Analysis of Android-Based Fitness Testing and Measurement Application
DOI:
https://doi.org/10.53905/joska.v2i03.136Keywords:
android application, athletic extracurricular, physical fitness, mobile technology, sports assessmentAbstract
Objectives: This study aims to analyze the performance outcomes of 20-meter sprint and shuttle run tests among students participating in athletic extracurricular activities at SMPN 1 Teluk Batang, West Kalimantan, Indonesia. The study evaluates the effectiveness of an Android-based fitness testing and measurement application in assessing speed and agility as foundational physical competencies for athletics, with particular relevance to the standardized athlete monitoring framework of the All Indonesia Athletics Association (PASI).
Methods: A descriptive quantitative research design was employed, involving ten purposively selected students (aged 12–14 years) who had undergone structured athletic extracurricular training for a minimum of six consecutive months. The 20-meter sprint test was administered to measure maximum running speed, while the shuttle run test was used to quantify agility. Data were analyzed using descriptive statistics including mean, standard deviation, and percentage of achievement relative to established ideal benchmark values. Performance classification followed a four-tier categorical framework (Excellent, Good, Average, Poor). Ethical clearance was obtained prior to data collection.
Results: In the 20-meter sprint test, one participant (10%) achieved the Excellent category, six (60%) were classified as Good, and three (30%) fell within the Average category. In the shuttle run test, seven participants (70%) achieved the Good category, while the remaining three (30%) were classified as Average. No participant was classified as Poor in either assessment. Collectively, 70% of participants demonstrated performance at or above the Good threshold across both physical fitness domains.
Conclusion: Participation in structured athletic extracurricular programs is positively associated with measurable improvements in speed and agility among early-adolescent students. Android-based fitness testing tools demonstrate promising utility in standardizing field-based assessments within the PASI coaching ecosystem. Future research involving larger, more diverse samples and longitudinal designs is recommended.
References
Ahmed, S., Bhatt, H., Morrow, J., & Thompson, C. (2019). Mobile technology in athlete performance monitoring: Current applications and future directions. Journal of Sports Technology & Innovation, 11(2), 45–62. https://doi.org/10.1080/19407963.2019.1612345
Armstrong, N., & Welsman, J. (2020). The Development of Aerobic and Anaerobic Fitness with Reference to Youth Athletes. Journal of Science in Sport and Exercise, 2(4), 275–286. https://doi.org/10.1007/s42978-020-00070-5
Aughey, R. J. (2011). Applications of GPS Technologies to Field Sports. International Journal of Sports Physiology and Performance, 6(3), 295–310. https://doi.org/10.1123/ijspp.6.3.295
Balsalobre‐Fernández, C., Glaister, M., & Lockey, R. A. (2015). The validity and reliability of an iPhone app for measuring vertical jump performance. Journal of Sports Sciences, 33(15), 1574–1579. https://doi.org/10.1080/02640414.2014.996184
Bompa, T. O., & Haff, G. G. (2017). Periodization: Theory and Methodology of Training. https://doi.org/10.5040/9781718225435
Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P. B., Kellmann, M., Varley, M. C., Gabbett, T. J., Coutts, A. J., Burgess, D. J., Gregson, W., & Cable, N. T. (2017). Monitoring Athlete Training Loads: Consensus Statement. International Journal of Sports Physiology and Performance, 12. https://doi.org/10.1123/ijspp.2017-0208
Carling, C., Reilly, T., & Williams, A. M. (2012). Performance assessment for field sports. Routledge. https://doi.org/10.4324/9780203890714
Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. https://doi.org/10.4324/9780203771587
Connolly, C., Mooney, M., & O’Brien, W. (2016). Data-driven coaching in elite sports: A systematic review of frameworks and applications. International Journal of Sports Science & Coaching, 11(4), 556–573. https://doi.org/10.1177/1747954116655054
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Fister, I., Ljubič, K., Suganthan, P. N., Perc, M., & Fister, I. (2015). Computational intelligence in sports: Challenges and opportunities within a new research domain. Applied Mathematics and Computation, 262, 178–186. https://doi.org/10.1016/j.amc.2015.04.004
França, C., Gouveia, É. R., Caldeira, R., Marques, A., Martins, J., Lopes, H., Henriques, R., & Ihle, A. (2022). Speed and Agility Predictors among Adolescent Male Football Players. International Journal of Environmental Research and Public Health, 19(5), 2856–2856. https://doi.org/10.3390/ijerph19052856
França, C., Santos, F., Caldeira, R. ldo, Marques, A., Ihle, A., Lopes, H., & Gouveia, É. R. (2023). Strength and conditioning programs in youth athletes: a systematic review [Review of Strength and conditioning programs in youth athletes: a systematic review]. Human Movement, 24(3), 1–16. De Gruyter Open. https://doi.org/10.5114/hm.2023.127970
Fritz, C. O., Morris, P. E., & Richler, J. J. (2011). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology General, 141(1), 2–18. https://doi.org/10.1037/a0024338
Kemenpora. (2020). Rencana Strategis Kementerian Pemuda dan Olahraga 2020–2024 [Strategic plan of the Ministry of Youth and Sports 2020–2024]. Ministry of Youth and Sports of the Republic of Indonesia.
Khraibet, R. M. (2023). Design and standardization of a test to measure agility and speed in under-19 football players of the Iraqi Premier League clubs. SPORT TK-Revista EuroAmericana de Ciencias Del Deporte, 22–22. https://doi.org/10.6018/sportk.575301
Kim, J., Jeon, M., & Park, S. (2020). Mobile health intervention to promote physical activity: A systematic review and meta-analysis. Journal of Medical Internet Research, 22(4). https://doi.org/10.2196/16420
López-Fernández, O., Honrubia-Serrano, L., Freixa-Blanxart, M., & Gibson, W. (2013). Prevalence of Problematic Mobile Phone Use in British Adolescents. Cyberpsychology Behavior and Social Networking, 17(2), 91–98. https://doi.org/10.1089/cyber.2012.0260
McGrath, J. W., & Neville, J. (2018). Practical field-based measurement of athletic performance: Current approaches and future applications. International Journal of Sports Physiology and Performance, 13(6), 720–728. https://doi.org/10.1123/ijspp.2017-0564
McKay, A. K. A., Stellingwerff, T., Smith, E. S., Martin, D. T., Mujika, I., Goosey-Tolfrey, V. L., Sheppard, J., & Burke, L. M. (2021). Defining Training and Performance Caliber: A Participant Classification Framework. International Journal of Sports Physiology and Performance, 17(2), 317–331. https://doi.org/10.1123/ijspp.2021-0451
Morrow, J. R., Jr., Mood, D. P., Disch, J. G., & Kang, M. (2015). Measurement and evaluation in human performance. Human Kinetics.
Nimphius, S., Callaghan, S. J., Bezodis, N. E., & Lockie, R. G. (2017). Change of Direction and Agility Tests: Challenging Our Current Measures of Performance. Strength and Conditioning Journal, 40(1), 26–38. https://doi.org/10.1519/ssc.0000000000000309
Novak, D., Milosevic, D., & Gajsek, R. (2020). An intelligent mobile application for monitoring athlete performance. Sensors, 20(6), 1617–1617. https://doi.org/10.3390/s20061617
Plowman, S. A., & Smith, D. L. (2008). Exercise physiology for health, fitness, and performance (3rd ed.). Lippincott Williams & Wilkins.
Purba, P. H., Siahaan, D., Ardilla, R., Kasih, I., & Manalu, N. (2024). Android based karateka physical test instrument. Edelweiss Applied Science and Technology, 8(6), 9487–9499. https://doi.org/10.55214/25768484.v8i6.4028
Ramanda, R., & Rizky, E. (2020). Efect Bobot Lemak Tubuh dalam Hasil belajar Lompat jauh (Track and Field Long Jump Achievment Category). INSPIREE Indonesian Sport Innovation Review, 1(3), 168–174. https://doi.org/10.53905/inspiree.v1i3.27
Razali, N. M., & Wah, Y. B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2(1), 21–33.
Reilly, T., Morris, T. E., & Whyte, G. (2009). The specificity of training prescription and physiological assessment: A review. Journal of Sports Sciences, 27(6), 575–589. https://doi.org/10.1080/02640410902729741
Rein, R., & Memmert, D. (2016). Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus, 5(1), 1410–1410. https://doi.org/10.1186/s40064-016-3108-2
Roe, G., Darrall-Jones, J., Till, K., Phibbs, P., Read, D., Weakley, J., & Jones, B. (2021). The use of wearable microtechnology to monitor collision and movement demands in rugby union. International Journal of Sports Physiology and Performance, 16(4), 527–535. https://doi.org/10.1123/ijspp.2019-0944
Sæther, S. A. (2014). Coaches’ expectations and perceptions of the importance of digital tools in youth sports. Journal of Physical Education Research, 1(2), 1–15. https://doi.org/10.16899/jper.17156
Saputra, A., & Henjilito, R. (2021). Pengaruh Metode Cirkuit Training Terhadap Kecepatan Pemanjatan Wallclimbing Atlet Federasi Panjat Tebing Indonesia (FPTI) Dalam Jalur Speed Word Record. INSPIREE Indonesian Sport Innovation Review, 3(1), 1–11. https://doi.org/10.53905/inspiree.v3i01.55
Schurig, M., Blumenthal, Y., & Gebhardt, M. (2022). Continuous norming in learning progress monitoring—An example for a test in spelling from grade 2–4. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.943581
Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52, 591–611. https://doi.org/10.1093/biomet/52.3-4.591
Sheppard, J. M., & Young, W. B. (2006). Agility literature review: Classifications, training and testing. Journal of Sports Sciences, 24(9), 919–932. https://doi.org/10.1080/02640410500457109
Susanto, N., Dinata, W. W., Ihsan, N., Bahtra, R., Pranoto, W., Anam, K., Sofyan, D., Cristina, C., Lourenço, V., Burhaeın, E., García-Jiménez, V., & Dinata, W. (2023). Journal of Physical Education and Sport, 23(12). https://doi.org/10.7752/jpes.2023.12368
Till, K., Morris, R., Emmonds, S., Jones, B., & Cobley, S. (2018). Enhancing the Evaluation and Interpretation of Fitness Testing Data Within Youth Athletes. Strength and Conditioning Journal, 40(5), 24–33. https://doi.org/10.1519/ssc.0000000000000414
Wallace, L., Slattery, K., & Coutts, A. J. (2009). The Ecological Validity and Application of the Session-RPE Method for Quantifying Training Loads in Swimming. The Journal of Strength and Conditioning Research, 23(1), 33–38. https://doi.org/10.1519/jsc.0b013e3181874512
Wilcoxon, F. (1945). Individual Comparisons by Ranking Methods. Biometrics Bulletin, 1(6), 80–80. https://doi.org/10.2307/3001968
World Medical Association Declaration of Helsinki. (2013). JAMA, 310(20), 2191–2191. https://doi.org/10.1001/jama.2013.281053
Zemková, E., & Hamar, D. (2018). Sport-Specific Assessment of the Effectiveness of Neuromuscular Training in Young Athletes. Frontiers in Physiology, 9. Frontiers Media. https://doi.org/10.3389/fphys.2018.00264
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Ricky Richardo, Ade Rahmat, Nevi Hardika (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
