Review of the interaction between lifestyle habits and personality disorders
Bibliographic record
Abstract
Introduction Individuals with personality disorders have a decreased life expectancy when compared to the general population in particular due to physical illnesses. Many factors can be associated with those physical illnesses such as lack of physical activity and bad nutritional habits. Moreover, physical activity and nutrition (lifestyle) intervention have shown great results in decreasing symptoms and improving condition in affective and anxiety disorders. However, little is known about the relation between lifestyle, and personality disorders. Objectives The purpose of this review is to regroup the available information on this topic. Methods In February 2021, we searched the literature using 4 databases for articles analyzing the relation between lifestyle and personality disorders. Twenty-one articles were included. Results In this review, we found few studies analyzing the relation between lifestyle and personality disorders. Most studies either used lifestyle measures as control variables or did not use such variables at all. Moreover, instruments used to measure lifestyle variables lacked precision at best. Two studies demonstrated a relation between early malnutrition and further development of personality disorders, but those results may be influenced by confounding variables and cannot indicate a clear link between nutrition and personality disorder. Conclusions Few evidences are available linking lifestyle to personality disorders in any way. This lack of evidence is surprising considering the multiple benefits individuals with personality disorders could get from it. More studies are needed to thoroughly analyze the impact of lifestyle on personality disorders and vice versa. Those studies need to use validated instruments to provide strong evidence about this relation. Disclosure No significant relationships.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".