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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

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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

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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.

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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

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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

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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

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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

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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

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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

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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).

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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.

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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

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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

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“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.

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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].

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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

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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).

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