Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data
Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2023-08-01
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| Series: | PLOS Digital Health |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468058/?tool=EBI |
| _version_ | 1827824927558860800 |
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| author | Francesca Romana Cavallo Christofer Toumazou |
| author_facet | Francesca Romana Cavallo Christofer Toumazou |
| author_sort | Francesca Romana Cavallo |
| collection | DOAJ |
| description | Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention. Author summary Mobile health applications employ wireless technology for healthcare to aid behaviour change and improve health. Mobile health applications have been developed to increase physical activity, but they present issues such as not using behavioural theory and not being personalised to the patient. In this work, we propose a mobile health intervention to increase physical activity and sedentary behaviour in people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes. The intervention is based on behavioural theory and consists of a recommendation system that is personalised to the patient’s characteristics and adapts to their skills. The system uses data from activity trackers, hormonal levels and genetic variants to issue highly personalised and effective recommendations. To prove the efficacy of the system, we simulated it using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrated that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention. |
| first_indexed | 2024-03-12T02:33:19Z |
| format | Article |
| id | doaj.art-e9694d3dcb2a421799f4e7da4e021acf |
| institution | Directory Open Access Journal |
| issn | 2767-3170 |
| language | English |
| last_indexed | 2024-03-12T02:33:19Z |
| publishDate | 2023-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLOS Digital Health |
| spelling | doaj.art-e9694d3dcb2a421799f4e7da4e021acf2023-09-05T05:32:02ZengPublic Library of Science (PLoS)PLOS Digital Health2767-31702023-08-0128Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank DataFrancesca Romana CavalloChristofer ToumazouMobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention. Author summary Mobile health applications employ wireless technology for healthcare to aid behaviour change and improve health. Mobile health applications have been developed to increase physical activity, but they present issues such as not using behavioural theory and not being personalised to the patient. In this work, we propose a mobile health intervention to increase physical activity and sedentary behaviour in people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes. The intervention is based on behavioural theory and consists of a recommendation system that is personalised to the patient’s characteristics and adapts to their skills. The system uses data from activity trackers, hormonal levels and genetic variants to issue highly personalised and effective recommendations. To prove the efficacy of the system, we simulated it using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrated that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468058/?tool=EBI |
| spellingShingle | Francesca Romana Cavallo Christofer Toumazou Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data PLOS Digital Health |
| title | Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data |
| title_full | Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data |
| title_fullStr | Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data |
| title_full_unstemmed | Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data |
| title_short | Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data |
| title_sort | personalised lifestyle recommendations for type 2 diabetes design and simulation of a recommender system on uk biobank data |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10468058/?tool=EBI |
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