Restriction lift date: 2021-05-18
Activity profiles of adults aged 50 - 70 years: functional data analysis
| dc.availability.bitstream | embargoed | |
| dc.check.date | 2021-05-18 | |
| dc.contributor.advisor | O'Sullivan, Kathleen (Catherine) | en |
| dc.contributor.advisor | Fitzgerald, Tony | en |
| dc.contributor.author | Weedle, Richard | |
| dc.date.accessioned | 2020-05-18T11:11:38Z | |
| dc.date.available | 2020-05-18T11:11:38Z | |
| dc.date.issued | 2019-10 | |
| dc.date.submitted | 2019-10 | |
| dc.description.abstract | Physical activity has a major impact on health. Questionnaires are the most common method of physical activity assessment. While cost effective, these are subjective and can correlate poorly with actual activity levels. Accelerometers have gained popularity given their accuracy, objectivity and ability to capture large amounts of data. Simple summary measures such as the total or average activity over the day are often used. However, these fail to exploit the longitudinal nature of the data and do not capture the variation in activity levels throughout the day. This study intends to capitalise on this nature by implementing a functional data analysis approach. Activity data was collected from a cohort of 475 people in Mitchelstown in 2011. The individuals wore wrist worn accelerometers in a free living environment for a week. This data was collapsed into 1 minute epochs and each epoch was then aggregated over the week to get an estimate of daily circadian activity. The discrete wavelet transform was chosen as the smoothing technique to reveal the underlying functional nature of the data. This allows every individual in the cohort to be represented by a smooth activity profile. This study aimed to identify and characterise subgroups within a cohort based on these activity profiles. Functional principal component analysis was applied to these activity profiles in order to explore the dominant patterns within the data. Each individual’s profile was approximated by a weighted sum of profiles and these weights were then used to perform a cluster analysis. Five distinct subgroups were identified. These differed from each other in both the magnitude of the activity and the times at which the activity occured. A more simplified approach, based purely on the distance between profiles, was also implemented. Two distinct clustering methods identified the exact same 5 subgroups in the cohort. To ensure their robustness, these results were subject to a sensitivity analysis with respect to the epoch length, smoothing technique and number of functional components utilised in the clustering. Other studies have clustered accelerometer data in terms of absolute activity volume, as in high or low activity groups. However, they do not place too much value in using the granularity of the data to determine what time of day people are active. In addition to the high, moderate and low activity subgroups, our analysis revealed two subgroups which have a propensity to be active in either the morning or evening. It is suggested that these are indicative of an individual’s biological rhythm or chronotype. The Mitchelstown cohort was re-screened 5 years later in 2016, which presents an exciting opportunity to examine changes in these profiles over time. | en |
| dc.description.status | Not peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Weedle, R. P. 2019. Activity profiles of adults aged 50 - 70 years: functional data analysis. MRes Thesis, University College Cork. | en |
| dc.identifier.endpage | 113 | en |
| dc.identifier.uri | https://hdl.handle.net/10468/9977 | |
| dc.language.iso | en | en |
| dc.publisher | University College Cork | en |
| dc.rights | © 2019, Richard Weedle. | en |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| dc.subject | Functional data analysis | en |
| dc.subject | Accelerometer | en |
| dc.title | Activity profiles of adults aged 50 - 70 years: functional data analysis | en |
| dc.type | Masters thesis (Research) | en |
| dc.type.qualificationlevel | Masters | en |
| dc.type.qualificationname | MRes - Master of Research | en |
