Diabetes disproportionately affects minority populations. Social support, and more specifically marriage, has been found to buffer the negative effects of diabetes and depression. Data collected from African Americans with type 2 diabetes in Atlanta and NHANES data were compared to examine whether marriage affects health status and mental health. Approximately, 1742 African Americans aged 18-80 were included in this study. Chi square analysis revealed that married men had lower rates of depression (15.9% vs. 24.7%) compared to unmarried men (p < 0.05), but the same effect was not found in women. The findings show that marriage was not associated with HbA1c, but was associated with rates of depression. The lack of association of HbA1c with marriage may be because marriage may not be the best proxy of social support in the African American community. Future research should focus on alternative forms of social support such as cohabitation, extend family, and friend.

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本研究旨在探討「健康、性格、習慣量表(HPH)」的臨床效度。HPH最初是由柯永河教授(民84)編製，後來廣泛使用在國內臨床場域中。發展至今已有中上程度的信效度支持，但過去較缺乏臨床上區辨與構念效度的研究，因此本研究旨在探討HPH區辨不同疾患的能力，以及以臨床疾患為受試時量尺之構念效度。 本研究回顧國內外類似測驗─MMPI、KMHQ、MCMI─的發展軌跡，並參照前人作法來進行HPH的臨床區辨效度研究。初步以臨床場域中常見的精神分裂症、重鬱症、低落型情感疾患、焦慮疾患，共257名患者為受試。先以共變數分析(ANCOVA)探討控制人口與臨床變項後，不同疾患組別在HPH的A、B、D類量尺的影響。再進一步使用羅吉斯迴歸(logistic regression)探討哪些量尺及其組合可以區辨兩兩疾患間的差異。最後，本研究也進行HPH的探索性因素分析(exploratory factor analysis)，以檢驗其臨床上的因素結構。 本研究發現，精神分裂症(A1)、躁症傾向(A2)、憂鬱自殺類(A3、B4、A4)、心理功能與健康(D1、D3、D4、D5、D6)量尺在共變數分析上的差異情形與假設大致相符，後續討論分析也支持強迫症(B5)量尺效度。羅吉斯迴歸中，A1、A3、B4、B5能在兩兩疾患間區辨有顯著預測力。其中A1能在精神分裂症與其他三組疾患的兩兩區辨中預測，A3能在重鬱症與另外兩組(精神分裂症、焦慮症)的兩兩區辨中預測，B4能在低落型情感與精神分裂症的兩兩區辨中預測，B5能在強迫症與其他疾患間的兩兩區辨中預測。但是在重鬱症與低落型情感疾患間，以及低落型情感與焦慮疾患間，沒有量尺能在兩者的區辨中有顯著預測力。而各兩兩疾患間整體區辨效果有中至高度的關聯性，分類正確率也多有七成以上，顯示HPH量表在臨床上的區辨效度獲得支持。 構念效度部分，A、D類量尺因素結構與當初編製的每個量尺構念相近，B類量尺構念雖與原量尺略有不同，但仍不違背原量尺編製架構，因此構念效度亦獲得支持。不過各量尺仍有值得編修之處，討論一節中針對結果提出HPH後續編修之建議。 最後，本研究也將此結果之臨床實務應用於討論一節中詳述，以供後續研究與實務者參考。 / The purpose of this study is to examine the clinical validity of the Health, Personality, and Habit Test (HPH). The HPH was developed by Dr. Yung-Ho Ko in 1995, and has been widely used in clinical settings. The HPH has demonstrated appropriate reliability and validity, but little research has been done on its differential and construct validity in the clinical settings. Therefore, the aim of this study is to explore the HPH’s ability to differentiate between disorders and its construct validity in clinical context. This research reviewed the developments of similar tests, such as MMPI, KMHQ, and MCMI, and examined validity of the HPH with the same methods. Subjects were 257 patients who suffered from common disorders in clinical settings, including schizophrenia, major depression, dysthymia, and anxiety disorders. ANCOVA was first used to explore whether different disorders have an effect on category A, B, and D scales after controlling demographic and clinical variables. Next, logistic regression was used to clarify which scales and combinations can differentiate between two of four disorders. Finally, exploratory factor analysis was conducted to examine the structure of HPH in clinical setting. The results of ANCOVA showed that the differences of schizophrenia scale (A1), manic scale (A2), depression/suicide scales (A3, B4, & A4), obsessive-compulsive disorder (OCD) scale (B5), and psychological function and health scales (D1, D3, D4, D5, D6) were partly consistent with assumptions, supporting the differential validity of HPH. The results of logistic regression analysis also supported the validity of A1, A3, B4, and B5 scales. More specifically, A1 was able to differentiate schizophrenia from any other three disorders, A3 was able to differentiate MDD from schizophrenia and anxiety disorders, B4 was able to differentiate dysthymia from schizophrenia, and B5 was able to differentiate OCD from other disorders. However, none of the scales was able to differentiate MDD from dysthymia, nor were they able to differentiate dysthymia from anxiety disorders. Moreover, each of the logistic regression functions showed moderate to high correlations, and most of them achieved high overall hit rates (above 70%), providing support for the clinical differential validity of the HPH. As for construct validity, these factors in category A and D scales were essentially similar to original scales. Similarly, factors in category B scales were compatible to original scales though difference was found. In sum, these results lent support to the construct validity of the HPH in the clinical settings. However, refining of the scales is needed and suggestions are discussed. Finally, the practical uses of the findings were also discussed.