## 1 Introduction
Furthermore, studies that employ professional equipment are difficult to conduct over a long period of time, as long-term continuous measurements are needed to monitor individual sleep responses that often change rapidly [37,38]. Similarly, investigations in laboratory conditions in which sleep and environmental factors are monitored by professional instruments have another substantial limitation associated with the fact that sleep parameters, such as total sleep time (TST) and sleep efficiency (SE), tend to decrease during laboratory experiments compared to sleeping at home [39]. As a result, sleep characteristics obtained from laboratory experiments may lead to incorrect or biased results [40]. In addition, small-scale laboratory studies tend to undermine the generalizability of research [41]. As an alternative, polysomnography and its more affordable alternative--polygraphy--is commonly used in laboratory and close-to-laboratory research [42,43]. However, outside the lab, i.e., in real-world conditions, this equipment does not allow for sleep monitoring in large population contingents, who are exposed to different environmental risk factors [44]. Considering these limitations, it is necessary to use alternative tools to monitor sleep and environmental risk factors that affect it in an easy-to-use manner [45; 46]. Various wearable sensors and AI-driven analysis tools demonstrate the research potential [47; 48]. In this context, recent progress in the development of sensor technologies has contributed to the emergence and popularization of affordable consumer devices, allowing long-term sleep monitoring under exposure to different environmental risk factors [14; 16; 49]. However, the use of such sensors is often restricted due to their relatively high cost and availability. As another alternative, smartphones (SPs) and smart watches (SWs) have been used in several recent studies to monitor the effect of environmental risk factors that affect sleep [23; 50]. Thus, in a recent study, [PERSON] et al. [49] tracked sleep quality over 51 nights using different types of SPs on which the 'iSleep' app was installed. This program uses an SP built-in microphone to monitor ambient noise and helps to perform a detailed analysis of irregular sleep patterns. In a separate study, [PERSON] et al. [14] conducted a 13-month study that evaluated the effect of nighttime light intensity on sleep quality using the app 'LUX Light Meter'. In another recent study, [PERSON] and [PERSON] [16] examined the combined effects of artificial light and noise on sleep using low-cost SPs and SWs. In this study, 72 volunteers used their SW/SP tandems for 30 days to monitor their sleep patterns and to determine the influence of artificial light and noise on sleep. Our study emphasizes the growing role of low-cost devices, such as SPs and SWs, in addressing the limitations of traditional monitoring methods. Although advanced models, such as machine learning-based optical fiber interferometers, gain popularity [51], low-cost devices provide a more accessible alternative, making them an important complement for widespread use in real-world settings. However, the precision of SPs and SWs as sleep and environmental monitoring instruments is largely unknown. Therefore, an interdisciplinary review of the literature is necessary to capture the breadth of recent studies in which such low-cost technological tools were used and summarize their coverage and reported accuracy. To the best of our knowledge, no systematic literature survey of this type has been carried out to date. The present study aims to fill this knowledge void. The study uses the Preferred Reporting Items for Systematic Reviews (PRISMA) framework that uses a Boolean search query to identify relevant publications while providing fully reproducible results [52; 53]. According to [PERSON] et al. [54] and [PERSON] et al.