Protocol for Machine Learning on Children Physical Activity: A Systematic Literature Review
Abstract
Aktivitas fisik berperan penting dalam kesehatan anak dan remaja baik saat ini maupun di masa depan. Mengukur partisipasi mereka dalam aktivitas fisik menjadi langkah krusial dalam pengembangan intervensi yang efektif. Studi ini bertujuan untuk mengidentifikasi penggunaan teknologi machine learning dalam mengukur level aktivitas fisik anak dan remaja. Selain itu, artikel ini melaporkan metodologi systematic review secara komprehensif dan transparan guna memberikan kontribusi berbasis bukti dalam bidang keilmuan aktivitas fisik. Pencarian literatur dilakukan pada basis data Scopus, Web of Science, PubMed, Medline, dan ProQuest Central. Artikel yang dipilih adalah penelitian yang menggunakan algoritma machine learning untuk klasifikasi, kuantifikasi, monitoring, atau prediksi aktivitas fisik anak dan remaja (usia 0–18 tahun). Data yang diekstraksi meliputi tahun publikasi, negara, jumlah dan karakteristik partisipan, jenis model dan algoritma machine learning yang digunakan, tujuan penelitian, tipe aktivitas fisik, instrumen akuisisi data, performance metrics, serta rekomendasi penelitian lanjutan. Protokol systematic review ini telah terdaftar di International Prospective Register of Systematic Reviews (PROSPERO) pada 9 Mei 2023 dengan nomor registrasi CRD42023421941.
Artikel ini diharapkan dapat memberikan wawasan bagi pengembangan metode berbasis machine learning dalam analisis aktivitas fisik anak dan remaja.
Keywords
Full Text:
PDF (Bahasa Indonesia)References
Ahmadi, M. N., Pavey, T. G., & Trost, S. G. (2020). Machine learning models for classifying physical activity in free-living preschool children. Sensors (Switzerland), 20(16), 1–14. https://doi.org/10.3390/s20164364
Alsareii, S. A., Awais, M., Alamri, A. M., AlAsmari, M. Y., Irfan, M., Aslam, N., & Raza, M. (2022). Physical Activity Monitoring and Classification Using Machine Learning Techniques. Life, 12(8). https://doi.org/10.3390/life12081103
Al-Shabandar, R., Hussain, A. J., Laws, A., Keight, R., & Lunn, J. (n.d.). Machine Learning Approaches to Predict Learning Outcomes in Massive Open Online Courses.
Bach, K., Kongsvold, A., Bårdstu, H., Bardal, E. M., Kjærnli, H. S., Herland, S., Logacjov, A., & Mork, P. J. (2021). A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living. Journal for the Measurement of Physical Behaviour, 5(1), 24–31. https://doi.org/10.1123/jmpb.2021-0015
Candelino, M., Tagi, V. M., & Chiarelli, F. (2022). Cardiovascular risk in children: a burden for future generations. In Italian Journal of Pediatrics (Vol. 48, Issue 1). BioMed Central Ltd. https://doi.org/10.1186/s13052-022-01250-5
Farrahi, V., Niemelä, M., Kangas, M., Korpelainen, R., & Jämsä, T. (2019). Calibration and validation of accelerometer-based activity monitors: A systematic review of machine-learning approaches. In Gait and Posture (Vol. 68, pp. 285–299). Elsevier B.V. https://doi.org/10.1016/j.gaitpost.2018.12.003
Fergus, P., Hussain, A. J., Hearty, J., Fairclough, S., Boddy, L., Mackintosh, K., Stratton, G., Ridgers, N., Al-Jumeily, D., Aljaaf, A. J., & Lunn, J. (2017). A machine learning approach to measure and monitor physical activity in children. Neurocomputing, 228, 220–230. https://doi.org/10.1016/j.neucom.2016.10.040
Gangal, A., Kumar, P., Kumari, S., & Saini, A. (2020). Prediction models for healthcare using machine learning: A review. In Handbook of Research on Disease Prediction Through Data Analytics and Machine Learning (pp. 70–91). IGI Global. https://doi.org/10.4018/978-1-7998-2742-9.ch005
Genovesi, S., Giussani, M., Orlando, A., Battaglino, M. G., Nava, E., & Parati, G. (2019). Prevention of Cardiovascular Diseases in Children and Adolescents. In High Blood Pressure and Cardiovascular Prevention (Vol. 26, Issue 3, pp. 191–197). Springer International Publishing. https://doi.org/10.1007/s40292-019-00316-6
Hagenbuchner, M., Cliff, D. P., Trost, S. G., Van Tuc, N., & Peoples, G. E. (2015). Prediction of activity type in preschool children using machine learning techniques. Journal of Science and Medicine in Sport, 18(4), 426–431. https://doi.org/10.1016/j.jsams.2014.06.003
Hamid, A., Duncan, M. J., Eyre, E. L. J., & Jing, Y. (2020). Predicting children’s energy expenditure during physical activity using deep learning and wearable sensor data. European Journal of Sport Science, 1–9. https://doi.org/10.1080/17461391.2020.1789749
Hoodbhoy, Z., Jeelani, M. M., & Aziz, S. (2021). Machine Learning for Child and Adolescent Health: A Systematic Review. In Pediatrics (Vol. 147, Issue 1). www.aappublications.org/news
Jain, V., & Chatterjee, J. M. (Eds.). (2020). Machine Learning with Health Care Perspective (Vol. 13). Springer International Publishing. https://doi.org/10.1007/978-3-030-40850-3
Janssen, I., & Leblanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. In International Journal of Behavioral Nutrition and Physical Activity (Vol. 7). http://www.ijbnpa.org/content/7/1/40
Jones, P. J., Catt, M., Davies, M. J., Edwardson, C. L., Mirkes, E. M., Khunti, K., Yates, T., & Rowlands, A. V. (2021). Feature selection for unsupervised machine learning of accelerometer data physical activity clusters – A systematic review. In Gait and Posture (Vol. 90, pp. 120–128). Elsevier B.V. https://doi.org/10.1016/j.gaitpost.2021.08.007
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
Kamiri, J., & Mariga, G. (2021). Research Methods in Machine Learning: A Content Analysis. In International Journal of Computer and Information Technology (Vol. 10, Issue 2). www.ijcit.com78
Langerhuizen, D. W. G., Janssen, S. J., Mallee, W. H., Van Den Bekerom, M. P. J., Ring, D., Kerkhoffs, G. M. M. J., Jaarsma, R. L., & Doornberg, J. N. (2019). What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review. In Clinical Orthopaedics and Related Research (Vol. 477, Issue 11, pp. 2482–2491). Lippincott Williams and Wilkins. https://doi.org/10.1097/CORR.0000000000000848
Mannocci, A., D’egidio, V., Backhaus, I., Federici, A., Sinopoli, A., Varela, A. R., Villari, P., & Torre, G. La. (2020). Are there effective interventions to increase physical activity in children and young people? An umbrella review. In International Journal of Environmental Research and Public Health (Vol. 17, Issue 10). MDPI AG. https://doi.org/10.3390/ijerph17103528
Messing, S., Rütten, A., Abu-Omar, K., Ungerer-Röhrich, U., Goodwin, L., Burlacu, I., & Gediga, G. (2019). How can physical activity be promoted among children and adolescents? A systematic review of reviews across settings. In Frontiers in Public Health (Vol. 7, Issue MAR). Frontiers Media S.A. https://doi.org/10.3389/fpubh.2019.00055
Neville, R. D., Lakes, K. D., Hopkins, W. G., Tarantino, G., Draper, C. E., Beck, R., & Madigan, S. (2022). Global Changes in Child and Adolescent Physical Activity during the COVID-19 Pandemic: A Systematic Review and Meta-analysis. JAMA Pediatrics, 176(9), 886–894. https://doi.org/10.1001/jamapediatrics.2022.2313
Oja, P., & Titze, S. (2011). Physical activity recommendations for public health: Development and policy context. In EPMA Journal (Vol. 2, Issue 3, pp. 253–259). https://doi.org/10.1007/s13167-011-0090-1
Rossi, L., Behme, N., & Breuer, C. (2021). Physical activity of children and adolescents during the COVID-19 pandemic—A scoping review. In International Journal of Environmental Research and Public Health (Vol. 18, Issue 21). MDPI. https://doi.org/10.3390/ijerph182111440
Sember, V., Jurak, G., Kovač, M., Đurić, S., & Starc, G. (2020). Decline of physical activity in early adolescence: A 3-year cohort study. PLoS ONE, 15(3). https://doi.org/10.1371/journal.pone.0229305
Tomaczkowski, L., & Klonowska, J. (2020). Physical activity of school children and youth. Baltic Journal of Health and Physical Activity, 12(2), 83–87. https://doi.org/10.29359/BJHPA.12.2.09
Trost, S. G., Wong, W. K., Pfeiffer, K. A., & Zheng, Y. (2012). Artificial neural networks to predict activity type and energy expenditure in youth. Medicine and Science in Sports and Exercise, 44(9), 1801–1809. https://doi.org/10.1249/MSS.0b013e318258ac11
Tsao, C. W., Aday, A. W., Almarzooq, Z. I., Alonso, A., Beaton, A. Z., Bittencourt, M. S., Boehme, A. K., Buxton, A. E., Carson, A. P., Commodore-Mensah, Y., Elkind, M. S. V., Evenson, K. R., Eze-Nliam, C., Ferguson, J. F., Generoso, G., Ho, J. E., Kalani, R., Khan, S. S., Kissela, B. M., … Martin, S. S. (2022). Heart Disease and Stroke Statistics-2022 Update: A Report from the American Heart Association. In Circulation (Vol. 145, Issue 8, pp. E153–E639). Lippincott Williams and Wilkins. https://doi.org/10.1161/CIR.0000000000001052
Ullah, H. A., Letchmunan, S., Zia, M. S., Butt, U. M., & Hassan, F. H. (2021). Analysis of Deep Neural Networks for Human Activity Recognition in Videos - A Systematic Literature Review. In IEEE Access (Vol. 9, pp. 126366–126387). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ACCESS.2021.3110610
van Sluijs, E. M. F., Ekelund, U., Crochemore-Silva, I., Guthold, R., Ha, A., Lubans, D., Oyeyemi, A. L., Ding, D., & Katzmarzyk, P. T. (2021). Physical activity behaviours in adolescence: current evidence and opportunities for intervention. In The Lancet (Vol. 398, Issue 10298, pp. 429–442). Elsevier B.V. https://doi.org/10.1016/S0140-6736(21)01259-9
Zhou, M., Fukuoka, Y., Goldberg, K., Vittinghoff, E., & Aswani, A. (2019). Applying machine learning to predict future adherence to physical activity programs. BMC Medical Informatics and Decision Making, 19(1). https://doi.org/10.1186/s12911-019-0890-0
DOI: https://doi.org/10.17509/jtikor.v8i2.81977
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Imas Damayanti, Nur Indri Rahayu, Kuston Sultoni, Jajat Jajat
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.