 I'm Julia Angonese and here is a short preview to my talk towards an objective measurement of individual listening preferences, trait consistency and state specificity. Let's assume we are at the coffee break of the conference and you want to understand what your German colleague is telling you. If I were to ask you which sound you prefer among these two that I will now play in terms of better intelligibility and lower listening effort, what would you choose? This one, or this one, and this is actually what our colleague was telling us. Some people might prefer higher background noise to maintain a good signal quality, others might want to maintain listening comfort, so low noise, despite having some signal distortions. This trade-off between noise and distortion is introduced by single microphone noise reduction algorithms in hearing aids. These algorithms provide an increasing signal to noise ratio by reducing the surrounding noise, but with this they also introduce distortions to the signal. Despite this drawback, however, these algorithms are an essential part of the fine tuning of hearing aid, since they improve the understanding of speech and noise. So can we measure this trade-off preferences to achieve information on the individual that will favor better individualized fitting? We implemented a mobile task to categorize noise and distortion haters, and we will discuss its psychometric qualities and whether these measured preferences are stable along the days. We will do so in a dataset of 185 unedited older adults with subjective reports of hearing loss who participated in a three-weeks-long ecological momentary assessment study. But for now, I thank you for listening and I will be more than happy to welcome you at my talk.