The Fertility Tracker Method

The Fertility Tracker Method falls under the umbrella of fertility awareness-based methods. By using these methods, women can distinguish their fertile from their non fertile phases of their menstrual cycle. In the Fertility Tracker method, a Fertility Tracker is used to intuitively distinguish the non-fertile from the fertile days. Using an integrated highly sensitive thermal sensor, the user measures her daily Body Basal Temperature (BBT) and documents her menstruation. The Fertility Tracker saves the data automatically and evaluates it using an integrated algorithm to narrow down the fertile window (1)

The fertility tracker method first came up in 1986. Dr, Hubertus Rechberg, a German entrepreneur and inventor, developed the first fertility tracker “Lady-Comp” under the roof of his company Valley Electronics GmbH.

The BBT Shift Detection Algorithm

The Fertility Tracker method is based on the logic of existing manual methods such as the calculothermal method and the symptothermal method and complements them with a self-learning algorithm designed to detect statistically significant shifts in BBT baselines.

While the calculothermal method uses a rigid calculation concept (the beginning of the fertile period is calculated on the basis of the shortest cycle minus 18 days, while the beginning of the infertile period is determined on the basis of temperature, see also Knaus-Ogino method (2), the Fertility Tracker method enables individual calculation of the fertile days after menstruation on the basis of previously measured personal cycles.

The Fertility Tracker method uses the possibility of determining the fertile days after menstruation based on the statistical analysis of the user’s previously measured cycles. It therefore learns to evaluate the individual cycle through the integrated algorithm and continuous data acquisition. The method assumes at the beginning of the application (due to insufficient data) that all of the days after menstruation could be fertile in the first cycles. These fertile days are continuously adjusted with each further cycle and increasingly precisely narrowed down by the learning algorithm.

The Fertility Tracker’s calculations are based on the daily measured temperature data and the significant change in basal body temperature after ovulation. The hormone progesterone, which is released by the corpus luteum (the yellow endocrine body formed from the follicle after releasing the egg), has a thermogenic effect that causes the BBT to rise by about 0.2–0.3°C after ovulation (3). Due to the corpus luteum secretions having a dominant effect on hormone levels, the progesterone increase and the associated rise in BBT after ovulation is a constant and safe retrospective index indicating that ovulation has taken place (4,5). Using this data, the Fertility Tracker can distinguish the non fertile from the fertile days with an accuracy of over 99% (6). The user’s data at the beginning and end of the menstrual cycle is also considered.

One day after the significant increase in BBT, the chance of successful fertilization is only 0.8%, and two days after the increase it is 0.4% (7). Based on these data, the Fertility Tracker can distinguish between infertile and fertile days with a very high degree of accuracy. Information provided by the user on the beginning and end of the menstrual cycle is also included.

Advantages of the Fertility Tracker Method

A disadvantage of the already established fertility awareness methods is reading and interpreting the measured data, resulting in human rounding and interpretation errors (8,9) and the use of non-uniformly calibrated measuring instruments (10). The Fertility Tracker method requires that the measurement of basal body temperature and the evaluation of individual fertility are combined into one device, thus eliminating potential human and technical inaccuracies.

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Taken as a whole, the Fertility Tracker method is based on four elements:

• The recording and learning of new data (the basal temperature measured daily, the beginning and end of menstruation, as well as the collected historical cycle data) by a Fertility Tracker.
• The evaluation of the statistically significant temperature increases after ovulation.
• The statistical calculation of non fertile days at the beginning of the cycle, starting from the earliest significant temperature increase of previous cycles.
• The reduced risk of human input and interpretation errors through the combination of hardware (measurement by the thermometer) and software (algorithm) in one device (fertility tracker)

Source:

1. Raith-Paula, E., Frank-Herrmann, P., Freundl, G. & Strowitzki, T. Natürliche Familienplanung heute. (Springer Berlin Heidelberg, 2013). doi:10.1007/978–3–642–29784–7.
2. Holt, J. G. H., H. Marriage and Periodic Abstinence. (1960).
3. Su, H.-W., Yi, Y.-C., Wei, T.-Y., Chang, T.-C. & Cheng, C.-M. Detection of ovulation, a review of currently available methods. Bioeng. Transl. Med. 2, 238–246 (2017).
4. Prior, J. C., Naess, M., Langhammer, A. & Forsmo, S. Ovulation Prevalence in Women with Spontaneous Normal-Length Menstrual Cycles — A Population-Based Cohort from HUNT3, Norway. PLOS ONE 10, e0134473 (2015).
5. Shilaih, M. et al. Modern fertility awareness methods: wrist wearables capture the changes in temperature associated with the menstrual cycle. (2018) doi:10.1042/BSR20171279.
6. van de Roemer, N et al. The performance of a fertility tracking device (2021). ​​The European Journal of Contraception & Reproductive Health Care
7. Colombo, B. & Masarotto, G. Daily fecundability: first results from a new data base. Demogr. Res. 3, [39] p. (2000).
8. Pallone, S. R. & Bergus, G. R. Fertility Awareness-Based Methods: Another Option for Family Planning. J Am Board Fam Med 22, 147–157 (2009).
9. Fehring, R. J., Schneider, M., Barron, M. L. & Pruszynski, J. Influence of Motivation on the Efficacy of Natural Family Planning. MCN Am. J. Matern. Nurs. 38, 352–358 (2013).
10. Händel, P. & Wahlström, J. Digital contraceptives based on basal body temperature measurements. Biomed. Signal Process. Control 52, 141–151 (2019).