Honors Department, Clarkson University, Potsdam, NY 13699, USA.
Department of Kinesiology, Indiana University, Bloomington, IN 47405, USA.
Sensors (Basel). 2023 Jul 23;23(14):6624. doi: 10.3390/s23146624.
Depressive mood states in healthy populations are prevalent but often under-reported. Biases exist in self-reporting of depression in otherwise healthy individuals. Gait and balance control can serve as objective markers for identifying those individuals, particularly in real-world settings. We utilized inertial measurement units (IMU) to measure gait and balance control. An exploratory, cross-sectional design was used to compare individuals who reported feeling depressed at the moment ( = 49) with those who did not ( = 84). The Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was employed to ensure internal validity. We recruited 133 participants aged between 18-36 years from the university community. Various instruments were used to evaluate participants' present depressive symptoms, sleep, gait, and balance. Gait and balance variables were used to detect depression, and participants were categorized into three groups: not depressed, mild depression, and moderate-high depression. Participant characteristics were analyzed using ANOVA and Kruskal-Wallis tests, and no significant differences were found in age, height, weight, BMI, and prior night's sleep between the three groups. Classification models were utilized for depression detection. The most accurate model incorporated both gait and balance variables, yielding an accuracy rate of 84.91% for identifying individuals with moderate-high depression compared to non-depressed individuals.
健康人群中抑郁情绪状态较为普遍,但往往报告不足。在健康个体中,自我报告抑郁存在偏差。步态和平衡控制可以作为识别这些个体的客观标志物,特别是在现实环境中。我们使用惯性测量单元 (IMU) 来测量步态和平衡控制。采用探索性横断面设计比较了当下感到抑郁的个体(=49 人)和未感到抑郁的个体(=84 人)。采用观察性队列研究和横断面研究的质量评估工具来确保内部有效性。我们从大学社区招募了 133 名年龄在 18-36 岁之间的参与者。使用各种仪器评估参与者的当前抑郁症状、睡眠、步态和平衡。步态和平衡变量用于检测抑郁,参与者被分为三组:无抑郁、轻度抑郁和中重度抑郁。使用 ANOVA 和 Kruskal-Wallis 检验分析参与者的特征,在三组之间未发现年龄、身高、体重、BMI 和前一晚睡眠有显著差异。使用分类模型进行抑郁检测。最准确的模型同时纳入了步态和平衡变量,对于识别中重度抑郁个体与无抑郁个体的准确率为 84.91%。