Detecting the fertile window in irregular menstrual cycles using a wearable medical device
Primary Research Question: Can the Ava bracelet identify hormonal patterns and fertile window (FW) in women with irregular cycles, including subjects with and without diagnosed polycystic ovaries syndrome (PCOS)?
Key Findings: Results included 161 cycles from women with irregular but undiagnosed/unknown PCOS (meanduration= 28.72 days [95% CI, 28.1-29.4]) and 61 cycles from women with confirmed PCOS (meanduration = 34.9 days [95% CI, 32.1-37.6]). Ava’s algorithm accurately identified fertile days in 82.1% of irregular cycles (95% CI, 80.0-83.8). Almost 30% of irregular cycles had more than one FW (mean=1.5 FW per cycle).
Citation: Rothenbühler M, Schmutz E, Hamvas G, et al. Detection of fertile window in irregular cycles using a wearable medical device. Poster presented at: International Federation of Fertility Societies 2019 World Congress; April 11-14, 2019; Shanghai, China.
Previous cycle tracking with a wearable multiparameter device reduces time to conception
Primary Research Question: Do real-world Ava users who cycle track with a wearable device prior to trying to conceive become pregnant faster than women who did not cycle track first?
Key Findings: Data from 12,540 women who purchased the Ava bracelet and reported a positive pregnancy test were included, with subjects divided between the prior cycle tracking (PCT; n=451) and no prior cycle tracking (NCT; n=12,089) groups. Time to pregnancy was significantly faster in the PCT group (mean=75 days, SD= 51 days) versus the NCT group (mean=89 days, SD=65 days; t(587)=-8.5936, p<.001).
Citation: Gibson S, Bilic A, Sakalidis V, Goodale BM, Shilaih M, Shulman L. Previous cycle tracking with a wearable multiparameter device reduces time to conception. Poster presented at: Central Association of Obstetricians and Gynecologists; Oct. 16-19, 2019; Cancun, Mexico.
Detection of the fertile window using a wearable medical device and the calendar method: A comparative study
Primary Research Question: When identifying the six-day fertile window, how does artificial intelligence-based predictions from wearable sensor technology compare to the calendar method in accuracy and precision?
Key Findings: The Ava bracelet’s accuracy in identifying the fertile days was 88.1% (SD=9.1%) compared to 76.8% (SD=5.1%) for the Standard Days method, 69.2% (SD=15.6%) for the Rhythm Method, and 67.6% (SD=16.1%) for the Alternative Rhythm Method. Furthermore, the wearable fertility tracker had the highest precision of any of the methods analyzed (70.3%, SD=21.9% v. 42.7%-47.7% for the calendar methods [SDs=7.6%-13.0%]).
Citation: Mouriki E, Bilic A, Goodale BM, et al. Detection of the fertile window using a wearable medical device and the traditional calendar method: A comparative study. Poster presented at: American Society of Reproductive Medicine Scientific Congress and Expo; Oct. 12-16, 2019; Philadelphia, PA, USA.
Capturing the physiological characteristics of early pregnancy using wrist worn wearables
Primary Research Question: Can wearable sensors on the Ava bracelet capture physiological changes associated with early pregnancy, in particular differences in heart rate variability (HRV), pulse rate, breathing rate, and wrist-skin temperature?
Key Findings: Analysis included 131 conceptive and 853 non-conceptive cycles from 330 women. In comparison to the late luteal phase of non-conceptive cycles, conceptive cycles were characterized by: an increase in pulse rate (1.43 beats/minute, p<.001), breathing rate (0.31 breaths/minute, p<.001), and wrist-skin temperature (0.05 C, p<.05). In addition, non-conceptive cycles were more likely to have lower HRV (-3.14 standard units, p<.001).
Citation: Shilaih M, Goodale BM, Falco L, Kübler F, Dammeier F, Leeners B. Capturing the physiological characteristics of early pregnancy using wrist worn wearables. Poster presented at: European Society of Human Reproduction and Embryology Annual Meeting; July 1-14, 2018; Barcelona, Spain.
Proof of concept pilot study: Digital women’s health based on wearables and big data
Primary Research Question: The goal of this pilot study was to determine if physiological data measured via a wrist worn wearable sensor could allow for an individualized, AI-driven form of natural family planning.
Key Findings: In a first step, wrist skin temperature (WST) and pulse rate readings were analyzed. Both showed significant differences between the follicular and luteal phase. The minimum average resting pulse rate occurred in the follicular phase (mean=55.5 beats/minute) and maximum resting pulse rate in the luteal phase (mean=59.3 beats/minute). WST followed the same pattern, with 34.3 compared to 34.7, respectively.
Citation: Stein P, Falco L, Kübler F, et al. Digital women’s health based on wearables and Big Data: New findings in physiological changes throughout the menstrual cycle. Poster presented at: Germany Society of Gynecology and Obstetrics (DGGG) Annual Meeting; Oct. 19-22, 2016; Stuttgart, Germany.