Kaufman Jaycee, Jeon Jouhyun, Oreskovic Jessica, Thommandram Anirudh, Fossat Yan
Klick Applied Sciences, Klick Health, Toronto, ON, Canada.
Faculty of Science, University of Ontario Institute of Technology, Oshawa, ON, Canada.
JMIR Form Res. 2025 Jan 9;9:e65448. doi: 10.2196/65448.
Identifying subtle changes in the menstrual cycle is crucial for effective fertility tracking and understanding reproductive health.
The aim of the study is to explore how fundamental frequency features vary between menstrual phases using daily voice recordings.
This study analyzed smartphone-collected voice recordings from 16 naturally cycling female participants, collected every day for 1 full menstrual cycle. Fundamental frequency features (mean, SD, 5th percentile, and 95th percentile) were extracted from each voice recording. Ovulation was estimated using luteinizing hormone urine tests taken every morning. The analysis included comparisons of these features between the follicular and luteal phases and the application of changepoint detection algorithms to assess changes and pinpoint the day in which the shifts in vocal pitch occur.
The fundamental frequency SD was 9.0% (SD 2.9%) lower in the luteal phase compared to the follicular phase (95% CI 3.4%-14.7%; P=.002), and the 5th percentile of the fundamental frequency was 8.8% (SD 3.6%) higher (95% CI 1.7%-16.0%; P=.01). No significant differences were found between phases in mean fundamental frequency or the 95th percentile of the fundamental frequency (P=.65 and P=.07). Changepoint detection, applied separately to each feature, identified the point in time when vocal frequency behaviors shifted. For the fundamental frequency SD and 5th percentile, 81% (n=13) of participants exhibited shifts within the fertile window (P=.03). In comparison, only 63% (n=10; P=.24) and 50% (n=8; P=.50) of participants had shifts in the fertile window for the mean and 95th percentile of the fundamental frequency, respectively.
These findings indicate that subtle variations in vocal pitch may reflect changes associated with the menstrual cycle, suggesting the potential for developing a noninvasive and convenient method for monitoring reproductive health. Changepoint detection may provide a promising avenue for future work in longitudinal fertility analysis.
识别月经周期中的细微变化对于有效追踪生育能力和了解生殖健康至关重要。
本研究旨在探讨使用每日语音记录，月经周期各阶段的基频特征如何变化。
本研究分析了16名自然月经周期女性参与者通过智能手机收集的语音记录，在1个完整月经周期内每天收集。从每个语音记录中提取基频特征（均值、标准差、第5百分位数和第95百分位数）。使用每天早上进行的促黄体生成素尿液测试估计排卵情况。分析包括卵泡期和黄体期这些特征的比较，以及应用变化点检测算法来评估变化并确定音调发生变化的日期。
与卵泡期相比，黄体期的基频标准差低9.0%（标准差2.9%）（95%可信区间3.4%-14.7%；P = 0.002），基频的第5百分位数高8.8%（标准差3.6%）（95%可信区间1.7%-16.0%；P = 0.01）。在基频均值或基频的第95百分位数方面，各阶段之间未发现显著差异（P = 0.65和P = 0.07）。对每个特征分别应用变化点检测，确定了语音频率行为发生变化的时间点。对于基频标准差和第5百分位数，81%（n = 13）的参与者在可育窗口内出现了变化（P = 0.03）。相比之下，分别只有63%（n = 10；P = 0.24）和50%（n = 8；P = 0.50）的参与者在可育窗口内出现了基频均值和第95百分位数的变化。
这些发现表明，音调的细微变化可能反映与月经周期相关的变化，提示开发一种无创且便捷的生殖健康监测方法的潜力。变化点检测可能为未来纵向生育分析工作提供一条有前景的途径。
