# The Use of Smart Rings in Health Monitoring--A Meta-Analysis
## 2 Materials and Methods
### Data Synthesis and Analysis  
To assess the consistency and accuracy of smart rings in health monitoring, we synthesised data from eligible studies and performed meta-analyses on various outcomes. Our criteria mandated that any outcome be assessed in at least three studies, and that the SR be compared with a gold-standard control device, grounding our findings in validated clinical metrics. According to the Cochrane Handbook for Systematic Reviews of Interventions [30], the use of rigorously validated control devices is essential for ensuring the accuracy and reliability of diagnostic comparisons. In the medical field, gold-standard devices like polysomnography (PSG) for sleep monitoring and pulse oximetry for oxygen saturation are widely validated [33; 34].  
We primarily employed the Bland-Altman method for our meta-analysis, a technique specifically tailored for comparing two methods of clinical measurement. This method evaluates the agreement between two quantitative measurements by constructing limits of agreement (LoAs). These LoAs are established around the mean difference between measurements and provide a range within which 95% of the differences between measurements lie. Using the Bland-Altman framework for meta-analysis, we utilised a limits of agreement (LoAs) approach described by [PERSON] [35]. This method was chosen not only because of its extensive application in primary Bland-Altman studies but also due to its capability to provide an estimate of the pooled LoAs in the larger population, transcending just the sampled individuals. It is worth noting that the 'population LoA' derived from this approach tends to be wider than those frequently reported in meta-analyses of Bland-Altman studies [35].  
The pooled LoAs were determined using the formula \(\delta\pm 2\surd\left(\tau^{2}+\sigma^{2}\right)\), where:  
* \(\delta\) represents the average bias across studies (mean difference between the two methods across all studies).
* \(\tau^{2}\) represents the between-study variation, capturing the variability in differences across studies.  
\(\sigma^{2}\) signifies the average within-study variation in differences.  
Estimates for both \(\delta\) and \(\sigma^{2}\) were derived by employing a weighted least-squares model, to account for the varying levels of precision in the studies included in our meta-analysis, providing a more balanced estimation, and minimizing the potential bias from smaller studies with high variability [36]. To calculate their standard errors (SEs), we adopted the robust variance estimation (RVE) technique. This decision was made given the presence of studies included in our review that implemented repeated-measures designs but did not accommodate for potential correlations between measurements [37]. For uniformity and ease of meta-analysis, results from individual studies were standardised (for example, HR was expressed in beats per minute (bpms); total sleep time and rapid eye movement duration were indicated in minutes). All computations were executed using the R-Studio statistical software (v.4.4.2, RStudio, PBC, Boston, MA, USA). The code relative to the data analyses is reported in the Supplementary Material (S2).