\chapter{Loneliness and Social Isolation Detection through Passive Sensing Methods: A Comprehensive Overview}
\markboth{2. Loneliness and Social Isolation Detection through Passive Sensing}{}
This chapter presents:

\begin{enumerate}
  \item An in-depth look at the literature on the methods used to detect loneliness and social isolation. The aim is to bridge the gap between clinical methods and passive sensing methods for loneliness and social isolation detection.
  \item A review of studies that used passive sensing technologies to detect loneliness and social isolation. We examined the type of populations that were studied, the different types of sensors that were used, and the efficacy and reliability of those methods.
  \item Evaluation of this scoping review in connection with previous research, while providing a synthesis of the key findings and the implications for future research is also presented.
\end{enumerate}

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

Passive sensing technologies provide an intuitive way of collecting data via personal devices such as smartphones and wearables. Scoping reviews, as compared to systematic reviews and other review-based techniques, are a relatively new method of searching and synthesizing the literature. They are useful for investigating broad areas across various study designs \cite{mays2005systematically,o2010scoping}, making them appropriate for examining emerging broader fields. There are four main reasons why researchers could conduct a scoping review in an unexplored or new area: to figure out the design and purpose of the study, to see if a systematic review is needed, to summarize the research results, and to find research gaps in the existing literature \cite{arksey2005scoping,levac2010scoping}. This review investigated the field of loneliness and social isolation detection through passive sensing, which is a relatively new research area. Therefore there was a need to conduct a scoping review in this area.

\section{Loneliness Detection Approaches: An Overview}

\subsection{Assessing Loneliness In Research And Clinical Settings}
Traditionally, the detection of loneliness has been based on personal assessments in a clinical setting. This includes using the well-known University of California at Los Angeles (UCLA) loneliness scale and other validated measures by clinicians \cite{russell1996ucla,vanderweele2011marginal,townley2014mind,hays1987short}. Several other verified measures can be used to find out how lonely a person is \cite{de1985development,de1999manual,gierveld20066,gierveld2010jong}. These tools measure feelings of loneliness through an investigation of subjective experiences of isolation, support networks, and social interactions in the form of different questions. While these scales have played an important role in improving the understanding of loneliness, their reliance on self-reported data in a specific setting makes them vulnerable to inherent biases and limitations. Due to the subjective nature of these assessments and the fact that they are primarily verified through correlations with "at risk" populations, there is an urgent need for more objective, reliable, and thorough methods for detection. 

\subsection{Technology Based Detection through Passive Sensing}
In recent years, there has been an increasing interest in using technology for more objective and unobtrusive monitoring of loneliness and social isolation detection. Passive sensing technologies are at the cutting edge of this advancement, and they made a shift in methodology from self-reported assessments to continuing, data-based assessments \cite{trifan2019passive}. These technologies include a wide range of tools, such as ambient sensors in smart homes, smartphones, and wearable devices. For instance, smartphones can track movement patterns, social interactions (calls, messages), and app usage, while wearable devices can monitor physical activity, sleep patterns, and physiological metrics such as heart rate and skin conductance \cite{cornet2018systematic}. Ambient sensors in smart homes can detect daily routines, social interactions, and environmental factors \cite{uddin2018ambient}. These diverse data streams can be analyzed to find patterns and anomalies that correlate with loneliness and social isolation. The aim is to collect various types of users' data passively and then process that data to look for the signs or digital biomarkers in the behaviours of users that may indicate loneliness or social isolation. For example, reduced physical activity, irregular sleep patterns, and a decline in social interactions may serve as indicators of increasing loneliness.

\begin{enumerate}
  \item \textbf{Smart-home-based Methods:} Smart-home technologies use ambient sensors to track everyday activities and well-being passively. This approach involves examining behavioural data, such as movement patterns, home appliance use, sleep schedules etc. to detect deviations from an individual's typical behaviour patterns that might suggest social isolation or loneliness indicators \cite{heath2008multi,wang2013toward,smith2005rfid}. For example, spending more time in certain sections of the home or abnormalities in sleep patterns may indicate social isolation or mental discomfort. Researchers have found that looking at these changes over time may be a more objective and long-term way to measure loneliness than using people to directly report their feelings \cite{madan2010social, golder2011diurnal, farrahi2008did}. This method improves our understanding of loneliness patterns and allows for early intervention without requiring people to directly report or answer questions, thereby preserving privacy and minimizing biases.

  \item \textbf{Mobile and Wearable Sensing Methods:} The increase in smartphone and wearable devices use has created new opportunities for monitoring health and well-being. These modern gadgets are equipped with a variety of sensors that may monitor physical activity, location, interactions with others (via call and SMS logs), and other data. By analyzing this sensor data, we can look for potential patterns of loneliness and social isolation like decreased mobility, less social engagement, and changes in communication habits. This method reduces the biases associated with self-reporting and gives insights into the constantly shifting dynamics of loneliness and social isolation in everyday life.
\end{enumerate}


\section{Review Methodology}

\subsection{Search Strategy and Selection Criteria}
A comprehensive literature search was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines \cite{liberati2009prisma} for scoping reviews \cite{tricco2018prisma}. The search included papers from January 2011 to January 2024. Given the interdisciplinary nature of our research, we included databases from both computer science and social science; IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and EMBASE. The search focused on using passive sensing technologies to detect loneliness and social isolation. The search keywords and corresponding results for each database are included in the Table \ref{CS_DBs_table}.

\begin{table}
\caption{Search Keywords and Result Statistics For Databases}
	\label{CS_DBs_table}

\begin{tabular}{p{0.36\textwidth}>{\centering}p{0.06\textwidth}>{\centering}p{0.06\textwidth}>{\centering}p{0.04\textwidth}>{\centering\arraybackslash}p{0.07\textwidth}>{\centering\arraybackslash}p{0.08\textwidth}>{\centering\arraybackslash}p{0.05\textwidth}>{\centering\arraybackslash}p{0.01\textwidth}>{\centering\arraybackslash}p{0.02\textwidth}}
\hline
\centering

\multirow{2}{*}{\shortstack{Keyword Combinations}}&\multicolumn{6}{c}{Databases}\\\cline{2-7}
&IEEE Xplore&Scopus&ACM&PubMed&WOS&EMBASE&\\
\hline
Loneliness AND Detection & 44  & 6487 & 1046 & 528 & 364 & 364\\

Loneliness AND Sensing & 53 & 1128 & 1339 & 1153 & 1845 & 74\\

Loneliness AND Passive Sensing & 3 & 94 & 64 & 21 & 38 & 4\\

Loneliness AND Monitoring & 50 & 6443 & 1148 & 473 & 634 & 538\\

Social Isolation AND Detection & 174 & 7329 & 432 & 2639 & 539 & 632\\

Social Isolation AND Sensing & 78 & 834 & 946 & 2539 & 1473 & 74\\

Social Isolation AND Passive Sensing & 1 & 127 & 21 & 43 & 29 & 4\\

Social Isolation AND Monitoring & 135 & 7063 & 846 & 1485 & 1264 & 1164\\

\hline

\end{tabular}
\end{table}

\subsection{Data Extraction and Analysis}
After an initial search, studies were selected based on specific inclusion and exclusion criteria. We selected studies that only focused on those using passive sensing technologies for detecting loneliness or social isolation. The data extraction from these studies involved examining the methodology, demographic characteristics, technology used, data collecting methods, and outcome indicators for loneliness or social isolation. This systematic method led to a comprehensive investigation of the effectiveness of passive sensing methods in different populations and settings, revealing important insight into the current research context in this developing field. The selection process is shown in Figure-\ref{sim:Fig1}.


\begin{itemize}
  \item \textbf{General description of the study:} Authors, year, country
  \item \textbf{Study design:} Population type and age, participants sample selection process, duration of the study, ground truth data collection methods, privacy handling.
  \item \textbf{Study tech insights:} Technology used for sensing, data collection streams, algorithm
  \item \textbf{Study outcome characteristics:} Indicators/identification markers, sensed outcomes.
\end{itemize}

\begin{figure}[htb]
\centering
\includegraphics[height=0.33\textheight,width=0.85\textwidth]{figures/Ch2/updated_PRISMA.pdf}
	\caption{ Flow diagram of literature search and selection process (adapted from Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA]).}
	\label{sim:Fig1}
\end{figure}

\section{Results}

\subsection{Demographics Characteristics}

\subsubsection{Population Focus}
Our review found a significant number of the studies focused on younger individuals mainly college students, which are 37\% (14 out of 38) of the studies \cite{wang2014studentlife,doryab2019identifying,jacobson2020digital,quiroz2018emotion,poudyal2019wearable,yow2014predicting,pulekar2016autonomously,li2016loneliness,ben2015next,wu2021improving,elmer2023modeling,yang2023loneliness,ahmed2022less,jafarlou2023objective}. Studies for older adults are 40\% (15 out of 38)  \cite{goonawardene2017sensor,hu2017elderly,walsh2014inferring,petersen2013unobtrusive,martinez2017predictive,austin2016smart,vandeweerd2016ambient,sanchez2015inferring,lyons2015pervasive,petersen2016phone,huynh2017towards,leese2021older,rees2023wearables,ji2023short,site2022machine}. Only a smaller portion, 18\% (7 out of 38), included participants from mixed age groups \cite{servia2017mobile,gu2017smartbuddy,costa2019emotions,gao2016smartphone,fulford2020smartphone,wetzel,sarhaddi2022predicting}. The number of participants in these studies ranged from as few as 5 to as many as 364, with study period extending from a week up to five years. While most studies employed a longitudinal design, collecting data over time to observe changes, one study \cite{quiroz2018emotion} used a cross-sectional design, examining participants only at a single point in time and two others did not specify their duration \cite{lyons2015pervasive,costa2019emotions}.

\subsubsection{Age and Age Groups}
40\% (15 out of 38) of the studies explicitly mentioned the ages of the participants \cite{quiroz2018emotion,poudyal2019wearable,wu2021improving,wetzel,pulekar2016autonomously,walsh2014inferring,goonawardene2017sensor,lyons2015pervasive,petersen2016phone,fulford2020smartphone,leese2021older,wu2021improving,sarhaddi2022predicting,ahmed2022less,jafarlou2023objective}. The other studies referred to age groups in a general manner without mentioning specific ages. Four studies focused on individuals aged 18 to 28 years \cite{wang2014studentlife,doryab2019identifying,jacobson2020digital,yow2014predicting,li2016loneliness,ben2015next}, whereas six studies focused on older adults \cite{walsh2014inferring,goonawardene2017sensor,lyons2015pervasive,petersen2016phone,fulford2020smartphone,leese2021older} and a few are mixed \cite{servia2017mobile,gu2017smartbuddy,costa2019emotions,gao2016smartphone}. One study included a broad age range from 18 to 78 years \cite{wetzel}, including both younger and older participants.

\subsubsection{Gender Distribution}
The gender distribution differed across the studies. Four Studies involving younger populations included both male and female participants \cite{jacobson2020digital,yow2014predicting,fulford2020smartphone,wu2021improving}, 3 studies having more female participants \cite{doryab2019identifying,quiroz2018emotion,wu2021improving} and others 2 studies with male participants \cite{pulekar2016autonomously,ben2015next}. Two studies solely consisted of females \cite{poudyal2019wearable,sarhaddi2022predicting}. Seven studies concerning older adults  reported their gender, and all of these studies show a female predominance except one. The female ratios in these studies are 60\% \cite{walsh2014inferring}, 81\% \cite{austin2016smart}, 74\% \cite{goonawardene2017sensor}, 54\% \cite{sanchez2015inferring}, 89\% \cite{lyons2015pervasive}, 44\% \cite{leese2021older} and 88\% \cite{petersen2016phone}. 

\subsubsection{Educational Level}
Participants' educational background was specified in 15 studies \cite{doryab2019identifying,pulekar2016autonomously,yow2014predicting,li2016loneliness,wu2021improving,ben2015next,austin2016smart,petersen2016phone,fulford2020smartphone,goonawardene2017sensor,leese2021older,wu2021improving,sarhaddi2022predicting,elmer2023modeling,ahmed2022less}. The younger participants mostly consisted of college students \cite{doryab2019identifying,pulekar2016autonomously,yow2014predicting,li2016loneliness,wu2021improving,ben2015next,ahmed2022less,elmer2023modeling,wu2021improving}, including first-year students and a combination of graduate and undergraduate students. Four Studies on older adults showed a diverse range of educational backgrounds \cite{austin2016smart,goonawardene2017sensor,petersen2016phone,leese2021older}. Two studies included participants who had completed their graduate studies  \cite{austin2016smart,leese2021older}, and the remaining two studies included participants with varying levels of education, with 46\% completing a college education in one study \cite{petersen2016phone} and 39\% completing primary level education and 39\% had no formal education in another study \cite{goonawardene2017sensor}.

\subsubsection{Ethnic Background}
The ethnic background of participants was reported in 10 studies, highlighting diversity, especially among younger populations \cite{doryab2019identifying,jacobson2020digital,yow2014predicting,li2016loneliness,ben2015next,goonawardene2017sensor,lyons2015pervasive,leese2021older,elmer2023modeling}. These younger groups often had an Asian cultural background, though studies also included participants from the United States \cite{doryab2019identifying,ben2015next}, China \cite{li2016loneliness}, and Singapore \cite{yow2014predicting}, representing a mix of ethnicities \cite{jacobson2020digital,li2016loneliness}. Older adult studies reported a majority of Chinese participants in one study \cite{goonawardene2017sensor} and a majority of White participants in another \cite{lyons2015pervasive}.

\subsection{Sensing Streams}
Smartphones and wearable fitness trackers are equipped with many sensors that help us track user movements and collect data on daily routines. The majority of the studies reviewed (58\%, or 22 out of 38) used smartphones or wearable devices as the main method for passive data collection tool \cite{wang2014studentlife,doryab2019identifying,jacobson2020digital,quiroz2018emotion,poudyal2019wearable,yow2014predicting,pulekar2016autonomously,li2016loneliness,ben2015next,wu2021improving,martinez2017predictive,sanchez2015inferring,servia2017mobile,gu2017smartbuddy,gao2016smartphone,fulford2020smartphone,wetzel,gao2014predicting,wu2021improving,rees2023wearables,yang2023loneliness,jafarlou2023objective}. The accelerometer and GPS are the most common sensors in these devices for passive data collection, with 72\% (16 out of 22) of the studies using both of them \cite{jacobson2020digital,quiroz2018emotion,poudyal2019wearable,gu2017smartbuddy,costa2019emotions,ben2015next,doryab2019identifying,yow2014predicting,li2016loneliness,wu2021improving,fulford2020smartphone,gao2016smartphone,sanchez2015inferring,rees2023wearables,yang2023loneliness,jafarlou2023objective}. Accelerometers are used because to their low power consumption and minimum privacy violation. GPS provides significant information on a user's travel routines, such as frequently visited places and the time spent at these places, which may help to extract social behaviours of users.

Bluetooth and microphones are the second most common sensors used in 45\% (10/22) of the studies for passive data collection \cite{doryab2019identifying,poudyal2019wearable,poudyal2019wearable,li2016loneliness,wu2021improving,gu2017smartbuddy,ben2015next,sanchez2015inferring,rees2023wearables,yang2023loneliness}. Bluetooth is used here as a proximity sensor which provides information about a user's sociability based on their physical closeness to others using Bluetooth-enabled devices. Microphones provide indirect indicators of sleep habits and social interactions. Additionally, a small number of studies (7\%, or 3 out of 38) used Wi-Fi MAC addresses to collect social interaction data, and one study used a smartphone light sensor to determine screen lock/unlock state \cite{gao2016smartphone}, providing indirect information on device usage habits.

Phone usage data, along with hardware sensors, was shown to be an important source of information, serving as an indicator of social isolation or interaction. 50\% (11 out of 22) of the studies used this approach to analyze SMS text messages and call logs to infer communication patterns \cite{doryab2019identifying,jacobson2020digital,yow2014predicting,martinez2017predictive,pulekar2016autonomously,li2016loneliness,sanchez2015inferring,gao2016smartphone,petersen2016phone,poudyal2019wearable}. Some studies monitored how often smartphones were locked and unlocked \cite{doryab2019identifying,gao2016smartphone}, while others analyzed app use patterns to infer mood and sociability. A study collected contact information, browsing history, and email data to get deeper insights into user behaviour \cite{pulekar2016autonomously}.

34\% (13 out of 38) of the studies shifted the focus from personal devices to ambient and physical sensors in houses \cite{goonawardene2017sensor,hu2017elderly,walsh2014inferring,petersen2013unobtrusive,austin2016smart,vandeweerd2016ambient,lyons2015pervasive,huynh2017towards,costa2019emotions,rees2023wearables,ji2023short,site2022machine,elmer2023modeling}. This method focused on monitoring indoor mobility and behaviour patterns using a range of sensors, such as passive infrared motion sensors for tracking movement, pressure sensors for detecting presence on furniture, sound sensors for recording household activities and interactions, and door sensors for observing entry and exit patterns. This approach provides a different perspective to wearable and smartphone sensors, improving the comprehension of participants' lives and social interactions inside their homes.


\subsection{Loneliness and Social Isolation Assessment: Methods and Tools}

Multiple different methods of assessment were used to validate the data collected passively via sensors in the research of loneliness and social isolation. 72\% of the studies (27 out of 38) relied on self-reports and questionnaires as their main methods for data collection. These tools provide a straightforward way to collect data on participants' health and well-being which could be either in a clinical environment or with using remote methods like through smartphone apps.

Self-reports and questionnaires were used to assess the physical and mental health of participants in 27\% of the studies \cite{jacobson2020digital,wu2021improving,goonawardene2017sensor,martinez2017predictive,gu2017smartbuddy,gao2016smartphone,fulford2020smartphone,wetzel}. These tools were crucial for assessing several aspects of social relationships, mental well-being, symptoms of anxiety or depression \cite{jacobson2020digital,poudyal2019wearable,li2016loneliness,ben2015next,walsh2014inferring,huynh2017towards,leese2021older}, and daily living activities \cite{yow2014predicting,wu2021improving,goonawardene2017sensor,servia2017mobile,gu2017smartbuddy,gao2016smartphone}. The selection of scales and questionnaires was customized to match the particular aspects of loneliness, depression, and anxiety. The UCLA loneliness scale \cite{russell1980revised}, the De Jong Gierveld Loneliness Scale \cite{gierveld2010jong}, and the ESTE-R Loneliness Scale \cite{sanchez2015inferring} were often used for measuring loneliness. Similarly, for the evaluation of depression and anxiety symptoms used the Social Interaction Anxiety Scale, the Depression Anxiety Stress Scale, and the Positive Affect Negative Affect (PANA) Schedule \cite{watson1988development}.

Some studies choose to use custom-designed questionnaires along with standard scales to meet their particular study requirements and target audiences. The ecological momentary assessment (EMA) is an example of a method that offers a customized way to record the nuances of participants' thoughts and behaviours as they occur. This approach enables researchers to gather data that is closely connected to their study's area and their requirements.

\subsection{Efficacy and Reliability}

\subsubsection{Detection Methods}
The studies reviewed used several indicators to identify changes in behaviour that indicate loneliness or social isolation. Of the indicators examined, the frequency of incoming and outgoing phone calls and SMS text messages is the most commonly studied, with 24\% (9 out of 38) of the studies focusing on it \cite{yow2014predicting,austin2016smart,pulekar2016autonomously,sanchez2015inferring,gao2016smartphone,petersen2016phone,jacobson2020digital,jafarlou2023objective,elmer2023modeling}. Another often-studied indicator is how much time people spend away from their homes, with an equal number of studies investigating its influence on levels of loneliness \cite{petersen2013unobtrusive,austin2016smart,vandeweerd2016ambient,goonawardene2017sensor,sanchez2015inferring,lyons2015pervasive,fulford2020smartphone}. Additional behavioural indicators examined include sleep cycles, voice activity and speech, physical movement levels, mobility and pace of walking, time spent in various areas of the home, types of applications used, and interactions with others identified through Bluetooth proximity detection \cite{walsh2014inferring,doryab2019identifying,goonawardene2017sensor,ben2015next}.

\subsubsection{Phone Calls or SMS Text Messages}

Nine studies investigated the relationship between the frequency of incoming and outgoing phone calls and the feeling of loneliness \cite{gao2016smartphone,petersen2016phone,sanchez2015inferring,li2016loneliness,wetzel,martinez2017predictive,pulekar2016autonomously,austin2016smart,elmer2023modeling}. One significant finding was that those who received fewer incoming calls felt lonelier than those who received more calls \cite{gao2016smartphone}. One study with older adults showed that the loneliest person got just 40\% of the phone calls compared to the least lonely person \cite{petersen2016phone}, indicating a significant difference in social interaction. The influence of communication between family and friend circles was also examined closely. A study found that in intimate relationships, both receiving and making calls to family members are important factors in determining feelings of loneliness inside the family and between spouses \cite{sanchez2015inferring}. In comparison to social isolation, the frequency of phone calls from acquaintances, as opposed to friends, was shown to be a more reliable indicator. This detailed comprehension helps in creating prediction algorithms to identify loneliness. Upon further examination, it was found that those with increased symptoms of loneliness received fewer phone calls on average \cite{petersen2016phone}. An analysis demonstrated a slight inverse relationship between the duration of phone conversations and loneliness, indicating that lengthier calls may be linked to reduced feelings of loneliness.

Additionally, the length of phone calls was shown to have an inverse relationship with social well-being, which could mean that long chats might have a beneficial impact on a person's social health. Two other studies also confirmed this result by identifying a negative relationship between call length and loneliness \cite{martinez2017predictive,pulekar2016autonomously}. The frequency of calls was significantly linked to the overall phone use time \cite{austin2016smart}. A study on students found that those who received more phone calls, particularly on weekdays, reported reduced feelings of loneliness, an easier adjustment to college life, and a better sense of community among their peers \cite{yow2014predicting}. Research indicates that those with increased levels of social anxiety or loneliness often get a lower number of SMS text messages \cite{gao2016smartphone}. The study indicated a little negative association between receiving SMS messages at night and loneliness. However, there was no significant connection between the frequency of messaging and feelings of loneliness or social well-being \cite{wetzel}. A study found a strong connection between the number of messages received and scores on the UCLA loneliness scale which explains the impact of digital communication on how loneliness is perceived and felt \cite{pulekar2016autonomously}.


\subsubsection{Web Social Activity and Communication Apps}
Five studies investigated the correlation between online social activity and loneliness \cite{li2016loneliness,wetzel,gao2016smartphone,yang2023loneliness,ahmed2022less}. A study conducted with a younger population found a positive correlation between loneliness and the frequency of app usage \cite{li2016loneliness}, suggesting that younger people who spend more time on social applications tend to feel more lonely. In comparison, a study on older people found that those who increased their social media use saw a significant decrease in symptoms of loneliness \cite{wetzel}. Researchers discovered that the social media networks of older people resulted in less loneliness and higher levels of overall well-being when compared to those of younger adults. Another study discovered that smartphone activities connected to web-based social interactions, such as using social or communication apps often, did not show any connection to feelings of social anxiety or loneliness \cite{gao2016smartphone}.

\subsubsection{Number of Computer Sessions or Hours on the Computer}
Furthermore, 2 studies examined computer use based on the frequency of sessions and total daily screen time \cite{austin2016smart,leese2021older}. An increasing number of computer sessions was found to be linked to increased feelings of loneliness. Previous research examining the correlation between computer usage and loneliness have reported conflicting results \cite{morahan2003loneliness}. While some argue that using computers might help relieve loneliness, others suggest that more computer use, especially among young individuals is linked with increased feelings of loneliness \cite{fokkema2007escape,caplan2006relations,ceyhan2008loneliness,morahan1999relationship}. A study found no significant correlation between phone usage, computer use, and loneliness \cite{austin2016smart}. The number of phone calls was significantly linked to the time spent on the phone, whereas the number of computer sessions had a strong connection to the time spent on the computer \cite{austin2016smart}.

\subsubsection{Bluetooth Sensing}
Five studies investigated the correlation between Bluetooth proximity sensing features related to social interactions and feelings of loneliness \cite{wu2021improving,wang2014studentlife,li2016loneliness,doryab2019identifying,wu2021improving}. A study found that individuals often report experiencing loneliness when many Bluetooth devices are detected nearby. The tendency might indicate instances when an individual experiences solitude in crowded public places with a significant number of Bluetooth devices present \cite{wu2021improving}. Another study found a significant relationship between Bluetooth device interactions and the Patient Health Questionnaire-9 depression scale \cite{wang2014studentlife}. A study found that there is no correlation between Bluetooth proximity and loneliness \cite{li2016loneliness}. A study used Bluetooth along with other sensor data including GPS, Wi-Fi, SMS text messages to improve the accuracy of predicting loneliness levels in individuals \cite{doryab2019identifying}.

\subsubsection{Daily Phone Use or Time on Mobile Phone}
Three studies examined the correlation between overall mobile phone use and loneliness \cite{petersen2016phone,doryab2019identifying,li2016loneliness}. A study found that less phone use on certain weekends and in the morning was linked to higher levels of loneliness in younger individuals \cite{doryab2019identifying}. Another study found that students who are at risk of loneliness tend to spend more time on their smartphones daily. Loneliness was shown to be positively linked to app use, regardless of the time of day \cite{li2016loneliness}.

\subsubsection{Demographic Characteristics}
Out of the 38 studies, 6 studies examined differences between genders in loneliness levels \cite{petersen2016phone,gao2016smartphone,gao2014predicting,wetzel,rees2023wearables,site2022machine}. Petersen et al \cite{petersen2016phone} found that gender significantly influenced the daily call frequency, with women making or receiving twice as many calls as males. A study investigated the gender distribution between high and low loneliness groups. The chi-square test showed that the gender-based difference was not statistically significant \cite{gao2016smartphone}. A study found that women with higher levels of well-being use reading applications and browsers more often than males \cite{gao2014predicting}. Four studies investigated passive sensing and participant behaviour in relation to age. A study investigated the ages of individuals in high and low loneliness groups; the chi-square test showed that there was no statistically significant age difference \cite{gao2016smartphone}. Another study found that age did not significantly affect the daily number of calls \cite{petersen2016phone}. Wetzel et al found a significant correlation between participants' age and time spent on social media \cite{wetzel}, showing that the link between social media usage and loneliness differs depending on age. Older individuals who spend more time on social media are likely to feel much less lonely. While younger people who spend more time on social media were linked to higher levels of reported loneliness \cite{wetzel}. Another study discovered that the level of activity and the frequency of messages and calls increased in relation to the age of individuals born prior to the year 2000 \cite{servia2017mobile}. Cognitive ability was considered a demographic variable in 2 studies. A study found that those with a chronic disease have a higher level of loneliness and more severe social well-being compared to those without a chronic illness \cite{wetzel}. Another study found a correlation between improved cognitive abilities and increased frequency of daily phone calls \cite{petersen2016phone}. This study investigated whether an individual's pain level may predict phone calls but found no significant correlation between pain level and call frequency has been found \cite{petersen2016phone}. Two studies investigated the correlation between an individual's personality characteristics and activity patterns inferred from smartphone data collected passively \cite{pulekar2016autonomously,servia2017mobile}. A study found a strong connection between emotional stability and extroverted personality traits and several smartphone-based features. Agreeableness, conscientiousness, and intellect personality traits are moderately linked to various smartphone activities, including messaging, browsing, calls, and contacts \cite{pulekar2016autonomously}. Another study found that thankfulness is linked to participants' communication habits, with the most thankful individuals mostly using their phones to communicate.


\subsubsection{Time Outside of Home and Time Spent at Home}
Seven studies evaluated the impact of time spent outside compared to at home on an individual's loneliness levels \cite{lyons2015pervasive,fulford2020smartphone,goonawardene2017sensor,austin2016smart,huynh2017towards,petersen2013unobtrusive,vandeweerd2016ambient}. A study with older people found that more time spent outside the house was linked to reduced feelings of loneliness \cite{lyons2015pervasive}. Other studies including participants of various age groups found that those who increased their time spent outside noticed a decrease in feelings of loneliness \cite{fulford2020smartphone}. Previous research studies have examined outdoor activities among older individuals by assessing their average daily time spent outside and the frequency of outdoor visits. Spending more time outside linked to reduced loneliness and increased social networking scores. Furthermore, two more studies found a significant correlation between the amount of time spent outside daily and feelings of loneliness \cite{austin2016smart,huynh2017towards}. Another study found that those who spend a significant amount of time at home have higher feelings of loneliness. The physical activity score correlates positively with the average time spent outside the home \cite{petersen2013unobtrusive}.

\subsubsection{Daily Activity and Movement}
Five studies investigated how everyday activities and movements, as well as their duration, influence levels of loneliness \cite{ben2015next,fulford2020smartphone,li2016loneliness,doryab2019identifying,goonawardene2017sensor}. A study found that individuals who engage in higher levels of physical activity tend to report lower levels of loneliness \cite{ben2015next}. In another study, a higher level of dispositional loneliness was linked to a significantly shorter average activity duration \cite{fulford2020smartphone}. Feelings of detachment from others and painful sentiments of isolation characterize dispositional loneliness  \cite{rossi2020anxiety}. Loneliness was shown to correlate positively with the frequency of visits to significant places \cite{fulford2020smartphone}. Loneliness is inversely associated with activity duration \cite{fulford2020smartphone}. A study with students found a significant negative relationship between loneliness and indoor mobility, such as the time spent moving inside throughout the day. Studies show that sedentary students are at a higher risk of feeling lonely. Another study found that those who participate in activities for longer periods experience less loneliness, regardless of the time of day or night \cite{li2016loneliness}. Another study found the same trend: increased overall physical activity and number of steps throughout the day and night lead to reduced feelings of loneliness \cite{doryab2019identifying}.

\subsubsection{Conversation Activity}
Three studies investigated how conversation and duration of conversations impact levels of loneliness \cite{ben2015next,fulford2020smartphone,wang2014studentlife}. Out of these, two studies reported no significant relationship between conversation duration and loneliness \cite{ben2015next,fulford2020smartphone}. While the other study found that college students who had fewer social interactions are more prone to loneliness \cite{wang2014studentlife}.

\subsubsection{Sleep}
Three studies examined the correlation between the duration of sleep and feelings of loneliness, depression and stress \cite{ben2015next,wang2014studentlife,goonawardene2017sensor}. A study found that there was no correlation between sleep duration and loneliness, but there was a negative relationship between sleep duration and daily stress \cite{ben2015next}. This indicate that those who sleep enough tend to have lower levels of stress. A study with students found that those who get less sleep are more prone to depression \cite{wang2014studentlife}. Please note that this study didn't explicitly analyze the relationship between sleep and loneliness. A study on older people revealed that those who noticed themselves as socially isolated had more midday naps  \cite{goonawardene2017sensor}.

\subsubsection{Privacy Issues and Ethical Concerns}
62\% (23/38) of the studies did not discuss privacy or ethical issues and did not specifically mention privacy concerns \cite{servia2017mobile,huynh2017towards,hu2017elderly,doryab2019identifying,martinez2017predictive,austin2016smart,gu2017smartbuddy,pulekar2016autonomously,li2016loneliness,vandeweerd2016ambient,goonawardene2017sensor,costa2019emotions,sanchez2015inferring,gao2016smartphone,ben2015next,lyons2015pervasive,wu2021improving,petersen2016phone,fulford2020smartphone,wu2021improving,rees2023wearables}. Five studies included details about ethical clearance from the appropriate committees and participant consent \cite{quiroz2018emotion,poudyal2019wearable,walsh2014inferring,wetzel,jacobson2020digital}. Two studies reported that they stored user data on a secure data server and used anonymized user IDs to protect users' privacy \cite{wang2014studentlife,bellini2021understanding}. Furthermore, a study collected statistical data on smartphone use only to protect the anonymity of participants \cite{yow2014predicting}. Another study used a video camera installed in a smart home to monitor users' activities. They recorded video clips for only 5 seconds each time activity was detected at the entrance \cite{petersen2013unobtrusive}.

\section{Discussion}
\subsection{Comparison With Previous Work}
Our scoping review contributes a new perspective to the existing literature on passive sensing by being the first one to focus on detecting loneliness and social isolation using passive sensing methods. Previous reviews have thoroughly discussed the use of passive sensing in monitoring different mental and physical health conditions. Our study addresses a gap in the literature by specifically examining loneliness and social isolation. This emphasis highlights the current challenges and possible future directions in the area, especially the limitations of current passive sensing techniques in reliably identifying these complex conditions. Existing studies on loneliness detection using passive sensing still have significant gaps. This thesis aims to contribute to filling some of these gaps.

\subsection{Target Group Selection and Technology Choice}
Selecting the target group is an important aspect for developing systems that detect loneliness or social isolation. Our review found that the majority of studies primarily focused on younger age groups. This inclination may come from the practical convenience of having a younger group, especially from educational institutions like colleges and universities, as opposed to the more complicated challenge of including older people, which is often spread out throughout different community scenarios, which makes them harder to reach via traditional channels. Existing studies support the observation that isolation tends to peak during the transitional phase of late adolescence and again in the post-retirement period, which highlights a distinct age-related pattern in social isolation and loneliness. Middle-aged individuals, particularly those who are jobless, are recognized as another demographic with a higher vulnerability to loneliness. The varying vulnerability at different life phases highlights the need for a precise selection of detection technologies customized for each age group. Another important observation from this review is that the methods used for loneliness assessment are different for different age groups. Younger people are primarily studied through the lens of smartphone usage, which may be based on assumptions about their higher engagement with digital devices and platforms. In contrast, studies focusing on older adults more frequently used ambient sensors for passive data collection; this could be possibly due to the less use of smart devices by this population group, and usually, older people are more critical about privacy issues. Many factors could contribute to the choice of using ambient sensors for older people. Firstly, many older people aren't comfortable with technology and find the interfaces on smart devices too complicated to use. Additionally, individuals suffering from conditions like dementia could face other practical challenges related to using such modern devices, such as remembering to carry, wear, or charge these devices etc. The difference in technology acceptance and preference for detecting loneliness between younger and older people highlights the need of creating systems that are suitable for different age groups and easy to use. Developers may improve the effectiveness of loneliness detection systems by recognizing and overcoming particular challenges encountered by older persons, therefore customizing solutions to accommodate the different needs of individuals based on the age group.

\subsection{Privacy Issues}
As we have seen, privacy concerns were one of the important aspects among participants. While a few studies mentioned taking steps to ensure privacy, such as obtaining participant consent for data collection and securing ethical clearance but there is still a significant portion of studies that did not explicitly discuss how privacy concerns were addressed. This oversight raises important questions about the overall acceptability of these detection methods to both participants and the general public, as well as potential risks associated with data and privacy breaches.

\subsection{The Need for Personalized and Dynamic Loneliness Detection}
In most of the studies reviewed, researchers relied on generic and static machine learning models that use all the available data to train a model for loneliness detection. This approach does not effectively account for the nuanced and diverse ways in which loneliness manifests across individuals. This also lacks the adaptability to capture the evolving nature of individual behaviours and the fluctuating nature of loneliness itself. By relying on a single, generalized model trained on the entire dataset, it may overlook subtle but crucial differences in behavioural patterns between individuals or subgroups. For example, it may not account for variations in phone usage patterns across different personality types or social contexts which could lead to misclassification of individuals with unique behaviours. Furthermore, generic models may struggle to capture the dynamic nature of loneliness, which can fluctuate over time and be influenced by various contextual factors. This is particularly relevant in the context of college students, where daily routines, social interactions, and individual preferences vary. There is a need for methods to recognize the heterogeneity within the population and then develop personalized models for loneliness detection that can effectively capture the unique ways in which loneliness is expressed, leading to improved detection accuracy and, ultimately, more targeted and effective interventions.

\subsection{Distinguishing Social and Emotional Loneliness}
There are a number of studies in the psychology literature that have examined types of loneliness, social and emotional loneliness \cite{maes2017intimate,qualter2002separateness,zammuner2008italians,ojembe2022understanding}, but there is not any study that investigates these types through passive sensing. While both types collectively contribute to the overall experience of loneliness, they have distinct facets with potentially unique behavioural manifestations \cite{qualter2002separateness}. Investigating these nuanced aspects of loneliness is important for finding their unique behavioural manifestations and differences. This can lead to improved precision of loneliness detection and more personalized interventions that address the specific needs of individuals experiencing a specific form of loneliness.

\subsection{The Loneliness-Depression Relationship}
Loneliness and depression are interconnected conditions and often co-occur while influencing each other in complex ways \cite{singh2013loneliness}. Studies have shown that loneliness can lead to depression and vice versa, creating a vicious cycle that exacerbates both conditions \cite{cacioppo2010perceived,lamers2015bidirectional}. All the reviewed studies in this scoping review have exclusively focused on loneliness detection using passive sensing, while there exists a separate body of literature that independently explores the use of passive sensing for depression detection \cite{narziev2020stdd,jacobson2020passive,de2022digital,mullick2022predicting}. No studies, to our knowledge, have explicitly investigated the relationship between loneliness and depression through the lens of passive sensing data. This highlights the need for studies that use passive sensing to explore the bidirectional relationship between loneliness and depression to provide a more comprehensive understanding of their interplay.

\subsection{Energy Consumption and Validation Challenges}
Energy consumption emerged as another critical concern. Continuous data collection from various sensors can drain a smartphone's battery quickly, which is inconvenient for users and can interrupt data collection. However, only a few studies mentioned this issue, either by planning to tackle it in the future or by implementing strategies like lowering the sensors' sampling rate when the battery is low or selectively using sensors that consume less power.

The comprehensive validation of loneliness detection systems before their public deployment is important to ensure that these systems are not only accepted by targeted users but also trusted for their accuracy, reliability, and sensitivity to privacy concerns. This validation process is critical as it provides key information about the systems' functionality and efficacy.

\section{Conclusion}
This chapter has highlighted several key gaps and challenges in the current research on loneliness and social isolation detection using passive sensing. The rest of this thesis aims to address some of the gaps discussed above, including personalized and dynamic loneliness detection, investigating social and emotional loneliness using passive sensing, and exploring the loneliness and depression relationships through the lens of passive sensing.