Universidade de Lisboa – Instituto Superior de Economia e Gestão
1st Year of the Masters in Sports Management
Analysis Report
Sports Motion
Diogo Cruz nº 64561
Gonçalo Silva nº 57563
Luís Gonçalves nº 64571
Marta Cabral nº 64587
Sara Teixeira nº 64596
Lecturer: Carlos Costa
Course Unit: Redes e Sistemas de Informação
2024/2025
Index
1. Introduction .................................................................................................................. 1
2. Query e Export.............................................................................................................. 2
3. Analysing data with Scopus tools ................................................................................. 2
3.2 Analysing published documents by year................................................................. 3
3.3 Analysing the documents by thematic area ............................................................. 4
4. Export data from SCOPUS to VOSviewer ................................................................... 5
5. Data analysis choices in VOSviewer ............................................................................ 6
6. Map construction and visualisation .............................................................................. 6
6.1 Minimum of 5 hits per keyword ............................................................................. 7
6.2 Minimum of 8 occurrences per keyword .............................................................. 10
7. Conclusion .................................................................................................................. 12
8. Annexes ...................................................................................................................... 13
Index of Figures
Figure 1. Export of the Scopus database ......................................................................... 2
Figure 2. Documents by country or territory ................................................................... 2
Figure 3. Documents by year ........................................................................................... 3
Figure 4. Documents by thematic area ............................................................................ 4
Figure 5. Exporting data from Scopus to Vosviewer ........................................................ 5
Figure 6. Data analysis process ....................................................................................... 6
Figure 7. Map illustration ................................................................................................ 7
Figure 8. No. of occurrences and no. of keywords........................................................... 7
Figure 9. Overlay Visualisation graphic .......................................................................... 9
Figure 10. No. of occurrences and keywords ................................................................. 10
Figure 11. Map illustration ............................................................................................. 11
Figure 12. Checking the selected keywords ................................................................... 13
1. Introduction
In today's context, data analysis plays a fundamental role in scientific research,
allowing trends and patterns to be identified in various areas of knowledge. As part of the
Networks and Information Systems curricular unit of the Masters in Sports Management
at the Instituto Superior de Economia e Gestão (ISEG), this report aims to explore the
application of digital tools in analyzing scientific production related to the intersection
between sport, movement and technology.
To do this, the Scopus database, one of the main platforms for indexing scientific
articles, was used to identify relevant publications in the area. Strategic keywords were
used, such as "sport", "motion" and "app", which resulted in 90 articles being identified
for analysis. Data exploration included identifying the countries with the highest scientific
output, the evolution of the number of publications over time and the thematic distribution
of the articles, with special emphasis on areas such as computer science, medicine,
engineering and sports science.
The data was then exported to VOSviewer software, a bibliometric network
visualization tool that allows the creation of keyword co-occurrence maps. The analysis
centered on identifying the main thematic clusters, highlighting the relationship between
technological advances and sports performance. Among the most relevant topics were the
role of artificial intelligence and wearable sensors in training and monitoring athletes, as
well as the impact of mobile applications in personalizing sports monitoring.
Therefore, this report not only describes the methodology applied to collect and
process the data, but also critically analyses the results obtained, highlighting the growing
importance of technology in sports research and its potential future developments.
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2. Query e Export
Figure 1. Export of the Scopus database
At this first point in the work, it was necessary to use Scopus, which is a database
of abstracts and citations from various sources, with the main aim of identifying the ideal
number of articles for carrying out the work. In this way, we began by defining the
appropriate keywords for the purpose of our work, which consisted of "sport", "motion"
and "app", giving a total of 90 articles found, a very significant and appropriate number
for the purpose identified.
3. Analyzing data with Scopus tools
We then clicked on "Analyze Results" in order to analyze the data from the 90
articles identified, in terms of the countries with the most scientific production, the
number of documents published per year and the distribution of documents by thematic
area.
3.1 Analysing the countries with the highest scientific output
Figure 2. Documents by country or territory
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The graph shows the distribution of documents related to the topic analyzed by
country or territory, making it possible to identify the regions with the most scientific
production. China clearly leads the way, with around 18 documents, demonstrating strong
research activity in the area. The United States and Germany followed with significant
contributions, reflecting the role of these countries as global centers of innovation and
scientific production.
India, Taiwan and Australia also have a significant output, demonstrating the
involvement of Asian and Oceania countries in scientific research. European countries
such as Spain, the United Kingdom, Austria and Italy have more modest figures but still
make a notable contribution to the advancement of knowledge. Brazil also stands out in
Latin America, representing the region's participation in this field.
Finally, Portugal is at the bottom of the list, with a smaller contribution in terms
of the number of documents. However, this does not invalidate the importance of these
contributions to the global context, especially in international collaborations or specific
areas of specialization. The graph therefore reflects a strong concentration of scientific
production in countries with advanced infrastructure and a growing geographical
diversification of research.
3.2 Analyzing published documents by year
Figure 3. Documents by year
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The graph shows the evolution of the number of documents published per year, reflecting
the growth of scientific production on the subject analysis. Between 2008 and 2012, the
number of publications was very low, indicating that the topic had not yet received much
attention from the scientific community. From 2013 onwards, there was a gradual
increase, with a more marked growth trend between 2016 and 2024.
This consistent growth suggests a growing interest in the area, possibly driven by
technological advances or the practical relevance of the topic. Production peaked in 2024,
with 16 documents published, representing the year with the highest scientific
productivity to date. However, it is interesting to note the sharp drop in 2025, which can
be explained by its proximity to the current year, indicating that not all the data has yet
been recorded or that the year is underway.
This pattern reflects the growing importance of the topic on the global scientific
scene, with a consolidation of the area in recent years. The upward trend until 2024
demonstrates a strengthening of research, while the drop in 2025 should be analyzed with
caution, considering possible delays in the registration of publications.
3.3 Analysing the documents by thematic area
Figure 4. Documents by thematic area
The graph shows the distribution of documents by thematic area, reflecting the
interdisciplinary nature of the subject being analyzed. Computer Science leads the way
with 24.7 per cent of the documents, highlighting the importance of digital technologies,
algorithms and artificial intelligence in the development and application of knowledge in
the area studied.
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Medicine emerged as the second most represented area, with 16 per cent of the
documents highlighting the impact of the topic on health, particularly with regard to
practical application in diagnosis, rehabilitation and movement monitoring. It was
followed by Engineering, with 14.4 per cent, reinforcing the relevance of technical and
innovative solutions, such as wearable sensors and biomechanical systems, which are
fundamental to advances in the field.
Mathematics (7.2%) and Biochemistry and Genetics (6.7%) reflect the need for
analytical, statistical and biological models to support scientific research. Areas such as
Physics and Astronomy (6.2%) show the application of physical principles in analyzing
movements and forces. Other domains, such as Health Sciences (8.8%) and Social
Sciences (3.1%), indicate the breadth of the topic and its relevance to well-being and
social impact.
Finally, the graph shows that the research is highly multidisciplinary, with
significant contributions from various areas, which reinforces the relevance of the topic
in different scientific and practical contexts.
4. Export data from SCOPUS to VOSviewer
At this first point, after using the Scopus database, with the main objective of
generating the ideal number of articles for our work, we moved on to the next step of
exporting all the information collected in Scopus to the software for building and
visualising maps based on bibliometric networks called "VOSviewer"
Figure 5. Exporting data from Scopus to Vosviewer
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5. Data analysis choices in VOSviewer
We then carried out all these steps to ensure that all the processes were carried out
properly, in order to achieve the desired graphics, as shown below.
Figure 6. Data analysis process
6. Map construction and visualization
In this analysis, we will compare the data visualizations using two minimum
occurrence criteria: 5 and 8. The aim of this comparison is to understand the impact that
different thresholds have on the composition of the network, the clusters formed and the
representativeness of the keywords identified.
The number 5 was chosen as a starting point because we believe it is a threshold
that offers a comprehensive basis, allowing us to include keywords that, although less
frequent, still have relevance in the context of the analysis. This initial criterion ensures
that we don't exclude potentially important information.
On the other hand, the limit of 8 occurrences was set as a more selective filter, in
order to highlight keywords with a greater presence in the database. This number is high
enough to ensure that only the most significant terms are included, but not so restrictive
as to exclude relevant contributions. Thus, comparing the two limits will make it possible
to assess how different criteria affect the quality, depth and representativeness of the
analysis, ensuring an appropriate balance between comprehensiveness and selectivity.
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6.1 Minimum of 5 occurrences per keyword
Figure 8. No. of occurrences and no. of keywords
Figure 7. Map illustration
The analysis of the graphs generated by VOSviewer, together with the color
segmentation of the clusters, makes it possible to identify the main themes, the relevance
of the keywords and the intensity of their connections, providing a comprehensive view
of the relationships between the concepts. Each cluster represents a distinct but
interconnected theme, which reflects the interdisciplinary nature of the study.
The blue cluster is centered on topics related to physiology, movement and
biomechanical
phenomena.
Keywords such
as
"physiology",
"exercise"
and
"biomechanics" indicate that this cluster addresses the study of the human body in
movement and the impact of biomechanical factors. This cluster is fundamental to
understanding how human movement can be studied and improved, with a focus on
movement science and exercise physiology. Terms such as "mobile application" and
"mobile applications" (16 and 14 occurrences respectively, with link strengths of 183 and
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155) suggest a practical link between science and technologies that help analyze
movements and apply biomechanical concepts. The words Human (with 31 occurrences
and 314 links) and Humans (with 23 occurrences and 258 links), as you can see, are the
largest, and Human ends up playing a central role in the graph.
The green cluster groups together keywords such as "male", "female", "adult" and
"young adult", which show an analysis orientated towards human populations and
demographic characteristics. This group explores how factors such as age and gender
influence movement or related studies. "Male" and "Female" stand out with 19 and 16
occurrences, respectively, and connecting links of 220 and 189, indicating that these
categories play a central role in the analysis. The term "adult" (17 occurrences and
strength of 196) also reflects the relevance of different age groups in the study. This
cluster shows the importance of demographics and population characteristics in
biomechanical and sports studies.
In turn, the red cluster highlights keywords such as "sports", "wearable sensors",
"artificial intelligence" and "virtual reality", indicating a strong link to advanced
technologies applied to sport. This group represents the intersection between sport and
technology, with special emphasis on innovations such as wearable sensors and artificial
intelligence. The term "sports" leads in relevance, with 36 occurrences and a link strength
of 138, showing that sport is a central theme in the network. Terms such as "wearable
sensors" (9 hits, 105 strength) and "artificial intelligence" (9 hits, 42 strength) suggest
the growing role of technology in improving sports performance and analyzing
movements.
The yellow cluster, although less prominent, connects themes such as "exercise"
and "sports medicine", acting as a bridge between the practical application of exercise
and the scientific and medical aspects related to sport. This cluster reflects the link
between sports practice and medicine, highlighting how physiological and biomechanical
knowledge can be applied to improve health and performance.
The network also shows the interconnection between the clusters, suggesting that
topics such as technology (red), demographics (green) and movement science (blue) are
not independent, but complementary. For example, the relevance of "human" (31
occurrences, strength of 314) demonstrates the centrality of the human being in the
interconnection of themes, while "sports" connects to both technological advances and
physiological and demographic impact.
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Thus, each cluster represents a specific theme, but they are all interconnected,
demonstrating the relationship between human physiology, demographics, technology
and sport. This segmentation helps to identify relevant thematic patterns and interactions,
allowing for a clear and organised analysis of the data.
Figure 9. Overlay Visualisation graphic
VOSviewer's "Overlay Visualization" graph shows the time distribution of
keywords, where the colors reflect the year in which they became most relevant. Purple
indicates older studies, while yellow represents more recent research. This temporal
evolution, when related to the research data obtained from Scopus, makes it possible to
understand the trends and progress of scientific research in the area being analyzed.
In the graph, terms such as "exercise", "mobile application", "humans" and
"physiology" appear predominantly in shades of purple. This indicates that these concepts
began to gain relevance several years ago and are the fundamental pillars of scientific
literature in this area. These topics reflect the beginning of scientific exploration around
biomechanics, the physiology of movement and the introduction of mobile technologies
for sport. They are therefore well-established topics that serve as a basis for further
research.
On the other hand, more recent terms, highlighted in yellow and green, such as
"wearable sensors", "artificial intelligence", "motion analysis" and "rehabilitation",
suggest that the current focus of research is on advanced technological applications. This
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highlight reflects the growing integration of wearable sensors and artificial intelligence,
which are transforming areas such as movement monitoring, sports performance analysis
and rehabilitation processes.
The relationship between the graph and the Scopus results shows this evolution.
While older studies focus on general and fundamental themes, more recent articles
address specific and technological issues, such as the reliability of low-cost motion
analysis systems and the use of artificial intelligence in sports apps. These advances are
particularly relevant to improving the accuracy of analyses and expanding their practical
applications.
In short, the graph reveals a clear transition in scientific research, from
fundamental concepts to the most advanced technological applications. This evolution
highlights how technological advances such as wearable sensors and artificial intelligence
are shaping contemporary research, enabling more precise and accessible responses to
emerging needs. The integration of these technologies into the analysis of sport and
human movement reflects continuous and promising progress in the area, as demonstrated
both by the visual patterns of the graph and by the most recent articles available on
Scopus.
6.2 Minimum of 8 occurrences per keyword
Figure 10. No. of occurrences and keywords
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Figure 11. Map illustration
With a minimum number of 8, sized by occurrences and with a high variation in
size (i.e. the ones with the most occurrences, "Sports" and "Human", are better noticed),
allowing less frequent terms to be excluded, reducing the density of the network and
eliminating potential insights into sub-themes or more specific issues. In this case, the
focus is on the most prominent clusters, such as red (technology and sport), blue
(physiology and movement) and green (demography and human populations), which help
to identify the pillars of research, but with less granularity.
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7. Conclusion
The analysis carried out throughout this report made it possible to explore
scientific production at the intersection of sport, movement and technology, highlighting
trends, advances and challenges in this area. Using the Scopus and VOSviewer tools, it
was possible to map geographical, temporal and thematic patterns, providing a clear
overview of research in this field.
Geographically, China, the United States and Germany stand out as the largest
scientific producers, reflecting their central role in innovation and technological
development in the area. At the same time, the growing participation of countries such as
India, Taiwan and Brazil, shows geographical diversification, promoting collaborations
and new perspectives.
Over time, there has been a marked increase in the number of publications since
2013, culminating in a peak in 2024. This increase reflects the growing interest in the
subject, driven by technological advances and its practical application in sport and health.
The thematic analysis revealed a strong interdisciplinary approach, with Computer
Science (24.7%), Medicine (16%) and Engineering (14.4%) standing out, highlighting
the integration of digital technologies, artificial intelligence and technical solutions in
sports research. Keyword co-occurrence maps reinforced this view, identifying clusters
that cover everything from biomechanical and physiological foundations (blue cluster),
through demographic characteristics (green cluster), to technological innovations applied
to sport (red cluster).
The comparison between the 5 and 8 criteria showed a balance between breadth
and depth. While criterion 5 captured a greater wealth of sub-themes, criterion 8
highlighted the central pillars of the investigation, such as "sports", "human" and "mobile
applications".
The results confirm the impact of advanced technologies, such as sensors and
artificial intelligence, on training personalization and performance monitoring. This
progress reinforces the importance of the subject, highlighting the potential of
multidisciplinary research to shape the future of sport and health.
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8. Annexes
Figure 12. Checking the selected keywords
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