Skip to main content

Advertisement

SpringerLink
Log in

A data mining-based analysis of cognitive intervention for college students’ sports health using Apriori algorithm

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In recent years, the importance of cognitive intervention promoting physical health and well-being among college students has gained significant attention. The cognitive dimension is pivotal in determining individuals’ behaviors and decisions, especially in domains where personal choices significantly impact well-being. However, with increasing awareness concerning the advantages of exercise and healthy behaviors, many college students require assistance prioritizing their physical health because of various problems, including current academic schedules and the stress of postgraduate college admissions. To address these issues, this study presented data mining algorithms to assess the efficacy of cognitive treatments in improving college students’ health and fitness through sports activity. It smoothly integrates sports education into a larger social development framework, emphasizing the vital significance of holistic well-being and the numerous benefits of rigorous fitness and physical training regimens. It analyzes the efficacy of college students’ sports learning using data mining to clarify the Apriori and decision tree algorithms. It addresses students’ sports perceptions, cognitive interventions for sports health and health standards in college physical education. This paper evaluates the intervention’s success based on sports value cognition, sports need cognition and sports attitude recognition to improve student performance. The analysis of undergraduate students’ post-intervention scores revealed a considerable rise in these areas, with the experimental group scoring 14.2 and the control group scoring 11.53. The specific scores for sports value cognition were 17.86 and 15.7, while those for sports attitudes and cognition were 11.28 and 9.56, respectively. The impact of the cognitive intervention is further analyzed concerning body shape and physical fitness. The results reveal that a higher proportion of students in the intervention group engaged in physical exercise than the control group, especially with more than three weekly exercise sessions. The intervention and control groups’ participation rates were 20.2% and 6.75%, respectively. Notably, the intervention group demonstrated a higher proportion (5.78%) of students engaging in five or more physical exercises per week than the control group (1.95%).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

Not applicable.

References

  • A, M. S. (2021) L 2–L∞ control for delayed singular markov switch system with nonlinear actuator faults. Int J Fuzzy Syst 23(7):2297–2308

    Article  MathSciNet  Google Scholar 

  • Ali M, Yin B, Kunar A, Sheikh AM et al. (2020) Reduction of Multiplications in Convolutional Neural Networks. In 2020 39th Chinese Control Conference (CCC). IEEE. pp. 7406–7411. https://doi.org/10.23919/CCC50068.2020.9188843

  • Bao X, Chen D, Shi L, Xia Y, Shi Z, Wang D (2022) The relationship between COVID-19-related prevention cognition and healthy lifestyle behaviors among university students: mediated by e-health literacy and self-efficacy. J Affect Disord 309:236–241

    Article  Google Scholar 

  • Bao N, Zhang T, Huang R, Biswal S, Su J, Wang Y, Cha Y (2023) A deep transfer learning network for structural condition identification with limited real-world training data. Struct Control Health Monit 2023:8899806

    Article  Google Scholar 

  • Chen Z (2019) Observer-based dissipative output feedback control for network T-S fuzzy systems under time delays with mismatch premise. Nonlinear Dyn 95:2923–2941

    Article  MATH  Google Scholar 

  • Chen Y, Chen Z, Guo D, Zhao Z, Lin T, Zhang C (2022) Underground space use of urban built-up areas in the central city of Nanjing: Insight based on a dynamic population distribution. Underground Space 7(5):748–766

    Article  Google Scholar 

  • Cheng B, Wang M, Zhao S, Zhai Z, Zhu D, Chen J (2017) Situation-aware dynamic service coordination in an IoT environment. IEEE/ACM Trans Netw 25(4):2082–2095

    Article  Google Scholar 

  • Daley MM, Shoop J, Christino MA (2023) Mental Health in the Specialized Athlete. Curr Rev Musculoskel Med 16:1–9

    Article  Google Scholar 

  • Ekin CC, Polat E, Hopcan S (2023) Drawing the big picture of games in education: a topic modeling-based review of past 55 years. Comput Educ 194:104700

    Article  Google Scholar 

  • Fan L, Zhang J, Wang F, Liu S, Lin T (2023) Exploring outdoor activity limitation (O.A.L.) factors among older adults using interpretable machine learning. Aging Clin Exp Res 35:1–12

    Article  Google Scholar 

  • Hazrat B, Yin B, Kumar A, Ali M, Zhang J, Yao J (2023) Jerk-bounded trajectory planning for rotary flexible joint manipulator: an experimental approach. Soft Comput 27(7):4029–4039. https://doi.org/10.1007/s00500-023-07923-5

    Article  Google Scholar 

  • Huang H, Hwang GJ, Jong MSY (2022) Technological solutions for promoting employees’ knowledge levels and practical skills: An SVVR-based blended learning approach for professional training. Comput Educ 189:104593

    Article  Google Scholar 

  • Izzat N, Abu-Farha R, Harahsheh MAM, Thiab S (2023) A qualitative assessment of anabolic–androgenic steroid use among gym users in Jordan: motives, perception, and safety. Int J Legal Med 137:1–10

    Article  Google Scholar 

  • Kang E, Kim MY, Lipsey KL, Foster ER (2022) Person-centered goal setting: a systematic review of intervention components and level of active engagement in rehabilitation goal-setting interventions. Arch Phys Med Rehabil 103(1):121–130

    Article  Google Scholar 

  • Kumar A, Shaikh AM, Li Y et al (2021) Pruning filters with L1-norm and capped L1-norm for CNN compression. Appl Intell 51:1152–1160. https://doi.org/10.1007/s10489-020-01894-y

    Article  Google Scholar 

  • Li Q, Hou J (2021) Fault detection for asynchronous T-S fuzzy networked Markov jump systems with new event-triggered scheme. IET Control Theory Appl 15(11):1461–1473

    Article  MathSciNet  Google Scholar 

  • Liu Y, Wang K, Liu L, Lan H, Lin L (2022) Tcgl: temporal contrastive graph for self-supervised video representation learning. IEEE Trans Image Process 31:1978–1993

    Article  Google Scholar 

  • Liu X, Zhou G, Kong M, Yin Z, Li X, Yin L, Zheng W (2023a) Developing multi-labelled corpus of twitter short texts: a semi-automatic method. Systems 11(8):390

    Article  Google Scholar 

  • Liu X, Shi T, Zhou G, Liu M, Yin Z, Yin L, Zheng W (2023b) Emotion classification for short texts: an improved multi-label method. Hum Soc Sci Commun 10(1):1–9

    Google Scholar 

  • Lu J, Liu L, Zheng J, Zhou Z (2022) Interaction between self-perceived disease control and self-management behaviours among Chinese middle-aged and older hypertensive patients: the role of subjective life expectancy. BMC Public Health 22(1):733

    Article  Google Scholar 

  • Lu S, Liu M, Yin L, Yin Z, Liu X, Zheng W (2023) The multi-modal fusion in visual question answering: a review of attention mechanisms. PeerJ Computer Sci 9:e1400

    Article  Google Scholar 

  • Papageorgiou E, Krommidas C, Digelidis N, Moustakas L, Papaioannou AG (2023) An online PETE course on intercultural education for pre-service physical education teachers: a non-randomized controlled trial. Teach Teach Educ 121:103920

    Article  Google Scholar 

  • Ponzano M, Tibert N, Brien S, Funnell L, Gibbs JC, Keller H, Laprade J, Morin SN, Papaioannou A, Weston Z, Wideman TH (2023) International consensus on the non-pharmacological and non-surgical management of osteoporotic vertebral fractures. Osteoporos Int. https://doi.org/10.1080/09638288.2023.2239146

    Article  Google Scholar 

  • Qian J, Wang W, Sun S, Wu M, Liu L, Sun Y, Yu X (2022) Exploring interactions between women who have experienced pregnancy loss and obstetric nursing staff: a descriptive qualitative study in China. BMC Pregnancy Childbirth 22(1):450

    Article  Google Scholar 

  • Rahe M, Wolff F, Jansen P (2022) Relation of mindfulness, heartfulness and well-being in students during the coronavirus-pandemic. Int J Appl Posit Psychol 7(3):419–438

    Article  Google Scholar 

  • Shahwan S, Yunjue Z, Satghare P, Vaingankar JA, Maniam Y, Janrius GCM, Lin TW, Roystonn K, Subramaniam M (2022) Employer and co-worker perspectives on hiring and working with people with mental health conditions. Community Ment Health J 58(7):1252–1267

    Article  Google Scholar 

  • Shao Z, Zhai Q, Guan X (2023) Physical-model-aided data-driven linear power flow model: an approach to address missing training data. IEEE Trans Power Syst. https://doi.org/10.1109/TPWRS.2023.3256120

    Article  Google Scholar 

  • So JCH, Ho YH, Wong AKL, Chan HC, Tsang KHY, Chan APL, Wong SCW (2023) Analytic study for predictor development on student participation in generic competence development activities based on academic performance. IEEE Trans Learn Technol. https://doi.org/10.1109/TLT.2023.3291310

    Article  Google Scholar 

  • Tóth-Nagy G, Utasi A, Neumanné VI, Sebestyén V (2023) Data-driven supporting of Schwartz attitude model for a deeper understanding of sustainability awareness in Eastern European countries. Environ Sustain Indic 17:100226

    Google Scholar 

  • Ullah R, Dai X, Sheng A (2020) Event-triggered scheme for fault detection and isolation of non-linear system with time-varying delay. IET Control Theory Appl 14(16):2429–2438

    Article  MathSciNet  Google Scholar 

  • Wang J (2023) Design of analysis platform for college students’ physical learning effect based on data mining algorithm. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE. pp. 1–6

  • Wang L, Zhai Q, Yin B,et al. (2019) Second-order convolutional network for crowd counting. Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980T. https://doi.org/10.1117/12.2540362

  • Wang Q, Lee KCS, Hoque KE (2023) The mediating role of classroom climate and student self-efficacy in the relationship between teacher leadership style and student academic motivation: evidence from China. Asia Pac Educ Res 32(4):561–571

    Article  Google Scholar 

  • Wu B, Gu Q, Liu Z, Liu J (2023) Clustered institutional investors, shared E.S.G. preferences and low-carbon innovation in family firm. Technol Forecast Soc Change 194:122676

    Article  Google Scholar 

  • Xiong Z, Liu Q, Huang X (2022a) The influence of digital educational games on preschool children’s creative thinking. Comput Educ 189:104578

    Article  Google Scholar 

  • Xiong Z, Weng X, Wei Y (2022b) SandplayAR: Evaluation of psychometric game for people with generalized anxiety disorder. Arts Psychother 80:101934

    Article  Google Scholar 

  • Xu H, Sun Z, Cao Y et al (2023) A data-driven approach for intrusion and anomaly detection using automated machine learning for the Internet of Things. Soft Comput. https://doi.org/10.1007/s00500-023-09037-4

    Article  Google Scholar 

  • Yao W, Guo Y, Wu Y, Guo J (2017) Experimental validation of fuzzy PID control of flexible joint system in presence of uncertainties. In 2017 36th Chinese Control Conference (CCC). IEEE. pp. 4192–4197. https://doi.org/10.23919/ChiCC.2017.8028015

  • Yin B, Khan J, Wang L, Zhang J, Kumar A (2019) Real-time lane detection and tracking for advanced driver assistance systems. In 2019 Chinese Control Conference (CCC). IEEE. pp. 6772–6777. https://doi.org/10.23919/ChiCC.2019.8866334

  • Yin B, Aslam MS et al (2023) A practical study of active disturbance rejection control for rotary flexible joint robot manipulator. Soft Comput 27:4987–5001. https://doi.org/10.1007/s00500-023-08026-x

    Article  Google Scholar 

  • Zenggang X, Mingyang Z, Xuemin Z, Sanyuan Z, Fang X, Xiaochao Z, Yunyun W, Xiang L (2022) Social similarity routing algorithm based on socially aware networks in the big data environment. J Signal Process Syst 94(11):1253–1267

    Article  Google Scholar 

  • Zhang W, Pan G, Han D, Liu T, Liang W, Dai M, Xie H, Qin D, Niu L (2023) Regulating bimetallic active centers for exploring the structure–activity relationship toward high-performance photocatalytic nitrogen reduction. Materials Today Nano 22:100323

    Article  Google Scholar 

Download references

Funding

No funding was provided for the completion of this study. No funds, grants, or other support was received.

Author information

Authors and Affiliations

  1. Department of Leisure-Sports, Pai Chai University, Daejeon, 35345, South Korea

    Qi Hao & Woong Jae Choi

  2. School of Sports and Health, Linyi University, Linyi, 276000, Shandong, China

    Jie Meng

Authors
  1. Qi Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Woong Jae Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jie Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Meng.

Ethics declarations

Conflict of interest

The authors have no financial or proprietary interests in any material discussed in this article. The authors declare that they have no conflict of interest.

Ethical approval

Not Applicable.

Informed consent

Not Applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hao, Q., Choi, W.J. & Meng, J. A data mining-based analysis of cognitive intervention for college students’ sports health using Apriori algorithm. Soft Comput 27, 16353–16371 (2023). https://doi.org/10.1007/s00500-023-09163-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-023-09163-z

Keywords

Search

Navigation