i.
To explore how fractal analysis can be used to analyse accelerometer data obtained from children of the
Millennium Cohort Study (MCS) with the statistical software R. ii.
To interpret results of the analysis. iii.
To provide guidelines on how the proposed methods can be used in paediatric studies.
There is an increasing concern that children are becoming less active. Lack of physical activity may lead to many health problems such as obesity. The Millennium Cohort Study (MCS) was conducted to monitor the levels of physical activity in children. The use of accelerometer-based activity monitors results in high frequency time series data. These time series are highly non-periodic and turbulent. The fractal analysis is an appropriate method to describe such complex time series. It is very useful in analyzing highly irregular models as it focuses on variability and quantifies the roughness of the series.
During the first few weeks at ICH, I did a literature review on how fractal analysis is applied to different scientific fields. Fractal analysis is found to be extensively applied to a diverse range of scientific fields such as biology, epidemiology, medicine, physics, chemistry and meteorology. Previous works have shown positive findings and the fractal indices obtained have proven to be a useful description of complex systems. The fractal dimension by box counting method and the Hurst Exponent by the Detrended Fluctuation Analysis are the more popular methods for analysis; hence, both were chosen to analyze the MCS accelerometer data.
Following this, I use the R software to apply the methods to the data. The learning curve was steep for me as I have no previous experience in R. I took a few days to get familiarize with the software. It was challenging, but nonetheless interesting, to write a functional script that is able to generate the fractal dimension and Hurst
Exponent from the raw accelerometer data in the MCS. Fortunately, my supervisor, Dr Geraci was very helpful in guiding me to write the codes. It is intriguing to know that the script is used on real data (>600 samples) collated across UK and it can be adapted by future researchers for their own research. After the indices were generated, they were further analyzed using statistical tests based on different variables. Some of the variables do show interesting results. For example, it was found that there is a significant difference in the fractal index between different seasons during the holiday period. However, it was also surprising to find no correlations in all continuous variables (i.e. height, BMI). Finally, I wrote a report detailing the literature review, data analysis, results and discussion.
This studentship has given me an insightful experience of what entails a research project and I found it to be both challenging and rewarding. It was exceptionally beneficial for me to experience work in the field that I am intending to pursue in the future. I have gained new skills not only in literature searching and scientific field, but has also benefitted from learning new software, R, which will definitely be an asset to future projects that I may be involved in. During my 8 weeks at ICH, I also had the opportunity to attend some seminars and presentations in biostatistics and epidemiology, making my studentship a wholesome experience. Finally, I would like to express my gratitude to the people in the department who has made my internship an enjoyable one, and to Dr
Marco Geraci for all the help and guidance in the project.