scholarly journals A self‐excited threshold autoregressive state‐space model for menstrual cycles: Forecasting menstruation and identifying within‐cycle stages based on basal body temperature

2019 ◽  
Vol 38 (12) ◽  
pp. 2157-2170 ◽  
Author(s):  
Ai Kawamori ◽  
Keiichi Fukaya ◽  
Masumi Kitazawa ◽  
Makio Ishiguro
Keyword(s):  
Body Temperature ◽  
State Space ◽  
Basal Body ◽  
State Space Model ◽  
Basal Body Temperature ◽  
Menstrual Cycles ◽  
Threshold Autoregressive ◽  
Space Model

scholarly journals Period tracker applications: What menstrual cycle information are they giving women?

2021 ◽  
Vol 17 ◽  
pp. 174550652110499
Author(s):  
Lauren Worsfold ◽  
Lorrae Marriott ◽  
Sarah Johnson ◽  
Joyce C Harper
Keyword(s):  
Big Data ◽  
Menstrual Cycle ◽  
Body Temperature ◽  
Basal Body ◽  
Cycle Length ◽  
Accurate Information ◽  
Basal Body Temperature ◽  
Menstrual Cycles ◽  
Conflicting Information ◽  
Fertile Window

Background: Period tracking applications (apps) allow women to track their menstrual cycles and receive a prediction for their period dates. The majority of apps also provide predictions of ovulation day and the fertile window. Research indicates apps are basing predictions on assuming women undergo a textbook 28-day cycle with ovulation occurring on day 14 and a fertile window between days 10 and 16. Objective: To determine how the information period tracker apps give women on their period dates, ovulation day and fertile window compares to expected results from big data. Methods: Five women’s profiles for 6 menstrual cycles were created and entered into 10 apps. Cycle length and ovulation day for the sixth cycle were Woman 1—Constant 28 day cycle length, ovulation day 16; Woman 2—Average 23 day cycle length, ovulation day 13; Woman 3—Average 28 day cycle length, ovulation day 17; Woman 4—Average 33 day cycle length, ovulation day 20; and Woman 5—Irregular, average 31 day cycle length, ovulation day 14. Results: The 10 period tracker apps examined gave conflicting information on period dates, ovulation day and the fertile window. For cycle length, the apps all predicted woman 1’s cycles correctly but for women 2–5, the apps predicted 0 to 8 days shorter or longer than expected. For day of ovulation, for women 1–4, of the 36 predictions, 3 (8%) were exactly correct, 9 predicted 1 day too early (25%) and 67% of predictions were 2–9 days early. For woman 5, most of the apps predicted a later day of ovulation. Conclusion: Period tracker apps should ensure they only give women accurate information, especially for the day of ovulation and the fertile window which can only be predicted if using a marker of ovulation, such as basal body temperature, ovulation sticks or cervical mucus.

PERIOVULATORY LEVELS OF PLASMA PROGESTERONE AND LUTEINIZING HORMONE IN WOMEN

1969 ◽  
Vol 62 (1) ◽  
pp. 82-88 ◽  
Author(s):  
Elof D. B. Johansson ◽  
Leif Wide
Keyword(s):  
Body Temperature ◽  
Luteinizing Hormone ◽  
Corpus Luteum ◽  
Basal Body ◽  
Significant Rise ◽  
Rapid Rise ◽  
Progesterone Concentration ◽  
Basal Body Temperature ◽  
Plasma Progesterone ◽  
Menstrual Cycles

ABSTRACT Plasma LH and progesterone levels were measured around the time of ovulation in 16 normal menstrual cycles. No increased levels of progesterone were found before the LH peak. The maximal LH peak levels lasted for 16–20 h at which time the plasma progesterone rose to a concentration of 1 to 2 ng/ml. Following the fall in the LH concentration, there was a rapid rise in the plasma progesterone concentration, indicating the formation of a corpus luteum. The lowest basal body temperature coincided with the first significant rise in LH levels.

Basal body temperature curves and endocrine pattern of menstrual cycles in Down syndrome

1996 ◽  
Vol 10 (2) ◽  
pp. 133-137 ◽  
Author(s):  
R. M. Cento ◽  
L. Ragusa ◽  
C. Proto ◽  
A. Alberti ◽  
C. Romano ◽  
...  
Keyword(s):  
Down Syndrome ◽  
Body Temperature ◽  
Basal Body ◽  
Basal Body Temperature ◽  
Menstrual Cycles

Monophasic basal body temperature in ovulatory menstrual cycles

1972 ◽  
Vol 113 (7) ◽  
pp. 933-937 ◽  
Author(s):  
E.D.B. Johansson ◽  
U. Larsson-Cohn ◽  
C. Gemzell
Keyword(s):  
Body Temperature ◽  
Basal Body ◽  
Basal Body Temperature ◽  
Menstrual Cycles

scholarly journals Fecundability in relation to use of mobile computing apps to track the menstrual cycle

2020 ◽  
Vol 35 (10) ◽  
pp. 2245-2252
Author(s):  
Joseph B Stanford ◽  
Sydney K Willis ◽  
Elizabeth E Hatch ◽  
Kenneth J Rothman ◽  
Lauren A Wise
Keyword(s):  
Menstrual Cycle ◽  
Body Temperature ◽  
Mobile Computing ◽  
Basal Body ◽  
Caffeine Intake ◽  
Time Varying ◽  
Basal Body Temperature ◽  
Menstrual Cycles ◽  
Fertile Window ◽  
History Of

Abstract STUDY QUESTION To what extent does the use of mobile computing apps to track the menstrual cycle and the fertile window influence fecundability among women trying to conceive? SUMMARY ANSWER After adjusting for potential confounders, use of any of several different apps was associated with increased fecundability ranging from 12% to 20% per cycle of attempt. WHAT IS KNOWN ALREADY Many women are using mobile computing apps to track their menstrual cycle and the fertile window, including while trying to conceive. STUDY DESIGN, SIZE, DURATION The Pregnancy Study Online (PRESTO) is a North American prospective internet-based cohort of women who are aged 21–45 years, trying to conceive and not using contraception or fertility treatment at baseline. PARTICIPANTS/MATERIALS, SETTING, METHODS We restricted the analysis to 8363 women trying to conceive for no more than 6 months at baseline; the women were recruited from June 2013 through May 2019. Women completed questionnaires at baseline and every 2 months for up to 1 year. The main outcome was fecundability, i.e. the per-cycle probability of conception, which we assessed using self-reported data on time to pregnancy (confirmed by positive home pregnancy test) in menstrual cycles. On the baseline and follow-up questionnaires, women reported whether they used mobile computing apps to track their menstrual cycles (‘cycle apps’) and, if so, which one(s). We estimated fecundability ratios (FRs) for the use of cycle apps, adjusted for female age, race/ethnicity, prior pregnancy, BMI, income, current smoking, education, partner education, caffeine intake, use of hormonal contraceptives as the last method of contraception, hours of sleep per night, cycle regularity, use of prenatal supplements, marital status, intercourse frequency and history of subfertility. We also examined the impact of concurrent use of fertility indicators: basal body temperature, cervical fluid, cervix position and/or urine LH. MAIN RESULTS AND THE ROLE OF CHANCE Among 8363 women, 6077 (72.7%) were using one or more cycle apps at baseline. A total of 122 separate apps were reported by women. We designated five of these apps before analysis as more likely to be effective (Clue, Fertility Friend, Glow, Kindara, Ovia; hereafter referred to as ‘selected apps’). The use of any app at baseline was associated with 20% increased fecundability, with little difference between selected apps versus other apps (selected apps FR (95% CI): 1.20 (1.13, 1.28); all other apps 1.21 (1.13, 1.30)). In time-varying analyses, cycle app use was associated with 12–15% increased fecundability (selected apps FR (95% CI): 1.12 (1.04, 1.21); all other apps 1.15 (1.07, 1.24)). When apps were used at baseline with one or more fertility indicators, there was higher fecundability than without fertility indicators (selected apps with indicators FR (95% CI): 1.23 (1.14, 1.34) versus without indicators 1.17 (1.05, 1.30); other apps with indicators 1.30 (1.19, 1.43) versus without indicators 1.16 (1.06, 1.27)). In time-varying analyses, results were similar when stratified by time trying at study entry (<3 vs. 3–6 cycles) or cycle regularity. For use of the selected apps, we observed higher fecundability among women with a history of subfertility: FR 1.33 (1.05–1.67). LIMITATIONS, REASONS FOR CAUTION Neither regularity nor intensity of app use was ascertained. The prospective time-varying assessment of app use was based on questionnaires completed every 2 months, which would not capture more frequent changes. Intercourse frequency was also reported retrospectively and we do not have data on timing of intercourse relative to the fertile window. Although we controlled for a wide range of covariates, we cannot exclude the possibility of residual confounding (e.g. choosing to use an app in this observational study may be a marker for unmeasured health habits promoting fecundability). Half of the women in the study received a free premium subscription for one of the apps (Fertility Friend), which may have increased the overall prevalence of app use in the time-varying analyses, but would not affect app use at baseline. Most women in the study were college educated, which may limit application of results to other populations. WIDER IMPLICATIONS OF THE FINDINGS Use of a cycle app, especially in combination with observation of one or more fertility indicators (basal body temperature, cervical fluid, cervix position and/or urine LH), may increase fecundability (per-cycle pregnancy probability) by about 12–20% for couples trying to conceive. We did not find consistent evidence of improved fecundability resulting from use of one specific app over another. STUDY FUNDING/COMPETING INTEREST(S) This research was supported by grants, R21HD072326 and R01HD086742, from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, USA. In the last 3 years, Dr L.A.W. has served as a fibroid consultant for AbbVie.com. Dr L.A.W. has also received in-kind donations from Sandstone Diagnostics, Swiss Precision Diagnostics, FertilityFriend.com and Kindara.com for primary data collection and participant incentives in the PRESTO cohort. Dr J.B.S. reports personal fees from Swiss Precision Diagnostics, outside the submitted work. The remaining authors have nothing to declare. TRIAL REGISTRATION NUMBER N/A.

scholarly journals The forecasting of menstruation based on a state-space modeling of basal body temperature time series

2017 ◽  
Vol 36 (21) ◽  
pp. 3361-3379 ◽  
Author(s):  
Keiichi Fukaya ◽  
Ai Kawamori ◽  
Yutaka Osada ◽  
Masumi Kitazawa ◽  
Makio Ishiguro
Keyword(s):  
Time Series ◽  
Body Temperature ◽  
State Space ◽  
Basal Body ◽  
Temperature Time Series ◽  
Basal Body Temperature ◽  
Space Modeling ◽  
State Space Modeling ◽  
Temperature Time
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