Effect of Data Visualization on Users’ Running Performance on

Treadmill

Thanaphon Amattayakul

and Puripant Ruchikachorn

Faculty of Commerce and Accountancy, Chulalongkorn University, Phayathai Road, Bangkok, Thailand

Keywords: Data Visualization, Data Representation, Data Perception, User Experience, Health, Running Activity, Time

to Exhaustion, Motivation, Behavior.

Abstract: This paper examined how real-time data visualization influences treadmill users’ performance and experience.

Traditional treadmill displays are often represented with texts, limiting user engagement and motivation. By

applying visualization techniques that align with human cognitive processing, such as line charts and progress

indicators, we proposed data visualization designs to represent running performance metrics more

meaningfully. The study applied 3 display conditions: traditional and two improved visualization displays.

Through a within-subjects experiment with 18 participants, metrics such as time to exhaustion, heart rate,

distance, and calorie expended were collected along with subjective feedback. Statistical analysis showed that

both visualization displays significantly improved running performance and satisfaction. Results showed that

real-time feedback with data visualization design can positively influence users’ understanding and

psychological connection to their fitness data. These findings highlight the potential of data visualization to

perceive and elevate user experience in exercise interfaces.

1 INTRODUCTION

In today’s data-driven era, data visualization plays a

crucial role in transforming information into insights,

influencing decision-making and behaviors. This is

particularly relevant in exercise contexts, where

treadmill users often rely on feedback to monitor their

performance. However, traditional treadmill displays

typically present data in simple text formats. Prior

studies have demonstrated the potential of data

visualizations to support engagement and behavioral

change. Stusak et al. (2014) explored how

personalized physical visualizations —such as 3D-

printed sculptures of running data could positively

affect runners’ performance, motivation, and social

interactions. Their work showed that physical

visualization feedback not only served as passive

information but also sparked competition,

conversation, and deeper personal engagement,

highlighting the importance of meaningful and

embodied data representation.

Similarly, Coenen and Vande Moere (2021)

examined how situated public visualizations of

https://orcid.org/0009-0004-3454-7298

https://orcid.org/0000-0002-2721-6915

running statistics on displays in public spaces

encouraged social reflection and community

dialogue. Their findings emphasized that accessible,

interactive visualizations could stimulate casual users

to compare, interpret, and discuss their performance

with others. Additionally, Kashanj et al. (2024)

demonstrated that runners responded more quickly

and preferred glanceable visualizations over text

when using smartwatches during running, due to the

ease of real-time interpretation and enhanced

situational awareness.

Building on these insights, this study investigates

the transformative potential of well-designed, real-

time visualizations embedded within treadmill

interfaces to enhance motivation, self-reflection, and

exercise performance. While prior research has

explored physical, public, and wearable displays that

apply to data visualization, a gap remains in

understanding the impact of dynamic, on-screen

visual feedback during treadmill workouts.

This research aims to bridge that gap through

experimental studies. To guide this experimental

investigation, we set two research questions:

Amattayakul, T., Ruchikachorn and P.

Effect of Data Visualization on Users’ Running Performance on Treadmill.

DOI: 10.5220/0013644700003967

In Proceedings of the 14th International Conference on Data Science, Technology and Applications (DATA 2025), pages 669-676

ISBN: 978-989-758-758-0; ISSN: 2184-285X

669

1. How does real-time visual presentation of

exercise data affect user’s running

performance?

2. Can improved display with visualization

designs lead to increased satisfaction and

motivation compared to traditional treadmill

displays?

In response to these questions, we apply

enhanced real-time data visualization techniques to

treadmill exercise interface, focusing on presenting

user’s activity performance with perceptual clarity,

goal motivation, and real-time feedback, which have

not been fully explored in previous treadmill

visualization studies.

2 RELATED WORKS

This section reviews related works in three areas:

exercise performance and feedback indicators,

visualization design principles, and the visual

feedback in physical activity. These topics provide a

foundation for understanding how visualization can

support user engagement and satisfaction. A review

of these areas also helps identify existing gaps and

challenges in designing effective feedback systems.

The insights drawn from prior studies inform the

development of the visualization strategies proposed

in this study.

2.1 Running Performance and User

Feedback Indicators

Running performance metrics that are frequently used

in previous related studies, Passafiume et al. (2022)

examined how visual feedback influences breathing

efficiency and time to exhaustion. Schiewe et al.

(2020) evaluated smartwatches displaying distance

and speed. To assess endurance and performance,

time to exhaustion, heart rate, distance covered, and

energy expended are frequently used (Nicolò et al.,

2019; Walsh et al., 2010). In terms of subjective

experience, perceived exertion has been defined as

the sense of effort during physical activity by Borg

(1982). For clarity and user familiarity, Helms et al.

(2016) proposed the revised scale to be a 0-10 scale.

User satisfaction is measured via questionnaires, as

proven in studies by Cawthon and Moere (2007),

Michelle et al. (2013), and Saket et al. (2019), who

evaluated visualization effectiveness using both

aesthetic and performance-based questions. Yang et

al. (2014), Alves et al. (2020), and Carvalho and

Chaves (2013) applied user preferences to study how

different visualization styles relate to user insights

and needs.

These metrics, both objective and subjective,

provide a framework for evaluating running

performance and user experience, enabling an

understanding of how data visualization can influence

physical outcomes and motivational responses.

2.2 Visualization Design

To find out how visualization should be designed,

Wills Graham (2011) mentioned that line charts and

scatterplots are effective options when both variables

measure continuous values, especially when one

variable is time. Areas may also be used, depending

on the quantity being displayed on the y dimension.

Myers (1985) suggested that users perceive systems

with progress indicators as faster and more satisfying.

Harrison et al. (2007) explained how progress bars

represent task progress and are usually applied to

modern interfaces. Yentes et al. (2012) surveyed that

progress bars can improve user enjoyment. This is

aligned with the “goal gradient effect” (Hull, 1932),

which suggested that effort increases as one

approaches a goal. Kivetz et al. (2006) found that this

effect also affects consumer behavior. Becker and

Pligt (2016) found that people put in more effort

toward the end of a goal.

Together, these insights showed that visualization

should also be thoughtfully designed to align with

human perception and motivation. By leveraging

visual elements such as progress indicators or goal-

oriented designs, visualizations can enhance user

satisfaction and drive toward action.

2.3 Visualization in Physical Activity

Table 1 compares studies that influence physiological

performance or user motivation. Ali-Hasan et al.

(2006) examined the concept of social data

visualization, which helps users to track their fitness

data and compare it with other users. Fister was

focused on post-session visualization, not real-time

feedback. The shift toward real-time visualization for

performance enhancement was marked by Crowell et

al. (2010), who examined real-time treadmill-based

visualization using waveform line chart visual

feedback. This approach helped reduce users’ impact

loading and supported learning and maintaining

proper running form technique. The coming of

wearable technology expanded real-time feedback

beyond treadmills and gym environments. Passafiume

et al. (2022) evaluated how real-time animated

breathing

feedback affected endurance. Unlike

DATA 2025 - 14th International Conference on Data Science, Technology and Applications

670

Table 1: Comparative Review of Visualization Techniques in Physical Activity. A check mark (✓) indicates the presence of

the corresponding characteristic in each study.

Moving

viewer +

Stationary

visualization

Display

treadmill

Real-time

feedback

Visual

activity

performance

Data visualization design

usage

Evaluate

with activity

performance

Evaluate

with

subjective

feedback

Line chart or

Bar chart

Progress

chart

Ali-Hasan et al. (2006)

✔ ✔

Nadalutti and Chittaro (2007)

✔ ✔

Crowell et al. (2010)

✔ ✔ ✔

✔

Stusak et al. (2014)

✔

Eikey et al. (2015)

✔

Lucas-Cuevas et al. (2018)

✔ ✔

Zhi et al. (2019)

✔

Schiewe et al. (2020)

✔

Coenen and Vande Moere (2021)

✔

✔ ✔

✔

Passafiume et al. (2022)

✔ ✔ ✔

✔

Kashanj et al. (2024)

✔ ✔ ✔ ✔

✔

Present study (2025)

✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔

distance- or pace-based visualizations, this study

showed that breathing synchronization with

animation can improve running efficiency, though it

did not significantly extend users’ time to exhaustion.

While various studies highlight the motivational

potential of visualization feedback, their findings are

inconsistent, particularly regarding which techniques

are most effective across different contexts. However,

few have explored how real-time visualizations can

be applied to support continuous performance and

motivation during treadmill running.

To address this

gap, this study experimentally investigates the impact

of goal-oriented real-time visualizations on users’

physical performance and motivation.

3 METHODS

This section outlines the methods used to investigate

the impact of visualization during treadmill running.

It includes experimental design and the development

of the display.

3.1 Experiment Design

Eighteen participants (10 females, 8 males) aged 20

to 26 with prior treadmill experience (minimum 3

hours total usage and regular weekly exercise) were

recruited. Each participants attend four treadmill

running session over four weeks, with different

treatment order to minimize bias. Sessions were

spaced approximately one week apart. All

participants experienced all conditions.

- T0: Baseline (no display)

- TT: Traditional Display

- T1: Improved Display 1 (line chart and circular

progress chart)

- T2: Improved Display 2 (line chart, circular

progress chart, and average indicator)

We collected objective and subjective measurement

metrics to analyze the difference between treatment

in each user.

- Objective measurements: Time to exhaustion,

heart rate, distance, calories expended.

- Subjective measurements: Perceived exertion

(10-level Borg scale), satisfaction (1-5 scale),

use preference.

After each session, participants rated their effort

using the 10-level Borg scale and completed a user

satisfaction survey. This feedback provided

additional insights into their perceptions of the

visualization designs.

3.2 Display Design

Line charts were used for speed and heart rate, as

related studies supported their use for time-series data

(Tufte, 1983; Munzner, 2014). For cumulative data

like distance and energy, use half-ring charts to

display progress in distance and energy metrics,

aligning with the goal-gradient theory (Kivetz et al.,

2006). Both Improved display 1 and 2 incorporate a

line chart and a half-ring progress chart. Display 2,

however, enhances analytical depth by including

average heart rate, average speed, and rate indicators

for calories and distance

In designing real-time visual feedback for users,

we prioritized clarity and minimized cognitive load.

We chose a line chart over a bar chart or a scatter plot

to represent the trend of speed and heart rate due to

Effect of Data Visualization on Users’ Running Performance on Treadmill

671

its superior performance in communicating

continuous data. A line chart allows users to quickly

notice and interpret changes over time. Previous

studies have shown that line charts outperform bar

charts and scatter plots in tasks involving time-series

interpretation and correlation detection (Saket et al.,

2018). For visualizations that reflect users’

progression, we selected a half-ring shape over a bar

format. Ohtsubo and Yoshida (2014) found that users

perceived faster progress with a circular design, and

a half-ring resulted in faster evaluation times

compared to a full-ring shape.

Figure 1: Display format. (A) Traditional treadmill display

presents basic metrics such as distance, pace, and speed in

text. (B) Improved Display 1 introduces dynamic elements

including a line chart of heart rate and a bar chart for speed

distribution, along with half-circle progress indicators for

calories burned and covered distance. (C) Improved

Display 2 builds upon Display 1 by integrating average

heart rate, average speed, and additional rate indicators for

calories and distance progress.

4 RESULTS

This section presents the experimental results,

focusing

two

main

areas:

the

analysis

running

performance data during the treadmill experiment and

the analysis of participant responses from the post-run

survey. Collected data were analyzed using both

parametric and non-parametric statistical tests to

examine potential differences in performance,

perceived exertion, satisfaction, and user preference

across different display conditions to examine

potential differences between treatments.

4.1 Running Performance Analysis

A repeated measure ANOVA was conducted to

analyze the differences across treatment groups for

the following metrics: duration, distance covered,

calories expended, average heart rate, maximum heart

rate, average speed, and maximum speed. The

analysis identified statistically significant differences

among groups for duration, distance covered, and

calories expended. To determine the most effective

treatment for running performances, a post-hoc

Bonferroni test was further performed to compare

treatments. The results are as follows:

- Improved Display 2 (T2) and Improved display

1 (T1) have significantly higher duration, and

calories expended compared to running without

a display (T0) and Traditional Display (TT).

- Improved Display 2 (T2) has significantly higher

distance covered compared to running without a

display (T0) and Traditional Display (TT).

- No significant difference was found between

Improved Display 2 and Improved Display 1.

Table 2: Performance Pairwise Comparison. Statistical

significance levels between treatments are indicated as

follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***).

Metrics Group1 Group2 t-statistic

Raw

-value

Adjusted

-value

Duration

T0 TT -1.370 0.188 1.000

T0 T1 -5.114 0.000 0.000***

T0 T2 -4.697 0.000 0.001***

TT T1 -3.097 0.006 0.039*

TT T2 -4.107 0.001 0.004**

T1 T2 -1.583 0.131 0.790

Distance

Covered

T0 TT -2.296 0.035 0.208

T0 T1 -3.326 0.004 0.024*

T0 T2 -4.951 0.000 0.000***

TT T1 -1.609 0.126 0.755

TT T2 -3.763 0.002 0.009**

T1 T2 -2.324 0.033 0.196

Calories

Expended

T0 TT -0.956 0.352 1.000

T0 T1 -3.639 0.002 0.012*

T0 T2 -4.184 0.001 0.004**

TT T1 -3.083 0.007 0.040*

TT T2 -4.028 0.001 0.005**

T1 T2 -1.574 0.134 0.804

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4.2 Survey Response Analysis

For perceived exertion level, to find the difference

across treatments for exertion levels, we conducted a

Friedman test, a non-parametric alternative to

repeated measures ANOVA for within-subject

design, as the normality assumption was violated in

at least one treatment group. We calculated actual

exertion levels for each participant using their

maximum heart rate (Max HR = 220 - Age) and then

determined the percentage of maximum heart rate for

each participant during treatments. To compare actual

exertion with perceived exertion, we mapped each

participant’s percentage of maximum heart rate to the

10-level Borg scale, where lower values represent

lower exertion and higher values indicate near-

maximal effort. By comparing actual exertion and

perceived exertion values, we analyzed the extent to

which treatments influenced users' perception of

effort relative to their actual physiological exertion

level. A Friedman test revealed no statistically

significant difference in exertion levels across

treatments (p = 0.404). This suggests that no single

treatment was significantly more or less demanding

than the other in terms of actual and perceived

exertion difference. Additionally, we conducted a

post-hoc pairwise comparison using the Wilcoxon

signed-rank test to further explore potential

differences between individual treatments. However,

no significant differences were found as well,

indicating that Improved Display treatment with real-

time data visualization feedback doesn’t influence

users to experience less perceived exertion level.

To analyze user satisfaction, a Friedman test

was performed to evaluate user satisfaction across

different treatments for the following metrics: overall

satisfaction, clarity of information, relevance of

information, aesthetic appeal, ease of use, motivation

level, and perceived utility. The analysis result

showed that it was significantly different among

groups for overall satisfaction, aesthetic appeal,

motivation level, and perceived utility.

After that we use Dunn’s test to compare between

treatment groups:

- Improved Display 2 (T2) has a significantly

higher score for overall satisfaction and

aesthetic appeal compared to Traditional

Display (TT).

- Both Improved Displays scored significantly

higher in motivation level and perceived

utility compared to Traditional Display (TT).

Table 3: User Satisfaction Pairwise Comparison. Statistical

significance levels between treatments are indicated as

follows: p < 0.05 (*), p < 0.01 (**), p < 0.001 (***).

Metric Grou

1 Grou

-value

Overall Satisfaction

T2 T1 1.000

TT T1 0.107

TT T2 0.034*

Aesthetic Appeal

T2 T1 0.893

TT T1 0.061

TT T2 0.002**

Motivation Level

T2 T1 1.000

TT T1 0.014*

TT T2 0.003**

Perceived Utility

T2 T1 1.000

TT T1 0.005**

TT T2 0.001***

After completing the experiment, participants

indicated their preferred treatment: 9 preferred

Improved Display 2, 6 chose Improved Display 1, and

3 favored the Traditional Display.

5 DISCUSSIONS

This section outlines the discussion of key findings

from the experimental results, linking them with prior

research and relevant theoretical perspectives. It

examines how data visualization influences user

performance, perceptions of exertion, and

satisfaction. In addition, the discussion highlights the

study’s limitations and suggests directions for future

work.

5.1 Running Performance

The experimental analysis results showed that

Improved Display 2 and Improved Display 1

significantly improved running performance for

running duration, covered distance, and calories

burned compared to running without display and

Traditional Displays that provide feedback to users in

the form of text. The findings align with several

previous studies that highlight the benefits of real-

time or physical representations of performance data

in enhancing exercise outcomes. For example, both

Concon et al. (2024) and Stusak et al. (2014)

demonstrated significant improvements in exercise

performance. A key similarity between their studies

and our study is the use of visualization-based

feedback that fosters a sense of achievement and

personal progress, which in turn motivates users to

exercise with greater effort and consistency. In

contrast, the study by Nadalutti & Chittaro, which

used the MOPET Analyzer, provided interactive

charts and spatial maps to visualize running patterns,

but did not offer real-time feedback during exercise.

Effect of Data Visualization on Users’ Running Performance on Treadmill

673

Likewise, the study by Passafiume et al. (2022),

which used only breathing animation without any

performance-related visual feedback, did not result in

significant performance improvements. One possible

explanation is that the breathing animation alone may

not be directly related to users’ perception of their

performance during exercise. Without performance-

related cues, users may lack the feedback needed to

maintain motivation, making the visual feedback less

effective in supporting running performance.

5.2 Perceived Exertion

An observation from the study revealed that

Improved Display 1 and Improved Display 2 showed

no significant difference from other treatments in

reducing perceived exertion, even though their

running performance results were significantly better

than Traditional Display. This suggests that effort

perception level can be influenced by other factors

than the way data is represented to users. Marcora et

al. (2009) found that perceived exertion does not

always correlate with actual physical effort,

suggesting that cognitive load of participants can

increase perceived exertion independently of

physiological effort levels. Stewart et al. (2022)

examined the difference between actual exertion and

perceived exertion in a VR games exercise

experiment. Their findings showed that participants

consistently reported lower perceived exertion

compared to actual exertion levels. The study

suggests that enjoyment of the VR game and the

environment (labs and gyms setting) influenced

perceived effort, leading users to experience less

perceived exertion level. These findings reinforce the

idea that perceived exertion is shaped by multiple

psychological and contextual factors, such as

cognitive load, emotional engagement, and

environmental immersion, which helps explain why

enhanced visual displays in our study did not reduce

perceived effort. It suggests that the user’s internal

state and environment may play a more significant

role in effort perception than the format of data

perception.

5.3 User Satisfaction

User satisfaction responses indicate that Improved

Display 2 received the highest scores in overall

satisfaction and aesthetic appeal. Both Improved

Display 1 and Improved Display 2 also scored higher

in motivation levels and perceived utility, while the

Traditional Display received the lowest scores across

all categories. Studies by Zhi et al. (2019) and

Kashanj et al. (2024) demonstrated that interactive or

real-time visualizations, such as linked layouts or

smartwatch gauges, not only improve task

performance but also enhance user enjoyment and

engagement. Similarly, Coenen and Vande Moere’s

(2021) study found that situating data visualizations

in public spaces fosters emotional and social

engagement. These studies, like our Improved

Displays, share key characteristics: relevance of data

to the user, ease of interpretation under physical or

cognitive load, and interactive or dynamic feedback

that reinforces self-awareness and motivation. In

contrast, a study by Eikey et al. (2015), which

manipulated basic visual elements such as the static

color of a progress bar chart, found no significant

impact on user self-efficacy. The use of color alone

lacked meaningful relevance to user progress and did

not offer personalization. making it less engaging,

especially for long-term goals like 10,000 steps.

While our study also used static color, it was applied

within a half-ring progress chart and involved a

shorter goal (1 km, approximately 1,200 - 1,500

steps). This smaller goal scale may have made

progress changes easier to perceive, helping

participants feel more motivated to complete the goal.

According to survey feedback, Improved

Display 2 was the most preferred, and Traditional

Display has the lowest score and is the least preferred.

Hildon et al. (2012) review examining found that

users often prefer bar charts, despite tables and

pictographs being generally better understood. Luera

et al. (2024) research shows that users generally

prefer visual representations (such as tables and

charts) over text-based information due to their

efficiency in interpreting trends and comparisons

insight. These findings show that users prefer

visualization feedback over the Traditional Display

that generally represents data in text format.

5.4 Limitations and Future Work

Although the result supports the benefits of real-time

data visualization, the study has some limitations that

should be addressed in future studies. The sample size

of 18 participants may have limited the statistical

power of the study. With a larger number of

participants, the results might have been more robust

and generalizable. However, recruiting participants

proved to be challenging due to constraints such as

time limitations, specific eligibility requirements, and

the intensive nature of the experiment.

A smaller

sample size increases variability and reduces the

generalizability of the study compared to a larger

population. Future research should consider using a

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larger sample to increase confidence in the findings.

A future study could examine whether users maintain

their exercise engagement and motivation over time

with visualization design. The study used a specific

type of visualization, but different designs may lead

to different results. Future studies should compare

different visual elements to determine effective

design for boosting performance. The study involved

a sample size in a controlled experimental setting,

which may not fully generalize to real-world

behavior. Future studies may investigate the

experiment to test visualization impact in real-world

settings, such as gyms or sports grounds.

6 CONCLUSIONS

This study shows the significant impact of real-time

data visualization in improving running performance

and user satisfaction. In terms of running

performance, both Improved Display 1 and Improved

Display 2 led to better outcomes in running duration,

distance covered, and calories burned compared to

the Traditional Display and the control condition.

Regarding user satisfaction, Improved Display 2

received the highest ratings in overall satisfaction and

aesthetic appeal. In terms of user preference, survey

responses confirmed that users favored Improved

Display 2, while the Traditional Display was the least

preferred. However, perceived exertion did not

significantly differ across display types, suggesting

that the format of data representation alone may not

reduce perceived effort, which could instead be

influenced by other cognitive or emotional factors.

These insights can inform the design of data and

represent interface improvements to foster better user

experience and user engagement.

ACKNOWLEDGEMENTS

The authors thank the participants for their valuable

contributions, Chulalongkorn University for research

support, and Assoc. Prof. Ronnapee Chaichaowarat,

Ph.D., for providing the venue and treadmill used in

this study. Additional thanks are extended to the

advisor for suggestions throughout the project.

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