## 3 Methods  
One of the five Nursing Homes (NHs) of the project was selected and its living room was considered in this study. The NH is located in Flanders (Belgium); the building hosting the facility dates back to the 1980s and it underwent several local renovations and technical adjustments over the past 30 years. The rationale for selecting this facility in particular was two-fold: (1) the living room of the department under investigation is slightly bigger in size (ca. double volume) and hosts on average more residents (ca. 10 more, on average), compared to the other four NH living rooms; (2) in the context of the AcustiCare project, this facility is the only one where retrofit interventions were implemented, aimed specifically at reducing reverberation time in the living room (which is an important factor of influence on acoustical capacity). Living rooms in the departments for people with dementia and the often related BPSD in the Nursing Homes of the project are conceived to host a number of residents, ranging approximately between 20 and 30 people at the time, including staff and visitors (e.g., resident's family members, friends, doctors, etc. ); for the case study, the number of residents typically ranges between 15 and 30 people. The layout of the living room (Figure 1) shows a few tables for 3-6 persons each spread across the space, with a desk/console/kitchen area for staff on one side, and a smaller, more flexible area where group activities might happen (soft gym, watching TV, listening to radio, etc.). The protocol for data collection is reported in Ref. [7]. Data collection for the living room included: reverberation time, ambient noise levels measurements for a 12-h period (07:00 am-07:00 pm), and occupancy (i.e., number of people in the living room) for a 12-h period (07:00 am-07:00 pm). Volumetric data of the living rooms were acquired; reverberation time (\(T\)) was measured in accordance with the ISO 3382-2:2008 standard [31]. Measurements were conducted in unoccupied conditions, while the rest of the facility was still functioning. Therefore, considering the proximity of potentially sensitive receivers, it was not suitable to use loud signals to elicit the rooms and \(T_{20}\) was used instead of \(T_{30}\). For noise levels, sensor nodes were installed in the living room during a typical day of activity of the facility; data would be sent over the internet to the Ghent University server infrastructure and subsequently processed further through an agent-based approach and eventually stored in a warehouse database [7]. These customized sensor nodes are conceived to provide remote data access and have been described in previous work [7], performing equivalently at least to \"Class 2\" equipment and being suitable for the measurement of the sound levels and their daily variation, as observed in the current study. A-weighted equivalent sound levels were calculated on a 30-min basis (\(L_{\text{Aeq},30\text{min}}\)). To the best of the author's knowledge, no specific protocol is available to formulate a proper uncertainty balance for the kind of measurements proposed in this study, using these specific devices. Yet, it is still possible to get a sense of the error the devices might incur in, and it is worthwhile highlighting that the sources of uncertainty that might emerge in indoor context and for a relatively narrow range of sound levels are likely less than those one might have to consider for outdoor environmental noise measurements (e.g., road traffic noise). Before deploying the sensor nodes at the nursing home, these were tested at the Ghent University Lab: they were calibrated using a Svantek SV 35A acoustic calibrator and the same procedure of indoor testing as proposed in Ref. [32], which is based on direct comparison of measurements with a Class 1 sound level meter, was performed, leading to an error of less than 0.2 dB on average. This is a well-accepted approach in literature, in order to check for the accuracy and reliability of acoustic sensor nodes [32; 33; 34; 35].