Patient-centred technologies targeting genetics, activity, and metabolism for lifestyle and healthcare personalisation
File(s)
Author(s)
Cavallo, Francesca Romana
Type
Thesis or dissertation
Abstract
The integration of technologies such as artificial intelligence, high-throughput sequencing and smart sensors is blurring the lines between the physical, digital, and biological domains. As these technologies become even more accurate and accessible, they produce an increasing amount of clinical, biometric, and behavioural data that is set to change how humans manage their life and health.
This thesis explores how personalised healthcare can be realised with the integration of available technologies and data sources, and how new technologies can be developed and tested to improve the outcomes and everyday life of people with type 2 diabetes. With this aim, this work firstly investigates how physical behaviours impact diabetes, and whether there are individual differences in disease phenotype and in the response to physical behaviours. Secondly, these differences are exploited to create an adaptive and personalised intervention to aid physical behaviour change. The intervention is a knowledge-based system combining genomics, proteomics, clinical data, and data from wearable sensors to provide personalised activity recommendations aimed at reducing the risk of cardiovascular disease in people with diabetes. Moreover, a point-of-care device based on a novel sensor is developed with the aim of integrating it in the system to provide a feedback signal to further personalise the intervention. Finally, this work covers the methodological considerations required to assess personalised interventions. A new computational method to infer daily activities from accelerometer data is developed to accelerate the evaluation of behavioural interventions. The effect of personalised nutrition on a biomarker of diabetes and obesity is assessed, and how randomised controlled trials can be designed and employed to test innovative interventions is discussed.
This work represents a step towards the integration of multiple data sources able to create point-of-care personalised and adaptive therapies with the potential of revolutionising the treatment of diseases triggered by lifestyle behaviours.
This thesis explores how personalised healthcare can be realised with the integration of available technologies and data sources, and how new technologies can be developed and tested to improve the outcomes and everyday life of people with type 2 diabetes. With this aim, this work firstly investigates how physical behaviours impact diabetes, and whether there are individual differences in disease phenotype and in the response to physical behaviours. Secondly, these differences are exploited to create an adaptive and personalised intervention to aid physical behaviour change. The intervention is a knowledge-based system combining genomics, proteomics, clinical data, and data from wearable sensors to provide personalised activity recommendations aimed at reducing the risk of cardiovascular disease in people with diabetes. Moreover, a point-of-care device based on a novel sensor is developed with the aim of integrating it in the system to provide a feedback signal to further personalise the intervention. Finally, this work covers the methodological considerations required to assess personalised interventions. A new computational method to infer daily activities from accelerometer data is developed to accelerate the evaluation of behavioural interventions. The effect of personalised nutrition on a biomarker of diabetes and obesity is assessed, and how randomised controlled trials can be designed and employed to test innovative interventions is discussed.
This work represents a step towards the integration of multiple data sources able to create point-of-care personalised and adaptive therapies with the potential of revolutionising the treatment of diseases triggered by lifestyle behaviours.
Version
Open Access
Date Issued
2022-09
Date Awarded
2023-02
Copyright Statement
Creative Commons Attribution NonCommercial Licence
Advisor
Toumazou, Christofer
Sponsor
Engineering and Physical Sciences Research Council
Publisher Department
Electrical and Electronic Engineering
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)