Investigating the relationship between diurnal rest-activity rhythms and mood disorders: a data-driven approach

Sangha, Natasha (2024) Investigating the relationship between diurnal rest-activity rhythms and mood disorders: a data-driven approach. PhD thesis, University of Glasgow.

Abstract

Within mental health disorders, mood disorders describe those that have a significant impact on an individual’s emotional state. These disorders represent a leading cause of disability worldwide and can have a severe impact on an individual’s quality of life, including frequently reported problems with diurnal patterns of rest and activity. As a result, sleep and activity patterns represent a target for future phenotypic markers and therapies, but further investigation is required.

Given the heterogeneity of symptoms, it is helpful to consider rest-activity patterns within sub-groups of mood disorders. Typically, these disorders are grouped into depressive disorders (characterised primarily by low mood or anhedonia) and bipolar disorders (characterised primarily by feeling unusually hyper or irritable, often with depressive episodes). Less acknowledgement has been given to a potential third group, unipolar mania, who experience episodes of mania but not depressive episodes. Within common diagnostic criteria (DSM-5 and ICD-11) unipolar mania is grouped with bipolar-I disorder, yet the limited research available suggests considerable differences in demographics and outcomes. Combining bipolar disorder and unipolar mania groups may be contributing to the variability of research findings in this area.

This thesis investigates the relationship between rest-activity rhythms and mood disorders in a large UK-based population (UK Biobank). Through a combination of statistical and machine learning methods it aims to (a) investigate whether these rhythms can help us validate the nosological status of unipolar mania; (b) characterise rest-activity rhythm differences in these mood disorder groups, including seasonal patterns; (c) determine how accurately mood disorder groups can be differentiated using rest-activity measures; and (d) compare rest-activity measures in criteria-driven vs data-driven mood disorder groupings.

• Item Type: Thesis (PhD)
• Qualification Level: Doctoral
• Subjects: R Medicine > R Medicine (General)
• Colleges/Schools: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Mental Health and Wellbeing
• Funder's Name: Medical Research Council (MRC)
• Supervisor's Name: Lyall, Dr. Laura, Cullen, Dr. Breda, Whalley, Dr. Heather and Smith, Professor Daniel
• Date of Award: 2024
• Depositing User: Theses Team
• Unique ID: glathesis:2024-84515
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 30 Aug 2024 08:51
• Last Modified: 30 Aug 2024 08:58
• Thesis DOI: 10.5525/gla.thesis.84515
• URI: https://theses.gla.ac.uk/id/eprint/84515

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