Ozturk Sekine, Feltman Scott, Klein Daniel N, Kotov Roman, Mohanty Aprajita
Department of Psychology, Stony Brook University, Stony Brook, NY, USA.
Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
Psychol Med. 2024 Oct 4;54(12):1-12. doi: 10.1017/S0033291724002010.
Adolescence is marked by a sharp increase in the incidence of depression, especially in females. Identification of risk for depressive disorders (DD) in this key developmental stage can help prevention efforts, mitigating the clinical and public burden of DD. While frequently used in diagnosis, nonverbal behaviors are relatively understudied as risk markers for DD. Digital technology, such as facial recognition, may provide objective, fast, efficient, and cost-effective means of measuring nonverbal behavior.
Here, we analyzed video-recorded clinical interviews of 359 never-depressed adolescents females via commercially available facial emotion recognition software.
We found that average head and facial movements forecast future first onset of depression (AUC = 0.70) beyond the effects of other established self-report and physiological markers of DD risk.
Overall, these findings suggest that digital assessment of nonverbal behaviors may provide a promising risk marker for DD, which could aid in early identification and intervention efforts.
青春期抑郁症发病率急剧上升，尤其是在女性中。在这个关键的发育阶段识别抑郁症（DD）风险有助于预防工作，减轻DD的临床和公共负担。虽然非言语行为在诊断中经常使用，但作为DD的风险标志物相对较少被研究。数字技术，如面部识别，可能提供客观、快速、高效且具有成本效益的非言语行为测量方法。
在此，我们通过商用面部情绪识别软件分析了359名从未患抑郁症的青少年女性的视频临床访谈。
我们发现，平均头部和面部运动能够预测未来首次抑郁症发作（AUC = 0.70），其作用超过了其他已确立的DD风险自我报告和生理标志物的影响。
总体而言，这些发现表明，非言语行为的数字评估可能为DD提供一个有前景的风险标志物，这有助于早期识别和干预工作。
