behavioural Grouping of Passive Sensing Data from College Students." IEEE Access (2023).
  \item \textbf{Qirtas, Malik Muhammad}, Evi Zafeiridi, Eleanor Bantry White, and Dirk Pesch. "Evolving AI for Wellness: Dynamic and Personalized Loneliness Detection Using Passive Sensing." Accepted for Publication in Springer Lecture Notes in Computer Science as Proceedings of AISoLA Conference 2023.
  \item \textbf{Qirtas, Malik Muhammad}, Evi Zafeiridi, Eleanor Bantry White, and Dirk Pesch. "The relationship between loneliness and depression among college students: Mining data derived from passive sensing." SAGE Digital Health 9 (2023).
  \item \textbf{Qirtas, Malik Muhammad}, Evi Zafeiridi, Eleanor Bantry White, and Dirk Pesch. "Differentiating and Predicting Social and Emotional Loneliness in Students Using Passive Sensing: An Exploratory Study." In Preparation for Submission
\end{enumerate}

\section{Thesis Outline}
In Chapter 2, we will present a comprehensive literature review on loneliness detection using passive sensing and will discuss different aspects of existing studies in this area. Chapter 3 presents the details of the datasets used, behavioural features extraction and overall methodology. Chapter 4 presents personalized detection through clustering and group-specific models. Chapter 5 presents a dynamic and personalized loneliness detection approach using incremental clustering and classification models. Chapter 6 explored social and emotional loneliness using passive sensing data. Chapter 7 investigated the relationship between loneliness and depression using passive sensing data. Chapter 8 concludes the thesis and presents limitations with future research directions.
 