Fertility Tracker Method

The Fertility Tracker Method (FTM) falls under the umbrella of fertility awareness-based methods. By using this method, women can distinguish their infertile from fertile days within their menstrual cycle with an accuracy of over 99%1. In the Fertility Tracker Method, the user uses a fertility tracker like the Lady-Comp. Using an integrated, highly sensitive thermal sensor, the user measures her basal body temperature (BBT) daily and documents her menstruation. The fertility tracker saves the data automatically and evaluates it independently using an integrated algorithm to narrow down the fertile window2.

The Fertility Tracker Method was first created in 1986. Dr. Hubertus Rechberg, a German entrepreneur and inventor, developed under the roof of his company, Valley Electronics, the first fertility tracker “Lady-Comp”. In the meantime, the 6th generation of fertility trackers is available with the current Lady-Comp.

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 it with an integrated self-learning algorithm that is tailored to detect significant differences in BBT as well as variables in cycle length.

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 method3), the Fertility Tracker Method enables individual calculation of the fertile days after menstruation on the basis of previously measured personal cycles.

In this way, the Fertility Tracker Method does not use a blanket calculation formula, but learns to evaluate the individual cycle more precisely through the integrated algorithm and continuous data collection. Thus, at the beginning of the application, the method assumes (due to the lack of sufficient data) that all days after menstruation could be fertile in the first cycles. These days, marked as fertile, are continuously and individually adjusted with each further cycle and are increasingly and precisely narrowed down by the learning algorithm.

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 individually 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), has a thermogenic effect that causes the basal body temperature to rise by about 0.2-0.3°C after ovulation4. 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 place5,6.Using this data, the fertility tracker can distinguish the infertile from the fertile days with a high degree of accuracy. The user's data at the beginning and end of the menstrual cycle is also considered.

Advantages and Summary of the Fertility Tracker Method

A disadvantage of the already established fertility awareness methods can be the uneducated reading and interpreting the measured data, resulting in human rounding and interpretation errors7,8 and the use of non-uniformly calibrated measuring instruments9. 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 inaccuracies10.

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 infertile 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).
The new Lady-Comp

1. van de Roemer N., Haile L., Koch C.K.,  The performance of a fertility tracking device. The European Journal of Contraception & Reproductive Health Care

2. 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.

3. Holt, J. G. H., H. Marriage and Periodic Abstinence. (1960).

4. 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).

5. 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).

6. 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.

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).