Training Volume and Intensity of Physical Activity among Young Athletes: The Health Promoting Sports Club (HPSC) Study

Aira, Tuula; Salin, Kasper; Vasankari, Tommi; Korpelainen, Raija; Parkkari, Jari; Heinonen, Olli J.; Savonen, Kai; Alanko, Lauri; Kannas, Lasse; Selänne, Harri; Villberg, Jari; Vähä‐Ypyä, Henri; Kokko, Sami

doi:10.4236/ape.2019.94019

Cited by 11 publications

(11 citation statements)

References 31 publications

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“…at least one hour per day on average) [ 27 ]. Corresponding results have previously been found in a sample of SC participants [ 30 ]. However, one should take into account that the population in the present study represents young people who are more active than the average in Finland; in fact the mean daily MVPA was eight minutes more than that found in a population-based study [ 31 ].…”

Section: Discussionsupporting

confidence: 88%

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Aira

Vasankari

Heinonen

et al. 2021

Int J Behav Nutr Phys Act

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Background Longitudinal studies demonstrate an average decline in physical activity (PA) from adolescence to young adulthood. However, while some subgroups of adolescents decrease activity, others increase or maintain high or low activity. Activity domains may differ between subgroups (exhibiting different PA patterns), and they offer valuable information for targeted health promotion. Hence, the aim of this study was to identify PA patterns from adolescence to young adulthood; also to explore the associations of (i) changes in PA domains and in sedentary time, (ii) sociodemographic factors, and (iii) self-rated health with diverging PA patterns. Methods The observational cohort study data encompassed 254 adolescents at age 15 and age 19. K-means cluster analysis for longitudinal data was performed to identify participant clusters (patterns) based on their accelerometry-measured moderate-to-vigorous PA (MVPA). Logistic regressions were applied in further analysis. Results Five PA patterns were identified: inactivity maintainers (n = 71), activity maintainers (n = 70), decreasers from moderate (to low) PA (n = 61), decreasers from high (to moderate) PA (n = 32), and increasers (n = 20). At age 15, participation in sports clubs (SC, 41–97%) and active commuting (AC, 47–75%) was common in all the patterns. By age 19, clear dropout from these activities was prevalent (SC participation mean 32%, AC 31–63%). Inactivity maintainers reported the lowest amount of weekly school physical education. Dropout from SC – in contrast to non-participation in SC – was associated with higher odds of being a decreaser from high PA, and with lower odds of being an inactivity maintainer. Maintained SC participation was associated with higher odds of belonging to the decreasers from high PA, and to the combined group of activity maintainers and increasers; also with lower odds of being an inactivity maintainer. Maintenance/adoption of AC was associated with decreased odds of being an inactivity maintainer. Self-reported health at age 19 was associated with the patterns of maintained activity and inactivity. Conclusions PA patterns diverge over the transition to adulthood. Changes in SC participation and AC show different associations with diverging PA patterns. Hence, tailored PA promotion is recommended.

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Section: Discussionsupporting

confidence: 88%

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Aira

Vasankari

Heinonen

et al. 2021

Int J Behav Nutr Phys Act

Self Cite

View full text Add to dashboard Cite

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“…Therefore, the pursuit of safety and privacy in sports IoT is a significant issue directly related to the evolution of the modern sports industry [ 5 ]. Based on the criticality of the environment in question in this work, a technique for optimizing cyber defense technologies using advanced machine learning methods is proposed.…”

Section: Introductionmentioning

confidence: 99%

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Xiao

Chen

2022

Computational Intelligence and Neuroscience

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Individual and team performance can be improved by utilizing “smart” devices and applications that are connected through networks. In sports, the Internet of Things (IoT) refers to all of the “smart” devices and applications linked through networks to reduce injuries to the bare minimum, develop advanced training techniques, and apply analytical advanced sports improvement methodologies to improve sports performance in general. The Internet of Things (IoT) in sports is closely related to the objective of both security and privacy in sports, which has become a topic of crucial concern for the sports business in recent years, as evidenced by the adoption of IoT in sports years. For this reason, security flaws can have catastrophic consequences, including the disclosure of personal data, the manipulation of statistical findings, the harming of organizations’ reputations, and enormous financial losses for the sporting organization. One or more of the consequences, as previously mentioned, is related to sports organizations and the athletes who are members of those organizations, and they have a direct impact on the corresponding set of sports-related, medical-related, and paramedical enterprises, specifically those that provide specialized sports equipment and associated services. A critical need to detect and quantify threats has long been recognized to better support decision-making when adopting or constructing a safe and reliable sports Internet-of-Things infrastructure, which is becoming increasingly common. Using advanced machine learning algorithms, this research provides a methodology for technology optimization in cybersecurity defenses that is then used in a unique case study utilizing volleyball players to demonstrate its effectiveness. In conjunction with a Monte Carlo optimization technique, an upgraded variant of fuzzy cognitive maps (FCM) is presented in greater detail. This model is utilized for a specific scenario of risk identification of volleyball industry, assessment, and optimization for IoT sports networks.

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“…A key innovation used by the entire sports industry is the athlete's involvement in capturing valuable information daily [ 4 ]. The time that the athlete must devote is usually identified with the hours of his daily training, and the data recorded is generally divided into three main categories [ 5 ]. Wellness: The athlete's well-being is recorded daily based on his answer to seven critical questions.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Sports Wearable Data Fusion Framework

Zhang

2022

Computational Intelligence and Neuroscience

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When the sports industry has access to advanced training and preparation techniques, the sports sector is entering a new era, where real-time data processing services have a crucial priority in improving physical fitness and avoiding injuries to athletes. The primary sports support methodology is based on multiple sensors, mainly wearables, often of different types and technology, which collect somatometric data in real time and are usually analyzed with deep learning technologies. And while modern athletes train and prepare intelligently using the innovative techniques of available technology, there is considerable concern about the use of personal data. There is great concern about cyberattacks and possible data leaks that could affect the sports industry and sports in general. To secure the personal data of athletes collected and analyzed by sports wearables, this paper presents a privacy-preserving sports wearable data fusion framework. This is an advanced methodology based on Lagrange's relaxation method for the problem of multiple assignments and synthesis of information by numerous sensors and the use of differential privacy to access databases with personal information, ensuring that this information will remain personal without a third entity may disclose the identity of the athlete who provided the data.

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Training Volume and Intensity of Physical Activity among Young Athletes: The Health Promoting Sports Club (HPSC) Study

Cited by 11 publications

References 31 publications

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Physical activity from adolescence to young adulthood: patterns of change, and their associations with activity domains and sedentary time

Tech Optimization in Cybersecurity Defenses by Advanced ML Methods: The Use Case of Volleyball Industry

Privacy-Preserving Sports Wearable Data Fusion Framework

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