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Title: Advancing our understanding of major depression and its assessment using experience sampling methodology
Author: Li, Yu-Mei
ISNI:       0000 0004 8510 1133
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2020
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The development of the classification systems of psychiatric disorders illustrates a long-term debate on whether a discrete and categorical or a continuum and spectral view best describes psychiatric disorders. This is because comorbidity between mental disorders is common and the symptoms of the same psychiatric disorders are heterogeneous. These all increase the difficulty to diagnose and to treat mental disorders. Generally, the diagnostic systems have attempted to develop diagnostic criteria of psychiatric disorders that resemble a solid medical model, where each disorder is described by a discrete category of symptoms. However, these attempts have been plagued by problems. Even though having a categorical classification system makes diagnoses easier than having nothing to work from, a categorical classification system cannot explain clinical conditions. Having alternative classification systems such as one based on dimensions (spectra) or a mixture of categorical and spectral classification systems may be better than using either classification system alone. It has been suggested that a mechanistic property clusters (MPC) would be a suitable alternative (Kendler et al., 2011). The MPC described psychiatric disorders are caused by multi-level causal loops and interactions. A similar view of symptoms forming a causal network – the network theory – has been advocated (Borsboom, 2008, 2017). The network theory hypothesizes that the present psychiatric symptoms are caused by the same and/or other psychiatric symptoms occurred earlier. This PhD project investigated the heterogeneous nature of the symptoms of major depression (MD) by using their repeated measures in daily-life settings in three studies. We used on-line questionnaires, mobile questionnaires, and activity sensors. MD symptoms at the moment were measured using mobile questionnaires and daily physiological signals were recorded using activity sensors. The on-line questionnaires were administered before and after the participants completed the momentary questionnaires. Study 1 measured the momentary MD symptoms using Android devices, Study 2 was a replicate of Study 1 and iOS devices were employed in Study 2. Study 3 measured the momentary MD symptoms and activity levels using Android and iOS devices and activity sensors. Study 3 was a replication of Studies 1 and 2. The replicability of the results across the three studies was tested. Meta-analysis was used to see whether there were similar patterns across studies. In addition to the network theory, discriminant and convergent validity of the mobile and retrospective MD assessments were tested. The heterogeneity of MD symptoms was examined in the relationships between momentary MD symptom ratings and controlling factors (i.e., age, gender, employment status, marital status, educational level, MD severity, circadian rhythm, personality traits and facets, daily activity and heart rate variability). The results suggested heterogeneity among MD symptoms in how they linked to other variables. These findings challenge the existing clinical practice of using the total sum-score of symptoms in clinical diagnoses. Marginal support for the network theory was reported. The findings showed moderate replicability across studies.
Supervisor: Mottus, Rene ; Bates, Timothy Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
Keywords: Major Depression ; experience sampling ; activity level ; personality ; network theory ; symptomatic heterogeneity