Percorrer por autor "Borges, Maria"
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- Are wearable sleep-tracking devices reliable alternatives to Polysomnography? A Systematic Review and Meta-AnalysisPublication . Agostinho, Margarida; Borges, Maria; Pereira, Telmo; Borges, Daniel Filipe; Soares, Joana Isabel; Borges, Daniel FilipePolysomnography (PSG) is the reference method for characterizing sleep architecture, but it is resource-intensive and difficult to scale for large cohort assess ments. This has increased interest in wearable devices for naturalistic sleep monitoring. This systematic review and meta-analysis evaluated how wearable sleep-tracking devices compare with laboratory PSG in healthy adults across standard sleep metrics and sleep stage durations. PubMed, Scopus, Scielo, Web of Science, and the Cochrane Library were searched following PRISMA guidelines. Eligible studies included healthy adults under going simultaneous wearable and PSG recordings. Mean differences were synthesized for total sleep time, sleep latency, sleep efficiency, wake after sleep onset, and time spent in light (N1+N2), deep (N3), and REM sleep using fixed or random effects models based on heterogeneity, with significance set at p < .01. Risk of bias and applicability were assessed using QUADAS-2. Sixteen studies met the inclusion criteria, and ten contributed to the meta- analysis. Wearable devices overestimated total sleep time and sleep efficiency and underestimated wake after sleep onset, with substantial variability between devices. No device demonstrated consistently superior performance. In individual studies, the closest agreement with PSG was observed for the Oura Ring (third generation) for sleep latency and sleep efficiency, and for selected Fitbit models for deep and REM sleep. Wearable devices provide reasonable estimates of global sleep metrics and may complement PSG for population monitoring and longitudinal self-tracking. However, variable performance, methodological heterogeneity, and risk-of-bias consid erations currently limit their use as stand-alone diagnostic tools or for detailed sleep- stage characterization.
- Wearable sleep staging technology as an alternative to polysomnography: a systematic review and meta-analysisPublication . Borges, Maria; Pereira, Telmo; Borges, Daniel Filipe; Soares, Joana IsabelIntroduction:Sleep is vital for health as it has regenerative and protective functions. During sleep, there is a cyclicity of different phases that are analysed and classified for clinical purposes using polysomnography (PSG), a costly and technically demanding method. The tremendous growth of sleep medicine, where demand for studies far outstrips supply, opens a window for the development of accurate, low-threshold sleep monitoring solutions that can be self-administered at home and could help avoid these issues of convenience, accessibility and reproducibility. Objective:This study aims to analyse the existing literature on the feasibility of wearable devices as an alternative to PSG for the classification of sleep stages. Methods: The literature search was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). All studies published in English or Portuguese with healthy adults who used wearables to record sleep were included. A meta-analysis was also performed to assess the mean values of the sleep metrics: total sleep time (TST), sleep latency (SL) and wake after sleep onset (WASO), all in minutes and time per stage (in minutes and as relative frequency of TST), as well as sleep efficiency (SE) (in %) and the corresponding statistics between the wearables used and the PSG. Results: Given the high variability of wearables, the analysed metrics whose values were closest to the PSG came from different devices. Nevertheless, the meta-analysis revealed that most wearables tend to overestimate these variables Conclusions: The performance of wearables demonstrates remarkable accuracy in sleep staging, rivalling the gold standard PSG in some variables while providing a more convenient and unobtrusive alternative. This review enriches our global knowledge of sleep measurement and summarizes the limitations that need to be overcome, as informed decision making depends on understanding the different device options, validation contexts and cost implications.