Development of Physical Exercise Prescription Application for Reduction of Functional Movement Limitation in College Students

Authors

DOI:

https://doi.org/10.60027/ijsasr.2023.3729

Keywords:

Application; , Physical Exercise Prescription; , Functional Movement Limitation; , College Students; , FMS

Abstract

Background and Aims: The physical decline of college students is greatly affected by the modern lifestyle. College students sit in bad posture for a long time and use electronic products for a long time, and at the same time lack exercise, resulting in increasingly serious restrictions on the functional movement of the body. In this regard, we hope to use modern advanced technology to build an application that can guide college students with functional movement limitations to perform physical training. This paper aims; (1) To investigate the limitations of functional movements among college students. (2) To design physical exercise prescriptions for reduction of functional movement limitation. (3) To construct an application for physical exercise prescription for reduction of functional movement limitation. (4) To experience the application to compare the reduction of functional movement limitation with pre-test and post-test.

Materials and Methods: In the two flexible test movements of active straight leg raise and shoulder flexible in FMS, we added 4 and 12 observation factors by Modify Delphi. After the physical exercise prescription was investigated by the expert IOC, all the content was recognized by the expert. Based on the preliminary content, we have built the Physical Exercise Prescription Application for Reduction of Functional Movement Limitation with an exact validity value equal to 1 and reliability while conducting the Chi-Square test of the application and expert group, we found that the application and experts are consistent. For physical exercise prescriptions, we compared the application and experts to find that the Chi-Square value is between 1 to 3 and has a large consistency. The Chi-Square Value of the evaluation process is 0.087, and the consistency evaluated by the application and experts is as high as 76.8%.

Results: We used the application to conduct an 8-week experimental intervention of 35 college students. Using the physical exercise prescriptions recommended by the application, through the t-test, students' shoulder flexibility and active straight leg raise are raised significantly (P <0.05).

Conclusion: The application can solve the problem of functional movement limitation of college students. In the future, with the increase of capital investment and the expansion of data volume, the application will be able to solve the basic problems of functional movement limitation to more different levels of motion pyramids. To encourage to exanthem case more healthily.

References

Alves, V., & Silvestrini, A. P. (2020). Achieving the Right to Work in the Face of Technological Advances: Reflections on the Occasion of the ILO's Centenary. U. Bologna L. Rev., 5, 226.

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular systems biology, 12(7), 878.

Baker, R., Coenen, P., Howie, E., Williamson, A., & Straker, L. (2018). The short-term musculoskeletal and cognitive effects of prolonged sitting during office computer work. International journal of environmental research and public health, 15(8), 1678.

Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010. 19th International Conference on Computational Statistics Paris France, August 22-27, 2010 Keynote, Invited and Contributed Papers (pp. 177 - 186). Physica-Verlag HD.

Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015, August). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1721-1730).

Castro, O., Vergeer, I., Bennie, J., Cagas, J., & Biddle, S.J. (2021). Using the behavior change wheel to understand university students’ prolonged sitting time and identify potential intervention strategies. International Journal of Behavioral Medicine, 28, 360-371.

Chau, R.C.W., Thu, K.M., Hsung, R.T.C., & Lam, W.Y.H. (2023). Teeth reconstruction using artificial intelligence: trends, perspectives, and prospects. Journal of the California Dental Association, 51(1), 2199910.

Chen, Y., Cai, X., Li, J., Lin, P., Song, H., Liu, G., & Ma, X. (2023). The values and barriers of BIM implementation combination evaluation based on stakeholder theory: a study in China. Engineering, Construction and Architectural Management, 30(7), 2814-2836.

Chen, Y., Hsu, C.Y., Liu, L., & Yang, S. (2012). Constructing a nutrition diagnosis expert system, Expert Systems with Applications, 39(2), 2132-2156.

Chimera, N.J., Knoeller, S., Cooper, R., Kothe, N., Smith, C., & Warren, M. (2017). PREDICTION OF FUNCTIONAL MOVEMENT SCREEN™ PERFORMANCE FROM LOWER EXTREMITY RANGE OF MOTION AND CORE TESTS. International journal of sports physical therapy, 12(2), 173–181.

Cook, G., Burton, L., & Hoogenboom, B. (2006). Pre-participation screening: the use of fundamental movements as an assessment of function - part 1. North American journal of sports physical therapy: NAJSPT, 1(2), 62–72.

Cook, G., Burton, L., & Hoogenboom, B. (2006). Pre-participation screening: the use of fundamental movements as an assessment of function - part 2. North American journal of sports physical therapy: NAJSPT, 1(3), 132–139.

Dong, S., Zeng, L., Che, X., Du, X., Xu, H., Ji, C., & Li, Z. (2023). Application of Artificial Intelligence in Logging Identification of Fractures in Tight Reservoirs. Earth Science, 48(7), 2443-2461.

Fan, L., Lang, L. A. N. G., Xiao, J., Zhang, S., Chong, Y., & Lyu, S. (2022). Intelligent fault diagnosis expert system for a multi-parameter monitor based on fault tree. Journal of Biomedical Engineering, 39(3), 586-595.

Flasiński, M. (2016). Rule-Based Systems. In: Introduction to Artificial Intelligence. Springer, Cham.

Géron, A. (2022). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media, Inc.

Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: Springer.

He, M., Liu, J., & Meng, L. (2019). Research on the Current Situation and Countermeasures of College Students' Physical Activity and Sedentary Behavior--Taking Wuhan Business School as an Example. Fujian Sports Science and Technology, 38(1), 43-46.

Huang Wenhu.(2022). The Crisis and Value Reconstruction of Go Culture in the Age of Artificial Intelligence. Journal of Huaqiao University (Philosophy and Social Sciences Edition), (2), 24-34.

Izquierdo, M., Merchant, R.A., Morley, J.E., Anker, S.D., Aprahamian, I., Arai, H., ... & Singh, M.F. (2021). International exercise recommendations in older adults (ICFSR): expert consensus guidelines. The journal of nutrition, health & aging, 25(7), 824-853.

Kastelic, K., Kozinc, Ž., & Šarabon, N. (2018). Sitting and low back disorders: an overview of the most commonly suggested harmful mechanisms. Collegium antropologicum, 42(1), 73-79.

Khurram, I. Qazi, H.K., Lam, B.X., Gaoxiang, O., Xunhe, Y. (2016). Classification of epilepsy using computational intelligence techniques. CAAI Transactions on Intelligence Technology, 1(02), 137-149

Krihua, P.R., & Xu, L. (1984). Determination of the Number of Sample Units in a Simple Random Sampling Survey. Jiangxi Forestry Science and Technology, (3), 52-53.

LeCun, Y., et al. (2015). Deep Learning. Nature, 521, 436-444. https://doi.org/10.1038/nature14539

Lee, Y., & Park, K.H. (2006). Health Practices That Predict Recovery from Functional Limitations in Older Adults. American Journal of Preventive Medicine, 31(1), 25-31.

Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in human behavior, 31, 343-350.

Li, L., Chen, Y., Jia, F., Hu, W., Yang, H., Zhang, Y., & Li, M. (2019). The current situation of poor posture of college students in three universities in Shijiazhuang. Chinese School Health, 40(7), 1099-1101.

Li, T., Wang, X., Fang, F., Gu, W., & Li, B. (2018). Observation on the effect of functional training of core muscle groups in preventing recruits from lower back pain and improving core muscle function. Journal of the Second Military Medical University, (5), 538-542.

Liao, T., Li, L., Wang, Y.T. (2019). Effects of Functional Strength Training Program on Movement Quality and Fitness Performance Among Girls Aged 12–13 Years. Journal of Strength and Conditioning Research, 33(6), 1534-1541.

Lu, L., Wang, Y., Ma, Y., & Xu, J. (2021). Research and Analysis of Sports Artificial Intelligence Based on Knowledge Graph. Journal of Capital Institute of Physical Education, 33(1), 14.

Lynn, S.K., & Noffal, G.J. (2010). Hip And Knee Moment Differences Between High and Low Rated Functional Movement Screen (FMS) Squats. Medicine & Science in Sports & Exercise, 42(5), 402.

Mao, Z., Ye, L., Ding, T., & Qiu, L. (2022). Research on Intervention Strategies for Large-scale Improvement of Students' Physical Fitness in School Physical Education in the New Era from the Perspective of "Three Precisions". Journal of Tianjin Institute of Physical Education, 37(02), 125-130

Mathew, A., Amudha, P., & Sivakumari, S. (2021). Deep learning techniques: an overview. Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020, 599-608.

McKinney, W. (2022). Python for data analysis. O'Reilly Media, Inc.

Nagi, S. Z. (1976). An Epidemiology of Disability among Adults in the United States. The Milbank Memorial Fund Quarterly. Health and Society, 54(4), 439-467.

Newell, A., Shaw, J.C., & Simon, H.A. (1959). Report on a general problem-solving program. In IFIP congress, 256, 64.

Paleyes, A., Urma, R.G., & Lawrence, N.D. (2022). Challenges in deploying machine learning: a survey of case studies. ACM Computing Surveys, 55(6), 1-29.

Peebles, R., & Jonas, C. E. (2017). Sacroiliac joint dysfunction in the athlete: diagnosis and management. Current sports medicine reports, 16(5), 336-342.

Reilly, T., Morris, T., & Whyte, G. (2009). The specificity of training prescription and physiological assessment: A review. Journal of Sports Sciences, 27(6), 575-589.

Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 4510-4520.

Scannapieco, M. (2006). Data Quality: Concepts, Methodologies and Techniques. Data-Centric Systems and Applications. Springer.

Shaffer, C.M., Deo, A., Tudor, A., Shenoy, R., Danesh, C. D., Nathan, D., & Chen, Y. (2021). Self‐Programming Synaptic Resistor Circuit for Intelligent Systems. Advanced Intelligent Systems, 3(8), 2100016.

Simon, H.A. (1988). The science of design: Creating the artificial. Design Issues, 67-82.

Smith, P.D., & Hanlon, M.P. (2017). Assessing the effectiveness of the functional movement screen in predicting noncontact injury rates in soccer players. The Journal of Strength & Conditioning Research, 31(12), 3327-3332.

Song, R., Yang, S., & Cheng, Q. (2021). Investigation and Research on the Status Quo of College Students in Medical Colleges - Taking Zhuhai Campus of Zunyi Medical University as an example. Neijiang Science and Technology, 42(04), 116-117.

Su, B., Li, J., Xu, H., Xu, Z., Meng, J., Chen, X., & Li, F. (2022). Scientific training aids: application of flexible wearable sensors for motion monitoring. Chinese Science: Information Science, 52(1), 54-74.

Sun, L., Qin, X., Xu, J., & Xue, Z. (2022). Density Peak Clustering Algorithm Based on K-Nearest Neighbors and Optimal Allocation Strategy. Journal of Software, 33(04), 1390-1411.

Tahran, Ö., & Yeşilyaprak, S.S. (2020). Effects of modified posterior shoulder stretching exercises on shoulder mobility, pain, and dysfunction in patients with subacromial impingement syndrome. Sports Health, 12(2), 139-148.

Tee, J.C., Klingbiel, J.F., Collins, R., Lambert, M.I., & Coopoo, Y. (2016). Preseason Functional Movement Screen Component Tests Predict Severe Contact Injuries in Professional Rugby Union Players. Journal of strength and conditioning research, 30(11), 3194–3203.

Verbrugge, L.M., & Jette, A.M. (1994). The disablement process. Social science & medicine, 38(1), 1–14.

Verma, A.A., Murray, J., Greiner, R., Cohen, J.P., Shojania, K.G., Ghassemi, M., & Mamdani, M. (2021). Implementing machine learning in medicine. Cmaj, 193(34), E1351-E1357.

Wang, Y. (2017). Re-understanding of the kinematic chain, chain reaction, and functional training from the perspective of system theory. Journal of Shandong Institute of Physical Education, (3), 92-99.

Wright, A., Stevens, J., Galloway, R., Donahue, P., Sha, Z., & McCoy, S. (2023). Aortic stiffness increases during prolonged sitting independent of intermittent standing or prior exercise. European Journal of Applied Physiology, 123(3), 533-546.

Wu, F., Fan, S., Zhang, L., Wang, Y., Wang, L., & Chang, Q. (2021). The effect of corrective movement training on the intervention effect of physical fitness training for recruits. South China Journal of Defense Medicine, (5), 364-367.

Xu, J., Li, L., Ma, X.Q., Zhang, M., Qiao, J., Redding, S.R., & Ouyang, Y.Q. (2023). Fertility Intentions, Parenting Attitudes, and Fear of Childbirth among College Students in China: A Cross-Sectional Study. Journal of Pediatric and Adolescent Gynecology, 36(1), 65-71.

Xu, R., Zuo, H., Ji, Y., Li, Q., Wang, Z., Liu, H., Wang, J., Wei, Z., Li, W., Cong, L., Li, H., Jin, H., & Wang, J. (2021). Effects of Short-Term Limitation of Movement of the First Metatarsophalangeal Joint on the Biomechanics of the Ipsilateral Hip, Knee, and Ankle Joints During Walking. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 27, e930081.

Yan, Q., Li, S., & Fu, B. (2012). A case study of applying IHP dual training method to improve lower limb motor function of elite hockey players. Journal of Beijing Sport University, (12), 126-129.

Zhang, L., Li, Z., & Ma, Z. (2022). Research progress of rehabilitation physical training in non-specific low back pain. Contemporary Sports Science and Technology, 17, 146-149.

Zhang, X., Chen, F., & Zhao, Z. (2009). Prevention and rehabilitation measures of ankle joint injuries in gymnasts. Chinese Journal of Sports Medicine, 4, 452-453.

Zhang, Y., Wu, L., & Wang, S. (2010). Survey on development of the expert system. Computer Engineering and Applications, 46(19), 43-47.

Downloads

Published

2023-10-20

How to Cite

Lin, P., Tongdecharoen, W. ., & Tasnaina, N. (2023). Development of Physical Exercise Prescription Application for Reduction of Functional Movement Limitation in College Students. International Journal of Sociologies and Anthropologies Science Reviews, 3(5), 421–446. https://doi.org/10.60027/ijsasr.2023.3729