Qualification Type: | PhD |
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Location: | Exeter |
Funding for: | UK Students, EU Students, International Students |
Funding amount: | £19,237 |
Hours: | Full Time, Part Time |
Placed On: | 11th September 2024 |
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Closes: | 4th November 2024 |
Reference: | 5239 |
Project description
About the GW4 BioMed2 Doctoral Training Partnership
The partnership brings together the Universities of Bath, Bristol, Cardiff (lead) and Exeter to develop the next generation of biomedical researchers. Students will have access to the combined research strengths, training expertise and resources of the four research-intensive universities, with opportunities to participate in interdisciplinary and 'team science'. The DTP already has over 90 studentships over 6 cohorts in its first phase, along with 58 students over 3 cohorts in its second phase.
Project Information
Research Theme: Population Health Sciences
Summary:
The menstrual cycle can influence mental wellbeing for a variety of biological and psychosocial reasons, but high-quality data are lacking. This is an exciting opportunity to work with people that menstruate to co-produce and trial a cutting-edge smart technology method to collect real-time data on menstrual experiences. You will advance our understanding of the important intersection of the menstrual cycle and mental health and develop skills highly valued in the FemTech industry.
Project Description:
The menstrual cycle and menstrual experiences can substantially impact an individual’s mental health and wellbeing. Perhaps the most well-known example is premenstrual syndrome (PMS), a set of symptoms that occur in the luteal phase, including anxiety, low mood, and irritability, with a severe form being Premenstrual Dysphoric Disorder (PMDD). However, menstruation can influence mental wellbeing in other ways and at different cycle stages, e.g. through physical discomfort, changes to behaviours, and social effects arising from stigma.
There are many unaddressed or unanswered questions about the association between the menstrual cycle and mental health, for example: What biopsychosocial mechanisms increase risk of low mood and mental distress during certain stages of the menstrual cycle? And why do some individuals show large variability in their mental wellbeing within or between cycles?
Previous studies have been hampered by a lack of high-quality real-time data on mental health and menstrual symptoms throughout the menstrual cycle. However, smartphone apps and wearable devices like fitness trackers make it possible to collect quality, prospective, real-time data on physical and emotional states.
Ecological Momentary Assessment (EMA) is a cutting-edge method that involves collecting frequent data on experiences close in time to the experience itself, either actively (e.g. self-report of mood via a smartphone app) or passively (e.g. wearable sensors measuring physiological features including heart rate, temperature, activity and sleep). Innovative statistical models are needed to integrate and analyse these complex data to help researchers better understand menstrual cycle-related fluctuations in mental wellbeing.
Research questions:
1) How can EMA be used to collect high quality real-time data on mental wellbeing throughout the menstrual cycle?
2) How can EMA data be effectively integrated, analysed and visualised to provide insights into menstrual cycle-related variation in mental wellbeing?
3) How do mental wellbeing-related EMA measures vary in association with the menstrual cycle and menstrual characteristics?
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