# PlanFitting: Tailoring Personalized Exercise Plans with Large Language
Models

Donghoon Shin  University of WashingtonSeattleWAUSA<EMAIL_ADDRESS>,  Gary Hsieh
University of WashingtonSeattleWAUSA<EMAIL_ADDRESS>and  Young-Ho Kim  NAVER AI
LabSeongnamRepublic of Korea<EMAIL_ADDRESS>
(2024)

###### Abstract.

A personally tailored exercise regimen is crucial to ensuring sufficient
physical activities, yet challenging to create as people have complex
schedules and considerations and the creation of plans often requires
iterations with experts. We present PlanFitting, a conversational AI that
assists in personalized exercise planning. Leveraging generative capabilities
of large language models, PlanFitting enables users to describe various
constraints and queries in natural language, thereby facilitating the creation
and refinement of their weekly exercise plan to suit their specific
circumstances while staying grounded in foundational principles. Through a
user study where participants ($N=18$) generated a personalized exercise plan
using PlanFitting and expert planners ($N=3$) evaluated these plans, we
identified the potential of PlanFitting in generating personalized,
actionable, and evidence-based exercise plans. We discuss future design
opportunities for AI assistants in creating plans that better comply with
exercise principles and accommodate personal constraints.

Exercise planning, Conversational interface, Generative AI, Large language
models, Implementation intention

††submissionid: 6043††copyright: acmcopyright††journalyear: 2024††doi:
XXXXXXX.XXXXXXX††conference: ACM CHI Conference on Human Factors in Computing
Systems; May 11–16, 2024; Honolulu, HI, USA††booktitle: CHI ’24: ACM CHI
Conference on Human Factors in Computing Systems, May 11–16, 2024, Honolulu,
HI, USA††price: 15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Human-centered
computing Natural language interfaces††ccs: Human-centered computing Empirical
studies in HCI

Figure 1. Key screen of the PlanFitting system. As the user chats with the
system on the chat panel (right), PlanFitting leads the conversation and
collects the user’s personal constraints (\footnotesize{A}⃝), recommends
exercise types (\footnotesize{B}⃝) based on the constraints, and sets up
personalized plan in the form of if-then rules (\footnotesize{C}⃝).

A key screen of the PlanFitting system, which consists of a chat panel on the
right side and a dashboard on the left side

##  1\. Introduction

Engaging in regular physical activity is a core constituent of maintaining a
healthy lifestyle, significantly impacting the overall well-being of
individuals. Not only do such activities help maintain optimal physical
health, but physical activities are also known to improve mental health
(VanKim and Nelson, 2013) and reduce the risk of various chronic diseases
(Durstine et al., 2013). Despite the widespread consensus on the importance of
consistent exercise, a large portion of the global population is known to fall
short of meeting the recommended physical activity guidelines. For example,
Tucker et al. (Tucker et al., 2011) revealed that less than 10% of individuals
meet the amount of physical activities as recommended by the U.S. national
guideline, leading to concerns at both individual and public health levels.

To better undertake daily physical exercises, it is beneficial for individuals
to adhere to personalized exercise regimes (Luszczynska et al., 2011;
Gollwitzer, 1999). However, creating individualized exercise plans is often
challenging (De Vet et al., 2011), and individuals often struggle to formulate
plans that align with their unique lifestyle constraints (Xu et al., 2022). A
potential solution is to involve professional exercise planners (e.g.,
personal trainers, medical practitioners) and seek their assistance in
tailoring the individual exercise plans. Yet, this approach comes with its own
setbacks, such as high costs, accessibility issues, and lack of customization
due to broad client bases (of Sports Medicine, 2023; Pelletier et al., 2020;
Castrillon et al., 2020; Melton et al., 2008).

Previous studies have attempted to bridge this gap by employing technology-
supported self-reflection (Lee et al., 2015; Xu et al., 2022) and leveraging
peer groups (Agapie et al., 2018) or crowdworkers (Agapie et al., 2016) to
formulate customized exercise plans. However, while these human-computational
approaches can bypass the needs of experts and result in plans comparable to
those generated by experts (Agapie et al., 2016, 2018), they still require
time and effort by people. They also depend on having the preferences, goals,
and schedule of the client well specified upfront. However, without guidance,
the information provided by the clients may be incomplete. Further, even the
best-designed plans need adaptations to fit with the changing contexts of the
clients. This can be challenging to achieve if peers, planners, or crowds are
expected to be available 24-7 to perform this type of adaptation as needed,
which becomes even more critical when iterating on the exercise routines for
the long-term exercise. To address these issues, in this study, we focus on
the potential of large language models (LLMs) for addressing these challenges
within the realm of exercise planning. More specifically, we highlight the
expressivity and comprehensibility of LLMs in steering the conversation to
gather highly personalized constraints required to plan for exercise regimens,
which we expected to streamline the process of personalizing exercise plans.

To understand the everyday practice and challenges of exercise planning
between experts and clients, we conducted preliminary interviews with
professional exercise planners ($N=5$) and lay individuals (client; $N=8$) who
have experience in setting up personalized exercise plans with planners. From
the interview, we characterized the procedure of formulating tailored exercise
plans between the experts and clients, which consists of goal setting,
collecting availabilities and expected obstacles, prescribing plans, and
iteration. In addition, we found that experts often struggle to integrate
exercise prescriptions within the irregular schedule of clients with limited
consideration of clients’ input during the iterative process of revising their
exercise plans.

Informed by these insights, we designed and developed PlanFitting (Figure 1),
a web interface that helps users create and iterate on their personalized
exercise plans through a conversation driven by an LLM. Specifically,
PlanFitting system engages users in an interactive dialogue, collecting
necessary information on their constraints (i.e., exercise goals,
availability, and potential obstacles that may inhibit adherence to the plan).
PlanFitting then recommends exercises and provides the exercise plan in the
form of implementation intention (Gollwitzer, 1999) (i.e.. IF-THEN rules),
which are succinct and generalized schedule format that associates the
intentions with the user’s specific events without stringently adhering to
time-based schedules (Cohen and Gollwitzer, 2008).

To understand how people interact with PlanFitting and the quality of AI-
crafted plans, we conducted an exploratory study with 18 people motivated to
plan their exercise. During the study, participants formulated a weekly plan
and refined it further with the assistance of PlanFitting. In this process,
PlanFitting successfully assisted users in articulating highly personalized
constraints, while accommodating their own unique chatting style. Also,
participants perceived PlanFitting system to be usable, and found the
generated plans to be personalized and actionable. In addition, expert
planners ($N=3$) who evaluated the generated outputs based on the exercise
principle (i.e., FITT (DeSimone, 2019)) evaluated the frequency, intensity,
and time composition of the generated plans to be above average, yet revealed
the opportunities for enhancing the combination of exercise types. From the
qualitative feedback from the participants and planners, we also discuss
future design implications for further enhancing the qualities of the plans
generated by an AI-infused exercise planner.

Our study contributes:

  1. (1)

A formative study ($N=13$) revealing the process and challenges of exercise
planning between clients and expert planners

  2. (2)

Design and development of PlanFitting, a novel exercise planning system driven
by LLMs that helps generate a personalized exercise plan and iterate on it

  3. (3)

Empirical results from an exploratory user study ($N=18$) demonstrating how
people leverage PlanFitting to craft their exercise plans and how professional
expert planners view the quality of the output plans

##  2\. Related Work

###  2.1. Personalized and Actionable Exercise Plans

Maintaining physical activities is an integral part of a healthy lifestyle,
but the majority of individuals find it challenging to incorporate adequate
exercise into their daily routines (De Vet et al., 2011; Xu et al., 2022). To
resolve this, setting up and maintaining exercise plans can significantly aid
in motivating individuals to maintain regular physical activity (Luszczynska
et al., 2011; Gollwitzer, 1999). Specifically, several empirically grounded
guidelines have been proposed to facilitate this process. For example, the
American College of Sports Medicine (ACSM) (ACSM, 2023) has developed
universally recognized guidelines that health professionals commonly use to
design effective exercise regimens (Piercy et al., 2018). Such guidelines
offer broad advice and guidance on the planning process of exercise regimen
(e.g., recommending at least 150 minutes of moderate-intensity exercise per
week), as well as the definition of exercise-related terminologies.

On top of adhering to such guidelines, prior studies have explored methods to
generate exercise plans that foster successful adoption and maintenance of
exercise regimens. Personalization of exercise plans is generally empashized
(Wackerhage and Schoenfeld, 2021), which takes into account various personal
factors such as individual preferences, constraints, and everyday conditions
that may be crucial to ensuring the individual’s chance of adapting to the
plans. However, tailoring such unique constraints of individuals demands
exercise professionals or health experts.

Another line of research in the fields of behavioral psychology and sports
medicine has explored the effective concept and format of exercise
prescriptions. One well-known approach is the implementation intention, which
comprises a specific plan linking a particular circumstance to a corresponding
action (Gollwitzer, 1999; Hagger and Luszczynska, 2013). Implementation
intentions are formatted as IF-THEN rules and often practically combined or
interchanged with action planning by including the environmental cues (Hagger
and Luszczynska, 2013). For example, one can set up an exercise plan in the
form of implementation intention like “IF I come back home in the evening,
THEN I will jog for 30 minutes.” By effectively turning intentions into
actionable steps, implementation intentions have demonstrated success in
various behavior change contexts, such as managing a healthy diet (Adriaanse
et al., 2011; Achtziger et al., 2008; Gratton et al., 2007; Reuter et al.,
2008), reducing bedtime procrastination (Valshtein et al., 2020), and aiding
in smoking cessation (McWilliams et al., 2019; Conner and Higgins, 2010).
Similarly, in the context of exercise, adopting implementation intention to
the exercise plans has been shown effective in promoting physical activities
(Lippke et al., 2004).

Building on this body of research, our paper introduces a technological
approach for incorporating individual constraints to generate customized
exercise plans in an implementation intention format. Specifically, aligning
with the earlier research emphasizing the significance of specificity and
context in generating implementation intentions (De Vet et al., 2011), we
believe that prescribing implementation intentions with the personalized
constraints sourced from the individuals would help create personalized and
actionable exercise plans. Furthermore, in this process, we posit that
technology-mediated interactions can assist individuals in articulating their
personal constraints and incorporating such constraints into implementation
intentions, thus enabling the personalized planning of implementation
intention without expert assistance.

###  2.2. Technology-mediated Exercise Planning

Advancements in computing technology have offered great potential in
leveraging digital tools to assist individuals during the process of
maintaining their daily physical activity and status (Klasnja et al., 2011),
such as encouraging physical activities (Consolvo et al., 2006; Lin et al.,
2006) and tracking physical activities (Anderson et al., 2007; Consolvo et
al., 2008). Another application area is exercise planning, where several
studies within the HCI community have explored technology-mediated
interactions in the context of planning for the individual’s exercise regimen
to ensure consistent physical activities. For example, previous studies
explored technologies that help maintain individuals’ exercise plans on their
own (Lee et al., 2015; Xu et al., 2022). Another line of research goes beyond
individuals by leveraging other people, such as peers (Agapie et al., 2016) or
crowd workers to generate custom exercise plans to facilitate health behavior
change (Agapie et al., 2018, 2016).

This line of research showcases the potential of leveraging technology in
constructing personalized exercise regimens. Nevertheless, prior technology-
mediated approaches often require substantial human involvement, such as self,
friends, or crowds from the goal-setting to the actual formulation of the
plan. Thus, considering the cost and availability of such human labor
associated with these approaches, the sustainability and scalability of these
approaches might be limited. Instead, in this study, we seek to mitigate such
issues and aim to build more sustainable systems that support iterable
planning with the help of large language models (LLMs) in assisting users in
planning and implementing a feasible exercise regimen.

###  2.3. Integration of Large Language Models in User Interfaces

Recent advances in large language models (LLMs) offer new opportunities for
equipping traditional interfaces with intelligence. Trained on a vast amount
of textual data with human iteration, LLMs (e.g., GPT (Brown et al., 2020;
OpenAI, 2023c), LLaMA (Meta, 2023), HyperCLOVA X (Cloud, 2023), PaLM
(Chowdhery et al., 2022)) demonstrate remarkable proficiency and pose great
potential in various natural language processing (NLP) tasks, ranging from
text summarization (e.g., (Brown et al., 2020; Wu et al., 2022)) to dialogue
generation (e.g., (Wei et al., 2023; Zhang et al., 2019)). Incorporated into
user interfaces, LLMs generally have two broad roles. First, LLMs augment core
system interactions with NLP tasks, such as text analysis (e.g., VISAR (Zhang
et al., 2023)), story generation (e.g., TaleBrush (Chung et al., 2022)),
inspiration generation (e.g., Sparks (Gero et al., 2022)), and data labeling
(e.g., PaTAT (Gebreegziabher et al., 2023)). Most of these tasks are
challenging to tackle with traditional NLP models or yield comparable quality
with LLMs with much smaller training samples (Brown et al., 2020).

Second, LLMs provide a conversational interaction component, as they can
generate responses considering both the task-specific contextual information
and the dialogue. Many LLM vendors provide generalized chatbot services (e.g.,
ChatGPT (OpenAI, 2023a), Bard (Google, Inc, 2023)) to demonstrate their LLM
capability, and HCI research prototypes leverage LLMs to perform more specific
tasks through conversational interaction (e.g., health data collection chatbot
(Wei et al., 2023), recommendation (Friedman et al., 2023; Chen, 2023)). For
example, Wang et al. proposed a conversational interface that supports users
to interact with mobile UI components through conversation (Wang et al.,
2023). In this work, we leverage LLMs for both purposes: (1) understanding
exercise context and constraints users described in natural language and
generating exercise plans from them, and (2) carrying on a free-form
conversation to maximize flexibility and expressivity of various exercise
constraints.

##  3\. Formative Study

To understand the current practice of personalized exercise planning and the
challenges that arise during those processes, we conducted a formative
interview study with exercise planners ($N=8$) and clients ($N=5$). The study
protocol was reviewed and approved by the institutional review board.

Exercise planners. From a corporate clinic and our internal network, we
recruited five experts (FP-1 – FP-5; three females and two males) who are
experienced in setting up personalized exercise plans for clients. Of the five
experts, three were physical therapists, another was a physiatrist, and the
other was a kinesiologist. The exercise planners aged between 26 and 42
($M=34.6$) and had an average of 9.8 years of relevant experience ($SD=4.5$).

Clients. We recruited eight individuals (FC-1 – FC-8; 6 females and 2 males)
by advertising our study on a local community platform and internal bulletin
boards in a giant enterprise in South Korea. Our inclusion criteria were
people to have experience setting up their personalized exercise plans under
the advice of exercise experts (e.g., clinicians, physical therapists,
personal trainers, etc.). Clients were aged between 26 and 45 ($M=35$). Three
participants responded that they have/had engaged in exercise under the
personalized exercise plans for less than three months, three participants for
3 to 6 months, and the other two participants for more than six months.

We invited each participant to 1-hour semi-structured interview session. Then,
we asked each exercise planner to primarily share insights into (i) their
planning procedures and (ii) the obstacles they encountered while setting up
plans for/with the clients. Likewise, clients were prompted to elaborate on
(i) their experiences and process of planning exercises with exercise planners
and (ii) any challenges they faced during the planning. The interviews were
audio-recorded and transcribed. We compensated 50,000 KRW (approximately 35
USD) and 30,000 KRW (approximately 22 USD) for each planner and client,
respectively.

We analyzed the interview transcripts using thematic analysis (Braun and
Clarke, 2006). Specifically, the process was done in a bottom-up approach,
where the authors first got familiarized with the raw responses, identified
emerging themes from the responses, and compared the themes until the authors
reached an agreement. As a result, we could derive the final themes as
described in the following section.

###  3.1. Practice of Personalized Exercise Planning

From the interview, we identified that planners primarily follow well-known
exercise guidelines, such as the ACSM guidebook, which emphasizes engaging in
a minimum of 150 minutes of moderate-intensity exercise per week. However,
these guidelines do not provide specific guidance on the personalization for
varying lifestyles: “Actually, even if you take a look at those exercise
planning guidebooks, there won’t be anything more detailed than [showing a
page that defined some case studies of individuals] (…) that’s the end of
‘evidence-based’ personalization.” (FP-4) As a result, planners use them as a
flexible framework rather than strict rules, making tailored modifications
while adhering to such high-level principles: “I’m just following a broad
guide and customizing a lot in that scope. Shouldn’t the details within it be
personalized?” (FP-2)

In addition, we were able to surface common information that the planners
collect to personalize the plans for each client, such as the personal goal of
the exercise, personal obstacles, and feedback (during the follow-up
sessions), delivered through either verbal communication or a combination of a
survey form and an oral interview with the client:

Understanding client’s main goals for exercise. Every planner responded that
they start by setting up the goal of the exercise, emphasizing the influence
of identifying the purpose and setting clear goals for exercise in the
motivation of clients. They are reported to engage in conversations with
clients to find out their own necessity and benefits of exercise to enhance
motivation, particularly for newcomers: “For managing exercise plans, it’s
crucial to first motivate by discussing goals first rather than just telling
them to do it.” (FP-1)

Surfacing available amount of times for exercise and potential obstacles.
Based on the goals of the exercises that the planners identified, planners ask
clients questions to surface how much time they would be available for
exercise: “For those who don’t have set regular office hours or for nurses
working 3/4 shifts, I ask and look at how much personal time the client can
exercise on a regular basis.” (FP-3) Also, planners ask what factors may
potentially make it challenging for them to exercise during those times (e.g.,
physical constraints, parenting), in order to make the exercise planning more
viable and realistic: “I told my planner when my menstrual cycle comes (…) And
(as a developer in a company) I told them whenever there is a schedule for
releasing a new version that my condition won’t be good for about three
following days.” (FC-5)

Prescribing plans. Based on collected exercise goals, availabilities, and
obstacles, planners create a personalized exercise plan for clients. While
planners are willing to provide detailed plans down to specific times, the
limited availability of planners makes this approach impractical: “I can’t do
detailed time planning (…) It seems inconsistent (with my current
availability) to generate highly detailed plans, like scheduling at a certain
time.” (FP-4) As a result, planners and clients typically receive a weekly
exercise plan with recommended days and hours, exercise types, allowing
clients to exercise at their own convenience to meet their requirements: “They
(planners) didn’t ask me to exercise at a specific time; they just told me to
do a certain amount of some exercises during the week.” (FC-4)

Revisiting regularly (e.g., weekly, bi-weekly) to share feedback and iterate
on the plan. Emphasizing the importance of viewing the exercise planning as a
feedback-driven iteration, rather than a one-time interaction, planners and
clients regularly meet (e.g., weekly, bi-weekly) to check if the exercises
need to be modified: “There are types of exercise that go in and out (…) After
solving the urgent problem, if I wanna get a nicer body shape, other exercises
may go in or out.” (FC-1) Gathering newly emerged feedback and constraints,
planners make adjustments to exercise types and/or duration: “Clients first
give it a try, and I gather feedback when they come back in the following week
based on their experience trying the exercise plan. If they think it won’t
work for any reason, I ask them to let me know, and we can start the revisions
from there, just like forming and iterating on a hypothesis.” (FP-2)

###  3.2. Challenges of Personalized Exercise Planning

In addition to understanding the practice of establishing personalized
exercise plans between planners and clients, we could also identify challenges
that they frequently encounter:

####  3.2.1. Difficulty of contextualizing the exercise within their own
schedule

After being prescribed the weekly exercise counts, clients are required to
incorporate these exercises into their own schedules by themselves. However,
during interviews, clients reported that such an ‘autonomous’ process, without
more specific guidance on when to exercise, makes it difficult for clients to
cope with unexpected variables (e.g., appointments, work schedules).
Consequently, adhering to the plans becomes highly reliant on their own
motivation, making clients prone to becoming complacent: “I think it’s mostly
about getting the number of exercises and then performing them on my own, so
my own willingness is the most important factor (…) If I suddenly have to work
at night, I just end up not doing exercise that day because there’s no one
pushing me to do and I feel like I can just do it later.” (FC-2)

Particularly, these issues are reported to worsen over time. Specifically, as
time passes, various triggers that may lower motivation emerge, such as
moments of stagnation in their exercise progress. In such situations, this
complacency is exacerbated, leading to a tendency to continuously postpone
exercise and eventually skip it: “If you aim for a weight loss, there are
times when you reach a point where you’re not losing any more weight (…) then
my motivation decreased a bit, so sometimes I took a day or two off, rested a
bit more, or skipped it in various other ways. So, I’m skipping more than I
did in the beginning.” (FC-6)

####  3.2.2. Limited availability of planners affecting the iteration process
and adaptation to fluctuating schedules

Clients expressed struggles around accommodating sudden, unexpected time
changes induced by their irregular lifestyles and work schedules. Often,
reaching out to planners for real-time schedule adjustments isn’t a practical
possibility, as planners too have other clients and personal commitments. As a
result, clients often expressed a desire for more flexibility in schedule
planning: “I have been meeting with my planner every week (…) it was sad to
see whenever I have a schedule change and need an alternative, I couldn’t ask
about the plan iterations right away for the other days.” (FC-6)

Such an issue is reported to ironically make the whole exercise schedules of
clients even more dependent on the planners’ decision-making process.
Consequently, if the weekly meeting is canceled as either the client or the
planner is unable to attend the weekly meeting, it often results in a
disruption of the exercise for the entire week: “There were instances when the
trainers were not available due to their other commitments (…) the whole
exercise for the following week messed up.” (FC-5)

####  3.2.3. Limited adaptability of planners in engaging with and
incorporating client feedback

Even when meeting to discuss the exercise regimen, clients often struggle to
have their concerns and input incorporated into the plans. Indeed, clients
shared several cases when they felt their opinions were dismissed, or they had
to spend a considerable amount of time advocating for their points to be
considered: “You know, I can’t see the planner every day and have to meet them
face to face, and my daily conditions are different every day (…) but I always
had to follow the same fixed program. I once went on a trip to [an
attraction], but even when I explained this situation in advance my planner
just asked me to keep exercising while traveling. It’s too inflexible and
feels too coercive.” (FC-2)

The prescribed regimen’s inability to cater to unique constraints such as
travel schedules could discourage clients from following through. In the worst
case, disagreements stemming from this lack of flexibility have sometimes even
led clients to discontinue their programs entirely: “I and planners had
disagreements on the types of exercise, and I discontinued planning for the
exercise with my personal trainer from that moment.” (FC-5)

##  4\. PlanFitting

Our formative study surfaced the overall planning process of personalized
exercise plans, as well as the difficulties that can emerge during such
processes. Informed by these insights, we designed and implemented
PlanFitting, a conversational interface aimed to help individuals set up their
personalized exercise plan and iterate on it. Focusing on the expressivity and
comprehensibility that LLMs offer, we designed our system using LLMs to foster
engaging interaction, while adapting to the unique constraints of users and
allowing them to iterate their plans. Informed by the procedure that our
expert interviewees follow, we organized the interaction process of
PlanFitting into the following three stages: (1) collecting exercise-related
user constraints (i.e., goals, availabilities, obstacles), (2) personalized
exercise recommendation, and (3) generating personalized weekly plan. In the
following, we describe the design of PlanFitting’s dialogue system with the
underlying LLM pipeline, user interface components, and implementation
details.

###  4.1. Interaction and Interface Design

PlanFitting was designed as a web application (Figure 1) consisting of two
primary elements: a chat panel on the right panel (Figure 1-Chat panel) and a
dashboard (Figure 1-Dashboard). The user mainly interacts with PlanFitting on
the chat panel via natural language, and the dashboard provides an overview of
the current status of the conversation, summarizing exercise-related
constraints (Figure 1-\footnotesize{A}⃝), recommended exercise list from the
system (Figure 1-\footnotesize{B}⃝), and the finalized exercise plans (Figure
1-\footnotesize{C}⃝). Every information on the dashboard is automatically
updated on every conversational turn so that the user can stay on track.

####  4.1.1. Collecting exercise-related user constraints

The user starts planning by entering their name into the system (See Figure
1-\footnotesize{D}⃝). The chatbot takes the lead by actively collecting
essential information required for crafting an exercise plan. Specifically,
the chatbot proactively asks questions aimed at gathering the personal
constraints of the user, listed in the following:

  1. (1)

Exercise goals: The user’s goal of exercise, either in a format of intended
purpose or the specific muscle group they aim to target

  2. (2)

Availability: The user’s available times for the exercise, either in the exact
time format (e.g., ‘7 pm’) or in a descriptive form (e.g., ‘after work’)

  3. (3)

Potential obstacles: Any expected obstacles they anticipate that could
potentially impede their exercise routine (e.g., ‘chance of working until late
night’)

####  4.1.2. Exercise type recommendation

After the user has shared all the necessary constraints, the chatbot proceeds
to offer personalized exercise recommendations. The system provides up to five
exercise options based on the curated list of exercises that contains 75
common exercises that the exercise experts summarized, drawing from the prior
work (Agapie et al., 2018). The list contained the name of the exercise, as
well as its well-known alternative name, intensity, laypeople description
(e.g., definition, how to perform), and the muscles involved (See Appendix B
for an example). The list is stored and loaded in CSV format, which can be
easily expanded by altering with external exercise databases in the future.

The recommended exercises are displayed on the dashboard with a brief
description, which summarizes the definition of the exercise and, if
available, the reasoning behind the recommendation (Figure
1-\footnotesize{B}⃝). For users seeking more comprehensive information about a
particular exercise, a ‘more’ button is provided where the users may click to
access additional details of the exercise. Then, users are asked to select
their desired exercises by either clicking on them on the dashboard or typing
the name of the exercise(s) into the chat screen in a free form; if they wish
to explore additional exercise options, they are also allowed to simply ask a
request to the chatbot, which will result in refreshing the recommendations.

####  4.1.3. Generating a personalized exercise plan

After the user selects the exercise types they want to include, PlanFitting
generates an exercise plan, which is displayed on the dashboard (Figure
1-\footnotesize{C}⃝).

Format of the plan. From the interview study, we found that prescribing
exercise broadly (e.g., specifying a weekly amount) could burden users with
scheduling and possibly lower motivation. Thus, to contextualize the exercise
plan within the user’s availabilities, PlanFitting offers each exercise plan
in an implementation intention (Gollwitzer and Sheeran, 2006) format, a
grounded strategy rooted in behavioral psychology that aligns the user’s
intentions with specific events, hence offering a structured format in well-
established IF-THEN statements. (i.e., “IF {availability (time or situation)},
THEN do {exercise type} for {amount} at {intensity}”) In addition, the system
offers a coping plan for each plan, which equips users with an alternative
plan to follow when the original plan cannot be executed due to the obstacles
that may happen. (i.e., “IF {obstacle}, THEN {alternative}”)

Grounding a plan to global exercise guidelines. To earn rigor for the
generated plans, the system applies a set of guidelines based on the
recommendations offered by ACSM (ACSM, 2023):

First, the system aims to allocate exercises totaling more than 150 minutes
per week. To comply with the ACSM guidelines, the system also accounts for
vigorous-intensity exercises by doubling their allocated time when calculating
the total exercise duration. In addition, to balance between cardio and
strength training, if the user had initially chosen exercises of either type
only, the system asks users to consider incorporating both types of exercise.
Lastly, the system tries to put a minimum of a one-day rest period between
exercise sessions, if possible, to prevent any potential negative effects of
consecutive days of exercising the same or adjacent muscle group.

Following the initial planning phase, when the user returns to the system,
PlanFitting inquires about their satisfaction with the existing plan. If the
user is satisfied with their plan, the system asks if they are willing to
extend the allotted time to adhere to the progression principle (i.e.,
gradually increase the engagement in exercise) of the exercise (ACSM, 2023).
However, if the user indicates dissatisfaction, the system solicits feedback
on the specific aspects that require revision, facilitating an iterative
approach to refining the plan. In this manner, the system approaches exercise
planning as an ongoing, open-ended process, conducive to continuous
improvement based on user input.

In summary, the conversational flow within our chatbot interface is structured
to facilitate user engagement, provide exercise recommendations, and enable
the creation of personalized exercise plans that adhere to recognized exercise
guidelines while allowing for the iteration of generated plans.

###  4.2. Conversational Pipeline Design

Figure 2. Illustration of how the PlanFitting computes and returns the next
dialogue of the chatbot and updates the dashboard, based on the current
dialogues

A figure that illustrates how the PlanFitting system computes and returns the
next dialogue of the chatbot and updates the dashboard, based on the current
dialogues

Figure 2 illustrates the pipeline of PlanFitting’s dialogue system. The
pipeline consists of two LLM-driven components: a response generator (Figure
2-\footnotesize{G}⃝) and the dialogue analyzer (Figure 2-\footnotesize{C}⃝).
The response generator generates the AI message based on a global instruction
(Figure 2-\footnotesize{F}⃝) and the current dialogue (Figure
2-\footnotesize{A}⃝). The user’s constraints and generated plans are
maintained in a data structure called “plan summary” (Figure
2-\footnotesize{B}⃝). The plan summary maintains the current status as well as
provides information to be displayed on the UI dashboard.

Plan summary update. Inspired by memory management techniques from the NLP
discipline (e.g., (Bae et al., 2022)), we designed the dialogue analyzer to
generate edit commands that modify the previous state of the plan summary. The
dialogue analyzer receives the latest turn pair (i.e., the AI message and the
user’s response; Figure 2-\footnotesize{A}⃝) and the plan summary of the
previous cycle (Figure 2-\footnotesize{B}⃝) as inputs and generates a list of
edit commands (e.g., add, update, and remove; Figure 2-\footnotesize{D}⃝) that
reflect the changes caused by the new messages. Then, the system applies the
edit commands to the plan summary and generates a new plan summary (Figure
2-\footnotesize{E}⃝). The system updates the plan summary every time before
the system generates a response. The dialogue analyzer runs with the following
input prompt:

> \- Analyze the input dialogue and return an array of JSON objects each of
> which denotes an update for this summary object.
> \- The user may mention multiple entities, such as goals and obstacles, or
> corrections to previous entities.
> \- You are allowed to use the following set of methods for update:
>
> For goal, availability, obstacle, recommended_exercise, and
> implementation_intention:
> {
>  target: "goal" | "availability" | "obstacle" | "recommended_exercise" | "implementation_intention",
>  method: "add" | "update" | "remove"
>  params: { // for update
>  id: string,
>  update: {} // will be overwritten to the corresponding element.
>  } | { // for addition
>  entity: {} // a new entity without ID; ID will be assigned by the system.
> Only for implementation_intention,
>  // assign a random ID in case you use the "parent_ids" property.
>  } | {// for removal
>  id: string
>  }
> }
> \---
> If there is nothing to be updated, return [].

Conversation. Once the plan summary is updated, the system formulates an
instruction prompt (Figure 2-\footnotesize{F}⃝) to be fed to the response
generator. The instruction includes the task descriptions (See Appendix A) on
how to carry on the conversation (Figure 2-\footnotesize{F}⃝, Task
descriptions), and the current plan summary to inform the model with which
constraints are missing, thus what needs to be asked in the following
dialogues (Figure 2-\footnotesize{F}⃝, Planning status).

When defining tasks for exercise type recommendation and generating plans, we
defined rules to append XML data to the message so the system can parse the
information and use it for the user interface. For example, we defined the
message rules for creating the plan as follows (See Appendix A.4 for the
detailed instructions):

> Using the exercise types that the user selected, plan for and return the
> user’s exercise plan in the implementation intention format
> ...
> Each implementation intention rule should be accompanied by corresponding
> coping plans that can be plan B when the user fails to adhere to meet the
> main rules. It should assume the failure of each of the user’s
> availabilities due to the obstacles the user mentioned
> ...
> Each exercise/coping plan should be described in an IF-THEN format along
> with AMOUNT inside
> ...
> (Example:
>  <If>Monday after work</If> <Then><Exercise>Running</Exercise> <Amount>60
> minutes - moderate intensity</Amount></Then>
>  <If>After running</If> <Then><Exercise>Pilates</Exercise> <Amount>30
> minutes - vigorous intensity</Amount></Then>
>  <If>Too sleepy after work on Monday</If> <Then>
> <CopingPlan>Do the same exercises on Tuesday</Coping
> Plan></Then>)

To compute the exercise recommendations, we employed a combination of function
calling (OpenAI, 2023b) and cosine similarity techniques. First, we embedded
the title and description of each exercise from our prepared list and saved
them as an embedding. Once the user finishes providing their constraints and
the function calling detects if the exercise recommendation is needed, a
function that takes the goal and obstacles as input and returns the
recommended exercises is triggered.

Specifically, the function is programmed to embed the parameter as an
embedding, which is then compared to each embedding of each exercise from the
list to calculate the cosine similarity and return the types of exercise that
have the top 5 cosine similarities to the user in a JSON format. Then, similar
to how the system does for generating the exercise plan, PlanFitting formats
the output JSON to the XML format through Regex postprocessing, which is then
populated in the dashboard (Figure 2-\footnotesize{B}⃝).

###  4.3. Implementation

PlanFitting system consists of two components: (i) a web interface and (ii) a
backend server, where the user interacts with the web interface whose chat is
computed to return the response from the backend server.

The web interface was built as a web-based application, which was built and
deployed using a Javascript-based framework (SvelteKit). For the backend, we
employed a Python server that takes the user’s name and chat message as inputs
and generates the subsequent message along with detected metadata, such as
exercise goals, availability, obstacles, and recommended/selected exercise
types. This server is connected to the server via the API interface.

We used OpenAI chat completion API (OpenAI, 2023d) to implement the
conversational pipeline components. For each API call, we ran GPT with the
following parameters: model = gpt-4-0613, temperature = 0.5, top_p = 1,
frequency_penalty = 0, and presence_penalty = 0.

##  5\. Exploratory User Study

To gain a comprehensive understanding of the use of the PlanFitting system, we
conducted an exploratory user study with 18 individuals. Specifically,
participants interacted with PlanFitting, setting up their exercise plans with
their own goals and constraints. To assess the rigor of the crafted plans, we
evaluated the plans that participants produced with three physical therapists.
The study protocol was approved by the institutional review board.

###  5.1. Participants

We advertised our study to a local online community platform and the company’s
bulletin board, where we required participants to be individuals who are (i)
aged over 19, (ii) motivated to do regular exercise, (iii) and not currently
doing exercise under the specific plan advised by planners, (iv) who can
participate in an in-person lab study. As a result, we recruited 18
participants (P1 – P18; 11 females and 7 males) who were aged between 19 and
54 ($M=33.2$). Of all, six were full-time/part-time employees by the time they
were participating in our study, six were college students, one was a retiree,
and five responded that they were either stay-at-home parents or unemployed.
We compensated 50,000 KRW (approximately 35 USD) as a gift card for their
participation.

###  5.2. Study Procedure & Tasks

To comprehensively explore how the clients create and refine their exercise
plans using the PlanFitting system, as well as understand their perceptions
toward these processes, we organized the user study with clients into the
following phases: (i) initial exercise planning, (ii) plan iteration, and
(iii) debriefing. Throughout the planning, we employed a think-aloud approach
to better surface their lively experience interacting with the system.

Initial exercise planning. The initial phase of the study involved clients
being guided through the process of configuring their exercise plans with the
assistance of the PlanFitting system. Within this phase, clients were asked to
interact with the system to articulate and input their specific exercise
goals, availabilities, and any potential obstacles that may arise when they
exercise.

Here, clients were asked to interact with the system by mainly following the
guidance provided by the chatbot. At the same time, they were also asked to
freely ask questions to the chatbot and iterate on their plans until they were
satisfied. As such, we aimed to mirror the process of tailoring exercise plans
to individual constraints based on the overall guidance of the PlanFitting
system.

Plan iteration. After setting up their weekly exercise plan initially,
participants were instructed to move on to the second phase. In this phase,
they were asked to imagine themselves in the upcoming week, having completed
their exercises successfully, and to also consider scenarios that may have
hindered their progress in the previous weeks. To assist them in this process,
we presented example scenarios for their reference (e.g., “I intended to swim
last week, but I’d rather avoid such location-dependent activities due to the
hassle of making reservations”). In cases where they had nothing to change, we
suggested they engage with the system as if they were completely satisfied
with their plan.

Once they had formulated their scenarios, participants were encouraged to use
the system to review and fine-tune their exercise plans over a designated time
frame. They were asked to freely describe adjustments to the system that they
would want to make, such as exercise availabilities, types, and amounts.

Debriefing. During the final debriefing phase, we conducted a survey and a
semi-structured interview with each client to gather their feedback, insights,
and reflections on both the planning process and their interactions with
PlanFitting system.

The survey was designed to assess their subjective evaluation of how
personalized and actionable the generated plan is, as well as their degree of
acceptance and adoption of the PlanFitting system. For evaluating the level of
personalization and actionability, we asked follow and fit for measuring
personalization, and specificity, encouragement, vocabulary, and accuracy for
measuring actionability on a 7-point Likert scale, following the rubric that
Agapie et al. (Agapie et al., 2018) formulated to evaluate the quality of the
plan based on ACSM principles. For evaluating the acceptance and adoption of
our system, we used the Technology Acceptance Model (TAM) scale (Venkatesh and
Bala, 2008).

Then, they were guided to the interview session, where we inquired about the
overall usability of our system, the quality of the generated plans, their
feedback on the iteration process, the obstacles they faced while interacting
with the PlanFitting system, and the potential future enhancements they were
envisioning. The overall procedure took approximately 1 hour for each client.

Expert evaluation. To assess the appropriateness and quality of the generated
plans from the perspective of experts, we recruited three expert planners (E1
– E3; one male and two females) from a corporate clinic. The experts were
nationally licensed physical therapists aged between 28 and 39 ($M=31.3$), and
had an average of 7 years in professional exercise planning ($SD=4.6$). We
asked the experts to evaluate plans from the initial exercise planning phase
both quantitatively and qualitatively, where the expert was randomly assigned
six plans each and asked to evaluate them. Specifically, the experts
holistically reviewed the plans as well as the constraints and conversation
history, with private information masked, presented on the PlanFitting
interface. For each plan, they filled out our evaluation form that consists of
7-point Likert scales of four items from the FITT principles (DeSimone,
2019)—frequency (i.e., how often the exercises in the plan are), intensity
(i.e., how intense the exercises consisting of the plan are), time (i.e.,
duration of the exercises consisting of the plan), and type (i.e., composition
of the types of exercise consisting of the plan)—a recognized and empirically
validated framework consisting of salient factors in exercise plan design and
assessment (1: highly unsatisfactory, 7: highly satisfactory). For each item,
we also included an open-ended field asking for the rationales for the
assessment.

###  5.3. Analysis

Similar to what we did for our formative study, we coded (i) participants’
responses and (ii) qualitative responses from expert evaluations using a
thematic analysis, where the authors identified the initial themes and teamed
to discuss and compare the themes until they reached a consensus.

To construct the sequence of actions that the user performed using
PlanFitting, we first associated each action within the interaction log with
the user-specified constraints (i.e., goal, availability, obstacles, and
exercise type). Then, we categorized each action as either add, edit, or
remove, indicating whether it aimed to introduce a new entity, modify an
existing one, or delete it. In addition to these basic actions, we defined and
characterized amount (asking to change the amount of exercise), question
(asking questions to the chatbot), and querying exercise list (asking the
chatbot for exercise recommendations based on the user-specified constraint).
These actions were then structured into a sequence for each individual client.

##  6\. Results

In this section, we report the results of our study in three parts: (1)
overall usage and interaction patterns, (2) exercise plans, and (3) subjective
feedback.

###  6.1. Collected Constraints and Interaction Patterns

Table 1. Exercise goals, availabilities, and potential challenges that our
participants described to the chatbot

ID |  Goal |  Availability |  Potential obstacles
---|---|---|---
P1 |  •  Weight loss |  |
•  Recover energy |  •  Weekdays at night after 6 pm |  |
•  Weekends in the morning |  •  Do not wanna do exercises that heavily affect knees |  |
•  Company dinner or other appointments |  |  |
P2 |  •  Maintain muscular strength |  |
•  Be more energetic in daily life
•  Weight loss
•  Maintain daily health
•  Cardio |  •  After waking up |  |
•  If it fails, exercise afternoon or at night instead
•  Light exercise after lunch |  •  Light exercise at night |  |
•  Hard to exercise on the day after drinking
•  Sudden schedules afternoon
•  Sudden schedules at night |  |  |
P3 |  •  Recover basic energy |  •  After school |  •  Difficult to exercise after heavy drinking
P4 |  •  Weight loss |  |
•  Overcome exercise shortage caused by COVID-19 |  •  Thu–Sun after 7 pm |  •  Don’t want to exercise on rainy days |
P5 |  •  Improve muscular strength |  |
•  Fix posture |  •  Everyday in the morning |  •  Want to exercise without equipment |
•  Not familiar with exercise |  |  |
P6 |  •  Weight loss |  |
•  Improve shoulder muscles
•  Relieve wrist pain |  •  Everyday in the morning except for late night |  •  Diagnosed with right shoulder subluxation |
P7 |  •  Recover energy |  |
•  Weight loss
•  Improve muscles |  •  Weekdays in the morning & at night |  •  Kids’ day off from school or appointment |
•  Kids/husband come back home early |  |  |
P8 |  •  Weight loss |  |
•  Recover energy
•  Relieve stress
•  Get hobbies |  •  Weekdays in the morning |  |
•  Weekdays afternoon
•  Weekends at any time |  •  Difficult to exercise after drinking or sleeping late |  |
•  Postpone the exercise if there is a schedule with others |  |  |
P9 |  •  Improve swimming skills |  |
•  Improve muscular strength |  •  Weekdays in the morning |  |
•  Unable to exercise on Mon–Fri as already doing swimming |  •  Difficult to exercise if a kid is sick |  |
P10 |  •  Weight loss |  |
•  Recover energy |  •  Weekdays after school at night |  |
•  Weekends afternoon
•  Tuesday afternoon–night |  •  Sleepy after school |  |
P11 |  •  Weight loss |  |
•  Cardio |  •  After dinner |  •  Location constraint |
P12 |  •  Weight loss |  •  Everyday in the morning & at night |  •  Diagnosed with back disc
P13 |  •  Weight loss |  •  Three times per week in the morning (9–12 am) |  •  Prefer indoor exercise Diagnosed with peripheral edema
P14 |  •  Weight increase |  |
•  Recover energy |  •  Everyday after 7 pm except for Sat |  •  Want to avoid excessively using the right index finger |
P15 |  •  Improve arm muscles |  |
•  Want to make waist look thinner |  •  Weekdays at night |  |
•  Weekends 10–12 am |  •  Weekday night party |  |
•  Wish to exercise three times per week |  |  |
P16 |  •  Weight loss |  |
•  Relieve waist pain
•  Get broad shoulders |  •  Tue–Thu after school |  |
•  Fri & Sat before work
•  Sun & Mon at anytime |  N/A (Provided no obstacle) |  |
P17 |  •  Improve golf ¿ backswing skills |  •  Mon at anytime |
•  Thu & Fri at night |  •  Economical exercises |  |
P18 |  •  Recover energy |  |
•  Improve muscles
•  Relieve back pain |  •  After work |  |
•  Weekends afternoon |  N/A (Provided no obstacle) |  |

A table that describes every constraint that the participants in our study
input using our system

Table 1 summarizes the goals, availabilities, and potential obstacles that
participants provide to PlanFitting during the initial planning phase.
Participants provided a wide range of constraints related to their lifestyle
(See Table 1). Throughout these interactions, participants shared 2.28
exercise goals ($SD=1.04$), 1.72 availabilities ($SD=0.80$), and anticipated
1.33 potential obstacles ($SD=0.88$) on average. Some common goals that the
participants described include weight loss ($N=11$), recovering daily energy
($N=8$), and maintaining/improving muscular strength ($N=5$). For
availability, only 5 participants described their availability in the exact
time format (e.g., after 7 pm); the others described all of their
availabilities freely in a descriptive form (e.g., after school). Lastly,
participants described their potential obstacles in a highly personalized
expression by drawing connections to various aspects of their own lifestyles
and circumstances, such as heavy drinking (P2, P3), kid’s schedule (P7, P9),
and party (P15).

Figure 3. Sequence of the edits that the participants made to tailor their
exercise plan during our exploratory study

A figure that illustrates the sequence of the edits that the participants made
to tailor their exercise plan during our exploratory study

Figure 3 illustrates how these constraints were provided and modified for each
participant across the two study phases. In the early stage, participants
generally followed the ordering of information that PlanFitting was programmed
to collect. As the interaction progressed, they iterated their constraints in
individual ways through flexible conversational interaction. Ten participants
(56%) also asked questions (Figure 3; gray rectangles) to PlanFitting about
exercise and other related topics.

###  6.2. User Evaluation of Exercise Plans and Planning

Figure 4 and Figure 4 illustrate the distribution of user scores evaluating
the quality of personalization and actionability, respectively. In the
following, we cover the participants’ quantitative evaluation and their
related feedback in debriefing about the two aspects.

####  6.2.1. Personalization

Overall, participants found the generated plans to be thoroughly personalized,
where they reported that they were generally likely to follow the plans
($M=5.83$, $SD=0.99$) and found the output plan to fit their personal
lifestyle ($M=6.00$, $SD=0.77$), scored on a 7-point Likert scale (1: strongly
disagree, 7: strongly agree).

During the study, participants reported that they felt PlanFitting could
tailor exercise plans according to their preferences and constraints. Also, by
incorporating those personal constraints into the guidelines, PlanFitting made
participants perceive the plans as both well-grounded and highly personalized.
P15 remarked, “It was refreshing to have schedules tailored to my personal
time and listen to my request. I was really surprised to see that AI could do
that well (…) once I requested, it extended the duration of each exercise
session by 15 minutes.” Similarly, P10 noted, “I wanted to do simple, sweat-
free, and noiseless exercises at home. Tailoring my plan using this system, it
was great to see that my preferences and conditions were reflected exactly in
the plan that looks easy to follow.”

Participants also appreciated that PlanFitting allowed them to reiterate their
existing plans and constraints afterward. P16 noted, “It is really nice having
the option to easily modify the existing exercise plan when a new goal arises
(…) For example, if I suddenly injure my leg and need rehabilitation, I’m sure
it would also be well-reflected in my plan.” Participants also emphasized that
PlanFitting could quickly adapt to unexpected changes. Without having to
abandon their exercise plans when their schedules abruptly changed or new
obstacles emerged, participants expressed the hope that they could easily
check and adjust their exercise plans using PlanFitting without relying on the
presence of an expert: “If there’s a change in my availability, being able to
make adjustments instantly like this, I believe I would use it frequently.”
(P14) With such support that enables users to freely iterate on their plans,
participants reported various future uses of PlanFitting, such as finding and
engaging in lightweight exercises that can be done on the go or when they
suddenly have some free time: “Let’s assume that I want to utilize some spare
moments, for example, when I finish lunch early and have about 20-30 minutes
left. Then I could easily use this system in my workplace to use those spare
moments.” (P5)

Figure 4. Survey results from our exploratory study with participants. The
results for each subset of (c) are calculated as the mean score of its sub-
questionnaires, and the rightmost points denote the responses with 7-point on
average

A figure that illustrates the results of the survey from our exploratory study
with participants

####  6.2.2. Actionability

As in Figure 4, participants also gave positive ratings on the actionability
of the plans. The plans were generally received to be specific with enough
details to act upon ($M=5.06$, $SD=1.51$). In addition, participants found the
presentation of the plan and its accompanying information encouraging
($M=5.56$, $SD=1.34$), described with straightforward vocabulary ($M=6.19$,
$SD=1.11$), and accurate ($M=5.72$, $SD=1.23$), responded on a 7-point Likert
scale (1: strongly disagree, 7: strongly agree).

From our qualitative analysis, we identified that participants found
PlanFitting-driven plans presented in IF-THEN format to be more practical and
adaptable, especially for individuals with fluctuating schedules. Avoiding
vague timing instructions (e.g., 3 times per week) or rigid time constraints
(e.g., 7 pm) while contextualizing the plan to the user-described situations,
the format made them perceive the plan as realistic and easy to remember,
making participants perceive the prescribed plans as more actionable. P6
noted, “I think it’s better when it tells you to do some exercise based on the
situation like this. Honestly, sticking to a set time isn’t always easy to
follow through with, in reality.”

Furthermore, participants reported the plans to be well-adhering to the
specific constraints they provided: “For every information I added to the
chat, the system successfully reflected those to my exercise plans.” (P3) The
plans were also reported to be presented in sufficient detail to follow by
specifying the exercise type, and amount. This level of specificity made
participants perceive the plan as clear and easy to follow: “What surprised me
was how it instructed me on what to do on each day, like there was a clear
outline. I liked that it was so specific. I tend to prefer clear instructions
(…) Nowadays, there are just too many choices, and I tend to dislike making
decisions. So, having such clear instructions made me appreciate why I should
use this and why I rated it highly.” (P4) With such specificity of the plans,
participants noted that the generated plans are systematic, making them
perceive the plan as more actionable: “I felt like I could systematically
handle various types of exercises a bit better. It gave me a feeling of being
well-grounded.” (P6)

On top of the specificity, participants noted that offering coping plans for
each exercise regimen further improved the actionability of the exercise plan.
Specifically, they expected that, even when they encountered obstacles that
could make them skip exercise, the coping plans would motivate them to attempt
the exercise: P18 remarked, “If I find myself unable to do my exercise and I’m
debating whether to skip it for the day, seeing this alternative [coping plan]
might make me think, ‘Well, if I can’t follow the original plan, I might as
well do the alternative one today,’ and it would induce to start exercising
anyway.”

###  6.3. Expert Evaluation of the Plans

As illustrated in Figure 5, expert planners generally evaluated the exercise
plans generated by PlanFitting positively, based on the core components of the
FITT principle—how adequately the frequency, intensity, time, and type of
exercises were recommended. Here, we describe the assessment and feedback we
gained from the experts and the potential room for further enhancing the
plans.

Figure 5. Survey results from our exploratory study with expert planners

A figure that illustrates the results of the survey from our exploratory study
with expert planners

####  6.3.1. Frequency

Experts generally rated the frequency of exercise of the plans to be well-
defined, with an average score of 5.67 on a 7-point Likert scale ($SD=1.53$).
In the subjective feedback, the experts attributed the success of frequency
mainly to the system’s ability to accommodate both the well-known guideline of
assigning 150 minutes per week and the participant’s desired exercise
frequency. They particularly valued the system’s approach to listening to the
participant’s preference while avoiding any excessive burden by distributing
the weekly exercise frequency evenly throughout the week: E1 reported, “It’s
highly commendable to reflect the exercise guideline by scheduling exercise
with the assigned time for at least 3 times a week and incorporating the
concept of rest on the day after exercise.”

At the same time, we could also observe some future considerations when
setting up the frequency of plans. First, the evaluation of experts implied
the need for prompting the system to set up the frequency of plans according
to the number of exercises the participants wish to do. For example, after
seeing a four-day regimen containing many (4) types of exercises that the user
selected, E3 proposed bumping up the frequency higher, to five or six days per
week: “Given the four different exercise types (that the participant mentioned
they wished to do), it may make sense to increase the exercise frequency from
the current four times a week to five or six.” Conversely, experts cautioned
against overly frequent exercise sessions with similar exercise types. After
reviewing a plan where one participant requested and was prescribed a seven-
day strength training planning, E2 stressed the need to consider muscle
fatigue, highlighting the system’s need to adjust the frequency when the
potential risk of injury exists: “The plan consists of 7 days of exercise
sessions that target the abdomen and lower body, which could potentially lead
to muscle fatigue. It’s essential to reduce the frequency.”

####  6.3.2. Intensity

The experts evaluated the intensity of the exercise plan as slightly positive,
averaging 4.28 ($SD=1.32$), indicating that while generally favorable, there
was room for some improvement in this aspect. Particularly, they positively
evaluated the role of the PlanFitting in preventing intensity-related issues
through the form of coping plans, based on the obstacles that the participants
provide. For instance, they highly appreciated the coping plan that recommends
one participant to discontinue exercise and consult with a professional if the
intensity of exercise posed a strain on participants with back pain: “I found
cautionary comments for the patients with back pain to be great, along with
the appropriate intensity of exercise offered.” (E2)

Despite these positive aspects, we also identified feedback from experts based
on their experience to enhance PlanFitting’s guidance for intensity.
Currently, PlanFitting recommends increasing the amount of exercise as a
progression measure if they are satisfied with the previous plan. In addition
to the time, E1 suggested that the intensity of the plans can also be used as
a measure for the progression: “In terms of the intensity of this plan, I
consider it appropriate. Given that the participant is healthy, I also
recommend the user start with moderate intensity and gradually progress to
higher intensity.” Additionally, PlanFitting is currently fed with the
commonly advised intensity information in our predefined exercise list to
inform the intensity of recommended exercises in the plans. However, after
seeing the recommendation of moderate-intensity exercises to participants
seeking weight loss or muscle strength improvement, E1 implied that these can
be customized based on the goals that the participants may have (e.g., perform
the exercise in high-intensity for achieving weight loss or improving muscle
strength goals): “To achieve weight loss, I believe it is necessary to include
high-intensity aerobic exercises that have a higher level of intensity.”

####  6.3.3. Time

From the plans that the participants generated, all participants were
prescribed plans that satisfied the guideline for exercise time (i.e., 150
minutes of moderate-intensity exercise or equivalent), except for two
participants who mentioned they were already engaging in another exercise for
their hobby prior to planning, and one participant who manually requested the
system to exclude a session from their exercise plan. Aligning with such
adherence of the PlanFitting-generated plans with the exercise guideline, the
evaluation of the time component within the exercise plans received a positive
rating, with an average of 5.06 ($SD=1.80$). Expressing their satisfaction,
experts additionally provided further recommendations to enhance the
flexibility of the plans, such as breaking down the session further to make
the plan more actionable: “I think the amount of time has been planned well.
If the client is unable to commit to a 30-minute exercise, you can also advise
them to break it down into three 10-minute sessions.” (E1)

Furthermore, we found the potential improvement of PlanFitting to
operationalize the time not only in weekly total duration but also in terms of
per-session duration. Currently, PlanFitting is designed to align with the
general guidelines set by the ACSM regarding the total weekly duration of
exercise. Although the experts assessed the plans as meeting these guidelines,
they were also evaluating the duration per session, pointing out session-wise
issues present in PlanFitting-generated plans. For instance, although our
system may offer a long vigorous-intensity exercise session if the user
provides limited time available for exercise and the system may have to
satisfy the guideline within fewer available exercise days, planners argued
that these long, intense sessions might result in overexertion and cautioned
against such exceptional cases: “For the case of high-intensity exercises,
prescribing a 50-minute session of strength training is excessive for the
participants.” (E2)

####  6.3.4. Type

Unlike other evaluation criteria, the type of exercises within the plans
received a rating slightly below satisfactory, averaging 3.89 ($SD=1.45$),
indicating that there was room for enhancement in tailoring the exercise
recommendations. From the feedback that the experts offered, we surfaced rooms
for the exercise types that PlanFitting provides to improve.

First, even if we prompted PlanFitting to induce the balance between aerobic
and resistance exercises, the final choice of exercise types still depended on
the participant’s intent, leading some participants to end up getting the plan
that consists of either type of exercise only. On such an account, E2
suggested presenting the ‘required’ exercises with a stronger tone during the
process of setting up the plan: “Only the exercises the user wanted to do were
included. However, as this is an interaction where AI sets exercise goals
together with the participant, ‘necessary exercises’ should also be guided.”
From the expert evaluation, we also noticed occasional inaccuracies in
exercise recommendations when specific muscle groups to improve were not
explicitly mentioned in participants’ goals. For example, when trying to
suggest exercises that might benefit one participant’s golfing skills,
PlanFitting recommended exercises by calculating the cosine similarity between
the keyword ‘golf’ and the description of various exercises–which may not have
the golf-related keyword, as pointed out by E3: “Other exercises that could
enhance golf performance were not adequately suggested (…) recommendations for
improving golf backswings should include exercises that enhance flexibility,
core strength, and lower body strength.” Thus, identifying and addressing the
specific muscle groups that need improvement based on the goal, even when they
are not explicitly mentioned, may enhance the precision of exercise
recommendations.

###  6.4. User Experience & Future Enhancements

Participants indicated a positive inclination towards adopting and using
PlanFitting. The perceived usefulness received a positive rating of 5.43 on
average ($SD=0.99$). Similarly, participants rated the system to be easy to
use, with an average score of 6.00 ($SD=1.12$), indicating that participants
found the system easy to navigate and utilize. As for the intention to
continue using the system, participants responded with an average rating of
5.52 ($SD=1.26$).

During our interviews with participants, we uncovered factors that made them
intend to keep using this system. First, generative enabled free-flowing and
flexible conversations allowed participants to respond in a non-linear manner:
“Even if I suddenly went back to a previous question or said something else,
the system seamlessly continued the conversation which made the chatting more
convenient.” (P3)

Secondly, preserving conversational history in the form of a dashboard and
making it visible allowed users to keep track of the constraints they
expressed and helped them to further adjust plans easily, without having to
manually look up all the previous chat history: “The dashboard neatly
organizes and updates the information every time I entered constraints, which
I find very convenient. Often times when I plan things like this, I have to
make separate notes on my phone, right? Now I can just input it to AI, and it
automatically organizes it for me (…) I consider this as a very useful
component.” (P12)

At the same time, participants also highlighted future integration of other
contextual information that can further personalize the plans generated by
PlanFitting. For instance, some participants suggested that incorporating
context-aware features, such as providing exercise recommendations based on
their current location and weather conditions, could significantly enhance the
system’s utility. Additionally, soliciting more detailed constraints from
participants, such as whether they have children (P13) or specific muscle
areas requiring rehabilitation (P15), was identified as a future enhancement
that would further make them perceive the system to be useful.

##  7\. Discussion

In this paper, we explored the potential of PlanFitting in its capacity to
generate personalized and evidence-based exercise plans. By leveraging large
language models (LLMs), PlanFitting streamlines the exercise planning process
by assisting users in articulating various constraints that may affect their
exercise, reflecting the constraints to their exercise plans, and allowing for
continuous iteration without any external human computation. Our user study
showcased that LLM-assisted exercise planning can effectively assist users in
creating exercise plans that are personalized and aligned with the guidelines.

During the iteration phase of the study, PlanFitting successfully adjusted the
plans per users’ request, demonstrating its potential for long-term use as an
exercise companion. After seeing PlanFitting reflecting various edits they
requested in their plans, participants expressed intention to use PlanFitting
in the long-term and frequently throughout their exercise journey.
Furthermore, they envisioned various possible use cases for PlanFitting, such
as an exercise scheduler during spare time and an on-the-go exercise planner.
These reports and potential uses of PlanFitting hint at its future role in
playing the proactive aide during the long-term exercise planning process and
its implementation through the continuous iteration of the plan.

Despite such efficacy, the evaluation from expert planners also suggests room
for future enhancements, such as incorporating further empirical guidelines
that are validated by professionals during the process of creating exercise
plans. From the evaluation, we identified several reports of planners that
pointed to certain edits that they would have applied to the plans based on
their own hands-on experiences, such as recommending a certain exercise
intensity for achieving specific exercise goals. This points out that,
although PlanFitting takes into account various individualized factors and
exercise guidelines during the exercise planning process, human expertise
remains paramount for assuring the quality and effectiveness of the plans. As
PlanFitting’s interactions are driven by the instructions fed into the LLM, we
anticipate that this can be easily implemented by listing up such empirical
guidance and adding them as a form of instruction. Moreover, future versions
of AI-based exercise planning systems like PlanFitting might consider setting
up a repository that contains expert knowledge and feedback and letting users
choose among these to further inform the planning with experts’ know-how.

On top of following the constraints collected from the users, our findings
further pose the potential of LLM’s generative capabilities in playing the
role of assisting users to think through several factors that the users may
have overlooked. Currently, the system predominantly tailors plans to match
user preferences. During the user study, however, instances arose where
PlanFitting’s strict adherence to user-specified availabilities resulted in
creating a strenuous exercise plan. As such, the expert planners called for a
more considerate approach by highlighting the importance of not overburdening
the user, especially when incorporating high-intensity exercise sessions into
their plans. By leveraging the generative capabilities of LLMs, we can
envision presenting AI-generated post-hoc checklists that prompt users to
think through various potential factors that might lead the plans to be
excessively demanding or pose harm to safety based on the initial version of a
plan. Not limited to preventing injury, we believe the future system may also
harness LLMs to create a plan that the user is more likely to stick to, by
predicting potential obstacles that the user forgot to add once the plan draft
is ready and asking users to think through them.

Another key aspect of our system involves the integration of implementation
intentions, where the users are provided with IF-THEN statements linked to
their availabilities collected through chatting with PlanFitting. From the
study, we identified that the participants perceived such situation-based
expressions as highly comprehensible and adaptable, compared to vague amount-
based or rigid time-based instructions. Similarly, as such implementation
intention strategies have been shown effective in a variety of behavior change
tasks (e.g., diet control (Adriaanse et al., 2011; Achtziger et al., 2008;
Gratton et al., 2007; Reuter et al., 2008), smoking cessation (McWilliams et
al., 2019; Conner and Higgins, 2010)), we posit that our approach is also
adaptable to various other behavior change contexts. Particularly, since our
system is composed of a set of easy-to-alter instructions in a natural
language that defines the constraints to be collected, we believe that the
adaptation process for various other tasks is significantly simple, and only
minimal changes would be required to tailor these instructions to reflect the
domain-specific constraints of each new context.

As the system scales up, we believe that incorporating context-aware
functionalities would make the plans even more contextualized. For instance,
integrating location-aware recommendations could enable PlanFitting to take
into account factors driven by real-time information, such as weather
conditions, nearby exercise facilities, or nearby routes that allow exercise
on the go (e.g., a specific route for running while going back home). Such a
level of contextualization would make the generated plans even more closely
connected to the user’s real-world context. Similarly, other features that may
reflect up-to-date health stats of the user could be incorporated into future
revisions of PlanFitting to create even richer and more personalized exercise
planning.

###  7.1. Limitation and Future Work

Although our user study offers an in-depth understanding of how users employ
PlanFitting to articulate their constraints related to exercise planning and
formulate the plans, conducting a real-world deployment study will further
enhance our understanding of how our system affects users’ real-world exercise
routines. We believe that the initial insights gained from our system will
provide valuable information for future research.

##  8\. Conclusion

In this study, we designed and developed PlanFitting, an LLM-infused web
interface that assists users in creating their personalized exercise plans
through conversation. From the user study consisting of individuals and the
expert evaluation of the plans generated from the user study, we highlighted
the potential of PlanFitting to produce personalized and guideline-informed
plans. We also discuss design implications for enhancing the design of AI
assistants for personalized exercise planning. To this end, we anticipate that
this work will serve as guidance to inform and inspire researchers in the HCI
and broad AI communities that leverage LLMs to foster flexible, sustainable,
and iterable exercise planning.

###### Acknowledgements.

We thank our study participants for their time and efforts. We also thank
Elena Agapie, who provided the exercise knowledge base dataset. Yuncheol Ha
and Hyosang Kim at NAVER Clinic helped us recruit exercise experts for this
study. This work was supported by NAVER AI Lab in terms of a research
internship and a research fund.

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##  Appendix A Summary of Instructions

###  A.1. General Rules for the Conversation

  * •

You are a helpful and supportive AI assistant that helps the user, named
"{{user_name}}", to establish exercise plans that fit their lifestyle and
requirements for the following week

  * •

Use casual, friendly Korean

  * •

Signal that you are sympathizing with the user by summarizing, paraphrasing,
or empathizing with a previous dialogue

  * •

Every chat should be in the question format, except for when wrapping up the
chat

###  A.2. Step 1: Gathering Constraints

  * •

First, collect information for the user goals for exercise, availability for
exercise, and any potential obstacles against exercise, step by step

    * –

When gathering information, ask if there’s additional information to add once
the user responds

    * –

When inquiring about obstacles, ask the user to think about the potential
expected challenges they might face while doing an exercise

    * –

When inquiring about obstacles, if the user has provided several available
times, you must inquire about the anticipated challenges for each schedule
once per each available time in a separate dialogue

    * –

When inquiring about obstacles, offer some possible examples based on the
exercise time the user provided (e.g., Availability: ’at night’ -> Question:
’Will your work/study schedule potentially affect your ability to exercise at
night?’)

###  A.3. Step 2: Recommending Exercises

Coupled with the function calling technique (OpenAI, 2023b) that we used, this
prompt is invoked when the function for retrieving exercise types is called.

  * •

Based on the information gathered, recommend the user with exercise types in
the following format: <Output><Exercise>[EXERCISE_NAME]</Exercise> (<Row
ID>[ROW_ID]</RowID>): [DESCRIPTION]</Output>

    * –

When recommending the exercise type, you should provide the name of each
exercise (wrapped with <Exercise> tag), its details (i.e., description,
muscles involved, how the exercise relates to the user’s obstacles or goals),
and the row_id of the exercise (wrapped with <RowID> tag). Each line must be
wrapped with <Output> tag

      * *

{few-shot examples}

    * –

If possible, include the rationale/reasons for choosing that exercise based on
the goals, availabilities, or obstacles that the user offered

    * –

Ask the user to choose exercise types that they are satisfied with among the
recommended exercise types offered

    * –

Include the rationale/reasons for choosing that exercise based on the
obstacles that the user mentioned

    * –

Ask the user to choose exercise types that they are satisfied with among the
recommended exercise types offered

    * –

If every exercise type that the user selected is either cardio or resistance,
ask the user if they want to add the other type of exercise

    * –

The summary of the exercise should be concise

###  A.4. Step 3: Creating the Exercise Plan

  * •

Using the exercise types that the user selected, plan and return the user’s
exercise plan in the implementation intention format, where the weekly
exercise/coping plans are described in an IF-THEN format along with AMOUNT.
Also, ask the user if they are satisfied with it

    * –

IF tag must contain the ’day’ of the week (e.g., Monday) that the exercise
will take place on (e.g., <If>Monday after work</If>)

    * –

If the user specified availability in a general form, split the availability
by the weekday (e.g., Availability: ’after work’ -> IF: [Monday after work,
... , Friday after work])

    * –

Each implementation intention rule should be accompanied by corresponding
coping plans that can be plan B when the user fails to meet the main rules
(written in the same format, except that the coping plan is in <CopingPlan>
tag) very specific to when each of the obstacles has occurred. It should
assume the failure of each of the user’s availabilities due to the obstacles
the user mentioned

    * –

If possible, add a day gap between the two exercise plans (e.g., if there is a
plan for Monday, Tuesday is skipped but Wednesday can be a day for exercise)

    * –

The total amount of exercise time should exceed 150 minutes per week. When a
vigorous-
intensity exercise is included in the plan, calculate its time by multiplying
it by the factor of 2 (e.g., vigorous: x min, moderate: y min -> total time:
2x+y min)

    * –

Each exercise/coping plan should be described in an IF-THEN format along with
AMOUNT inside (e.g., IF the time is Monday 7 pm, THEN you do the exercise for
AMOUNT).

    * –

IF and THEN part should be wrapped with <If> and <Then> and <Amount> tag,
respectively (e.g., <If>After Monday 7pm</If> <Then><Exercise>Walk</Exercise>
for <Amount>30 minutes</Amount></Then>)

    * –

AMOUNT should also include the intensity of the exercise in a short form

      * *

{few-shot examples}

##  Appendix B Example of the Exercise Data

  * •

Running

    * –

Alternative name and keywords: jogging, running, cross country running, 10k
training

    * –

Intensity: moderate (6 miles per hour)

    * –

Laypeople description: Running is sometimes referred to as jogging. Moderate
speed running would be running about 10 minutes per mile (6 mph). This can be
done outside or on a treadmill. Running outside makes the activity slight
ly more intense

    * –

Muscles involved: Lower extremity muscles including quads, hamstrings, glutes,
calves; cardio