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caps-anxiety.tex
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\journal{Travel Behaviour and Society}
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pdftitle={Exploring the Link Between Travel Behavior and Mental Health},
pdfauthor={Emily K. Youngs; Gregory S. Macfarlane; Jared A. Nielsen},
pdfkeywords={travel behavior, mental
health, motivation, suicidality, activity types, DBSCAN-TE},
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\begin{document}
\begin{frontmatter}
\title{Exploring the Link Between Travel Behavior and Mental Health}
\author[1]{Emily K. Youngs%
%
}
\ead{emmykae@byu.edu}
\author[1]{Gregory S. Macfarlane%
%
}
\ead{gregmacfarlane@byu.edu}
\author[2]{Jared A. Nielsen%
%
}
\ead{jarednielsen@byu.edu}
\affiliation[1]{organization={Civil and Construction Engineering
Department, Brigham Young University},addressline={430
EB},city={Provo},country={USA},countrysep={,},postcode={84602},postcodesep={}}
\affiliation[2]{organization={Psychology Department, Brigham Young
University},addressline={1070
KMBL},city={Provo},country={USA},countrysep={,},postcode={84602},postcodesep={}}
\cortext[cor1]{Corresponding author}
\begin{abstract}
This study explores the critical link between travel behavior and mental
health, focusing on young adults who have expressed suicidal ideation.
By analyzing how daily activities and movement patterns impact mental
well-being, the research aims to explore travel related mental health
approaches for individuals. The importance of this work lies in its
potential to improve the quality of life for individuals by informing
more personalized and effective mental health strategies.
Through comprehensive data analysis and statistical modeling, the
research aims to uncover insights into how travel behavior influences
motivation levels and mental well-being across different neurological
and physiological groups. The dataset used in this study includes
location-based services (LBS) data collected from participants over
varying periods of time, allowing for a longitudinal analysis of travel
patterns and mental health parameters for individuals.
Participants were part of either autism, social anxiety, or control
groups, and their travel behavior and mental health data were analyzed
using statistical models. The relationship between suicidality,
motivation levels, and travel behaviors was explored, along with the
impact of engagement in activities at different location types on
motivation levels. The study used the DBSCAN-TE (density-based spatial
clustering of applications with noise, time, and entropy) algorithm to
identify activities from the LBS data, with efforts made to address data
quality issues and impute missing activity information.
Significant differences in activity engagement and motivation levels
were observed among individuals in the autism, social anxiety, and
control groups. The study found that those in the control group took
part in more activities and expressed higher levels of motivation than
those in the autism or social anxiety groups. In addition, simply
increasing activity engagement may not be sufficient to significantly
boost motivation levels. However, for those in the control group, more
activities in parks lead to a statistically significant increase in
motivation and for those in the autism group, more activities to grocery
stores lead to a statistically significant decrease in motivation. The
research highlights the complex interplay between travel behavior,
activity engagement, and mental health outcomes, emphasizing the need
for tailored interventions based on individual needs.
The study faced limitations related to data quality, including sparse
data due to participants turning off their phones or the app failing to
record data accurately. The lack of detailed information on activity
duration and the inability to confirm activity engagement for all
participants were also noted as limitations.
All in all, this research sheds light on the complex relationship
between travel behavior and mental health among young adults with
suicidal ideation. By understanding how travel patterns impact
motivation levels and mental well-being, tailored interventions can be
developed to support individuals grappling with mental health
challenges. Future studies should focus on improving data quality and
incorporating measures to enhance the reliability of activity data
collection for more robust analyses.
\end{abstract}
\begin{keyword}
travel behavior \sep mental
health \sep motivation \sep suicidality \sep activity types \sep
DBSCAN-TE
\end{keyword}
\end{frontmatter}
\bookmarksetup{startatroot}
\section{Introduction}\label{introduction}
In the United States, the prevalence of mental illnesses among adults,
aged 18 or older, rose from 19.1\% in 2018 to 22.8\% in 2021 (Mackett,
2021; {``Mental {Health By} the {Numbers},''} 2023). The essence of
mental health extends far beyond the absence of illness, encompassing
emotional, psychological, and social dimensions essential for holistic
well-being. This holistic perspective highlights mental health's
pervasive influence on personal relationships, work efficiency, and
lifestyle choices, accentuating the need to examine its intersection
with individual daily behaviors and choices. The allocation of time and
its impact is particularly intriguing, as it closely ties to travel
behavior, including both patterns and decisions, related to moving from
place to place. These behaviors include trip frequency, destination
choices, and daily travel decisions (Timmermans et al., 2003).
Understanding the impact of these travel patterns on mental well-being
is potentially crucial for empowering individuals to make informed
decisions that promote positive mental health outcomes while avoiding
behaviors detrimental to well-being.
In this thesis, through comprehensive data analysis and statistical
modeling, we aim to uncover insights into how travel behavior impacts
motivation levels and mental health across different neurological or
psychological groups, exploring variations across autism, social
anxiety, and control groups. Significant differences in activity
engagement and motivation among individuals in the autism, social
anxiety, and control groups underscore the complex interplay between
these factors and mental health. Additionally, we investigate the
relationship between suicidality, motivation levels, and travel
behaviors within these groups. We also examine the impact of engagement
in activities at different location types on motivation levels. By
investigating this dataset, we aim to illuminate the relationship
between various mental health parameters and individuals' daily activity
engagement, thereby providing invaluable insights into overall mental
wellness. Unraveling the connection between individual travel patterns
and mental well-being holds potential in supporting those grappling with
mental health challenges.
This thesis explores the connection between mental health and travel
behavior patterns currently discussed in the literature. It outlines the
methods used to analyze activities and integrate survey responses into a
cohesive dataset. Models are then presented to analyze the relationship
between mental health and travel behavior. Following this, the
discussion focuses on the travel patterns and mental health outcomes
across the three different groups. Finally, the thesis concludes
summarizing the key findings and by offering insights for future
research.
\bookmarksetup{startatroot}
\section{Literature Review}\label{literature-review}
Mental health and well-being are complex issues influenced by a
multitude of factors, including social and environmental elements.
Addressing these factors is essential for promoting individual
well-being (Barry, 2009). Additionally, travel behavior has been
recognized as a significant factor impacting social and mental health
(Delbosc \& Currie, 2011). In this review, we explore current research
on the impacts of built and natural environments, social isolation,
travel behavior, and activity types on mental health and well-being. We
also examine the well-being and travel behavior of individuals with
autism and social anxiety. Ultimately, we integrate these factors to
highlight how our research aims to fill an existing gap related to the
literature.
\subsection{Impacts of Built and Natural
Environments}\label{impacts-of-built-and-natural-environments}
Part of the increase in mental health problems come from factors that
limit people's engagement with the world around them since positive
mental health coincides with people's ability to access the natural
environment. Recent researchers have underscored the therapeutic
benefits on mental health of engaging with green and blue spaces,
ranging from parks and forests to oceans and rivers, in alleviating
stress and anxiety, enhancing mood, and bolstering cognitive function
(Pouso et al., 2021; White et al., 2021). Green and blue spaces help
support good mental health, but these spaces, the very thing that can
help with mental health issues, are often inaccessible. Urbanization,
for example, magnifies the predicament by reducing access to these
spaces, thereby impeding efforts to foster mental well-being. In
addition, the implementation of lockdowns and social distancing measures
during COVID-19 further exacerbated this issue by limiting many
individuals' interactions with nature, which was one observed major
shift in travel patterns (Pouso et al., 2021).
In addition to facilitating access and engagement with the world, the
built or urban environment significantly impacts well-being. Researchers
in Brussels, Belgium, found that noise pollution, air pollution, and
lack of green space were linked to higher levels of depressive symptoms
and stress, whereas proximity to green spaces was associated with lower
levels of stress and depressive symptoms (Pelgrims et al., 2021). A
systematic review of the literature by (Rautio et al., 2018) confirmed
these findings, underscoring the relationship between the living
environment and depressive mood. \clearpage Overall, access to natural
spaces within urban areas is crucial for mental well-being. Urban
planning should prioritize green and blue spaces to mitigate stress and
depressive symptoms associated with urbanization. Balancing development
with the preservation of these natural spaces is essential for promoting
public mental health.
\subsection{Impacts of Social
Isolation}\label{impacts-of-social-isolation}
Social isolation, or social exclusion, affects individuals' mental
health. When looking specifically at the effects of the COVID-19
pandemic, (Loades et al., 2020) focused on the impact of social
isolation and loneliness on the mental health of children and
adolescents. The authors conducted a rapid systematic review of existing
literature on the topic and found that social isolation and loneliness
can have a negative impact on mental health, including increased
symptoms of anxiety, depression, and post-traumatic stress disorder.
(Barry, 2009) reviewed the determinants of positive mental health,
emphasizing the importance of social support and social connectedness, a
sense of control and autonomy, self-esteem, and meaning and purpose in
life. She argues that mental health promotion should focus on building
resilience, enhancing positive emotions, and developing skills and
competencies to manage adversity. Many of these competencies are related
to social interactions and avoiding social isolation (Barry, 2009).
While significant periods of isolation, like during the COVID-19
lockdowns, reduced social interactions, everyday sociality is influenced
by one's ability to travel. Researchers in Melbourne, Australia, studied
the link between mobility, social exclusion, and well-being. They found
that increased mobility improved well-being and reduced social
exclusion, positively impacting mental health (Stanley et al., 2011).
Another study in Melbourne, Australia, explored how transport
disadvantage and social exclusion affect well-being. Using survey data,
researchers found that both factors negatively impact well-being, with
social exclusion having a stronger effect (Delbosc \& Currie, 2011). The
ability and choice to make trips affects well-being and mental health of
individuals.
Overall, these results suggest that social exclusion and isolation,
transport disadvantage, and loneliness can negatively impact individual
well-being and mental health. On the other hand, mobility and contact
with others can have a positive impact on individual well-being. These
findings highlight the importance of social support networks and access
to transportation to facilitate social interactions.
\subsection{Impacts of Travel
Behavior}\label{impacts-of-travel-behavior}
Research on travel patterns highlights the relationship between travel
behavior and individuals' social and mental well-being. (Syahputri et
al., 2022) conducted a study in the Bandung Metropolitan area to
investigate the effect of travel satisfaction, linked to mental health,
and activity travel patterns of household members. This study showed
that travel satisfaction is positively correlated with social health and
mental health. Individuals who spent more time working or studying at
home had higher travel satisfaction, leading to better social and mental
health. However, extended time on obligatory activities limited
interactions with household members, which negatively affected mental
health. Encouraging travel for those with significant commitments can
enhance travel satisfaction, social health, and mental health.
Additionally, regular daily activity travel patterns with breaks from
obligations were linked to improved mental health.
Similarly, another study investigated how travel affects emotional
well-being and life satisfaction. The researchers pointed out that those
who regularly traveled to work experienced less life satisfaction than
those who worked from home. However, the study revealed a positive
association between general travel and emotional well-being, indicating
that travel experiences contribute to improved mood and life
satisfaction. Overall, these findings underscore the importance of
integrating travel into daily routines to foster better mental health
and overall well-being (Friman et al., 2017).
The reviewed studies highlight the significant influence of travel
behavior on social and mental health outcomes. Factors such as travel
satisfaction and activity travel patterns of household members play
crucial roles in shaping these outcomes. Additionally, positive travel
experiences are associated with improved emotional well-being and life
satisfaction.
\subsection{Impacts of Activity Types}\label{impacts-of-activity-types}
People engage in various types of activities which can impact mental
health and overall well-being. For instance, (Takiguchi et al., 2022)
found that the types of activities people engage in can significantly
affect the extent of positive emotions experienced. By analyzing various
activity settings like green spaces, grocery stores, libraries, and
social recreation spaces, we aim to uncover the potential benefits and
implications of these activity locations on mental health and overall
well-being.
\subsubsection{Green Spaces}\label{green-spaces}
The role of green spaces in enhancing mental well-being has increasingly
captured attention, with numerous studies attesting to their beneficial
effects across diverse demographics and environments. This section
explores the impact of spending time in green spaces on mental health.
Primarily, exposure to nature is consistently correlated with reduced
stress, anxiety, and depression levels. For instance, participants who
embarked on a 90-minute walk in natural settings exhibited decreased
activity in the subgenual prefrontal cortex---a region linked to
rumination and heightened mental health risks---compared to urban
walkers (Bratman et al., 2015). Similarly, a meta-analysis of 25 studies
indicated that natural environments positively influenced overall mental
well-being, indicated predominantly through participants' self-reported
emotional states (Bowler et al., 2010).
Long-term exposure to green spaces also yielded positive mental health
outcomes. Research by (Gascon et al., 2018) revealed lower anxiety and
depression levels in adults residing near green and blue spaces.
Similarly, participants perceiving significant greenery in their
neighborhoods exhibited notably better physical and mental health
outcomes (Sugiyama et al., 2008). Furthermore, another study found that
living in areas with increased green space concentration was correlated
with lower salivary cortisol levels, indicative of reduced stress (Roe
et al., 2013). Time spent in green spaces is associated with reduced
levels of stress, anxiety, and depression.
While the relationship between green spaces and mental health is
intricate, recent studies uniformly advocate for their positive
influence. In sum, exposure to green spaces alleviates stress, anxiety,
and depression, rendering them a promising avenue for enhancing mental
health and overall well-being.
\subsubsection{Grocery Stores}\label{grocery-stores}
Supermarkets and grocery stores are integral to modern life, serving as
essential hubs for purchasing food and household necessities. However,
the experience of shopping in these environments can influence
individuals' mental well-being. This section aims to investigate the
effects of time spent in grocery stores on mental health.
A study surveyed 239 volunteers across 29 focus groups, exploring stress
in the context of grocery shopping. Participants expressed stress
related to time pressure, crowd density, employee attitudes, and store
layout, as well as environmental factors such as music and lighting, and
product availability (Aylott \& Mitchell, 1998). No quantitative data
were gathered and stress was not measured before or after spending time
grocery shopping - these were just stresses that participants expressed
experiencing while grocery shopping. While this study is from 1998, it
sheds light on the stress experienced in the context of grocery shopping
and shows that stress while grocery shopping is not a new issue.
As previously mentioned, time pressure experienced at grocery stores can
induce stress. In one study, 1,023 participants responded to a
questionnaire about grocery shopping and shopping satisfaction,
specifically addressing how shopping satisfaction related to time
pressure. Researchers found that time pressure had varying effects on
satisfaction depending on whether it was a major shopping trip or a
quick shopping trip for a few items. They found that overall, women were
less satisfied with their shopping experience when under time pressure
and men were satisfied with their shopping experience when under time
pressure. Overall, time pressure had a negative impact on shopping
satisfaction, although other factors also influenced customer
experiences (Nilsson et al., 2017).
\subsubsection{Libraries}\label{libraries}
Libraries serve as indispensable community resources, providing access
to information, educational materials, and spaces for learning and
social interaction. Beyond their traditional roles, libraries have
increasingly been acknowledged for their potential to positively impact
mental health and well-being. This section seeks to explore the diverse
ways in which libraries can influence mental health outcomes.
Existing literature strongly supports the notion that libraries promote
positive mental health and well-being. One literature review highlighted
the mental health support offered by many libraries, noting the
comforting atmosphere provided to patrons, often attributed to the
supportive library staff (Elia, 2019). Similarly, another study
described public libraries as offering a therapeutic landscape that is
welcoming, comforting, calming, empowering, and overall conducive to
well-being (Brewster, 2014).
Moreover, libraries often offer specialized programs and services aimed
at promoting mental health and well-being. For instance, they may host
mental health workshops, employ social workers, or introduce therapy dog
programs to provide emotional support and coping strategies for patrons
facing mental health challenges (Elia, 2019). Some libraries even have
employees trained in bibliotherapy, a therapeutic approach that uses
books and literature to support mental health outcomes. A systematic
literature review synthesized evidence on bibliotherapy services at
public libraries, revealing its effectiveness in aiding individuals with
anxiety, stress, depression, and other issues (Zanal Abidin et al.,
2023). This coupling of access to literature with trained bibliotherapy
professionals enhances mental health outcomes for library patrons.
In sum, libraries play a multifaceted role in promoting mental health
and well-being through resource accessibility, community engagement, and
supportive environments. Their positive impact on individuals' mental
health outcomes is evident in the literature.
\subsubsection{Social Recreation}\label{social-recreation}
Social recreation spaces, such as restaurants, theaters, malls, and
leisure spots, play a significant role in providing opportunities for
socialization, relaxation, and entertainment. Despite their ubiquity,
the impact of time spent in these environments on mental health is
complex. This section explores the effects of social recreation spaces
on mental health outcomes, drawing insights from recent research.
Leisure activities, which individuals choose to engage in when free from
obligations, enhance well-being by eliciting positive emotions and
fostering long-term stress-coping mechanisms (Takiguchi et al., 2022).
Longitudinal studies support the positive link between leisure
satisfaction and well-being over time (Kuykendall et al., 2015).
However, the quality of social interactions within these spaces can
significantly affect individuals' well-being. A study found that
negative interactions were linked to lower quality of life, while
supportive interactions corresponded with higher quality of life(Yanos
et al., 2001). This highlights the importance of social interaction
dynamics in shaping well-being.
Furthermore, limited research exists on the impact of social deprivation
or isolation on adolescents and adults. Self-reported loneliness is
linked to mental health issues, but disentangling the causal
relationship between loneliness and mental well-being remains
challenging. It is often unclear whether loneliness causes poor mental
health or vice versa. Experimental studies on human loneliness face
hurdles, as loneliness is not solely due to social deprivation as some
individuals feel lonely even in crowded environments (Orben et al.,
2020).
Social recreation spaces have a multifaceted influence on mental health
outcomes, offering platforms for social interaction and stress
alleviation while harboring potential risk factors contingent upon the
nature of these interactions. The intricate interplay between activities
in these environments and mental well-being suggests that visits to
social recreation locations may either bolster or diminish overall
well-being, contingent upon the nature of social interactions
experienced therein.
\subsection{Neurological and Psychological
Typology}\label{neurological-and-psychological-typology}
Navigating the intersection between mental health and travel behavior
unveils a complex relationship that significantly influences
individuals' daily lives. In this section, we look at the relationship
between specific neurological and psychological conditions---autism
spectrum disorder (ASD) and social anxiety---and their corresponding
impacts on travel behaviors and overall well-being. Through recent
research, we explore how autistic individuals and individuals with
social anxiety navigate transportation challenges, revealing unique
patterns that shape their mobility and mental health outcomes.
\subsubsection{Autism}\label{autism}
ASD, commonly referred to as autism, presents as a lifelong
neurodevelopmental condition characterized by challenges in social
communication and social interaction. In addition, autistic individuals
tend to engage in restricted and repetitive behaviors. We use the term,
``autistic'', as recommended by many self-advocates we know who prefer
the identify-first label ``autistic'' over person-first terminology
``individual with autism spectrum disorder (or condition)'' (Kenny et
al., 2016). Although typically diagnosed in early childhood, many
autistic individuals may remain undiagnosed until later in life (Ridgway
et al., 2024). Research comparing the well-being of autistic and
non-autistic individuals highlighted significant disparities, with
autistic individuals often exhibiting lower levels of well-being,
particularly evident among college students who grapple with challenges
in social connectivity and balancing academic demands (Bailey et al.,
2020; Ridgway et al., 2024).
Despite its prevalence in many fields, autism remains relatively
underexplored in transportation literature, with existing transportation
surveys failing to neurologically categorize individuals. Recent studies
have begun to address this gap, shedding light on the travel behaviors
and experiences of autistic individuals. Surveys conducted among
autistic adults in New Jersey underscored their reliance on others for
transportation, often resulting in missed opportunities due to the
unavailability of rides. Moreover, firsthand accounts from autistic
individuals and their caregivers highlight feelings of isolation and
depression stemming from transportation-related struggles (Deka et al.,
2016; Lubin \& Feeley, 2016).
These findings collectively suggest a potential link between lower
well-being and less activity engagement illustrated by travel behaviors
among autistic individuals, emphasizing the need for tailored support
and inclusive transportation systems.
\subsubsection{Social Anxiety}\label{social-anxiety}
Social anxiety involves an overwhelming fear of embarrassment or
rejection from others and refers to the anxiety stemming from
anticipated or real interpersonal judgement in social situations. These
fears and anxieties tend to lead socially anxious people to be more
socially isolated than others (Leichsenring \& Leweke, 2017; Öztürk \&
Mutlu, 2010; Ye et al., 2021). Frequently mistaken for shyness, social
anxiety remains underrecognized, with a lifetime prevalence of 13\% and
a 12-month prevalence of 8\% among adults and adolescents in the United
States (Leichsenring \& Leweke, 2017).
Two studies examining the relationship between social anxiety and
well-being found a negative correlation between the two variables. These
studies looked at social anxiety experienced by participants, and not
necessarily at participants who have social anxiety disorder, which is a
persistent state of social anxiety that can cause major impacts or
changes in one's daily life (Leichsenring \& Leweke, 2017). In one
study, (Öztürk \& Mutlu, 2010) investigated the correlation between
social anxiety and well-being among university students. They observed
that higher levels of social anxiety were associated with lower overall
well-being. Similarly, (Ye et al., 2021) found a significant negative
correlation between social anxiety and well-being among Chinese college
students.
When considering the impact of anxiety disorders on travel patterns,
(Ratering et al., 2024) studied the travel behavior of 40 Dutch adults
diagnosed with anxiety disorders. They noted that individuals with
anxiety disorders, including social anxiety, often faced mobility
limitations due to anxiety-related concerns. Participants in the study
used coping mechanisms such as traveling with familiar companions,
avoiding certain locations, or staying at home altogether (Ratering et
al., 2024).
Overall, heightened social anxiety is associated with reduced well-being
and may prompt individuals to modify their travel behaviors, potentially
resulting in fewer trips and less activity engagement.
\subsection{Current State of the
Research}\label{current-state-of-the-research}
Recently, a research group used mobile phone-based sensing to study
daily activities and environmental exposures in relation to anxiety
symptoms. (Lan et al., 2022) explored how individuals' activities in
different environmental settings contributed to anxiety levels, using
cross-sectional data from mobile phone sensors like global position
services (GPS) and accelerometers to track spatial movements and
activity patterns. Anxiety symptoms were measured with a standardized
questionnaire, and environmental exposures such as air pollution, noise,
and green space availability were assessed.
The study found that time spent in areas with higher air pollution and
noise was associated with increased anxiety, while time in green spaces
was linked to lower anxiety symptoms. Physical activity and social
interactions in various environments also correlated with reduced
anxiety. This study began to explore how individuals' travel can affect
anxiety symptoms, but this study had limitations. GPS data were
collected over a seven-day period, with anxiety levels assessed using a
single questionnaire asking participants to reflect on the past two
weeks. The study tracked exposure to various areas but did not pinpoint
where specific activities occurred. Additionally, participants were not
categorized into specific groups based on neurological or psychological
conditions.
To address these gaps, this thesis takes a longitudinal approach,
collecting location-based services (LBS) data from participants over one
month to a year. Participants completed mental health surveys up to
twice daily, allowing for more detailed and frequent assessments of
mental health in relation to daily travel habits. Unlike the previous
study, we identifed and analyzed specific activities rather than just
GPS trajectories, ensuring accurate identification of where activities
occurred. Furthermore, participants were categorized into
groups---autism, social anxiety, and control---based on psychological
evaluations. This approach enables a deeper analysis of travel behavior
and mental health across different population segments, providing
insights that can inform more targeted interventions.
\subsection{Summary}\label{summary}
Overall, this literature review examines various factors impacting
mental health and well-being, focusing on the built and natural
environments, social isolation, travel behavior, activity types, and the
unique challenges faced by autistic individuals or those with social
anxiety.
Access to green and blue spaces is crucial for mental health, as they
help reduce stress and improve mood. However, urbanization and COVID-19
restrictions have limited access, worsening mental health issues. High
pollution and lack of green spaces in urban areas are linked to
increased stress and depression. Social isolation, exacerbated by the
pandemic, has heightened anxiety and depression. Mobility and social
support are key to mitigating these effects, making transportation
access and social interactions essential. Positive travel experiences
and regular travel are associated with better mental health. Green
spaces significantly reduce stress, while grocery shopping can be
stressful. Libraries and social recreation areas support well-being,
whereas individuals with autism and social anxiety face unique travel
challenges, leading to isolation and limited mobility.
Recent studies using mobile phone-based sensing have explored the
relationship between environmental exposures, activities, and anxiety.
This thesis addresses gaps by using longitudinal data to assess mental
health impacts of travel behavior across three groups of individuals,
aiming to understand the link between travel behavior and mental health.
\bookmarksetup{startatroot}
\section{Methodology}\label{methodology}
To examine the relationship between travel behavior and mental health,
it was necessary to clean, process, and analyze the data. This process
involved evaluating the data quality, ensuring its integrity, and using
the DBSCAN-TE (density-based spatial clustering of applications with
noise, time, and entropy) algorithm to identify activities. Various
statistical models were subsequently applied to understand the
connection between travel behavior and mental health. This section
offers a detailed overview of the research methods used to investigate
the correlation between travel behavior and mental health among young
adults with suicidal ideation.
\subsection{Study Data}\label{study-data}
In this research, we explored a unique dataset of young adults who have
expressed suicidal ideation in professional therapy contexts. This study
was conducted by the Brigham Young University's (BYU) Counseling and
Psychology Services (CAPS) office. The study included 88 young adults
residing in Utah County, each belonging to one of four groups based on a
psychological evaluation: autism, social anxiety, control, or no group.
Those who were part of the no-group initially thought that they were
part of the social anxiety group or the autism group, but after the
psychological evaluation, it was found that they were not part of either
of those groups. The individuals in the no-group were grouped together
with the control group for this analysis. Of the 88 participants in the
study, 28 belong to the social anxiety group, 29 belong to the autism
group, and the remaining 31 are part of the control group.
Table~\ref{tbl-descriptivestats} provides the participants' age, IQ
score, sex, and race descriptive statistics by group. The average
participant was 23.8 years old with an average IQ score of 120.8. The
autism group had the highest average age and average IQ score of the
three groups. In all, there were 65 individuals assigned female at birth
and 23 individuals assigned male at birth. While the self-identified
gender of each participant was also recorded, the sex assigned at birth
was used for this analysis. The three groups had a similar distribution
of males and females. The majority of participants were White, but
Hispanic or Latino and Asian participants were also part of the study.
\begin{table}
\caption{\label{tbl-descriptivestats}Descriptive Statistics by Group:
Age, IQ Score, Sex, and Race}
\centering{
\centering
\resizebox{\linewidth}{!}{
\begin{tabular}[t]{llcccccc}
\toprule
\multicolumn{2}{c}{ } & \multicolumn{2}{c}{Control (N=31)} & \multicolumn{2}{c}{Autism (N=29)} & \multicolumn{2}{c}{Social Anxiety (N=28)} \\
\cmidrule(l{3pt}r{3pt}){3-4} \cmidrule(l{3pt}r{3pt}){5-6} \cmidrule(l{3pt}r{3pt}){7-8}
& & Mean & Std. Dev. & Mean & Std. Dev. & Mean & Std. Dev.\\
\midrule
Age & & 22.8 & 2.7 & 25.9 & 4.9 & 22.8 & 2.3\\
IQ & & 120.2 & 12.0 & 122.6 & 14.1 & 119.7 & 11.4\\
\midrule
& & N & Pct. & N & Pct. & N & Pct.\\
Sex & Female & 23 & 74.2 & 20 & 69.0 & 22 & 78.6\\
& Male & 8 & 25.8 & 9 & 31.0 & 6 & 21.4\\
Race & White & 25 & 80.6 & 26 & 89.7 & 27 & 96.4\\
& Asian & 0 & 0.0 & 2 & 6.9 & 0 & 0.0\\
& Hispanic or Latino & 6 & 19.4 & 1 & 3.4 & 1 & 3.6\\
\bottomrule
\end{tabular}}
}
\end{table}%
To begin gathering data, participants installed an application on their
phone that collected cellular LBS data. The participants participated to
varying degrees with some providing 1 month of LBS data and others
providing up to 1 year of data. There was a period of time, about 1
month, when no LBS data was collected by the application. In addition to
collecting LBS data, the application prompted participants to complete a
survey answering questions about their mental health. Surveys were
administered in the mornings and evenings and asked questions such as,
``Have you felt stressed since you last took a survey?'' ``How would you
gauge your motivation in the past day?'' and ``Have you thought about
killing yourself in the past 12 hours or since you last took a survey?''
among others. Participants were monetarily incentivized to complete the
mental health surveys.
With the integration of LBS data and mental health survey responses,
this dataset enabled us to analyze the impact of individual travel
behavior on mental well-being, with a particular focus on understanding
the interaction between travel behavior, mental health, and individuals
from the three different groups. Before analyzing these connections, we
cleaned and processed the data.
\subsection{Cleaning the Data}\label{cleaning-the-data}
The raw LBS data included the ID associated with each participant
(userID), the timestamp consisting of the date and time when the data
was collected, and the geometry, meaning latitude and longitude, of each
data point. The first step in cleaning the data was to prepare the raw
LBS data to be distilled into semantic activities and activity
locations. To prepare the data for further analysis, we determined the
activity day, implemented a scoring algorithm, and trimmed down the LBS
data.
Traditionally, we think of each day ending at 11:59 PM and then the next
day starting at 12:00 AM. We decided that to best capture individuals'
daily travel, we would shift the 24-hour period to be from 3:00 AM to
2:59 AM. This shift accounts for the travel that people engage in after
12:00 AM, but before 3:00 AM. Ultimately, this adjustment ensures that
any timestamps recorded between 12:00 AM and 2:59 AM were attributed to
the preceding calendar date, classifying them as the final data points
of the preceding day rather than the initial data points for the
following day. The data points in the 24-hour period from 3:00 AM to
2:59 AM make up the activity day. Moreover, the evening mental health
survey closed at 3:00 AM each day, so a survey taken between 12:00 AM
and 3:00 AM would be the evening survey and would be associated with the
activity day as previously described.
Once the activity days were established and the data points were
categorized and grouped by activity day, we assessed the quality and
completeness of the LBS data for each combination of userID and activity
day. There were 12,051 userID-activity days, each with varying quality
of LBS data. To assess the LBS data, the data were grouped by userID,
activity day, and hour of the day and a scoring algorithm was
implemented. This algorithm identified userID-activity days that have a
high score, indicating a sufficient quantity and distribution of LBS
data points. The scoring mechanism is based on two factors: the hour of
the day and the number of LBS points recorded in that hour. For hours
from 8:00 AM to 11:59 PM, which are hours where the likelihood of
significant activity engagement is greater, a score of 3 was assigned to
the hour and for the remaining hours of each activity day, a score of 1
was assigned to the hour. This aspect of the scoring algorithm is
described in Equation~\ref{eq-hourly-score} where \(H_t\) is the hourly
score and \(t\) is the hour of the day
\begin{equation}\phantomsection\label{eq-hourly-score}{
H_t = \begin{cases}
3 & \text{if } 8 \leq t \leq 23 \\
1 & \text{otherwise}
\end{cases}
}\end{equation}
The other aspect adjusted the overall score based on the number of LBS
points recorded within the given hour. The algorithm categorized each
hour into a tier based on the number of LBS points and assigned a score
corresponding to each tier. The tiers are described as follows: for an
hour with less than 500 points, a score of 0 was assigned; for an hour
with between 500 and 1500 points, a score of 1 was assigned; for an hour
with between 1500 and 2500 points, a score of 2 was assigned; and for an
hour with more than 2500 points, a score of 3 was assigned. This aspect
of the scoring algorithm is described in Equation~\ref{eq-lbs-score}
where \(P_t\) is the hourly LBS score and where \(LBS_t\) is the number
of LBS points in that hour
\begin{equation}\phantomsection\label{eq-lbs-score}{
P_t =
\begin{cases}
0 & \text{if } \text{LBS}_t < 500 \\
1 & \text{if } 500 \leq \text{LBS}_t < 1500 \\
2 & \text{if } 1500 \leq \text{LBS}_t < 2500 \\
3 & \text{if } \text{LBS}_t \geq 2500
\end{cases}
}\end{equation}
To determine the combined score for each hour, the score based on the
hour of the day and the score based on the number of LBS points were
multiplied together. Equation~\ref{eq-combined-score} describes this
step and \(S_t\) is the combined score for the given hour
\begin{equation}\phantomsection\label{eq-combined-score}{
S_t = H_t \times P_t
}\end{equation}
Then to determine the final score of the activity day, the scores for
each hour of the day were summed to determine the total daily score for
the activity day. \clearpage Equation~\ref{eq-total-score} shows this
step in the calculation and \(S_{\text{day}}\) is the total daily score.
The \(S_{\text{day}}\) is for the activity day which is from 3:00 AM to
2:59 AM, as previously described
\begin{equation}\phantomsection\label{eq-total-score}{
S_{\text{day}} = \sum_{t=3}^{2} S_t
}\end{equation}
Based on this scoring algorithm, the highest possible score for an
activity day is 168 points. By summing the scores for the day and
selecting high scoring days, the algorithm provided a comprehensive
assessment of the quality of LBS points collected on a given day for
each userID-activity day combination. We determined that a ``high
scoring'' day had a final score of 95 points or more. This threshold was
chosen as a cutoff to retain only those userID-activity day combinations
with a relatively high quality LBS dataset to ensure that only
sufficiently complete and accurate data was retained for further
analysis in determining the trip patterns for the participants.
Figure~\ref{fig-allScoredDays} illustrates the distribution of daily
scores across all userID-activity days. The average total daily score
for these days was around 70 points, which is slightly under the
midpoint of the potential score range of 84 points. Notably, 2,965 of
the 12,051 userID-activity days resulted in a total daily score of 0.
This observation suggests that many of these days were characterized by
sporadic and incomplete data collection. The efficacy of the scoring
algorithm in identifying such instances with unreliable, low-quality