Peebles Elizabeth, Finlay William, Nguyen Thao-Mi, Barrett Samuel, Thirumalaraju Prudhvi, Kanakasabapathy Manoj Kumar, Kandula Hemanth, Sarcione Carrie, James Kaitlyn E, Shafiee Hadi, Mahalingaiah Shruthi
Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States.
Department of Obstetrics & Gynecology, Massachusetts General Hospital, Boston, MA, United States.
J Med Internet Res. 2025 Aug 5;27:e73028. doi: 10.2196/73028.
Females with irregular or unpredictable cycles, including those with polycystic ovary syndrome (PCOS), have limited options for validated at-home ovulation prediction. The majority of over-the-counter ovulation prediction kits use urinary luteinizing hormone (LH) indicators that were optimized for those with regular menstrual cycles exhibiting a predictable mid-cycle LH surge. Artificial intelligence (AI) holds potential to address this health deficit via a smartphone-based salivary ferning ovulation test. Research on populations with irregular menstruation and PCOS can be challenging due to the duration and frequency of menstrual cycles.
The objective of this study was to evaluate the feasibility for participants with diverse menstrual cycle lengths to complete study tasks designed to train and develop a potential future AI model for salivary ferning-based ovulation prediction.
Participants were recruited for 2 menstrual cycles where retention, engagement, and adherence were evaluated. Participation entailed remotely collecting and uploading daily data (saliva, LH values), attending lab visits, and returning biological saliva samples.
Of the 133 females recruited from February to October 2023 via targeted patient messages and a public research website, 69 (51.9%) were eligible (age 19-35 years at enrollment, currently menstruating, able to read and comprehend English, weigh more than 110 lb, have an active primary care or gynecological provider, and able to commute to the Massachusetts General Hospital (MGH) main campus within 10 days of their ovulatory event). Of the 43 (62.3%) eligible participants who consented and completed the baseline survey, the majority were White (n=24, 55.8%), employed (n=33, 76.7%), and highly educated (college or more; n=32, 74.4%) and had a mean BMI of 28.9 (SD 7.8) kg/m. Of those who received a study kit (n=29, 42%), 17 (58.6%) participants began data collection, 9 (31%) provided data for completed study tasks for 1 menstrual cycle, and 7 (24.1%) completed the study. Furthermore, 19 (44.2%) eligible participants who completed the baseline survey withdrew from the study, citing menstrual cycles being too irregular for the study timeline (n=5, 26.3%), becoming pregnant (n=4, 21.1%), moving outside the study area (n=4, 21.1%), no time to dedicate to the study (n=2, 10.5%), ineligibility (n=2, 10.5%), and stress related to observing anovulation (n=2, 10.5%).
To optimize future scaled participant completion, the study design would include a more targeted recruitment message to address the high ineligibility status, streamline study procedures to ease the participant burden, and incorporate health education to equip participants with ovulatory health information to ameliorate the potential stress impacts of observing anovulation. After optimization, when scaled, this study design could provide an AI model with sufficient data to develop a smartphone-based ovulation predictor specifically tested on females with irregular or unpredictable cycles, including those with PCOS. A well-informed study design is the foundation to AI advancement and femtech (the technology sector focused on enhancing female health) growth, particularity for ovulatory and fertility digital health.
月经周期不规律或不可预测的女性，包括多囊卵巢综合征（PCOS）患者，在经过验证的家庭排卵预测方面选择有限。大多数非处方排卵预测试剂盒使用尿促黄体生成素（LH）指标，这些指标是针对月经周期规律且在周期中期出现可预测LH峰的女性进行优化的。人工智能（AI）有望通过基于智能手机的唾液结晶排卵测试来弥补这一健康缺陷。由于月经周期的持续时间和频率，对月经不规律和PCOS人群的研究可能具有挑战性。
本研究的目的是评估月经周期长度各异的参与者完成旨在训练和开发未来基于唾液结晶的排卵预测AI模型的研究任务的可行性。
招募参与者进行2个月经周期的研究，评估其留存率、参与度和依从性。参与包括远程收集和上传每日数据（唾液、LH值）、参加实验室检查以及返还生物唾液样本。
通过定向患者信息和公共研究网站在2023年2月至10月招募的133名女性中，69名（51.9%）符合条件（入组时年龄19 - 35岁，目前正在月经，能够阅读和理解英语，体重超过110磅，有活跃的初级保健或妇科医生，并且能够在排卵事件发生后10天内前往麻省总医院（MGH）主校区）。在43名（62.3%）同意并完成基线调查的符合条件参与者中，大多数为白人（n = 24，55.8%）、有工作（n = 33，76.7%）且受教育程度高（大学及以上；n = 32，74.4%），平均BMI为28.9（标准差7.8）kg/m²。在收到研究试剂盒的参与者中（n = 29，42%），17名（58.6%）参与者开始数据收集，9名（31%）参与者为1个月经周期的完整研究任务提供了数据，7名（24.1%）完成了研究。此外，19名（44.2%）完成基线调查的符合条件参与者退出了研究，原因包括月经周期对研究时间线来说太不规律（n = 5，26.3%）、怀孕（n = 4，21.1%）、搬离研究区域（n = 4，21.1%）、没有时间投入研究（n = 2，10.5%）、不符合条件（n = 2，10.5%）以及观察到无排卵相关的压力（n = 2，10.5%）。
为了优化未来大规模参与者的完成情况，研究设计应包括更具针对性的招募信息以解决高不符合条件率问题，简化研究程序以减轻参与者负担，并纳入健康教育以使参与者具备排卵健康信息，以减轻观察到无排卵的潜在压力影响。优化后，扩大规模时，本研究设计可为AI模型提供足够数据，以开发专门针对月经周期不规律或不可预测的女性（包括PCOS患者）进行测试的基于智能手机的排卵预测器。精心设计的研究是AI进步和女性科技（专注于改善女性健康的技术领域）发展的基础，尤其是对于排卵和生育数字健康而言。
