How are poor sleepers with other clinical conditions affected by maladaptive personality traits? A neural network-based analysis DOI Creative Commons
Habibolah Khazaie, Farzin Rezaei, Ali Zakiei

и другие.

Frontiers in Psychiatry, Год журнала: 2024, Номер 15

Опубликована: Июль 12, 2024

Background Psychopathology research mainly focused on the cross-sectional and longitudinal associations between personality psychiatric disorders without considering moment-to-moment dynamics of in response to environmental situations. The present study aimed both cluster a young sample according three mixed clinical conditions (poor sleep quality, depression, somatization) predict derived clusters by maladaptive traits sex differences using deep machine learning approach. Methods A 839 adults aged 18-40 years (64% female) from west Iran were clustered analysis techniques. An Artificial Neural Network (ANN) modeling is used biological gender. receiver operating characteristic (ROC) curve was identify independent variables with high sensitivity specific clusters. Results techniques suggested fully stable acceptable four-cluster solution for Depressed Poor Sleepers, Nonclinical Good Subclinical Clinical Sleepers. ANN model led identification one hidden layer two units. results Area under ROC Curve relatively completely acceptable, ranging from.726 to.855. Anhedonia, perceptual dysregulation, depressivity, anxiousness, unusual beliefs are most valuable importance higher than 70%. Conclusion approach can be well traits. Future test complexity normal connected conditions.

Язык: Английский

Personality and Sleep Psychopathology: Associations Between the DSM‐5 Maladaptive Trait Domains and Multiple Sleep Problems in an Adult Population DOI Open Access
Ali Zakiei, Habibolah Khazaie,

Mohammadreza Alimoradi

и другие.

Personality and Mental Health, Год журнала: 2025, Номер 19(1)

Опубликована: Фев. 1, 2025

ABSTRACT Given the lack of sufficient studies exploring nature sleep problems from perspective alternative model personality disorders (AMPD) proposed by DSM‐5, present study is aimed at determining associations between five trait domains such as negative affectivity and (insomnia, parasomnia, hypersomnia, circadian rhythm disorder, restless legs syndrome, sleep‐disordered breathing) in an adult population. Adults aged 18–65 western Iran were invited to via virtual platforms ( N = 928; 62% female) responded online Brief Form Personality Inventory for DSM‐5 Holland Sleep Disorder Questionnaire assess problems. The regression analyses indicated that AMPD could significantly predict both specific R 2 ranges 0.13 0.17; all p ≤ 0.001) total score 0.23; < 0.001). Psychoticism β 0.26 0.39; 0.14 0.29; 0.002) strongest associated with findings highlighted links maladaptive multiple unique profiles each problem are useful selecting treatments tailored adults.

Язык: Английский

Процитировано

0
How are poor sleepers with other clinical conditions affected by maladaptive personality traits? A neural network-based analysis DOI Creative Commons
Habibolah Khazaie, Farzin Rezaei, Ali Zakiei

и другие.

Frontiers in Psychiatry, Год журнала: 2024, Номер 15

Опубликована: Июль 12, 2024

Background Psychopathology research mainly focused on the cross-sectional and longitudinal associations between personality psychiatric disorders without considering moment-to-moment dynamics of in response to environmental situations. The present study aimed both cluster a young sample according three mixed clinical conditions (poor sleep quality, depression, somatization) predict derived clusters by maladaptive traits sex differences using deep machine learning approach. Methods A 839 adults aged 18-40 years (64% female) from west Iran were clustered analysis techniques. An Artificial Neural Network (ANN) modeling is used biological gender. receiver operating characteristic (ROC) curve was identify independent variables with high sensitivity specific clusters. Results techniques suggested fully stable acceptable four-cluster solution for Depressed Poor Sleepers, Nonclinical Good Subclinical Clinical Sleepers. ANN model led identification one hidden layer two units. results Area under ROC Curve relatively completely acceptable, ranging from.726 to.855. Anhedonia, perceptual dysregulation, depressivity, anxiousness, unusual beliefs are most valuable importance higher than 70%. Conclusion approach can be well traits. Future test complexity normal connected conditions.

Язык: Английский

Процитировано

2