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Approved Research

Wearable tech as a novel diagnostic tool

Principal Investigator: Miss Renske Lok
Approved Research ID: 63099
Approval date: September 2nd 2020

Lay summary

There is growing interest in the area of "wearable tech" and its relationship to health. These devices usually contain an accelerometer that can yield continuous information on activity levels. Previous studies have indicated correlational relationships between the shape of these activity patterns and the display of symptoms of Alzheimer disease. This was the first study showing the existence of such relationships, and this study highlighted the importance of the relationship between activity patterns and disease in the diagnosis of the disease. It is likely that such relationships also exist between 24-h activity patterns and other diseases. Therefore, it is timely to further investigate this. The aim of this study is therefore to investigate on a large scale whether there are (correlational) relationships between 24-h activity patterns and various diseases. The UK Biobank provides a unique opportunity to clarify whether these relationships exist, since it contains thousands of activity patterns as well as coinciding health related outcomes. 

 Given the amount of data stored in the Biobank and the time consuming procedure of activity data analysis, this phase of the project may take up to 8 months. Hereafter, the second phase of the project will consist of generating correlation analysis between activity patterns and health related outcomes, which will take approximately another 2 months. The total project duration will therefore be approximately 10 months.

It is essential to investigate the relationship between 24-hours activity patterns and various diseases, since changes in activity often comprise one of the first symptoms. If correlations were to be found between various patterns of activity and disease, it can be investigate whether such relationships are displayed at the onset of the disease and therefore offer a diagnostic tool. Investigating the relationship between 24-h patterns and disease is therefore a first step towards developing activity patterns as a diagnostic tool. This could impact the entire population.