Exploring Sleep Patterns and Influencing Factors in Elite Female Football Athletes
Description
This dataset contains a novel collection of data from 21 elite female footballers who were continuously monitored for 17 days. The dataset includes measures of actigraphy, well-being, caffeine consumption, screen time and daily hand strength tests. The main objective is to gain a deeper understanding of the interactions between lifestyle, sleep and athletic performance.
Sleep is essential for physical and mental recovery, memory performance and brain development. Athletes' sleep quality can be significantly affected by various factors, such as rigorous training schedules, stress, light exposure and caffeine consumption. By closely examining these factors, this dataset supports the creation of personalised training models that take into account the individual sleep patterns and recovery needs of each athlete. Such personalised approaches aim to optimise training and recovery strategies to ultimately improve the overall performance and well-being of athletes.
Files
REST.zip
Files
(44.4 MB)
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Additional details
Dates
- Available
-
2024-06
Software
- Repository URL
- https://github.com/simula/REST
- Programming language
- Python
- Development Status
- Active