Mapping predictors of physical morbidity and mortality in schizophrenia
Lead Research Organisation:
University of Manchester
Department Name: School of Health Sciences
Abstract
Schizophrenia reduces life expectancy by 20 years. Rather than than suicide, this premature mortality is mainly due to cardiovascular and respiratory disease, driven by smoking, poor diet, lack of exercise, sedentary lifestyle and the metabolic effects of antipsychotic medication. Crude measures of sedentary lifestyle predict mortality independent of vigorous exercise in recent general population surveys. Schizophrenia sufferers exercise little, with negative symptoms and sedation probably also make them particularly sedentary. We hypothesise that this will emerge as a key element in an advanced statistical model of risk based on a two phase survey with a novel combination of measures.
The project's first experiment will assess feasibility and acceptability of actigraphy and diaries measures of general activity and circadian rhythms (CR) over 1 week in 8 schizophrenia sufferers using MAHSC & Greater Manchester West (GMW) services.
In the second experiment, 300 schizophrenia suffers will be asked to complete a survey to gather information on smoking, exercise, daily time spent sitting, CR and sleepiness, family history, morbidity and adherence to medication. This data will then link to metabolic and clinical data from Salford's unique single electronic service records and key worker estimates of symptom severity.
During the final experiment, a subsample will be identified and recruited for a detailed assessment of morbidity, fitness, actigraphy measures of activity and CR, and negative symptoms.
These data will be used to construct a population model of risk for physical mortality and morbidity. The project will examine the role of CR, sedentary lifestyle, symptoms and reversible risk factors.
These new metric and analytic techniques can then be applied to a range of populations, with precise measurement of critical variables like activity. Understanding the interplay of risk predictors will guide interventions and demonstrate what targets have the greatest value, including reducing time spent sitting. The student will be well equipped to apply such techniques to surveying other groups or supporting evaluation of interventions.
The project's first experiment will assess feasibility and acceptability of actigraphy and diaries measures of general activity and circadian rhythms (CR) over 1 week in 8 schizophrenia sufferers using MAHSC & Greater Manchester West (GMW) services.
In the second experiment, 300 schizophrenia suffers will be asked to complete a survey to gather information on smoking, exercise, daily time spent sitting, CR and sleepiness, family history, morbidity and adherence to medication. This data will then link to metabolic and clinical data from Salford's unique single electronic service records and key worker estimates of symptom severity.
During the final experiment, a subsample will be identified and recruited for a detailed assessment of morbidity, fitness, actigraphy measures of activity and CR, and negative symptoms.
These data will be used to construct a population model of risk for physical mortality and morbidity. The project will examine the role of CR, sedentary lifestyle, symptoms and reversible risk factors.
These new metric and analytic techniques can then be applied to a range of populations, with precise measurement of critical variables like activity. Understanding the interplay of risk predictors will guide interventions and demonstrate what targets have the greatest value, including reducing time spent sitting. The student will be well equipped to apply such techniques to surveying other groups or supporting evaluation of interventions.
Organisations
Publications
Berry A
(2018)
Investigating the Agreement Between Cardiovascular Disease Risk Calculators Among People Diagnosed With Schizophrenia
in Frontiers in Psychiatry
Stubbs B
(2016)
How much physical activity do people with schizophrenia engage in? A systematic review, comparative meta-analysis and meta-regression
in Schizophrenia Research
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
MR/N013751/1 | 30/09/2016 | 29/09/2025 | |||
1789937 | Studentship | MR/N013751/1 | 30/09/2016 | 30/03/2020 | Alexandra Berry |
Description | Flexible Training Supplement Award |
Amount | £3,637 (GBP) |
Funding ID | P119489/ZL07 |
Organisation | Medical Research Council (MRC) |
Sector | Public |
Country | United Kingdom |
Start | 05/2019 |
End | 10/2019 |