Internalized stigma, disease severity, quality of life, anxiety and depression in axillary hyperhidrosis

Background: Axillary hyperhidrosis (AH) is characterized by excessive underarm sweating. It is a chronic autonomic disorder that can lead to social embarrassment, impaired quality of life (QoL), anxiety and depression. Internalized stigma (IS), defined as the acceptance of negative societal attitudes and stereotypes about an individual's illness, has not been previously studied in AH. The aim of this study was to evaluate the level of IS in patients with AH and the relationships between IS, disease severity, quality of life, anxiety, and depression. Patients and methods: One hundred and four patients with AH were included in the study. Demographic and clinical characteristics of the patients were recorded. The Hyperhidrosis Disease Severity Scale (HDSS) was used to define disease severity. Assessment was made using the Internalized Stigma Scale (ISS) (between 29 and 116, the higher the score the greater the stigma), Hospital Anxiety and Depression Scale (HADS) and Dermatology Life Quality Index (DLQI). Results: The mean age of the patients was 34.1 +/- 10.9 years. The HDSS grade was mostly moderate to severe. The mean ISS score was 57.5 +/- 6.5. Median HADS scores were 7 [interquartile range (IQR) 2-12] and 5 [IQR 2-10], respectively. HADS scores >= 8 were observed respectively in 39.4% and 8.7% of patients. The median DLQI score was 14 [IQR 4-24]. A DLQI score >= 11 was observed in 75% of patients. Significant correlation was found between ISS score and HDSS (r = 0.445, p < 0.001), HADS-A (r = 0.455, p < 0.001), DLQI (r = 0.478, p < 0.001) scores and symptom duration (r = 0.207, p = 0.035). The relationship between ISS and HADS depression scores was not statistically significant. Conclusion: IS is common in patients with AH. Disease severity, symptom duration and anxiety increased IS. Patient's quality of life is reduced in AH. (c) 2024 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.