Page 1 of 19
Investigating the Role of Context in the Delivery of Text Messages
for Supporting Psychological Wellbeing
Ananya Bhattacharjee Joseph Jay Williams Jonah Meyerhof
ananya@cs.toronto.edu williams@cs.toronto.edu jonah.meyerhof@northwestern.edu
Computer Science, University of Computer Science, University of Preventive Medicine, Northwestern
Toronto Toronto University
Canada Canada USA
Harsh Kumar Alex Mariakakis Rachel Kornfeld
harsh@cs.toronto.edu mariakakis@cs.toronto.edu rachel.kornfeld@northwestern.edu
Computer Science, University of Computer Science, University of Preventive Medicine, Northwestern
Toronto Toronto University
Canada Canada USA
ABSTRACT
Without a nuanced understanding of users’ perspectives and con- texts, text messaging tools for supporting psychological wellbeing
risk delivering interventions that are mismatched to users’ dynamic
needs. We investigated the contextual factors that infuence young
adults’ day-to-day experiences when interacting with such tools.
Through interviews and focus group discussions with 36 partic- ipants, we identifed that people’s daily schedules and afective
states were dominant factors that shape their messaging prefer- ences. We developed two messaging dialogues centered around
these factors, which we deployed to 42 participants to test and ex- tend our initial understanding of users’ needs. Across both studies,
participants provided diverse opinions of how they could be best
supported by messages, particularly around when to engage users
in more passive versus active ways. They also proposed ways of
adjusting message length and content during periods of low mood.
Our fndings provide design implications and opportunities for
context-aware mental health management systems.
CCS CONCEPTS
• Human-centered computing → Empirical studies in HCI.
KEYWORDS
text messages, mental wellbeing, contextual factors, JITAI, daily
schedule, mood, energy
ACM Reference Format:
Ananya Bhattacharjee, Joseph Jay Williams, Jonah Meyerhof, Harsh Ku- mar, Alex Mariakakis, and Rachel Kornfeld. 2023. Investigating the Role
of Context in the Delivery of Text Messages for Supporting Psychological
Wellbeing. In Proceedings of the 2023 CHI Conference on Human Factors in
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for proft or commercial advantage and that copies bear this notice and the full citation
on the frst page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specifc permission
and/or a fee. Request permissions from permissions@acm.org.
CHI ’23, April 23–28, 2023, Hamburg, Germany
© 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-9421-5/23/04. . . $15.00
https://doi.org/10.1145/3544548.3580774
Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany. ACM,
New York, NY, USA, 19 pages. https://doi.org/10.1145/3544548.3580774
1 INTRODUCTION
Digital mental health (DMH) tools enable users to access resources
and strategies for managing their psychological wellbeing at their
own convenience [9, 86]. However, a common concern about these
tools is that they often deliver interventions that are mismatched
to users’ needs at a particular time [31]. This is of particular con- cern for push-based tools that initiate interactions with the user,
such as text messages and notifcations. Push-based DMH tools
can potentially support users at moments when they may not have
the motivation or forethought to proactively engage, yet they risk
being perceived as insensitive to the user’s current mental state or
availability [31, 87]. These issues can cause frustration and eventu- ally contribute to quitting or disengaging from digital interventions
altogether [61, 94].
Incorporating information about a user’s context has the po- tential to overcome this fundamental challenge of disengagement
with push-based tools, helping DMH systems deliver interven- tions that will be perceived as timely, appropriate, and relevant
[48, 80, 88]. While context is a broad term that can have many inter- pretations[20, 22], HCIresearchers generally acknowledge contexts
by dividing them into several contextual factors (e.g., location, time
of day, activity level). HCI researchers generally integrate context
into their work by gathering data about dynamic situational factors
and using them to inform the delivery of an intervention. These
dynamic factors may be calculated (e.g., time of day) [6, 75], gath- ered through sensors and digital traces (e.g., location, movement,
social proximity, engagement with the phone) [48, 80], or actively
reported through brief assessments (e.g., mood, energy) [27]. For
just-in-time adaptive interventions (JITAIs), these contextual fac- tors are incorporated into algorithms and machine learning models
that personalize the timing and content of an intervention [35, 87].
Although JITAIs have shown promise for sustaining engagement
and motivating health behavior change in more personalized ways,
HCI literature has argued that they are still far from accounting
for the dynamic changes in people’s lives [118, 129]. This may be
particularly true in a mental health context, where contextual fac- tors like social interaction and movement can have complex and
Page 2 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
highly individualized relationships to a user’s mental state and need
for intervention [63, 89, 106]. Furthermore, many DMH tools have
historically been designed in a top-down process by experts with- out refecting a nuanced understanding of users’ perspectives or
preferences. When users have been involved in the design process,
it has often been to the extent of content generation [53]; rarely
have users been asked to give input on how they perceive con- texts to shape their need for and receptivity to support as they go
about their day-to-day lives, nor have they been asked to identify
specifc contextual data and decision rules that should underlie
an intervention. Hence, literature recommends that to implement
contextually-tailored systems, designers should carry out forma- tive investigations with actual users to understand their needs and
expectations and how these shift over time [22, 53].
Our research is motivated by this gap in the literature. We seek
to understand users’ perspectives to inform the thoughtful selection
of contextual factors in context-aware DMH tools and to defne
how dynamic factors might impact users’ receptivity towards dif- ferent intervention approaches. We limit our investigation to text
messaging systems — one of the most promising digital platforms
for promoting psychological wellbeing. Relative to alternatives like
mobile applications and online programs, text messaging systems
are more ubiquitous and accessible to the general population [25].
There are a number of text messaging systems that help support
psychological wellbeing [16, 50, 64, 84, 99], and a few of these adapt
themselves based on factors like the time of day and users’ activity
level [51, 94]. To inform the design of such JITAIs, we aim to answer
the following research questions:
• RQ1: Which contextual factors are perceived to infuence the
user experience of a text messaging service for psychological
wellbeing?
• RQ2: Whatspecifc elements of text messaging interventions
need to be tailored to refect the users’ dynamic contexts?
We focus our research on North American young adults aged 18–
25. This age group is vulnerable to many mental health problems,
exhibiting increasing rates of depression and anxiety over the past
several years [46, 104]. Mobile phone usage is also extremely high
in this population [44], as a recent survey has shown that nearly
100% of American young adults own a mobile phone [15]. Com- bined, these trends make DMH tools distributed overtext messaging
particularly relevant to this population.
Our investigation started with formative work, consisting of
interviews and focus group sessions with 36 participants who were
asked to discuss the contextual factors they thought would impact
their engagement with a text messaging service for psychological
wellbeing management. Participants anticipated that their daily
schedule and afective state (i.e., an individual’s mood and energy
level at a given moment) would be the most important contextual
factors. They were divided in whether they would be more willing
to receive messages in the morning versus the late afternoon or
evening. However, they anticipated that messages calling for pas- sive engagement, such as support messages or refection prompts,
could help them manage periods of low mood.
For a subsequent deployment study, we created two interactive
text messaging dialogues that allowed us to explore the role of
contextual factors: one dialogue centered around the impact of
the recipient’s schedule and the other around the impact of the
recipient’s afective state. We deployed these dialogues in a text
messaging probe to 42 participants to observe how those contex- tual variables impact people’s experiences. Individual interviews
with 20 participants confrmed several fndings and provided addi- tional nuances regarding users’ changing needs and preferences.
They afrmed the importance of adjusting the timing of message
delivery to maximize receptivity. Contrary to our formative work,
however, participants in the deployment study also expressed in- terest in getting reminder messages during work or school hours
as long as they did not demand an immediate response or action.
There were some design tensions as well; some participants wanted
fewer questions about their emotional state to more readily access
the intervention content itself, while others wished for extended
dialogues to express their current mood and energy level.
To summarize, our contributions include:
• The identifcation of key contextual variables that infuence
users’ experiences with a text messaging system aimed to
promote psychological wellbeing,
• The identifcation of specifc messaging elements that should
adapt based on those contextual factors, and
• A set of design considerations for building context-aware
DMH tools,such asthe incorporation of various data streams
to gather contextual information.
2 RELATED WORK
In this section, we frst discuss how text messages have been used
as a medium for promoting behavior change and mental wellness.
We then describe past work related to interventions that are per- sonalized according to the user’s context.
2.1 Text Messaging as a Medium for Promoting
Behavior Change and Psychological
Wellbeing
Text messaging services are now commonly used for promoting
health behavior change [111]. These services have demonstrated
success in supporting behavior change for various physical and
mental health challenges [41, 42, 120, 133]. For example, one do- main in which text messaging services have had signifcant impact
is in reducing alcohol consumption among individuals with alcohol
use disorder. Sufoletto et al. [120] lowered drinking among young
adults by frst prompting them to set a drinking limit and then
delivering a combination of motivational messages, self-efcacy
support, ecological momentary assessment (EMA) queries [113],
and reminders to help them stay within their self-assigned limit.
Glasner et al. [34] created an intervention to improve medication
adherence and mitigate heavy drinking among adults with HIV
and substance use disorders. A similar text messaging service was
created by Liao et al. [69] to promote smoking abstinence among
adult smokers. Their intervention resulted in lower cigarette con- sumption rate among participants, particularly among those who
received messages more frequently (3–5 messages per day). Other
areas where text messaging has been applied include weight man- agement [21, 115], physical activity promotion [56, 85, 116], and
patient engagement [95, 133], among many others.
Page 3 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
There has been a recent proliferation of text messaging services
for promoting psychological wellbeing [1, 16, 50, 61, 64, 84, 99, 119].
Such services can vary widely in the type of content they deliver
and the outcome they aim to achieve. In terms of content, many
services are centered around therapeutic approaches from clinical
psychology, such as cognitive behavioral therapy (CBT) [132], di- alectical behavior therapy (DBT) [71], acceptance and commitment
therapy (ACT) [43], and motivational interviewing [45]. A text mes- saging system can also provide support by sending motivational
quotes [47], recommending physical exercises [23], or describing
how a peer has overcome similar challenges [6, 92]. Messages can
also provide important reminders about healthcare services, treat- ment protocols, or appointment attendance [4]. Agyapong et al.
[1] deployed a text messaging service to help people manage their
mental wellness during the COVID-19 pandemic. The service sent
daily supportive messages to people along with requests to refect
on their stress, anxiety, and depression level. The authors found
that their messages were able to signifcantly reduce self-reported
anxiety scores among users. Levin et al. [68] delivered messages
ranging from psychoeducational texts, reminders, and EMA queries
to help people with bipolar disorder and hypertension manage
their symptoms. Arps et al. [2] were able to reduce depressive
symptoms among adolescents by sending them daily gratitude mes- sages. Lastly, researchers have explored the design of artifcially
intelligent chatbots that try to promote self-refection by engaging
users in human-like conversations [50, 59, 123].
Yet, asthe nextsubsection explores, text messaging interventions
may be even more successful in promoting mental wellness by
acknowledging the dynamic user contexts. We now discuss how
DMH tools have attempted to achieve such fexibility in the past
and how our work contributes to this space.
2.2 Contextual Factors in DMH Tools
Context-aware computing is a sub-domain of ubiquitous comput- ing and human-computer interaction that seeks to achieve a grand
vision where “computation is embedded into the fabric of the world
around us” [22, 82, 109]. However, the term context has been de- fned in many ways. For example, Dey [20] states the following:
“Context is any information that can be used to characterize the
situation of an entity. An entity is a person, place, or object that
is considered relevant to the interaction between a user and an
application, including the user and application themselves.” On the
other hand, Dourish [22] views context as a relational property
among objects or activities. To operationalize context in a way that
can be characterized by technologies, researchers often break down
context into contextual factors such as location, time of day, and
activity level [91].
In this work, we are particularly interested in understanding
dynamic contextual factors that can change within a span of min- utes, hours, or days. Furthermore, we are interested in how these
factors may impact the user experience of a text messaging service
for psychological wellbeing. This can be contrasted with examina- tions of relatively stable contexts within a user’s life, such as race,
ethnicity, cultural background, or profession. Even well-motivated
interventions can come across as irrelevant or inappropriate when
they fail to adapt to the rapid, frequent, and unexpected changes
in users’ lives [129], particularly since messages must compete for
users’ time in the presence of other activities and issues that require
their attention. Thus, delivering the wrong type of intervention or
intervening at an inconvenient time will likely lead to content be- ing ignored or creating a negative user experience [87], which can
potentially cause users to quit using tools early [30, 66]. Among in- dividuals with mental health concerns, primary reservations about
using an automated messaging program related to the potential for
messages to be intrusive or too generic [62], further suggesting the
importance of better adapting messaging to users’ contexts.
2.2.1 The Role of Context in Mental Health. Past literature in psy- chology suggests a large number of contexts of daily life that are
linked to mental health conditions. For example, there is a body of
literature that connects mental health with circadian rhythms [6,
28, 75, 128] — the physical, mental, and behavioral patterns that
follow a 24-hour cycle [127]. Mental health conditions may involve
distortions of the typical rhythms; as an example, individuals may
fnd themselves waking up, going to bed, eating, and socializing
at inconsistent times from day-to-day. These disruptions can lead
to a sense of being ungrounded and worsen symptoms. Studies
have also found that symptoms of depression and anxiety may
manifest most acutely at certain times of the day, including the
early morning [38] and nighttime [8, 122]. Mental health symptoms
have also been linked to physical activity, movement, and social
proximity, among other dynamic factors [5]. For instance, lower
levels of depression have been observed when individuals are more
physically active [49, 57, 121], spend more time outside of the house
or visit particular types of locations [105], and engage socially with
others [117]. Mental health conditions are also susceptible to spe- cifc stressors. For example, Brown et al. [10] reported that stress
in early adolescents is majorly impacted by their homework load.
2.2.2 Just-in-time Adaptive Interventions. Attempts to address con- textual factors in the design of DMH tools have mostly come
through the proliferation of just-in-time adaptive interventions
(JITAIs) — “an intervention design aiming to provide the right
type/amount of support, at the right time, by adapting to an individ- ual’s changing internal and contextual state” [87]. In other words,
such technologies leverage information about the user’s context to
decide on the ideal time to deliver specifc interventions. The con- textual variables are often collected through sensors, self-reported
scores, or prediction algorithms [58].
Several JITAIs have been developed in the recent past to promote
healthy behavior and mental wellness [26, 32, 35, 36, 40, 60, 70,
97], drawing upon the diverse contextual factors described earlier.
Ismail et al. [51] designed an adaptive text messaging app that used
the user’s goals, current step count, and information about their
surrounding environment to promote physical activity. Theirresults
suggest that the app was more successful in breaking sedentary
behavior compared to static reminder messages. Clarke et al. [17]
explored the utility of a stress management tool that automatically
sent the user a message whenever the sensors on their smartwatch
detected an elevated heart rate. A more sophisticated version of this
system by Howe et al. [48] adapted stress-reduction interventions
not only based on changes in heart rate, but also on the time of
day, the user’s facial expression, and the user’s volume of emails
and calendar events. Another work by Paredes et al. [94] relied on
Page 4 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
user-reported depression scores and mobile phone sensor data (e.g.,
accelerometer, screen status) to deliver interventions.
While promising, the JITAIs described above have largely selected
their contextual variables and set decision rules according to theo- ries from psychology instead of being guided by an understanding
of users’ own experiences or priorities. There is a rich history in HCI
of seeking to understanding how a person’s context infuences their
receptivity to user experiences, especially to motivate prolonged
use of a product [54, 67, 91, 124]. Interestingly, such approaches are
not yet routinely applied to the design of JITAIs [53]. Therefore,
existing JITAIs may not fully capture the nuances of users’ lives or
how context is experienced to shape one’s specifc needs from a
technology.
Following the approach advocated by Kabir et al. [53], we address
this gap in the literature with respect to adaptive DMH tools. We
examine qualitative data from our formative study to identify the
contextual variables participants thought would be most important
in shaping their needs when using a text messaging service to
support their psychological wellbeing. The subsequent deployment
of a text-message probe further reveals how dynamic variables
impact users’ experiences of receiving automated messaging and
their receptivity towards diferent intervention approaches.
3 FORMATIVE STUDY DESIGN
We conducted our formative study with 36 individuals to under- stand which contextual factors they anticipated would be crucial
to their interaction with a text messaging system for psychological
wellbeing. We also used this opportunity to elicit people’s sugges- tions on how DMH tools can adapt to refect dynamic changes in
those factors. Below, we describe the protocol for this investigation.
Research activities took place in two North American universities
and were approved by both institutions’ Research Ethics Boards.
3.1 Participants
Recruitment for the individual interviews and focus group discus- sions wasfacilitated by Mental HealthAmerica (MHA), a community- based non-proft organization that is dedicated to promoting men- tal wellbeing in the United States. MHA hosts screening surveys
that are widely used by young people seeking to better under- stand their mental health and connect to resources. People showing
moderate levels of depression and anxiety according to the Pa- tient Health Questionnaire-9 (PHQ-9) [65] and General Anxiety
Disorder-7 (GAD-7) [72] (scores of 10 or higher) were invited to
learn more about the project through a link that appeared with
their screening survey scores. Potential participants completed an
additional study-specifc screening survey and were deemed eligi- ble if they met all of the following criteria: (1) located in the United
States; (2) between 18–25 years old, or 19–25 years old if they were
in Nebraska; and (3) owned a mobile phone.
Of the 725 individuals who originally expressed interest in the
study by completing the screening survey, 106 were deemed to be
eligible. We invited 30 individuals (FP1–FP30) to take part in indi- vidual interviews, selecting participants such that the fnal sample
would be representative of MHA’s audience with regards to mental
health symptoms as well as demographic characteristics such as
age, gender identity, and race. From these 30 participants, nine
(FP1–FP9) also took part in focus group discussions; again, these
participants refected MHA’s audience. Focus group participants
were invited to attend as many sessions as they wished upon receiv- ing their initial invitation, and six participants attended more than
one (range: 1 to 4). We conducted 5 focus groupsin total. Facilitators
were trained on strategies to encourage input from new and quieter
group members, such as asking each member to write down their
answer before going around to each member and giving them the
chance to speak.
We were also interested in exploring multiple possible pathways
of reaching young adults who would beneft from a DMH tool
distributed over text messaging. The growing mental health con- cerns among university students [6, 114] motivated us to recruit
6 students (FP31–FP36) from a large North American University
using a combination of snowball sampling [37] and word-of-mouth.
The research team members invited students from an introductory
programming course and an HCI research lab to participate in the
study and encouraged them to pass on the invitations to their peers
and colleagues. These participants were between 18–25 years old,
and at the time of the study, they were studying computer science,
cognitive science, or psychology. They did not need to meet any
criteria regarding mental health symptoms in order to participate
in the study.
Participants were recruited in a rolling manner, and we con- tinued recruitment until we achieved data saturation from our
semi-structured interviews and focus group sessions. We did not
make attempts to have balanced groups across recruitment path- ways or mental health status since drawing explicit comparisons
between these two groups is beyond the scope of the work. Instead,
we were more concerned with identifying important contextual fac- tors that impact the text messaging experience across these groups.
The mean age of our fnal participant cohort was 21.7 years old.
They identifed with multiple genders (30 women, 4 men, 1 non- binary, and 1 undisclosed) and racial groups (16 White, 10 Asian, 1
Black/African American, 1 American Indian or Alaskan Native, 4
mixed, and 4 undisclosed).
3.2 Study Procedure
We anticipated that some people might feel more comfortable dis- cussing their mental health in a more private setting because of
the topic’s sensitivity, whereas a group setting allows for people to
build upon and respond to one another’s contributions to yield new
insights and areas of convergence or divergence [33]. Therefore, we
collected data both through individual interviews and focus group
discussions. We frst asked participants to share their experience
of using DMH tools to generate a baseline of their prior experi- ences. We then asked participants to project how a text messaging
service can better support them in managing their psychological
wellbeing; in particular, we sought to collect data on ways that
text messaging systems should adapt based on a user’s daily and
moment-to-moment context. Interview questions were developed
iteratively by two of the authors (RK and JM) as part of a broader
efort to understand the needs and preferences for self-management
of young adults who were experiencing mental health symptoms
Page 5 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
and were not interested in formal psychotherapy, but were inter- ested in using DMH tools to manage their symptoms. This study
reports on responses to questions in the interviews and focus group
sessions that were targeted to understand the association between
key contextual factors in participants’ lives and various elements
of text messaging like content, volume, frequency, and suggested
follow-up action. Questions included, but were not limited to:
• Are there factors or variables that change in your life and
that would afect how receptive you would be to mental
health-related text messages?
• How might those things change the type of text messages
you would want to receive?
• Which of these factors do you think should be considered
more important while text messaging programs are being
designed, and why?
• What are some times when you would be more open to
receiving messages? When would you be less open?
• Might you want diferent message frequency based on when
it is more convenient for you to interact with them versus
when it is less convenient? How?
The interviews were semi-structured in nature, allowing us to
deviate from the interview script as needed to ask additional follow- up questions. We also provided participants with explanations and
examples whenever necessary.
The individual interviews were conducted by one member of
the research team via telephone or the Zoom videoconferencing
platform. The focus groups were hosted on Zoom and proctored
by two team members. The size of the focus groups ranged from
2–5 participants each. Individual interviews took 15–30 minutes,
and focus group discussions lasted 60–75 minutes. All participants
were compensated at a rate of $20 USD per hour.
3.3 Data Analysis
We followed a thematic analysis approach [18] to analyze the qual- itative data. After transcribing interviews and focus group dis- cussions, two members from the research group (referred to as
“coders”) reviewed all transcripts to become familiar with the data.
The coders then followed an open-coding process [55] to assign
codes to segments of the data, and each developed a preliminary
codebook on their own. This initial round generated roughly 50
codes, including “low mood”, “messages during the morning”, and
“refection during work”. The coders then engaged in several dis- cussions to refne the code defnitions, identify overlapping codes,
and exclude codes that were not central to our research questions.
Next, they applied their codebook to a subset of the data (10 inter- view transcripts and 1 focus group discussion) and met to refne
the codebook further. This iterative process was repeated until the
coders reached a consensus in their understanding of code defni- tions. Finally, they applied the fnal codebook to separate halves of
the data. We focus below on several dominant themes that arose
from this analysis, centering on participants’ daily schedule and
their afective state.
3.4 Ethical Considerations
Ourteam membersincluded faculty members and graduate students
with training in human-computer interaction, clinical psychology,
and cognitive science. Research on promoting psychological wellbe- ing can raise several ethical considerations, so we took measures to
addressthese issuesthroughout our work. We informed participants
at the beginning of the interviews and focus group discussions that
they could skip any questions or stop the conversation altogether if
they felt uncomfortable at any point. Interviewers were also trained
to follow the Columbia-Suicide Risk Assessment protocol [96] if
participants indicated thoughts of suicide or self-harm. They were
also trained to deliver safety planning or refer participants to crisis
services as needed. However, none of the aforementioned risks
emerged during the study.
4 FORMATIVE STUDY FINDINGS
The participants in our formative study were typically frequent
users of technologies for work, school, and socializing. While many
reported previously using mental health smartphone apps, their
usage was short-lived either due to forgetting or loss of motivation
to engage. A few participants indicated using other DMH tools like
emails or web-based services, but none indicated using an auto- mated text messaging service for managing psychological wellbeing.
Nevertheless, participants suggested that text messaging had the
potential to better sustain their engagement than apps since they
are already deeply immersed in text messaging with their friends,
family, and colleagues.
Our focus groups and interviews revealed that two contextual
factors dominated users’ thoughts regarding DMH tools: (1) daily
schedule and (2) afective state. We elaborate below on why these
were viewed as important in shaping receptivity and preferences
for messaging.
4.1 Role of Daily Schedule in Shaping Message
Receptivity and Preferences
Participants in our formative study repeatedly mentioned the im- portance of their daily schedule in relation to their ability to engage
with text messages. However, we were given diverse responses
when we asked them to share their specifc preferred times for re- ceiving messages. Participants generally indicated the early morn- ing and late afternoon as suitable times for receiving messages,
although some people were in strong favor of one versus the other.
Those who believed that they would prefer to receive messages
during the morning expressed that they have moments of time to
themselves before leaving their home for work or school, which
could not be said about other parts of the day that were more hectic
and socially involved. A number of participants saw the morning
period as an opportune moment for receiving an encouraging mes- sage that could uplift their mood and give them confdence to face
the day. FP32 said,
“I prefer morning messages because they can be inspir- ing. It is the beginning of the whole day, and I need to
have motivation to do the tasks. . . . If, in the morning, I
get a message that gives me motivation, I will be happy
for the rest of the day.” (FP32)
In contrast, others were opposed to receiving messages in the
morning. Participants like FP7 said that they generally wake up
late and therefore have to hurry to get to class or work on time.
Understandably, people in this situation would either delay reading
Page 6 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
or engaging with messages orignore them altogether. Many of these
same participants speculated that they would have more bandwidth
to read and act upon messages in the afternoon or evening when
they are less rushed.
Another reason that many individuals presumed that they would
like messages later in the afternoon was that they felt the need
for support after work hours. Some people reported experiencing
loneliness and unhelpful thought patterns towards the end of their
workday, leading them to overthink their perceived failures and
shortcomings or to worry about the future, which could eventually
lead to sleep troubles. The experience was captured in the following
comment by FP16:
“I feel sending those messages out in the afternoon or
closer to when people are going to bed is probably the
best thing because, from my experience and from what
other people have told me, when they kind of start head- ing of to sleep is when . . . I wish I had someone with
me. Because when I wake up in the morning, if I’m not
having the best day, I still have to get ready and then I
go see my family and take my dog on a walk and those
things kind of distract me. But when I’m on my way
to start going to bed, it’s kind of like when I am alone
more often.” (FP16)
Participants expressed disinterest in receiving messages during
the middle of the day, particularly during the workweek. They
suspected that their attention would be preoccupied with their work
or classes, so they would not get much time to process or act upon
a message. In the event that a message had to be received during
intense working hours, participants suggested that the message
should be easy to read and should not prompt any action requiring
signifcant efort.
4.2 Role of Afective States in Shaping Message
Receptivity and Preferences
Participants unanimously acknowledged that their afective state —
their mood and energy level at a given moment — is an important
indicator of their receptivity towards text messages. While mood
references the valence of one’s afective state, and energy refer- ences the level of arousal [108], participants in this study saw these
dimensions as highly correlated, and sometimes used the terms
‘mood’ and ‘energy’ interchangeably.
Just as with their perspectives on the importance of their daily
schedule, there were diverse suggestions regarding their preferred
message content in diferent afective states. Some participants
anticipated that they would only want to receive messages from
text messaging services for psychological wellbeing when they are
experiencing negative emotions, as refected in FP1’s comment:
“You wouldn’t particularly want any messages when
you’re doing well. But on days that are really tough,
those messages would actually be pretty welcome, and
you’d be pretty receptive to them.” (FP1)
However, this sentiment was not unanimous since others saw value
in receiving messages during periods of positive emotion. For exam- ple, some participants were open to receiving interactive cohesive
dialogues during periods of high mood. FP15 felt that such dia- logues would help them notice and sustain positive emotions for a
longer time.
Participants were also willing to dedicate extra time towards
explaining their emotions to a system so that it could deliver per- sonalized messages targeted to their needs. FP5 commented:
“I would start of saying I’m feeling a certain way, like a
mood. And then the system will text me back whatever
advice they have regarding what I’m feeling.” (FP5)
When asked about their preferred message content during low
mood, some participants expressed interest in receiving simple
check-in messages that would cater to their current feelings. Par- ticipants saw several benefts in these messages. For example, FP9
felt that check-in messages would actually prompt them to refect
on their feelings and emotions, making them more aware of their
thought process. Another type of message that participants ex- pected to be useful during periods of low mood were those that
describe simple coping strategies. Several participants noted that
during difcult times, they tend to focus on their own sufering
and forget to appreciate the supportive people or helpful aspects in
their lives,so P1 suggested that messages might introduce strategies
related to gratitude. Participants also suggested that they would be
willing to hear from others who are going through similar experi- ences. FP3 posited that a message from a peer might normalize the
experiences of depression and even illustrate actionable strategies
that could be useful in overcoming their struggle.
While recognizing the benefts of receiving messages during
periods of low mood and energy, participants also conveyed that
text messaging services should not overwhelm users by demanding
a response or sending repeated reminders. Some of them posited
that if they read a suggestion to partake in an activity requiring
signifcant efort, there would be a low probability that they would
act upon that suggestion immediately. In those moments, repeated
prompts to engage with the messages could come across as over- whelming. Participants also advised against other message types
that were perceived to demand high efort responses from users
experiencing low mood, such as physical activity prompts or writ- ing exercises (e.g., where the user is expected to compose a long
free-text message). FP6 explained:
“If they’re having a hard time motivating or encourag- ing themselves, they might not feel like this is something
they could do.” (FP6)
They went on to describe that activities requiring moderate efort
might be more appropriate “for someone who is like not [experiencing
low mood] or has like a milder case”.
While most participants endorsed that mood and energy were
strongly correlated, a few participants distinguished between the
two constructs. For example, FP25 refected on times when they
felt tired but were not experiencing negative emotions. The cause
for low energy in these cases was often busyness from other obli- gations.
Implications for Deployment Study: Our formative study fnd- ings motivated the design of our subsequent deployment study. The
two contextual factors we identifed during our interviews and fo- cus group discussions guided design of two message dialogues: one
Page 7 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
catering to daily schedule (Daily Schedule Dialogue) and the other
to individual afective states (Afective State Dialogue). The message
contents within each dialogue were also inspired by formative study
participants’ opinions. The daily schedule dialogue asked partici- pants to engage in brief activities or respond to refective questions
twice a day, enabling us to observe people’s reactions to both forms
of message in the morning and afternoon. The afective state dia- logue aimed to deliver messages based on participants’ mood and
energy level. Participants received passive supportive texts if they
were experiencing negative emotions; otherwise, they were asked
to draft supportive texts for others. The following section explains
our deployment study design in much more detail.
5 DEPLOYMENT STUDY DESIGN
Although our formative study helped us understand people’s ex- pectations for adaptive DMH tools, prior literature has suggested
that such investigations should be followed by deployments so that
researchers can understand how those expectations translate to
people’s real-world experiences [103, 112]. Hence, we conducted a
deployment study to confrm, refute, and extend our fndings from
the formative work. We frst describe the design of the probes we
used to elicit feedback, and then we describe the protocol that was
used to deploy and evaluate the probes.
5.1 Formation of Message Dialogues
We designed two separate dialogues to investigate the key con- textual variables that emerged from our formative work. The frst
dialogue was designed to investigate people’s receptiveness to text
messages at diferent times of day, and the second dialogue was de- signed to understand how people would react when text messages
were tailored to their afective state. The messages in both dialogues
were developed by the research team, which consisted of faculty
members and graduate students in human-computer interaction,
psychology, and cognitive science. The dialogues went through
an iterative design process where the research team held multiple
meetings to ensure that the messages refected fndings from the
formative work. The dialogues are illustrated in Fig. 1 and Fig. 3;
however, we provide a short description of each dialogue below.
5.1.1 Daily Schedule Dialogue. Participants in our formative study
expressed interest in receiving messages in the morning or in the
late afternoon depending on their daily schedule and habits. To ex- plore the importance of this factor, participants who engaged with
this dialogue were sent two sequences of messages each day: one
starting at 9:00 AM and another at 4:30 PM in their local timezone.
We limited our protocol to these two time windows since the forma- tive study participants were worried about getting overwhelmed by
too many messages within the same day. Each sequence started with
one or two messages designed to help people manage their stress or
negative emotions. These messages were randomly selected from a
message bank composed of two broad types of messages, examples
of which are provided in Table 1:
• Brief activities: These messages prompted participants to
engage in a small activity that could be completed within
a few minutes, such as breathing or mindfulness exercises.
Activities like these have been found to improve moods and
reduce momentary stress [24, 94].
• Refective questions: These messages asked participants
to refect on various elements of life, such as their career,
relationships, and health. Doing so allowed participants to
re-evaluate their thought patterns and fnd alternative per- spectives in a structured manner [3].
One hour after the initial message(s), participants were asked
whether they enjoyed reading the messages. If they said ‘yes’, they
received an acknowledgement message that read, “We’re glad you
liked it! Thank you for the feedback.”; if they said ‘no’, they received
an acknowledgement message that read, “Sounds like this didn’t
really work for you. Thank you for the feedback.” Participants who
did not provide a response did not receive an acknowledgement
message. This procedure was repeated each morning and afternoon
irrespective of past engagement. Participants were not sent the
same message sequence twice on the same day.
5.1.2 Afective State Dialogue. Participants in our formative study
also expressed interest in receiving messagesthat would allow them
to express their emotional states and receive responsive content
accordingly. Literature suggests that emotion labeling (i.e., “using a
specifc word to describe an emotion” [27]) can help people observe,
express, and accept emotional responses without self-judgment and
eventually help them engage in activities more mindfully [52, 110].
By being more aware of their psychological state, a person may also
build a more nuanced understanding of how their feelings relate
to their thoughts and behaviors, thereby motivating changes in
behavior and thought patterns [132].
Motivated by these opportunities and benefts, we created a
check-in message that assessed the user’s state of mind based on
the circumplex model of emotions [108]. According to this model,
which is illustrated in Fig. 2, human emotions are distributed in a
two-dimensional space. The model’s horizontal axis encapsulates
valence, which pertains to how positive or negative the user’s
emotional state or mood is. The model’s vertical axis encapsulates
arousal or energy level. For our check-in message, participants
were asked to rate both their energy and mood levels as either
’high’ or ’low’. They were then asked to select the emotion that best
represented their state of mind from a list that was dynamically
generated according to their previous answers. For example, if
someone reported a high mood and low energy, the list would
contain emotional states corresponding to positive valence and low
arousal: calm, relaxed, and content. Participants were also able to
indicate that none of the emotions sufciently represented their
state of mind.
Participants in this dialogue received the check-in message at
9:30 AM in their local timezone. If the participant reached the point
of the dialogue where they had selected a term to describe their
current state, the system sent a message that acknowledged their
emotions (e.g., “Sounds like you are feeling relaxed right now.”). If
the participant could not fnd a suitable match from our list, the
system acknowledged the shortcoming and provided them with
a link to a website containing a larger set of emotions. Next, the
dialogue asked participants to complete one of two activities that
required diferent levels of involvement:
• Passive support reception: Participants received a short
supportive message if they were experiencing an emotion
Page 8 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
Table 1: Examples of messages used in the daily schedule dialogue.
Message Type Examples
Brief Activities Please fnd a comfortable seating position. First, exhale completely through your mouth,
making a woosh sound. Then, close your mouth gently and inhale through your nose for
4 seconds, hold your breath for 7 seconds and exhale through your mouth for 8 seconds.
Repeat this cycle 3 times.
Refecting gratefully on your day, right before you go to bed, can lead to better sleep. Set
aside a few "gratitude" minutes right before you go to bed tonight.
Refective Questions Is there a small step you’ve taken recently that you didn’t give yourself enough credit
for?
What is one thing you can remove from your everyday schedule to create more space for
rest and self-care?
Figure 1: Example conversations in the morning and late afternoon within the daily schedule dialogue.
associated with low mood or they could not fnd an emo- of this message sequence, the participant was asked whether
tion that refected their state of mind. Each message was they would like to receive the same message again in future.
designed to provide social support, such as validation or en- • Active writing: Participants received this activity sugges- couragement. The messages also included a sentence at the tion if they were experiencing an emotion with high mood.
beginning to explain the message source. Table 2 shows the The activity entailed drafting a text message to help some- four diferent message we used for this dialogue. At the end one else experiencing any negative emotion. Participants
Example conversations in the morning and late afternoon within the daily schedule dialogue
Page 9 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
Figure 2: An illustration of the circumplex model [108], which defnes emotions along on two dimensions: mood (valence) and
energy (arousal).
who saw this message were able to see example submissions
provided by the research team. Once the participant fnished
writing their message, they were asked if they would be
willing to share their message with others in future, and if
they would like to see it when they themselves were in a low
mood in the future.
Both sequences required responses at multiple points in order for
the next message to be sent. If participants did not send a response
at these junctions, the sequence was eventually dropped.
5.2 Participants
We recruited 42 participants between the ages of 18 and 25 to en- gage with our probe; we refer to them as DP1–DP42. Participants
were recruited using two distinct methods. The frst group of partic- ipants (DP1–DP6 and DP30–DP42) were enrolled via a combination
of snowball sampling and word of mouth, as in the formative study.
These participants did not have to meet any inclusion criteria other
than a general interest in testing a text message-based service for
managing psychological wellbeing. The other group of participants
(DP7–DP29) were recruited via targeted ads on MHA’s website,
the same community-based non-proft organization that facilitated
the formative study. These participants were recruited only if they
reported meeting clinical cutof scores for symptoms of depres- sion or anxiety according to the PHQ-9 or GAD-7 (scores of 10 or
higher). Five of these participants (DP7–DP11) also took part in our
formative study.
As before, we recruited participants in a rolling manner until we
reached data saturation. We were again not interested in drawing
comparisons between sources of participants, resulting in slightly
imbalanced group sizes. The mean age of our participants was
22.0 years old. Participants spanned two genders (29 women, 13
men) and multiple racial groups (17 Asian, 15 White, 4 African
American, 2 American Indian or Alaskan Native, 2 mixed, and 2
undisclosed). At the time of the study, all participants were living
in North America.
5.3 Study Procedure
Our investigation of contextual factors was part of a broader project
exploring people’s engagement with text messages of diverse types
(e.g., narrative messages, reminders, didactic lessons). Participants
were recruited in several waves between September 2020–February
2021. They were recruited to receive daily messages for a total
of 1–2 weeks depending on the number of daily dialogues that
were being tested in the broader project at the time. This paper
reports their experiences with a subset of dialogues centering on
contextual factors. Those in the earlier waves (DP1–DP11) received
the afective state dialogue on one day of the study, while the rest of
the participants (DP12–DP42) received the daily schedule dialogue
on one day of the study and the afective state dialogue on another.
We used Twilio, a message delivery platform, to send the mes- sages. Research team members manually sent messages using a
Wizard-of-Oz approach [90] so that unanticipated responses could
be handled using human judgment (e.g., open-ended response to a
close-ended question, spelling mistakes). Upon enrollment, partic- ipants were informed that their responses would be reviewed by
research team members for this purpose. The team members were
An illustration of the circumplex model, which defines emotions along two dimensions: mood (valence) and energy (arousal).
Page 10 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
Figure 3: Two example conversations under the afective state dialogue: (a) a conversation involving passive support reception,
and (b) a conversation involving active writing.
Two example conversations under the mood and energy dialogue: (a) a conversation involving passive support reception, and (b) a conversation involving active writing.
Two example conversations under the mood and energy dialogue: (a) a conversation involving passive support reception, and (b) a conversation involving active writing.
Page 11 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
Table 2: Examples of texts used for providing passive support in the afective state dialogue.
Message Type Messages
Professional Message Here is a message from one of our mental health professionals for
people going through rough days: "Focusing on the good in life is a
skill that gets better with practice."
Peer Message Here is a message from another person using the texting program:
"There are still good things in you and good things in the world."
General Message Here is a message written for people having hard days: "There are
still good things in you and good things in the world."
Self-written Message Here is a message you saved to send to yourself when you are hav- ing a rough day: [a supportive message written by the participant
themselves in an earlier study day as part of a diferent dialogue]
provided with a detailed script that contained instructions on the
branches conversations should follow based on responses of the
participants. After completing the study, participants were invited
to take part in a semi-structured interview to provide their feedback
on the two message dialogues and refect upon how the contextual
factors impacted their receptivity towards diferent messages. Our
questions included, but were not limited to:
• How did you feel about being asked to rate both mood and
energy, and then picking from a list of emotions? How would
you improve upon this?
• Did you notice any diferences in how responsive you were
to messages depending on morning versus afternoon? Or
based on your level of busyness?
• How did the message dialogues help you manage your mood
and negative emotions?
The interviewstook 10–30 minutes. They were conducted by one
member of the research team via telephone or the Zoom telecon- ference platform. We did not compensate participants for engaging
with our text messaging probe to ensure that the payment would
not infuence their interaction level, but participants who agreed to
give interviews were compensated at a rate of $20 USD per hour.
5.4 Data Analysis
We analyzed participants’ responses to the message dialogues using
mixed methods. Quantitatively, we report the response rates across
all participants for both of the message dialogues. We calculate
response rate as the number of response messages sent by a partici- pant relative to the number of responses expected. Qualitatively, we
analyze interview data using the same thematic analysis procedures
[18] used in our formative work, albeit with a separate codebook.
5.5 Ethical Considerations
We informed participants at the beginning of the study that the
messaging program was not intended to be a crisis service. We did
notsolicitsuicide-related information from participants at any point
during the study, but given the open-ended nature of text messaging,
we anticipated the unlikely possibility that someone could express
suicidal thoughts or other risk-related information while engaging
with our probe. Hence, we developed several measures to ensure
the safety of all participants. Participants were provided with the
contact information of several crisis services (e.g., suicide hotlines,
crisis text lines), and all text responses were reviewed on a daily
basis by the research team. If any message indicated a risk ofsuicidal
ideation or self-harm, team members were trained to reach out to
the sender of that message and conduct the Columbia-Suicide Risk
Assessment protocol [96], as previously described in Section 3.4.
No such risks emerged during the study, and therefore, we did not
need to conduct any follow-up assessments.
6 DEPLOYMENT STUDY FINDINGS
In this section, we frst briefy describe the amount of engagement
that participants had with the text message probes. We then report
participants’ feedback on the individual dialogues, followed by
insights that were revealed at the intersection of the underlying
contextual factors.
6.1 Engagement Across Both Dialogues
It is difcult to measure engagement for a text messaging service
like ours since people can choose to not respond to a dialogue
despite having read and followed its suggestions. Hence, we refrain
from making any statistical claims about participants’ preferences,
but we report quantitative metrics of engagement here as it may
relate to our qualitative observations.
For the daily schedule dialogue, the response rate across all par- ticipants was 72.6% (45/62), and 25 out of 31 participants responded
at least once. The response rate was 67.7% (21/31) for the mes- sages sent in the morning, and 77.4% (24/31) for the ones sent in
the afternoon. For the afective state dialogue, the number of re- sponses required to complete the dialogue varied depending on the
branch they followed. The response rate across all participants was
80.3% (106/132), and 37 out of 42 participants provided response at
least once. However, 19 participants fell short of providing three
responses required to receive a passive supportive text or a sugges- tion to do active writing (in response to the questions about mood,
energy, and discrete emotions).
6.2 Qualitative Feedback on Individual Message
Dialogues
6.2.1 Daily Schedule Dialogue. Our deployment study supported
many of the fndings from our formative work as far as how users’
daily schedule impacted their receptivity to text messages. Partici- pants were again divided in their choice between the morning and
Page 12 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
afternoon being their preferred time for receiving messages, and
these inclinations were both strong and personal. Preferences were
often based on a confuence of factors like the user’s availability,
their social situation, and the degree to which they were feeling
contemplative or connected to their emotions at particular times of
day. Those with preferences for morning messages also mentioned
that the interventions gave them the chance to set the tone or plan
for the coming day.
However, relative to participants from our formative study, par- ticipants who engaged directly with our probes provided a slightly
diferent perspective regarding their receptiveness to messages dur- ing work or school hours. These opinions were typically formed
when participants received a message in the morning but did not
have time to interact with it until a few hours later in the day. Af- ter these experiences, participants proposed that certain messages
could be well suited for busy periods of the day, provided they do
not demand too much time or immediate efort. For example, some
participants acknowledged that they often feel overworked, so they
appreciated text messages that reminded them to take breaks or
prioritize their mental health (e.g., “What is one thing you can re- move from your everyday schedule to create more space for rest
and self-care?”).
That being said, many deployment study participants echoed the
challengesthat were anticipated by the formative study participants
regarding messages during busy periods. They commented on the
pressure to maintain productivity and the difculty of switching
their attention during work hours, particularly when messages de- manded some degree of physical or mental exertion. Participants
like DP32 described that if they were asked to do an exercise or
outdoor activity during busy hours, they would simply not be able
to carry out the suggestion even if they found it appealing or poten- tially helpful. These high-efort suggestions would be postponed,
forgotten, or ignored. Participants like DP38 advocated that mes- sages should explicitly convey to users that it is reasonable for them
to follow through with the suggestion later in the day:
“Don’t ask people ‘Do you want to do this quick exercise
that you can do to help you relax?’. I might just say
‘no’ because I am doing something else, and now just
because I say ‘no’, you’re not sending me any follow-up.
But later on, when I am in the mood to do the exercise
or whatever, I might do it.” (DP38)
DP21 and DP29 provided similar opinions, stating that they often
read messagesimmediately upon reception but acted on them hours
later when it was more convenient. According to them, follow-up
and reminder messages can increase the probability that people do
the activities, but such messages should be sent in moderation to
avoid irritating users.
Related to this,some participantssuggested that messagesshould
be strategically distributed throughout the day to provide sustained
benefts and to maximize the likelihood of follow-through. DP34
suggested that even if a user prefers doing activitiesin the afternoon,
it may be helpful to provide some background information earlier
in the day to help them understand the rationale for proposing such
behavior changes or to mentally prepare for future messages. DP34
also suggested that users who prefer doing activities in the morning
may appreciate a follow-up message the following morning to serve
as a reminder:
“If I were to wake up, and then I see the text message on
my phone, it might not be the frst thing that I start my
day with. But then at 5 PM, if I get a question ‘What
was your favorite part?’, then I’m like, ‘Oh right, the
exercise! Ishould do that.’ . . . I think that being prompted
later in the day, might act as another reminder to go
and actually do the activity that was recommended.”
(DP34)
Although our fndings generally support expanding the times of
day we might considersending messages, there were also important
caveats. Specifcally, a few participants suggested that messaging
systems should not only try to send messages at users’ preferred
times but also focus on minimizing the probability of sending mes- sages at the most inconvenient times. They suspected that even
receiving a single message during a highly inconvenient time (e.g.,
during an exam or an important meeting) would have a signif- cantly negative impact on their engagement and retention. Thus,
in addition to asking users about their preferred time for receiv- ing messages upon enrollment, DP30 recommended that systems
should ask users to report the times during which they do not want
to receive any messages at all.
6.2.2 Afective State Dialogue. Participants generally appreciated
the messagesthat asked about and catered to their mood and energy
level. Confrming the expectations of participants in the formative
study, many people found the emotion labeling to be helpful, noting
that the activity facilitated emotional awareness. DP1 said,
“Having to look at all the diferent emotions is like,
‘Oh, actually, I’m feeling like this?’ Or like, it kind of
makes you separate what you’re feeling between like,
is it sadness, or is it just anxiousness.” (DP1)
Participants also felt that emotion labeling at the beginning of the
dialogue gave them a sense of control over the type of content they
received, which made the user experience feel more personal.
Some of the participants stated that they were unmotivated to
even look at their messages while they were experiencing low
mood and energy, but when they did, they appreciated the fact that
the corresponding passive support messages were less demanding.
Refecting on a peer message, for example, DP8 felt that it “really
resonated with how I was feeling.” During periods of high mood and
energy, the writing activity was also well received since it could be
used as a fun break from work and an opportunity to help others.
Although participants appreciated the ability to receive mes- sages tailored to their mood and energy, we received contradictory
opinions about the number of questions we sent to achieve this
functionality. Some participants felt that the dialogue included too
many back-and-forth questions to achieve emotion labeling. Dur- ing situations when they were experiencing low energy, DP27 felt
insufciently motivated to answer so many questions and wanted
to receive an activity suggestion more rapidly. On the other hand,
participants like DP22 and DP29 were willing to answer even more
questions. DP29 said,
“If I say something like my mood is high, I think that it
would be good to maybe include a question that says
Page 13 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
“What contributed to your high mood today?” so that
it can give the participant an opportunity to refect on
their day, see what’s contributing to that mood . . .If it’s
a low mood, then they will fully check-in and identify
the trigger for it so that they can make that connection.”
(DP29)
Continuing the feedback regarding the emotion labeling portion
of the dialogue, DP30 and DP31 suggested that there should be
more options for specifying their mood as they felt that the given
options failed to cover how they were feeling. DP10 noted that
a person can experience several emotions at the same time, so
the dialogue should have allowed users to select multiple options.
Finally, while we included a message to acknowledge the emotion
that the user had selected (e.g., “Sounds like you are feeling angry
right now”), a couple participants like DP32 felt that the system’s
repetition of what user had just said came across as condescending
and judgemental.
7 DISCUSSION
In our discussion, we frst summarize the key fndings of our work
and emphasize our contribution as it relates to adaptive DMH tools
and context-aware computing. We then provide some design rec- ommendations based on our fndings, after which we conclude by
enumerating some of the limitations of our work.
7.1 Key Insights
7.1.1 RQ1: Types of Contextual Factors That Were Identified. Our
work contributes to the long line of work on context-aware com- puting [6, 22, 63, 82, 98, 109] by recognizing the role of context
dictating people’s experiences with digital interventions. We saw
that people’s daily schedule and afective state are perceived to be
important contextual factors that infuence how they interact with
a text messaging system for managing psychological wellbeing.
These observations support fndings from prior HCI literature in
the digital mental health domain; for example, Bhattacharjee et al.
[6] advocated accounting for users’ schedules to increase interven- tion relevance, while Kornfeld et al. [63] suggested that DMH tools
should bifurcate the user experience depending on whether they
are experiencing low versus high mood and energy.
To extend this literature, we report diverse perspectives regard- ing how these factors impact one’s receptivity toward diferent
intervention content. For example, some people felt that they would
appreciate an encouraging and positive message in the beginning
of the day; as the day progresses, they would be less likely to check
their phone because of their daily commitments like work or school.
Another group of participants expressed a desire for receiving mes- sages after work hours since that was when they had free time or
experienced loneliness and distressing thoughts that messaging
could counteract. Past work has also found the need for support
after work hours particularly for individuals who tend to deviate
from the usual circadian cycle (e.g., going to sleep late at night and
waking up late in the morning) [28, 128]. Extending Kornfeld et
al.’s commentary on afective state [63], participants across both
studies generally expressed the need forsupport while experiencing
low mood and energy, preferring more passive forms of support.
While they were willing to complete an emotional labeling task
in order to receive appropriate forms of support, participants had
conficting opinions about the number of interactive messages to
which they would be willing to respond. Some found answering
the three questions in our afective state dialogue overwhelming,
while others wished for longer conversations to achieve further
customization and allow for deeper self-refection.
Extending the fndingsfrom our formative work, our deployment
study revealed additional associations between people’s daily sched- ule and afective state, providing insights on how people experience
the temporality of everyday life changes [98]. Several participants
mentioned feeling stressed due to anticipating an upcoming busy
schedule, while others shared that their emotions varied depending
on whether they were alone or surrounded by people.
7.1.2 RQ2: Adjusting Text Messaging Based on Contextual Factors.
Throughout our investigations, participants identifed several key
dimensions of text messaging that may be adjusted in relation to
the user’s context to support psychological wellbeing:
6.3 Associations Between Contextual Variables
While we created two separate dialogues to explore the importance
of contextual factors independently, our fndings revealed that par- ticipants frequently drew connections between their daily schedule
and their afective state. They pointed out how their mood and en- ergy levels fuctuate during diferent parts of the day. For example,
DP4 noted that their energy level tends to be low in the morning
just after waking up:
“If it’s early in the morning when I just get up from bed,
I’m really irritated. Like, I don’t want to say anything
and I don’t want to reply.” (DP4)
Confrming initial concerns voiced during our formative study,
participants occasionally experienced anxiety in the evening as
they refected on the workday that had passed. Several people also
said that they experienced undue stress as they anticipated a busy
schedule, whetherit was at the start of the busy day orthe preceding
night. DP32 refected on this connection:
“On Monday, I am super busy. So if the program knows
in advance about my busy days, it could kind of prompt
me before the next day . . . probably like in the evening. I
usually feel very overwhelmed on Sunday night.” (DP32)
People also shared that they required less support when they
were surrounded by other people. DP34 mentioned that when they
attended parties in the afternoon, they wanted to enjoy the time
with their friends rather than be distracted by messages. In fact,
they speculated that a message during those times could have even
worsened their mood by reminding them of their negative emotions.
Some people expressed that they did not want to be reminded of
strategies for managing their negative emotions even while being
surrounded by coworkers for similar reasons. However, in the era
of remote work induced by the COVID-19 pandemic, the distinc- tion between working and non-working hours became blurred, so
people like DP30 who were working from home recognized the
value of suggestions for mental health support during work hours.
Page 14 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
• Message volume: Past work has suggested that users have
very diferent preferences about the volume of messagesthey
wish to receive from a digital health intervention [7, 78]. Our
fndings build on this work by suggesting that, even for the
same individual, tolerance for messages fuctuates widely
based on context. Generally, participants did not appreciate
interruptions during work hours. Too many message notif- cations might distract users from their tasks, creating annoy- ance and an overall negative user experience [6, 131]. How- ever, these fndings also suggest that follow-up messages
and reminders are sometimes important. Our participants
proposed strategies for strategically scheduling follow-up
messages, such as sending an informational message in the
morning to prepare them for an activity in the afternoon.
Several people also advocated that messaging systemsshould
be respectful of their decreased willingness to be responsive
during periods of low mood and energy, so sending too many
notifcations during those times could be overwhelming.
• Required efort to engage: Literature on education and
social media has shown that diferent digital activities can
require varying degrees of cognitive efort [13, 39, 126]. For
example, researchers have shown that browsing a news feed
necessitates less efort than contributing to one. Our fnd- ingsreveal that such a diferentiation between passive versus
active engagement also extends to automated text messag- ing programs [29], with some passive support messages not
requiring deep engagement and other messages requiring
users to respond or carry out activities. Furthermore, we fnd
that the amount of efort a person is willing to exert depends
upon their context. Although active engagement with con- tent is generally more helpful for improving learning and
wellbeing [12, 13, 125], our results suggest that users are
often unwilling to exert signifcant efort during periods of
low mood and energy. At such times, we observe that passive
engagement can play a key role in maintaining a baseline
connection to the support system without overwhelming
users.
• Time sensitivity: Related to the required efort commanded
by messages is the urgency with which the message conveys
its suggestions [77]. Participants often objected when the
system prompted a particular task they did not have time to
complete; on the other hand, brief support messages and re- minders were generally wellreceived even when participants
were busy since they demanded less task-switching. Busy
users often delayed larger tasks tangential to their work, so
they felt that an intervention would be more acceptable if
it acknowledged and normalized delays (e.g., “Here is an
exercise to try when you have time”). Such strategies may be
particularly helpful during work hours or when the user’s
schedule is unknown.
These fndings can provide useful guidelines for selecting tailor- ing variables and creating decision rules in JITAIs for mental health
management [53]. In particular, they suggest ways of adapting spe- cifc elements of text messaging (e.g., volume, required efort, time
sensitivity) to account for the user’s context, some of which we
propose in Section 7.2. By highlighting the personal and complex
interaction between contextual factors and people’s intervention
needs, our work demonstrates how designers can carry out forma- tive investigation work with users to understand the subtle changes
in their expectations regarding context-aware DMH tools.
7.2 Design Recommendations
Based on the design tensions we observed in both of our studies, we
provide recommendations for how DMH tools designed to support
psychological wellbeing can adapt their functionality based on
contextual factors:
7.2.1 Gathering Detailed Schedule Information. Despite the fact
that our fndings indicate the need for adapting messages according
to people’s schedules, text messaging platforms are not ideally
suited for gathering such information on a daily basis. A suboptimal
way to accomplish this would be to send daily requests for people to
answer questions about their schedule, whether as a single question
that requires them to type out all of their activities or as a series of
yes-or-no questions about their availability at diferent hours. This
approach can either be cumbersome or insufcient for capturing the
nuances of a person’s schedule [6]. Another way to accommodate
people’sschedulesis by allowing for on-demand messaging wherein
users can request messages when they feel the need for support.
However, we recognize that some people might lack the energy
or forget to reach out for support as they experience negative
emotions.
As an alternate approach, text messaging systems could be in- tegrated with digital calendars, which have become a pervasive
tool for organizing events in both personal and professional set- tings [14]. By showing the time periods when a person is available
or unavailable, calendars can help text messaging systems better
integrate themselves into a person’s schedule. For example, days
with many meetings and fewer breaks have been found to be an
indicator of increased workplace stress [48, 74]. A text messaging
system might use calendar information to identify when a person is
attending consecutive meetings or classes and remind them to take
short breaks while also keeping the volume of messages acceptably
low. Such integration could be particularly important for people
who have unusual routines, such as those who work at night or
weekends. Access to one’s digital calendar would also make it eas- ier to build cohesive dialogues over the course of the day, seeding
background information before meetings and commitments while
sending reminders later in the day after these commitments are
over. Regardless of how digital calendars are integrated into a text
messaging system, careful consideration should be taken to ensure
user privacy when such information is monitored.
7.2.2 Incorporating Sleep and Physical Activity Information. Across
both of our studies, people often indirectly talked about their sleep
as an important factor on their ability to engage with messages. Peo- ple who wake up late in the morning found themselves rushing to
their classes or work and, as a result, they did not fnd time to notice
morning messages. Some people also shared that they experience
irrational thought patterns after their workday, leading them to
have sleep troubles. Sleep is an important indicator of one’s mental
state [75, 79, 100], making it relevant to one’s mood and energy.
The same can be said for physical activity and movement [93, 107].
Page 15 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
Mobile phone sensors are capable of tracking sleep and physical
activity with moderate accuracy [19, 100]. By simply identifying
when a person frst opens their phone in the morning, a text mes- saging system could infer an upper bound for the user’s wakeup
time. Comparing that time to the user’s typical sleep schedule, a
text messaging system can decide whether they have sufcient
time to read a message or if they are going to feel rushed while
getting ready for the day. Conversely, detecting that the user is
not asleep after their normal bedtime may indicate that they are
having trouble falling asleep due to distressing thoughts. In such
situations, a text messaging system could provide supportive mes- sages and strategies for improving their sleep cycle. Mobile phone
sensors like accelerometers, gyroscopes, and GPS sensors can also
provide precise and accurate information about a user’s movement
(e.g., walking, running, in vehicle, on bicycle) [80, 130]. Together
with digital calendars, these sensors can be used to detect when
an individual is available and to personalize the message content
accordingly.
7.2.3 Mitigating Negative User Experiences. JITAIs ideally fade into
the background of a person’s day-to-day experiences, interven- ing only when necessary to initiate or sustain positive behavior
changes [87]. Sensor-based technologies and algorithms for detect- ing the user’s context can occasionally be incorrect, leading to the
delivery of interventions at inopportune moments. Literature on
human-centered design suggests that users remember negative ex- periences more strongly and for a longer period than positive ones,
ultimately having greater impact on their behavior [73]. Consis- tent with this fnding, some of our deployment study participants
suggested that systems should not only account for the most op- portune moments for intervening but also inconvenient times for
delivering an intervention. Participants informed us of several sit- uations when they would not like to receive a message, such as
mornings when they are rushing to prepare for the day or during
exams or important meetings. Hence, text messaging applications
should actively collect information about inconvenient times for
content delivery using the aforementioned data sources (e.g., digital
calendars and mobile phone sensors).
Negative user experiences may also be infuenced by the content
or language used in a given message. Participants in our studies
occasionally raised concerns about the perceived urgency of our
message suggestions, so future interventions may want to consider
explicitly giving users the fexibility to follow through whenever
it is most convenient for them. Doing so may result in partici- pants delaying and forgetting about the messages altogether; hence,
follow-up and reminder messages should be strategically placed.
Giving users the option to snooze messages for a short period of
time may also provide users with control and reduce feelings of
being rushed or overwhelmed.
7.2.4 Balancing Demand with Mood and Energy. Our fndings sug- gest a few design tensions with regards to how messages should be
constructed to support people experiencing low mood and energy.
Participants across both studies generally said that they were not
keen to respond to messages when they experience negative emo- tions with the exception of brief check-in messages. However, our
afective state dialogue required users to answer three questions in
order to receive such a personalized message, which was perceived
as too demanding by a subset of participants. On the other hand,
some people wanted the fexibility to express their emotions in a
way that often required more than three questions. One way to
address this tension could be by giving users an open-ended prompt
where they can express their feelings with as much detail as they
would like and leveraging sentiment analysis techniques to infer
their afective state automatically. The length of the user’s response
may also be an indicator of their availability, with shorter responses
suggesting that the user is unlikely to respond to follow-up ques- tions.
7.2.5 Facilitating Social Connection and Difuse Sociality. Our fnd- ings touched upon some social aspects of the text messaging ex- perience. For example, some participants shared that they did not
wish to engage with text messages when they were around other
people they knew; however, they generally expressed willingness
to connect with people experiencing similar problems to their own
via text messaging platforms. We observed that when their mood
was high, the participants from the deployment study appreciated
drafting supportive texts to help others going through difcult
times. When their mood was low, receiving messages could provide
a sense of being less alone. Our fndings are consistent with the
need to support “difuse sociality” [11] (i.e., the feeling of connec- tion with others without direct interactions) in DMH interventions.
Many individuals with depression prefer to engage with DMH tools
independently [102], but may still beneft from building a sense of
connection with others through opportunities for difuse sociality.
Text messaging platforms can facilitate difuse sociality in several
ways beyond the peer-to-peer message exchange examined here.
For example, users can engage with peers’ frst-person narratives
of recovery so that they can draw explicit connections between
narrative characters and themselves [7]. In addition, guided chat
could be applied to buttress more in-depth peer-to-peer support
interactions. For example, multi-step structured prompts have pro- vided guidance both for the sharing of personal experiences and
for provision of personalized support [92]. Based on our fndings,
some of these strategies may come across as overwhelming during
periods of low mood or high busyness as they require users to
read and respond to a sequence of messages; however, strategically
deploying them throughout the week at opportune moments can
mitigate such concerns.
7.3 Limitations
Our text messaging probe was designed specifcally to support
young adults between the ages of 18 and 25. Although they spanned
diferent ethnicities, our participants were living in North America
at the time we ran our study. Hence, our fndings should be situated
within a particular cultural context. Although our fndings uncov- ered daily schedule, mood, and energy level as critical contextual
factors for DMH tools to consider, other issues like busyness and
social proximity were occasionally surfaced. With a diferent popu- lation, these issues might have been even more prominent. Similar
lines of work could also explore how the role of contextual factors
may vary depending on individuals’ habits of phone usage.
We also recognize that our deployment study examined each
contextual factor in relation to bespoke message dialogues with
Page 16 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
a handful of message varieties. Although we encouraged partic- ipants to comment on other types of messages they anticipated
would be better suited to varied contexts, future work could further
investigate alternative message categories.
Some people also reported that the options we provided during
the emotion labeling portion of the afective state dialogue failed to
covertheir experiences. This could be due to ourframing around the
circumplex model, which has its limitations despite being widely
used model in the literature [134]. Most notably, researchers have
criticized the circumplex model for not relying upon clear theoreti- cal guidelines in the mapping between its dimensions and various
emotions [83, 101]. Future work could explore other models for
operationalizing emotion labeling, such as Plutchik’s model [81] or
the Pleasure-Arousal-Dominance model [76].
Finally, we note that what participants might appreciate most at
a particular moment is not necessarily what will bring the great- est beneft regarding reducing symptoms. Therefore, DMH tools
should be evaluated not only according to how well they can reach
and engage users, but also according to how well they can reduce
symptoms of depression and anxiety by producing new thoughts
and behavior patterns.
8 CONCLUSION
Understanding how users view dynamic contextual factors in their
lives is important to the design of DMH tools, as it enables the de- livery of interventions that are well-matched to their needs. In this
work, we investigated how such variables infuence the reception
of text messaging systems for supporting psychological wellbeing.
Our formative work and deployment study revealed one’s daily
schedule and afective state to be prominent contextual factors
that shape how users receive and act upon messages. Participants
across both studies felt that text messaging systems should adapt
the message volume, time sensitivity, and required efort for the
interventions according to those contextual factors. We hope that
our work represents a major frst step towards the development of
user-centered, context-aware DMH tools.
ACKNOWLEDGMENTS
We are grateful to the young adults who participated in this work,
and to Theresa Nguyen and Kevin Rushton at Mental Health Amer- ica. We also thank Bei Pang, Jehan Vakharia, and Alvina Lai for
their help in collecting these data, and Zichen Gong for help in
drawing fgures and proofreading the manuscript. This work was
supported by grants from the National Institute of Mental Health
(K01MH125172, R34MH124960), the Ofce ofNaval Research (N00014-
18-1-2755, N00014-21-1-2576), and the Natural Sciences and Engi- neering Research Council of Canada (RGPIN-2019-06968). In addi- tion, we acknowledge a gift from the Microsoft AI for Accessibility
program to the Center for Behavioral Intervention Technologies
that, in part, supported this work (http://aka.ms/ai4a).
REFERENCES
[1] Vincent Israel Ouoku Agyapong, Marianne Hrabok, Wesley Vuong, Reham
Shalaby, Jasmine Marie Noble, April Gusnowski, Kelly J Mrklas, Daniel Li, Liana
Urichuk, Mark Snaterse, et al. 2020. Changes in stress, anxiety, and depression
levels of subscribers to a daily supportive text message program (Text4Hope)
during the COVID-19 pandemic: cross-sectional survey study. JMIR Mental
Health 7, 12 (2020), e22423.
[2] Emily R Arps, Myron D Friesen, and Nickola C Overall. 2018. Promoting
youth mental health via text-messages: a new zealand feasibility study. Applied
Psychology: Health and Well-Being 10, 3 (2018), 457–480.
[3] Eric PS Baumer. 2015. Refective informatics: conceptual dimensions for design- ing technologies of refection. In Proceedings of the 33rd Annual ACM Conference
on Human Factors in Computing Systems. 585–594.
[4] Sofan Berrouiguet, Enrique Baca-García, Sara Brandt, Michel Walter, Philippe
Courtet, et al. 2016. Fundamentals for future mobile-health (mHealth): a sys- tematic review of mobile phone and web-based text messaging in mental health.
Journal of medical Internet research 18, 6 (2016), e5066.
[5] Alexandra H Bettis, Taylor A Burke, Jacqueline Nesi, and Richard T Liu. 2022.
Digital technologies for emotion-regulation assessment and intervention: A
conceptual review. Clinical Psychological Science 10, 1 (2022), 3–26.
[6] Ananya Bhattacharjee, Jiayu Pang, Angelina Liu, Alex Mariakakis, and
Joseph Jay Williams. 2022. Design Implications for One-Way Text Messaging
Services that Support Psychological Wellbeing. ACM Transactions on Computer- Human Interaction (TOCHI) (2022).
[7] Ananya Bhattacharjee, Joseph Jay Williams, Karrie Chou, Justice Tomlinson,
Jonah Meyerhof, Alex Mariakakis, and Rachel Kornfeld. 2022. " I Kind of Bounce
of It": Translating Mental Health Principles into Real Life Through Story-Based
Text Messages. Proceedings of the ACM on Human-Computer Interaction 6,
CSCW2 (2022), 1–31.
[8] G Bhuvaneswari and E Emiline Joy. 2021. Assess the level of stress, sleep
disturbance, depression with nomophobia among undergraduate students. In- ternational Journal of Advanced Psychiatric Nursing 3, 2 (2021), 24–27.
[9] Judith Borghouts, Elizabeth Eikey, Gloria Mark, Cinthia De Leon, Stephen M
Schueller, Margaret Schneider, Nicole Stadnick, Kai Zheng, Dana Mukamel,
Dara H Sorkin, et al. 2021. Barriers to and facilitators of user engagement
with digital mental health interventions: systematic review. Journal of medical
Internet research 23, 3 (2021), e24387.
[10] Stephen L Brown, Brandye D Nobiling, James Teufel, and David A Birch. 2011.
Are kids too busy? Early adolescents’ perceptions of discretionary activities,
overscheduling, and stress. Journal of school health 81, 9 (2011), 574–580.
[11] Eleanor R Burgess, Kathryn E Ringland, Jennifer Nicholas, Ashley A Knapp,
Jordan Eschler, David C Mohr, and Madhu C Reddy. 2019. " I think people are
powerful" The Sociality of Individuals Managing Depression. Proceedings of the
ACM on Human-Computer Interaction 3, CSCW (2019), 1–29.
[12] Moira Burke and Robert E Kraut. 2016. The relationship between Facebook use
and well-being depends on communication type and tie strength. Journal of
computer-mediated communication 21, 4 (2016), 265–281.
[13] Moira Burke, Cameron Marlow, and Thomas Lento. 2010. Social network activity
and social well-being. In Proceedings of the SIGCHI conference on human factors
in computing systems. 1909–1912.
[14] Daniel Buzzo and Nicolo Merendino. 2015. Not all days are equal: investigating
the meaning in the digital calendar. In Proceedings of the 33rd Annual ACM
Conference Extended Abstracts on Human Factors in Computing Systems. 489–
501.
[15] Pew Research Center. 2022. Mobile Fact Sheet. Retrieved 2022-11-10 from
https://www.pewresearch.org/internet/fact-sheet/mobile/
[16] Prerna Chikersal, Danielle Belgrave, Gavin Doherty, Angel Enrique, Jorge E
Palacios, Derek Richards, and Anja Thieme. 2020. Understanding client support
strategies to improve clinical outcomes in an online mental health intervention.
In Proceedings of the 2020 CHI Conference on Human Factorsin Computing Systems.
1–16.
[17] Shanice Clarke, Luis G Jaimes, and Miguel A Labrador. 2017. mstress: A mobile
recommender system for just-in-time interventions for stress. In 2017 14th IEEE
Annual Consumer Communications & Networking Conference (CCNC). IEEE, 1–5.
[18] Harris Ed Cooper, Paul M Camic, Debra L Long, AT Panter, David Ed Rindskopf,
and Kenneth J Sher. 2012. APA handbook of research methodsin psychology, Vol
2: Research designs: Quantitative, qualitative, neuropsychological, and biological.
(2012).
[19] Nediyana Daskalova, Jina Yoon, Yibing Wang, Cintia Araujo, Guillermo Bel- tran Jr, Nicole Nugent, John McGeary, Joseph Jay Williams, and Jef Huang.
2020. SleepBandits: Guided fexible self-experiments for sleep. In Proceedings of
the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.
[20] Anind K Dey. 2001. Understanding and using context. Personal and ubiquitous
computing 5, 1 (2001), 4–7.
[21] EL Donaldson, S Fallows, and M Morris. 2014. A text message based weight
management intervention for overweight adults. Journal of Human Nutrition
and Dietetics 27 (2014), 90–97.
[22] Paul Dourish. 2004. What we talk about when we talk about context. Personal
and ubiquitous computing 8, 1 (2004), 19–30.
[23] Lígia Duro, Pedro F Campos, Teresa Romão, and Evangelos Karapanos. 2019.
Visual Quotes: Does Aesthetic Appeal Infuence How Perceived Motivating Text
Messages Impact Short-Term Exercise Motivation?. In Extended Abstracts of the
2019 CHI Conference on Human Factors in Computing Systems. 1–6.
[24] Ashley B Elefant, Omar Contreras, Ricardo F Muñoz, Eduardo L Bunge, and Yan
Leykin. 2017. Microinterventions produce immediate but not lasting benefts in
Page 17 of 19
Investigating the Role of Context in the Delivery of Text Messages CHI ’23, April 23–28, 2023, Hamburg, Germany
mood and distress. Internet interventions 10 (2017), 17–22.
[25] Anam Feroz, Farina Abrejo, Sumera Aziz Ali, Rozina Nuruddin, and Sarah
Saleem. 2019. Using mobile phones to improve young people’s sexual and
reproductive health in low-and middle-income countries: a systematic review
protocol to identify barriers, facilitators and reported interventions. Systematic
reviews 8, 1 (2019), 1–7.
[26] Caroline A Figueroa, Nina Deliu, Bibhas Chakraborty, Arghavan Modiri, Jing Xu,
Jai Aggarwal, Joseph Jay Williams, Courtney Lyles, and Adrian Aguilera. 2022.
Daily Motivational Text Messages to Promote Physical Activity in University
Students: Results From a Microrandomized Trial. Annals of Behavioral Medicine
56, 2 (2022), 212–218.
[27] Skye Fitzpatrick, Jennifer Ip, Lillian Krantz, Richard Zeifman, and Janice R Kuo.
2019. Use your words: The role of emotion labeling in regulating emotion
in borderline personality disorder. Behaviour research and therapy 120 (2019),
103447.
[28] Russell G Foster, Stuart N Peirson, Katharina Wulf, Eva Winnebeck, Céline
Vetter, and Till Roenneberg. 2013. Sleep and circadian rhythm disruption in
social jetlag and mental illness. Progress in molecular biology and translational
science 119 (2013), 325–346.
[29] Victoria Franklin, Alexandra Greene, Annalu Waller, Stephen Greene, Claudia
Pagliari, et al. 2008. Patients’ engagement with “Sweet Talk”–a text messaging
support system for young people with diabetes. Journal of medical Internet
research 10, 2 (2008), e962.
[30] Yoshimi Fukuoka, Caryl Gay, William Haskell, Shoshana Arai, Eric Vittinghof,
et al. 2015. Identifying factors associated with dropout during prerandomization
run-in period from an mHealth physical activity education study: the mPED
trial. JMIR mHealth and uHealth 3, 2 (2015), e3928.
[31] Sandra Garrido, Chris Millington, Daniel Cheers, Katherine Boydell, Emery
Schubert, Tanya Meade, and Quang Vinh Nguyen. 2019. What works and what
doesn’t work? A systematic review of digital mental health interventions for
depression and anxiety in young people. Frontiers in psychiatry 10 (2019), 759.
[32] Asma Ghandeharioun, Asaph Azaria, Sara Taylor, and Rosalind W Picard. 2016.
“Kind and Grateful”: A context-sensitive smartphone app utilizing inspirational
content to promote gratitude. Psychology of well-being 6, 1 (2016), 1–21.
[33] Anita Gibbs. 1997. Focus groups. Social research update 19, 8 (1997), 1–8.
[34] Suzette Glasner, Kevin Patrick, Michele Ybarra, Cathy J Reback, Alfonso Ang,
Seth Kalichman, Ken Bachrach, Hélène Chokron Garneau, Alexandra Venegas,
and Richard A Rawson. 2022. Promising outcomes from a cognitive behavioral
therapy text-messaging intervention targeting drug use, antiretroviral therapy
adherence, and HIV risk behaviors among adults living with HIV and substance
use disorders. Drug and Alcohol Dependence 231 (2022), 109229.
[35] Stephanie P Goldstein, Brittney C Evans, Daniel Flack, Adrienne Juarascio,
Stephanie Manasse, Fengqing Zhang, and Evan M Forman. 2017. Return of the
JITAI: applying a just-in-time adaptive intervention framework to the devel- opment of m-health solutions for addictive behaviors. International journal of
behavioral medicine 24, 5 (2017), 673–682.
[36] Suat Gonul, Tuncay Namli, Sasja Huisman, Gokce Banu Laleci Erturkmen, Is- mail Hakki Toroslu, and Ahmet Cosar. 2019. An expandable approach for design
and personalization of digital, just-in-time adaptive interventions. Journal of
the American Medical Informatics Association 26, 3 (2019), 198–210.
[37] Leo A Goodman. 1961. Snowball sampling. The annals of mathematical statistics
(1961), 148–170.
[38] Peter Graw, Kurt Kräuchi, Anna Wirz-Justice, and Walter Pöldinger. 1991. Diur- nal variation of symptoms in seasonal afective disorder. Psychiatry research 37,
1 (1991), 105–111.
[39] Paul Haidet, Robert O Morgan, Kimberly O’malley, Betty Jeanne Moran, and
Boyd F Richards. 2004. A controlled trial of active versus passive learning
strategies in a large group setting. Advances in Health Sciences Education 9, 1
(2004), 15–27.
[40] Wendy Hardeman, Julie Houghton, Kathleen Lane, Andy Jones, and Felix
Naughton. 2019. A systematic review of just-in-time adaptive interventions (JI- TAIs) to promote physical activity. International Journal of Behavioral Nutrition
and Physical Activity 16, 1 (2019), 1–21.
[41] Severin Haug, Michael P Schaub, Vigeli Venzin, Christian Meyer, and Ulrich
John. 2013. Efcacy of a text message-based smoking cessation intervention for
young people: a cluster randomized controlled trial. Journal of medical Internet
research 15, 8 (2013), e171.
[42] Severin Haug, Michael P Schaub, Vigeli Venzin, Christian Meyer, Ulrich John,
and Gerhard Gmel. 2013. A pre-post study on the appropriateness and efec- tiveness of a Web-and text messaging-based intervention to reduce problem
drinking in emerging adults. Journal of medical Internet research 15, 9 (2013),
e196.
[43] Steven C Hayes, Kirk D Strosahl, Kara Bunting, Michael Twohig, and Kelly G
Wilson. 2004. What is acceptance and commitment therapy? In A practical
guide to acceptance and commitment therapy. Springer, 3–29.
[44] Kristin E Heron, Kelly A Romano, and Abby L Braitman. 2019. Mobile technology
use and mHealth text message preferences: an examination of gender, racial, and
ethnic diferences among emerging adult college students. Mhealth 5 (2019).
[45] Jennifer Hettema, Julie Steele, William R Miller, et al. 2005. Motivational inter- viewing. Annual Review of Clinical Psychology(2005) 1, 1 (2005), 91–111.
[46] Viviana E Horigian, Renae D Schmidt, and Daniel J Feaster. 2021. Loneliness,
mental health, and substance use among US young adults during COVID-19.
Journal of psychoactive drugs 53, 1 (2021), 1–9.
[47] Gabrielle N Horner, Stephen Agboola, Kamal Jethwani, Aswita Tan-McGrory,
and Lenny Lopez. 2017. Designing patient-centered text messaging interven- tions for increasing physical activity among participants with type 2 diabetes:
qualitative resultsfrom the text to move intervention. JMIR mHealth and uHealth
5, 4 (2017), e6666.
[48] Esther Howe, Jina Suh, Mehrab Bin Morshed, Daniel McDuf, Kael Rowan,
Javier Hernandez, Marah Ihab Abdin, Gonzalo Ramos, Tracy Tran, and Mary P
Czerwinski. 2022. Design of Digital Workplace Stress-Reduction Intervention
Systems: Efects of Intervention Type and Timing. In CHI Conference on Human
Factors in Computing Systems. 1–16.
[49] Galen Chin-Lun Hung, Pei-Ching Yang, Chen-Yi Wang, and Jung-Hsien Chiang.
2015. A smartphone-based personalized activity recommender system for
patients with depression. In Proceedings of the 5th EAI International Conference
on Wireless Mobile Communication and Healthcare. 253–257.
[50] Becky Inkster, Shubhankar Sarda, and Vinod Subramanian. 2018. An empathy- driven, conversational artifcial intelligence agent (Wysa) for digital mental
well-being: real-world data evaluation mixed-methods study. JMIR mHealth
and uHealth 6, 11 (2018), e12106.
[51] Tasnim Ismail, Dena Al Thani, et al. 2022. Design and Evaluation of a Just- in-Time Adaptive Intervention (JITAI) to Reduce Sedentary Behavior at Work:
Experimental Study. JMIR Formative Research 6, 1 (2022), e34309.
[52] Jon Kabat-Zinn and Thich Nhat Hanh. 2009. Full catastrophe living: Using the
wisdom of your body and mind to face stress, pain, and illness. Delta.
[53] Kazi Sinthia Kabir, Stacey A Kenfeld, Erin L Van Blarigan, June M Chan, and
Jason Wiese. 2022. Ask the Users: A Case Study of Leveraging User-Centered
Design for Designing Just-in-Time Adaptive Interventions (JITAIs). Proceedings
of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, 2
(2022), 1–21.
[54] Evangelos Karapanos, John Zimmerman, Jodi Forlizzi, and Jean-Bernard Martens.
2009. User experience over time: an initial framework. In Proceedings of the
SIGCHI conference on human factors in computing systems. 729–738.
[55] Shahedul Huq Khandkar. 2009. Open coding. University of Calgary 23 (2009),
2009.
[56] Bang Hyun Kim and Karen Glanz. 2013. Text messaging to motivate walking
in older African Americans: a randomized controlled trial. American journal of
preventive medicine 44, 1 (2013), 71–75.
[57] Seoyoun Kim, Hyunwoo Yoon, Patricia Morton, and Yuri Jang. 2022. Longi- tudinal links between behavioral activation coping strategies and depressive
symptoms of US adults living alone during the COVID-19 pandemic. Plos one
17, 5 (2022), e0267948.
[58] Predrag Klasnja, Eric B Hekler, Saul Shifman, Audrey Boruvka, Daniel Almi- rall, Ambuj Tewari, and Susan A Murphy. 2015. Microrandomized trials: An
experimental design for developing just-in-time adaptive interventions. Health
Psychology 34, S (2015), 1220.
[59] Rafal Kocielnik, Lillian Xiao, Daniel Avrahami, and Gary Hsieh. 2018. Refection
companion: A conversationalsystem for engaging usersin refection on physical
activity. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous
Technologies 2, 2 (2018), 1–26.
[60] Artie Konrad, Victoria Bellotti, Nicole Crenshaw, Simon Tucker, Les Nelson,
Honglu Du, Peter Pirolli, and Steve Whittaker. 2015. Finding the adaptive sweet
spot: Balancing compliance and achievement in automated stress reduction. In
Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing
Systems. 3829–3838.
[61] Rachel Kornfeld, Jonah Meyerhof, Hannah Studd, Ananya Bhattacharjee,
Joseph Jay Williams, Madhu Reddy, and David C Mohr. 2022. Meeting Users
Where They Are: User-centered Design of an Automated Text Messaging Tool
to Support the Mental Health of Young Adults. In CHI Conference on Human
Factors in Computing Systems. 1–16.
[62] Rachel Kornfeld, David C Mohr, Rachel Ranney, Emily G Lattie, Jonah Mey- erhof, Joseph J Williams, and Madhu Reddy. 2022. Involving Crowdworkers
with Lived Experience in Content-Development for Push-Based Digital Mental
Health Tools: Lessons Learned from Crowdsourcing Mental Health Messages.
Proceedings of the ACM on Human-computer Interaction 6, CSCW1 (2022), 1–30.
[63] Rachel Kornfeld, Renwen Zhang, Jennifer Nicholas, Stephen M Schueller,
Scott A Cambo, David C Mohr, and Madhu Reddy. 2020. " Energy is a Fi- nite Resource": Designing Technology to Support Individuals across Fluctuating
Symptoms of Depression. In Proceedings of the 2020 CHI Conference on Human
Factors in Computing Systems. 1–17.
[64] Kira Kretzschmar, Holly Tyroll, Gabriela Pavarini, Arianna Manzini, Ilina Singh,
and NeurOx Young People’s Advisory Group. 2019. Can your phone be your
therapist? Young people’s ethical perspectives on the use of fully automated con- versational agents (chatbots) in mental health support. Biomedical informatics
insights 11 (2019), 1178222619829083.
Page 18 of 19
CHI ’23, April 23–28, 2023, Hamburg, Germany Ananya Bhatacharjee et al.
[65] Kurt Kroenke, Robert L Spitzer, and Janet BW Williams. 2001. The PHQ-9:
validity of a brief depression severity measure. Journal of general internal
medicine 16, 9 (2001), 606–613.
[66] Brian Yoshio Laing, Carol M Mangione, Chi-Hong Tseng, Mei Leng, Ekaterina
Vaisberg, Megha Mahida, Michelle Bholat, Eve Glazier, Donald E Morisky, and
Douglas S Bell. 2014. Efectiveness of a smartphone application for weight loss
compared with usual care in overweight primary care patients: a randomized,
controlled trial. Annals of internal medicine 161, 10_Supplement (2014), S5–S12.
[67] Efe Lai-Chong Law, Virpi Roto, Marc Hassenzahl, Arnold POS Vermeeren, and
Joke Kort. 2009. Understanding, scoping and defning user experience: a survey
approach. In Proceedings of the SIGCHI conference on human factors in computing
systems. 719–728.
[68] Jennifer B Levin, Martha Sajatovic, Mahboob Rahman, Michelle E Aebi, Curt
Tatsuoka, Colin Depp, Clint Cushman, Elaine Johnston, Kristin A Cassidy, Carol
Blixen, et al. 2019. Outcomes of Psychoeducation and a Text Messaging Adher- ence Intervention Among Individuals With Hypertension and Bipolar Disorder.
Psychiatric Services 70, 7 (2019), 608–612.
[69] Yanhui Liao, Qiuxia Wu, Brian C Kelly, Fengyu Zhang, Yi-Yuan Tang, Qianjin
Wang, Honghong Ren, Yuzhu Hao, Mei Yang, Joanna Cohen, et al. 2018. Ef- fectiveness of a text-messaging-based smoking cessation intervention (“Happy
Quit”) for smoking cessation in China: a randomized controlled trial. PLoS
medicine 15, 12 (2018), e1002713.
[70] James J Lin, Lena Mamykina, Silvia Lindtner, Gregory Delajoux, and Henry B
Strub. 2006. Fish’n’Steps: Encouraging physical activity with an interactive
computer game. In International conference on ubiquitous computing. Springer,
261–278.
[71] Marsha Linehan. 2014. DBT? Skills training manual. Guilford Publications.
[72] Bernd Löwe, Oliver Decker, Stefanie Müller, Elmar Brähler, Dieter Schellberg,
Wolfgang Herzog, and Philipp Yorck Herzberg. 2008. Validation and standard- ization of the Generalized Anxiety Disorder Screener (GAD-7) in the general
population. Medical care (2008), 266–274.
[73] Dominik Pascal Magin, Andreas Maier, and Stefen Hess. 2015. Measuring
negative user experience. In International Conference of Design, User Experience,
and Usability. Springer, 95–106.
[74] Gloria Mark, Shamsi Iqbal, and Mary Czerwinski. 2017. How blocking dis- tractions afects workplace focus and productivity. In Proceedings of the 2017
ACM International Joint Conference on Pervasive and Ubiquitous Computing and
Proceedings of the 2017 ACM International Symposium on Wearable Computers.
928–934.
[75] Mark Matthews, Erin Carroll, Saeed Abdullah, Jaime Snyder, Matthew Kay,
Tanzeem Choudhury, Geri Gay, and Julie Kientz. 2014. Biological rhythms
and technology. In CHI’14 Extended Abstracts on Human Factors in Computing
Systems. 123–126.
[76] Albert Mehrabian. 1996. Pleasure-arousal-dominance: A general framework
for describing and measuring individual diferences in temperament. Current
Psychology 14, 4 (1996), 261–292.
[77] Arielle Mendel, Anthony Lott, Lisha Lo, and Robert Wu. 2018. A matter of ur- gency: reducing clinical text message interruptions during educational sessions.
Journal of Hospital Medicine 13, 9 (2018), 616–622.
[78] Jonah Meyerhof, Theresa Nguyen, ChrisJ Karr, Madhu Reddy, Joseph J Williams,
Ananya Bhattacharjee, David C Mohr, and Rachel Kornfeld. 2022. System design
of a text messaging program to support the mental health needs of non-treatment
seeking young adults. Procedia Computer Science 206 (2022), 68–80.
[79] Helen M Milojevich and Angela F Lukowski. 2016. Sleep and mental health
in undergraduate students with generally healthy sleep habits. PloS one 11, 6
(2016), e0156372.
[80] David C Mohr, Mi Zhang, and Stephen M Schueller. 2017. Personal sensing:
understanding mental health using ubiquitous sensors and machine learning.
Annual review of clinical psychology 13 (2017), 23.
[81] Manshad Abbasi Mohsin and Anatoly Beltiukov. 2019. Summarizing emotions
from text using Plutchik’s wheel of emotions. In 7th Scientifc Conference on
Information Technologies for Intelligent Decision Making Support (ITIDS 2019).
Atlantis Press, 291–294.
[82] T Moran and P Dourish. 2001. Human-Computer Interaction Journal, Special
Issue on Context-Aware Computing, 16, 2–4. Lawrence Erlbaum, Mahwah, New
Jersey. Raghvan, VV, Sever, H. On the Reuse of Past Optimal Queries. In: Proceedings
of ACM SIGIR 95 (2001), 344–350.
[83] Rick L Morgan and David Heise. 1988. Structure of emotions. Social Psychology
Quarterly (1988), 19–31.
[84] Robert R Morris, Kareem Kouddous, Rohan Kshirsagar, and Stephen M Schueller.
2018. Towards an artifcially empathic conversational agent for mental health
applications: system design and user perceptions. Journal of medical Internet
research 20, 6 (2018), e10148.
[85] Elizabeth L Murnane, Xin Jiang, Anna Kong, Michelle Park, Weili Shi, Connor
Soohoo, Luke Vink, Iris Xia, Xin Yu, John Yang-Sammataro, et al. 2020. Designing
ambient narrative-based interfaces to refect and motivate physical activity. In
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems.
1–14.
[86] I Myin-Germeys. 2022. Digital Mental Health: Towards Personalised Care in
Psychiatry. European Psychiatry 65, S1 (2022), S4–S5.
[87] Inbal Nahum-Shani, Shawna N Smith, Bonnie J Spring, Linda M Collins, Katie
Witkiewitz, Ambuj Tewari, and Susan A Murphy. 2018. Just-in-time adaptive
interventions (JITAIs) in mobile health: key components and design principles
for ongoing health behavior support. Annals of Behavioral Medicine 52, 6 (2018),
446–462.
[88] Felix Naughton, Chloë Brown, Juliet High, Caitlin Notley, Cecilia Mascolo, Tim
Coleman, Garry Barton, Lee Shepstone, Stephen Sutton, A Toby Prevost, et al.
2021. Randomised controlled trial of a just-in-time adaptive intervention (JITAI)
smoking cessation smartphone app: the Quit Sense feasibility trial protocol.
BMJ open 11, 4 (2021), e048204.
[89] Ada Ng, Rachel Kornfeld, Stephen M Schueller, Alyson K Zalta, Michael Bren- nan, and Madhu Reddy. 2019. Provider perspectives on integrating sensor- captured patient-generated data in mental health care. Proceedings of the ACM
on human-computer interaction 3, CSCW (2019), 1–25.
[90] Oda Elise Nordberg, Jo Dugstad Wake, Emilie Sektnan Nordby, Eivind Flobak,
Tine Nordgreen, Suresh Kumar Mukhiya, and Frode Guribye. 2019. Designing
chatbots for guiding online peer support conversations for adults with ADHD.
In International Workshop on Chatbot Research and Design. Springer, 113–126.
[91] Marianna Obrist, Manfred Tscheligi, Boris de Ruyter, and Albrecht Schmidt.
2010. Contextual user experience: how to refect it in interaction designs? In
CHI’10 Extended Abstracts on Human Factors in Computing Systems. 3197–3200.
[92] Kathleen O’Leary, Stephen M Schueller, Jacob O Wobbrock, and Wanda Pratt.
2018. “Suddenly, we got to become therapists for each other” Designing Peer
Support Chats for Mental Health. In Proceedings of the 2018 CHI Conference on
Human Factors in Computing Systems. 1–14.
[93] Scott A Paluska and Thomas L Schwenk. 2000. Physical activity and mental
health. Sports medicine 29, 3 (2000), 167–180.
[94] Pablo Paredes, Ran Gilad-Bachrach, Mary Czerwinski, Asta Roseway, Kael
Rowan, and Javier Hernandez. 2014. PopTherapy: Coping with stress through
pop-culture. In Proceedings of the 8th International Conference on Pervasive
Computing Technologies for Healthcare. 109–117.
[95] Angelica Perreira. 2020. Text messaging to enhance patient engagement in diabetes
self-care. Ph. D. Dissertation. University of Hawai’i at Manoa.
[96] K Posner, D Brent, C Lucas, M Gould, B Stanley, G Brown, P Fisher, J Zelazny,
A Burke, MJNY Oquendo, et al. 2008. Columbia-suicide severity rating scale
(C-SSRS). New York, NY: Columbia University Medical Center 10 (2008).
[97] Mashfqui Rabbi, Angela Pfammatter, Mi Zhang, Bonnie Spring, Tanzeem Choud- hury, et al. 2015. Automated personalized feedback for physical activity and
dietary behavior change with mobile phones: a randomized controlled trial on
adults. JMIR mHealth and uHealth 3, 2 (2015), e4160.
[98] Amon Rapp. 2022. How do people experience the temporality of everyday
life changes? Towards the exploration of existential time in HCI. International
Journal of Human-Computer Studies 167 (2022), 102899.
[99] Amy Leigh Rathbone and Julie Prescott. 2017. The use of mobile apps and
SMS messaging as physical and mental health interventions: systematic review.
Journal of medical Internet research 19, 8 (2017), e295.
[100] Ruth Ravichandran, Sang-Wha Sien, Shwetak N Patel, Julie A Kientz, and Laura R
Pina. 2017. Making sense of sleep sensors: How sleep sensing technologies
support and undermine sleep health. In Proceedings of the 2017 CHI conference
on human factors in computing systems. 6864–6875.
[101] Nancy A Remington, Leandre R Fabrigar, and Penny S Visser. 2000. Reexamining
the circumplex model of afect. Journal of personality and social psychology 79,
2 (2000), 286.
[102] Brenna N Renn, Theresa J Hoeft, Heather Sophia Lee, Amy M Bauer, and Pa- tricia A Areán. 2019. Preference for in-person psychotherapy versus digital
psychotherapy options for depression: survey of adults in the US. NPJ digital
medicine 2, 1 (2019), 1–7.
[103] Ryan E Rhodes and Gert-Jan de Bruijn. 2013. How big is the physical activity
intention–behaviour gap? A meta-analysis using the action control framework.
British journal of health psychology 18, 2 (2013), 296–309.
[104] Victoria Rideout, Susannah Fox, et al. 2018. Digital health practices, social media
use, and mental well-being among teens and young adults in the US. (2018).
[105] Darius A Rohani, Maria Faurholt-Jepsen, Lars Vedel Kessing, and Jakob E
Bardram. 2018. Correlations between objective behavioral features collected
from mobile and wearable devices and depressive mood symptoms in patients
with afective disorders: systematic review. JMIR mHealth and uHealth 6, 8
(2018), e9691.
[106] Darius A Rohani, Andrea Quemada Lopategui, Nanna Tuxen, Maria Faurholt- Jepsen, Lars V Kessing, and Jakob E Bardram. 2020. MUBS: A personalized
recommender system for behavioral activation in mental health. In Proceedings
of the 2020 CHI Conference on Human Factors in Computing Systems. 1–13.
[107] John Rooksby, Alistair Morrison, and Dave Murray-Rust. 2019. Student per- spectives on digital phenotyping: The acceptability of using smartphone data
to assess mental health. In Proceedings of the 2019 CHI Conference on Human
Factors in Computing Systems. 1–14.
Page 19 of 19
Investigating the Role of Context in the Delivery of Text Messages
[108] James A Russell. 1980. A circumplex model of afect. Journal of personality and
social psychology 39, 6 (1980), 1161.
[109] Bill Schilit, Norman Adams, and Roy Want. 1994. Context-aware computing
applications. In 1994 frst workshop on mobile computing systems and applications.
IEEE, 85–90.
[110] Zindel Segal, Mark Williams, and John Teasdale. 2018. Mindfulness-based cogni- tive therapy for depression. Guilford Publications.
[111] Reham Shalaby, Medard K Adu, Hany M El Gindi, and Vincent IO Agyapong.
2022. Text Messages in the Field of Mental Health: Rapid Review of the Reviews.
Frontiers in Psychiatry 13 (2022).
[112] Paschal Sheeran and Thomas L Webb. 2016. The intention–behavior gap. Social
and personality psychology compass 10, 9 (2016), 503–518.
[113] Saul Shifman, Arthur A Stone, and Michael R Huford. 2008. Ecological mo- mentary assessment. Annu. Rev. Clin. Psychol. 4 (2008), 1–32.
[114] Sang-Wha Sien, Shalini Mohan, and Joanna McGrenere. 2022. Exploring Design
Opportunities for Supporting Mental Wellbeing Among East Asian University
Students in Canada. In CHI Conference on Human Factors in Computing Systems.
1–16.
[115] G Siopis, T Chey, and M Allman-Farinelli. 2015. A systematic review and meta- analysis of interventions for weight management using text messaging. Journal
of Human Nutrition and Dietetics 28 (2015), 1–15.
[116] Diana M Smith, Laura Duque, Jef C Hufman, Brian C Healy, and Christopher M
Celano. 2020. Text message interventions for physical activity: a systematic
review and meta-analysis. American journal of preventive medicine 58, 1 (2020),
142–151.
[117] Nili Solomonov, Jennifer N Bress, Jo Anne Sirey, Faith M Gunning, Christoph
Flückiger, Patrick J Raue, Patricia A Areán, and George S Alexopoulos. 2019.
Engagement in socially and interpersonally rewarding activities as a predictor
of outcome in “Engage” behavioral activation therapy for late-life depression.
The American Journal of Geriatric Psychiatry 27, 6 (2019), 571–578.
[118] Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Jessie YC
Chen, Jianming Dong, Vincent G Dufy, Xiaowen Fang, Cali Fidopiastis, Gino
Fragomeni, Limin Paul Fu, et al. 2019. Seven HCI grand challenges. International
Journal of Human–Computer Interaction 35, 14 (2019), 1229–1269.
[119] Elizabeth Stowell, Mercedes C Lyson, Herman Saksono, Reneé C Wurth, Holly
Jimison, Misha Pavel, and Andrea G Parker. 2018. Designing and evaluating
mHealth interventions for vulnerable populations: A systematic review. In
Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.
1–17.
[120] Brian Sufoletto, Tammy Chung, Frederick Muench, Peter Monti, and Dun- can B Clark. 2018. A text message intervention with adaptive goal support to
reduce alcohol consumption among non-treatment-seeking young adults: non- randomized clinical trial with voluntary length of enrollment. JMIR mHealth
and uHealth 6, 2 (2018), e35.
[121] Kristin L Szuhany and Michael W Otto. 2020. Efcacy evaluation of exercise as
an augmentation strategy to brief behavioral activation treatment for depression:
a randomized pilot trial. Cognitive behaviour therapy 49, 3 (2020), 228–241.
[122] Marijn Ten Thij, Krishna Bathina, Lauren A Rutter, Lorenzo Lorenzo-Luaces,
Ingrid A van de Leemput, Marten Schefer, and Johan Bollen. 2020. Depression
alters the circadian pattern of online activity. Scientifc reports 10, 1 (2020), 1–10.
[123] Myrthe L Tielman, Mark A Neerincx, Marieke Van Meggelen, Ingmar Franken,
and Willem-Paul Brinkman. 2017. How should a virtual agent present psychoed- ucation? Infuence of verbal and textual presentation on adherence. Technology
and Health Care 25, 6 (2017), 1081–1096.
[124] Phil Turner. 2017. A psychology of user experience: Involvement, afect and
aesthetics. Springer.
[125] Patti M Valkenburg. 2017. Understanding self-efects in social media. Human
Communication Research 43, 4 (2017), 477–490.
[126] Philippe Verduyn, Nino Gugushvili, and Ethan Kross. 2021. The impact of social
network sites on mental health: distinguishing active from passive use. World
Psychiatry 20, 1 (2021), 133.
[127] Martha Hotz Vitaterna, Joseph S Takahashi, and Fred W Turek. 2001. Overview
of circadian rhythms. Alcohol research & health 25, 2 (2001), 85.
[128] William H Walker, James C Walton, A Courtney DeVries, and Randy J Nelson.
2020. Circadian rhythm disruption and mental health. Translational psychiatry
10, 1 (2020), 1–13.
[129] Liyuan Wang and Lynn Carol Miller. 2020. Just-in-the-moment adaptive inter- ventions (JITAI): A meta-analytical review. Health Communication 35, 12 (2020),
1531–1544.
[130] Rui Wang, Min SH Aung, Saeed Abdullah, Rachel Brian, Andrew T Camp- bell, Tanzeem Choudhury, Marta Hauser, John Kane, Michael Merrill, Emily A
Scherer, et al. 2016. CrossCheck: toward passive sensing and detection of mental
health changes in people with schizophrenia. In Proceedings of the 2016 ACM
international joint conference on pervasive and ubiquitous computing. 886–897.
[131] Tilo Westermann, Sebastian Möller, and Ina Wechsung. 2015. Assessing the rela- tionship between technical afnity, stress and notifcations on smartphones. In
Proceedings of the 17th International Conference on Human-Computer Interaction
with Mobile Devices and Services Adjunct. 652–659.
CHI ’23, April 23–28, 2023, Hamburg, Germany
[132] Rob Willson and Rhena Branch. 2019. Cognitive behavioural therapy for dummies.
John Wiley & Sons.
[133] Tae-Jung Yun and Rosa I Arriaga. 2013. A text message a day keeps the pul- monologist away. In Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems. 1769–1778.
[134] Ke Zhong, Tianwei Qiao, and Liqun Zhang. 2019. A study of emotional communi- cation of emoticon based on Russell’s circumplex model of afect. In International
Conference on Human-Computer Interaction. Springer, 577–596.