ABSTRACT
This study aimed to explore how natural menstrual cycle phases and dosage of oral hormonal contraceptives (OC) influence the diurnal rhythm of distal skin temperature (DST) under real-life conditions. Participants were 41 healthy females (23.9 ± 2.48 y), comprising 27 females taking monophasic hormonal oral contraceptives (OC users) and 14 females with menstrual cycles (non-OC users). Wrist DST was continuously recorded and averaged over two consecutive 24-hour days during (pseudo)follicular and (pseudo)luteal menstrual phases. Diurnal rhythm characteristics, i.e. acrophase and amplitude, describing timing and strength of the DST rhythm, respectively, were calculated using cosinor analysis. Results show that non-OC users experienced earlier diurnal DST maximum (acrophase, p = 0.019) and larger amplitude (p = 0.016) during the luteal phase than during the follicular phase. This was observed in most (71.4%) but not all individuals. The OC users showed no differences in acrophase or amplitude between pseudoluteal and pseudofollicular phases. OC users taking a higher dosage of progestin displayed a larger amplitude for DST rhythm during the pseudoluteal phase (p = 0.009), while estrogen dosage had no effect. In conclusion, monophasic OC cause changes in diurnal DST rhythm, similar to those observed in the luteal phase of females with menstrual cycles, suggesting that synthetic progestins act in a similar manner on skin thermoregulation as progesterone does.
Subject(s)
Circadian Rhythm , Menstrual Cycle , Skin Temperature , Humans , Female , Circadian Rhythm/drug effects , Circadian Rhythm/physiology , Adult , Skin Temperature/drug effects , Young Adult , Menstrual Cycle/drug effects , Contraceptives, Oral, Hormonal/pharmacology , Contraceptives, Oral, Hormonal/administration & dosage , Luteal Phase/drug effects , Luteal Phase/physiology , Body Temperature Regulation/drug effectsABSTRACT
BACKGROUND: Recent advancements in artificial intelligence have revolutionized dermatological diagnostics. These technologies, particularly machine learning (ML), including deep learning (DL), have shown accuracy equivalent or even superior to human experts in diagnosing skin conditions like melanoma. With the integration of ML, including DL, the development of at home skin analysis devices has become feasible. To this end, we introduced the Skinly system, a handheld device capable of evaluating various personal skin characteristics noninvasively. MATERIALS AND METHODS: Equipped with a moisture sensor and a multi-light-source camera, Skinly can assess age-related skin parameters and specific skin properties. Utilizing state-of-the-art DL, Skinly processed vast amounts of images efficiently. The Skinly system's efficacy was validated both in the lab and at home, comparing its results to established "gold standard" methods. RESULTS: Our findings revealed that the Skinly device can accurately measure age-associated parameters, that is, facial age, skin evenness, and wrinkles. Furthermore, Skinly produced data consistent with established devices for parameters like glossiness, skin tone, redness, and porphyrin levels. A separate study was conducted to evaluate the effects of two moisturizing formulations on skin hydration in laboratory studies with standard instrumentation and at home with Skinly. CONCLUSION: Thanks to its capability for multi-parameter measurements, the Skinly device, combined with its smartphone application, holds the potential to replace more expensive, time-consuming diagnostic tools. Collectively, the Skinly device opens new avenues in dermatological research, offering a reliable, versatile tool for comprehensive skin analysis.
