Aim: Assessing symptoms and daily functioning in patients with major depressive disorder (MDD) can be challenging, as their limited self-monitoring abilities may result in behavior observed during structured interviews not accurately reflecting their daily lives. This study aimed to determine if specific occupational behaviors could distinguish individuals with MDD from healthy individuals.
Methods: Baseline data were collected from medical records and activity programs. Three occupational therapists conducted content analysis to assess occupational performance characteristics. Chi-squared tests compared the prevalence of these characteristics between patients with MDD and healthy controls. Multivariable logistic regression controlled for potential confounders, with independent variables selected based on clinical relevance and sample size ( < 0.01). Discriminant analysis was used to enhance group differentiation, assessing prediction rates using area under the curve (AUC) values.
Results: A total of 69 occupational performance characteristics were identified, with 12 showing significant differences between 27 patients with MDD and 43 healthy controls. Key discriminators included "Ask questions and consult" ( < 0.001, odds ratio [OR] = 0.051, 95% confidence interval [CI] = 0.009-0.283), "Concentrate on work" ( = 0.003, OR = 0.078, 95% CI = 0.015-0.416), "Choose simple work" ( = 0.004, OR = 17.803, 95% CI = 2.446-129.597), and "Punctual" ( = 0.017, OR = 0.030, 95% CI = 0.002-0.530). Discriminant analysis using these variables yielded a Wilks' of 0.493 ( < 0.001), achieving an 88.6% accuracy rate. The receiver operating characteristic curve's AUC value was 0.911 (sensitivity = 95.3%, specificity = 77.8%).
Conclusion: This study highlights the importance of occupational performance characteristics in tailoring treatment strategies for MDD, providing insights beyond traditional assessment methods.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598739 | PMC |
| http://dx.doi.org/10.1002/pcn5.70038 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFSuccessful Multi-Modal Laser Intervention and Histopathological Evaluation of Multiple Glomangiomas.
Lasers Surg Med
December 2024
Department of Dermatology, Veterans Health Administration, San Antonio, Texas, USA.
Objectives: Glomangiomas are benign vascular malformations that exist within the spectrum of glomuvenous malformations which consist of varying amounts of glomus cells, vascular spaces, and smooth muscle. Glomangiomas are often treated due to associated pain, particularly when located on pressure areas such as the back or extensor surfaces, which can cause difficulty with certain activities and occupational functions. Histologically glomangiomas consist of prominent dilated vascular spaces lined by glomus cells typically situated in the deep-dermis to subcutaneous fat which limits treatment to modalities capable of reaching the depth of the tumor including excision, sclerotherapy, and laser therapy.
View Article and Find Full Text PDFResearch on health information avoidance behavior and influencing factors of cancer patients-an empirical analysis based on structural equation modeling.
BMC Public Health
December 2024
Bengbu Medical University, School of Health Management, Bengbu, China.
Objective: To explore the health information avoidance behaviors and influencing factors of cancer patients, and to construct a structural equation model to analyze the mediating roles of self-efficacy and negative emotions in the process of generating health information avoidance behaviors of cancer patients.
Methods: A face-to-face electronic questionnaire was used to collect data. Applying a chi-square test and multivariate logistic regression model to analyze the role of different socio-demographic factors in influencing health information avoidance behavior of cancer patients; applying structural equation modeling to analyze the role mechanism of health information avoidance behavior of cancer patients.
Thyroid cancer survival and prognostic factors in Yantai, China (2012-2022): a population-based study.
BMC Cancer
December 2024
Department of Medicine, Shandong College of Traditional Chinese Medicine, Yantai, 264199, China.
Background: Although thyroid cancer is associated with low mortality rates, significant racial disparities in thyroid cancer outcomes have not been adequately studied in Asia. Moreover, the Asian population consists of different ethnic groups that are not homogeneous. This study aimed to perform a population-based analysis of survival outcomes and prognostic factors in thyroid cancer patients.
View Article and Find Full Text PDFA high-throughput, turbulent-mixing, condensation aerosol concentrator for direct aerosol collection as a liquid suspension.
J Aerosol Sci
November 2024
National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, Cincinnati, OH, 45226, United States.
Trace measurement of aerosol chemical composition in workplace atmospheres requires the development of high-throughput aerosol collectors that are compact, hand-portable, and can be operated using personal pumps. We describe the design and characterization of a compact, high flow, Turbulent-mixing Condensation Aerosol-in-Liquid Concentrator (TCALC) that allows direct collection of aerosols as liquid suspensions, for off-line chemical, biological, or microscopy analysis. The TCALC unit, measuring approximately 12 × 16 × 18 cm, operates at an aerosol sample flowrate of up to 10 L min, using rapid mixing of a hot flow saturated with water vapor and a cold aerosol sample flow, thereby promoting condensational growth of aerosol particles.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!