Global fertility rates are declining. In China, the total fertility rate dropped sharply from 5.59 in 1971 to 1.15 in 2021. Chinese university students often lack adequate fertility awareness, yet no validated instrument exists to assess this construct in this population, despite its recognized importance for informed reproductive decision-making.
Methods
The Fertility Awareness Scale for University Students (FASUS) was developed through literature search, semi-structured interviews, and two rounds of Delphi expert consultation. Psychometric properties were tested using exploratory and confirmatory factor analysis, Cronbach's α, split-half reliability, and test-retest reliability. A cross-sectional survey of 750 students examined fertility awareness levels and influencing factors via univariate analysis and multiple linear regression.
Results
The final FASUS Scale consisted of 22 items across three dimensions: fertility-related knowledge, reproductive health attitudes, and reproductive health skills, with a cumulative variance contribution rate of 67.952%. The scale demonstrated strong reliability (Cronbach's α = 0.97, split-half reliability = 0.885, test-retest reliability = 0.981). A survey of 750 students revealed an average fertility awareness score of 78.01 ± 16.44, indicating a low to moderate level. Key influencing factors included gender, ethnicity, education level, only-child status, and prior fertility knowledge education.
Conclusion
This study utilized a self-developed scale to assess fertility awareness among Chinese university students. The results showed that the fertility awareness level of Chinese university students was generally low. We suggest that health care institutions implement targeted intervention measures to improve reproductive health outcomes.
PMID 42245376 42245376 DOI 10.3389/fpubh.2026.1818259 10.3389/fpubh.2026.1818259 Niu et al. 2026, Niu 2026
Cite this article
Niu, C., Lu, X., Ren, Y., Yang, F., Ma, S., Yu, Y., Gao, Y., Shen, N., & Shu, J. (2026). Fertility awareness among Chinese university students: scale development and cross-sectional study.. Frontiers in public health, 14, 1818259. https://doi.org/10.3389/fpubh.2026.1818259
Niu C, Lu X, Ren Y, Yang F, Ma S, Yu Y, Gao Y, Shen N, Shu J. Fertility awareness among Chinese university students: scale development and cross-sectional study.. Frontiers in public health. 2026;14:1818259. doi:10.3389/fpubh.2026.1818259
Keywords
fertility awareness, influence factors, level classification, reliability and validity, university students
Several studies have evaluated the reliability of using temperature sensors placed in different locations on the body to identify the day of ovulation. However, such demonstrations are lacking for axillary temperature wearable devices. This study aimed to evaluate the accuracy with which an axillary temperature armband sensor (Tempdrop) identifies the day of ovulation and the fertile window, using the Clearblue Connected Ovulation Test System as the reference method. A total of 194 cycles were analyzed from 125 women that participated in the study between April 2023 and June 2024. The performance parameters were high: the sensitivity (96.8% (95% CI 95.6; 97.7)), specificity (99.1% (98.8; 99.4)), accuracy (98.6% (98.2; 98.9)), positive predictive value (96.8% (95.6; 97.7)) and negative predictive value (99.1% (98.8; 99.4)). Furthermore, the results revealed a remarkably clear and better-than-expected change in temperature around the time of ovulation. This axillary temperature wearable sensor is an effective alternative to urine ovulation tests for determining the timing of ovulation. Another advantage is that it provides a clear temperature curve that can be used to evaluate the quality of the luteal phase.
Patient Education · Fertility Knowledge and Misconceptions
Wainwright E et al., 2025·Reprod Health·
Open Access
Fertility rates in the UK are at an all-time low, with infertility affecting approximately 1 in 7 couples. Despite the rising demand for fertility services, fertility awareness, specifically knowledge of ovulation and the fertile window, remains low among women of reproductive age. Most existing studies offer a broad perspective, lacking focus on women actively trying to conceive (TTC). This study aims to assess the level of understanding surrounding the fertile window among women TTC, identifying factors associated with knowledge gaps. A retrospective, cross-sectional analysis of 97,414 women actively TTC who answered an online health assessment was conducted. Participants provided information on menstrual cycle characteristics, previous pregnancies, and fertility knowledge, including the timing of the fertile window. Frequencies, percentages were calculated and chi-squared tests performed to assess differences in categorical data. Logistic regression models were used to calculate odds ratios (ORs) to better understand factors significantly associated with not knowing the fertile window. Out of the total respondents (97,414), over a third (33,756, 41%) could not accurately identify the fertile window, with substantial misconceptions observed across all age groups and ethnicities. Women with previous pregnancies were more likely to correctly identify the fertile window (OR = 1.45, 97.5% CI: 1.20-1.75, p < 0.001). However, knowledge was significantly lower among those with irregular cycles, non-White ethnicities, younger age groups and longer time TTC. Additionally, misconceptions about cycle regularity were apparent, of 60,322 women describing their cycles as regular 10% did not know their cycle length (66,95) and a further 2.9% fell outside of the clinically regular 21-35 day range. These misconceptions followed a similar trend with younger age groups, non-white ethnicities and longer time TTC having significantly increased rates of misidentifying regular cycles. This further increased the odds of not knowing their fertile window (OR = 2.99, 97.5% CI: 2.83-3.17, p < 0.001). The findings reveal gaps in fertility awareness among women actively TTC. Addressing these knowledge gaps through targeted educational interventions could potentially reduce time-to-pregnancy and the reliance on assisted reproductive technologies. Improved fertility education focusing on cycle tracking and ovulation timing is essential to assist women with accurate information during their TTC journey.
Measurement and Statistics · Instrument Development and Validation
Bouchard TP et al., 2025·Womens Health Rep (New Rochelle)·
Open Access
Measuring quantitative menstrual cycle hormones at home may help women better understand their postpartum and perimenopause fertility transitions, but these quantitative fertility monitors require validation. This study included 16 North American women, aged 28-51, during either the postpartum (n = 8, cycles = 18) or perimenopause (n = 8, cycles = 35) fertility transitions testing daily first-morning urine testing with both the Mira Monitor and ClearBlue Fertility Monitor (CBFM) along with menstrual cycle parameter tracking. The main outcome measures were a rise in estrone-3-glucuronide (E13G) and luteinizing hormone (LH) urine hormone values from the Mira monitor correlated to low, high, or peak values on the CBFM. Both in the postpartum and perimenopause transitions, the identification of the day of ovulation based on the LH surge on the Mira and CBFM monitors was highly correlated (R = 0.94 and 0.83, p < 0.001). The E13G levels on the Mira monitor were significantly higher for a CBFM reading of "High" compared with "Low" for both the postpartum and perimenopausal cycles (all p < 0.001). Similarly, the LH levels on the Mira monitor were significantly higher for a CBFM reading of "Peak" (LH surge) compared with "High" for both the postpartum and perimenopausal cycles (all p < 0.001). The LH surge and levels of E13G in urine identified on the quantitative Mira fertility monitor strongly correlate to the LH surge and the shift from low to high on the CBFM during the postpartum and perimenopause transitions.
Schoeman EM et al., 2025·Human reproduction (Oxford, England)
Can a panel of plasma protein biomarkers be identified to accurately and specifically diagnose endometriosis?
A novel panel of 10 plasma protein biomarkers was identified and validated, demonstrating strong predictive accuracy for the diagnosis of endometriosis.
Endometriosis poses intricate medical challenges for affected individuals and their physicians, yet diagnosis currently takes an average of 7 years and normally requires invasive laparoscopy. Consequently, the need for a simple, accurate non-invasive diagnostic tool is paramount.
STUDY DESIGN, SIZE, This study compared 805 participants across two independent clinical populations, with the status of all endometriosis and symptomatic control samples confirmed by laparoscopy. A proteomics workflow was used to identify and validate plasma protein biomarkers for the diagnosis of endometriosis.
PARTICIPANTS/MATERIALS, SETTING, A proteomics discovery experiment identified candidate biomarkers before a targeted mass spectrometry assay was developed and used to compare plasma samples from 464 endometriosis cases, 153 general population controls, and 132 symptomatic controls. Three multivariate models were developed: Model 1 (logistic regression) for endometriosis cases versus general population controls, Model 2 (logistic regression) for rASRM stage II to IV (mild to severe) endometriosis cases versus symptomatic controls, and Model 3 (random forest) for stage IV (severe) endometriosis cases versus symptomatic controls.
MAIN A panel of 10 protein biomarkers were identified across the three models which added significant value to clinical factors. Model 3 (severe endometriosis vs symptomatic controls) performed the best with an area under the receiver operating characteristic curve (AUC) of 0.997 (95% CI 0.994-1.000). This model could also accurately distinguish symptomatic controls from early-stage endometriosis when applied to the remaining dataset (AUCs ≥0.85 for stage I to III endometriosis). Model 1 also demonstrated strong predictive performance with an AUC of 0.993 (95% CI 0.988-0.998), while Model 2 achieved an AUC of 0.729 (95% CI 0.676-0.783).
LIMITATIONS, The study participants were mostly of European ethnicity and the results may be biased from undiagnosed endometriosis in controls. Further analysis is required to enable the generalizability of the findings to other populations and settings.
In combination, these plasma protein biomarkers and resulting diagnostic models represent a potential new tool for the non-invasive diagnosis of endometriosis.
STUDY FUNDING/COMPETING INTEREST(S): Subject recruitment at The Royal Women's Hospital, Melbourne, was supported in part by funding from the Australian National Health and Medical Research Council (NHMRC) project grants GNT1105321 and GNT1026033 and Australian Medical Research Future Fund grant no. MRF1199715 (P.A.W.R., S.H.-C., and M.H.). Proteomics International has filed patent WO 2021/184060 A1 that relates to endometriosis biomarkers described in this manuscript; S.B., R.L., and T.C. declare an interest in this patent. J.I., S.B., C.L., D.I., H.L., K.P., M.D., M.M., M.R., P.T., R.L., and T.C. are shareholders in Proteomics International. Otherwise, the authors have no conflicts of interest.
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