| Application: | 12184, Type 2 diabetes, physical activity, sedentary behaviour and sleep. |
| Title: | Curating a longitudinal research resource using linked primary care EHR data–a UK Biobank case study |
| Size: | 35.0 MB |
| Archived: | 4 Aug 2022 |
| Stability: | Complete |
| Personal: | Contains individual-level data |
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Notes
Primary care EHR data are often of clinical importance to cohort studies however they require careful handling. Challenges include determining the periods during which EHR data were collected. Participants are typically censored when they deregister from a medical practice, however, cohort studies wish to follow participants longitudinally including those that change practice. Using UK Biobank as an exemplar, we developed methodology to infer continuous periods of data collection and maximize follow-up in longitudinal studies. This resulted in longer follow-up for around 40% of participants with multiple registration records (mean increase of 3.8 years from the first study visit). The approach did not sacrifice phenotyping accuracy when comparing agreement between self-reported and EHR data. A diabetes mellitus case study illustrates how the algorithm supports longitudinal study design and provides further validation. We use UK Biobank data, however, the tools provided can be used for other conditions and studies with minimal alteration.Application 12184
Type 2 diabetes, physical activity, sedentary behaviour and sleep.
Many lifestyle behaviours are associated with chronic disease, including Type-2 diabetes. Regular engagement in physical activity reduces risk whereas sedentary behaviour and sleep restriction / elongation increase risk. Questionnaires and accelerometry are commonly used to measure physical activity, sedentary behaviour and sleep patterns at a population wide level. We will use these measures to observe the association between physical activity, sedentary behaviour and sleep by self-report and accelerometry in people with Type 2 diabetes on a large scale. When data becomes available we will measure the impact of these lifestyle behaviours as well as fitness, on glycemic control. Chronic disease, including Type 2 diabetes poses a serious financial, personal and societal threat to the UK, with incidence rates continually rising. Evidence suggests that lifestyle behaviour modification could prevent the majority of Type 2 diabetes cases. This research will help us to establish the importance of physical activity, sedentary behaviours and sleep as lifestyle mediators in Type 2 diabetes prevention and treatment at a population wide level. Establishing the discrepancy between subjective and objective methods on a large scale will provide evidence for future lifestyle interventions, which will move towards better care for Type 2 diabetes patients. Standard epidemiological case control techniques will be employed to explore physical activity, sedentary behaviour and sleep in patients with Type 2 diabetes (cases) and those without (controls). Differences in self-report and device measured (accelerometry) will be measured in participants when both sets of data are available. When biomarker data and fitness data is available we will look at associations between glucose control (HbA1c and glucose) and levels of fitness, physical activity, sedentary behaviour and sleep. We require repeat assessment data to enable us to do a longitudinal analysis on the impact of physical activity on chronic disease risk. We require data (not samples) for the full cohort. We will then ourselves identify subgroups for analysis of lifestyle variables, initially focussing on individuals with a diagnosis of Diabetes above the age of 35 years, excluding those who took insulin within their first year, will be included along with 4 controls per case. Prospective data will also be requested.
| Lead investigator: | Professor Mike Trenell |
| Lead institution: | Newcastle University |
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