The Fertility Tracker Method 

The Fertility Tracker Method is a fertility awareness-based method automated in an integrated device that combines hardware (basal body temperature sensor) and software (a self-learning algorithm). 

The Fertility Tracker Method (or FTM) comes under the umbrella of fertility awareness-based methods. FTM combines the logic of existing manual fertility awareness methods including the Calendar Method, the Calculothermal Method, and the Symptothermal Method in the automated process of the fertility tracker, using a self-learning algorithm to calculate the user’s fertile and infertile days. This process avoids the user error-based misinterpretations that can occur with manual fertility awareness methods. (Pallon 2009; Barron 2005) 

The first Fertility Tracker was developed by Dr. Hubertus Rechberg in 1986. His company Valley Electronics continues to develop and innovate Fertility Trackers worldwide, including the Pearly, Lady-Comp, Baby-Comp, and Daysy. 

The Fertility Tracker both tracks and documents the user’s basal body temperature, then making an independent calculation of the fertile and infertile days using this data. The Fertility Tracker automatically stores the data and evaluates it using an intelligent, self-learning algorithm. Additionally the algorithm continuously checks its own calculation results against its previously collected data, and thus learns to reliably distinguish between fertile and infertile days. 

All users have to do to practice FTM accurately is to regularly measure their basal body temperature and correctly input their menstruation days. The Fertility Tracker takes over all further steps and determines the fertility status of the user. 

The Fertility Tracker Method is based, in part, on the Calculothermal Method and consists of a combination of three elements:

  1. The recording and learning of new data (the daily basal temperature, the beginning and end of menstruation, as well as the collected historical cycle data) by a Fertility Tracker. 
  2. The statistically significant evaluation of the post-ovulation temperature rise, with current and historical data from the database, by an algorithm adapted to the female cycle. 
  3. The avoidance of human input and interpretation errors through the combination of hardware (the sensor) and software (the algorithm) in one device (the Fertility Tracker).

The Fertility Tracker Method

The Fertility Tracker (hardware/sensor) 

The Fertility Tracker uses a very precise sensor for basal body temperature measurement. Uniquely, this sensor waits for the final temperature value to stabilize. This is why the Fertility Tracker may take up to 60 seconds to record the temperature. Your basal body temperature does not jump, the sensor has to warm up until it reaches its final temperature value. When it's warming up, you get a curve that is rising toward the final temperature value. Other basal body temperature monitoring devices have very fast measurement speed because they extrapolate the rising curve to “guess” or approximate the final temperature value. 

The Fertility Tracker waits until the final temperature value has stabilized, to get a result that is reliable. And if the temperature falls again (for example due to breathing and cold air coming into the mouth during a measurement), the value is not taken until it rises again and stabilizes. This method ensures a temperature measurement as precise as possible.

The self-learning algorithm (software)

The algorithm uses the menstrual start date as the beginning of a new cycle. Basal body temperature (BBT) is measured daily to establish the pre-ovulation phase until the current temperature value has increased by a characteristic shift. Due to the lack of enough data in the first few cycles using the device, the algorithm assumes that all days (after menstruation) up to ovulation could be fertile. That is the reason you will have more red (possible fertile) days in the first few cycles (figure 2 A). 

This mechanism is adjusted after continuous use of the device, in which infertile days during the pre-ovulation phase are calculated by learning from previously entered data and daily basal body temperature.

After this learning period when the Fertility Tracker gathers your personal data, the tracker will begin to pinpoint your ovulation and start your personal fertile window 5 days before your earliest likely predicted ovulation. This is calculated using the new data you have provided (basal body temperature, menstrual cycle start and end date, historical cycle data) and compared to statistical analysis of the database of menstrual cycle data. The algorithm ascertains the earliest day in your cycle you are likely to ovulate. The fertile phase will then continue until ovulation has been confirmed.

As soon as ovulation is assumed to have happened and the end of the fertile time is identified, by a statistical temperature shift, the algorithm starts to assign post-ovulatory, infertile statuses to the preceding days. Each new day, statistical analyses are used to re-evaluate whether you are still in the high-temperature luteal phase. The device waits until you enter menstruation, signaling a new cycle, or a prolonged high temperature phase that could indicate successful conception (figure 2 B).

Ovulatory Phase_EN.jpgfigure 2 A

Algorithm_EN.jpgfigure 2 B

The Temperature-Only Method

The Temperature-Only Method is focused on the phase after ovulation. The, often much longer, pre-ovulation phase is considered to be potentially fertile. The method assumes infertile days after ovulation. With a relatively regular cycle, users can assume that about 20-30% of the days are infertile (including menstruation) with the Temperature-Only method. With the Fertility Tracker Method, women with a regular cycle the distribution of fertile and infertile days is about fifty-fity.

The Calendar Method

The Calendar Method calculation of the fertile window is based on the average length of the previous cycles. It is assumed that the post ovulation phase, the second half of the cycle after ovulation, is always 14 days long (1). The fertile window opens four days before and closes three days after the predicted time. Information from the current cycle (i.e. daily BBT measurement)  is not taken into account in this type of calculation. Since ovulation and thus the individual fertile window can fluctuate by an average of 5 days within a year, this method is not accurate at all (2)

Covered by the Fertility Tracker Method

Covered by the Fertility Tracker Method

The Calculothermal Method

The classic Calculothermal Method, combines Basal Body Temperature with Ogino’s calendar calculation, which is the shortest cycle minus 18 to identify the start of the fertile time, with temperature to identify the end of the fertile time (3).

Based on the findings of the scientific work on natural family planning (NFP), Valley Electronics has developed a unique Method that allows a unique evaluation procedure through statistical and mathematical calculations.

While the classical Calculothermal Method is based on a rigid construct, the Fertility Tracker Method makes use of the possibility to individually extend the infertile days after menstruation on the basis of already measured cycles. In a nutshell, the Calculothermal Method tries to predict the fertile window, while the Fertility Tracker Method (due to the lack of sufficient data) assumes that in the first few cycles all days after menstruation could be fertile. 

These infertile days after menstruation are adjusted to the individual optimum with each cycle - the Fertility Tracker Method is actively learning. 

As soon as the ovulation is assumed to have happened, based on the measured BBT, the Fertility Tracker Method starts to assign mathematical post-ovulatory, infertile status to the days. Each new day, this mathematical test is (re)evaluated to decide if you are still in the infertile luteal phase. 


Peripheral basal body temperature

A pilot study on peripheral basal body temperature as used by many fitness treackers such as the current Apple Watch 8, Oura Ring or Fitbit shows the basic problem: Although a certain correlation to the cycle phases was found, the temperature rise at ovulation showed a considerable range of variation in this study and in 18% (!) of the 437 proven ovulatory cycles no temperature rise at all could be detected4. In a further 5%, there was a safety-related misdiagnosis: ovulation occurred after the fertile window defined by Produkt.  In another study, ovulation was within the fertile window in 83% of cycles5

A second important point is that Daysy provides over 99% accuracy in distinguishing fertile from non-fertile day for all users6. A basis for this high accuracy is that the basal body temperature measured orally in the morning is the same for all women, while for example the percentage of body fat7, the environment or even the season can have a direct influence on the skin temperature. 

In general, development in the use of peripheral basal body temperature has made great strides and is suitable for roughly determining which phase of the cycle the user is in, but is not suitable for distinguishing infertile from fertile days.

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Daysy is an intelligent fertility tracker that lets you get to know your very own menstrual cycle.

1) Colombo, B. and Scarpa, B. Calendar methods of fertility regulation: A rule of thumb. Statistica , 56(1):3–14, 1996

2) Johnson, S., Marriott, L. & Zinaman, M. Can apps and calendar methods predict ovulation with accuracy? Curr. Med. Res. Opin. 34, 1587–1594 (2018)

3) Holt, J. G. H. Marriage and Periodic Abstinence , 2nd Edition (1st Edition 1937). Longmans, London, 1960

4) ShilaihM, (2017), Modern fertility awareness methods: wristwearables capture the changes of temperature associatedwith themenstrual cycle. BiosciRep. 

5) MaijalaA, (2019), Nocturnal finger skin temperature in menstrual cycle tracking: ambulatory pilot study using a wearable Oura ring

6) van de Roemer N (2021), Performance of an Fertility Tracking Device

7) Salamunes A (2017), The effect of body fat percentage and body fat distribution on skin surface temperature with infrared thermography