Dergiler / Türk Psikiyatri Dergisi / 2017 / Cilt: 28 - Sayı: 4
Premenstrüel Sendromu Olan Kadınlarda Yüzden Duygu İfadesi Tanıma Becerilerinin Premenstrüel Sendromu Olmayan Kadınlarla Karşılaştırılması
- Sayfa
- 234–239
- DOI
- —
Özet
Amaç: Depresif duygudurum, anksiyete ve irritabilite gibi afektif belirtileri içeren premenstrüel sendromun (PMS) yüzden duygu ifadesi tanımayı etkilediği vurgulanmaktadır. Menstrüel siklusun sağlıklı kadınlarda bile yüzden duygu ifadesi tanımayı etkilediği bilinmektedir. Bu çalışmada PMS'si olan ve olmayan sağlıklı kadınlarda menstrüel siklusun yüzden duygu ifadesi tanıma üzerindeki etkisini incelemeyi amaçladık
Abstract
with and without Premenstrual SyndromeObjectıve: It is emphasized that premenstrual syndrome (PMS) includes affective symptoms, such as depressed mood, anxiety and irritability, all of which may influence the recognition of facial emotion expressions. Also it is known that menstrual cycling may effect facial emotion recognition in healthy females. In the present study, we aimed to investigate how menstrual cycling effects of facial emotion recognition facial emotions in women with and without PMS. .Methods: Sixty healthy women were included to the study. They were divided two group labeled women with PMS (n=33) and without PMS (n=27), which is accordance with the Premenstrual Assessment Form. Then, The Facial Emotion Recognition Test (56 mixed photos with happy, surprised, fearful, sad, angry, disgusted and neutral facial expressions from Ekman & Friesen’s series) was performed on each group in both the luteal and follicular phases.Results: The women with PMS were significantly worse in recognizing sad (p=0.003) and surprised (p=0.019) faces in the luteal phase compared to the follicular phase, whereas women without PMS were significantly worse in recognizing sad faces (p=0.008) in the luteal phase compared to the follicular phase. There were no significant differences between women with and without PMS in either the luteal phases or in the follicular phases according to facial emotion recognition (for each, p>0.05)