Red and Green LED Light Therapy: A Comparative Study in Androgenetic Alopecia.
Abstract
BACKGROUND: Androgenetic alopecia (AGA) affects both men and women, characterized by progressive hair thinning. While current treatments like minoxidil and finasteride have efficacy limitations and side effects, low-level light therapy (LLLT) using red or near-infrared light has emerged as a promising alternative. Recent animal studies suggest potential benefits from green LED light, though human data are sparse. METHODS: This study utilized an innovative LED helmet emitting red and green LED light on respective halves of the frontal scalp, delivering an energy density of 40 J/cm2 over 20 min. Clinical photography, physician evaluations on a 7-point scale, patient satisfaction, and measurements of hair density and hair diameter were employed. Data were analyzed using linear mixed-effects models, with significance set at p < 0.05. RESULTS: Seventeen participants (47.1% male, 52.9% female, average age 46.47 years) demonstrated notable improvements after 6 months of treatment. Red and green LEDs both significantly increased hair diameter, non-vellus hair density, and satisfaction scores. Notably, the red LED therapy resulted in a statistically significant decrease in vellus hair density and achieved a greater increase in hair diameter compared to the green LED therapy. Minimal adverse effects were reported, primarily consisting of tolerable scalp heat and mild redness. CONCLUSION: Both red and green LED therapies effectively enhanced hair growth, increasing density and thickness over 6 months. Red LED demonstrated superior improvements in specific measures. Consequently, both therapies present safe and viable alternatives for the management of AGA, expanding the repertoire of available treatment options.
En bref
Low‐level light therapy (LLLT) using red or near‐infrared light has emerged as a promising alternative for Androgenetic alopecia, and recent animal studies suggest potential benefits from green LED light, though human data are sparse.
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