Assessing Lifestyle in Psychiatric Disorders
Bibliographic record
Abstract
Introduction: Major depressive disorder (MDD), generalized anxiety disorder (GAD), and bipolar disorder (BD) are mental illnesses associated with socio-cognitive functional impairments, reduced quality of life, and increased risk of medical and psychiatric comorbidities. These disorders are also associated with unhealthy patterns in several fundamental lifestyle areas according to the current notions of lifestyle psychiatry, such as diet, physical activity, substance use, sleep, stress management, and social relationships. With the rising prevalence rates of poor mental well-being following the onset of the COVID-19 pandemic, it is essential to understand the relationship between a multifactorial lifestyle and the presence of psychiatric symptoms. Thus, the aim of this thesis was to assess the association between a multidimensional lifestyle and symptoms of MDD and GAD during the COVID-19 pandemic, and lifestyle patterns among symptomatic individuals with BD. We additionally reviewed the literature on lifestyle interventions for improvement of outcomes related to BD. Results: Unhealthy lifestyle behaviours were associated with symptoms of MDD and GAD during the COVID-19 pandemic in Spain, Brazil, and Canada. Machine learning analyses revealed strong predictive power for detecting the presence of these symptoms through lifestyle behaviours and perceptions. Individuals with BD engage in more unhealthy lifestyles than healthy individuals across all the core areas of lifestyle psychiatry, regardless of the polarity of the mood episode. Furthermore, to date, traditional lifestyle domains such as diet, physical activity, and sleep have been the most frequently targeted domains for interventions to improve mood symptoms and functional outcomes of BD, while domains such as substance use, stress management and social relationships have been more neglected. In addition, multidimensional lifestyle interventions have demonstrated a higher efficacy rate of improving outcomes of BD than single-domain interventions, however, there has been a lack of interventions for BD targeting majority of the core lifestyle domains. Conclusion: The findings suggest that multidimensional unhealthy lifestyles are associated with symptoms of MDD, GAD, and BD. These results support the current notions of lifestyle psychiatry, indicating that a multidimensional assessment of lifestyle behaviours and perceptions can be a beneficial approach towards understanding the cumulative impact of various lifestyle patterns on psychiatric symptoms. This work highlights the importance of imposing a holistic approach towards studying the association between lifestyle factors and psychiatric disorders in order to implement effective, personalized preventive and treatment strategies for mental health disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".