research paper in physical fitness

• Open access
• Published: 21 May 2024

The bright side of sports: a systematic review on well-being, positive emotions and performance

• David Peris-Delcampo 1 ,
• Antonio Núñez 2 ,
• Paula Ortiz-Marholz 3 ,
• Aurelio Olmedilla 4 ,
• Enrique Cantón 1 ,
• Javier Ponseti 2 &
• Alejandro Garcia-Mas 2  

BMC Psychology volume  12 , Article number:  284 ( 2024 ) Cite this article

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The objective of this study is to conduct a systematic review regarding the relationship between positive psychological factors, such as psychological well-being and pleasant emotions, and sports performance.

This study, carried out through a systematic review using PRISMA guidelines considering the Web of Science, PsycINFO, PubMed and SPORT Discus databases, seeks to highlight the relationship between other more ‘positive’ factors, such as well-being, positive emotions and sports performance.

The keywords will be decided by a Delphi Method in two rounds with sport psychology experts.

Participants

There are no participants in the present research.

The main exclusion criteria were: Non-sport thema, sample younger or older than 20–65 years old, qualitative or other methodology studies, COVID-related, journals not exclusively about Psychology.

Main outcomes measures

We obtained a first sample of 238 papers, and finally, this sample was reduced to the final sample of 11 papers.

The results obtained are intended to be a representation of the ‘bright side’ of sports practice, and as a complement or mediator of the negative variables that have an impact on athletes’ and coaches’ performance.

Conclusions

Clear recognition that acting on intrinsic motivation continues to be the best and most effective way to motivate oneself to obtain the highest levels of performance, a good perception of competence and a source of personal satisfaction.

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Introduction

In recent decades, research in the psychology of sport and physical exercise has focused on the analysis of psychological variables that could have a disturbing, unfavourable or detrimental role, including emotions that are considered ‘negative’, such as anxiety/stress, sadness or anger, concentrating on their unfavourable relationship with sports performance [ 1 , 2 , 3 , 4 ], sports injuries [ 5 , 6 , 7 ] or, more generally, damage to the athlete’s health [ 8 , 9 , 10 ]. The study of ‘positive’ emotions such as happiness or, more broadly, psychological well-being, has been postponed at this time, although in recent years this has seen an increase that reveals a field of study of great interest to researchers and professionals [ 11 , 12 , 13 ] including physiological, psychological, moral and social beneficial effects of the physical activity in comic book heroes such as Tintin, a team leader, which can serve as a model for promoting healthy lifestyles, or seeking ‘eternal youth’ [ 14 ].

Emotions in relation to their effects on sports practice and performance rarely go in one direction, being either negative or positive—generally positive and negative emotions do not act alone [ 15 ]. Athletes experience different emotions simultaneously, even if they are in opposition and especially if they are of mild or moderate intensity [ 16 ]. The athlete can feel satisfied and happy and at the same time perceive a high level of stress or anxiety before a specific test or competition. Some studies [ 17 ] have shown how sports participation and the perceived value of elite sports positively affect the subjective well-being of the athlete. This also seems to be the case in non-elite sports practice. The review by Mansfield et al. [ 18 ] showed that the published literature suggests that practising sports and dance, in a group or supported by peers, can improve the subjective well-being of the participants, and also identifies negative feelings towards competence and ability, although the quantity and quality of the evidence published is low, requiring better designed studies. All these investigations are also supported by the development of the concept of eudaimonic well-being [ 19 ], which is linked to the development of intrinsic motivation, not only in its aspect of enjoyment but also in its relationship with the perception of competition and overcoming and achieving goals, even if this is accompanied by other unpleasant hedonic emotions or even physical discomfort. Shortly after a person has practised sports, he will remember those feelings of exhaustion and possibly stiffness, linked to feelings of satisfaction and even enjoyment.

Furthermore, the mediating role of parents, coaches and other psychosocial agents can be significant. In this sense, Lemelin et al. [ 20 ], with the aim of investigating the role of autonomy support from parents and coaches in the prediction of well-being and performance of athletes, found that autonomy support from parents and coaches has positive relationships with the well-being of the athlete, but that only coach autonomy support is associated with sports performance. This research suggests that parents and coaches play important but distinct roles in athlete well-being and that coach autonomy support could help athletes achieve high levels of performance.

On the other hand, an analysis of emotions in the sociocultural environment in which they arise and gain meaning is always interesting, both from an individual perspective and from a sports team perspective. Adler et al. [ 21 ] in a study with military teams showed that teams with a strong emotional culture of optimism were better positioned to recover from poor performance, suggesting that organisations that promote an optimistic culture develop more resilient teams. Pekrun et al. [ 22 ] observed with mathematics students that individual success boosts emotional well-being, while placing people in high-performance groups can undermine it, which is of great interest in investigating the effectiveness and adjustment of the individual in sports teams.

There is still little scientific literature in the field of positive emotions and their relationship with sports practice and athlete performance, although their approach has long had its clear supporters [ 23 , 24 ]. It is comforting to observe the significant increase in studies in this field, since some authors (e.g [ 25 , 26 ]). . , point out the need to overcome certain methodological and conceptual problems, paying special attention to the development of specific instruments for the evaluation of well-being in the sports field and evaluation methodologies.

As McCarthy [ 15 ] indicates, positive emotions (hedonically pleasant) can be the catalysts for excellence in sport and deserve a space in our research and in professional intervention to raise the level of athletes’ performance. From a holistic perspective, positive emotions are permanently linked to psychological well-being and research in this field is necessary: firstly because of the leading role they play in human behaviour, cognition and affection, and secondly, because after a few years of international uncertainty due to the COVID-19 pandemic and wars, it seems ‘healthy and intelligent’ to encourage positive emotions for our athletes. An additional reason is that they are known to improve motivational processes, reducing abandonment and negative emotional costs [ 11 ]. In this vein, concepts such as emotional intelligence make sense and can help to identify and properly manage emotions in the sports field and determine their relationship with performance [ 27 ] that facilitates the inclusion of emotional training programmes based on the ‘bright side’ of sports practice [ 28 ].

Based on all of the above, one might wonder how these positive emotions are related to a given event and what role each one of them plays in the athlete’s performance. Do they directly affect performance, or do they affect other psychological variables such as concentration, motivation and self-efficacy? Do they favour the availability and competent performance of the athlete in a competition? How can they be regulated, controlled for their own benefit? How can other psychosocial agents, such as parents or coaches, help to increase the well-being of their athletes?

This work aims to enhance the leading role, not the secondary, of the ‘good and pleasant side’ of sports practice, either with its own entity, or as a complement or mediator of the negative variables that have an impact on the performance of athletes and coaches. Therefore, the objective of this study is to conduct a systematic review regarding the relationship between positive psychological factors, such as psychological well-being and pleasant emotions, and sports performance. For this, the methodological criteria that constitute the systematic review procedure will be followed.

Materials and methods

This study was carried out through a systematic review using PRISMA (Preferred Reporting Items for Systematic Reviews) guidelines considering the Web of Science (WoS) and Psycinfo databases. These two databases were selected using the Delphi method [ 29 ]. It does not include a meta-analysis because there is great data dispersion due to the different methodologies used [ 30 ].

The keywords will be decided by the Delphi Method in two rounds with sport psychology experts. The results obtained are intended to be a representation of the ‘bright side’ of sports practice, and as a complement or mediator of the negative variables that have an impact on athletes’ and coaches’ performance.

It was determined that the main construct was to be psychological well-being, and that it was to be paired with optimism, healthy practice, realisation, positive mood, and performance and sport. The search period was limited to papers published between 2000 and 2023, and the final list of papers was obtained on February 13 , 2023. This research was conducted in two languages—English and Spanish—and was limited to psychological journals and specifically those articles where the sample was formed by athletes.

Each word was searched for in each database, followed by searches involving combinations of the same in pairs and then in trios. In relation to the results obtained, it was decided that the best approach was to group the words connected to positive psychology on the one hand, and on the other, those related to self-realisation/performance/health. In this way, it used parentheses to group words (psychological well-being; or optimism; or positive mood) with the Boolean ‘or’ between them (all three refer to positive psychology); and on the other hand, it grouped those related to performance/health/realisation (realisation; or healthy practice or performance), separating both sets of parentheses by the Boolean ‘and’’. To further filter the search, a keyword included in the title and in the inclusion criteria was added, which was ‘sport’ with the Boolean ‘and’’. In this way, the search achieved results that combined at least one of the three positive psychology terms and one of the other three.

Results (first phase)

The mentioned keywords were cross-matched, obtaining the combination with a sufficient number of papers. From the first research phase, the total number of papers obtained was 238. Then screening was carried out by 4 well-differentiated phases that are summarised in Fig.  1 . These phases helped to reduce the original sample to a more accurate one.

Phases of the selection process for the final sample. Four phases were carried out to select the final sample of articles. The first phase allowed the elimination of duplicates. In the second stage, those that, by title or abstract, did not fit the objectives of the article were eliminated. Previously selected exclusion criteria were applied to the remaining sample. Thus, in phase 4, the final sample of 11 selected articles was obtained

Results (second phase)

The first screening examined the title, and the abstract if needed, excluding the papers that were duplicated, contained errors or someone with formal problems, low N or case studies. This screening allowed the initial sample to be reduced to a more accurate one with 109 papers selected.

Results (third phase)

This was followed by the second screening to examine the abstract and full texts, excluding if necessary papers related to non-sports themes, samples that were too old or too young for our interests, papers using qualitative methodologies, articles related to the COVID period, or others published in non-psychological journals. Furthermore, papers related to ‘negative psychological variables’’ were also excluded.

Results (fourth phase)

At the end of this second screening the remaining number of papers was 11. In this final phase we tried to organise the main characteristics and their main conclusions/results in a comprehensible list (Table  1 ). Moreover, in order to enrich our sample of papers, we decided to include some articles from other sources, mainly those presented in the introduction to sustain the conceptual framework of the concept ‘bright side’ of sports.

The usual position of the researcher of psychological variables that affect sports performance is to look for relationships between ‘negative’ variables, first in the form of basic psychological processes, or distorting cognitive behavioural, unpleasant or evaluable as deficiencies or problems, in a psychology for the ‘risk’ society, which emphasises the rehabilitation that stems from overcoming personal and social pathologies [ 31 ], and, lately, regarding the affectation of the athlete’s mental health [ 32 ]. This fact seems to be true in many cases and situations and to openly contradict the proclaimed psychological benefits of practising sports (among others: Cantón [ 33 ], ; Froment and González [ 34 ]; Jürgens [ 35 ]).

However, it is possible to adopt another approach focused on the ‘positive’ variables, also in relation to the athlete’s performance. This has been the main objective of this systematic review of the existing literature and far from being a novel approach, although a minority one, it fits perfectly with the definition of our area of knowledge in the broad field of health, as has been pointed out for some time [ 36 , 37 ].

After carrying out the aforementioned systematic review, a relatively low number of articles were identified by experts that met the established conditions—according to the PRISMA method [ 37 , 38 , 39 , 40 ]—regarding databases, keywords, and exclusion and inclusion criteria. These precautions were taken to obtain the most accurate results possible, and thus guarantee the quality of the conclusions.

The first clear result that stands out is the great difficulty in finding articles in which sports ‘performance’ is treated as a well-defined study variable adapted to the situation and the athletes studied. In fact, among the results (11 papers), only 3 associate one or several positive psychological variables with performance (which is evaluated in very different ways, combining objective measures with other subjective ones). This result is not surprising, since in several previous studies (e.g. Nuñez et al. [ 41 ]) using a systematic review, this relationship is found to be very weak and nuanced by the role of different mediating factors, such as previous sports experience or the competitive level (e.g. Rascado, et al. [ 42 ]; Reche, Cepero & Rojas [ 43 ]), despite the belief—even among professional and academic circles—that there is a strong relationship between negative variables and poor performance, and vice versa, with respect to the positive variables.

Regarding what has been evidenced in relation to the latter, even with these restrictions in the inclusion and exclusion criteria, and the filters applied to the first findings, a true ‘galaxy’ of variables is obtained, which also belong to different categories and levels of psychological complexity.

A preliminary consideration regarding the current paradigm of sport psychology: although it is true that some recent works have already announced the swing of the pendulum on the objects of study of PD, by returning to the study of traits and dispositions, and even to the personality of athletes [ 43 , 44 , 45 , 46 ], our results fully corroborate this trend. Faced with five variables present in the studies selected at the end of the systematic review, a total of three traits/dispositions were found, which were also the most repeated—optimism being present in four articles, mental toughness present in three, and finally, perfectionism—as the representative concepts of this field of psychology, which lately, as has already been indicated, is significantly represented in the field of research in this area [ 46 , 47 , 48 , 49 , 50 , 51 , 52 ]. In short, the psychological variables that finally appear in the selected articles are: psychological well-being (PWB) [ 53 ]; self-compassion, which has recently been gaining much relevance with respect to the positive attributional resolution of personal behaviours [ 54 ], satisfaction with life (balance between sports practice, its results, and life and personal fulfilment [ 55 ], the existence of approach-achievement goals [ 56 ], and perceived social support [ 57 ]). This last concept is maintained transversally in several theoretical frameworks, such as Sports Commitment [ 58 ].

The most relevant concept, both quantitatively and qualitatively, supported by the fact that it is found in combination with different variables and situations, is not a basic psychological process, but a high-level cognitive construct: psychological well-being, in its eudaimonic aspect, first defined in the general population by Carol Ryff [ 59 , 60 ] and introduced at the beginning of this century in sport (e.g., Romero, Brustad & García-Mas [ 13 ], ; Romero, García-Mas & Brustad [ 61 ]). It is important to note that this concept understands psychological well-being as multifactorial, including autonomy, control of the environment in which the activity takes place, social relationships, etc.), meaning personal fulfilment through a determined activity and the achievement or progress towards goals and one’s own objectives, without having any direct relationship with simpler concepts, such as vitality or fun. In the selected studies, PWB appears in five of them, and is related to several of the other variables/traits.

The most relevant result regarding this variable is its link with motivational aspects, as a central axis that relates to different concepts, hence its connection to sports performance, as a goal of constant improvement that requires resistance, perseverance, management of errors and great confidence in the possibility that achievements can be attained, that is, associated with ideas of optimism, which is reflected in expectations of effectiveness.

If we detail the relationships more specifically, we can first review this relationship with the ‘way of being’, understood as personality traits or behavioural tendencies, depending on whether more or less emphasis is placed on their possibilities for change and learning. In these cases, well-being derives from satisfaction with progress towards the desired goal, for which resistance (mental toughness) and confidence (optimism) are needed. When, in addition, the search for improvement is constant and aiming for excellence, its relationship with perfectionism is clear, although it is a factor that should be explored further due to its potential negative effect, at least in the long term.

The relationship between well-being and satisfaction with life is almost tautological, in the precise sense that what produces well-being is the perception of a relationship or positive balance between effort (or the perception of control, if we use stricter terminology) and the results thereof (or the effectiveness of such control). This direct link is especially important when assessing achievement in personally relevant activities, which, in the case of the subjects evaluated in the papers, specifically concern athletes of a certain level of performance, which makes it a more valuable objective than would surely be found in the general population. And precisely because of this effect of the value of performance for athletes of a certain level, it also allows us to understand how well-being is linked to self-compassion, since as a psychological concept it is very close to that of self-esteem, but with a lower ‘demand’ or a greater ‘generosity’, when we encounter failures, mistakes or even defeats along the way, which offers us greater protection from the risk of abandonment and therefore reinforces persistence, a key element for any successful sports career [ 62 ].

It also has a very direct relationship with approach-achievement goals, since precisely one of the central aspects characterising this eudaimonic well-being and differentiating it from hedonic well-being is specifically its relationship with self-determined and persistent progress towards goals or achievements with incentive value for the person, as is sports performance evidently [ 63 ].

Finally, it is interesting to see how we can also find a facet or link relating to the aspects that are more closely-related to the need for human affiliation, with feeling part of a group or human collective, where we can recognise others and recognise ourselves in the achievements obtained and the social reinforcement of those themselves, as indicated by their relationship with perceived social support. This construct is very labile, in fact it is common to find results in which the pressure of social support is hardly differentiated, for example, from the parents of athletes and/or their coaches [ 64 ]. However, its relevance within this set of psychological variables and traits is proof of its possible conceptual validity.

Analysing the results obtained, the first conclusion is that in no case is an integrated model based solely on ‘positive’ variables or traits obtained, since some ‘negative’ ones appear (anxiety, stress, irrational thoughts), affecting the former.

The second conclusion is that among the positive elements the variable coping strategies (their use, or the perception of their effectiveness) and the traits of optimism, perfectionism and self-compassion prevail, since mental strength or psychological well-being (which also appear as important, but with a more complex nature) are seen to be participated in by the aforementioned traits.

Finally, it must be taken into account that the generation of positive elements, such as resilience, or the learning of coping strategies, are directly affected by the educational style received, or by the culture in which the athlete is immersed. Thus, the applied potential of these findings is great, but it must be calibrated according to the educational and/or cultural features of the specific setting.

Limitations

The limitations of this study are those evident and common in SR methodology using the PRISMA system, since the selection of keywords (and their logical connections used in the search), the databases, and the inclusion/exclusion criteria bias the work in its entirety and, therefore, constrain the generalisation of the results obtained.

Likewise, the conclusions must—based on the above and the results obtained—be made with the greatest concreteness and simplicity possible. Although we have tried to reduce these limitations as much as possible through the use of experts in the first steps of the method, they remain and must be considered in terms of the use of the results.

Future developments

Undoubtedly, progress is needed in research to more precisely elucidate the role of well-being, as it has been proposed here, from a bidirectional perspective: as a motivational element to push towards improvement and the achievement of goals, and as a product or effect of the self-determined and competent behaviour of the person, in relation to different factors, such as that indicated here of ‘perfectionism’ or the potential interference of material and social rewards, which are linked to sports performance—in our case—and that could act as a risk factor so that our achievements, far from being a source of well-being and satisfaction, become an insatiable demand in the search to obtain more and more frequent rewards.

From a practical point of view, an empirical investigation should be conducted to see if these relationships hold from a statistical point of view, either in the classical (correlational) or in the probabilistic (Bayesian Networks) plane.

The results obtained in this study, exclusively researched from the desk, force the authors to develop subsequent empirical and/or experimental studies in two senses: (1) what interrelationships exist between the so called ‘positive’ and ‘negative’ psychological variables and traits in sport, and in what sense are each of them produced; and, (2) from a global, motivational point of view, can currently accepted theoretical frameworks, such as SDT, easily accommodate this duality, which is becoming increasingly evident in applied work?

Finally, these studies should lead to proposals applied to the two fields that have appeared to be relevant: educational and cultural.

Application/transfer of results

A clear application of these results is aimed at guiding the training of sports and physical exercise practitioners, directing it towards strategies for assessing achievements, improvements and failure management, which keep them in line with well-being enhancement, eudaimonic, intrinsic and self-determined, which enhances the quality of their learning and their results and also favours personal health and social relationships.

Data availability

There are no further external data.

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Researchers at Stanford Medicine and their colleagues conducted nearly 10,000 measurements in nearly 20 types of tissues, learning about the effects of exercise on the immune system, stress response, energy production and metabolism. Alan Poulson Photography/Shutterstock.com

Exercise. It’s associated with increased muscle strength, improved heart health, lower blood sugar and just about every other physical improvement you can name. But how does regularly puffing away on a treadmill, biking up a steep hill or going for a brisk lunchtime walk confer such a dizzying array of health benefits?

We’re now closer to finding out, thanks to a vast new study led by Stanford Medicine. Researchers conducted nearly 10,000 measurements in nearly 20 types of tissues to uncover the effect of eight weeks of endurance exercise in laboratory rats trained to run on rodent-sized treadmills.

Their results highlight striking effects of exercise on the immune system, stress response, energy production and metabolism. They uncovered significant links between exercise, molecules and genes already known to be involved in myriad human diseases and tissue recovery.

The study is one of a series of papers published May 1 by members of a multicenter research group meant to lay the groundwork for understanding — on a bodywide, molecular level — exactly how our tissues and cells react when we push them to perform.

“We all know exercise is beneficial for us,” said professor of pathology Stephen Montgomery , PhD. “But we don’t know much about the molecular signals that manifest across the body when people exercise, or how they may change when people train. Our study is the first to take a holistic, bodywide look at molecular changes, from proteins to genes to metabolites to fats and energy production. It’s the broadest profiling yet of the effects of exercise, and it creates an essential map to how it changes the body.”

Montgomery, who is also a professor of genetics and of biomedical data science, is a senior author of the  paper , which published on May 1 in  Nature . Other senior authors are  Michael Snyder , PhD, the Stanford W. Ascherman, MD, FACS Professor in Genetics, and associate professor of medicine  Matthew Wheeler , MD. First authors are former genetics PhD student Nicole Gay, PhD; former postdoctoral scholar David Amar, PhD; and Pierre Jean Beltran, PhD, a former postdoctoral scholar at the Broad Institute.

Additional papers by Stanford Medicine researchers include a related published report in  Nature Communications  investigating the effect of exercise-induced changes in genes and tissues known to be involved in disease risk as well as a  paper published on May 2 in  Cell Metabolism , which focuses on the effects of exercise on the cellular energy factors called mitochondria in various tissues. Montgomery is the senior author of the  Nature Communications paper and postdoctoral scholar  Nikolai Vetr , PhD, is its lead author. Instructor of cardiovascular medicine  Malene Lindholm , PhD, is the senior author of the  Cell Metabolism paper, and Amar is the lead author.

Stephen Montgomery

“These papers further highlight the multiple impacts exercise training has on metabolism and health,” Montgomery said.

A coordinated look at exercise

The researchers involved in the study and the other simultaneous publications are part of a national group called the Molecular Transducers of Physical Activity Consortium, or MoTrPAC, organized by the National Institutes of Health. The effort was launched in 2015 to investigate in detail exactly how physical exercise improves health and prevents disease.

The Stanford Medicine team took on a lot of the heavy lifting, studying the effects of eight weeks of endurance training on gene expression (the transcriptome), proteins (the proteome), fats (the lipidome), metabolites (the metabolome), the pattern of chemical tags placed on DNA (the epigenome), the immune system (the…you get the idea).

Let’s just call it the sweat-ome.

They performed 9,466 analyses on multiple tissues in rats as the animals were trained to run increasing distances and compared the results with those of rats that loafed about in their cages. They paid special attention to the muscles of the leg, the heart, the liver, the kidney and a type of fat called white adipose tissue (the kind of fat that accumulates as pounds pile on); other tissues included the lungs, brain and brown adipose tissue (a more metabolically active type of fat that helps burn calories). The combination of multiple assays — think of all those -omes! — and tissue types pumped out results numbering in the hundreds of thousands for non-epigenetic changes to more than 2 million distinct changes in the epigenome. The results will keep scientists hopping for years.

Although this study served primarily to create a database for future analysis, some interesting nuggets vaulted to the top. First, they noted that the expression of 22 genes changed with exercise in all six of the tissues they focused on. Many of these genes were involved in what are known as heat shock pathways, which stabilize the structure of proteins when cells undergo stress including changes in temperature (feel that burn?), infection or tissue remodeling (hello new muscle fibers!). Others have been implicated in pathways that reduce blood pressure and increase the body’s sensitivity to insulin, which lowers blood sugar levels.

The researchers also noted that the expression of several genes involved in Type 2 diabetes, heart disease, obesity and kidney disease was reduced in exercising rats as compared with their sedentary counterparts — a clear link between their studies and human health.

Sex differences

Finally, they identified sex differences in how multiple tissues in male and female rats responded to exercise. Male rats lost about 5% of their body fat after eight weeks of exercise while female rats didn’t lose a significant amount. (They did, however, maintain their starting fat percentage while the sedentary females packed on an additional 4% of body fat during the study period.) But the largest difference was observed in gene expression in the rats’ adrenal glands. After one week, genes associated with the generation of steroid hormones like adrenaline and with energy production increased in male rats but decreased in female rats.

Despite these early, tantalizing associations, the researchers caution that exercise science is nowhere near the finish line. It’s more like the starting gun has just fired. But the future is exciting.

“In the long term, it’s unlikely we will find any one magic intervention that reproduces what exercise can do for a person,” Montgomery said. “But we might get closer to the idea of precision exercise — tailoring recommendations based on a person’s genetics, sex, age or other health conditions to generate beneficial whole-body responses.”

A full list of researchers and institutions involved in the study can be found online.

The MoTrPAC study is supported by the National Institutes of Health (grants U24OD026629, U24DK112349, U24DK112342, U24DK112340, U24DK112341, U24DK112326, 612 U24DK112331, U24DK112348, U01AR071133, U01AR071130, 613 U01AR071124, U01AR071128, U01AR071150, U01AR071160, U01AR071158, U24AR071113, U01AG055133, U01AG055137, 615 U01AG055135, 5T32HG000044, P30AG044271 and P30AG003319), the National Science Foundation, and the Knut and Alice Wallenberg Foundation.

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• Published: 21 May 2024

The effect of physical fitness on psychological health: evidence from Chinese university students

• Shuzhen Ma   ORCID: orcid.org/0009-0009-9325-4539 1 , 2 ,
• Yanqi Xu 3 ,
• Simao Xu 4 &
• Zhicheng Guo 5  

BMC Public Health volume  24 , Article number:  1365 ( 2024 ) Cite this article

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Despite frequent discussions on the link between physical and mental health, the specific impact of physical fitness on mental well-being is yet to be fully established.

This study, carried out between January 2022 and August 2023, involved 4,484 Chinese University students from eight universities located in various regions of China. It aimed to examine the association between physical fitness on psychological well-being. Descriptive statistics, t-tests, and logistic regression were used to analyze the association between physical fitness indicators (e.g., Body Mass Index (BMI), vital capacity, and endurance running) and mental health, assessed using Symptom Checklist-90 (SCL-90). All procedures were ethically approved, and participants consented to take part in.

Our analysis revealed that BMI, vital capacity, and endurance running scores significantly influence mental health indicators. Specifically, a 1-point increase in BMI increases the likelihood of an abnormal psychological state by 10.9%, while a similar increase in vital capacity and endurance running decreases the risk by 2.1% and 4.1%, respectively. In contrast, reaction time, lower limb explosiveness, flexibility, and muscle strength showed no significant effects on psychological states ( p  > 0.05).

Improvements in BMI, vital capacity, and endurance running capabilities are associated with better mental health outcomes, highlighting their potential importance in enhancing overall well-being.

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In China, while University students were traditionally perceived as being ‘blessed by fortune’ and less prone to mental distress or disorders, the rapid expansion of universities and universities during significant socio-economic transitions has brought unprecedented attention to mental health issues among this demographic in recent decades [ 1 , 2 ]. Both male and female university students commonly experience psychological challenges stemming from environmental changes, academic pressures, emotional setbacks, and health issues [ 3 ]. Mental disorders during this period can lead to significant adverse outcomes, including university dropout, academic underachievement, strained relationships, and diminished emotional well-being, ultimately compromising physical health and future career prospects [ 4 ]. The Symptom Checklist-90 (SCL-90) is frequently employed in China for assessing the mental health status of university students [ 5 ]. According to Ren (2009), the SCL-90 was utilized in 63.8% of the published articles addressing mental health among university students [ 6 ].

Wang introduced the SCL-90 to China in 1984 [ 7 ]. Since its translation by Wang from English to Chinese, the scale has gained widespread usage in China [ 8 ]. It comprises 90 self-report items, with each question utilizing a 5-point Likert scale, ranging from 0 (not at all) to 4 (extremely). The SCL-90 effectively identifies individuals with existing psychiatric symptoms, screens for potential symptoms, determines their type and severity, and highlights the urgency for personalized intervention based on higher total scores [ 9 ].

Given that the World Health Organization (WHO) regards mental and physical dimensions as fundamental components of overall health and well-being [ 10 ], a robust correlation has been identified between mental and physical health [ 11 , 12 , 13 , 14 ]. Dr. Kishore, in an article published in the Bulletin of the WTO, notably asserted that “true physical health cannot exist without mental health” [ 15 ]. Ohrnberger (2017) discovered robust cross-effects between physical and mental health, even when adjusting for confounding variables [ 16 ]. In prior research, several cross-sectional studies have identified mental health as a significant correlate of physical health [ 17 , 18 , 19 ], however, there is a lack of studies investigating the dynamic association between the two [ 16 ].

Physical health, encompassing cardiorespiratory endurance, muscular strength endurance, flexibility, and body composition, serves as a critical indicator of health [ 20 , 21 ]. The Ministry of Education of China released the National Physical Health Standards for Students (revised in 2014, NPHSS) to assess the physical health status of young individuals, including university students, thus reflecting their overall physical fitness level. These standards are evaluated annually, with fitness measures assessed according to the 2014 revised Chinese National Student Physical Fitness Standard (CNSPFS), covering various aspects such as aerobic capacity, upper body strength, flexibility, body mass index (BMI), abdominal strength, and trunk strength [ 21 ]. The national standards aid educators in establishing the desired objectives for students to accomplish by the conclusion of their academic endeavors [ 22 ]. Since its initial introduction, the NPHSS has been instrumental in shaping physical education policies in China. Studies have demonstrated that structured physical fitness programs, aligned with these standards, not only enhance physical health but also contribute to academic performance and psychological resilience among students. Furthermore, longitudinal data suggest that continuous engagement with NPHSS-guided activities significantly improves health outcomes over time [ 23 ].

Our study employs the 2014 revised NPHSS to assess the physical fitness level of Chinese university students, alongside the use of the SCL-90 as a screening tool for evaluating their mental health status. We analyze the discrepancy in average scores of the sports quality index between students with regular and abnormal mental health statuses and examine the impact of sports quality index scores on students’ psychological well-being using a binary logistic regression model. To provide some suggestions on how to improve the mental health status of students with abnormal mental health status in university through some physical exercises.

Participants

Between January 2022 and August 2023, a cross-sectional study was conducted in three regions of China—North and Northeast, Northwest, and Southwest—focusing on the psychological and physical health of university students. This study was approved by the Ethics Committee of Guangxi Normal University and involved eight universities. Within each of the four academic levels at every university, 150 students were selected, totaling 4800 participants. Students were recruited on a voluntary basis from various departments within each university. A random sampling technique was applied across the different majors to ensure a representative sample, reflecting the diversity of academic disciplines. Recruitment was facilitated through university instructors in physical education and mental health, and participants were offered academic credit as an incentive, a practice approved by the ethics committee for its educational value. Recruitment and data collection were carried out from January 2022 to August 2023. Recruitment was initiated in January 2022 and completed by April 2022. Each university conducted its recruitment independently, allowing for data collection to proceed until August 2023. The participants were primarily first through fourth-year undergraduate students, typically aged 18 to 22. After excluding participants with incomplete physical fitness tests or questionnaires, data from the remaining participants were analyzed. Specifically, if a participant left any item blank or provided evidently non-serious responses (such as marking the same answer across multiple items without considering the content), we considered the data incomplete or the responses erroneous, thus excluding them from our analysis. To address the ethical consideration of incentivizing participation with academic credit, it is important to note that this practice was carefully reviewed and approved by the ethics committee. The incentive was deemed appropriate given its direct relevance to the educational outcomes of the students involved in the study. Moreover, the use of academic credit as an incentive aligns with the educational goals of the participants, enhancing their engagement in activities that contribute to their academic and personal development.

The research involved the evaluation of participants through a combination of physical fitness assessments and questionnaire surveys. The physical fitness evaluations adhered to the criteria outlined in the NPHSS provided by the Ministry of Education of China. These standards encompass various parameters such as BMI, lung capacity, 50-meter sprint, sit-and-reach flexibility, standing long jump, pull-ups (for men) or 1-minute sit-ups (for women), and either a 1000-meter run (for men) or an 800-meter run (for women). The physical fitness assessment greatly benefits students by assisting them in achieving higher academic credits and preparing them for the workforce with improved physical conditions.

The Symptom SCL-90 is a widely recognized tool in psychiatric assessment [ 24 ]. Its reliability has been confirmed by previous studies [ 25 ]. The scale comprises nine subscale dimensions: Somatization, Obsessive-Compulsive, Interpersonal-Sensitivity, Depression, Anxiety, Hostility, Phobic-Anxiety, Paranoid Ideation, and Psychoticism [ 9 ]. In this study, teachers from eight universities utilized mobile phones to distribute the questionnaire to their students, who were instructed to complete it accurately. The questionnaire exhibited a 100% response rate and a 94% effectiveness rate, demonstrating its utility in capturing relevant data.

Variable table

This study integrates a thorough assessment of physical fitness encompassing seven primary dimensions. Due to inherent differences in physical characteristics between genders, variations in indicator selection are observed. The overall physical quality test comprises seven aspects: BMI, vital capacity, 50m run, standing long jump, sitting forward bend, 1000m run (male)/800m run (female), and pull-up (male)/one-minute sit-up (female). The implications of these seven indicators are delineated in Table  1 below. The ratings of ‘Excellent’, ‘Good’, ‘Pass’, and ‘Failure’ in this study are determined based on the specific scoring criteria set by the NPHSS, which adjust scoring thresholds for different physical indicators according to gender.

The SCL-90 scale comprises 90 items divided into 10 distinct factors. Each factor and its corresponding items are as follows:

Somatization (items 1, 4, 12, 27, 40, 42, 48, 49, 52, 53, 56, and 58, totaling 12 items): Reflects distress arising from perceptions of bodily dysfunction.

Obsessive-compulsive (items 3, 9, 10, 28, 38, 45, 46, 51, 55, and 65, totaling 10 items): Indicates an inclination towards repetitive thoughts and compulsive behaviors.

Interpersonal Sensitivity (items 6, 21, 34, 36, 37, 41, 61, 69, and 73, totaling 9 items): Measures feelings of inadequacy and inferiority, particularly in comparison to others.

Depression (items 5, 14, 15, 20, 22, 26, 29, 30, 31, 32, 54, 71, and 79, totaling 13 items): Assesses symptoms associated with mood disturbance, including melancholy, hopelessness, and the lack of interest in life.

Anxiety (items 2, 17, 23, 33, 39, 57, 72, 78, 80, and 86, totaling 10 items): Evaluates general signs of anxiety such as nervousness, tension, and tremulousness.

Hostility (items 11, 24, 63, 67, 74, and 81, totaling 6 items): Concerns feelings of anger and irritability.

Phobic Anxiety (items 13, 25, 47, 50, 70, 75, and 82, totaling 7 items): Represents persistent and irrational fears about specific objects, people, or situations.

Paranoid Ideation (items 8, 18, 43, 68, 76, and 83, totaling 6 items): Involves thoughts or beliefs of being harmed by others.

Psychoticism (items 7, 16, 35, 62, 77, 84, 85, 87, 88, and 90, totaling 10 items): Encompasses a range of symptoms suggestive of psychosis, including isolation and withdrawal.

Other (items 19, 44, 59, 60, 64, 66, and 89, totaling 7 items): Items that do not neatly fall into the above categories.

Each item employs a 5-level scoring system with the following instructions: None (1 point), Very mild (2 points), Moderate (3 points), Severe (4 points), and very severe (5 points). Factor scores are computed by summing the scores of each item within the factor and dividing by the number of items within that factor. A score of 1–2 indicates a normal result, while a score greater than 2 indicates an abnormal result.

Equity, diversity and inclusion

This research targeted university students across China, with recruitment strategies meticulously designed to accommodate the accessibility requirements, geographical diversity, educational attainment, and socioeconomic statuses of participants. To achieve a balanced and diverse sample, recruitment efforts spanned multiple provinces, deliberately including individuals of varying genders and ethnic backgrounds. The composition of the author team reflects a commitment to diversity, evidenced by a gender balance and a wide array of research disciplines represented.

Data analysis

This study conducted descriptive statistics analysis on the total score of Sport Quality test and SCL-90 scale test results of the total sample, respectively; And according to gender differences, descriptive statistics analysis was also conducted for the total score of Sport Quality test and SCL-90 scale test results. This study uses statistical software SPSS for Independent sample t-test and logistic regression analysis. Specifically, independent sample t test was used to compare differences between psychological state among seven Sport Quality indicators. Logistic regression analysis was used to assess the impacts of scores of sport quality indicators on students’ psychological state. In the logistic regression analysis, this study used the 10 factors (somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism and others) in the SCL-90 scale as the dependent variables and the scores of the seven aspects (BMI, vital capacity, reaction rate, lower limb explosive force, flexibility, endurance running and muscle strength) of physical testing as the independent variables for regression analysis. The dependent variable is divided into two categories: normal (record as 1) and abnormal (record as 2), this study uses the binary logistic regression model to explore the impacts of scores of sport quality indicators on students’ psychological state. Making adjustments in logistic regression involves several crucial steps, including variable selection, model diagnostics, and validation to ensure robustness and relevance. The variable selection method of the manual selection based on theoretical understanding was used in the current study. Variables with a high p-value (above a threshold, typically 0.05) are considered insignificant and can be excluded. This study uses tests and plots to check for the adequacy of the model fit.

In the physical fitness assessments, adjustments were made to account for potential confounders such as weather conditions and venue characteristics. These variables were selected due to their potential impact on the physical performance outcomes measured in the study. By controlling for these environmental and situational variables during the testing phase, we aimed to ensure that the data on sport quality indicators accurately reflect the students’ physical capabilities, minimizing any external influences that could affect the outcomes. In this study, our initial target was to include 4,800 university students. However, a total of 4,484 students participated in the physical and psychological assessments, leading to a participation rate of 9%. The primary reasons for non-participation included absenteeism on the scheduled days of testing and incomplete survey responses. We estimated the required sample size using G*Power software, based on an effect size of 0.138 derived from previous studies [ 27 ]. The analysis indicated that a minimum sample size of 1832 was necessary. Our actual sample size of 4484 significantly exceeds this threshold, confirming the statistical robustness of our findings.

Descriptive statistics of total score of sport quality test results

In 2023, a total of 4,484 university students in China participated in this study, comprising 3,565 males and 919 females, who underwent physical and psychological testing. The results of the sports quality test are presented in Table  2 . It shows that only five students achieved an ‘excellent’ total score, representing a mere 0.1% of participants. Additionally, 268 students scored ‘good,’ accounting for 6.0% of the total; 3,731 students received a ‘pass,’ making up 83.2% and representing the largest proportion; and 480 students were categorized as ‘fail,’ comprising 10.7% of the total.

Descriptive statistics of SCL-90 scale test results

Table  3 displays the frequency distribution of SCL-90 scale test results, indicating that a higher number of students exhibited normal psychological outcomes compared to those with abnormal results, representing 8.7% of the total sample. Among the students, 8.5% of males and 9.5% of females showed abnormal psychological test outcomes. The predominant dimensions observed in the overall sample were obsessive-compulsive disorder (13.4%), interpersonal sensitivity (9.3%), and depression (8.3%), with somatization presenting the lowest incidence at 4.6%. For males, the primary issues were obsessive-compulsive disorder (13.0%), interpersonal sensitivity (9.3%), and depression (7.8%), with somatization at 4.5%. Among females, the main dimensions identified were obsessive-compulsive disorder (14.8%), interpersonal sensitivity (9.5%), and depression (10.3%), with somatization at 5.0%.

Differences of sport quality indicators between normal and abnormal state of psychological test ( N  = 4484)

Table  4 displays the differences in sport quality indicators between students with normal and abnormal psychological test states. Across the overall sample, as well as separately within the male and female groups, the mean BMI was significantly lower in those with a normal psychological state compared to those with an abnormal state ( p  < 0.01). Additionally, the mean scores for vital capacity and endurance running were significantly higher in the normal psychological state than in the abnormal state ( p  < 0.01). However, no significant differences were observed in the sport quality indicators of reaction rate, lower limb explosive force, flexibility, and muscle strength between the two groups ( p  > 0.05).

Logistic regression analysis

In order to explore the impact of physical fitness on psychological health of university students from a quantitative perspective. This study used the 10 factors (somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism and others) in the SCL-90 scale as the dependent variables and the scores of the seven aspects (BMI, vital capacity, reaction rate, lower limb explosive force, flexibility, endurance running and muscle strength) of physical testing as the independent variables for regression analysis. In this study, because the dependent variables (overall psychological state, somatization, obsessive-compulsive, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, psychoticism and others) are divided into two categories: normal (record as 1) and abnormal (record as 2), this study uses the binary logistic regression models to explore the impacts of scores of sport quality indicators (BMI, vital capacity, reaction rate, lower limb explosive force, flexibility, endurance running and muscle strength) on students’ psychological state. The results are shown in Table  5 , 6 and 7 .

Firstly, in binary logistic regression, the overall psychological state is taken as the dependent variable. It can be seen from Table  5 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ psychological state in the overall sample, namely, BMI, vital capacity, and endurance running. From Table  5 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ psychological state being abnormal increases by 10.9%; when the vital capacity score increases by 1 point, the risk of a student’s psychological state being abnormal decreases by 2.1%; when the endurance running score increases by 1 point, the risk of a student’s psychological state being abnormal decreases by 4.1%.

Next, in binary logistic regression, the factor of somatization is taken as the dependent variable. From Table  5 , it is observed that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ somatization in the overall sample, namely, BMI, vital capacity and endurance running. From Table  5 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ somatization being abnormal increases by 8.8%; when the vital capacity score increases by 1 point, the risk of a student’s somatization being abnormal decreases by 1.8%; when the endurance running score increases by 1 point, the risk of a student’s somatization being abnormal decreases by 2.7%.

In the binary logistic regression, the factor of obsessive-compulsive is taken as the dependent variable. It can be seen from Table  5 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ obsessive-compulsive in the overall sample, namely, BMI, vital capacity, and endurance running. From Table  5 , it can be further seen that for every 1-point increase in BMI score, the risk of students’ obsessive-compulsive being abnormal increases by 7.8%; when the vital capacity score increases by 1 point, the risk of a student’s obsessive-compulsive being abnormal decreases by 2.0%; when the endurance running score increases by 1 point, the risk of a student’s obsessive-compulsive being abnormal decreases by 3.0%.

In the binary logistic regression, the factor of interpersonal sensitivity is taken as the dependent variable. It can be seen from Table  5 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ interpersonal sensitivity in the overall sample, namely, BMI, vital capacity, and endurance running. From Table  5 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ interpersonal sensitivity being abnormal increases by 9.2%; when the vital capacity score increases by 1 point, the risk of a student’s interpersonal sensitivity being abnormal decreases by 1.6%; when the endurance running score increases by 1 point, the risk of a student’s interpersonal sensitivity being abnormal decreases by 3.1%.

In the binary logistic regression, the factor of depression is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other five independent variables BMI ( p  > 0.05), reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are two factors that affect students’ depression in the overall sample, namely, vital capacity and endurance running. From Table  6 , it can be further seen that when the vital capacity score increases by 1 point, the risk of a student’s depression being abnormal decreases by 5.6%; when the endurance running score increases by 1 point, the risk of a student’s depression being abnormal decreases by 16.5%.

In the binary logistic regression, the factor of anxiety is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01), lower limb explosive force ( p  < 0.05) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other three independent variables reaction rate ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are four factors that affect students’ anxiety in the overall sample, namely, BMI, vital capacity, lower limb explosive force and endurance running. From Table  6 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ anxiety being abnormal increases by 8.3%; when the vital capacity score increases by 1 point, the risk of a student’s anxiety being abnormal decreases by 1.9%; when the lower limb explosive force score increases by 1 point, the risk of a student’s anxiety being abnormal increases by 2.0%;when the endurance running score increases by 1 point, the risk of a student’s anxiety being abnormal decreases by 1.7%.

In the binary logistic regression, the factor of hostility is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ hostility in the overall sample, namely, BMI, vital capacity and endurance running. From Table  6 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ hostility being abnormal increases by 8.8%; when the vital capacity score increases by 1 point, the risk of a student’s hostility being abnormal decreases by 1.5%; when the endurance running score increases by 1 point, the risk of a student’s hostility being abnormal decreases by 2.3%.

In the binary logistic regression, the factor of phobic anxiety is taken as the dependent variable. It can be seen from Table  6 that the regression coefficients of the independent variables, BMI ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other five independent variables vital capacity ( p  > 0.05), reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are two factors that affect students’ phobic anxiety in the overall sample, namely, BMI and endurance running. From Table  6 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ phobic anxiety being abnormal increases by 9.5%; when the endurance running score increases by 1 point, the risk of a student’s phobic anxiety being abnormal decreases by 1.7%.

In the binary logistic regression, the factor of paranoid ideation is taken as the dependent variable. It can be seen from Table  7 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ paranoid ideation in the overall sample, namely, BMI, vital capacity and endurance running. From Table  7 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ paranoid ideation being abnormal increases by 10.4%; when the vital capacity score increases by 1 point, the risk of a student’s paranoid ideation being abnormal decreases by 1.4%; when the endurance running score increases by 1 point, the risk of a student’s paranoid ideation being abnormal decreases by 1.9%.

In the binary logistic regression, the factor of psychoticism is taken as the dependent variable. It can be seen from Table  7 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ psychoticism in the overall sample, namely, BMI, vital capacity and endurance running. From Table  7 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ psychoticism being abnormal increases by 11.0%; when the vital capacity score increases by 1 point, the risk of a student’s psychoticism being abnormal decreases by 1.9%; when the endurance running score increases by 1 point, the risk of a student’s psychoticism being abnormal decreases by 2.0%.

In the binary logistic regression, the factor of Others is taken as the dependent variable. It can be seen from Table  7 that the regression coefficients of the independent variables, BMI ( p  < 0.01), vital capacity ( p  < 0.01) and endurance running ( p  < 0.01) were significant, while the regression coefficients of the other four independent variables reaction rate ( p  > 0.05), lower limb explosive force ( p  > 0.05), flexibility ( p  > 0.05) and muscle strength ( p  > 0.05) were not significant. From this, it can be concluded that there are three factors that affect students’ Others in the overall sample, namely, BMI, vital capacity and endurance running. From Table  7 , it can be further seen that for every 1 point increase in BMI score, the risk of students’ Others being abnormal increases by 8.3%; when the vital capacity score increases by 1 point, the risk of a student’s Others being abnormal decreases by 1.6%; when the endurance running score increases by 1 point, the risk of a student’s Others being abnormal decreases by 2.7%.

This study reveals a potential correlation between physical fitness and mental health, highlighting the beneficial effects of lowering BMI, enhancing lung capacity, and engaging in endurance running on various aspects of mental well-being, such as somatization, obsessive tendencies, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychosis. These findings offer valuable insights into strategies for enhancing overall health and well-being.

In our study, we found that for every one-point increase in BMI, students face an 8.8% higher risk of abnormal somatization, a 7.8% higher risk of obsessive-compulsive disorder, a 9.2% higher risk of abnormal interpersonal sensitivity, an 8.3% higher risk of abnormal anxiety, an 8.8% higher risk of abnormal hostility, a 9.5% higher risk of abnormal phobic anxiety, a 10.4% higher risk of abnormal paranoid ideation, and an 11.0% higher risk of abnormal psychoticism. Extensive documentation exists on the physical health implications of obesity, indicating a consistent association between elevated BMI and heightened risks of chronic diseases and mortality [ 28 , 29 , 30 ]. Nevertheless, a growing body of research examining the psychological effects of obesity produces inconsistent results. Most studies indicate an inverse correlation between body weight and psychological well-being [ 31 , 32 , 33 , 34 ]. However, some studies suggest a positive association instead [ 35 ], while others find either a neutral or insignificant correlation [ 36 ]. In our study, we provided a more precise depiction of the association of BMI with individuals’ mental well-being. Engaging in regular exercise and making dietary changes to lower BMI could potentially alleviate somatic symptoms linked to psychological distress. Those with lower BMI typically report fewer physical complaints and demonstrate enhanced coping mechanisms for stressors.

Our research revealed that for every one-point increase in lung capacity, the risk of abnormal somatization decreases by 1.8%, the risk of abnormal obsessive-compulsive disorder decreases by 2.0%, the risk of abnormal interpersonal sensitivity decreases by 1.6%, the risk of abnormal depression decreases by 5.6%, the risk of abnormal anxiety decreases by 1.9%, the risk of abnormal hostility decreases by 1.5%, the risk of abnormal paranoid ideation decreases by 1.4%, and the risk of abnormal psychoticism decreases by 1.9%. Increasing evidence in current research suggests a close association between obstructive pulmonary diseases such as asthma, chronic bronchitis, and emphysema, and psychological health issues like depression and anxiety [ 37 , 38 , 39 , 40 , 41 ]. Previous research conducted among adult clinical and general practice populations has revealed elevated rates of anxiety and mood disorders, particularly major depression [ 42 , 43 , 44 , 45 , 46 , 47 ]. Community-based studies have confirmed and expanded upon the general validity of the association between asthma, chronic obstructive pulmonary disease, and mental disorders [ 39 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. Our study aligns with previous research but provides a more nuanced examination of the association between lung capacity and psychological well-being.

Our study indicates that for every one-point increase in endurance running, the risk of abnormal somatization decreases by 2.7%, the risk of abnormal obsessive-compulsive disorder decreases by 3.0%, the risk of abnormal interpersonal sensitivity decreases by 3.1%, the risk of abnormal depression decreases by 16.5%, the risk of abnormal anxiety decreases by 1.7%, the risk of abnormal hostility decreases by 2.3%, the risk of abnormal phobic anxiety decreases by 1.7%, the risk of abnormal paranoid ideation decreases by 1.9%, and the risk of abnormal psychoticism decreases by 2.0%. Additionally, we found that for every point increase in lower limb explosive force, there is a 2.0% increase in the risk of abnormal anxiety among students, indicating an adverse effect on mental health. There is considerable evidence substantiating the link between physical activity and different mental health results throughout all stages of life [ 55 , 56 , 57 ]. Long-term running interventions frequently enhance mental health metrics, particularly depression indicators, among individuals with psychosis [ 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]. Some prior research suggests that running enhances mood, especially when conducted outdoors, across various intensities, except for an intensity significantly above the lactate threshold [ 67 , 68 , 69 ]. Our study supplements previous research by emphasizing the impact of endurance running scores on psychological resilience and validating the significant role of endurance running in various psychological indicators, with particular effectiveness observed in depression indicators.

The study delves into the complex association between physical fitness and mental health, revealing a potential correlation between these two domains. It demonstrates the substantial impact of interventions aimed at lowering BMI, enhancing lung capacity, and engaging in endurance running on various facets of mental well-being, including somatization, obsessive tendencies, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychosis. These findings underscore the pivotal role of physical activity in promoting mental health. This potential correlation between physical fitness and mental health can be elucidated through various underlying mechanisms [ 70 ]. Firstly, interventions targeting BMI reduction through regular exercise and dietary adjustments show promise in alleviating somatic symptoms associated with psychological distress. These effects may be attributed to exercise-induced physiological changes, such as improved cardiovascular health and hormonal regulation, which positively impact mood and emotional well-being. Similarly, enhancements in lung capacity and engagement in endurance running have been associated with reduced obsessive-compulsive tendencies, possibly mediated by neurotransmitter modulation in the brain. Furthermore, the release of endorphins and serotonin during exercise may further contribute to the amelioration of depressive and anxious symptoms, thereby enhancing mental well-being. Moreover, endurance running has been shown to facilitate emotional regulation and empathy by fostering social bonding and communication skills, thereby enhancing interpersonal sensitivity. Physical activity also serves as a constructive outlet for managing stress and aggression, leading to decreased hostility and improved emotional well-being. Additionally, interventions aimed at enhancing lung capacity and engaging in endurance running may mitigate symptoms of phobic-anxiety disorders by promoting relaxation and stress relief. Furthermore, strategies targeting BMI reduction and regular physical activity maintenance can contribute to reduced paranoid ideation by bolstering self-esteem and self-efficacy.

In summary, these findings offer valuable insights into strategies for enhancing overall health and well-being, emphasizing the importance of integrating physical activity into mental health management approaches. By understanding the potential correlation between physical fitness and mental health and implementing appropriate interventions, individuals can take proactive steps towards improving their mental well-being and achieving a better quality of life.

Limitations

While our study offers valuable insights into the correlation between physical fitness and mental health, it is not without limitations. The cross-sectional design limits our ability to determine causality, and selection bias may arise since participants likely have higher physical activity levels than the general population, potentially skewing mental health outcomes positively. Additionally, the use of self-reported mental health measures might introduce reporting bias, affecting the accuracy of associations. The generalizability of our findings could also be influenced by the demographic and geographic characteristics of our sample. Furthermore, this study did not employ multilevel modeling, despite the hierarchical nature of the data, due to complexity and sample size constraints, which might limit the added value of this approach for our specific research questions. Addressing these biases and limitations in future longitudinal studies, and considering multilevel models, could strengthen the validity of our findings and enable a more comprehensive interpretation of interactions across different levels of data. Future research involving diverse populations across various settings is essential to validate and expand our conclusions on a global scale.

In summary, lowering BMI, increasing lung capacity, and improving endurance running have shown promising benefits for various dimensions of mental health. Incorporating regular physical activity into lifestyle interventions may serve as an effective strategy for promoting holistic well-being and reducing the burden of mental health disorders. Further research is warranted to explore the mechanisms underlying these associations and to develop targeted interventions for improving mental health outcomes through physical fitness interventions.

Data availability

No datasets were generated or analysed during the current study.

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May 14, 2024

Understanding how exercise affects the body

At a glance.

• A study of endurance training in rats found molecular changes throughout the body that could help explain the beneficial effects of exercise on health.
• Large differences were seen between male and female rats, highlighting the need to include both women and men in exercise studies.

Exercise is one of the most beneficial activities that people can engage in. Regular exercise reduces the risk of heart disease, diabetes, cancer, and other health problems. It can even help people with many mental health conditions feel better.

But exactly how exercise exerts its positive effects hasn’t been well understood. And different people’s bodies can respond very differently to certain types of exercise, such as aerobic exercise or strength training.

Understanding how exercise impacts different organs at the molecular level could help health care providers better personalize exercise recommendations. It might also lead to drug therapies that could stimulate some of the beneficial effects of a workout for people who are physically unable to exercise.

To this end, researchers in the large, NIH-funded Molecular Transducers of Physical Activity Consortium (MoTrPAC) have been studying how endurance exercise and strength training affect both people and animals. The team is examining gene activity, protein alterations, immune cell function, metabolite levels, and numerous other measures of cell and tissue function. The first results, from rat studies of endurance exercise, were published on May 2, 2024, in Nature and several related journals.

Both male and female rats underwent progressive exercise training on a treadmill over an 8-week period. By the end of training, male rats had increased their aerobic capacity by 18%, and females by 16%. Tissue samples were collected from 18 different organs, plus the blood, during the training period and two days after the final bout of exercise. This let the researchers study the longer-term adaptations of the body to exercise.

Changes in gene activity, immune cell function, metabolism, and other cellular processes were seen in all the tissues studied, including those not previously known to be affected by exercise. The types of changes differed from tissue to tissue.

Many of the observed changes hinted at how exercise might protect certain organs against disease. For example, in the small intestines, exercise decreased the activity of certain genes associated with inflammatory bowel disease and reduced signs of inflammation in the gut. In the liver, exercise boosted molecular changes associated with improved tissue health and regeneration.

Some of the effects differed substantially between male and female rats. For example, in male rats, the eight weeks of endurance training reduced the amount of a type of body fat called subcutaneous white adipose tissue (scWAT). The same amount of exercise didn’t reduce the amount of scWAT in female rats. Instead, endurance exercise caused scWAT in female rats to alter its energy usage in ways that are beneficial to health. These and other results highlight the importance of including both women and men in exercise studies.

The researchers also compared gene activity changes in the rat studies with those from human samples taken from previous studies and found substantial overlap. They identified thousands of genes tied to human disease that were affected by endurance exercise. These analyses show how the MoTrPAC results from rats can be used to help guide future research in people.

“This is the first whole-organism map looking at the effects of training in multiple different organs,” says Dr. Steve Carr, a MoTrPAC investigator from the Broad Institute. “The resource produced will be enormously valuable, and has already produced many potentially novel biological insights for further exploration.”

Human trials are expected in the next few years. Information on participating can be found here .

—by Sharon Reynolds

Related Links

• Gut Microbes May Affect Motivation to Exercise
• Exercise-Induced Molecule Reduces Obesity in Mice
• Testing Ways to Encourage Exercise
• Hormone Links Exercise with Cognitive Benefits
• Exercise-Induced Protein May Reverse Age-Related Cognitive Decline
• Getting Active Later in Life Brings Benefits
• Get Active Together: Social Support Can Help Keep You Moving
• Personalized Exercise? How Biology Influences Fitness
• Maintain Your Muscle: Strength Training at Any Age
• Molecular Transducers of Physical Activity Consortium (MoTrPAC)
• Participating in MoTrPAC

References:  Temporal dynamics of the multi-omic response to endurance exercise training. MoTrPAC Study Group; Lead Analysts; MoTrPAC Study Group. Nature . 2024 May;629(8010):174-183. doi: 10.1038/s41586-023-06877-w. Epub 2024 May 1. PMID: 38693412. Sexual dimorphism and the multi-omic response to exercise training in rat subcutaneous white adipose tissue. Many GM, Sanford JA, Sagendorf TJ, Hou Z, Nigro P, Whytock KL, Amar D, Caputo T, Gay NR, Gaul DA, Hirshman MF, Jimenez-Morales D, Lindholm ME, Muehlbauer MJ, Vamvini M, Bergman BC, Fernández FM, Goodyear LJ, Hevener AL, Ortlund EA, Sparks LM, Xia A, Adkins JN, Bodine SC, Newgard CB, Schenk S; MoTrPAC Study Group. Nat Metab . 2024 May 1. doi: 10.1038/s42255-023-00959-9. Online ahead of print. PMID: 38693320. The impact of exercise on gene regulation in association with complex trait genetics. Vetr NG, Gay NR; MoTrPAC Study Group; Montgomery SB. Nat Commun . 2024 May 1;15(1):3346. doi: 10.1038/s41467-024-45966-w. PMID: 38693125.

Funding:  NIH’s Office of the Director (OD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), National Institute on Aging (NIA), National Human Genome Research Institute (NHGRI), National Heart, Lung, and Blood Institute (NHLBI), and National Library of Medicine (NLM); Knut and Alice Wallenberg Foundation; National Science Foundation (NSF).

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Top 10 International Priorities for Physical Fitness Research and Surveillance Among Children and Adolescents: A Twin-Panel Delphi Study

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The measurement of physical fitness has a history that dates back nearly 200 years. Recently, there has been an increase in international research and surveillance on physical fitness creating a need for setting international priorities that could help guide future efforts.

This study aimed to produce a list of the top 10 international priorities for research and surveillance on physical fitness among children and adolescents.

Using a twin-panel Delphi method, two independent panels consisting of 46 international experts were identified (panel 1 = 28, panel 2 = 18). The panel participants were asked to list up to five priorities for research or surveillance (round 1), and then rated the items from their own panel on a 5-point Likert scale of importance (round 2). In round 3, experts were asked to rate the priorities identified by the other panel.

There was strong between-panel agreement (panel 1: r s  = 0.76, p  < 0.01; panel 2: r s  = 0.77, p  < 0.01) in the priorities identified. The list of the final top 10 priorities included (i) “conduct longitudinal studies to assess changes in fitness and associations with health”. This was followed by (ii) “use fitness surveillance to inform decision making”, and (iii) “implement regular and consistent international/national fitness surveys using common measures”.

Conclusions

The priorities identified in this study provide guidance for future international collaborations and research efforts on the physical fitness of children and adolescents over the next decade and beyond.

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1 Introduction

Physical fitness consists of multiple components such as cardiorespiratory fitness (CRF), musculoskeletal fitness (MSF; i.e., muscular strength, power, endurance, and flexibility), agility, speed, balance, coordination, and body composition, which collectively reflect an individual’s ability to perform physical activity [ 1 ]. Measurement of physical fitness has a long history that dates back more than 200 years to Adolphe Quételet, a pioneer in anthropometry [ 2 , 3 ]. In 1835, Quételet began measuring the handgrip strength of Belgian boys and girls [ 4 , 5 ]. From the early 1900s, fitness testing of children and adolescents expanded beyond anthropometry and isometric muscle strength to include exercise capacity and motor performance (e.g., sprinting, jumping) [ 6 , 7 ]. During the two World Wars (1914–1918 and 1939–1945) there was an international focus on measuring and improving performance-related fitness (i.e., having the skills and physical abilities to engage in a competitive environment) for military preparedness [ 6 ]. However, in the 1970s, because of research demonstrating that low physical fitness was significantly associated with poor health outcomes among adults [ 8 , 9 ], physical fitness testing started to shift from a performance-related to a health-related focus [ 6 ]. The evidence supporting health-related fitness (i.e., the fitness components significantly linked with current and future health [ 6 ]) among children and adolescents arrived later, with research beginning to appear in the early 1990s for CRF [ 10 , 11 ] and the early 2000s for MSF [ 12 , 13 ].

Findings from cross-sectional studies suggest that high CRF and MSF among children and adolescents is associated with a range of health benefits, such as better cardiovascular health, skeletal health, motor competence, cognitive ability, mental health, and self-esteem [ 11 , 12 , 14 , 15 , 16 ]. In addition, CRF levels are a stronger predictor of cardiovascular disease risk factors among youth than objectively measured physical activity levels [ 17 ]. Longitudinal epidemiological studies have shown that physical fitness levels persist (i.e., track) across the life course [ 18 , 19 , 20 , 21 ], and that high CRF and MSF in childhood, adolescence, or early adulthood is prospectively associated with a healthier cardiovascular profile [ 13 , 22 , 23 , 24 ], reduced disability [ 25 , 26 ], and a decreased risk of premature mortality [ 27 , 28 ] in adulthood. An individual’s physical fitness level, especially their CRF, provides a reasonable objective indication of their moderate to vigorous intensity physical activity levels in recent months, as it summarizes the physiological response to their physical activity profile [ 29 ]. In addition, physical fitness testing is feasible, cost effective, and suitable for population surveillance [ 30 , 31 ]. For these reasons, there has been a strong international call to universally measure physical fitness among children and adolescents for global health surveillance, monitoring, and clinical screening [ 6 , 14 , 32 , 33 ].

Anthropometric measures (i.e., body mass index, waist circumference) have long been an important indicator of health in research, surveillance, and clinical practice [ 34 ]. The same cannot be said for other components of physical fitness (e.g., CRF, MSF) despite mounting evidence of their importance [ 31 ]. In light of declining international levels of some aspects of fitness (e.g., CRF, leg power, abdominal/core endurance) among children and adolescents [ 35 , 36 , 37 ], there is a need to refocus international efforts to identify the priorities that can help address major literature gaps and guide future physical fitness research and health surveillance. The Delphi method is described as a systematic approach to gather expert opinions and arrive at consensus [ 38 ]. This Delphi approach has been previously used to identify priorities in physical activity and sedentary behavior research [ 39 ]. Thus, the objective of this research was to conduct a twin-panel Delphi study to determine an international list of the top 10 priorities for physical fitness research and surveillance among children and adolescents over the next decade.

2.1 Overview

This study implemented a twin-panel Delphi procedure, which allowed two independent groups (the Delphi panels) of experts to address our research objective based on their subjective opinions [ 38 ]. Over the course of several rounds, the Delphi procedure allowed the two expert panels to systematically refine their responses to arrive at a final list of priorities [ 40 ]. The twin-panel approach is an improvement from a traditional single-panel Delphi because it allows expert panels to cross-validate the ranked priorities identified by each panel.

2.2 Participant Sampling Strategy

2.2.1 panel 1.

Sampling for panel 1 took place as part of a large international fitness meeting hosted by the Public Health Agency of Canada on August 19, 2021. The meeting aimed to discuss and explore potential directions to address international priority areas in fitness research and health surveillance. See the electronic supplementary material (ESM) for a brief outline of the meeting agenda. Meeting delegates (i.e., experts) were selected based on the lead organizers’ (JJL, BJF) knowledge of individuals who were actively engaged in fitness research and surveillance. The final group of attendees included 45 participants: 17 were Canadian fitness experts who worked in policy, programs, or surveillance; 12 were fitness experts from Canadian universities; and 16 were international experts from outside Canada. Academic experts were identified if they had published a peer-reviewed research article that assessed or interpreted youth fitness within the last 5 years. PhD students were considered if their dissertation was directly related to fitness assessment or surveillance. The majority of the meeting participants were invited to participate in the Delphi study, with the final response rate being 62% (28/45).

2.2.2 Panel 2

To identify research experts to include as part of panel 2, a SciVal list of the top 100 authors worldwide based on the topic cluster “Cardiorespiratory Fitness; Skinfold Thickness; School Children” (Topic T.7814) was used on August 4, 2021. These experts were then ranked by scholarly output (i.e., the total count of research outputs) to identify the most productive researchers in this SciVal research category. From this list, 10 researchers were excluded because they participated in panel 1. The remaining 57 researchers who had been a first or senior (i.e., last/corresponding) author on a relevant publication and had an h-index of ≥ 5 were invited, with 32% (18/57) agreeing to participate.

2.3 Survey Procedure

The Delphi included three rounds of data collection and analysis. All surveys were created and administered in Google Forms (Mountain View, CA, USA). For each round, participants were provided with a direct web link to the survey via emails. All participants were allowed 3 weeks to complete each round, with a reminder email sent after 2 weeks. All three rounds were completed between August and November 2021. Participants were not required to complete all three rounds to retain their responses. Google Sheets (Mountain View, CA, USA) was used to organize responses and to conduct data analyses. Each panel conducted the Delphi independently, following the same methods. Participants were not made aware of the other panel (i.e., blinded) until round 3. Those who completed all three rounds of the Delphi study were invited to contribute to this research article as a co-author.

2.3.1 Round 1

All participants were provided with a cover letter and asked to answer the following question: “In your opinion, what is the most important priority area for physical fitness research and surveillance among children and adolescents that should be addressed over the next 10 years?” Participants were asked to describe the priority in one or two sentences. They were then asked to provide supporting details, such as examples or supporting literature, for the identified priority area. Participants were provided the opportunity to identify five priority areas. One researcher (JJL) reviewed all priorities submitted by the participants. Similar priorities were combined into a single overarching priority theme. A second researcher (BJF) reviewed the priority themes for accuracy. Discussions took place between the two researchers (JJL, BJF) to resolve any disagreement, with a third researcher (GRT) consulted for any unresolved disagreement.

2.3.2 Round 2

During round 2, participants were provided with a cover letter and asked to review the list of overarching priority themes identified by their respective panel during round 1. Participants were notified that their responses were merged with similar priority areas to create overarching priority themes that may not directly reflect their original wording. Participants were asked to rate the level of importance over the next 10 years for each priority theme using a 5-point Likert scale (0 = don’t know, 1 = somewhat important, 2 = moderately important, 3 = important, 4 = very important, 5 = extremely important). Mean scores were calculated and ranked in descending order from highest to lowest. The standard deviation was used as a tiebreaker with lower standard deviations being ranked higher. Participants who responded as ‘don’t know’ were coded as a missing value that did not contribute to the denominator in calculating mean scores.

2.3.3 Round 3

In round 3, participants were provided with a cover letter and asked to rate the level of importance of the priorities identified by the other panel using the same 5-point Likert scale from round 2. For instance, panel 1 rated the 25 priorities identified by panel 2, and panel 2 rated the 36 priorities identified by panel 1. Like round 2, mean scores were calculated to rank priorities, and standard deviations were used as a tiebreaker.

2.4 Statistical Analysis

Spearman’s rank correlation coefficients were used to assess the level of between-panel agreement in the ranked priorities. Using responses from round 3, one correlation coefficient was calculated for the agreement for panel 1’s ranked priorities, and a second correlation coefficient was calculated for the agreement on panel 2’s ranked priorities. Correlations of 0.1, 0.3, and 0.5 were used as thresholds for weak, moderate, and strong agreement, respectively [ 41 ]. To identify the top 10 priorities, an a priori decision was made to combine the ranked lists for panels 1 and 2 using the overall or mean (if the priority was included in both panel lists) Likert scale response from round 2.

3.1 Participant Demographics

Table 1 describes the participant characteristics. Panel 1 included participants from all career stages (0–5 years, 6–10 years, 11–20 years, and 21+ years). The panel 1 participants resided in six continents across all country income levels, with the majority from North America. Panel 2 was smaller and did not include students, or participants living in Africa, or low-income countries. The study retention was strong with 89% (25/28) and 72% (13/18) of panel 1 and 2 participants completing all three rounds of the study, respectively (Fig.  1 ).

Flow chart depicting the participant retention across all three rounds of the twin-panel Delphi study

3.2 Delphi Results

During round 1, panel 1 submitted 104 unique responses that were qualitatively reduced into 36 unique priority themes (Table 2 ). Panel 2 submitted 71 responses that were reduced into 25 priority themes (Table 3 ). Eight priorities overlapped between the panels. An overview of the unique responses by priority theme is provided in the ESM.

In round 2, participants were asked to rate the level of importance for each priority identified by their respective panel. The mean Likert-scale scores ranged from 1.96 to 4.46 and 2.71 to 4.43 for panels 1 and 2, respectively. Of the eight overlapping priorities, four emerged in the top 10 priorities for panel 1 and six emerged in the top 10 priorities for panel 2. For panel 2, the top five priorities were also identified by panel 1. Both panels identified “conduct longitudinal studies to assess changes in fitness and associations with health” as the number one priority. “Use fitness surveillance to inform decision making”, “implement regular and consistent international/national fitness surveys using common measures”, and “develop universal health-related fitness cut-points” were common priorities that were ranked in the top 10 for both panels.

During the final round, expert participants were asked to rate the level of importance for each of the other panel’s priorities. The between-panel agreement was strong for both panel 1 ( r s  = 0.76, p  < 0.01) and panel 2 ( r s  = 0. 77, p  < 0.01) using responses from round 3. Given the strong agreement between panels, the priorities identified by both panels were combined to identify the top 10 overall priorities (Table 4 , Fig.  2 ).

Top 10 international priorities for physical fitness research and surveillance among children and adolescents identified by international experts in fitness

4 Discussion

To our knowledge, this is the first study to have used a twin-panel Delphi method to identify a list of international priority areas for physical fitness research and surveillance among children and adolescents. The top 10 priorities reflect diverse fields of study, from epidemiology to social science, and notably, to achieve many of the priorities, international collaboration is required. Below, we summarize topical evidence related to these ten research priorities.

4.1 Priority 1: Conduct Longitudinal Studies to Assess Changes in Fitness and Associations with Health

In recent decades, several longitudinal studies have established that physical fitness in adolescence is a significant inverse and independent predictor of disease outcomes, including premature mortality in adulthood [ 13 , 23 , 24 , 25 , 26 , 27 , 28 ]. Some studies on adolescents investigated changes in fitness levels (i.e., CRF and MSF) and associations with health outcomes using follow-up periods of several years [ 42 , 43 , 44 ], whilst others identified that improvements in MSF from childhood to adolescence were associated with reduced adiposity [ 13 , 45 ]. There is a need for future studies to link fitness (both CRF and MSF) measured in young childhood (of both sexes) with clinical outcomes in adulthood in nationally representative cohorts to establish longitudinal links with key health outcomes [ 13 , 22 , 24 , 27 ]. Such studies could provide valuable insights into physical fitness and the associated risk of developing and dying from a chronic disease (i.e., relative risk), that could be used to calculate the population attributable fraction. There is also a need to better understand the link between childhood fitness and future mental disorders, given the increasing burden of mental health problems in some countries [ 46 ], especially in the context of the COVID-19 pandemic [ 47 ]. Furthermore, cohorts with multiple follow-ups allow for an assessment of changes and trajectories in fitness over time which can be used to calculate the meaningful clinically important difference (i.e., what is the minimum improvement in fitness required for meaningful changes in physical health status?). An example is the Aerobic Centre Longitudinal Study cohort for which statistically significant reductions in all-cause and cardiovascular disease mortality were found among men who maintained or improved their physical fitness over a 5-year period [ 48 ].

4.2 Priority 2: Use Fitness Surveillance to Inform Decision Making

Public health surveillance is essential to guide health promotion efforts. Many countries collect and report regularly on body composition and self-reported physical activity through national health surveillance systems [ 49 ]. However, surveillance systems can be expanded to report on other important measures of physical fitness such as CRF and MSF [ 32 , 50 ]. Some countries, including Slovenia, Hungary, and Japan, have implemented routine national fitness surveillance [ 31 , 51 ]. While others, such as Australia, have recently scaled back ongoing national fitness surveillance efforts [ 52 ]. National fitness surveillance efforts in Slovenia identified a 13% decline in the fitness levels of youth aged 6–15 years following 2 months of COVID-19-related lockdowns [ 53 ]. Other countries used national fitness surveillance to identify regions/groups with low fitness levels and in need of intervention [ 54 ]. The approach to incorporate national fitness surveillance efforts have also been used to track the effectiveness of national policy efforts aimed at increasing the physical activity levels of children and youth in the school context [ 55 ]. Countries could further benefit from leveraging the measurement of physical fitness (CRF and MSF) to inform and track the effectiveness of policy and programming to improve the health of children and adolescents.

4.3 Priority 3: Implement Regular and Consistent International/National Fitness Surveys Using Common Measures

The 2018 Global Matrix 3.0 of Physical Activity Report Cards for Children and Youth, for the first time, included physical fitness as an indicator [ 56 ]. Unfortunately, over half (55%) of the included countries were unable to report a grade for physical fitness due to a lack of available data [ 56 ]. This suggests that most countries do not implement regular fitness surveys/testing among children and adolescents. Of the countries that do implement regular fitness surveys, the measurement protocols varied substantially both within and between countries. For instance, CRF is measured nationally using a submaximal step test in Canada, a treadmill test in the USA, and a variety of field-based tests (e.g., the 20-m shuttle run test, distance runs, timed runs) in Japan, Estonia, and Hungary [ 31 , 35 ]. There is more international consistency with the measurement of MSF (especially for muscular strength, which is commonly assessed as isometric maximal handgrip strength), but still, major international differences in protocols and reporting exist [ 57 ]. Implementing regular and consistent international and national fitness surveys, similar to efforts conducted for physical activity [ 49 , 58 ], would help better describe the global health status of children and adolescents.

4.4 Priority 4: Implement Scalable School-Based Interventions to Improve and Promote Fitness

Many countries have recently observed declines in measures of physical fitness among children and adolescents [ 35 , 36 , 59 ], likely resulting in meaningful reductions in population health. There is a need to promote fitness among children and adolescents using safe, equitable, and inclusive approaches [ 60 ]. Although it is not always the case, most youth spend a substantial part of their day in the school environment. As a result, schools provide a unique opportunity to implement interventions aimed at improving fitness (e.g., via increased quality and quantity of physical activity throughout the day [ 61 ]). Several systematic reviews have found positive improvements in the physical fitness levels (i.e., MSF and CRF) of children and adolescents associated with school-based interventions [ 62 , 63 , 64 , 65 ]. More recently, school-based interventions using high-intensity interval training have demonstrated promising improvements for youth CRF and other important health markers [ 66 ]. However, gaps and limitations persist. For example, future interventions need to better assess the sustained impact of interventions by including longer follow-up times [ 63 ], and their potential scalability while incorporating implementation science frameworks [ 67 ]. Future interventions aimed at increasing physical activity in the school environment could use objective measures of physical fitness as the primary study outcome [ 68 ]. Lastly, the development of scalable and cost-effective school-based interventions that successfully promote physical fitness among children and adolescents remains a large gap requiring international research focus over the next decade [ 69 , 70 , 71 ].

4.5 Priority 5: Develop Universal Health-Related Fitness Cut-Points

The World Health Organization led major efforts to establish universal health-related cut-points for body mass index to detect overweight and obesity among children and adolescents aged 5–19 years [ 72 ]. For waist circumference, the age- and sex-specific 90th percentile has been proposed as an international cut-point to detect central obesity among children and adolescents aged 6–18 years [ 73 ]. Less international consensus exists for other measures of physical fitness. In 2016, Ruiz et al. conducted a meta-analysis of health-related cut-points for CRF and identified values of 42 and 35 mL/kg/min for boys and girls, respectively [ 74 ]. A major limitation of the Ruiz meta-analysis was a lack of age-specific cut-points. A more recent systematic review concluded that the variability in published CRF cut-points precludes the ability to identify universal age- and sex-specific cut-points [ 75 ]. There is a need for future studies using standardized CRF measures and similar health outcomes to improve the ability to identify universal sex- and age-specific CRF cut-points. There is a similar need for standardized measures of MSF to reduce heterogeneity in conducting meta-analyses for universal cut-points [ 76 ]. There is also a need for consensus on appropriate scaling methods to help account for body size when measuring physical fitness, which might be an important first step before developing universal health-related fitness cut-points.

4.6 Priority 6: Investigate Interventions to Improve Fitness

Aside from school-based interventions, home-, family-, and community-based interventions could complement the promotion of physical fitness among children and adolescents [ 77 , 78 ]. However, home-, family-, and community-based interventions have received less attention in the literature, with a particular gap existing for interventions targeting physical fitness as the primary outcome [ 79 ]. Most home-, family-, and community-based intervention studies have focused on physical activity levels as the primary outcome [ 79 ]. In addition, web-based or app-based interventions for health promotion have gained attention more recently [ 80 , 81 ]. These types of studies are promising, especially as the world continues to grapple with the unique challenges that children and adolescents have faced because of the COVID-19 pandemic [ 82 ].

4.7 Priority 7: Assess the Reliability and Validity of Fitness Measures

Reliability and validity are used to evaluate the quality of a fitness test and have important implications for fitness surveillance, the assessment of fitness-enhancing polices and interventions, and for linking fitness components to health outcomes. Existing tools and frameworks are available to help evaluate the quality of outcome measures [ 83 ]. Several comprehensive systematic reviews of the reliability [ 84 , 85 ] and criterion validity of field-based fitness tests have been published [ 84 , 86 , 87 , 88 ]. Reliability and validity data from these reviews have been used to develop field-based fitness test batteries for population health surveillance among children and adolescents. For example, information on the health-related predictive validity, criterion validity, reliability, and feasibility of field-based fitness tests was used to develop the ALPHA (Assessing Levels of Physical Activity) health-related fitness test battery for children and adolescents [ 84 ]. The ALPHA recommends the 20-m shuttle run test for CRF, handgrip strength and standing broad jump tests for MSF, and height, body mass, waist circumference, and skinfolds (triceps and subscapular) for body composition. Despite the widespread evidence regarding the reliability and validity of many fitness tests for school-aged children, few studies have validated fitness tests for preschoolers and school-aged children from low- and middle-income countries [ 89 , 90 , 91 ]. A better understanding of the criterion validity of field-based MSF tests (where appropriate laboratory-based criterion measures are used), and the reliability and validity of motor fitness tests (speed, agility, balance, coordination), is required [ 92 ].

4.8 Priority 8: Develop a Common/Universal International Field-Based Fitness Test Battery

Fitness test batteries include a variety of standardized fitness measures often covering several components (e.g., CRF, MSF, body composition) that collectively indicate an individual’s overall physical fitness level. Worldwide, there are more than 15 field-based fitness test batteries for children and adolescents [ 93 ]. The most commonly used include the FitnessGram ® [ 94 ], Eurofit [ 95 ], and ALPHA [ 84 ] test batteries [ 31 ]. Therefore, it is challenging to pool data internationally given the difficulty of standardizing fitness test performances (e.g., because of differences in tests/protocols, performance metrics, age metrics, reporting procedures). There is a pressing need for collaboration to develop a universal field-based fitness test battery that can be implemented internationally. A scalable test battery requires a set of measures that are easily implemented with non-specialized personnel, have evidence of operating at a large scale, are effective (i.e., valid, reliable, high completion rate), and low cost [ 30 ]. A widely accepted protocol (e.g., core outcome set) for reporting results is also required, an issue that has been discussed in detail elsewhere [ 6 , 96 ].

4.9 Priority 9: Investigate and Reduce Inequalities in Fitness

Evidence from international comparison studies suggest that trends in CRF [ 35 ], standing broad jump [ 36 ], and sit-up performance [ 37 ] among children and adolescents have declined substantially since the start of the millennium. Some research suggests that the country trends in those with high fitness levels have not changed substantially, but trends in those with low fitness have declined substantially in more recent years, resulting in larger country-specific temporal inequalities among youth [ 97 , 98 ]. There is also evidence that CRF varies substantially between countries, with the fittest children and youth residing in Africa and Northern Europe and those with the lowest fitness residing in South and Central America [ 99 ]. There is a need to address these inequalities both within (e.g., regional variations [ 54 , 100 ]) and between countries to provide every child with the potential to attain healthy levels of physical fitness. An equity approach should always be implemented when investigating fitness, similar to approaches used in physical activity research [ 101 ]. However, scalable national and international approaches to reverse these fitness inequalities are unknown and represent a substantial area of future research.

4.10 Priority 10: Develop an International Fitness Data Repository

There exist several international data repositories for physical activity, including the International Children’s Accelerometry Database (ICAD) [ 102 ], the Physical Activity Cohort Repository (PACE) [ 103 ], and the World Health Organization Global Health Observatory Data Repository for several health-related indicators, including body mass index and physical inactivity [ 104 ]. These data repositories provide easy access to aggregate data for harmonization by region or country, and they promote standardized data collection within countries for certain measures. The European FitBack project is an important effort that could evolve into a new international fitness data repository [ 105 ]. However, there remain issues with retaining data submitted through the FitBack portal, and with allowing researchers to access these raw data for research purposes. Future work is needed to expand existing platforms or to create a new data repository that can mirror efforts in physical activity and body mass index.

4.11 Strengths and Limitations

This study has many strengths including a broad international representation of experts, the use of purposive and systematic sampling procedures to identify experts, a twin-panel design to cross-validate priorities, the use of a Delphi method with participant blinding, and three structured rounds of data collection. The findings from our study are the subjective opinion of the expert panel and may not represent the opinions of other experts who were not included in this study. During the panel 1 international meeting, content from the round 1 survey (i.e., the most reported priority areas identified by the panel) were discussed and may have introduced bias during round 2 responses. However, this bias was likely small given the strong agreement between panels. Most of the participants in panel 1 were Canadian, and we had limited representation from low- and middle-income countries and countries in Africa. Including more experts from these regions may have identified different priorities. It is also important to note that research is constantly evolving, and priorities may change in the future. For this reason, it will be important to revisit this Delphi exercise in the next decade to examine what work has been done and to update the international priorities in this area of research and surveillance.

5 Conclusions

Using a systematic Delphi twin-panel approach with an international group of experts, we identified the top 10 international areas for physical fitness research and surveillance over the next decade. Priorities included, among others, the use of longitudinal studies, fitness surveillance to inform decision making, international fitness testing using valid, reliable and standardized measures, and the development of interventions to improve fitness among children and adolescents. The priorities identified in this study should help guide international collaborations and research efforts over the next decade and beyond.

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Lang, J.J., Zhang, K., Agostinis-Sobrinho, C. et al. Top 10 International Priorities for Physical Fitness Research and Surveillance Among Children and Adolescents: A Twin-Panel Delphi Study. Sports Med 53 , 549–564 (2023). https://doi.org/10.1007/s40279-022-01752-6

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A systematic review of intention to use fitness apps (2020–2023)

• Salvador Angosto   ORCID: orcid.org/0000-0001-7281-794X 1 , 2 ,
• Jerónimo García-Fernández   ORCID: orcid.org/0000-0001-6574-9758 2   na1 &
• Moisés Grimaldi-Puyana   ORCID: orcid.org/0000-0003-4722-1532 2   na1  

Humanities and Social Sciences Communications volume  10 , Article number:  512 ( 2023 ) Cite this article

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Technology advances and digital transformation are constantly growing, resulting in an increase in the number of sports-related technologies and apps on the market, particularly during the COVID-19 pandemic. The aim of this study is to update a comprehensive evaluation of the literature published since 2020 on the desire to use and embrace fitness and physical activity-related apps. Using the PERSiST adapted from the PRISMA 2020 statement, a total of 29 articles that provide assessment models of sports consumers’ desires to utilise fitness applications were discovered. Several major conclusions emerge from the findings: (1) the use of alternative models to the Technology Acceptance Model has increased in recent years with new theories not derived from that model now being associated with it; (2) studies in Europe are increasing as well as a specifical interest in fitness apps; (3) the UTAUT and UTAUT2 model are more widely used within the sport sector and new models appear connected with behaviour intentions; and (4) the number of exogenous and endogenous variables that are linked to the main technology acceptance variables and their behavioral intentions is diverse within the academic literature. These findings could help technology managers to increase user communication, physical activity levels and participation in their fitness centres, as well as to modify the policies and services of sports organisations.

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Introduction.

In recent years, the number of smartphone users has steadily increased throughout the world, with nearly half of the population now owning a device (Newzoo, 2021 ). As a result, the smartphone is quickly becoming a vital instrument in the lives of the general public (Byun et al., 2018 ). This digital change can also be found in the sports and fitness industry, where the digital explosion in the usage of smartphones and wearables has allowed fitness apps to become one of the market’s most important categories (Jones et al., 2020 ).

Fitness apps are swamping the mobile app market (Beldad and Hegner, 2018 ), with almost one in every five users downloading this type of app on their device (Fox and Duggan, 2021 ). Due to the lockdown placed on people and the requirement to stay at home, the demand for fitness apps has grown significantly since the onset of the COVID-19 pandemic (Clement, 2020 ; Ting et al., 2020 ). A fitness app is a third-party programme for smartphones or wearables that may help consumers in recording physical activity data, guiding sports learning and leading a healthy lifestyle (Eshet and Bouwman, 2015 ). A recent study conducted a social comparison of fitness-related posts on social media platforms by fitness app users. Specifically, Kim ( 2022 ) found that when fitness comparison decreased there was a decrease in user self-efficacy towards physical activity, whereas if fitness comparison increased, self-efficacy towards physical activity increased. Consequently, Kim ( 2022 ) highlighted that self-efficacy is a key element for fitness app users’ motivation and participation in physical activity, and they should be compared to high-performing individuals. In addition, gamification is another important element concerning fitness apps for user satisfaction, and a specific design adapted to the type of user is necessary given the number of existing elements in gamification, highlighting feedback and rewards (Yin et al., 2022 ).

The popularity of fitness apps has grown over the years, coinciding with a greater understanding of the value and advantages of physical activity and a healthy lifestyle (Lim and Noh, 2017 ). Fitness apps have become a trend in the worldwide fitness sector, resulting in new patterns of training behaviour (Hu et al., 2023 ; Kercher et al., 2022 ; Thompson, 2022 ). These new behaviour patterns are connected to physical activity monitoring, a shift in health-care perceptions, and changes in lifestyle habits (Lin et al., 2019 ). Middelweerd et al. ( 2014 ), for their part, emphasise that fitness apps employ many behaviour modification approaches such as goal planning, self-control, feedback, the use of contingent incentives and social support.

In the fitness context, it is also important to address the importance that apps can have in the management of sports centres as a two-way communication tool between the organisation (managers or trainers) and users. In this way, Ferreira-Barbosa et al. ( 2021 ) consider that the use of notifications and communications through the fitness app costs less and produces a greater and better interaction with the client. Thus, the use of applications in fitness centres can enable more direct and dynamic communication with users, providing a better and more personalised service.

Despite this, while studies have begun to find the factors that lead to the desire of using technologies such as apps in numerous fields (Gao et al., 2012 ), a deeper knowledge of the intention to use using certain apps is required (Cho et al., 2020 ). As a result, there are several theoretical frameworks in the scientific literature that explain the acceptance of new technology by sports customers. This ‘acceptance of technology’ refers to an individual’s readiness to adopt technology (Dillon, 2001 ).

The technology adoption model (TAM) developed by Davis ( 1989 ) and Davis et al. ( 1989 ) is the principal model utilised in most research to quantify consumer acceptance of new technologies. The TAM assumes an extension of Ajzen and Fishbein’s ( 1980 ) Theory of Reasoned Action, in which the behavioural intention is decided by the attitude towards this conduct (Davis, 1989 ). According to this author, attitudes are developed around two beliefs: perceived usefulness (PU) and perceived ease of use (PEOU). PU is described as the individual’s belief about the worth of a system, such as its performance or efficiency, in order to gain an advantage, while PEOU is defined as the degree to which the individual believes that the system requires no physical or mental effort and is easily accessible (Davis, 1989 ; Davis et al., 1989 ). PU and PEOU provide for the prediction of user intentions in relation to the adoption of both devices and mobile apps (Kim et al., 2016 ; Koenig-Lewis et al., 2015 ). The TAM has been employed in a variety of areas, including finance, tourism, gaming, health and sports (Rivera et al., 2015 ).

A number of TAM-based theories have been established, including the technology readiness and acceptance model (TRAM), which is derived from the TAM and the "Technology Readiness" (TR) model. Parasuraman ( 2000 ) created the TR with the goal of reflecting consumers’ views and dispositions to implement new technologies, linking their usage with the fulfilment of personal or work objectives. The TRAM has been used in a variety of apps, including social innovation (Rahman et al., 2017 ), branding (Jin, 2020 ) and sports technology (Kim and Chiu, 2019 ). Venkatesh and Davis ( 2000 ) introduced the TAM2 model, which integrates social influence and cognitive belief processes. Other models developed from the TAM are those proposed by Venkatesh et al. ( 2003 ), who suggested the Unified Theory of Acceptance and Use of Technology (UTAUT), its extension called UTAUT2 proposed by Venkatesh et al. ( 2012 ) and UTAUT3 proposed by Farooq et al. ( 2017 ). These theories are concerned with both customers and users (Ferreira et al., 2021 ). According to Venkatesh et al. ( 2003 ), the UTAUT model identifies four elements that influence ‘intention to use’: (i) performance expectancy (PE), or the degree to which individuals believe that using the system will allow them to improve their work performance; (ii) effort expectancy (EE), or the degree to which individuals believe that using the system will allow them to improve their work performance; (iii) social influence (SI), defined as the degree to which individuals believe that their social referents believe that they should use the system; and (iv) facilitating conditions (FC), identified as the degree to which the individual believes in the existence of a technical and organisational benefit.

In addition to the four factors derived from the UTAUT model, the UTAUT2 approach integrates three additional variables (Venkatesh et al., 2012 ): (i) hedonic motivation (HM), which reflects the individual’s intrinsic motivations for accepting new technology; (ii) price value (PV) considered as acceptance of the cost involved in using new technology; and (iii) habit (HA) or the degree to which the individual tends to use the new technology automatically after a learning process. Regarding the UTAUT3 model, Farooq et al. ( 2017 ) introduce a new variable, Personal Innovativeness (PI). Dutta et al. ( 2015 ) indicate that personality traits, such as PI, play an essential role in Information Technology (IT) adoption. As a trait, PI is stable and situation-specific and has a high tendency to influence IT adoption and acceptance (Farooq et al., 2017 ; Thatcher and Perrewé, 2002 ). Thus, PI can be defined as the perceived predisposition or personal attitude of individuals that reflect their tendency to independently experience and adopt new developments in IT (Schillewaert et al., 2005 ). This means that PI can be conceptualised as the willingness to adopt the latest technological gadgets or be linked to trying out new IT features and developments (Farooq et al., 2017 ).

Figure 1 shows the conceptual model of the different theories discussed (TAM, UAUT, UAUT2, UTAUT3). The UTAUT and the UTAUT2 models were performed to investigate consumer acceptance and usage of new technologies (Beh et al., 2021 ), and have been used in a variety of research in the sports, fitness and wearable sectors (Beh et al., 2021 ; Dhiman et al., 2020 ; Yuan et al., 2015 ). However, the UTAUT3 model has not yet been used in the sport context, but it has been employed in other contexts such as tourism (Pinto et al., 2022 ), virtual communication (Gupta et al., 2022 ) and education (Gunasinghe et al., 2020 ).

TAM (Davis, 1989 ), UAUT (Venkatesh et al., 2003 ), UAUT2 (Venkatesh et al., 2012 ), UTAUT3 (Farooq et al., 2017 ). Source: Own elaboration.

In conclusion, despite the recent systematic review conducted by Angosto et al. ( 2020 ) on research that examined the intentions to use and implement apps in the fitness and health sector, or a recent meta-analysis of the Intention to use wearable devices in health and fitness (Gopinath et al., 2022 ), more research is needed. Regarding the need for a new review update, this is necessary for three reasons: (a) the previous review developed by Angosto et al. ( 2020 ) has some shortcomings that will be addressed in the discussion; (b) to analyse the evolution of TAM-derived models such as UTAUT, UTAUT2 or UTAUT3; and (c) the previous review was conducted just before the COVID-19 pandemic, a period in which digitalisation underwent a major evolution to respond to the needs of society. The pandemic has impacted the need to adopt modern technology to monitor, record and control physical activity for both people and sports groups (Núñez Sánchez et al., 2022 ; Ruth et al., 2022 ). As a result, the study’s aim is to perform a comprehensive systematic review that updates the number of studies that have investigated the intention to use or adopt fitness apps from 2020 to May 2023.

Review design and protocol

The Prisma in Exercise, Rehabilitation, Sports Medicine and SporTs science (PERSiST) guidelines (Ardern et al., 2022 ) based on the sports science adaptation of the Prisma 2020 statements (Page et al., 2021 ) were followed for this systematic review. The systematic review was not registered on the PROSPERO platform because, not being in the field of health, it did not meet the requirements for registering the systematic review protocol. Therefore, a prior search protocol was not established and all aspects were marked directly in the methodology of this study.

Inclusion and exclusion criteria

This systematic review includes empirical research published in peer-reviewed journals. However, grey literature was excluded, as were assessment reports, periodic reports, dissertations, abstracts and other forms of publishing. The following criteria were used to include studies in the search: (i) peer-reviewed journal articles; (ii) usage of any form of sports and fitness app; (iii) assessment of the intentions using the app through a survey and (iv) publications in English and Spanish. The following items were excluded: (i) books, book chapters, congress proceedings, or other forms of publications; (ii) qualitative approaches, theoretical research, or reviews; (iii) studies written in a language other than English or Spanish; (iv) no mobile apps were utilised in the sports environment; and (v) duplicate articles.

Search strategy

Table 1 shows the categories of terms that were utilised in the search across multiple databases. Six databases were chosen in an attempt to cover a wide variety of topics linked to this multidisciplinary study, such as sports science, health, psychology and marketing. The databases employed were Pubmed, Web of Science, PsycINFO, Scopus, ABI/Inform and SPORTDiscus. The search lasted from December 27, 2021, through May 26, 2023. The search included all years and there were no restrictions on document type or language from 2020 to the present, considering the previous work by Angosto et al. ( 2020 ).

Figure 2 illustrates the flow chart of all the points proposed by the PRISMA 2020 methodology for conducting systematic reviews (Page et al., 2021 ). The first database search found 8647 results, which were reduced to 3471 once duplicates were removed. A thorough scan of titles and abstracts was carried out by one reviewer, in addition to a full-text review of the selected studies after applying the inclusion and exclusion criteria. A second reviewer evaluated the abstracts of the publications that remained at the abstract level ( n  = 12) to check their eligibility, and there were no disagreements with the first reviewer.

This conceptual diagram shows the protocol of the systematic review process (Page et al., 2021 ).

Assessment of methodological quality

The methodological quality analysis was tested using a rating scale measure of 20 items developed by Angosto et al. ( 2020 ) in the sport consumer research type framework where there were no intervention methods on the themes of the CONSORT checklist (Schulz et al., 2010 ). Two reviewers independently assessed each study by examining the multiple elements that make up an investigation. Each element scored one point if the study met the criterion satisfactorily or zero if the research did not meet the criterion or if the element was not applicable to this study. When disagreement emerged, the reviewers resolved this by re-examining the study until an agreement was reached. Supplementary Table S2 (see the section “Data availability”) indicated the methodological quality evaluation results for each research.

Data extraction

For data extraction, an Excel form was created that includes the following characteristics: (a) publishing year; (b) country of study , country of the institution of the first author of the study; (c) number of participants , total of the sample used in the study; (d) gender , percentage of males and females in the sample; (e) age of participants , average age or age ranges of the study sample; (f) type of Application evaluated , fitness or sport apps and their combination with other types of apps such as health or diet apps.; (g) theory used , evaluation model used in the study; (h) analyses performed , types of analysis used in the results; and (i) variables included , assessed variables included in the model proposed in the study. Supplementary Table S3 (see the “Data availability” section) showed the individual data of each study.

Analysis of the assessment of methodological quality

To assess methodological quality, the analysis of the 29 research papers reviewed in the study (Supplementary Table S2 ) found that 16 studies had the best rating of 15 points or more out of a possible 20. There have been 12 studies with an average score between 10 and 15 points, and one research had a score of <10 points (Jeong and Chung, 2022 ). It should be noted that none of the studies reviewed estimated the sample needed for the generalisability of the results, which could be attributed to the fact that all the studies selected their samples by convenience within a certain group. Furthermore, none of the research defined inclusion criteria for the sample selection. Three studies revealed which author performed each phase of the study (García-Fernández et al., 2020 ; Vinnikova et al., 2020 ; Yu et al., 2021 ), and nine studies indicated whether or not they received funding.

Summary of reported intervention outcomes

Supplementary Table S3 shows the descriptive data taken from each research. According to the findings, this issue of assessing the intention to use applications in the sports marketing industry has garnered considerable attention in recent years. A total of 29 research works were chosen, based on the studies published following the systematic review conducted by Angosto et al. ( 2020 ) that focused on the quantitative evaluation of the intention to use sports applications, using either paper-based or online surveys. The results showed that 2022 was the year with the highest number of publications ( n  = 12), while nine articles were published in 2021, there were five articles published in 2020 and three articles in 2023. The location of the research revealed that 64% of the total articles published were from Asia ( n  = 18), ~32% were from Europe ( n  = 9) and 4% were from America ( n  = 1). Among the countries with the highest number of publications, the following should be highlighted China which had the most papers, with six, followed by Spain with four articles, and Hong Kong, Taiwan, and Germany, each with three articles.

A total of 22,942 respondents were examined in the sample of studies, with a range of total size between 200 and 8840 participants, and an average of 791.1 participants per research work. With respect to the type of the sample, the vast majority considered fitness users or community members, with ten and nine articles respectively. To a limited extent, the authors used students ( n  = 6) or the general population ( n  = 2). The sociodemographic data of the sample revealed that the majority of the studies had a greater proportion of females than males ( n  = 18), with an average of 46.1% males and 53.1% females. Seven articles indicated the average age of the participants, with an average age for all 30 years old. A total of 19 articles indicated age by range, with 10 articles having a higher proportion of young people under 30 years, eight articles having a higher population between 30 and 50 years, and one article with a majority of participants over 50 years. Two articles did not indicate age in any of the above ways. Regarding the type of apps used within the sports context, they were fitness apps used in sports centres ( n  = 18), followed by sports apps ( n  = 6), four used apps that also had a health aspect and one included diet-related aspects.

Analysing the theoretical background on which the authors have based their studies, the use of the TAM model still stands out ( n  = 12), and there was an increase in the number of articles that used the UTAUT or its derivatives (UAUT = 4; UTAUT2 = 6). In addition, three studies were based on another TAM-derived model, TRAM, while one article relied on the expectation-confirmation model (ECM), or the theory of normative social behavior (TNSB), and another study encompassed several models such as the theory of consumption values (TCV) and the theory of perceived risk (TPR). When examining the link between the various constructs studied, 25 studies used structural equation analysis (SEM), while one used regression analysis and another used correlation analysis. The SEM analysis was carried out using the PLS and AMOS statistical tools.

One issue to take into account in the variables used is that intention to use (ITU) is a common variable as it is a criterion for inclusion. Although the intention to use is referred to in many different ways, the concept is the same. The results show that more than 40 variables have been directly or indirectly associated with UTI in the different articles published. The most analysed variables are those that form the basis of the TAM. PU or PE was another of the most important factors analysed together with UTI, appearing in 26 articles, followed by PEOU or EE, which was evaluated in a total of 23 articles. Among the most frequently used variables associated with the different models were Perceived Enjoyment (PEN) in eight articles, Satisfaction (SA) in five articles, Innovativeness (INN) in four studies, and Health Consciousness (HC), Optimism (OP) and Subjective Norms (SN) with three articles each.

The constructs associated with the UTAUT or UTAUT2 models have also been studied in almost all the articles that have considered these models. Among them, the use of SI stands out in eight articles, while other factors such as HA, HM, or FC have been analysed in five studies and PV in four studies. Other variables associated with the UTAUT or UTATU2 models include Self-efficacy (SE) in four articles, and PI, perceived playfulness, goal setting, attractiveness, privacy protection and barriers in one article. Other factors linked with other models that have been studied once were Insecurity, Discomfort, Need for interaction, Personal attachment, Word-of-mouth, Commitment and Quality aspects or Motivations. Appendix B shows all the variables analysed in each individual study.

Finally, considering the main results, it has been shown that, although the TAM factors (PU and PEOU) are widely studied and evidence has been found of the influence of both on UTI and PEOU on PU, there are many factors that also both directly and indirectly influence, using these two constructs as mediators of UTI. For example, PEN is a variable that eight studies have found to influence UTIs. SI and HA were other factors that also significantly influence UTI ( n  = 5 for each one). Other elements from the UTAUT/UTAUT2 models that have also been shown to influence UTI, to a lesser extent across studies, have been PV ( n  = 3), FC ( n  = 2), and HM ( n  = 3). Other aspects external to the TAM-based models that directly and significantly influence ITU were Innovativeness, Subjective Knowledge, Trust, Commitment, Perceived Playfulness, Health Consciousness, Personal Innovativeness, Autonomous Motivation, Self-efficacy, Attractiveness, Perceived Privacy Protection, Subjective Norms, Goal Setting, Risk Perception, Physical Appearance, Affiliation, Condition, Privacy Risk and Security Risk.

As for the indirect effects of the external variables considering PEOU/EE, PU/PE, or PEN as mediating variables, the influence of factors common to these three variables such as Innovativeness, Insecurity, Optimism, Perceived Attractiveness, Information Quality,and System Quality has been evidenced. Other external factors that significantly influenced both PEOU/EE and PU/PE were Subjective Knowledge, Task-Technology Fit, Accuracy, SE, PEN and Subjective Norms. While certain factors only influenced some of the variables considered, especially PU/PE, which was influenced by a greater number of external variables (Discomfort, Confirmation of Expectations, Trustworthiness, Perceived Benefits, Risk Perception, Perceived Threats), PEN only influenced Discomfort and PEOU/EE e-Lifestyles. Therefore, it was observed that there is no consensus in the scientific literature when it comes to addressing common external variables for further research in several contexts.

The aim of this systematic review was to update research that has analysed the intention to use or adopt fitness apps from 2020 to May 2023, following the study conducted by Angosto et al. ( 2020 ). It is relevant to highlight the differences between this review and the previous one by Angosto et al. ( 2020 ). For this purpose, it is important to consider the review of studies that used UTAUT or UTAUT2 developed by Venkatesh et al. ( 2016 ) as a model. In this review, the author argues the need to expand existing reference models with new exogenous, endogenous, moderating, or outcome mechanisms, as well as theorising influences at different levels. As a clear example in this line, the author himself increased the number of endogenous variables of the UAUT model including HM, PV and HA resulting in the UTAUT2 model or, in the case of Farooq et al. ( 2017 ), incorporating PI to obtain the UTAUT3 model. In addition, Davis ( 1989 ) proposed the initial TAM model by inducing external or exogenous variables in order to be able to analyse in different contexts.

Based on these aspects, the review previously carried out by Angosto et al. ( 2020 ) presents a clear limitation as it only focuses on analysing the influence of TAM or TAM2 factors, omitting the possible influences of exogenous, endogenous, or moderating variables. In this way, it should be noted that these authors do not carry out an in-depth analysis of user behaviour and its effects (both direct and indirect) that influence the ITU fitness app. On the other hand, another error is observed because the authors discriminated the variables of the UTAUT or UTAUT2 models, only focusing in the end on the studies based on TAM, TAM2, or TRAM. Therefore, when they conducted their analysis on the influence of variables, they omitted data from these studies as well. It should be noted that the UTAUT and UTAUT2 models are based on TAM, thus PE is the equivalent of PU, while EE is the equivalent of PEOU.

In view of the previous reasons, together with the period experienced by the world population as a result of the COVID-19 pandemic, it is necessary to update the previous review carried out by Angosto et al. ( 2020 ). It should be remembered that during the pandemic the population was forced to be confined to their homes. This has represented a milestone in the digitalisation of society and sports and fitness services. In fact, it can be observed that while in the review by Angosto et al. ( 2020 ), the authors identified 19 articles, from the beginning of the pandemic to the present day this review has found a total of 29 articles that met the inclusion/exclusion criteria. In short, the number of publications has more than doubled in the last three years. It is true that five research works overlapped with the prior review, which might explain why these studies were published in the press, and by assigning them a journal number, they seem published at a later date. This review emphasises the significance of this topic’s rising popularity in the fitness sector from several domains such as sociology, psychology and management (Cai et al., 2022 ).

To summarise, the results of this review and the previous review by Angosto et al. ( 2020 ) will be compared. In general, regarding the location of the studies, an increase in the number of studies conducted in Europe was observed compared to the previous review (Acikgoz et al., 2022 ; Baubonytė et al., 2021 ; Damberg, 2021 ; Ferreira et al., 2021 ; García-Fernández et al., 2020 ; Gómez-Ruiz et al., 2022 ; Pérez-Aranda et al., 2021 ; Schomakers et al., 2022 ; Yang and Koenigstorfer, 2021 ), and a decrease in the number of studies in the Americas (Won et al., 2023 ). Concerning countries, there is an exponential increase in the number of studies conducted by authors in Chinese universities and, when compared to the previous review, there is a majority of studies from South Korea.

In relation to gender, both reviews obtained similar results in which the proportion of female participants was higher than male participants in most of the studies. Although the gender of the customers or users studied was primarily female, Baubonyte et al. ( 2021 ) believe this to be rather immaterial in research that compared the intention to use new technologies based on gender. When the mean age was analysed, this review showed that the mean age of the participants was around 30 years old, while in the review by Angosto et al. ( 2020 ), this was 24 years old. Also, it should be noted that the age groups with the highest representation and the highest proportion of users were either very young (<23 years) or adult (30–50 years), while in this review most studies have a higher proportion of the population under 30 years versus adults. The reason for these results may be due to the fact that females tend to prioritise collective practice over individual practice (Vogler et al., 2008 ), and therefore there is a higher proportion of users of fitness centres or communities, while young people present fewer digital barriers when it comes to using apps than, perhaps, the adult population (Schreurs et al., 2017 ).

Depending on the type of app analysed in the different studies, variations have also been observed with respect to the previous review. The previous review emphasised that most studies considered fitness and diet apps while fitness or sports apps were the least considered. This review reports completely inverse results where the large majority of apps analysed were fitness apps followed by sport, while diet-fitness apps have been the least evaluated, with only one study. This change in trend may be clearly influenced by the context of the COVID-19 pandemic where the population forced to stay at home due to confinement felt the need to do physical exercise to be active and use leisure time in a more entertaining way. A significant proportion of the scientific literature highlights the features and functions and results of using fitness and sports apps (Kim et al., 2017 ), despite the fact that some studies have evaluated other health-related apps alongside this type of app (Aboelmaged et al., 2022 ; Chiu et al., 2021 ; Chiu and Cho, 2021 ; Zhu et al., 2023 ), or that of diet (Chiu et al., 2021 ). It is vital to highlight that the link between physical activity, fitness and health is extremely close, as is eating to live a healthy lifestyle.

Most research that has analysed technology adoption or intention to use has used the TAM model, which offers an understanding of why people embrace these technologies based on their PU and PEOU views (Márquez et al., 2020 ). However, this study found that recent research increasingly employs theories developed from the TAM, such as the TRAM model (Aboelmaged et al., 2022 ; Chiu and Cho, 2021 ), the UTAUT (Guo, 2022 ; Pérez-Aranda et al., 2021 ; Vinnikova et al., 2020 ; Wei et al. 2021 ), or the UTAUT2 model (Damberg, 2021 ; Dhiman et al., 2020 ; Ferreira-Barbosa et al., 2021 ; Kim and Lee, 2022 ; Schomakers et al., 2022 ; Yang and Koenigstorfer, 2021 ). In addition, other theories also appear in different articles such as the ECM (Chiu et al., 2021 ; Zhang and Xu 2020 ), the TNSB (Yeoh et al. 2022 ) or the TCV/TPR (Zhu et al., 2023 ). An interesting aspect to note is that, although no study based on the UTAUT3 model suggested by Farooq et al. ( 2017 ) has been found, Dhiman et al. ( 2020 ) proposed the UAUT2 model, but incorporated the PI variable which is included as a new endogenous variable within the UTAUT3.

In general, previous research on the acceptance of new technologies in the sports industry has found that PEOU (Mohammadi and Isanejad, 2018 ), or PU are the primary influences on the ‘intention to use’ (Kim et al., 2017 ). According to Venkatesh ( 2000 ), when a customer or user sees a technology to be simple to use, he or she would also regard it to be valuable. According to Cho and Kim ( 2015 ), PEOU typically has a benefit for users since it helps them to carry out activities with a more comfortable and simple method while driving the desire to continue using the app. In this regard, Liu et al. ( 2017 ) revealed that PEOU was the most important belief since the majority of fitness users thought apps were easy and simple to use when they met their expectations. Based on one research work, if the user must make an effort to learn how to use the app, this will favourably affect the consumer’s propensity to use the app (Lin et al., 2020 ). When a customer has a strong desire to use the app, the person is more likely to promote it to others (Cheng et al., 2021 ). As a result, the usage of fitness apps will be related to an increase in physical activity levels and, consequently, in health (Kim, 2022 ; Litman et al., 2015 ).

However, in spite of this more than contrasted evidence in the scientific literature, it is important to address the extent to which other variables (exogenous, endogenous, or moderating) can influence the ITU fitness app. To begin with the influence of exogenous variables, the TR model has been shown in different studies to have an external influence on TAM factors (Aboelmaged et al., 2022 ; Chen and Lin, 2018 ; Chiu and Cho, 2021 ). For example, PEOU is moderately influenced by Innovativeness and slightly influenced by Optimism and Insecurity, while PU is moderately influenced by Optimism and slightly influenced by Innovativeness, Discomfort and Insecurity (Aboelmaged et al., 2022 ; Chang et al., 2023 ; Chiu and Cho, 2021 ). Furthermore, Chiu and Cho ( 2021 ) found that both positive (Innovativeness and Optimism) and negative (Discomfort and Insecurity) factors of TR significantly influenced PEN. In another context, Raman and Aashish ( 2022 ), evaluating wearables, revealed that positive aspects of the TR positively influenced PEOU and PU, while negative aspects of TR negatively influenced these variables.

In contrast, Acikgoz et al. ( 2022 ) found a moderate influence of Innovativeness on PU and Subjective Knowledge on both PEOU and PU. Chang et al. ( 2023 ) reported a slight influence of the variable Task-Technology Fit on PEOU and PU. Other influential variables on PEOU have also been shown to be Self-efficacy (Dhiman et al. 2020 ), e-Lifestyles (García-Fernández et al., 2020 ), Perceived Attractiveness (Gómez-Ruiz et al., 2022 ; Jeong and Chung, 2022 ), Accuracy (Jeong and Chung, 2022 ), Information Quality and System Quality (Won et al., 2023 ) and Subjective Norms (Yu et al., 2021 ). As for external influential variables also in PU/PE, there are Confirmation of Expectations (Chiu et al., 2021 ), Perceived Attractiveness (Gómez-Ruiz et al., 2022 ), Accuracy and Trustworthiness (Jeong and Cheung, 2022 ), Self-efficacy, Perceived Barriers, Perceived Benefits, Risk Perception, and Perceived Threats (Wei et al., 2021 ), Information Quality and System Quality (Won et al. 2023 ) and Subjective Norms (Yu et al., 2021 ). Won et al. ( 2023 ) also found the influence of Information Quality and System Quality on PEN.

Some studies have also assessed the effects of exogenous or endogenous variables on attitudes as a moderator with ITU. Some variables that had a significant influence were PU/PE (García-Fernández et al., 2020 , Pérez-Aranda et al., 2021 ; Yu et al., 2021 ), PEOU/EE (Pérez-Aranda et al., 2021 ; Yu et al., 2021 ), PEN, Gamification and Satisfaction (Pérez-Aranda et al., 2021 ). Cai et al. ( 2022 ) found that Satisfaction acted as a moderating variable for PEOU, PU and Trust with ITU. Regarding the influence of endogenous variables that influenced ITU in addition to PEOU, PU, or PEN we found Subjective Knowledge (Acikgoz et al., 2022 ), Commitment (Chiu et al., 2021 ; Cho et al., 2020 ), PV (Damberg, 2021 ; Dhiman et al., 2020 ; Yang and Koenigstorfer, 2021 ), HA (Damberg, 2021 ; Dhiman et al., 2020 ; Ferreira et al. 2021 ; Schomakers et al. 2022 ; Yang and Koenigstorfer, 2021 ), Health Consciousness (Damberg, 2021 ), Perceived Playfulness (Damberg, 2021 ), SI (Dhiman et al., 2020 ; Ferreira et al., 2021 ; Guo, 2022 ; Vinnikova et al., 2020 ), PI (Dhiman et al., 2020 ), HM (Ferreira et al., 2021 ; Schomakers et al., 2022 ); FC (Ferreira et al., 2021 ; Yang and Koenigstorfer, 2021 ), Perceived Trust (Gómez-Ruiz et al., 2022 ), Autonomous Motivation (Guo, 2022 ), SE (Huang and Ren, 2020 ; Vinnikova et al., 2020 ), Privacy Perceived Protection (Kim and Lee, 2022), Subjective Norms (Pérez-Aranda et al., 2021 ) and Goal-setting (Vinnikova et al., 2020 ).

Particularly interesting are the studies that did not rely on TAM models or derivatives that found different variables that significantly influenced ITU. For example, Zhu et al. ( 2023 ) showed that the variables of General Health, Affiliation, Physical appearance, Condition, Perceived Risk and Security Risk influenced UTI. Yeoh et al. ( 2022 ) indicated that Outcome Expectation, Descriptive Norms and Perceived Behavioural Control influence UTI. Pérez-Aranda et al. ( 2023 ) found that attitudinal, cognitive and behavioural antecedents increase the intention to continue using a sports app. Finally, according to the influence on outcome variables, Cheng et al. ( 2021 ) observed that the ITU significantly influenced the Word-of-Mouth outcome variable. On the other hand, Ferreira et al. ( 2021 ) found that ITU influenced current use and Satisfaction, and Guo ( 2022 ) that ITU and Controlled Motivation also influenced current use. At the same time, SI, SE and Goal-setting also influenced current use (Vinnokova et al., 2020 ).

Lastly, we will discuss some evidence reported by other studies focused on the sport context, but which did not take into account fitness apps. For example, Wang et al. ( 2022 ) noted in a fitness software that SI, PE and EE significantly affected the ITU of university students. In an e-Sport game during a pandemic, Ong et al. ( 2023 ) showed that HA was the most significant factor in UTI, followed by usability, FC, SI and HM. In a similar vein, Yang et al. ( 2022 ) found that HA was the only predictor for the use of metaverse technology for basketball learning in college students. Ahn and Park ( 2023 ) showed that hedonic, user burden, pragmatic and social values were key predictors of fitness app user satisfaction. Gu et al. ( 2022 ) observed that attitudes toward exercise and the use of sports apps have a significant impact on physical activity intentions. Finally, Ferreira et al. ( 2023 ) demonstrated that the relationship between UTIs and members’ overall satisfaction with the gym is positively mediated by e-Lifestyles.

Limitations and future research

There are obvious limitations to this systematic review. The first point to mention is maybe the shorter time restriction compared to the prior review by Angosto et al. ( 2020 ). However, this is required since the COVID-19 pandemic is still active and national governments are implementing preventative measures based on the pandemic’s progress (Ferrer, 2021 ; Official State Bulletin, 2021 ). Many nations are enacting new temporary confinements, which may encourage the usage of exercise or health applications. Other potential constraints include publication bias, which occurs when journals publish research with favourable and significant results while rejecting papers with irrelevant outcomes. Another source of bias might have been the language, since there may have been publications in languages other than those specified in the inclusion criteria (English, Spanish and Portuguese). Another constraint might be the choice of search databases, because missing specific databases may result in prospective articles not being detected for inclusion in the review. A third issue is inclusion bias, which occurs when the inclusion or exclusion criteria itself prejudices against a research work. The last limitation is that the great diversity of variables analysed by the authors does not allow the generation of an adequate database that would enable a more in-depth analysis of the results through a meta-analysis beyond the TAM variables such as PEOU and PU.

Future research should try to assess sports consumers or users in other European or American contexts, with the possibility of analysing the results according to socio-demographic characteristics such as gender, age, sport, or digital experience. Age is an interesting aspect to investigate since, depending on the generation to which the person belongs, he or she will identify with new technologies to different degrees. In addition, there are variables such as those in the UTAUT model and derivatives or TR that have been more common than others, but there is still a need to increase the number of studies that use them. Other studies could take a longitudinal approach, assessing the consumer’s desire to use and actual use of the application, as well as whether or not this affects their behaviour towards a more active or healthy life.

Future lines of research relating to the evaluation of the intention to use fitness apps, or any other form of app or wearable, should examine the differences between the models in the same population using the TAM model and some of the other derived models such as the UTAUT or UTATU2. Furthermore, the proposed theoretical models should be assessed by linking them to other factors related to smartphones or other technical devices, such as attachment to the gadget, social influence for its usage, or actual use of the item, among others. Theoretical models such as the TAM, TAM2, UTAUT, UTAUT2 or UTAUT3 should be examined in various sports settings such as the usage of apps for managerial duties, sports training, or marketing/sports products.

Another key issue that has not been studied is the variation in intention to use across the different age groups of the population, since the elderly population may have a different aim than the younger population. Along similar lines, additional elements such as educational level or socioeconomic position may impact the inclination to use the fitness app or any other gadget or technology. Finally, longitudinal research might be utilised to determine how well the intention to use fitness apps matches the actual use of them.

Conclusions

This systematic review update highlights that research on the usage intention and adoption of fitness apps is a topic of interest within the digital sports marketing industry. In recent years there has been a significant increase in the number of publications, with an increasing number of European studies focusing on fitness or sports apps themselves and not associated with health or diet. In addition, the models used beyond the TAM itself are becoming more diversified, as well as the number of exogenous, endogenous and moderating variables in the different studies. Although there is no consensus on analysing the same variables in greater depth in order to generate data for a better joint analysis, there is no consensus on analysing the same variables in greater depth in order to generate data for a better joint analysis.

Finally, a practical aspect of sports organisation management is the desire that this sort of study may assist in learning the opinions of users or customers while adopting or establishing new policies with a digital transformation. This is especially important because it allows for improving the organisation’s communication in a bidirectional way. In short, the implementation of the use of apps in sports centres implies more direct and closer communication with users. In addition, physical activity and management might be monitored without eliminating travel and human interaction. For example, sports organisations make extensive use of sports digital marketing, through the use of social tools, to make the organisation more visible and to offer a more direct image and contact with current or future consumers (Angosto et al., 2022 ). However, not all users have the same social media, therefore the use of push notifications and in-app communication in a venue allows for better notification of relevant news and at a lower cost.

Furthermore, the theoretical models reviewed above identify factors that influence the ITU of technology, such as PU, PEOU, SI and FC. Sport managers can therefore use these models to identify and assess which factors are relevant in their particular context. This will help them to understand the needs and preferences of their users and to adapt their strategies accordingly.

Also, PU is a critical factor in the intention to use technology. Therefore, sports managers should assess how their users perceive the usefulness of technology in their sport context. Among the actions to be taken, they can conduct surveys, interviews or focus groups to collect data on how users feel technology can enhance their sport experience. This will allow sports managers to identify areas for improvement or additional features that can add value to the user experience. Similarly, PEOU is also an important factor in the acceptance and use of technology. In this regard, sports managers must ensure that the technology they use is easy to use and accessible to their users. This involves providing clear instructions, intuitive interfaces and adequate training to ensure that users feel comfortable using the technology.

Another variable that has been shown to influence ITU is SI. In this regard, sports managers could leverage these positive SI to promote the adoption of technology in their sports community. For example, they can collaborate with influential athletes or well-known coaches to support and promote the use of technology. They could also encourage social interaction among technology users by creating online communities or support groups. Finally, FC and perceived barriers have also been shown to influence the intention to use. Sports managers should identify and address any potential barriers that may hinder the adoption and use of technology in their sport environment. This may include a lack of technology resources, resistance to change, or privacy and security concerns. By proactively addressing these barriers, sports managers could encourage greater acceptance and use of technology.

Data availability

The datasets generated during and/or analysed during the current study are available in the Figshare repository, https://figshare.com/s/d0a13d89538847f00b67 .

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This research was funded by the Junta de Andalucía, Regional Ministry of Economic Transformation, Industry, Knowledge and Universities (grant number AT 21_00031). SA is funded by the European Union—NextGenerationEU through a postdoctoral contract with Margarita Salas.

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Salvador Angosto

Department of Physical Education and Sports, Faculty of Educational Sciences, Universidad de Sevilla, 41013, Seville, Spain

Salvador Angosto, Jerónimo García-Fernández & Moisés Grimaldi-Puyana

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Angosto, S., García-Fernández, J. & Grimaldi-Puyana, M. A systematic review of intention to use fitness apps (2020–2023). Humanit Soc Sci Commun 10 , 512 (2023). https://doi.org/10.1057/s41599-023-02011-3

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• Published: 14 May 2024

Assessment of nutritional status, physical fitness and physical activity of school going adolescents (12–15 years) in Delhi

• Shanza Ferozi 1 ,
• Anu Gupta Taneja 1 &
• Neha Bakshi 1  

BMC Pediatrics volume  24 , Article number:  331 ( 2024 ) Cite this article

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Adolescence is a distinct period that is crucial for setting the foundation for long-term health.

To assess the nutritional status, physical fitness, and physical activity of adolescents.

The present cross-sectional study recruited 100 adolescents purposively. Information regarding general profile and lifestyle-related factors was collected using a questionnaire. Anthropometric data such as height, weight, BMI, and body fat% were collected using appropriate equipment. Physical fitness was assessed using a battery adapted from FITNESSGRAM® and PAQ-A assessed the physical activity. Dietary intake was analysed using a 2-day 24-hour dietary recall.

The study revealed, 19% of the participants were overweight and 6% were obese. The majority (74%) were physically inactive and 15% had high body fat %. There was lower consumption of energy, carbohydrates, iron, and calcium, than the recommendations. Also, physical activity scores were negatively associated with macronutrient intake and trunk lift (strength and flexibility) [ p  < 0.05]. Data showed lower physical fitness scores. BMI and hand-grip strength was positively correlated [ p  < 0.05]. Push Ups (endurance) and Standing Broad Jump (power) showed a negative correlation with body fat%. Tennis ball throw and PACER (cardiorespiratory fitness) were positively associated with protein intake. A multiple regression analysis significantly showed that a unit increase in cell phone usage increases body fat% by 11.64 units. Standing broad jump increases by 38.6 cm and decreases with 28.76 cm with a unit increase in playing outside and tuitions timings respectively.

Poor nutritional status, physical fitness, and physical activity were reported among adolescents. It is imperative to plan intervention strategies to improve the overall health of adolescents.

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Introduction

Adolescence (aged 10–19 years) is an important phase in a child’s growth with rapid physical, psychological, and cognitive development [ 1 ]. There are 1.3 billion adolescents in the world today, more than ever before, making up 16% of the world’s population [ 2 ].

In India, there are 253 million adolescents between 10 and 19 years. This age group requires good nutrition, learning, knowledge, counselling, and direction to ensure their development into healthy adults [ 3 ]. Considering this important phase of growth and development, malnutrition either under or over-nutrition is a major reason for concern among adolescents. The global prevalence of underweight among children and adolescents is 8.4% for girls and 12.4% for boys. Emerging evidence suggests that over-nutrition is also a growing population health concern among adolescents in low- and middle-income countries [ 4 ]. According to CNNS 2016-18 data 24% of adolescents were thin for their age (BMI for age <-2 SD), and 5% of adolescents were overweight or obese (BMI for age > + 1 SD). CNNS 2016-18 data also suggested that overall, food consumption patterns were similar between boys and girls which showed imbalanced dietary intake [ 5 ]. The short-term complications of undernutrition (thinness or stunting) are poor performance at school and risk of frequent infections. In the long term, under-nutrition among adolescents is associated with poor general health, and less economic productivity [ 6 , 7 ]. On the other hand, over-nutrition contributes to the early development of non-communicable diseases such as diabetes, hypertension, coronary heart diseases, sleep apnoea, and cancer [ 8 ].

Physical fitness and physical activity are two important factors that affect health besides dietary intake. While physical activity is considered a behaviour with a degree of choice on behalf of the individual involved, physical fitness is an attribute, and fitness components include cardio-respiratory endurance, muscle strength, flexibility, and body composition [ 9 , 10 ]. Despite all the health benefits of physical activity, most adolescents worldwide are physically inactive. It is estimated that 77.6% of boys and 84.7% of girls aged 11 to 17 years are physically inactive [ 11 ]. The modern era has brought changes in ways of life and work that are associated with lower levels of physical activity [ 12 ]. Also, decline in physical fitness are often recorded [ 13 ], which are likely influenced by the decreasing trend in physical activity and changes in body composition [ 14 , 15 ].

The capacity for physical activity is intimately linked to the multi-component concept of physical fitness [ 16 , 17 ]. Due to the favourable effects of high levels of fitness during childhood and adolescence on adult health, it is a significant health marker [ 18 , 19 ]. Higher levels of physical fitness also reduce the chance of health issues and allow involvement in a wider range of physical activities [ 20 , 21 ].

Changes in physical fitness, physical inactivity and poor nutrition status can lead to health problems later in life, such as obesity, diabetes, osteoporosis, back pain, cardiovascular disease, and cancer [ 22 ].

Studies on Indian children and adolescents have shown an association between diet patterns, physical activity level and overweight/obesity [ 23 , 24 , 25 ]. Considering this crucial stage of adolescence which lays the foundation for future health status and wellbeing, it is imperative to assess their physical fitness levels along with nutrition status and physical activity. Assessment of physical fitness ensures body’s ability to perform activities required for healthy living. Physical fitness can be achieved through adequate nutrition, appropriate physical activity, and adequate rest. Furthermore, there is limited research work to comprehend the relationship between different components of physical fitness with nutrition status in the Indian adolescent population. Hence, the present study aimed to assess the physical fitness, nutritional status, and physical activity of school-going adolescents (12–15 years).

This cross-sectional study recruited 100 adolescents in the age group of 12–15 years from a government and an aided school in North Delhi. Before conducting the research, ethical clearance was taken from the Lady Irwin College Institutional Ethical Committee. Informed Consent and Assent were also taken from the parents and children respectively after providing the required information about the study. Students with any kind of disability, with chronic diseases which have an impact on physical activity and fitness like diabetes, asthma etc. and adolescents regularly involved in any sports activities were excluded from the study to maintain sample homogeneity.

General information regarding basic subject profile like, educational qualification, family income, socio-economic status, number of family members, etc. was gathered using a questionnaire. An e-questionnaire was made for data collection. The questions were asked from the participants and filled in by the researcher.

Nutritional assessment was done using anthropometric measures like weight, height, BMI, and body fat % was assessed using Tanita body composition analyser. The height and weight of adolescents were taken using valid and reliable tools. BMI was computed using Quetlet’s Index and the adolescents were classified into different categories of nutritional status using WHO 2007 growth charts [ 26 ] and IAP 2015 revised growth charts [ 27 ]. WHO Anthro plus Software was used to categorise adolescents according to BMI for age standards by WHO. For classification of nutrition status according to IAP standards, the 75th percentile and 95th percentile were characterized as overweight and obese respectively. The 3rd percentile was used to define thinness. Body fat % of adolescents was classified using standards given by Khadgawat et al., (2013) [ 28 ]. The percentage of body fat < 85th centile was considered as having normal body fat, those with a percentage of body fat between 85-95th centile as having moderate body fat, and individuals > 95th centile were considered as having elevated body fat.

The researcher collected information from the respondents on the nature and quantities of food consumed over the past 24 h. The nutritional requirements increase as the child grows and some of the nutrients in which a surge can be seen are energy, protein, calcium, magnesium, folate, iron, vitamin A, etc [ 29 ]. Two days 24-hour recalls were recorded, and the percent adequacy was calculated for energy, protein, fat, carbohydrate, iron, vitamin A and calcium. Then a comparison was made with ICMR, 2020 recommendations for dietary allowances [ 29 ] and was analyzed using dietary software. For comparison of food group intake by adolescents, NIN Dietary Guidelines for Indians (2011) were used [ 30 ]. Physical Activity was assessed using the Physical Activity Questionnaire – Adolescents (PAQ-A), which is a validated tool to assess general levels of physical activity for the last seven days. It provides a summary of physical activity score derived from eight items, each scored on a 5-point scale [ 31 ]. In the original version of this questionnaire, the first question listed 23 different physical activities. Since many of them weren’t applicable in Indian contexts, the questionnaire was changed, and 13 applicable activities were eventually retained in the final version. A score of 2.75 (> 60 min of moderate-vigorous physical activity per day) was used to detect adolescents performing enough physical activity [ 32 ].

In the study, the test battery that was used for assessing the physical fitness components like cardiorespiratory fitness, muscular strength, endurance, flexibility, and power consisted of 8 test items. PACER was used for assessing cardiorespiratory fitness, Curl Up for abdominal strength and endurance, Trunk Lift for trunk extensor strength and flexibility, Push Up for upper body endurance, Back Saver Sit and Reach for flexibility, Tennis Ball Throw for upper body power, Standing Broad Jump for lower body power, and Hand Grip Strength for maximum isometric strength. The 8 test items that are included in the test battery have appeared in Fitnessgram® 2013 [ 33 ] and are also included in various research papers [ 34 , 35 , 36 , 37 , 38 ]. The 8 test items included in the test battery are given in following Table  1 . The Fitnessgram® (2013) reference standards were used for classifying adolescent boys and girls according to their performance on different physical fitness tests. Administration of the test battery was done by the school physical education teacher while the researcher recorded the results. On a single day, two or three physical fitness tests were administered based on the schedule of the adolescents. For Trunk Lift, Tennis Ball Throw, and Standing Broad Jump better of two attempts was recorded whereas for other tests the score for a single attempt was recorded as continued performance can improve the results which can create bias.

Statistical analysis

The descriptive statistics of the participants’ baseline characteristics and responses were provided as frequency and percentage for categorical variables that were presented differently for boys and girls. A bivariate analysis was performed, using the Spearman correlation coefficient (rs), Kruskal-Wallis H test value, and Pearson correlation coefficient to indicate the strength of the association between different variables; p  values below 0.05 were considered to indicate statistical significance. Multiple regression was run with lifestyle factors that may have an impact on dependent variables such as physical fitness, physical activity, nutrition status, and dietary intake whereas lifestyle factors like gender, the total number of family members, number of earning members, educational qualification of parents, annual income of the family, participation in any sports activity in school or neighbourhood, time spent playing outside with friends, availability of personal cell phone, use of the phone for education purpose, time spent on the phone on an average in a day, time spent watching TV, additional tuitions, frequency of tuitions, helping parents at home in household and other chores, frequency of getting involved in household chores, meals consumed in a day, and teaching about benefits of participating in sports and other activities were taken as independent variables.

General profile of the participants

The data gathered showed that 90% of the sample consisted of boys; the majority of them lived in a nuclear family (61%) and had one earning family member (82%). Only 16% of the parents were graduates and 62% of the fathers were businessmen. None of the participants had any medical condition. The majority of them were non-vegetarian (85%) and consumed 3 major meals in a day (71%). Most of the adolescents involved in the study had a screen time of 1–2 h (74%) according to IAP (2021) guidelines recommend balancing screen use with other activities such as physical activity, schooltime, and sleep [ 39 ]. Most of the adolescents (69%) claimed that they help their parents at home. 78% of the adolescents were found to spend 1–2 h playing outside.

Data revealed that 100% of participants were consuming breakfast. It was found in our study that about 62% of adolescents were getting 9–10 h of sleep (Table  2 ).

Anthropometric information

The data showed that overweight (19%) and obesity (6%) were prevalent among adolescents according to IAP 2015 growth charts, and according to WHO 2007 growth charts only 2% of adolescents were found to be severely wasted, 4% were found to be wasted, 78% had normal BMI, 10% were overweight and 2% were obese. According to body fat % cut-offs by Khadgawat et al., (2013) [ 28 ] 85% of adolescents were found to be normal while 15% were found to be moderately fat (Table  3 ).

Table  3 demonstrates how body fat percentage classified adolescents as normal, who were overweight (11%), obese (3%), and thin (5%) according to their BMI (IAP,2015). Only 8% of adolescents with overweight BMIs were classified as overweight by body fat % cut-offs. Furthermore, 2% of normal and 3% of obese adolescents according to BMI were also classified as overweight by body fat %. A similar trend was observed in BMI classification by WHO, 2007 and body fat %. Thus, it can be said that BMI accompanied by body fat % gives a better picture of the adiposity among the sample.

Dietary intake

Food Group Intake : Fig.  1 showed that adolescents had lower consumption of milk and milk products, green leafy vegetables, other vegetables, roots and tubers, fruits, cereals, millets, and fats and oils as compared to the values given in NIN Dietary Guidelines (2011) [ 30 ].

Food group intake by adolescents

Nutrient Intake : The participants’ nutrient intake was calculated for vital nutrients such as proteins, iron, calcium, vitamin A, carbohydrates, fats, and energy using dietcal software. The percent adequacy was calculated and compared to the estimated average requirements for Indian adolescents by the ICMR-NIN expert committee [ 29 ]. Figure  2 shows that the percent adequacy of the nutrients specified is above 50% of the requirements. However, the intake of energy, carbohydrates, iron, and calcium was lower than the recommendations.

Nutrient intake by the adolescents

• Physical activity

The data gathered regarding physical activity among adolescents using PAQ-A showed that only 26% of adolescents were sufficiently active, i.e., they got a score of 2.75 which means that they were performing > 60 min of moderate-vigorous physical activity per day [ 32 ]. WHO (2020) guidelines recommend an average of 60 min per day of moderate to vigorous-intensity physical activity. Furthermore, it was found that physical activity showed a significant and negative correlation with energy ( R (98) = -0.206, p  = 0.04), protein ( R (98) = -0.2, p  = 0.045), and fat ( r (98) = -0.26, p  = 0.009) intake indicating that an increase in these nutrients may have a negative impact on physical activity.

• Physical fitness

The data gathered regarding the physical fitness of adolescents using physical fitness battery showed lower scores in different fitness tests measuring cardio-respiratory fitness (PACER), abdominal strength and endurance (Curl Up), trunk extensor strength and flexibility (Trunk Lift), upper body endurance (Push Up), flexibility (Back Saver Sit and Reach), upper body power (Tennis Ball Throw), lower body power (Standing Broad Jump), and muscular strength (Hand Grip Strength) (Table  1 ). Figure  3 depicts that most of the participants need improvement in different fitness tests.

Physical fitness level of adolescents

Association of physical fitness with various parameters

The association of physical fitness with physical activity, nutrition status and nutrient intake is depicted in Table  4 .

Physical fitness & physical activity

A significant negative association ( R (98) = -0.206, p  = 0.04) of physical activity was observed with trunk lift only, which assesses trunk extensor strength and flexibility. It thus indicates that the higher the physical activity level, lower the trunk extensor strength and flexibility. The data also revealed a negative association with most of the fitness tests except for sit and reach though the results weren’t significant. Thus, it could be said that higher physical activity is not synonymous to good physical fitness (Table  4 ).

Physical fitness and BMI

A significant positive correlation ( R (98) = 0.316, p  = 0.001) was observed between BMI and physical fitness test that measures muscular strength, i.e., Hand Grip Strength which indicates that with an increase in BMI muscular strength increases. Correlations of BMI with physical fitness tests that measure strength, endurance, and flexibility were found positive, but they were not significant. A negative correlation was observed between BMI and physical fitness tests that measure abdominal strength (Curl Up), cardiorespiratory fitness (PACER), and lower body power (Standing Broad Jump), indicating that an increase in BMI, decreases fitness scores, but the results were not significant (Table  4 ).

Physical fitness and body fat %

A significant negative correlation was observed between body fat% and push-up (endurance) ( R (98) = -0.326, p  = 0.001) and Standing Broad Jump (lower body power) ( R (98) = -0.273, p  = 0.006) indicating that performance in fitness tests decreases as the body fat % increases. Also, a significant positive correlation is observed between body fat % and Sit and Reach (flexibility) ( R (98) = 0.313, p  = 0.002) which showed higher body fat% is associated with higher sit and reach scores indicating that fat% does not affect flexibility (Table  4 ).

Physical fitness and diet intake

The fitness parameters were assessed for their association with all the major and micronutrients. The data presented depicts only significant values of the association between them. Push-ups scores showed a significant negative correlation with fat ( R (98) = -0.233, p  = 0.02) intake and a significant positive correlation with calcium ( R (98) = -0.216, p  = 0.03) indicating that higher the fat intake was correlated to lower endurance level whereas for calcium intake higher intake was correlated with increased endurance levels. Curl Up scores showed a positive correlation with most of the nutrients assessed and but a significant result was observed with energy ( R (98) = 0.202, p  = 0.04), iron ( R (98) = 0.265, p  = 0.008), and PUFA ( R (98) = 0.223, p  = 0.025) indicating that an increase in these nutrients may increase abdominal strength and endurance. Tennis ball throw showed a significant and positive correlation with protein intake ( R (98) = 0.078, p  = 0.048) indicating that an increase in protein intake may increase upper body power. PACER showed a positive and significant correlation with protein intake ( R (98) = 0.328, p  = 0.039) indicating that an increase in protein intake may improve cardiorespiratory fitness (Table  4 ).

Physical fitness and lifestyle factors

A multiple regression analysis of various independent variables like gender, the total number of family members, number of earning members, educational qualification of parents’ etc. was run with physical fitness tests that were taken as dependent variables.

It was found that a unit increase in time spent playing outside increased the standing broad jump (lower body power) scores by 38.6 cm. Similarly, a unit increase in time spent on taking tuition in a week reduces the standing broad jump by 28.76 cm. These results were found to be statistically significant (Table  5 ). Through a multiple regression analysis of various lifestyle factors described above (independent variables) with body fat % (dependent variable) it was found that a unit increase in the usage of the phone may significantly increase the body fat % by 11.64% ( p  = 0.014) (Table  5 ).

Through a multiple regression analysis of nutrient intake (independent variable) with BMI (dependent variables) it was found that a unit increase in energy, protein, and carbohydrate intake significantly increases BMI by 0.18 kg/m 2 , 0.73 kg/m 2 , and 0.72 kg/m 2 respectively (Table  6 ).

Adolescence is an essential developmental stage that establishes the foundation for future health. Hence, it is crucial to keep a close watch on their growth and development. Adolescents’ overall health and development are greatly influenced by several factors, including physical fitness, nutritional consumption, and physical activity.

Indian research on children and adolescents has revealed a link between dietary habits, degree of physical activity, and overweight/obesity [ 23 , 24 , 25 ]. However, little research has been done to understand the association between various aspects of physical fitness and nutritional status in the teenage Indian population. Therefore, the current study’s objective is to evaluate the physical fitness, nutritional condition, and physical activity of adolescents (12–15 years old) at North Delhi Schools.

Maintaining optimum nutrition status during this phase of life might be beneficial in adult life. Obesity during childhood can harm the body in a variety of ways, now and in the future. For children and adolescents, BMI screens for potential weight and health-related issues. Adolescents were classified according to WHO 2007 growth charts [ 26 ] and IAP 2015 revised growth charts [ 27 ]. A higher proportion of adolescents were found to be overweight and obese using IAP 2015 charts (as against the WHO 2007 references) and similar results were observed by Oza et al., (2021) [ 41 ]. Body fat % categorised a higher proportion of adolescents in the normal category and less proportion of adolescents fall under the overweight-obese category when compared with BMI (Table  3 ). Another previous study also depicted that the prevalence of overweight and obesity was found to be less with body fat % as compared with BMI [ 42 ].

Lifestyle factors like breakfast consumption, sleep hours, screen time, etc. are some of the factors that are very important for appropriate growth and development. Adolescents generally require about 9–9.30 h of sleep per night [ 43 ]. According to IAP (2021) guidelines adolescents’ screen time should be balanced with other activities that are required for overall development [ 39 ]. Excessive screen time among school-going children has been associated with physical inactivity and poor eating behaviour which could lead to an increased risk of being overweight and obese [ 44 ]. The present study showed appropriate screen time and sleep duration among the recruited adolescents (Table  2 ). Maintaining appropriate nutrition status requires adequate nutrient intake, adolescence is a challenging stage when it comes to food selection and opting for healthy food items. Previous studies have shown a lower intake of proteins, fruits, and vegetables and a higher intake of high-fat salt and sugar foods (HFSS) [ 45 , 46 ]. The present study also depicted a lower intake of milk and milk products and fruits and vegetables.

Physical fitness is an important component of health that can play a critical role in enhancing the overall health and fitness of an individual. It is even more important for adolescents, as this phase lays the foundation for adult life and health. Improved physical fitness has positive health benefits like improved bone health, mental health, and quality of life. It also aids in the prevention of obesity and cardiovascular disease later in life. In the present study, only a small percentage of the study sample showed a sufficient level of physical fitness. Similar results were observed in previous studies also [ 47 , 48 ].

Physical activity plays a crucial role in maintaining the overall health of adolescents. Regular physical activity can help children and adolescents improve cardiorespiratory fitness, build strong bones and muscles, control weight, reduce symptoms of anxiety and depression, and reduce the risk of developing health conditions [ 49 ]. Only 26% of adolescents were found to be sufficiently active while 74% were found to be inactive. WHO Physical Activity Profile 2022 [ 50 ] also showed similar trends and found that 72% of Indian adolescent boys and 76% of girls were physically inactive. The present study showed a significant negative association between physical activity and trunk lift scores which assesses trunk extensor strength and flexibility. That is, the higher the physical activity level, the lower the trunk lift scores. The data also revealed a negative association with most of the fitness tests except sit and reach though the results weren’t significant. Thus, it could be said that it is not necessary that with increased physical activity, fitness also increases. Similar results were given by Malina (2001) suggesting that a large part of the variability (80–90%) in health-related fitness is not accounted for by physical activity [ 51 ].

Most of the adolescents who performed well in all the tests had normal BMI & body fat % and our result is in line with the previous findings [ 37 , 51 ]. The present study also showed that grip strength performance which assesses muscular strength was better in adolescents with higher BMI and this result is consistent with a previously done study [ 52 ]. Push-ups (endurance) and standing broad jump (power) showed a negative significant correlation with body fat % indicating that performance in these tests decreases as the body fat % increases. It could be because with increased fat % body weight also increases and standing broad jump is affected by increased weight and our result was in line with the previous study depicting impact of body fat% on the physical fitness levels [ 53 ].

The assessment of diet in relation to physical fitness showed that Curl Up had a positive and significant relation with energy, iron, and PUFA indicating that an increase in their intake may increase abdominal strength and endurance. Also, tennis ball throw and PACER showed a significant and positive correlation with protein indicating that an increase in protein intake may increase upper body power and may improve cardiorespiratory fitness (Table  4 ).

Through a multiple regression analysis, it was significantly found that a unit increase in time spent playing outside increased the standing broad jump (power) scores by 38.6 cm. Similarly, a unit increase in time spent on taking tuition in a week reduces the standing broad jump by 28.76 cm (Table  5 ). Hence it is crucial to focus on the physical fitness of an adolescent along with academics. Analysing and interpreting physical fitness along with physical activity and nutrition status is imperative to assess the wholistic development of adolescent is the main inference of this study. Though, the study had few limitations such as the study was performed on a small sample size, hence, the results cannot be generalized for the population. Students especially girls were not willing to perform fitness tests; hence, gender differences could not be studied. PACER and Standing Broad Jump were time-consuming tests, due to time constraints they could not be performed on the entire sample size of 100 adolescents.

However, the study emphasizes that physical activity, physical fitness, dietary intake, and nutrition status are all interrelated with each other. Therefore, adolescents need to be enlightened about the importance of good dietary intake along with appropriate physical activity and fitness regimes so that they have improved overall health.

Adolescence being an important phase of life needs sufficient care, good nutrition, adequate physical fitness, and physical activity, these factors play a critical role in growth and development. The present study lays the foundation for future interventional research studying the impact of physical fitness and health-related aspects among adolescents. Therefore, adolescents need to be motivated to include good nutrition and appropriate physical fitness and physical activity regimes in their daily routine.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Shanza Ferozi, Anu Gupta Taneja & Neha Bakshi

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SF, NB and AGT conceived and wrote the protocol for this study. SF did data collection. NB and AGT supervised the study. SF did data analysis for this study and wrote the draft manuscript. SF and NB revised the manuscript for scientific input. All authors agreed to submit the current manuscript as the final version.

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Ferozi, S., Taneja, A.G. & Bakshi, N. Assessment of nutritional status, physical fitness and physical activity of school going adolescents (12–15 years) in Delhi. BMC Pediatr 24 , 331 (2024). https://doi.org/10.1186/s12887-024-04733-y

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SYSTEMATIC REVIEW article

Effect of functional training on physical fitness among athletes: a systematic review.

• 1 Department of Sports Studies, Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan, Malaysia
• 2 Department of Sports Studies, Faculty of Education Studies, Universiti Putra Malaysia, Seri Kembangan, Malaysia
• 3 Department of Science and Technical Education, Faculty of Educational Studies, Universiti Putra Malaysia, Seri Kembangan, Malaysia
• 4 Department of Sports Studies, Faculty of Education Studies, Hunan Normal University, Changsha, China
• 5 Faculty for Sport and Physical Education, University of Montenegro, Podgorica, Montenegro
• 6 Montenegrin Sports Academy (MSA), Podgorica, Montenegro
• 7 Montenegrosport, Podgorica, Montenegro

There is evidence that functional training is beneficial for the overall physical fitness of athletes. However, there is a lack of a systematic review focused on the effects of functional training on athletes' physical fitness. Thus, the aimed of the present review is to clarify the effects of functional training on physical fitness among athletes. In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) Statement guidelines, the systematic search of PubMed, SCOPUS, EBSCOhost (SPORTDiscus), and CINAHL Plus databases was undertaken on the 2nd November 2020 to identify the reported studies, using a combination of keywords related to functional training, physical fitness, and athletes. From the 145 studies , only nine articles met all eligibility criteria and were included in the systematic review. The assessment was performed on the Pedro scale, and the quality of the study included in the nine studies was fair (ranging from 3 to 4). The results showed that speed ( n = 6) was the aspect of physical fitness studied in functional training interventions, followed by muscular strength ( n = 5), power ( n = 4), balance ( n = 3), body composition ( n = 3), agility ( n = 3), flexibility (n = 1) and muscular endurance ( n = 1). Existing evidence concludes that functional training significantly impacts speed, muscular strength, power, balance, and agility. Furthermore, there are still limit numbers of evidence showing effect of functional training on flexibility and muscular endurance. In contrast, no significant improvement was found in body composition where functional training was conducted.

Systematic Review Registration: https://www.crd.york.ac.uk/prospero , identifier: CRD4202123092.

Introduction

Athletes' successful performance is usually attributed to the unique combination of talent and physical fitness, technical, tactical, and psychological qualities ( Smith, 2003 ). Among those criteria, physical fitness is considered the most critical quality to determine athletes' competitive ability ( Gabbett et al., 2007 ). Excellent physical fitness is essential for improving the athletes' technical and tactical level and performance and is the basic requirement for competing athletes under high-intensity training ( Chunlei, 2016 ). The loss of an athlete's physical fitness component can jeopardize this ability and lead to sports injuries ( Dengguang and Yang, 2007a ). For example, in tennis players, decreased muscle strength and postural control limits the ability to start quickly and change direction, which further hinders their ability to hit the ball effectively and maintain a stable body, and also increased the likelihood of sports injuries ( Kovacs, 2006 ).

A substantial number of publications proved a significant positive correlation between physical fitness components and exercise training intervention. The American College of Sports Medicine guidelines support the use of traditional resistance training, traditional resistance training enhances physical fitness performance by gradually increasing exercise load during the training process (Feito et al. , 2018 ). However, the training specificity literature has shown that the benefits of traditional resistance training for improving physical fitness is rarely transferred to sports performance (Li et al. , 2019 ; Li , 2021 ). Most of the traditional resistance training methods are not multi-articular and multiplanar; these aspects seem fundamental for eliciting greater sports performance (Fernandez-Fernandez et al. , 2016 ; Santos-rosa et al. , 2020 ). On the other hand , a new exercise training method that has recently received much attention to developing athletes' physical fitness is functional training ( Feito et al., 2018 ). Several studies have confirmed that functional training can enhance speed ( Tomljanović et al., 2011 ; Sander et al., 2013 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Baron et al., 2020 ; Keiner et al., 2020 ), muscular strength ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Elbadry, 2014 ; Cherepov and Shaikhetdinov, 2016 ; Keiner et al., 2020 ), power ( Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Keiner et al., 2020 ), balance ( Oliver and Brezzo, 2009 ; Elbadry, 2014 ; Yildiz et al., 2019 ), body composition ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ), agility ( Tomljanović et al., 2011 ; Cherepov and Shaikhetdinov, 2016 ; Yildiz et al., 2019 ), flexibility ( Yildiz et al., 2019 ) and muscular endurance ( Oliver and Brezzo, 2009 ). Additionally, other research has discovered positive effects of functional training on physical fitness in football players ( Oliver and Brezzo, 2009 ; Sander et al., 2013 ; Baron et al., 2020 ; Keiner et al., 2020 ), handball players ( Elbadry, 2014 ; Alonso-Fernández et al., 2017 ), martial artists ( Cherepov and Shaikhetdinov, 2016 ), tennis players ( Yildiz et al., 2019 ) and volleyball players ( Oliver and Brezzo, 2009 ). Despite the significance of functional training for improving the physical fitness components among athletes, there is no publication that summarized crucial information on the impacts of functional training protocols on physical fitness among athletes.

Conceptually, functional training refers to the training of partial chains and connections in the human motion chain that involves completing specific target actions, including multi-dimensional motion trajectory acceleration, deceleration, and stability training activities that meet the characteristics of particular target actions ( Cook, 2012 ). The action mode of functional training involves acceleration, deceleration and stability of multiple joints and planes. The action mode determines the broad participation and effective pertinence of functional training ( National Academy of Sports Medicine, 2001 ). Moreover, Boyle believes that the essence of functional training is purposeful training. It is a multi-plane exercise in stable control and weight-bearing. It is a series of exercises that involve balance and proprioception and are supported by body parts ( Boyle, 2016 ). Therefore, functional training differs from traditional resistance training; it can be any exercise performed to enhance a specific movement or activity ( Pacheco et al., 2013 ). With a definition this broad, the literature on functional training has incorporated various exercise programs with varying designs and focuses. The principle of functional training is the specificity of training, which means that training in a specific activity is the best way to maximize the performance in that particular area ( Hawley, 2008 ). In other words, the closer the training is to the desired outcome (i.e., a specific task or performance criterion), the better the result will be. For example, when the functional training program includes the element of strength training, the training improves the outcome of muscle strength ( Skelton et al., 1995 ; Alexander et al., 2001 ; Giné-Garriga et al., 2010 ). The results presented by the different studies on functional training effects on physical fitness components among athletes are encouraging, but limited scientific information is available to determine its possible benefits on the different physical fitness components of performance. Therefore, this systemtic review aimed of the present review is to clarify the effects of functional training on physical fitness among athletes.

Protocol and Registration

The data selection, collection and analysis of this review were performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines ( Moher et al., 2009 ) and were prospectively registered on the International Prospective Register of Systematic Reviews; https://www.crd.york.ac.uk/prospero , CRD4202123092.

Search Strategy

The literature search was undertaken in four international databases: the SCOUPS, PubMed, EBSCOhost (SPORTDiscus), and CINAHL Plus. The search was conducted on the 2th November , 2020. In each database, a search was conducted by title, taking a predefined combination of keywords: (“functional training” OR “functional exercise” OR “functional skill * ” OR “functional task training” OR “therapeutic exercise”) AND (“physical fitness” OR “physical endurance” OR “cardiorespiratory fitness” OR “physical conditioning” OR “skill-related fitness” OR “skill related fitness” OR “skill related physical” OR “skill-related physical” OR “skill related physical fitness” OR “skill-related physical fitness” OR “fitness, physical” OR “speed” OR “power” OR “reaction time” OR “agility” OR “balance” OR “coordination” OR “health related physical fitness” OR “health related physical” OR “health related fitness” OR “health-related physical” OR “health-related fitness” OR “health-related physical fitness” OR “aerobic endurance” OR “muscular strength” OR “muscular endurance” OR “body composition” OR “flexibility”) AND (“player * ” OR “athlete * ” OR “sportsman * ” OR “sportswoman * ” OR “sportsperson * ” OR “Jock * ”). We also explored other relevant articles in the reference lists of the studies included in the review and examined the reference lists of previous related reviews. All titles were manually searched for potential inclusion. Reference lists of retrieved papers, authors' names, and review articles were retrieved manually for additional relevant citations.

Eligibility Criteria

We used the PICOS (population, intervention, comparison, outcome, study designs) criteria as the inclusion criteria, is presented in Table 1 . Only records presenting functional training on aspect of physical fitness of athletes were included. Thus, studies were included if they met the following criteria: (1) A full text, peer-reviewed study published in English, describing the use of athletes (male and female) to explore the effects of functional training interventions on physical fitness, randomized controlled trial (RCT), non-randomized controlled trial (Non-RCT) with two or more groups, and single-group trials with pretest and post-test design; (2) In this study, only included studies on planned and organized functional training intervention to improve or maintain physical fitness. Notably, studies using functional training or combinations of functional training and other exercise training interventions (e.g., resistance training) were also included from this review; (3) Investigate the effects of functional training on physical fitness among athletes and assess at least one physical fitness component outcome; (4) There were no restrictions on the sample size, study location, and intervention time for the included studies.

Table 1 . Inclusion criteria according to the PICOS conditions.

Studies were excluded if they met several exclusion criteria: (1) Studies that combined functional training interventions with additional non-exercise training (e.g., psychological interventions) and interventions including unsupervised training courses were not included in the study; (2) Studies published articles, meeting abstracts, case reports, and short communications in languages other than English were excluded; and (3) Observational studies and interventions focusing solely on counseling for functional training implementation were excluded.

Study Selection

The retrieved studies were imported into Mendeley reference management software to remove any duplicates. Firstly, the search strategies were assisted by an experience librarian. Secondly, two independent reviewers (Xiao, Bai) screened the titles and abstracts of all the identified articles in the initial screening phase to identify relevant studies. Irrelevant materials were removed from the database before assessing all other titles and abstracts using our predetermined inclusion and exclusion criteria. Articles that remain at the end and enter a qualitative synthesis must have the whole text, and the whole text must be read. Items for which the full text is not available are dropped. If there were any disagreements, a third reviewer (Soh) was consulted until a consensus was achieved.

Data Extraction and Quality Assessment

After the data search was complete, data were obtained from eligible studies in a predetermined extraction form [Including, (1) Author, title, publication year; (2) Research design; (3) Sample size, control group; (4) Participant characteristics (age, gender, etc.); (5) Intervention features (type, length, and frequency); (6) Measures index, and (7) Research outcomes]. One author abstracted information into the standard form and the other author checked it.

The PEDro scale ( www.pedro.org.au ) has been proven to be a useful measure of the quality of experimental methodology in developing a systematic review, and has good validity and reliability ( Lima et al., 2013 ). The PEDro scale is designed to evaluate the four fundamental methodological aspects of a study, such as random process, blind technique, group comparison and data analysis. The assessment of the 11 items in the PEDro scale was performed by two well-trained, independent raters using a yes (1 point) or No (0 points) response rating scale, and disagreements were resolved by a third rater. However, the eligibility criteria were not considered in the total score since this was related to external validity. The total PEDro score ranges from 0 to 10 points, and higher scores reflect a better methodological quality. The higher the PEDro score, the higher the quality of the corresponding method. Studies scoring 8 to 10 were considered to be methodologically excellent in quality, those ranging from 5 to 7 to be good in quality, while a score between 3 and 4 is fair in quality, and those scoring below 3 to be poor in quality ( Foley et al., 2003 ). The judgment of overall scientifific evidence was based on number, methodological quality and consistency of outcomes of the studies in three levels of evidence: (1) strong evidence, provided by generally consistent findings in multiple (≥2) number and results studies, (2) moderate evidence, when only one study is available or findings are inconsistent in multiple (≥2) studies, (5) no evidence, when no case-control studies are found.

The search results were screened and read by formulating literature inclusion and exclusion criteria. This systematic review contains nine articles involving RCT and Non-RCT on the effects of functional training on physical fitness among athletes. They were published between the years of 2009–2020. In Table 2 , the studies' characteristics are presented.

Table 2 . Characteristics of the studies examined in the present review.

Figure 1 shows the flow chart of records selection. A total of 143 potential articles were identified through the electronic database search (36 from PubMed; 107 from SCOPUS; 0 from EBSCOhost (SPORTDiscus); 0 from CINAHL Plus), and additional relevant articles in screening the reference lists of studies that were included in the review and reference lists of previous related reviews ( n = 1), and Google Scholar ( n = 1). After exclusion of the duplicates (15), the title and abstract of 130 were assessed for eligibility. After elimination at the title and abstract level 48 articles, the remaining 82 articles were subsequently read. After reading, another 73 articles were eliminated, leaving nine relevant articles that satisfied the inclusion criteria and were included in the qualitative synthesis.

Figure 1 . PRISMA flow chart of the study selection process.

Study Quality Assessment

An assessment of the study quality, according to the PEDro list, is presented in Table 3 . The mean PEDro score of the included studies was 3.44 (range 3–4), which indicates that the included studies were of fair quality, and none of the studies met all the PEDro list quality criteria. All studies specified their eligibility criteria, similar baseline group, between-group comparisons, point measure and variability. None of the studies reported on allocation concealment, blind subject, blind therapist, blind assessor, or intention to treat analysis, except for three studies which described random allocation ( Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ), and only one study reported follow-ups ( Oliver and Brezzo, 2009 ). Nevertheless, it is challenging to include blind subjects, blind therapists, and blind assessors as participants and assessors, since the included studies were exercise training interventions. This situation calls for higher quality and better evidence level studies to be conducted in the future.

Table 3 . Summary of methodological quality assessment scores.

Population Characteristics

The nine included studies' population characteristics were reported based on the following aspects: (1) Athlete classification. In the included literature, only one article did not report the athlete classification ( Tomljanović et al., 2011 ) but only reported moderately trained athletes, and eight articles reported the type of athlete, including football players ( Oliver and Brezzo, 2009 ; Sander et al., 2013 ; Baron et al., 2020 ; Keiner et al., 2020 ), martial artists ( Cherepov and Shaikhetdinov, 2016 ), handball players ( Elbadry, 2014 ; Alonso-Fernández et al., 2017 ), tennis players ( Yildiz et al., 2019 ) and volleyball players ( Oliver and Brezzo, 2009 ); (2) Sample size. In total, the nine studies consisted of 330 subjects, ranging from 14 ( Alonso-Fernández et al., 2017 ) to 121 ( Sander et al., 2013 ) participants, with a median of 26 ( Oliver and Brezzo, 2009 ) and mean of 36.7; (3) Gender. All nine studies focused on athletes, three studies focused on females ( Oliver and Brezzo, 2009 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ), one study focused on male ( Tomljanović et al., 2011 ), and the remaining five studies did not report gender ( Sander et al., 2013 ; Cherepov and Shaikhetdinov, 2016 ; Yildiz et al., 2019 ; Baron et al., 2020 ; Keiner et al., 2020 ); (4) Age. Most studies report the subjects' age, except for one ( Cherepov and Shaikhetdinov, 2016 ), and only one study reported the age range of the subjects ( Tomljanović et al., 2011 ). An analysis of age reports in seven studies found that the age range of the subjects ranged from 9.6 years to 25 years ( Oliver and Brezzo, 2009 ; Sander et al., 2013 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Baron et al., 2020 ; Keiner et al., 2020 ); (5) Body Mass Index. Most studies reported the height and weight of the subjects ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Sander et al., 2013 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Keiner et al., 2020 ), only two studies reported the BMI of the subjects ( Alonso-Fernández et al., 2017 ; Baron et al., 2020 ), and only one study did not state the weight, height, BMI of the subjects ( Cherepov and Shaikhetdinov, 2016 ). For the consistency of literature analysis, the following formula was used to calculate the BMI of the subjects in the relevant studies: BMI = weight (kg)/height 2 (m). The BMI of the participants in the study ranged from 17.26 to 24.4 kg/m 2 ; (6) Training background. Among the nine studies, five studies reported the training background of athletes ( Tomljanović et al., 2011 ; Sander et al., 2013 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ) while the other four studies did not describe the training background ( Oliver and Brezzo, 2009 ; Cherepov and Shaikhetdinov, 2016 ; Baron et al., 2020 ; Keiner et al., 2020 ). For the consistency of literature analysis, the training background of the athletes was recorded in months. The training background of the subjects ranged from 36 months to 146 months.

Interventions Characteristics

The nine included studies' intervention characteristics were reported based on the following aspects: (1) Training length. The shortest intervention length is 8 days ( Sander et al., 2013 ) and the longest being 10 months ( Keiner et al., 2020 ); (2) Duration of each training session. Most studies reported the duration of each training session ( Oliver and Brezzo, 2009 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Baron et al., 2020 ; Keiner et al., 2020 ), only three studies did not state the duration ( Tomljanović et al., 2011 ; Sander et al., 2013 ; Cherepov and Shaikhetdinov, 2016 ). The duration of each training session analysis of 6 research reports found that they ranged from 10 min ( Oliver and Brezzo, 2009 ; Alonso-Fernández et al., 2017 ) to 90 min ( Baron et al., 2020 ); (3) Training frequency. Among the nine studies included, six studies reported frequency of training ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Keiner et al., 2020 ) while the other three studies did not ( Sander et al., 2013 ; Cherepov and Shaikhetdinov, 2016 ; Baron et al., 2020 ). The frequency analysis of 6 research reports found that the frequency ranged from 2 times/week to 4 times/week ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Keiner et al., 2020 ).

Outcome and Measures

The outcomes for the present study were grouped according to the effects of functional training on different physical fitness components among athletes. All authors of this study independently classified the papers according to other research topics (components). Disagreements were resolved through discussion among all authors until a consensus was reached.

Effect of Functional Training on Speed

Six of the nine studies included in this systematic review presented inferences about the effect of functional training on speed performance ( Tomljanović et al., 2011 ; Sander et al., 2013 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Baron et al., 2020 ; Keiner et al., 2020 ). The speed tests used in these studies included linear sprint test of 5 m ( Sander et al., 2013 ), 10 m ( Tomljanović et al., 2011 ; Sander et al., 2013 ; Yildiz et al., 2019 ; Baron et al., 2020 ), 15 m ( Sander et al., 2013 ), 20 m ( Tomljanović et al., 2011 ; Sander et al., 2013 ; Keiner et al., 2020 ), 25 m ( Sander et al., 2013 ) and 30 m ( Sander et al., 2013 ). They also included change of direction sprint test (5 m left and right test, 10 m left and right test) ( Sander et al., 2013 ; Keiner et al., 2020 ), repeated sprint ability test ( Alonso-Fernández et al., 2017 ), and acceleration test (0–5 m, 5–10 m, 10–20 m, 10–30 m, 0–30 m) ( Tomljanović et al., 2011 ; Baron et al., 2020 ). The subjects included young football players ( Sander et al., 2013 ; Baron et al., 2020 ; Keiner et al., 2020 ), moderately trained athletes ( Tomljanović et al., 2011 ), handball female players ( Alonso-Fernández et al., 2017 ) and prepubertal tennis players ( Yildiz et al., 2019 ). Four studies showed improvement in linear sprint test ( Sander et al., 2013 ; Yildiz et al., 2019 ; Keiner et al., 2020 ), change of direction sprint test ( Sander et al., 2013 ; Keiner et al., 2020 ) and repeated sprint ability test ( Alonso-Fernández et al., 2017 ). Additionally, Baron et al. (2020) observed a significant improvement for 5–10 m test and 10–30 m test, contrary to the 0–5 m test and 0–30 m test. However, only one study did not observe any significant change in 10, 20, and 10–20 m tests ( Tomljanović et al., 2011 ).

Effect of Functional Training on Muscular Strength

Muscular strength was only evaluated in five of the studies that were included in this review ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Elbadry, 2014 ; Cherepov and Shaikhetdinov, 2016 ; Keiner et al., 2020 ). The aspects valued and assessment tools used were pull up ( Cherepov and Shaikhetdinov, 2016 ), medicine ball throwing ( Tomljanović et al., 2011 ; Cherepov and Shaikhetdinov, 2016 ), pronequadra-ped core test, single-leg squat test ( Oliver and Brezzo, 2009 ), 1 repetition maximum ( Keiner et al., 2020 ), handgrip strength test and static strength test ( Elbadry, 2014 ). The subjects include martial artists ( Cherepov and Shaikhetdinov, 2016 ), moderately trained athletes ( Tomljanović et al., 2011 ), collegiate women athletes (volleyball and soccer players) ( Oliver and Brezzo, 2009 ), elite adolescent soccer players ( Keiner et al., 2020 ) and young handball players ( Elbadry, 2014 ). Studies conducted by Cherepov and Shaikhetdinov (2016) and Keiner et al. (2020) revealed a significant increase in muscular strength after the functional training intervention. Nonetheless, three studies observed a significant improvement on standing overarm medicine ball throw ( Tomljanović et al., 2011 ), leg squat (right and left) ( Oliver and Brezzo, 2009 ), back strength test ( Elbadry, 2014 ), but no significant change on lying medicine ball throw ( Tomljanović et al., 2011 ), quadra-ped left and right test ( Oliver and Brezzo, 2009 ), leg strength test and handgrip strength test ( Elbadry, 2014 ).

Effect of Functional Training on Power

Among the nine studies included in this review, four studies reported on power ( Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Keiner et al., 2020 ), and five studies did not report on it ( Oliver and Brezzo, 2009 ; Sander et al., 2013 ; Elbadry, 2014 ; Cherepov and Shaikhetdinov, 2016 ; Baron et al., 2020 ). The aspects valued and assessment tools used involved vertical countermovement jump test (jump height, air time, ground contact time, power peak) ( Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ; Yildiz et al., 2019 ; Keiner et al., 2020 ). The subjects include moderately trained athletes ( Tomljanović et al., 2011 ), female handball players ( Alonso-Fernández et al., 2017 ), prepubertal tennis players ( Yildiz et al., 2019 ) and elite adolescent soccer players ( Keiner et al., 2020 ). One study had an 8-week intervention period ( Yildiz et al., 2019 ), while the other study had an intervention period of 10 months ( Keiner et al., 2020 ). The results of these studies reveal that functional training can improve power ( Yildiz et al., 2019 ; Keiner et al., 2020 ). On the other hand, Tomljanović et al. (2011) observed a significant improvement in the countermovement jump test (jump height, ground contact time, power peak) but not for the vertical counter movement jump test (air time) ( Tomljanović et al., 2011 ). However, only one study reported that the 8-week functional training did not yield significant results in the vertical counter movement jump test (flight time, jump height, ground contact time, and power peak) ( Alonso-Fernández et al., 2017 ).

Effect of Functional Training on Balance

Balance (static and dynamic balance) was valued only in three of the nine studies included in this review. The measurement tools were the dynamic balance (right and left), static balance ( Yildiz et al., 2019 ), biodex balance test (left and right) ( Oliver and Brezzo, 2009 ) and standing stork test ( Elbadry, 2014 ). The subjects include prepubertal tennis players ( Yildiz et al., 2019 ), collegiate women athletes (volleyball and soccer players) ( Oliver and Brezzo, 2009 ), and young handball players ( Elbadry, 2014 ). One study reported an 8-week intervention period ( Yildiz et al., 2019 ), while the other studies had an intervention period of 10 weeks ( Elbadry, 2014 ). The results of these studies reveal that functional training can improve balance ( Elbadry, 2014 ; Yildiz et al., 2019 ). Nevertheless, one study reported that 13 weeks of functional training did not significantly improve balance ( Oliver and Brezzo, 2009 ).

Effect of Functional Training on Body Composition

The body composition aspect appeared to be undervalued by the studies included in this review. Only three of the studies assessed this aspect by using different measurement tools like body weight ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ), body height ( Tomljanović et al., 2011 ), body fat mass percentage ( Tomljanović et al., 2011 ), lean body mass ( Tomljanović et al., 2011 ), total body water ( Tomljanović et al., 2011 ), body mass index ( Oliver and Brezzo, 2009 ; Alonso-Fernández et al., 2017 ) and body fat ( Oliver and Brezzo, 2009 ; Alonso-Fernández et al., 2017 ). The subjects include moderately trained athletes ( Tomljanović et al., 2011 ), handball players ( Alonso-Fernández et al., 2017 ) and collegiate women athletes (volleyball and soccer players) ( Oliver and Brezzo, 2009 ). Two studies observed no significant effect of functional training on body composition ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ). However, Alonso-Fernández et al. (2017) observed a significant improvement in body fat, but not for body weight and body mass index ( Alonso-Fernández et al., 2017 ).

Effect of Functional on Agility

Agility was not the main aspect in many of the studies included in this review. Only three studies valued this criterion through four exercises: shuttle run 3 × 10 m ( Cherepov and Shaikhetdinov, 2016 ), agility 5–10–5 test, hexagon test ( Tomljanović et al., 2011 ) and T -test ( Yildiz et al., 2019 ). The subjects include martial artists ( Cherepov and Shaikhetdinov, 2016 ), moderately trained athletes ( Tomljanović et al., 2011 ) and prepubertal tennis players ( Yildiz et al., 2019 ). Studies conducted by Cherepov and Shaikhetdinov (2016) and Yildiz et al. (2019) revealed significant increases in agility after the functional training intervention. In contrast, Tomljanović et al. (2011) observed a significant improvement in the hexagon test but no significant change on the agility 5–10–5 test.

Effect of Functional Training on Flexibility

Only one study included in this systematic review presented inferences about the effect of functional training on flexibility. The flexibility was measured based on the sit and reach test, commonly used in health-related and physical fitness test batteries to evaluate the hamstring and lower back flexibility ( Hui and Yuen, 2000 ). The participants of this study were pre-pubertal tennis players. This study revealed a highly significant improvement in flexibility after 8 weeks of functional training ( Yildiz et al., 2019 ).

Effect of Functional Training on Muscular Endurance

Muscular endurance was assessed in one out of the nine studies included in this review ( Oliver and Brezzo, 2009 ). This study uses the one-minute sit up test to evaluate muscular endurance ( Oliver and Brezzo, 2009 ). The study subjects included female collegiate volleyball and soccer players. Oliver and Brezzo (2009) reported positive results in this aspect after the intervention.

This systematic review provides a comprehensive overview of the impact of functional training physical fitness among athletes and their bey relevant knowledge for athletes to improve their physical fitness. This revision is intended to be different from other published studies on using the functional training intervention among athletes. The main findings indicated that functional training could increase physical fitness (speed, strength, power, flexibility, agility, balance, aerobic, and muscular endurance) among athletes. However, no data was found in reaction time and coordination reporting. The reviewed papers varied significantly regarding the participants (type of athletes, age, and gender) and the physical fitness components studied. Nonetheless, functional training may be an effective physical fitness intervention among athletes based on positive findings in these studies. Following the framework in the “Results” section, the physical fitness components of the studies were analyzed in detail.

Most sports experts agree that speed, an elementary motor skill, is vital to succeeding in many sports disciplines ( Karalejić et al., 2014 ). Six studies evaluated this component in their research by using the linear sprint test ( Sander et al., 2013 ; Yildiz et al., 2019 ; Keiner et al., 2020 ), change of direction sprint test ( Sander et al., 2013 ; Keiner et al., 2020 ), and repeated sprint ability test ( Alonso-Fernández et al., 2017 ), which yielded significantly positive results. However, one study reported that some of the measures of repeated sprint ability test (5–10 m, 10–30 m) exhibited a significant increase, but there was no significant change on repeated sprint ability test (0–5 m, 0–30 m) after functional training ( Baron et al., 2020 ). Furthermore, only one study did not observe a significant effect of functional training on linear sprint test (10, 20, 10–20 m test) ( Tomljanović et al., 2011 ). This finding may be a result of improvements in functional status and increased speed. Campa et al. (2019) also demonstrated that better movement patterns might improve speed performance. However, wrong movement patterns will negatively impact the ability to perform fundamental movement patterns with precision and appropriate efficiency, besides increasing the risk of athletic injuries ( Kollock et al., 2019 ).

Strength has a great influence on physical fitness components. Athletes must intensify strength training to improve their skills and maintain a good competitive state in their respective fields ( Dengguang and Yang, 2007b ). Meanwhile, muscle strength can be divided into upper limb muscle strength study ( Tomljanović et al., 2011 ; Elbadry, 2014 ; Cherepov and Shaikhetdinov, 2016 ) and lower limb muscle strength study ( Oliver and Brezzo, 2009 ; Elbadry, 2014 ; Keiner et al., 2020 ). Three studies reported on upper limb strength; one study reported significant improvement in muscular strength (pull up, 3 kg stuffed ball overhead throwing) ( Cherepov and Shaikhetdinov, 2016 ) while the other study reported no significant increases in static strength test (handgrip strength and back strength test) ( Elbadry, 2014 ). However, only one study reported that some of the measures of upper limb muscular strength have significant increases (standing overarm medicine ball throw), but there was no significant improvement in the muscular strength test (lying medicine ball throw) ( Tomljanović et al., 2011 ).

Regarding those studies that assessed muscle strength of lower limbs, one study reported significant improvement in muscular strength (1 RM) ( Keiner et al., 2020 ). On the contrary, no significant improvement in muscular strength (static strength test: leg strength test) was found in another study ( Elbadry, 2014 ). Furthermore, one study reported that some of the measures of lower limb muscular strength (left leg squat and right leg squat) showed significant improvements, but the findings were not statistically significant (quadra-ped left and right test) ( Oliver and Brezzo, 2009 ). In terms of upper limb muscular strength, better results are observed in studies with long-term interventions ( Cherepov and Shaikhetdinov, 2016 ; Keiner et al., 2020 ). In addition, only one of the five included studies on muscle strength reported on upper limb muscular strength and lower limb muscular strength ( Elbadry, 2014 ). Therefore, more research should analyze strength.

Based on the findings of four studies that analyze the benefits of functional training on power, it was not possible to draw a definite conclusion on this aspect. Two studies confirmed that functional training programs increase athletes' performances in the vertical countermovement jump test ( Yildiz et al., 2019 ; Keiner et al., 2020 ). However, these two studies alone are not enough to support the idea that functional training is beneficial in improving athletes' power, and it is worth noting that this hypothesis has been supported by literature from non-athlete participants ( Miszko et al., 2003 ; Liu et al., 2014 ).

In addition, Tomljanović et al. (2011) reported that some of the measures of vertical countermovement jump test (jump height, ground contact time, power peak) were statistically significant, but there was no significant increase in the vertical countermovement jump test (air time) ( Tomljanović et al., 2011 ). The functional training program consisted of mostly upper body exercises, which did not test the performance of the lower body. This improvement may be mainly related to neural coordination of movement ( Tomljanović et al., 2011 ). However, another study reported that high-intensity interval training protocols based on functional exercises program had no significant increases in power (flight time, jump height, jump speed) ( Alonso-Fernández et al., 2017 ). This finding is consistent with the studies by Buchheit et al. (2009) and Rey et al. ( Viaño-Santasmarinas et al., 2018 ). However, these data opposed the reported results by Dello Iacono et al. (2016) involving handball players because the functional training program might not be enough to stimulate the neuromuscular system related to power ( Luo et al., 2005 ). Power in the upper/lower extremities is necessary to produce explosive actions among athletes ( Girard and Millet, 2009 ; Chelly et al., 2010 ). However, the included study only reported the effect of functional training on lower body power but ignored the effect of functional training on upper body power, which was an important gap in the existing literature. Therefore, it is necessary to consider and correct the research on power in functional training.

The static and dynamic balance were tested in three of the nine studies included in this review. Two studies confirmed that a functional training program increases static balance and dynamic balance ( Elbadry, 2014 ; Yildiz et al., 2019 ). This finding may be explained by the adaptations that occurred in all the sensory systems assisting postural control, such as the vestibular, visual, and the somatosensory and motor systems controlling muscular output ( Taube et al., 2008 ; Latorre Román et al., 2015 ).

However, only one study conducted was observed that performance in the biodex balance test (left, right) was not significantly improved ( Oliver and Brezzo, 2009 ). This observation may result from all the subjects being in-season, not only in practice and competition, but also in a regimen strength and conditioning program ( Oliver and Brezzo, 2009 ). Therefore, the interference of other factors (e.g., exercise training factors) should be avoided in future research.

Three studies showed no significant effect of functional training on body weight ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ), body height ( Tomljanović et al., 2011 ), body mass index ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Alonso-Fernández et al., 2017 ), lean body mass ( Tomljanović et al., 2011 ), and total body water ( Tomljanović et al., 2011 ). The study by Alonso-Fernández et al. (2017) reported statistically significant improvement in body fat, whereas two other studies showed no statistical significance in terms of body composition ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ). These results are in line with those obtained by Camacho-cardenosa et al. (2016) who found no statistically significant reduction in body fat. Considering that calorie intake and food monitoring have a statistically significant impact on this variable, it is safe to assume that athletes with more regular and stable eating habits may enhance body composition quality ( Mettler et al., 2010 ).

Effect of Functional Training on Agility

Agility is an essential component in most field and team sports. Traditional definitions of agility have simply identified speed in directional changes as the defining component ( Draper and Lancaster, 1985 ). Out of the three studies that investigated agility ( Tomljanović et al., 2011 ; Cherepov and Shaikhetdinov, 2016 ; Yildiz et al., 2019 ), only one of them reported that the agility 5-10-5 test did not demonstrate significant improvement ( Tomljanović et al., 2011 ). The explanation of these results may be the improved power qualities and enhanced postural control of the subjects ( Marković et al., 2007 ). In contrast, the impact of power and explosive strength is lower in speed-led agility tests (e.g., agility 5–10–5) ( Tomljanović et al., 2011 ).

Furthermore, in a study that compared the functional movement screen training and traditional training on agility in 62 elite male high school baseball players, the training program included static stretching, and it was showed that functional movement screen program improves flexibility (trunk flexion forward, trunk extension backward, the splits) ( Song et al., 2014 ). Despite the findings reported in previous studies, functional training may be an effective way to increase agility. Future research should consider exercise items in functional training and only determine the effect of functional training on speed-led agility tests.

The sit and reach is a field test used to assess hamstring and lower back flexibility ( Baltaci et al., 2003 ). This study found that the functional training group showed significant improvement at sit and reach, whereas no significant improvements were observed in the traditional training and control groups ( Yildiz et al., 2019 ). Similarly, Weiss et al. reported that the 7 weeks functional training program intervention resulted in significant improvements in the flexibility of college students (non-athletes) ( Weiss et al., 2010 ), which is different from the participants included in the study. However, the functional training intervention can significantly improve the flexibility of the participants. Therefore, these results can only be regarded as weak evidence at present, and they need to be compared to more exercise training interventions.

Muscular endurance was measured with a one-minute sit up test ( Pritchard et al., 2001 ). In the studies, the no intervention group also showed significant improvement on the one-minute sit up test. The intervention group's significant improvement and the non-intervention group may be due to the routine training program during the season ( Oliver and Brezzo, 2009 ). However, the sit up test measures rectus abdominal endurance and not deep core musculature ( Diener, 1995 ), which may be why the intervention group and non-intervention group did not show significant improvement.

Limitations

Overall, this review provides substantial evidence of fair quality and the beneficial effects of different functional training programs on physical fitness among athletes. However, there are several limitations to this review. Firstly, only four studies reported the gender of athletes ( Oliver and Brezzo, 2009 ; Tomljanović et al., 2011 ; Elbadry, 2014 ; Alonso-Fernández et al., 2017 ). If present, it could be important, as there are differences in assessing physical fitness components based on gender. This may impact the final research results. Secondly, none of the studies included in this review stated the sample size calculation method. Determining the sample size is influenced by several factors, including the purpose of the study, population size, the risk of selecting a “bad” sample, and the allowable sampling error ( MacCallum et al., 1999 ). Thus, inappropriate, inadequate, or excessive sample sizes can influence quality and accuracy ( Rodríguez del Águila and González-Ramírez, 2014 ). If the sample size calculation method in the included research is wrong, it may influence the outcome of the study. Thirdly, most studies did not document or control exercises that were performed by participants outside of the study setting. Additionally, most studies did not consider the influence of temperature, time, and other factors on physical fitness among athletes. Finally, the studies did not have any short-term or long-term follow-up, making it difficult to predict the long-term impact of functional training on physical fitness among athletes.

The present analysis of this systematic review provides strong evidence that functional training improved physical fitness in terms of speed, muscular strength, power, balance, and agility, while there is moderate evidence of the effect on flexibility and muscular endurance. No significant improvement was found in body composition. The results support the principle of specificity in training, where the best gains in performance are achieved when the training closely mimics the performance ( Hawley, 2008 ; Reilly et al., 2009 ). Furthermore, functional training is a relatively new training modality, but it recently has gained momentum among physical fitness training and has been identified as a “Top 10 Fitness Trend” in 2018 ( Thompson, 2017 ), with four of the nine studies being published in the past 3 years. Moreover, review trials show that functional training was most common in resistance and strength training. Nevertheless, it is necessary to be cautious about the results in view of the limitations outlined in the present study. To better understand the effectiveness of functional training in improving athletes' physical fitness, additional research to examine the effect of functional training on physical fitness components according to the difference in the type of athletes is encouraged. It will help verify the effectiveness of functional training to improve the physical fitness components among different types of athletes and promote functional training in the field of modern sports science ( Osipov et al., 2017 ).

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material„ further inquiries can be directed to the corresponding author.

Author Contributions

The literature search and selection of studies was performed by authors WX and KS. Following an initial screen of titles and abstracts WX, full scrutiny of potentially eligible studies was independently screened by WX and KS using the specific inclusion criteria. OT arbitrated any disagreements in study inclusion. Study quality assessment was performed by WX. All authors contributed to manuscript revision, read, and approved the submitted version.

The study was supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Science, grant numbers: 2020-JKCS-022; and scientific research fund project of Tianjin Education Commission of China phased research results of the research project on the current situation and mode of integration of Tianjin sports and medicine, grant numbers: 2018SK145.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: flexibility, muscular endurance, body composition, balance, speed

Citation: Xiao W, Soh KG, Wazir MRWN, Talib O, Bai X, Bu T, Sun H, Popovic S, Masanovic B and Gardasevic J (2021) Effect of Functional Training on Physical Fitness Among Athletes: A Systematic Review. Front. Physiol. 12:738878. doi: 10.3389/fphys.2021.738878

Received: 09 July 2021; Accepted: 13 August 2021; Published: 06 September 2021.

Reviewed by:

Copyright © 2021 Xiao, Soh, Wazir, Talib, Bai, Bu, Sun, Popovic, Masanovic and Gardasevic. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kim Geok Soh, kims@upm.edu.my

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Article Contents

Introduction, acknowledgments, supplementary material, conflict of interest statement.

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Effect of Exercise Training Programs on Physical Fitness Domains in Military Personnel: A Systematic Review and Meta-Analysis

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Chelsea Smith, Kenji Doma, Brian Heilbronn, Anthony Leicht, Effect of Exercise Training Programs on Physical Fitness Domains in Military Personnel: A Systematic Review and Meta-Analysis, Military Medicine , Volume 187, Issue 9-10, September-October 2022, Pages 1065–1073, https://doi.org/10.1093/milmed/usac040

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Physical training is important to prepare soldiers for the intense occupational demands in the military. However, current physical training may not address all fitness domains crucial for optimizing physical readiness and reducing musculoskeletal injury. The effects of nontraditional military physical training on fitness domains have been inconsistently reported, which limits the design of the ideal training program for performance optimization and injury prevention in the military. The aim of this systematic review was to identify the effects of exercise training on various fitness domains (i.e., aerobic fitness, flexibility, muscular endurance, muscular power, muscular strength, and occupationally specific physical performance) that contribute to occupational performance and musculoskeletal injury risk in military personnel.

An extensive literature search was conducted in January 2021 and was subsequently updated in July 2021 and December 2021. Included studies consisted of comparative groups of healthy military personnel performing traditional and nontraditional military physical training with at least one assessment representative of a fitness domain. Study appraisal was conducted using the PEDro scale. Meta-analysis was conducted via forest plots, standard mean difference (SMD, effect size), and intertrial heterogeneity ( I 2 ).

From a total of 7,350 records, 15 studies were identified as eligible for inclusion in this review, with a total of 1,613 participants. The average study quality via the PEDro score was good (5.3/10; range 4/10 to 6/10). Nontraditional military physical training resulted in greater posttraining values for muscular endurance (SMD = 0.46; P  = .004; I 2  = 68%), power (SMD = 1.57; P  < .0001; I 2  = 90%), strength via repetition maximum testing (SMD = 1.95; P  < .00001; I 2  = 91%), and occupationally specific physical performance (SMD = 0.54; P  = .007; I 2  = 66%) compared to the traditional group. There was no significant difference for aerobic fitness (SMD = −0.31; P  = .23; I 2  = 86%), flexibility (SMD = 0.58; P  = .16; I 2  = 76%), and muscular strength via maximal voluntary contraction (SMD = 0.18; P  = .28; I 2  = 66%) between training groups.

The current systematic review identified that nontraditional military physical training had a greater posttraining effect on muscular endurance, power, strength measured via repetition maximum, and occupationally specific physical performance compared to traditional military physical training. Overall, these findings suggest that nontraditional military physical training may be beneficial in optimizing occupational performance while potentially reducing musculoskeletal injury risk.

The arduous nature and intense occupational demands of the military are well known. 1 , 2 Soldiers must undertake many physically demanding tasks, from carrying heavy loads over long distances and uneven terrain to sprinting across the battlefield to seek cover and negotiate obstacles. 1 , 2 During deployment, these physical activities can be conducted over many hours or days, placing great physical strain on soldiers. 3 Soldiers may also face additional stressors, including sleep deprivation, calorie restriction, and environmental extremes during training and on deployment. 1 These occupational demands therefore require unique training with high levels of strength, endurance, speed, power, and agility crucial for soldiers to be physically and mentally prepared to effectively operate. 2 , 3

Currently, the primary goal of military physical training is to improve or maintain the physical performance of soldiers in preparation for the battlefield. 2 , 4 , 5 Historically, this focus has led to a traditional physical training program heavily geared toward the development of aerobic fitness and muscular endurance. 1–4 , 6 While little evidence exists on the benefits of traditional training on combat readiness, 7 improved performance during annual physical fitness tests, which may or may not reflect real-world mission requirements, results in the continuation of this training regimen. 2 , 4 However, traditional military physical training may not address other fitness domains, such as flexibility, muscular power, and muscular strength, aspects crucial for optimizing physical readiness and reducing musculoskeletal injury (MSKI) risk. 2 , 3 , 8 Preventable MSKI accounts for nearly 60% of soldiers’ “limited duty days in the U.S.” Army 9 and 40% of clinical presentations in the Australian Defence Force, 10 costing billions of dollars and compromising the readiness and occupational performance of military personnel. 11 Therefore, military physical training that targets a range of fitness domains may be beneficial for both optimal performance and reduced MSKI risk. 1 , 12–14

As indicated above, traditional military physical training typically consists of exercises to improve aerobic fitness and muscular endurance that are adapted to a specific military group and have been used for a substantial amount of time over the last 20-30 years. 2 , 4 In contrast, nontraditional military physical training involves nontraditional activities or practices (e.g., advanced or structured resistance training programs) that some military groups have chosen to adopt in recent years. 1 , 6 , 15 Previous studies have examined the effects of traditional and nontraditional military physical training on various fitness domains in an effort to determine the optimal training regime for the military. 1 , 12 , 13 , 16 However, the effects of these training programs on fitness domains in military personnel have been inconsistent. For example, some studies have reported greater improvements in muscular strength with resistance training compared to traditional military physical training, 17–19 while other studies have found poorer muscular strength. 20 Similar inconsistencies have also been observed with the addition of power exercises to military physical training with greater 12 , 13 and poorer 3 muscular power reported following this training regime compared to traditional physical training.

In an attempt to synthesize the research, several narrative reviews have examined the physical performance implications of specific, nontraditional military physical training (e.g., combined high-intensity muscular strength and aerobic endurance training or high-intensity fitness training). 5 , 21 These reviews concluded that combining high-intensity strength and endurance training, 5 as well as high-intensity fitness training, 21 induced superior improvements in muscular strength and aerobic fitness than traditional training. However, these studies failed to review the effects of these training programs on other fitness domains, such as flexibility and muscular power. 5 , 21

Based on the inconsistencies in the research to date and limited systematic reviews on the effects of nontraditional military physical training on fitness domains, it is extremely difficult for physical training instructors to design the ideal training program for performance optimization and injury prevention in the military. A greater understanding of the impact of training would assist the military with the appropriate allocation of time, resources, and expertise to enhance soldier performance. 11 The aim of this systematic review was to identify the effects of physical training on various fitness domains (i.e., aerobic fitness, flexibility, muscular endurance, muscular power, muscular strength, and occupationally specific physical performance) that contribute to occupational performance and MSKI risk in military personnel. We hypothesized that nontraditional military physical training would result in greater increases in fitness domains compared to traditional military physical training.

This systematic review and meta-analysis was conducted in accordance with the guidelines provided by the Preferred Reporting Items for Systematic Review and Meta-Analyses statement 22 and followed the population, intervention/exposure, comparison, and outcome (PICO) approach. The review protocol was registered in PROSPERO (CRD42021234402).

Studies that met the following PICO criteria were considered eligible and were included in this review:

Population: Healthy military personnel in the army, navy, air force, or marines.

Intervention or exposure: Nontraditional military physical training program of at least 4 weeks’ duration.

Comparison: Traditional military physical training program of at least 4 weeks’ duration.

Outcome: The effects of the training program on fitness domains (i.e., aerobic fitness, flexibility, muscular endurance, muscular power, muscular strength, and occupationally specific physical performance).

Studies were excluded if: (1) the participants were injured before the commencement of the study; (2) the results of physical fitness assessments were not reported before and following the intervention; (3) the training for the traditional training or control (CON) group was unspecified; (4) the training for the nontraditional training or experimental (EXP) group was similar to that of traditional military physical training; (5) they were published in a language other than English; and (6) they were reported as abstracts, reviews or case reports.

For the purpose of this review, traditional military physical training (CON) was defined as an exercise training program consisting of aerobic running/walking exercises, calisthenics, and/or muscular endurance exercises (i.e., involving resistance training when intensity <70% 1 repetition maximum (RM) or with a load that permitted >12 repetitions). In contrast, nontraditional military physical training (EXP) was defined as an exercise training program that consisted of training with mode altered (e.g., adding resistance or power-oriented exercises in conjunction with traditional aerobic and muscular endurance exercises) or resistance training with an intensity ≥70% 1 RM or with a load that permitted ≤12 repetitions.

The outcome measures for this systematic review were reported for each of the fitness domains, including: (1) aerobic capacity (i.e., multistage fitness test, time trial, treadmill maximal oxygen consumption (VO 2max ); (2) flexibility (i.e., joint range-of-motion, sit and reach); (3) muscular endurance (i.e., push-ups); (4) muscular power (i.e., countermovement jump); (5) muscular strength (i.e., maximum voluntary contraction [MVC], RM); and (6) occupationally specific physical performance (i.e., load carriage, 30-m run with combat gear and dummy rifle).

Given that time trial results are interpreted in the opposite direction to other valid measures of aerobic fitness (i.e., a smaller time trial value indicated better performance), time trial results were converted into the completed time trial speed (ms −1 ) to enable comparison with other relevant and positive measures of aerobic fitness (i.e., VO 2max ). Additionally, the relationship between mean and standard deviation for time trial results was maintained for the converted speed results (e.g., a time trial mean of 804s ± 114 s was converted to speed as follows: 3,218 m/804 s  = 4.00 ms −1 , while the standard deviation was 114 s/804 s × 4.00 ms −1  = 0.57 s).

A literature search was performed on January 25, 2021, and subsequently updated on July 6, 2021, and December 6, 2021, across five major electronic databases (CINAHL, Medline, Scopus, SportDiscus, and Web of Science). For the Medline search, four groupings of MeSH terms were utilized in combination. A free text search was also conducted in Medline (January 2019 to current) for studies that were “in-press” or did not have assigned MeSH terms. The MeSH and free text search terms can be found in Supplementary Table S1. Equivalent free text searches, without a time limit, were conducted in CINAHL, Scopus, SportDiscus, and Web of Science. The reference lists of all included studies were also screened for any studies that could be considered for possible inclusion in the review. An independent reviewer completed the computed literature search with a random sample of 40% of the titles/abstracts screened by a further two independent reviewers to assess interrater reliability. A weighted Kappa statistic value of 0.79 (95% confidence interval: 0.70-0.88) was obtained, which was considered acceptable for interrater reliability. 23

The methodological quality of each included study was screened using the PEDro scale with the overall score per study reported as a sum. 24 The PEDro criterion, “There was blinding of all subjects,” was removed from methodological evaluation as a participant could not be blinded when assigned an exercise training intervention. Therefore, methodological quality was scored out of a maximum of 10: excellent (8-10); good (5-7); fair (3-4); and poor (<3). 24

A meta-analysis was conducted using Reviewer Manager Software 5. All data from included studies were reported as mean ± standard deviation with the level of statistical significance set at <0.05. 25 The heterogeneity of studies was assessed via I 2 statistic tests with I 2 values of 25%, 50%, and 75% classified as low, moderate, and high heterogeneity, respectively. 26 To evaluate the effectiveness of the training programs, postintervention data were compared between EXP and CON groups and reported via forest plots using a random-effects model. 27 The standardized mean difference (SMD) was calculated to determine the magnitude of between-group differences (i.e., EXP vs. CON) with values of <0.2, 0.2, 0.5, and 0.8 classified as trivial, small, medium, and large, respectively. 28

The search identified 7,350 records with the removal of duplicates, screening of title and abstract and full-text screening resulting in 15 original articles included for evaluation (Supplementary Table S2).

A total of 1,613 participants were included in the analysis, with 1,037 and 576 participants comprising the EXP and CON groups, respectively (Supplementary Table S3). The mean age of participants ranged from 18 to 37 years in each of the EXP and CON groups. The mean body mass index ranges were 21.3-26.9 kg.m −2 (EXP) and 22.0-28.1 kg.m −2 (CON), while the mean height ranges were 1.71-1.84 m (EXP) and 1.73-1.86 m (CON). Overall, the physical characteristics of each group were similar. Included participants were employed from all three forces (i.e., army, 1 , 3 , 6 , 12 , 13 , 16 , 29 air-force, 17 , 30 and navy 31 ) across a range of expertise levels (i.e., recruits to experienced) and from a number of different countries (i.e., Australia, 1 , 6 Brazil, 12 , 13 , 16 Canada, 30 Denmark, 20 , 32 Finland, 18 , 19 , 33 Thailand, 17 and the United States 3 , 29 , 31 ) (Supplementary Table S3). Of the 11 studies that included details of sex, 1 , 6 , 16–20 , 29 , 31–33 1,286 participants were male and 60 participants were female. Eight of these studies included male participants exclusively. 1 , 16–19 , 29 , 31 , 33

A variety of nontraditional military physical training programs were evaluated in the included studies and involved increasing resistance training intensity ( n  = 5), 6 , 12 , 13 , 18 , 19 parodied resistance training ( n  = 2), 1 , 29 the addition of resistance training to traditional aerobic and muscular endurance exercises ( n  = 5), 3 , 17 , 20 , 32 , 33 targeted cervical resistance training ( n  = 2) 30 , 31 and passive flexibility training ( n  = 1) 16 (Supplementary Table S4).

The PEDro scores for included studies ranges from 4 (fair) to 6 (good). with the average score being 5.3, indicating good quality (Supplementary Table S4). The average quality of the studies included for each fitness domain was also rated as “good” as follows: aerobic fitness (5.7); flexibility (5.5); muscular endurance (5.3); muscular power (5.3); muscular strength via MVC (5.3); muscular strength via RM (5.4); occupationally specific physical performance (5.6). The following quality criteria were included in all studies: (1) eligibility criteria were specified; (8) all subjects for whom outcome measures were available received the intervention or control condition as allocated; and (10) the study provided both point measures and measures of variability for at least one key outcome. None of the following criteria were identified in the included studies: (3) allocation was concealed; (6) blinding of all therapists; and (7) blinding of all assessors who measured at least one key outcome (Supplementary Table S5).

Measures of aerobic fitness, flexibility, muscular endurance, muscular power, muscular strength, and occupationally specific physical performance were included in the meta-analyses.

Nontraditional military physical training resulted in greater posttraining values for muscular endurance (small SMD; P  = .004; Fig. 1A ), power (large SMD; P  < .0001, Fig. 1B ), strength via RM testing (large SMD; P  < .00001; Fig. 2A ) and occupationally specific physical performance (medium SMD; P  = .007; Fig. 2B ) with moderate-high heterogeneity noted (66%-91%). In contrast, there was no significant difference between EXP and CON groups for aerobic fitness (small SMD; P  = .23; Fig. 3A ), flexibility (medium SMD; P  = .16; Fig. 3B ), and muscular strength via MVC testing (trivial SMD; P  = .28; Fig. 3C ). Moderate-high interstudy heterogeneity was identified for these latter outcomes (66%-86%).

A forest plot of meta-analysis of (A) muscular endurance and (B) muscular power.

A forest plot of meta-analysis of (A) muscular strength via RM and (B) occupationally specific physical performance.

A forest plot of meta-analysis of (A) aerobic fitness, (B) flexibility, and (C) muscular strength via MVC.

The current systematic review quantitatively identified the effects of traditional and nontraditional military physical training on various fitness domains (i.e., aerobic fitness, flexibility, muscular endurance, power, strength, and occupationally specific physical performance) that contribute to military-specific performance and MSKI risk in military personnel. The meta-analyses showed that nontraditional training resulted in significantly greater posttraining values for muscular endurance, power, strength measured via RM, and occupationally specific physical performance compared to traditional training. However, no significant group differences were noted for the posttraining values of aerobic fitness, flexibility, and muscular strength measured via MVC. These results partially supported our hypothesis that nontraditional training would result in greater increases in fitness domains compared to traditional training. The results of this review have shown that nontraditional training can induce several benefits to support the development of training programs to enhance soldier performance and to potentially reduce MSKI risk.

Nontraditional military physical training resulted in a significantly greater posttraining muscular strength measured via RM, aligning with previous qualitative reviews. 5 , 21 Classically, traditional training incorporates circuit activities (e.g., push-ups, jerry carry, and box lift) 6 and calisthenics for the purpose of muscular endurance development. 6 , 12 , 13 However, this focus may not be specific to enhance strength. In contrast, incorporation of heavy resistance training exercises, such as deadlifts, squats, and chest presses may be more appropriate to specifically develop strength with clear benefits (i.e., large SMD) noted primarily in the included studies that integrated these exercises. 1 , 6 , 12 , 13 For example, Heilbronn et al. 1 specifically prescribed a resistance training program to induce myofibrillar hypertrophy, increase motor unit recruitment, and alter neural recruitment patterns in order to develop muscular strength. 1 The current findings provide further support that targeted and advanced resistance training can significantly improve strength, a key component reported for military performance. 1 , 2 , 4 In contrast, traditional training examined in the current review resulted in lower posttraining strength assessments and therefore may be sub-optimal for strength development and reduction of MSKI risk. 34

While muscular strength via RM was demonstrated to be greater for the nontraditional training group, the current meta-analysis identified no differences for MVC measures between groups. 18–20 , 30 , 33 A possible explanation for this lack of difference may be because of the inherent nature of muscular strength assessment protocols. Maximal voluntary contraction tests are typically isometric assessments, whereby the contraction velocity is controlled and performed using monoarticular motion (e.g., elbow flexion or knee extension). This muscular strength measurement does not effectively reflect the entire movement pattern observed during resistance training, which consists of concentric and eccentric contractions and multiarticular actions. This limitation is further demonstrated during isometric strength assessments, 18–20 , 30 , 33 where the muscle force generation capacity does not vary during the entire joint range-of-motion. On the other hand, RM testing replicates the movement patterns of resistance exercises and, thus, may be more pertinent to detect improvements in muscular strength with military-based resistance training. 35 Despite these results, the significantly greater RM in the EXP group reinforce the positive effects of heavy resistance training on strength development and its potential to enhance benefits beyond traditional military training.

Given that strength (via RM) was greater following nontraditional training, it was no surprise that greater muscular power was also noted posttraining. 12 Nontraditional training that incorporated resistance training in combination with power exercises, such as jump squats and countermovement jump, produced the greatest posttraining results and SMD ( Fig. 1B ) compared to studies that incorporated resistance training alone. 12 , 13 Previous research has identified the overriding importance of strength and power in the modern battlefield where the execution of sprinting, lifting, pulling, crawling, and climbing swiftly, while carrying heavy loads is of particular significance. 36 Therefore, the implementation of heavy resistance training together with power exercises (i.e., conduction of nontraditional training) may enhance the development of both muscular strength and power for improved combat readiness. Future studies are encouraged to specifically examine the impact of nontraditional training on combat performance to provide greater support for its inclusion in the preparation of military personnel.

Similarly, it was no surprise that greater muscular endurance was also noted following non-traditional training. While the focus on muscular endurance training was similar between training groups (Supplementary Table S4), the ability of heavy resistance training to induce improvements in muscular strength and endurance may explain the observed posttraining outcomes. Previous research has demonstrated that the performance of heavy bench presses for 6 weeks increases the number of push-ups one can perform. 37 This observed improvement is likely due, in part, to the greater strength developed from resistance training which results in endurance tests being performed at a lower submaximal level. This being said, our meta-analyses also showed that nontraditional training that incorporated heavy resistance training also resulted in no significant difference for aerobic fitness between traditional and nontraditional training groups. 6 , 29 , 32 This result highlighted that resistance training improved muscular strength, power, and endurance without diminishing aerobic fitness development, highlighting yet another advantage of nontraditional training to prepare military personnel.

Furthermore, our meta-analysis also found better performance of occupationally specific physical assessments (i.e., load carriage, 30-m run with combat gear, and dummy rifle) for the nontraditional training group compared to the traditional training group. Regardless of their military occupational specialty (e.g., maintenance, stores, driving, combat, ship maintenance, and medical), soldiers must be capable of performing physically demanding tasks. 38 , 39 Hence, a crucial aim of military physical training is to prepare soldiers for this, with regular monitoring needed for this preparation. 7 However, occupationally specific physical performance, which encompasses multiple fitness domains, may not be assessed regularly during annual physical fitness tests. 2 , 4 For example, annual physical fitness tests across the armed forces typically comprise of a timed run, push-ups, and sit-ups, which focus on aerobic capacity and muscular endurance only. 2 The link between these fitness tests and traditional military physical training, which is heavily geared toward aerobic and muscular endurance development, is obvious. 2 , 4 However, the link between these tests, and subsequently training, and the occupationally specific physical demands of the military is less clear. These simple physical fitness tests do not represent the multidimensional reality of the military, where high levels of other domains such as strength, speed, power, and agility are imperative for soldier performance in any occupational specialty. 2 , 7 , 38 , 39 The results of the meta-analysis further reinforce. the importance of nontraditional training for greater occupational performance in military personnel.

According to the meta-analysis, posttraining values for flexibility were similar between nontraditional and traditional training. However, only one of the analyzed studies specifically incorporated flexibility training as part of their nontraditional training program. 16 Soares et al. 16 reported that joint range-of-movement was significantly greater following the addition of stretching exercises in EXP group one (training large to small joints for passive flexibility) (+12.6%) and EXP group two (training small to large joints for passive flexibility) (+12.1%), compared to traditional training without flexibility training. 16 These results highlight the importance of including flexibility-specific training to improve flexibility, with further studies needed to confirm this benefit.

While some military groups are advancing their training practices to adopt a more nontraditional approach, large variations in standard military physical training continue to exist between countries, forces, and individual units as shown in the current studies of this review (Supplementary Table S4) and anecdotal discussions. As this area continues to evolve, unit commanders and physical training instructors may utilize the findings from this review in their military practice with the prescription of nontraditional military physical training that combines heavy resistance with power exercises to improve strength, power, and endurance without compromising aerobic fitness. This advanced training regime is likely to benefit military personnel to not only optimize physical performance but also reduce MSKI risk. Previous studies reported that poor performance in multiple fitness domains, such as aerobic fitness, flexibility, muscular endurance, etc., was strongly associated with an increased incidence of MSKI in military populations. 8 , 34 The current results reinforce that targeted military physical training can alter physical fitness domains that potentially contribute to reduced MSKI risk. 34 For example, the positive muscular strength adaptations from heavy resistance training can protect bones, joints, and muscles when under stress/impact. 40 Prior research reported a significant reduction in MSKI risk for military personnel undertaking resistance training. 41 Ultimately, our recommendations for practice are to incorporate heavy resistance training with power exercises as part of traditional training in order to optimize physical performance and potentially reduce MSKI risk. In addition, the inclusion of flexibility-specific training in the military also has the potential to reduce MSKI risk through the prevention of muscle strains during activities involving the stretch-shortening cycle. 8 , 16 Improvements in flexibility have been shown to be of benefit to the military with reductions in lower extremity overuse injuries obtained from jumping and sprinting activities, which are exacerbated by uneven terrain and heavy load carriage. 8 We encourage future studies to elaborate on the findings of this review by researching the effects of nontraditional military physical training including flexibility-specific training on MSKI incidence in military personnel.

Although the current review examined a range of studies, a number of limitations within the included studies should be acknowledged. Firstly, the majority of studies had modest sample sizes, with 10 of the 15 included studies (66.7%) having ≤20 participants per group, an unpowered sample size for most investigations. 42 Modest sample sizes may give rise to a false-negative result, 42 with future research encouraged to incorporate larger sample sizes. Secondly, only 4.5% of participants in the studies were female (of studies that reported sex). Given the greater role that women are playing in the military, it is important for future research to consider sex differences, such as hormonal variations and lower baseline physical fitness in females, that will impact the efficacy of physical training. 43 Thirdly, the ages of the military personnel reported in these studies varied by 19 years between the youngest and oldest participant (18-37 years old). This large variation in age may have an effect on the military experience and previous physical training history of participants, in turn, influencing the effectiveness of the studied training programs.

A novel and important strength of this systematic review was that we assessed a broad range of fitness domains, unlike previous reviews that only focused on one or two fitness domains (e.g., aerobic fitness and muscular strength). 5 , 21 As a result, the transferability of findings to physical training within the military context was enhanced with the effects of military physical training on multiple fitness domains evaluated at once. Although a broad range of fitness domains were assessed that have been found to contribute to MSKI risk, 2 , 3 , 8 MSKI risk was not an outcome in the included studies and this review. Hence, the relationship between traditional and nontraditional training and MSKI risk can only be hypothesized. To confirm this, future studies are encouraged to look at the direct impact between training, the fitness domains, and MSKI risk. Other limitations of this review that require further consideration include the pretraining differences noted between EXP and CON groups for some studies. 20 , 32 Consequently, while the meta-analysis may have found little between-group differences posttraining, there may have been greater improvements for one group. Additionally, high heterogeneity was evident within the meta-analyses owing to the variations in populations of, as well as the differences in, training programs’ length and type between forces, occupations, and countries in the included studies. The lack of standardization of military physical training in the world made comparisons, particularly of the CON (traditional training) group, difficult. The current study focused on a definition of military physical training based upon fitness domains historically. However, these definitions may be evolving with nontraditional training, as defined in the current study, becoming the standard or traditional form for some countries and/or forces. Future studies can expand upon the current results and explore the intricacies of evolving military training practices worldwide.

In conclusion, the current systematic review identified that nontraditional military physical training had a greater posttraining effect on muscular endurance, power, strength measured via RM, and occupationally specific physical performance. These findings demonstrate the benefits of nontraditional training to optimize occupational performance while potentially reducing MSKI risk. This will assist the military with the appropriate allocation of time, resources, and expertise when designing the ideal training program to enhance soldier performance. Future research should focus on determining the effects of nontraditional training on MSKI incidence to provide insight into the ideal military training program for performance and well-being.

None declared.

SUPPLEMENTARY MATERIAL is available at Military Medicine online.

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Author notes

Previously presented as an oral poster presentation at the Defence Health Sciences Symposium 2021, Defence Science and Technology Group, Melbourne, Australia, November 29, 2021.

The views, opinions, and/or findings contained in this review are those of the authors and should not be construed as official Australian Defence Force position, policy, or decision.

• physical fitness
• military personnel
• muscle strength

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Physical Activity and Physical Fitness among University Students—A Systematic Review

Vidran kljajević.

1 Secondary Vocational School, Bijelo Polje 84000, Montenegro; [email protected]

Mima Stanković

2 Faculty of Sport and Physical Education, University of Niš, 18000 Nis, Serbia; moc.liamg@5991civejdrojdnasud (D.Đ.); moc.liamg@57civonavojedar (R.J.)

Dušan Đorđević

Drena trkulja-petković.

3 Faculty of Kinesiology, University of Zagreb, 10110 Zagreb, Croatia; [email protected] (D.T.-P.); [email protected] (K.P.); moc.liamg@0cilosrooiram (M.O.); [email protected] (G.S.)

Rade Jovanović

Kristian plazibat, mario oršolić, mijo Čurić.

4 Faculty of Kinesiology, University of Osijek, 31000 Osijek, Croatia; [email protected]

Goran Sporiš

Associated data.

Not applicable.

The aim of this systematic review was to examine the scientific evidence regarding physical activity and physical fitness among university students. The search and analysis of the studies were done in accordance with the PRISMA guidelines. An electronic databases search (Google Scholar, PubMed, Science Direct, and Scopus) yielded 11,839 studies. Subsequently, the identified studies had to be published in English between 2011 and 2021, the experimental study had to have included males and females attending a faculty, and the participants had to have been evaluated for physical activity and fitness. A total of 21 studies were included in the quantitative synthesis, with a total of 7306 participants, both male and female. After analyzing the obtained results, it could be concluded that university students show a satisfactory level of physical activity and physical fitness. However, the results vary due to different factors involved, mostly related to the cultural differences and educational systems in different countries. As this study observes mediocre results of physical activity and physical fitness among university students, it is crucial to get their attention and awareness, to at least maintain a satisfactory level of physical activity and physical fitness.

1. Introduction

Diet and physical activity directly affect the health status of adults and children [ 1 , 2 , 3 , 4 , 5 , 6 ]. Due to the fact that the largest part of the world’s population is physically inactive, physical inactivity is considered to be a public health problem, as opposed to an individual problem. According to the report of the World Health Organization, physical inactivity is a risk factor, along with smoking, obesity, and hypertension [ 7 ]. Stress, obesity, and movement disorders such as hypokinesia are the most common causes of premature death, along with chronic non-communicable diseases from which neither children nor young people are immune [ 8 ]. Economically stable countries (about 60–70% of them) do not achieve even the minimum level of physical activity recommended by the World Health Organization in order to maintain health and energy balance [ 9 , 10 ].

Based on the abovementioned facts, the negative consequences of physical activity decline are also reflected in the student population, where the reduction of physical activity could also lead to decreased physical fitness. Health-related physical fitness is also influenced by many other factors, such as body weight and socioeconomic status. For example, the physical activity level of individuals of low socioeconomic status likely suffers due to their living areas providing less access to parks [ 11 ] and neighborhood walkability [ 12 ]. Additionally, their health is also negatively affected by the cost of healthy food compared to that of junk food [ 13 ]. Bodyweight disorder is very common in students and it can be often traced back to being overweight in childhood and adolescence [ 14 ]. Nevertheless, one of the most common negative external factors that influenced the exercise of physical activity in the students’ population is the lack of free time due to the schedule at the faculty, obligations in social and family life.

According to Caia et al. [ 15 ], low strength was peculiar to 61% of students and 28% had below-average strength. Kubieva et al. [ 16 ] concluded that students have problems with body mass index and strength, regardless of their physical activity level. Likewise, low cardiorespiratory fitness is also an important predictor and could be prevented only with lifestyle modifications [ 17 ], such as increasing physical activity and promoting dietary changes [ 18 ]. Kwan et al. [ 19 ] have already noticed the evident decline of physical activity when enrolling a university and according to several authors [ 20 , 21 , 22 ], already one-third of high school students are insufficiently active after transitioning to university life. This was confirmed by the study that investigated physical activity patterns among American, Asians, Africans and Hispanic university students. The authors have found that 46.7% of them didn’t engage in physical activity and 16.7% were physically inactive [ 23 ]. Several studies have also noted a weak physically active lifestyle and it is on the rise among university students [ 24 , 25 , 26 ].

Based on the above-mentioned facts, and due to the fact that many students feel pressure during the engagement in academic activities, with no time for physical activities, it was necessary to determine the current state of physical activity and physical fitness among university students. Therefore, the aim of this systematic review was to examine the scientific evidence regarding the level of physical activity and physical fitness among university students.

2. Materials and Methods

2.1. literature identification.

Studies were searched and analyzed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 27 ]. In addition, this study is approved by the Ethical Board of Faculty of Sport and Physical Education, University of Niš. The research included studies conducted from 2011 to 2021 and the literature relevant for this type of research, available with the following databases: Google Scholar, PubMed, Science Direct, and Scopus.

The following keywords were used to search for the articles reporting on physical activity and physical fitness among university students: (“exercise” OR “physical activity” OR “accelerometer” OR “physical fitness” OR “strength” OR “endurance”) AND (“students” OR “adults” OR “students population” OR “university students” OR “college students”).

A descriptive method was used to analyze the data obtained, and all titles and abstracts were reviewed for possible study inclusion. At the same time, the identification strategy was modified and adapted to the particular database to increase the sensitivity. After a detailed identification process, studies were considered to be relevant if they met the inclusion criteria.

The search for studies, assessment of their value and data extraction were conducted independently by two authors (M.S. and D.Đ.), and the lists of references from previously assessed and original research were also reviewed. After that, each author cross-examined the identified studies, which were then taken for further analysis or rejected.

2.2. Inclusion Criteria

For the study to be included in the final analysis, it had to meet the following criteria: year of publication (2011 to 2021), the studies published in English, the experimental study included males and females attending faculty and that the participants were tested for the evaluation of physical activity and physical fitness.

2.3. Risk of Bias Assessment

The risk of bias was assessed according to the PRISMA guidelines, that is, using the PEDro scale [ 28 ] to determine the quality of reviewed studies and the potential risk of bias. Two independent authors (D.Ð. and M.S.) assessed the quality and risk of bias using checklists. Concordance between reviewers was estimated using k-statistics data to review the full text and assess relativity and risk of bias. In case of discordance as to findings of the risk of bias assessment, the obtained data was assessed by the third reviewer (K.P.), who also gave the final decision. The k rate of concordance between reviewers’ findings was k = 0.91.

2.4. Data Extraction

When the cross-examination was conducted, if the data were adequate, the necessary information was extracted and then moved to an Excel spreadsheet. The standardized data extraction protocol was applied (Cochrane Consumer and Communication Review Group’s) to extract the characteristics, such as authors and year of study, sample size, age, types of experimental program, duration, frequency, and study results.

3.1. Quality of the Studies

Of the total number of studies that were included in the quantitative synthesis, and based on the points each study scored on the PEDro scale, the final study assessment scores were defined. According to Maher et al. [ 29 ], a score between 8–11 is considered to be optimal, but if the study gains between 0–3 points, that study will be classified with “poor” quality, 4–5 points with “fair” quality, 6–8 points with “good” quality, and 9–10 points with “excellent” quality. Of all studies included in this systematic review, 2 studies showed fair quality, 17 of them showed good quality, and the other 2 studies showed excellent quality, which is shown in Table 1 .

PEDro scale results.

Legend: 1—eligibility criteria; 2—random allocation; 3—concealed allocation; 4—baseline comparability; 5—blind subject; 6—blind clinician; 7—blind assessor; 8—adequate follow-up; 9—intention-to-treat analysis; 10—between-group analysis; 11 —point estimates and variability; Y—criterion is satisfied; N—criterion is not satisfied; ∑—total awarded points.

3.2. Selection and Characteristics of Studies

A search of electronic databases and scanning the reference lists yielded 11,839 studies. After removing duplicates, a total of 3938 studies were screened. Additional 3892 studies were excluded based on inclusion criteria and a total of 46 studies were screened and selected for eligibility. After increased sensitivity and in-deeper check, 25 studies with nonrelevant outcomes, editorials, and executive summaries were additionally excluded. Lastly, a total of 21 full-text studies were included in the systematic review ( Figure 1 ).

Collecting adequate studies on the basis of pre-defined criteria (PRISMA flow chart).

Table 2 and Table 3 show in more detail the studies that met the set conditions and entered the qualitative analysis.

Review of studies.

Legend: N—total number of participants; M—male, F—female; X—no data; EG—experimental group; CG—control group; PF—physical fitness tests; PA—physical activity; PEC—physical education classes; CMJ—countermovement jump; CRP—cardiorespiratory fitness; Els—explosive legs strength; Rls—repetitive legs strength; DB—dynamic balance; SB—static balance; mif—maximal isometric force; sq—squat; dhg—dynamometer hand grip; dynb—dynamometry back muscles; dynl—dynamometry legs; dl—deadlift; bdps—bench dips; KPA—Criminal Police Academy students; TsgUni—Tsinghua University students; PolNatUni—Poliand National University students; FilUni—Filipino University students; SPE—special physical education; ImT—isometric training; ItT—isotonic training; CRT—cardiorespiratory fitness; VO2max—maximal oxygen uptake; T—treadmill; CE—cycle ergometer; PH—physiotherapy students; FSPE—faculty of sport and physical education students; JapUni—Japanese University students; KorUni—Korean Unversity students; ChiUni—Chinese University students; RusUniH—Russian University students of humanities specialties; RusUniT—Russian University students of technical specialties; BuchUni—Bucharest University; IndUniP—Indian University Polytechnic College; ST—strength training; STIAT—strength training and intensive aerobic training; Flex—flexibility; Pu—push-ups; Plps—pull-ups; Su—sit-ups; LJ—long jump; Z—zipper test; Cu—curl up test; HJ—high jump; IFff—isometric force of the finger flexor; DA—dominant arm; RG—rhytmic gymnastics classes; AP—aqua-pilates; sq—squat; pnk—plank; Agr—academic grades; MAT—modified abdomen test; SR—shuttle run; SaR—sit and reach; Bst—back scratch test; Bd—Bamby dance; Ag—agility; BB—basketball; VC—vital capacity; LS—lifestyle; * significant improvement; ** significant difference between groups; ++ positive correlation.

Physical activity evaluation.

Legend: N—total number of participants; M—male; F—female; CT—circuit training; A—aerobic; PH—physiotherapy students; FSPE—faculty of sport and physical education students; FilUni—Filipino University students; RusUniH—Russian University students of humanities specialties; RusUniT—Russian University students of technical specialties; ChiUni—Chinese University students; KorUni—Korean University students; JapUni—Japanese University students; TsgUni—Tsinghua University students; LA—learning activity; TA—traffic activity; DA—domestic work; IPAQ—International Physical Activity Questionnaire; SAQ—Self-Assessment Questionnaire; CL-IPAQ—Chinese long format of IPAQ; PA—physical activity; GPA—average grade point; MVPA—moderate-vigorous intensity physical activity; CS-IPAQ—Chinese short format of IPAQ; Q—questionnaire, BSIQ—Body Self Image Questionnaire; FC—fitness center documents; SS—sport school documents; PiA—physical inactivity; FE—fast eating; CRF—cardiorespiratory fitness; Acc—accelerometer; METs—metabolic equivalent; EEPA—energy exposure originating from physical activity per day; Ds—daily steps; * significant improvement; ** significant difference between groups.

There were a total of 7675 participants. The highest number was 2324 [ 48 ], the lowest was 20 [ 43 ], while for only one study there were no data about the total number of participants [ 32 ]. The sample of participants was only female in five studies [ 34 , 38 , 40 , 41 , 43 ], while only one study had male participants only [ 47 ]. There were eight studies where the sample of participants was from the Faculty of Sport and Physical Education students [ 30 , 31 , 33 , 35 , 37 , 40 , 41 , 43 ], only one study had Criminal Police Academy students [ 36 ], while the rest of the studies had participants from different universities and faculties [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].

For measuring physical fitness, it should be mentioned Criminal Police Academy students [ 36 ], who were using standard battery test, prescribed by plan and program “Special physical education”. Two studies had students from Poland National University [ 45 , 46 ], one had Tsingua University students from China [ 44 ], and all three mentioned were using their own standardized battery test. Aqua-Pilates program [ 40 ], Bamboo dance program [ 39 ], hiking activities [ 35 ], and fitness yoga [ 38 ] were also evaluated and there were also two studies that conducted VO2max evaluation on the treadmill [ 49 ] and cycle ergometer [ 50 ]. The rest of the studies were using standard and already known physical fitness tests. There were evaluations of physical fitness based on practicing basketball games [ 32 ] and rhythmic gymnastics, both conducted on faculty [ 37 ], and concurrent power training with high-intensity interval cycling [ 43 ].

Two types of relationships were conducted. The first was the relationship between physical fitness and academic performance among Chinese University students [ 48 ] and the second one [ 50 ] was a relationship between depression, daily physical activity, physical fitness, and daytime sleep time in Japanese University students. In addition, only one study [ 49 ] was determining predictors of metabolic syndrome (eating speed, physical activity and cardiorespiratory fitness) in Korean University students.

Evaluation of physical activity was various. International Physical Activity Questionnaire (IPAQ) was used in four studies [ 42 , 44 , 47 , 48 ] and among those mentioned, only Osipov et al. [ 47 ] were using fitness center and sport school documents as evaluation of the physical activity. The self-assessment questionnaire was conducted only by Kaminska et al. [ 31 ], Kang et al. [ 49 ] had a regular questionnaire, while Shimamoto et al. [ 50 ] was the only study that monitored and evaluated physical activity using the accelerometer. Additionally, the mentioned questionnaires are valid and reliable [ 51 , 52 , 53 ], while the details of the accelerometer measurement have already been described in previous studies [ 54 , 55 ].

4. Discussion

The current study aimed to examine the scientific evidence regarding physical activity and physical fitness among university students. The universities are an ideal environment for the promotion of physical fitness and physical activity. The main findings of the current review are that university students show moderate levels of physical fitness and physical activity. However, when considering the results, it should be done with caution due to cultural differences, different faculties included in the review as well as the difference in educational systems. Moreover, some studies included students from the faculty of sports who have physical activities within the curriculum that must be taken into account. This was confirmed by Kaminska et al. [ 31 ] who found that students of the faculty of sports have better physical fitness compared to physiotherapy students, which was explained by a higher degree of physical activity in classes.

Additionally, when it comes to analyzing the impact of physical activities, such as aqua-Pilates [ 40 ], it was concluded that the group that implemented the program made statistically significant progress in terms of muscle strength, flexibility and balance. Meredith-Jones et al. [ 56 ] have also found similar results, that water exercises can lead to beneficial effects on cardiorespiratory fitness, strength and body fat. The weight loss in water removes the body load and helps in increasing the range of motion according to normal conditions, as well as significant energy expenditure [ 57 ]. In addition, such results are a consequence of the application of exercises that develop the mentioned abilities, because as already mentioned, the application of physical activities can increase the already acquired level of fitness and physical abilities. Fitness is an activity that consists of exercises that require the engagement of the whole body and develop both coordination and strength, as well as flexibility and balance [ 38 ].

Jourkesh et al. [ 30 ] have found that females performed better in flexibility tests than males, which is in full accordance with Ortega et al. [ 58 ]. Hiking activities conducted by Citozi et al. [ 35 ] found gender differences in balance tests, which is lined with other findings [ 59 , 60 ]. In accordance to previously mentioned studies, Zou et al. [ 39 ] have examined the influence of Bamboo dance and at the end of the program, the experimental group showed significant progress in balance (3.6%), agility (0.18%), strength (0.33%) and explosive power (0.42%). As the authors have considered low physical activity in university students, Bamboo dance interventions are needed. According to all mentioned facts, the main focus should be placed on reducing barriers that students experience that may impact their physical activity [ 61 ].

Wang [ 44 ] results showed that students with a higher degree of physical activity have 2.39 times better results in the strength test and 1.39 times in the long jump test. Osipov et al. [ 47 ] also used a standardized questionnaire to assess physical activity to divide participants into groups, and proved that participants with a higher degree of physical activity had better physical fitness compared to those with a lower degree. The results were even compared with the obtained results of some other countries, where Russian students show significantly better results compared to students from African countries [ 62 , 63 ], Turkey [ 64 ], Iran [ 65 ] and Ukraine [ 66 ], as well as several more European countries [ 67 , 68 ]. Two similar studies conducted by Griban et al. [ 45 , 46 ] gave two new answers. The first indicates the problem of increasing the level of fitness in higher institutions and the second is consisted of analyzing the dynamics of fitness on the same sample. Likewise, Adriana et al. [ 32 ] found that younger students have poorer results in physical fitness compared to older ones, in exact same variables. Suri et al. [ 41 ] are suggesting that there is a strong need for more active physical education programs that are appropriate for developing fitness and improving the health status of college-going students. Although the current state of physical fitness of these students was at a very unsatisfactory level, the authors emphasize that the system primarily does not provide the required level of physical fitness and work ability, which is why there is a great need to identify an adequate and efficient program that will improve the traditional physical education system. Kang et al. [ 49 ] found physical inactivity and poor cardiorespiratory function to metabolic equivalent of task risk. Students who are busy with their studies, part-time jobs and extracurricular sport clubs–physical activities that are normally considered to be beneficial [ 69 , 70 ] may cause excessive negative side effects [ 50 ]. In accordance with the current findings, similar results have been found in Western [ 71 , 72 , 73 , 74 ] and Asian countries [ 75 , 76 , 77 ].

Unlike all other studies in which the authors wanted to show that applying physical activity has a positive impact on the physical fitness of students, Mitrović et al. [ 36 ] decided to examine the impact of the absence of special physical education on physical fitness in an 8-month period. They came to the conclusion that the absence of this type of physical activity in students causes a significant decline in physical fitness, which may be an indication that already acquired physical fitness may decline if physical activity is not practiced. If there is more physical activity, the more physically fit individuals at the adequate level are there [ 78 ]. According to Rossomanno et al. [ 79 ], if we want to secure that these students could perform their duties, it is necessary to apply the monitored program for the development of physical fitness and physical activity during the whole year.

The main limitation of this study lies in cultural differences, where we included a wide range of universities, as well as differences in educational systems. As studies about physical activity interventions have rarely been enforced in some parts of included studies, further research should promote regular exercise in the university students. Despite the mentioned limitations, this study should be an important contribution to physical activity, as well as physical fitness research, and be beneficial to elucidating the primary negative factors.

5. Conclusions

Based on the obtained results, university students show a satisfactory level of physical activity and physical fitness. However, the results vary due to the different factors involved, which are mostly related to cultural differences and educational systems in different countries. The results for the students of the faculty of sport showed that physical activity has a positive effect on the development and maintenance of physical fitness and activity. As far as students from different faculties, this study suggests that further investigations should be conducted to promote daily exercise, as it may be beneficial for physical fitness and activity related to students health.

Author Contributions

Conceptualization, V.K. and G.S.; methodology, V.K. and G.S.; software, M.O.; validation, K.P. and D.T.-P.; formal analysis, M.O. and R.J.; investigation, D.Đ.; resources, M.S. and D.Đ.; data curation, M.Č.; writing—original draft preparation, D.Đ.; writing—review and editing, D.T.-P. and K.P.; visualization, M.Č.; supervision, D.T.-P., R.J. and K.P.; project administration, M.S. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Physical Fitness Research Paper Topics

Looking for captivating physical fitness research paper topics ? You’ve come to the right place! This page is your ultimate resource, providing an extensive list of research paper topics that delve into the fascinating world of physical fitness. With ten categories, each containing ten unique physical fitness research paper topics, you’ll discover a wide range of subjects to explore, analyze, and present in your research. From exercise physiology to nutrition, psychology to biomechanics, this comprehensive list covers various dimensions of physical fitness. So, whether you’re passionate about understanding the effects of exercise on cardiovascular health or exploring the role of nutrition in athletic performance, these topics will ignite your curiosity and help you embark on a rewarding research journey in the realm of physical fitness.

100 Physical Fitness Research Paper Topics

The field of physical fitness offers a rich landscape for research, providing numerous opportunities for students to explore various aspects of human health, exercise, and performance. This comprehensive list of physical fitness research paper topics is designed to inspire and guide health science students in their quest for compelling research ideas. The list is divided into ten categories, each containing ten unique topics, offering a diverse range of subjects to delve into. Whether you are interested in the physiological, psychological, or social aspects of physical fitness, there is something for everyone in this extensive compilation.

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Exercise Physiology

• The impact of different exercise modalities on cardiovascular health.
• Effects of resistance training on muscle strength and hypertrophy.
• The role of aerobic exercise in improving cognitive function.
• Exploring the physiological mechanisms behind exercise-induced fatigue.
• Exercise and its impact on metabolic disorders such as diabetes.
• The influence of exercise on bone health and prevention of osteoporosis.
• Effects of high-intensity interval training (HIIT) on athletic performance.
• The relationship between exercise and immune function.
• Exploring the effects of exercise on sleep quality and duration.
• The impact of exercise on mental health and well-being.

Nutrition and Physical Fitness

• The role of macronutrients in optimizing athletic performance.
• Exploring the effects of different diets on body composition and fitness.
• The influence of nutritional supplements on exercise recovery and performance.
• The impact of hydration status on exercise performance and physiological responses.
• Nutritional strategies for optimizing muscle glycogen storage and utilization.
• Exploring the relationship between nutrition, gut microbiota, and physical fitness.
• The effects of fasting and intermittent fasting on exercise adaptations.
• Nutritional considerations for vegan and vegetarian athletes.
• The role of antioxidants in mitigating exercise-induced oxidative stress.
• Investigating the effects of pre- and post-exercise nutrition timing on performance and recovery.

Psychology of Exercise

• The psychological benefits of physical activity on stress reduction.
• Exploring the motivational factors influencing exercise adherence.
• The role of exercise in improving body image and self-esteem.
• Examining the relationship between exercise and cognitive function in older adults.
• The impact of exercise on mood disorders such as depression and anxiety.
• Investigating the psychological effects of team sports participation.
• Exploring the psychological strategies for enhancing exercise performance.
• The influence of exercise on cognitive and academic performance in students.
• The role of exercise in promoting healthy aging and cognitive longevity.
• Psychological factors influencing exercise behavior among different populations.

Biomechanics and Kinetics

• Investigating the biomechanics of human gait and its implications for injury prevention.
• The role of biomechanical analysis in optimizing sports performance.
• Understanding the mechanics of resistance training exercises for muscle activation.
• Examining the biomechanical factors influencing running economy and performance.
• Biomechanical analysis of joint loading during different types of exercise.
• The influence of footwear on biomechanics and injury risk in athletes.
• Exploring the mechanics of human balance and stability during exercise.
• The role of motion capture technology in studying human movement patterns.
• Biomechanical analysis of sports-specific movements and injury prevention.
• Investigating the mechanics of plyometric training and its impact on power development.

Sports Medicine and Injury Prevention

• Exploring the most common sports-related injuries and their prevention strategies.
• The role of rehabilitation and physical therapy in sports injury recovery.
• Investigating the effects of preventive measures on reducing concussion risk in contact sports.
• Understanding the biomechanics of common overuse injuries in athletes.
• Exploring the psychological factors influencing injury rehabilitation and return to sport.
• The impact of sports specialization on injury risk and long-term athlete development.
• Investigating the efficacy of different injury prevention programs in specific sports.
• The role of bracing and protective equipment in injury prevention.
• Exploring the influence of environmental factors on injury risk in outdoor sports.
• The effects of fatigue on injury occurrence and prevention in sports.

Physical Fitness Assessment and Testing

• Evaluating the validity and reliability of different fitness assessment methods.
• The role of body composition analysis in assessing physical fitness and health.
• Investigating the accuracy of wearable fitness trackers in monitoring exercise intensity.
• Examining the utility of field-based fitness tests in predicting athletic performance.
• Comparing the effectiveness of laboratory-based and field-based fitness assessments.
• Exploring novel approaches to assessing muscular strength and power.
• The role of cardiovascular fitness testing in predicting health outcomes.
• Investigating the assessment of flexibility and its relationship with injury risk.
• Examining the utility of functional movement screening in assessing physical fitness.
• Evaluating the psychometric properties of self-report physical activity questionnaires.

Exercise Prescription and Training Programs

• The effects of different exercise intensity and duration on fitness outcomes.
• Investigating the impact of periodization models on long-term athletic development.
• Optimizing resistance training program design for muscle hypertrophy.
• The role of concurrent training in maximizing strength and endurance gains.
• Exploring the benefits of high-intensity interval training (HIIT) in various populations.
• Examining the effects of different exercise modalities on body composition changes.
• Investigating the efficacy of exercise programs for older adults in improving functional capacity.
• The impact of exercise programming on cardiovascular health and disease prevention.
• Exploring the effects of exercise on insulin sensitivity and metabolic health.
• The role of exercise prescription in promoting weight loss and weight management.

Exercise and Special Populations

• Investigating the effects of exercise on pregnancy outcomes and maternal health.
• Exercise interventions for individuals with chronic diseases such as diabetes and cardiovascular disorders.
• The impact of exercise on bone health in postmenopausal women.
• Exploring exercise programs for individuals with physical disabilities.
• The role of exercise in managing symptoms and improving quality of life in cancer patients.
• Exercise interventions for individuals with mental health conditions such as depression and anxiety.
• Investigating the effects of exercise on cognitive function in children and adolescents.
• The impact of exercise on sleep quality and patterns in different populations.
• Exercise programs for older adults to enhance mobility, balance, and functional independence.
• The role of exercise in promoting well-being and reducing stress in the workplace.

Exercise and Public Health

• Investigating the impact of physical activity interventions on population health outcomes.
• The role of exercise in preventing and managing non-communicable diseases.
• Examining the socioeconomic factors influencing physical activity levels in different populations.
• Exploring the effectiveness of community-based exercise programs in promoting health.
• The impact of built environment and neighborhood design on physical activity levels.
• Investigating the relationship between physical fitness and academic performance in school settings.
• Exploring strategies to promote physical activity in sedentary populations.
• The role of exercise in reducing healthcare costs and burden on the healthcare system.
• Investigating the effects of policy and environmental changes on physical activity promotion.
• The role of exercise in promoting healthy aging and preventing age-related chronic diseases.

Emerging Trends and Innovations in Physical Fitness

• Investigating the effects of wearable technology on exercise motivation and behavior change.
• Exploring the potential of virtual reality in enhancing exercise experiences.
• The impact of exergaming on physical activity levels and health outcomes.
• Investigating the use of artificial intelligence in personalized exercise prescription.
• Exploring the effects of biofeedback techniques on performance and exercise adherence.
• The role of genomics in understanding individual responses to exercise.
• Investigating the effects of mind-body exercise modalities on physical and mental well-being.
• Exploring the potential of outdoor adventure and nature-based activities in promoting physical fitness.
• The impact of social media and online platforms on exercise motivation and support.
• Investigating the effects of environmental factors on exercise performance and adherence.

This comprehensive list of physical fitness research paper topics offers a vast array of possibilities for students to explore in their research endeavors. From exercise physiology to sports medicine, psychology to emerging trends, there are numerous avenues to delve into the fascinating field of physical fitness. Whether you have a specific interest in a particular category or wish to explore cross-disciplinary topics, this list provides a solid foundation for selecting a compelling research topic. So, let your curiosity guide you, and embark on a journey of discovery and knowledge in the realm of physical fitness research.

Physical Fitness: Exploring the Range of Research Paper Topics

Physical fitness is a multidimensional concept that encompasses various aspects of health, performance, and well-being. As a student of health sciences, delving into the realm of physical fitness research can provide you with a rich opportunity to explore a wide range of captivating topics. From understanding the physiological adaptations to exercise to investigating the psychological aspects of physical activity, the field of physical fitness offers an expansive landscape for research. In this article, we will explore the diverse range of physical fitness research paper topics, providing you with a comprehensive understanding of the exciting possibilities that lie ahead.

Exercise Physiology: Unraveling the Mysteries of Human Performance

Exercise physiology is a fundamental area of study within physical fitness research. It focuses on understanding how the body responds and adapts to exercise. One fascinating research area within exercise physiology is the investigation of physiological adaptations to different types of exercise. You can explore the effects of various exercise modalities, such as aerobic training, resistance training, or high-intensity interval training, on cardiovascular health, muscular strength, endurance, and body composition. Additionally, examining the impact of exercise on metabolic disorders, bone health, immune function, and fatigue can provide valuable insights into the physiological mechanisms underlying human performance.

Psychology of Physical Activity: Understanding the Mind-Body Connection

Understanding the psychological aspects of physical activity is crucial for promoting and maintaining engagement in exercise. The psychology of physical activity encompasses a broad range of physical fitness research paper topics that explore the factors influencing exercise motivation, adherence, and the interplay between physical activity and mental health. You can investigate the role of motivation in initiating and sustaining exercise behavior, exploring strategies to enhance exercise adherence and overcome barriers to physical activity participation. Furthermore, exploring the relationship between exercise and mental health outcomes, such as depression, anxiety, stress management, and cognitive function, can shed light on the potential psychological benefits of physical fitness.

Sports Nutrition: Fueling the Body for Optimal Performance

Nutrition plays a critical role in supporting physical fitness and performance. Researching the impact of nutrition on exercise performance and recovery is a dynamic field within the realm of physical fitness. You can explore topics such as the influence of macronutrient composition on endurance or strength performance, the effects of hydration on exercise capacity, the role of dietary supplements in enhancing athletic performance, and the timing and composition of pre- and post-exercise meals. Investigating the nutritional requirements of specific populations, such as athletes, older adults, or individuals with chronic diseases, can provide valuable insights into optimizing nutrition strategies for diverse populations.

Injury Prevention and Rehabilitation: Ensuring Safe and Effective Exercise

Injury prevention and rehabilitation are essential components of physical fitness research. Exploring topics related to injury prevention and rehabilitation can encompass a wide range of areas, including the identification of risk factors for exercise-related injuries, the development of effective training programs to reduce injury rates, the investigation of rehabilitation techniques to facilitate recovery and return to physical activity, and the evaluation of preventive strategies in specific populations. Understanding the mechanisms underlying injuries and developing strategies to mitigate their occurrence can contribute to safer and more effective exercise practices.

Exercise Prescription and Programming: Tailoring Fitness Interventions

Exercise prescription and programming focus on the design and implementation of exercise interventions tailored to individual needs and goals. This research area encompasses topics such as the development of personalized exercise programs for different populations, the optimization of training variables (intensity, duration, frequency) for specific outcomes, the evaluation of novel training methods and technologies, and the use of wearable devices and digital technologies in exercise prescription. Investigating exercise prescription and programming can provide valuable insights into the most effective strategies for achieving desired fitness outcomes, improving overall health and well-being, and promoting behavior change.

Biomechanics and Movement Analysis: Exploring Human Motion

Biomechanics and movement analysis involve the study of human motion and the forces that act upon the body during physical activities. This research area explores topics such as the mechanics of joint movement, muscle function, gait analysis, balance and coordination, and the effects of external factors on movement performance. Investigating biomechanics and movement analysis can contribute to a deeper understanding of optimal movement patterns, injury mechanisms, ergonomics, and the development of assistive devices or rehabilitation strategies.

Environmental and Occupational Health: Exploring the Impact of Work and Environment on Health

Environmental and occupational health focuses on the effects of work and environmental factors on human health and well-being. This research area encompasses topics such as the impact of physical activity in occupational settings, the effects of environmental pollutants on health outcomes, the role of physical fitness in occupational performance, and the development of strategies to promote a healthy work environment. Investigating environmental and occupational health can provide insights into the relationship between physical fitness, work-related factors, and overall health and safety.

Public Health and Health Promotion: Advancing Population Health

Public health and health promotion research aim to improve the health and well-being of populations through disease prevention, health education, and promotion of healthy behaviors. This research area explores topics such as the impact of physical fitness on chronic disease prevention, the effectiveness of health promotion interventions in promoting physical activity, strategies for increasing physical activity in underserved populations, and the development of policies to support physical fitness initiatives. Investigating public health and health promotion can contribute to the development of evidence-based interventions and policies to enhance population health.

Geriatric Exercise Science: Enhancing Health in Aging Populations

Geriatric exercise science focuses on promoting health and functional independence in older adults through exercise and physical activity. This research area explores topics such as the effects of exercise on age-related declines in muscle strength, balance, and mobility, the role of physical activity in preventing age-related chronic diseases, and the development of exercise programs for older adults with specific health conditions. Investigating geriatric exercise science can provide valuable insights into maintaining health and well-being in aging populations and improving the quality of life for older adults.

The field of physical fitness research offers a vast array of topics to explore, ranging from exercise physiology and psychology of physical activity to sports nutrition, injury prevention and rehabilitation, exercise prescription and programming, biomechanics and movement analysis, environmental and occupational health, public health and health promotion, and geriatric exercise science. By choosing a research topic that aligns with your interests and career aspirations, you can contribute to the advancement of knowledge in the field while gaining a deeper understanding of the intricacies of physical fitness. Embrace the opportunities that physical fitness research presents and let your passion for health science drive your exploration of these captivating topics.

Choosing Physical Fitness Research Paper Topics

Selecting an engaging and relevant research topic is a crucial step in the process of writing a research paper on physical fitness. With a wide range of possibilities within the field, it can be challenging to narrow down your focus and identify a topic that aligns with your interests and academic goals. In this section, we will provide expert advice on how to choose physical fitness research paper topics that are compelling, meaningful, and contribute to the existing knowledge in the field.

• Identify Your Interests : Start by reflecting on your personal interests within the realm of physical fitness. Consider the aspects of exercise, health, performance, or well-being that fascinate you the most. Are you passionate about exercise physiology, psychology of physical activity, sports nutrition, injury prevention and rehabilitation, exercise prescription and programming, biomechanics and movement analysis, environmental and occupational health, public health and health promotion, or geriatric exercise science? By identifying your interests, you can focus on areas that resonate with you and spark your curiosity.
• Stay Informed : Keep up-to-date with the latest research and advancements in the field of physical fitness. Subscribe to academic journals, attend conferences, and follow reputable websites and research institutes dedicated to exercise science. By staying informed, you will gain insights into current trends, emerging topics, and gaps in knowledge that may inspire your research interests.
• Conduct a Literature Review : Before finalizing your research topic, conduct a comprehensive literature review to explore existing studies, theories, and gaps in knowledge. Identify areas where further research is needed or where conflicting findings exist. A literature review will help you refine your research question and ensure that your topic contributes to the existing body of knowledge.
• Consult with Faculty or Experts : Reach out to your faculty members or experts in the field for guidance and advice. They can provide valuable insights, suggest potential research directions, and help you refine your research topic. Utilize their expertise to gain a deeper understanding of the field and identify relevant research questions.
• Consider Practical Applications : Think about the practical applications and implications of your research topic. How can your findings contribute to real-world situations, enhance practice, or inform policy decisions? Identifying the practical significance of your research can add value and relevance to your study.
• Balance Specificity and Feasibility : Strive for a research topic that is specific enough to provide depth and focus but also feasible within the constraints of your research project. Consider the available resources, time, and access to data or participants when determining the scope of your research topic. Finding the right balance will ensure that your research is manageable and achievable within the given timeframe.
• Collaborate and Network : Collaborate with peers, researchers, or professionals in the field to broaden your perspective and generate new ideas. Engaging in discussions and exchanging thoughts with others can spark creativity and open doors to potential research collaborations.
• Think Outside the Box : Don’t be afraid to think outside the box and explore innovative or unconventional research topics within physical fitness. Consider interdisciplinary approaches or emerging areas of research that intersect with exercise science, such as technology, digital health, or social determinants of health. Embracing innovative ideas can lead to exciting discoveries and contribute to the evolution of the field.
• Consider Ethical Considerations : When choosing a research topic, consider the ethical implications and potential risks associated with your study. Ensure that your research adheres to ethical guidelines and protects the rights and well-being of participants. Consulting with ethics committees or institutional review boards can help ensure that your research is conducted ethically and responsibly.
• Seek Feedback and Refine Your Topic : Once you have identified a potential research topic, seek feedback from mentors, peers, or academic advisors. They can provide constructive criticism, suggest modifications, or help you clarify your research objectives. Use their input to refine your research topic and ensure that it aligns with your academic goals and the requirements of your research paper.

Choosing a research topic in the field of physical fitness requires careful consideration and alignment with your interests, academic goals, and the existing knowledge in the field. By following these expert tips, you can select a compelling research topic that contributes to the advancement of knowledge, engages your passion, and offers opportunities for meaningful exploration. Embrace the journey of research and let your curiosity drive you to uncover new insights in the fascinating world of physical fitness.

How to Write a Physical Fitness Research Paper

Writing a research paper on physical fitness requires careful planning, organization, and adherence to academic conventions. In this section, we will provide you with expert advice on how to write a compelling and well-structured physical fitness research paper. By following these guidelines, you can effectively communicate your research findings, contribute to the existing body of knowledge, and showcase your understanding of the subject matter.

• Define Your Research Objective : Start by clearly defining the objective of your research paper. What is the specific question or problem that your study aims to address? Clearly articulate your research objective to guide your literature review, data collection, and analysis.
• Conduct a Comprehensive Literature Review : Before diving into the writing process, conduct a thorough literature review to familiarize yourself with existing research on the topic. Identify key theories, methodologies, and findings that will inform your study. Analyze and critically evaluate the literature to identify gaps in knowledge that your research can fill.
• Develop a Solid Research Methodology : Outline your research methodology, including the study design, sample size, data collection methods, and data analysis techniques. Clearly explain how you will collect and analyze data to answer your research question. Ensure that your methodology is rigorous, ethical, and aligned with the standards of your academic institution.
• Organize Your Paper : A well-organized research paper follows a logical structure. Start with an introduction that provides background information, states the research question, and outlines the significance of your study. Follow this with a literature review that synthesizes existing research and highlights the gaps in knowledge. Next, present your research methodology, including the sample characteristics, data collection procedures, and statistical analysis methods. Present your findings in a clear and concise manner, using tables, graphs, or charts as appropriate. Finally, conclude your paper by summarizing your findings, discussing their implications, and suggesting avenues for future research.
• Write Clearly and Concisely : Use clear and concise language to convey your ideas. Avoid jargon or technical terms that may be unfamiliar to your readers. Explain complex concepts in a way that is accessible to a broad audience. Ensure that your writing is well-structured, with paragraphs that flow logically and smoothly.
• Support Your Arguments with Evidence : Back up your arguments and claims with credible evidence. Use scholarly sources, peer-reviewed articles, and reputable databases to support your statements. Include proper citations and references to acknowledge the work of other researchers and avoid plagiarism.
• Analyze and Interpret Your Findings : Once you have collected and analyzed your data, interpret the results in the context of your research question. Discuss the implications of your findings and consider alternative explanations or limitations of your study. Engage in critical thinking and provide thoughtful insights based on your analysis.
• Address Limitations : Acknowledge the limitations of your study and discuss potential sources of bias or confounding factors. This demonstrates a critical understanding of the research process and adds credibility to your work. Suggest areas for future research that can overcome these limitations and contribute to further knowledge in the field.
• Follow Proper Formatting and Citation Style : Adhere to the formatting guidelines specified by your academic institution or the journal you intend to submit your research paper to. Use the appropriate citation style, such as APA, MLA, or Chicago, and ensure consistency throughout your paper. Pay attention to details, such as margins, font size, headings, and references.
• Revise and Edit : Before submitting your research paper, revise and edit it thoroughly. Check for grammar and spelling errors, sentence structure, and overall coherence. Read your paper aloud or ask a colleague to review it for clarity and flow. Make necessary revisions to improve the quality and readability of your paper.

Writing a physical fitness research paper requires careful planning, diligent research, and effective communication of your findings. By following these guidelines, you can craft a well-structured and informative paper that contributes to the field of physical fitness. Embrace the process of writing and view it as an opportunity to share your knowledge, insights, and passion for the subject matter. With dedication and attention to detail, your research paper can make a valuable contribution to the body of knowledge in physical fitness.

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