# PITCH: Productivity and Mental Well-being Coaching through Daily
Conversational Interaction

Adnan Abbas  Virginia TechBlacksburgUSA<EMAIL_ADDRESS>and  Sang Won Lee
Virginia TechBlacksburgUSA

(2024)

###### Abstract.

Efficient task planning is essential for productivity and mental well-being,
yet individuals often struggle to create realistic plans and reflect upon
their productivity. Leveraging the advancement in artificial intelligence
(AI), conversational agents have emerged as a promising tool for enhancing
productivity. Our work focuses on externalizing plans through conversation,
aiming to solidify intentions and foster focused action, thereby positively
impacting their productivity and mental well-being. We share our plan of
designing a conversational agent to offer insightful questions and reflective
prompts for increasing plan adherence by leveraging the social interactivity
of natural conversations. Previous studies have shown the effectiveness of
such agents, but many interventions remain static, leading to decreased user
engagement over time. To address this limitation, we propose a novel rotation
and context-aware prompting strategy, providing users with varied
interventions daily. Our system, PITCH, utilizes large language models (LLMs)
to facilitate externalization and reflection on daily plans. Through this
study, we investigate the impact of externalizing tasks with conversational
agents on productivity and mental well-being, and the effectiveness of a
rotation strategy in maintaining user engagement.

Conversational Agent, Mental Well-being, Reflection, Rotation, Large Language
Model, Externalization, Time Management Planning, Intention

††copyright: acmlicensed††journalyear: 2024††conference:  Designing (with) AI
for Wellbeing; May, 2024; Honolulu, HI††isbn: 978-1-4503-XXXX-X/24/05††ccs:
Human-centered computing Interactive systems and tools††ccs: Human-centered
computing User studies

##  1\. Introduction

Planning and keeping track of tasks is crucial for productivity and users’
mental well-being. However, we often underestimate the time it takes to
complete tasks (Buehler et al., 1994) or fail to plan at all, leading to low
productivity and stress (nih, [n. d.]; Adams and Jex, 1999; Kimani et al.,
2019). There are several task management tools for planning and keeping track
of tasks, for e.g., calendars, to-do lists, project management software,
notebooks, email, scraps of paper, and word documents (Ahmetoglu et al., 2021;
Haraty et al., 2015; Bernstein et al., 2008). However, despite such
strategies, users still struggle with getting into the habit of planning
(Haraty et al., 2015) and making realistic plans (Buehler et al., 2010). Time
management literature suggests that short-term planning behaviors show the
most significant relationship to one’s desired outcomes (Tim, 2007; Kearns and
Gardiner, 2007; Lund and Wiese, 2021). Our work focuses on how to facilitate
workers’ daily planning, or time management planning (TMP), which is defined
as follows: determining tasks to be performed on a particular day,
prioritizing tasks, and associating an approximate completion time with each
task (Parke et al., 2017). With the advancement of artificial intelligence and
large-scale models, researchers have studied the potential of creating an
intelligent agent that a worker can converse with regards to TMP (Kimani et
al., 2019; Grover et al., 2020; Kocielnik et al., 2018a).

Externalizing plans with a conversational agent could help solidify intentions
and make them more deliberate. Simple reminders about one’s plans, even before
it is time to perform them, have the power to increase the likelihood of
following the externalized plan (Wicaksono et al., 2019). Using conversation
to support externalization can be a promising approach due to the social
interactivity that can emerge in a natural conversation. For example, personal
coaches often employ a technique of repeated inquiries to uncover concealed
motivations (Lee et al., 2015). This method suggests that such conversations
can prompt contemplative and meta-cognitive thinking (Kocielnik et al.,
2018b). Previous works experimented with different Conversational Agents (CAs)
prototypes to support daily planning through conversation (Kimani et al.,
2019; Grover et al., 2020), to facilitate reflection and goal-setting
(Kocielnik et al., 2018b; Meyer et al., 2014; Kocielnik et al., 2018a), and
supporting people to gain awareness and reflect on their health and activity
data (Kinnafick et al., 2014; Lee et al., 2019). Commercial apps such as
Rosebud (ros, [n. d.]) and Lark (lar, [n. d.]) serve as a “Chatbot,” actively
initiating conversations with people by asking about their daily activities
and well-being. Some of the findings from these studies suggest the success of
employing a conversational agent in TMP; users feel more productive and
satisfied with an emotionally expressive virtual agent (Grover et al., 2020)
and follow their exercise plans consistently (Luo et al., 2020).

While these works investigated people’s TMP behaviors with the support of a
chatbot, most of them employ a static intervention in terms of conversational
interaction, i.e., prompting the same set of questions repeatedly. Static
interventions aimed at behavioral regulation are shown to decline in
effectiveness over time as users begin to ignore them (Kovacs et al., 2018).
For instance, in the FitTrack study (Bickmore and Picard, 2005), some subjects
mentioned their lack of motivation to use the system because of its repetitive
dialog. One exception to the static intervention is work by Kocielnik et al.,
where different conversational mini-dialogues and follow-ups are used to
initiate reflection on physical activity data (Kocielnik et al., 2018b). The
mini-dialogues are generated from a set of 275 prompts gathered through user
workshops. However, these mini-dialogues are used only in the context of data
sense-making in the fitness domain.

We aim to introduce a rotation and context-aware prompting strategy, where
users experience different interventions every day through different
prompts/questions with the use of Large Language Models (LLMs). The
advancement in natural language processing (NLP), especially the recent surge
of LLMs, has opened up exciting opportunities for designers and developers to
customize chatbots that engage people in more natural and fluent conversations
(Seo et al., 2024; Han et al., 2021; Harrington and Egede, 2023; Wei et al.,
2023). Rotation strategy is defined as cycling through different approaches
over time to maintain sustained effectiveness and engagement, just like a
human coach iterates with novel strategies. Context-aware prompting means the
system personalizes questions according to the user’s context, which involves
users’ previous responses. Novel and relatable questions coming from rotated
and context-aware prompting strategies can increase effectiveness since
novelty can influence encoding online information into long-term memory,
which, in turn, may raise awareness of behavioral changes (Kormi-Nouri et al.,
2005). Moreover, systems that personalize interventions (Kaptein et al., 2015)
have found positive effects in behavioral change.

Currently, we are in the process of developing an intelligent LLM-based
conversational system (PITCH, Personal and Intelligent Task-management
Conversation Helper) that can support users in externalization and reflection
about their daily plans using a rotational strategy. We aim to answer the
following research questions for our study:

  * •

RQ1: How does externalization of tasks and follow-up by/with an external agent
help users with productivity and mental well-being?

  * •

RQ2: How does the rotation strategy of prompting different questions every day
help maintain users’ interests in the conversational system for reflection?

##  2\. PITCH - Conversational Agent for Reflecting on Task Management

We conducted a pilot study to understand the effectiveness of using a
conversational system in supporting users with externalization and reflection
on their daily tasks. Based on the findings from the pilot study and
literature review, we came up with the following design goals for PITCH to
answer the research questions:

###  2.1. Design Goals

####  2.1.1. Help users to externalize their plans through conversation at the
beginning of their day

As discussed in the Introduction, users struggle with planning their day and
often miscalculate their plans, resulting in stress and lower productivity
(Ahmetoglu et al., 2021). We aim to bridge the gap between users’ work
expectations and reality by making their plans well-intentioned and
achievable. Therefore, we need to design a chat interface for the exchange
between the user and the agent. We will prompt workers with a task-management
question twice per day: morning and evening. The morning question will be
generated with a randomly selected goal that is relevant to either
productivity or mental-well being and will be focused on short-term planning.
Based on the user’s response, the system will ask further questions to help
workers externalize and incorporate the task into their day’s routine. We also
want to tailor the timing of the morning check-in according to the user’s
schedule so it is easier for users to adopt the habit. An example of morning
interaction at the start of the user’s day can be seen in Figure 1(a) and
2(a).

####  2.1.2. Facilitate personalized reflection on users’ externalized plan
and detachment from work through context-aware prompting

Reflection of time spent can reduce absolute time on unimportant activities as
indicated by the user studies (Monge Roffarello and De Russis, 2021; foc, [n.
d.]). Recent research suggests that the psychological benefits of disclosure
and reflection with an agent are similar to reflection with another human (Ho
et al., 2018). Moreover, reflecting on one’s plans at the end of one’s day can
help psychologically detach from work and support a healthy work-life balance
(Williams et al., 2018; Kimani et al., 2019; Smit, 2015). However, users need
a nudge to reflect upon their activities as reflection does not come naturally
to humans (Fleck and Fitzpatrick, 2010). Encouraging reflection on deeper
levels, it is useful to ask contextual questions from users and doing this
exercise with an agent can allow one to see more than one could possibly see
alone (Fleck and Fitzpatrick, 2010). Therefore, we need to implement a
conversation storage and retrieval module that can ask personalized reflective
questions to users at the end of their day. We will create prompts that take
in the stored morning conversation’s context to generate personalized
reflection questions that can help a worker understand the time spent during
the day. Similar to the morning check-in, we also want to tailor the timing of
the evening reflection according to the user’s schedule so it is easier for
users to adopt the habit. An example interaction at the end of the user’s day
can be seen in Figure 1(b) and 2(b).

(a) Morning conversation

(b) Evening conversation

Figure 1. Scenario 1 - Morning and Evening example conversation

(a) Morning conversation

(b) Evening conversation

Figure 2. Scenario 2 - Morning and Evening example conversation

####  2.1.3. Maintain user’s interest by introducing randomness in
externalization inquiry and reflection prompts

As mentioned in the Introduction, users can lose interest in behavioral change
systems because of static interventions. Therefore, we aim to achieve
rotational strategy (Kovacs et al., 2018) by asking different
prompts/questions everyday. Another way to incorporate unpredictability in our
system is by leveraging temporal randomness on user’s contextual information
as a seed for prompt generation, for instance, the system asks the user “You
wanted to book tickets for your summer trip a few days ago. Did you manage to
get that sorted?” as shown in Figure 2(b). To achieve this, we will build an
LLM prompting strategy that randomly selects a goal related to productivity
and task management. Based on the literature and brainstorming, we developed a
set of goals that the conversational agent can have per daily conversation;
the goals are listed in Table 1. The morning check-in question and evening
reflection prompt will be generated by creating a prompt with a randomly
selected goal from the set. The goal set will have two categories of goals,
one focusing on productivity and the other focusing on mental well-being with
regard to their work. We aim to design a system that can be personalized to
each user and allow users to specify their preference between productivity and
well-being so that the random selection will be weighted based on the users’
selection.

We will also explore a crowd-sourcing approach to add new goals to the prompt
model. Based on user interaction, a conversational agent will explicitly ask
to specify their goal related to productivity and mental well-being. Then, the
identified goal will be added to the goal table for the system to later use to
generate inquiries. Added goals can be used for other users as well.
Leveraging crowd-sourcing, we can increase the social value of the system and
help users get a wider perspective of everyday life (Hong et al., 2015).

Table 1. Goals used in prompt design to generate questions from the LLM Prompt Focus |  Theme |  Goals
---|---|---
Productivity |  Externalization |  Help a worker externalize their plan on one task that they want to finish today.
Focus |  Help a worker find available time for them to focus without distraction.
Prioritization |  Help a worker differentiate an urgent task from an important task in their daily plan.
Collaboration |  Help a worker understand the importance of collaboration and delegation among the daily tasks.
Reflection |  Help a worker reflect on whether their daily plan matches their core values.
Well-being |  Awareness |  Help a worker be aware of their emotional state and understand how that can impact their day on a particular day.
Breaks |  Help a worker plan their breaks for the day, which will help them manage their time efficiently.
Positivity |  Help a worker have a positive beginning of the day.
Activity |  Help a worker find time for an active lifestyle during the day at work.
Stress management |  Help a worker identify and regulate the stress that they may have for the day.

##  3\. ONGOING EFFORTS

In this section, we lay out our ongoing work in terms of the software
development, user study design, and evaluation of the system.

###  3.1. Development

Currently, we are developing a Slackbot (sla, [n. d.]) using SlackAPI (sla,
[n. d.]) along with OpenAI GPT 3.5 model to incorporate the design goals
described in the section above. We are developing the intial prototype in
Slack (sla, [n. d.]) because it is an application centered in the productivity
domain. We are also exploring various prompting strategies to implement our
design goals related to reflection and rotation. An initial strategy is to
design the prompts from a goals-focused perspective where the LLM is provided
with a random goal from Table 1 and the conversation is centered around that
goal.

###  3.2. User study

In order to answer our research questions and seek ecologically valid
insights, we need to isolate and analyze the effect of rotation and
personalized reflection as independent variables. In that vein, we plan to
conduct a field deployment within-subjects study with two versions of
ProductivityPartner:

  * •

The baseline version, which only supports static intervention.

  * •

The full system, which has a rotation strategy implemented in the prompts
along with the context-aware features.

During the study, participants will receive question prompts at the beginning
and end of their working days over the course of some weeks delivered through
Slack which can be accessed using a Desktop or Mobile phone. We also plan to
distribute daily surveys based on the user condition which can include
quantitative Likert-scale questions that measure participants’ perceived
productivity (“I was productive on the day when I used
ProductivityPartnerBaseline/ProductivityPartnerFullSystem”) or perceived
effectiveness of the system they used (“It was helpful to use
ProductivityPartnerBaseline/ProductivityPartnerFullSystem to reflect and
better plan my day”). On top of this, we can distribute weekly surveys with
questions related to longitudinal reflection over the past week since it might
take time for users to perceive changes in their productivity. After the field
study, we aim to conduct semi-structured interviews to understand the concerns
and perceptions users had while using the system.

###  3.3. Evaluation

In order to evaluate the results of our study, we will do a qualitative
analysis of the dialogue exchanges (Hu and Lee, 2020). We will assess
engagement by analyzing the logged participant interactions, such as the
number of dialogues responded to, the time until a response was made, as well
as the length, and content of responses. Moreover, we will perform
quantitative analysis by performing repeated-measure ANOVA on the Likert-scale
data gathered from the surveys.

## References

  * (1)
  * foc ([n. d.]) [n. d.].  focus booster — pomodoro app and time tracker for online and web — focusboosterapp.com.  https://www.focusboosterapp.com/.  [Accessed 25-02-2024].
  * lar ([n. d.]) [n. d.].  Home - Lark Health — lark.com.  lark.com.  [Accessed 25-02-2024].
  * ros ([n. d.]) [n. d.].  Rosebud - AI Journal for Personal Growth — rosebud.app.  https://www.rosebud.app/.  [Accessed 25-02-2024].
  * sla ([n. d.]) [n. d.].  Slack.  https://www.slack.com/.  [Accessed 25-02-2024].
  * nih ([n. d.]) [n. d.].  Zeigarnik’s sleepless nights: How unfinished tasks at the end of the week impair employee sleep on the weekend through rumination - PubMed — pubmed.ncbi.nlm.nih.gov.  https://pubmed.ncbi.nlm.nih.gov/27101340/.  [Accessed 24-02-2024].
  * Tim (2007) 2007\.  A review of the time management literature.  _Personnel Review_ 36, 2 (2007), 255–276.  https://doi.org/10.1108/00483480710726136
  * Adams and Jex (1999) Gary Adams and Steve Jex. 1999.  Relationships Between Time Management, Control, Work–Family Conflict, and Strain.  _Journal of occupational health psychology_ 4 (01 1999), 72–7.  https://doi.org/10.1037/1076-8998.4.1.72
  * Ahmetoglu et al. (2021) Yoana Ahmetoglu, Duncan P. Brumby, and Anna L. Cox. 2021.  To Plan or Not to Plan? A Mixed-Methods Diary Study Examining When, How and Why Knowledge Work Planning is Inaccurate.  _Proc. ACM Hum.-Comput. Interact._ 4, CSCW3, Article 222 (jan 2021), 20 pages.  https://doi.org/10.1145/3432921
  * Bernstein et al. (2008) Michael Bernstein, Max Van Kleek, David Karger, and m.c. schraefel. 2008.  Information scraps: How and why information eludes our personal information management tools.  _ACM Trans. Inf. Syst._ 26, 4, Article 24 (oct 2008), 46 pages.  https://doi.org/10.1145/1402256.1402263
  * Bickmore and Picard (2005) Timothy W. Bickmore and Rosalind W. Picard. 2005.  Establishing and maintaining long-term human-computer relationships.  _ACM Trans. Comput.-Hum. Interact._ 12, 2 (jun 2005), 293–327.  https://doi.org/10.1145/1067860.1067867
  * Buehler et al. (2010) Roger Buehler, Dale Griffin, and Johanna Peetz. 2010.  Chapter One - The Planning Fallacy: Cognitive, Motivational, and Social Origins.  Advances in Experimental Social Psychology, Vol. 43. Academic Press, 1–62.  https://doi.org/10.1016/S0065-2601(10)43001-4
  * Buehler et al. (1994) Roger Buehler, Dale Griffin, and Michael Ross. 1994.  Exploring the ”planning fallacy”: Why people underestimate their task completion times.  _Journal of personality and social psychology_ 67, 3 (1994), 366.
  * Fleck and Fitzpatrick (2010) Rowanne Fleck and Geraldine Fitzpatrick. 2010.  Reflecting on reflection: framing a design landscape. In _Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction_ (Brisbane, Australia) _(OZCHI ’10)_. Association for Computing Machinery, New York, NY, USA, 216–223.  https://doi.org/10.1145/1952222.1952269
  * Grover et al. (2020) Ted Grover, Kael Rowan, Jina Suh, Daniel McDuff, and Mary Czerwinski. 2020.  Design and evaluation of intelligent agent prototypes for assistance with focus and productivity at work. In _Proceedings of the 25th International Conference on Intelligent User Interfaces_ (Cagliari, Italy) _(IUI ’20)_. Association for Computing Machinery, New York, NY, USA, 390–400.  https://doi.org/10.1145/3377325.3377507
  * Han et al. (2021) Xu Han, Michelle Zhou, Matthew J. Turner, and Tom Yeh. 2021.  Designing Effective Interview Chatbots: Automatic Chatbot Profiling and Design Suggestion Generation for Chatbot Debugging. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ (, Yokohama, Japan,) _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, Article 389, 15 pages.  https://doi.org/10.1145/3411764.3445569
  * Haraty et al. (2015) Mona Haraty, Joanna McGrenere, and Charlotte Tang. 2015.  How and why personal task management behaviors change over time. In _Proceedings of the 41st Graphics Interface Conference_ (Halifax, Nova Scotia, Canada) _(GI ’15)_. Canadian Information Processing Society, CAN, 147–154.
  * Harrington and Egede (2023) Christina N. Harrington and Lisa Egede. 2023.  Trust, Comfort and Relatability: Understanding Black Older Adults’ Perceptions of Chatbot Design for Health Information Seeking. In _Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems_ (, Hamburg, Germany,) _(CHI ’23)_. Association for Computing Machinery, New York, NY, USA, Article 120, 18 pages.  https://doi.org/10.1145/3544548.3580719
  * Ho et al. (2018) Annabell Ho, Jeff Hancock, and Adam Miner. 2018.  Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot.  _Journal of Communication_ 68 (05 2018).  https://doi.org/10.1093/joc/jqy026
  * Hong et al. (2015) Hwajung Hong, Eric Gilbert, Gregory D. Abowd, and Rosa I. Arriaga. 2015.  In-group Questions and Out-group Answers: Crowdsourcing Daily Living Advice for Individuals with Autism. In _Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems_ (Seoul, Republic of Korea) _(CHI ’15)_. Association for Computing Machinery, New York, NY, USA, 777–786.  https://doi.org/10.1145/2702123.2702402
  * Hu and Lee (2020) Donghan Hu and Sang Won Lee. 2020.  ScreenTrack: Using a Visual History of a Computer Screen to Retrieve Documents and Web Pages. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_ (¡conf-loc¿, ¡city¿Honolulu¡/city¿, ¡state¿HI¡/state¿, ¡country¿USA¡/country¿, ¡/conf-loc¿) _(CHI ’20)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3313831.3376753
  * Kaptein et al. (2015) Maurits Kaptein, Panos Markopoulos, Boris de Ruyter, and Emile Aarts. 2015.  Personalizing persuasive technologies.  _Int. J. Hum.-Comput. Stud._ 77, C (may 2015), 38–51.  https://doi.org/10.1016/j.ijhcs.2015.01.004
  * Kearns and Gardiner (2007) Hugh Kearns and Maria Gardiner. 2007.  Is it time well spent? The relationship between time management behaviours, perceived effectiveness and work‐related morale and distress in a university context.  _Higher Education Research & Development_ 26, 2 (2007), 235–247.  https://doi.org/10.1080/07294360701310839 arXiv:https://doi.org/10.1080/07294360701310839
  * Kimani et al. (2019) Everlyne Kimani, Kael Rowan, Daniel McDuff, Mary Czerwinski, and Gloria Mark. 2019.  A Conversational Agent in Support of Productivity and Wellbeing at Work. In _2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII)_. 1–7.  https://doi.org/10.1109/ACII.2019.8925488
  * Kinnafick et al. (2014) Florence-Emilie Kinnafick, Cecilie Thøgersen-Ntoumani, and Joan Duda. 2014.  Physical Activity Adoption to Adherence, Lapse, and Dropout A Self-Determination Theory Perspective.  _Qualitative health research_ 24 (04 2014).  https://doi.org/10.1177/1049732314528811
  * Kocielnik et al. (2018a) Rafal Kocielnik, Daniel Avrahami, Jennifer Marlow, Di Lu, and Gary Hsieh. 2018a.  Designing for Workplace Reflection: A Chat and Voice-Based Conversational Agent. In _Proceedings of the 2018 Designing Interactive Systems Conference_ (Hong Kong, China) _(DIS ’18)_. Association for Computing Machinery, New York, NY, USA, 881–894.  https://doi.org/10.1145/3196709.3196784
  * Kocielnik et al. (2018b) Rafal Kocielnik, Lillian Xiao, Daniel Avrahami, and Gary Hsieh. 2018b.  Reflection Companion: A Conversational System for Engaging Users in Reflection on Physical Activity.  _Proc. ACM Interact. Mob. Wearable Ubiquitous Technol._ 2, 2, Article 70 (jul 2018), 26 pages.  https://doi.org/10.1145/3214273
  * Kormi-Nouri et al. (2005) Reza Kormi-Nouri, Lars-Göran Nilsson, and Nobuo Ohta. 2005.  The novelty effect: Support for the Novelty-Encoding Hypothesis.  _Scandinavian journal of psychology_ 46 (04 2005), 133–43.  https://doi.org/10.1111/j.1467-9450.2005.00443.x
  * Kovacs et al. (2018) Geza Kovacs, Zhengxuan Wu, and Michael S. Bernstein. 2018.  Rotating Online Behavior Change Interventions Increases Effectiveness But Also Increases Attrition.  _Proc. ACM Hum.-Comput. Interact._ 2, CSCW, Article 95 (nov 2018), 25 pages.  https://doi.org/10.1145/3274364
  * Lee et al. (2019) Minha Lee, Sander Ackermans, Nena van As, Hanwen Chang, Enzo Lucas, and Wijnand IJsselsteijn. 2019.  Caring for Vincent: A Chatbot for Self-Compassion. In _Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems_ (Glasgow, Scotland Uk) _(CHI ’19)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3290605.3300932
  * Lee et al. (2015) Min Kyung Lee, Junsung Kim, Jodi Forlizzi, and Sara Kiesler. 2015.  Personalization revisited: a reflective approach helps people better personalize health services and motivates them to increase physical activity. In _Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing_ (Osaka, Japan) _(UbiComp ’15)_. Association for Computing Machinery, New York, NY, USA, 743–754.  https://doi.org/10.1145/2750858.2807552
  * Lund and Wiese (2021) John R. Lund and Jason Wiese. 2021.  Less is More: Exploring Support for Time Management Planning. In _Proceedings of the 2021 ACM Designing Interactive Systems Conference_ (Virtual Event, USA) _(DIS ’21)_. Association for Computing Machinery, New York, NY, USA, 392–405.  https://doi.org/10.1145/3461778.3462133
  * Luo et al. (2020) Yuhan Luo, Bongshin Lee, and Eun Kyoung Choe. 2020.  TandemTrack: Shaping Consistent Exercise Experience by Complementing a Mobile App with a Smart Speaker. In _Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems_ (¡conf-loc¿, ¡city¿Honolulu¡/city¿, ¡state¿HI¡/state¿, ¡country¿USA¡/country¿, ¡/conf-loc¿) _(CHI ’20)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3313831.3376616
  * Meyer et al. (2014) André N. Meyer, Thomas Fritz, Gail C. Murphy, and Thomas Zimmermann. 2014.  Software developers’ perceptions of productivity. In _Proceedings of the 22nd ACM SIGSOFT International Symposium on Foundations of Software Engineering_ (Hong Kong, China) _(FSE 2014)_. Association for Computing Machinery, New York, NY, USA, 19–29.  https://doi.org/10.1145/2635868.2635892
  * Monge Roffarello and De Russis (2021) Alberto Monge Roffarello and Luigi De Russis. 2021.  Coping with Digital Wellbeing in a Multi-Device World. In _Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems_ (, Yokohama, Japan,) _(CHI ’21)_. Association for Computing Machinery, New York, NY, USA, Article 538, 14 pages.  https://doi.org/10.1145/3411764.3445076
  * Parke et al. (2017) Michael Parke, Justin Weinhardt, Andrew Brodsky, Subrahmaniam Tangirala, and Sanford DeVoe. 2017.  When Daily Planning Improves Employee Performance: The Importance of Planning Type, Engagement, and Interruptions.  _Journal of Applied Psychology_ 103 (11 2017).  https://doi.org/10.1037/apl0000278
  * Seo et al. (2024) Woosuk Seo, Chanmo Yang, and Young-Ho Kim. 2024.  ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events. In _Proceedings of the CHI Conference on Human Factors in Computing Systems_ (Honolulu, HI, USA) _(CHI’24)_. Association for Computing Machinery, New York, NY, USA.  https://doi.org/10.1145/3613904.3642152
  * Smit (2015) Brandon Smit. 2015.  Successfully leaving work at work: The self-regulatory underpinnings of psychological detachment.  _Journal of Occupational and Organizational Psychology_ 2015 (11 2015), n/a–n/a.  https://doi.org/10.1111/joop.12137
  * Wei et al. (2023) Jing Wei, Sungdong Kim, Hyunhoon Jung, and Young-Ho Kim. 2023.  Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Data.  arXiv:2301.05843 [cs.HC]
  * Wicaksono et al. (2019) Adhi Wicaksono, Robert Hendley, and Russell Beale. 2019.  Investigating the Impact of Adding Plan Reminders on Implementation Intentions to Support Behaviour Change.  _Interacting with Computers_ 31 (03 2019), 177–191.  https://doi.org/10.1093/iwc/iwz012
  * Williams et al. (2018) Alex C. Williams, Harmanpreet Kaur, Gloria Mark, Anne Loomis Thompson, Shamsi T. Iqbal, and Jaime Teevan. 2018.  Supporting Workplace Detachment and Reattachment with Conversational Intelligence. In _Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems_ (, Montreal QC, Canada,) _(CHI ’18)_. Association for Computing Machinery, New York, NY, USA, 1–13.  https://doi.org/10.1145/3173574.3173662