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All Journal Medikora: Jurnal Ilmiah Kesehatan Olahraga Jurnal Keolahragaan Indonesian Journal of Sport Management
Kuston Sultoni
Universitas Pendidikan Indonesia
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Hubungan screen time dan tingkat aktivitas fisik mahasiswa di masa covid-19 dengan health related quality of life Raden Cyntani Araya; Yati Rukhayati; Imas Damayanti; Adang Suherman; Nur Indri Rahayu; Jajat Jajat; Kuston Sultoni
MEDIKORA Vol 21, No 1 (2022): April
Publisher : Faculty of Sports Sciences, Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21831/medikora.v21i1.47258

Abstract

Penelitian ini bertujuan untuk menguji hubungan screen time dan tingkat aktivitas fisik mahasiswa di masa covid-19 dengan health related quality of life. Metode yang digunakan dalam penelitian ini deskriptif korelasi dengan pendekatan kuantitatif. Sampel dalam penelitian sebanyak 360 orang mahasiswa aktif Universitas Pendidikan Indonesia. Instrumen pengambilan data mengunakan Global Physical Activity Questionnaire (GPAQ), Questionnaire For Screen Time Of Adolescents (QUEST), dan Health Related Quality Of Life SF-36 (HRQoL SF-36). Hasil dari analisis data yng diketahui bahwa dapat disimpulkan bahwa rata- rata MET mahasiswa UPI pada pandemi COVID- 19 berkisar 1027 MET. Level aktivitas fisik mahasiswa UPI pada pandemi COVID-19 tergolong sedang (nilai MET 600- 3000). Terdapat 8 aspek dalam kualitas hidup (HRQOL SF-36). Berikut 8 kualitas hidup yang terdiri dari: fungsi fisik, peran fisik, rasa nyeri, kesehatan umum, fungsi sosial, vitalitas, peran emosi, kesehatan mental. Hasil pengolahan data dalam penelitian ini menunjukan bahwa terdapat hubungan yang signifikan antara aktivitas fisik dan HRQOL dikarenakan nilai P = 0,000 0,05. Hal ini menunjukkan bahwa aktifitas fisik dapat menjadi salah satu faktor penyumbang kualitas hidup, tetapi screen time tidak menunjukan hubungan yang signifikan karena nilai P = 0,762 0,05.The relationship of screen time and physical activity level during covid-19 with health-related quality of life  among university studentsAbstractThis study aimed to test the relationship between screen time and physical activity levels of students during the Covid-19 period with health-related quality of life. The method used in this research was a descriptive correlation with a quantitative approach. The sample in the study was 360 active students at an Indonesian Education University. Physical activity was assessed using the Global Physical Activity Questionnaire (GPAQ). Screen time was measured using the Questionnaire For Screen Time of Adolescents (QUEST), and quality of life was assessed with the Health Related Quality of Life SF-36 (HRQoL SF-36). The results of the data analysis showed that the average the total metabolic equivalent of task (MET) per week  was 1027 MET, thus the level, was classified as moderate. There  were significant relationship between each 8 HRQoL subscale (i.e. Physical Function, Physical Role, Pain, General Health, Social Function, Vitality, Emotional Role, Mental Health) and. physical activity and HRQOL ( P value 0.000).  No significant correlation however was found between physical activity and screen time. This indicates that physical activity can be a contributing factor to quality of life, but screen time does not show a significant relationship because P value = 0.762 0.05.  
Hubungan physical activity dengan fine motor skills pada anak usia 4 tahun Nur Indri Rahayu; Aini Dewi Monica; Jajat Jajat; Kuston Sultoni
Jurnal Keolahragaan Vol 9, No 1: April 2021
Publisher : Program Studi Ilmu Keolahragaan Program Pascasarjana Universitas Negeri Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1851.807 KB) | DOI: 10.21831/jk.v9i1.34156

Abstract

Tujuan penelitian ini adalah menguji hubungan antara physical activity dengan fine motor skills pada anak usia 4 tahun. Metode yang digunakan adalah metode penelitian kuantitatif dengan pendekatan korelasional. Populasi dalam penelitian ini yaitu anak usia 4 tahun yang sedang menempuh pendidikan anak usia dini di PAUD, TK, dan KB di Kota Bandung. Jumlah sampel sebanyak 53 anak dengan teknik pengambilan sampel menggunakan purposive sampling. Instrumen yang digunakan berupa Accelerometer Actigraph dan 9-Hole Peg Test. Accelerometer Actigraph digunakan untuk mengukur tingkat physical activity atau aktivitas fisik dengan hasil yang menunjukan bahwa anak – anak paling banyak menghabiskan waktu di skor light daripada sedentary, moderate-to-vigorous dan vigorous. 9-Hole Peg Test digunakan untuk mengukur tingkat kemampuan motorik halus atau fine motor skills anak dengan hasil menunjukan bahwa anak lebih terampil dalam menggunakan tangan yang dominan. Data kemudian dianalisis dengan menggunakan Spearman Correlation Test. Hasil analisis data menunjukan tidak terdapat korelasi antara physical activity dengan fine motor skills baik pada tangan dominan (p=0,6780,05) maupun dengan tangan non dominan (p=0,1670,05) yang berarti tidak terdapat hubungan yang signifikan antara physical activity dengan fine motor skills pada anak usia 4 tahun. The relationship between physical activity and fine motor skills in 4-year-old children Abstract:The purpose of this study was to examine the relationship between physical activity and fine motor skills in 4-year-old children. The method used is a quantitative research method with the correlation research approach. The population in this study were 4-year-old children who were taking early education in PAUD, TK, and KB in Bandung City. A total of 53 4-year-old children participated in this study by using a purposive sampling technique. The instrumen used were Accelerometer Actograph and 9-Hole Peg Test. The accelerometer actigraph is used to measure the level of physical activity and the results show that children spend the most time on the light score rather than sedentary, moderate-to-vigorous and vigorous score. 9-Hole Peg Test is used to measure the level of fine motor skills of children and the results showing that children are more skilled in using the dominant hand. Data were analyzed using the Spearman Correlation Test. The results of data analysis showed there is no correlation between physical activity and fine motor skills both in dominant hand (p=0.6780,05) and with the non-dominant hand (p=0,1670,05) which meant there are no significant relationship between physical activity and fine motor skills in 4-year-old children.
Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method Syifa Wandani; Adang Suherman; Jajat; Kuston Sultoni; Yati Ruhayati; Imas Damayanti; Nur Indri Rahayu
Indonesian Journal of Sport Management Vol. 3 No. 2 (2023): Indonesian Journal of Sport Management
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/ijsm.v3i2.7173

Abstract

Actigraph is a widely used accelerometer for classifying physical activity levels in children, adolescents, adults, and older people. The classification of physical activity levels on Actigraph is determined through time calculations using cut-point formulas. The study aims to classify physical activity in young children according to the guidelines of the World Health Organization (WHO) using accelerometer data and machine learning methods. The study involved 52 young children (26 girls and 26 boys) aged 4 to 5 years in West Java, with an average age of 4.58 years. Physical activity and sedentary behavior of these early childhood were simultaneously recorded using the Actigraph GT3X accelerometer for seven days. The data from the Actigraph were analyzed using two algorithm models: the decision tree and support vector machine, with the Rapidminer application. The results from the decision tree model show a classification accuracy of 96.00% in categorizing physical activities in young children. On the other hand, the support vector machine model achieved an accuracy of 84.67% in classifying physical activities in young children. The decision tree outperforms the support vector machine in accurately classifying physical activities in early childhood. This research highlights the potential benefits of machine learning in sports and physical activity sciences, indicating the need for further development.
Co-Authors Adang Suherman Aini Dewi Monica Imas Damayanti Jajat Jajat, Jajat Nur Indri Rahayu Raden Cyntani Araya Syifa Wandani Yati Ruhayati Yati Rukhayati