{"title":"对重度抑郁症患者和健康人的职业表现特征进行判别分析。","authors":"Tomonari Hayasaka, Izumi Nagashima, Miku Hoshino, Koji Teruya, Yasuyuki Matumoto, Masami Murao, Taku Maruki, Masako Watanabe, Takeshi Katagiri, Yayoi Imamura, Mariko Kurihara, Yuki Oe, Yoshikazu Takaesu, Takashi Tsuboi, Koichiro Watanabe, Hitoshi Sakurai","doi":"10.1002/pcn5.70038","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>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.</p><p><strong>Methods: </strong>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 (<i>p</i> < 0.01). Discriminant analysis was used to enhance group differentiation, assessing prediction rates using area under the curve (AUC) values.</p><p><strong>Results: </strong>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\" (<i>p</i> < 0.001, odds ratio [OR] = 0.051, 95% confidence interval [CI] = 0.009-0.283), \"Concentrate on work\" (<i>p</i> = 0.003, OR = 0.078, 95% CI = 0.015-0.416), \"Choose simple work\" (<i>p</i> = 0.004, OR = 17.803, 95% CI = 2.446-129.597), and \"Punctual\" (<i>p</i> = 0.017, OR = 0.030, 95% CI = 0.002-0.530). Discriminant analysis using these variables yielded a Wilks' <i>λ</i> of 0.493 (<i>p</i> < 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%).</p><p><strong>Conclusion: </strong>This study highlights the importance of occupational performance characteristics in tailoring treatment strategies for MDD, providing insights beyond traditional assessment methods.</p>","PeriodicalId":74405,"journal":{"name":"PCN reports : psychiatry and clinical neurosciences","volume":"3 4","pages":"e70038"},"PeriodicalIF":0.0000,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598739/pdf/","citationCount":"0","resultStr":"{\"title\":\"Discriminant analysis of occupational performance characteristics in patients with major depressive disorders and healthy individuals.\",\"authors\":\"Tomonari Hayasaka, Izumi Nagashima, Miku Hoshino, Koji Teruya, Yasuyuki Matumoto, Masami Murao, Taku Maruki, Masako Watanabe, Takeshi Katagiri, Yayoi Imamura, Mariko Kurihara, Yuki Oe, Yoshikazu Takaesu, Takashi Tsuboi, Koichiro Watanabe, Hitoshi Sakurai\",\"doi\":\"10.1002/pcn5.70038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Aim: </strong>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.</p><p><strong>Methods: </strong>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 (<i>p</i> < 0.01). Discriminant analysis was used to enhance group differentiation, assessing prediction rates using area under the curve (AUC) values.</p><p><strong>Results: </strong>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\\\" (<i>p</i> < 0.001, odds ratio [OR] = 0.051, 95% confidence interval [CI] = 0.009-0.283), \\\"Concentrate on work\\\" (<i>p</i> = 0.003, OR = 0.078, 95% CI = 0.015-0.416), \\\"Choose simple work\\\" (<i>p</i> = 0.004, OR = 17.803, 95% CI = 2.446-129.597), and \\\"Punctual\\\" (<i>p</i> = 0.017, OR = 0.030, 95% CI = 0.002-0.530). Discriminant analysis using these variables yielded a Wilks' <i>λ</i> of 0.493 (<i>p</i> < 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%).</p><p><strong>Conclusion: </strong>This study highlights the importance of occupational performance characteristics in tailoring treatment strategies for MDD, providing insights beyond traditional assessment methods.</p>\",\"PeriodicalId\":74405,\"journal\":{\"name\":\"PCN reports : psychiatry and clinical neurosciences\",\"volume\":\"3 4\",\"pages\":\"e70038\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11598739/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PCN reports : psychiatry and clinical neurosciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/pcn5.70038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PCN reports : psychiatry and clinical neurosciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/pcn5.70038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Discriminant analysis of occupational performance characteristics in patients with major depressive disorders and healthy individuals.
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 (p < 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" (p < 0.001, odds ratio [OR] = 0.051, 95% confidence interval [CI] = 0.009-0.283), "Concentrate on work" (p = 0.003, OR = 0.078, 95% CI = 0.015-0.416), "Choose simple work" (p = 0.004, OR = 17.803, 95% CI = 2.446-129.597), and "Punctual" (p = 0.017, OR = 0.030, 95% CI = 0.002-0.530). Discriminant analysis using these variables yielded a Wilks' λ of 0.493 (p < 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.