The displays of the majority of electronic devices nowadays are illuminated by Light-Emitting Diodes (LEDs) or Organic Light-Emitting Diodes (OLEDs). These types of light sources have certain advantages regarding colour variety, contrast, resolution and the ability to construct thinner screens. Nevertheless, recent research raises concern of possible negative biological impact of these display types on visual health and the circadian rhythm. The biological basis of the concern lies in the emission spectra of the light sources. The white LEDs used as backlights in LED screens have a characteristic emission spectrum with a peak at 450 nm and the Red-Green-Blue (RGB) OLED emission spectrum has a blue peak. Both of them are very close to the 460nm where the melanopsin retina pigment presents the maximum absorption. In order to reduce the blue light emission several techniques have been developed including hardware adjustments, external filters and software applications that control the emission display characteristics. This study aims to record the performance of several available software applications on different mobile phone models. The spectral power distributions of the mobile phone screen were recorded by means of a commercial radiospectrometer, without and with the use of the blue light reducing software application, for various blue light filtering levels depending on the application. Several photometric and circadian parameters were calculated from the available spectra such as circadian light input, photopic illuminance and melatonin suppression index. The results of the study are the recordings of the respective differences in mobile screen output with and without the use of the blue light reduction application, presented in terms of spectral power and biologically relevant parameters. The analysis of the measuring procedure and the obtained results lead to an evaluation of the application performance variation depending on the mobile phone type and a standardised measurement protocol in order to have comparable results that could be used for blue light reducing software applications performance evaluation.

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