Introduction
Chronic pain (CP) is a complex multidimensional experience severely affecting individuals’ quality of life. Multiple cognitive, affective, emotional, and interpersonal factors play a major role in CP. Furthermore, the psychological, social, and physical circumstances leading to CP show high inter-individual variability, thus making it difficult to identify core syndrome characteristics. In a biopsychosocial perspective, we aim at identifying a pattern of psycho-physical impairments that can reliably discriminate between CP individuals and healthy controls (HC) with high accuracy and estimated generalizability using machine learning.
Methods
A total of 118 CP and 86 HC were recruited. All individuals were administered several scales assessing quality of life, physical and mental health, personal functioning, anxiety, depression, beliefs about medical treatments, and cognitive ability. These features were trained to separate CP from HC using support vector classification and repeated nested cross-validation.
Results
Our psycho-physical classifier was able to discriminate CP from HC with 86.5% balanced accuracy and significance (
p
= 0.0001). The most reliable features characterizing CP were anxiety and depression scores, and belief of harm from prolonged pharmacological treatments; for HP, the most reliable features were physical and occupational functioning, and vitality levels.
Conclusion
Our findings suggest that, using psychological and physical assessments, it is possible to classify CP from HC with high reliability and estimated generalizability via (i) a pattern of psychological symptoms and cognitive beliefs characteristic of CP, and (ii) a pattern of intact physical functioning characteristic of HC. We think that our algorithm enables novel insights into potential individualized targets for CP-related early intervention programs.
Background
Substance Use Disorder (SUD) causes a great deal of personal suffering for patients. Recent evidence highlights how defenses and emotion regulation may play a crucial part in the onset and development of this disorder.
The aim of this study was to investigate potential differences in the defensive functioning between SUD patients and non-clinical controls. Secondly, we aimed at investigating the relationships between alexithymia and maladaptive/assimilation defenses.
Methods
The authors assessed defensive functioning (Response Evaluation Measure-71, REM-71), personality (MMPI-II), and alexithymia (TAS-20) of 171 SUD patients (17% female; mean age = 36.5), compared to 155 controls. Authors performed a series of ANOVAs to investigate the defensive array in SUD patients compared to that of non-clinical controls. Student t test for indipendent samples was used to compare clinical characteristics between the SUD group and the controls. To investigate the role of single defenses in explaining alexithimia’s subscores, stepwise multiple regression analysis were carried out on socio-demographic characteristics of participants (gender, age, and years of education), with REM-71 defenses as predictors.
Results
SUD patients presented a more maladaptive/assimilation (Factor 1) defensive array (p < .001). Among SUD sub-groups, Alcohol Use Disorder patients showed more disfuncional defenses. Factor 1 defenses were related to a worse psychological functioning. In addition, alexyhimia (particularly DIF) was strongly related to Factor 1 defenses, expecially Projection (38% of variance explained, β = .270, p < .001).
Conclusion
The REM-71 and the TAS-20 might be useful screening instruments among SUD patients.
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