De Panfilis Chiara, Torre Mariateresa, Cero Sara, Salvatore Paola, Dall'Aglio Elisabetta, Marchesi Carlo, Cabrino Chiara, Aprile Sonja, Maggini Carlo
Unit of Psychiatry, Department of Neuroscience, Parma University Hospital, Parma, Italy.
Gen Hosp Psychiatry. 2008 Nov-Dec;30(6):515-20. doi: 10.1016/j.genhosppsych.2008.06.003. Epub 2008 Aug 3.
Some personality features, as measured by the Temperament and Character Inventory (TCI), have recently been found to be related to successful weight outcome after both behavioral and surgical therapies for obesity. However, personality features could possibly influence attendance in obesity treatments as well. Thus, the aim of this study was to explore whether personality variables assessed by the TCI predict attrition from a behavioral weight-loss program for obesity.
The TCI was administered to 92 obese patients [body mass index (BMI) >30 kg/m2] applying for a 6-month behavioral weight-loss program. Logistic stepwise regression analysis was performed to evaluate whether TCI scores predicted 6-month treatment attrition, after controlling for baseline psychiatric comorbidity, current age, gender, age at onset of obesity and initial BMI.
Sixty-two subjects (67.4%) completed the 6-month program, while 30 (32.6%) dropped out. Treatment attrition was predicted only by low reward dependence (P=.03) and the presence of mental disorders (P=.004).
Personality features denoting difficulty relying on others' support (low reward dependence) are associated with treatment noncompletion in obese patients attending a behavioral weight-loss program. These data may possibly serve to inform clinicians how to proceed in order to reduce dropout risk.
最近发现，通过气质与性格量表（TCI）测量的一些人格特征与肥胖行为治疗和手术治疗后的成功减重结果有关。然而，人格特征也可能影响肥胖治疗的参与度。因此，本研究的目的是探讨通过TCI评估的人格变量是否能预测肥胖行为减重计划的退出情况。
对92名申请为期6个月行为减重计划的肥胖患者[体重指数（BMI）>30kg/m²]进行TCI测试。在控制基线精神疾病共病、当前年龄、性别、肥胖发病年龄和初始BMI后，进行逻辑逐步回归分析，以评估TCI评分是否能预测6个月的治疗退出情况。
62名受试者（67.4%）完成了6个月的计划，30名（32.6%）退出。只有低奖励依赖（P=0.03）和精神障碍的存在（P=0.004）能预测治疗退出。
表示难以依赖他人支持的人格特征（低奖励依赖）与参加行为减重计划的肥胖患者治疗未完成有关。这些数据可能有助于告知临床医生如何采取措施以降低退出风险。
