RAMADAN SERIES

Listening In: Introducing the Ultrahuman Cycle Tracking & Ovulation Pro (C&O Pro) Feature Powered by OvuSenseTM Technology

Kanika Gupta a, Al Pirrie b, Aditi Shanmugam a, Kate Davies b, Apurva Hendi a, Robert Milnes b, Aditi Bhattacharya a

a Ultrahuman Healthcare Pvt. Ltd., India

b viO HealthTech Ltd., United Kingdom

Your Cycle, Your Insights, Your Early Warning System

  • Menstrual cycle is described by the American College of Obstetrics and Gynecology as the fifth vital sign (with heart rate, respiratory rate, temperature, and blood pressure) since its patterns are a direct mirror of endocrine, metabolic and emotional health.1, 2
  • Textbook cycles are rare. In a study of over 600,000 app-logged cycles, the mean length was 29.3 days, with only ~13% exactly 28 days and 87% falling between 23–35 days.3 Clinical definitions of regular and irregular cycles are broader and not based solely on the ideal 28-day length.4
  • One woman, many rhythms. While attention is often focused on the number of cycle days, variation can also occur at the level of ovulation timing (early or late) or even anovulation (where the egg is not released).5
  • Oligomenorrhea (infrequent/missed periods) can occur due to various factors including polycystic ovarian syndrome/disorder (PCOS/D), thyroid dysfunction, premature ovarian insufficiency, functional hypothalamic amenorrhea (FHA), eating disorders, mental health conditions, and anatomical changes such as Asherman’s syndrome.6
  • Fertility awareness methods can significantly improve conception rates. One study reported a 38% pregnancy rate over 8 months in subfertile couples after training, rising to 56% in those trying for 1–2 years.7 Another found a 20–28% higher fecundability in women tracking cervical mucus and temperature.8
  • Hence, there is a critical need for personalized cycle-pattern awareness to enable informed planning and decision-making.

Temperature as a Digital Proxy for Progesterone Levels: Cause and Effect

How Cycle & Ovulation Pro Empowers You to “Listen In”

  • OvuSense sensor and algorithmic suite is FDA-, EU-, CMDCAS-, TGA-, and HSA- (Singapore) cleared medical-grade sensing system for temperature tracking in the context of women’s health.12
  • Based on the OvuSense algorithm trained on data from OvuCore® vaginal sensor, for over 260,000+ cycles in an ongoing real-world study (Ethical approvals from WakeForest University (USA, IRB00083129) and the University of Bristol (UK, FREC project number 67281)).
  • Skin-temperature sensor has 90% accuracy in ovulation confirmation, while the vaginal temperature sensor achieves 99% accuracy.13, 14
  • Clinically proven to detect ovulation and anovulation in regular, irregular, PCOS/D, endometriosis, and other cycle types.14
  • Serves as a proven digital proxy for progesterone11 and has potential to accelerate conception by several months, as indicated by pregnancy-related discontinuation patterns among users.

Ultrahuman Ring AIR <> OvuSenseTM Algorithmic Performance

Compatibility tests were run in two scenarios. 

  1. Sensor datastream equivalence was assessed by comparing the OvuSense Skin Worn Sensor (SWS) and Ultrahuman Ring AIR in a controlled water bath experiment (8 h, 5 min sampling) to simulate physiological conditions (shown in Figure 1 and Table 1).
Figure 1: Sensor temperature equivalence between Ring AIR and OvuSenseTM SWS. A) Representative time-series of Ring AIR and SWS temperature measurements, sampled every 5 min. B) Bland–Altman plot illustrating bias and 95% limits of agreement (LOA, defined as bias ± 1.96 × SD) for n = 557 data points.

Table 1: Overlap of raw temperature data streams captured by the Ultrahuman Ring AIR and OvuSenseTM SWS sensor (n= 557 data points, sampled at every 5 min). MAE = Mean Absolute Error (MAE), Bias = systematic bias (mean difference), LOA = 95% limits of agreement, and MLM variance = variance of residuals. 

  1. Algorithm compatibility for temperature data from Ultrahuman Ring AIR user cycles, confirmed for ovulation using an independent hormone-based ovulation prediction kit (OPK, Inito), were run through the OvuSense algorithm. The OPK determined ovulation based on the combined patterns of four hormonal metabolites, offering a more comprehensive signal than traditional LH surge–based methods. For cohort information please see here. The algorithm identified all true ovulations (100% sensitivity) and correctly recognized all non-ovulatory phases/cycles (100% negative predictive value). It achieved a 91.7% positive predictive value, indicating that when it predicted an ovulation, it was correct 91.7% of the time. The overall accuracy of the algorithm compared to OPK results was 92.9% (Figure 2).
Figure 2: Performance metrics of the OvuSenseTM algorithm for temperature data from the Ultrahuman Ring AIR user cycles, validated against hormone-based ovulation prediction.

Conclusions

Women’s menstrual cycles have long been oversimplified, and this lack of nuance often leads to delayed recognition of health changes, allowing preventable disease states to go unaddressed until a major medical event occurs. With the OvuSense advanced, medically proven algorithm, C&O Pro expands the power of Ultrahuman’s women’s health tracking by:

  • encompassing a range of cycle types, including confirmation of anovulation;
  • delivering best-in-class accuracy for both fertile window prediction and ovulation confirmation; and
  • identifying temperature patterns recognized in medical literature as being associated with specific gynaecological conditions.

The side-by-side comparison of the OvuSense SWS and external anchoring of ovulation confirmation using the multi-metabolite urine test demonstrates the seamless integration of the OvuSense algorithm with the Ultrahuman Ring AIR’s temperature-sensing capabilities. Additionally, a cross-sectional analysis was conducted in a separate white paper to survey the prevalence of cycle flags within the current Ultrahuman women users. The full findings can be found here

In summary, by integrating clinically validated insights into the everyday functionality of the Ultrahuman Ring AIR, accessing your body’s hidden signals and translating them into clear, actionable feedback has never been easier. This means fewer surprises, faster answers, and most importantly, the confidence to proactively take charge of your hormonal health long before problems arise.

Disclaimer: Cycle and Ovulation Pro involves data streams from Ultrahuman Ring AIR and visualizes insights generated by the OvuSenseTM algorithm on the Ultrahuman app. It is not a diagnostic tool and is not intended for contraception, conception planning, or medical diagnosis. This white paper is provided “as is” without any express or implied warranties.

Reach out to partnerships@ultrahuman.com for commercial queries and science@ultrahuman.com for scientific queries.

1. CAH Care, (2015). Menstruation in girls and adolescents: using the menstrual cycle as a vital sign. Obstet. Gynecol, 126, e143-e146. PMID: 26595586 DOI: 10.1097/AOG.0000000000001215

2. Vollmar, A. K. R., Mahalingaiah, S., & Jukic, A. M. (2024). The Menstrual Cycle as a Vital Sign: a comprehensive review. F&S Reviews, 100081. PMID: 39906529 DOI: 10.1016/j.xfnr.2024.100081

3. Bull, J. R., Rowland, S. P., Scherwitzl, E. B., Scherwitzl, R., Danielsson, K. G., & Harper, J. (2019). Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles. NPJ digital medicine, 2(1), 83. PMID: 31482137 PMCID: PMC6710244 DOI: 10.1038/s41746-019-0152-7

4. Teede, H. J., Tay, C. T., Laven, J., Dokras, A., Moran, L., Piltonen, T., ... & Joham, A. E. (2023). International evidence-based guideline for the assessment and management of polycystic ovary syndrome 2023. PMID: 37580314 DOI: 10.1210/clinem/dgad463

5. Li, H., Gibson, E. A., Jukic, A. M. Z., Baird, D. D., Wilcox, A. J., Curry, C. L., ... & Mahalingaiah, S. (2023). Menstrual cycle length variation by demographic characteristics from the Apple Women’s Health Study. NPJ Digital Medicine, 6(1), 100. PMID: 37248288 DOI: 10.1038/s41746-023-00848-1

6. Riaz, Y., & Parekh, U. (2020). Oligomenorrhea. PMID: 32809410

7. Frank-Herrmann, P., Jacobs, C., Jenetzky, E., Gnoth, C., Pyper, C., Baur, S., ... & Strowitzki, T. (2017). Natural conception rates in subfertile couples following fertility awareness training. Archives of gynecology and obstetrics, 295, 1015-1024. PMID: 28185073 DOI: 10.1007/s00404-017-4294-z

8. Stanford, J. B., Willis, S. K., Hatch, E. E., Rothman, K. J., & Wise, L. A. (2019). Fecundability in relation to use of fertility awareness indicators in a North American preconception cohort study. Fertility and sterility, 112(5), 892-899. PMID: 31731946 DOI: 10.1016/j.fertnstert.2019.06.036

9. Baker, F. C., Siboza, F., & Fuller, A. (2020). Temperature regulation in women: effects of the menstrual cycle. Temperature, 7(3), 226-262.PMID: 33123618 DOI: 10.1080/23328940.2020.1735927

10. Rai, A. K., Singh, A., & Neelabh. (2023). Progesterone: Thermogenic Effect. In Encyclopedia of Sexual Psychology and Behavior (pp. 1-5). Cham: Springer International Publishing. DOI: 10.1007/978-3-031-08956-5_245-1

11. Knowles, T. G., García-Velasco, J. A., Toribio, M., Garrido, N., Barrio Pedraza, A. I., Colomé Rakosnik, C., ... & Milnes, R. (2025). Continuous overnight monitoring of body temperature during embryo transfer cycles as a proxy for establishing progesterone fluctuations by comparison with P4 blood progesterone results: a prospective, observational study. Reproductive Biology and Endocrinology, 23(1), 17. PMID: 39901176 DOI: 10.1186/s12958-024-01329-0

12. https://www.ovusense.com/uk/approvals/ Accessed on 26th May, 2025.

13. Papaioannou, S., Delkos, D., Pardey, J., Milnes, R. C., & Knowles, T. G. (2014). Vaginal core body temperature assessment identifies pre-ovulatory body temperature rise and detects ovulation in advance of ultrasound folliculometry. In Human Reproduction (Vol. 29, pp. 219-220). 

14. Hurst, B.S., Davies, K., Milnes, R.C., & Knowles, T. G. (2022). Novel technique for confirmation of the day of ovulation and prediction of ovulation in subsequent cycles using a skin-worn sensor in a population with ovulatory dysfunction: a side-by-side comparison with existing basal body temperature algorithm and vaginal core body temperature algorithm. Frontiers in Bioengineering and Biotechnology, 10, 807139. PMID: 35309997 DOI: 10.3389/fbioe.2022.807139

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