Mental health data science in rich longitudinal population cohorts
Lead Research Organisation:
University of Bristol
Department Name: Bristol Medical School
Abstract
Online social networking sites such as Twitter, Facebook and Instagram have changed the way we live our lives. For recent generations, all aspects of human behaviour, both good and bad, are now reflected in the online as well as the offline worlds. This means that these networks are home to a vast wealth of information about our mental states, which researchers have begun to tap into to improve our understanding of the causes and consequences of mental health and wellbeing. However, although a huge amount of data is available, its true value to medicine can only be realised when we are able to link it to what we know about the lives of specific people in the offline world. Only by doing this can we validate our online measurements of mental health and understand the effect that the social media revolution is having on what it means to be human.
Research chapters will include:
1) Epidemiology of social media use
2) Validating existing social media predictors of mental health and developing new ones using gold-standard ground truth measures in epidemiological samples
3) High-resolution time-series analyses of genetic and environmental influences on mental health over time and in response to events
4) Causal analyses of the impact that social media use has on mental health
Research chapters will include:
1) Epidemiology of social media use
2) Validating existing social media predictors of mental health and developing new ones using gold-standard ground truth measures in epidemiological samples
3) High-resolution time-series analyses of genetic and environmental influences on mental health over time and in response to events
4) Causal analyses of the impact that social media use has on mental health
People |
ORCID iD |
Claire Haworth (Primary Supervisor) | |
Nina Di Cara (Student) |
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
MR/N013794/1 | 01/10/2016 | 30/09/2025 | |||
2359899 | Studentship | MR/N013794/1 | 01/10/2018 | 03/05/2022 | Nina Di Cara |
MR/S502455/1 | 01/10/2018 | 31/03/2022 | |||
2359899 | Studentship | MR/S502455/1 | 01/10/2018 | 03/05/2022 | Nina Di Cara |
Description | Mood Music: Inferring Wellbeing from Spotify |
Amount | £4,644 (GBP) |
Organisation | University of Bristol |
Department | Jean Golding Institute |
Sector | Academic/University |
Country | United Kingdom |
Start | 01/2020 |
End | 07/2020 |
Description | Mapping Community Resilience in Wales with Open Data |
Organisation | Public Health Wales NHS Trust |
Country | United Kingdom |
Sector | Public |
PI Contribution | This collaboration was led by the PI of my lab - as part of the lab team I was involved in doing work with Public Health Wales. Outputs are described below. |
Collaborator Contribution | Data science skills and time towards the COVID-19 map. Publication writing. |
Impact | COVID-19 Community Response Map & web app (see Software) - multidisciplinary: data science, statistics, data visualisation, software engineering, epidemiology. In press/accepted publication in International Journal of Population Data Science describing this work. Collaboration extended into a grant from the Health Foundation. |
Start Year | 2019 |
Title | COVID-19 Community Response Map |
Description | A web application that collates data from multiple administrative data sources and plots them using a bespoke data visualisation tool made using d3.js. This map can be used to select community attributes that might suggest a community is more in need of support, and others that suggest a community has existing support. These can then be used to create comparison of datasets that are of interest to the user without the need for them to do any data manipulation themselves. Data on the map includes |
Type Of Technology | Webtool/Application |
Year Produced | 2020 |
Open Source License? | Yes |
Impact | This piece of software was created in collaboration with Public Health Wales in response to the COVID-19 pandemic. This collaboration has been the basis of a further successful funding bid led by Public Health Wales to the Health Foundation's COVID-19 programme, through which we will continue to develop the map. We have also had a manuscript accepted by the International Journal of Population Data Science that describes this piece of work. |
URL | https://covidresponsemap.wales/ |