The diagnostic accuracy of wearable digital technology in detecting fertility window and menstrual cycles: a systematic review and Bayesian network meta-analysis

This systematic review and Bayesian network meta-analysis assessed the diagnostic accuracy of wearable digital technology (WDT) in monitoring women's fertility window compared to conventional methods. 8 databases were searched until January 1, 2025. 27 studies were included in the analysis, where 13 studies applied WDT in tracking ovulation. We evaluated the accuracy, sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and summary receiver operating characteristic (SROC) of WDT, and compared the performance of different designs of WDT by NMA analysis. The revised QUADAS-2 tool was used for quality assessment. Our results demonstrated that WDT presented a pooled accuracy of 0.88 (95% CI: 0.86-0.90), with a sensitivity of 0.79 (95% CI: 0.70-0.87), specificity of 0.80 (95% CI: 0.60-1.00), PLR of 5.87 (95% CI: 2.49-13.88), NLR of 0.25 (95% CI: 0.13-0.51), DOR of 23.39 (95% CI: 3.45-158.71), and SROC of 0.75. Notably, WDT provided best detection for 3 days surrounding ovulation. Ring-type device, the use of multi-physiological parameters and the random forest algorithm method improved efficiency for WDT in the detection fertility window. Overall, WDT holds promise for fertility window tracking and could offer tentative support for optimizing pregnancy planning and monitoring women's reproductive health.