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Title: Fecundability in relation to use of mobile computing apps to track the menstrual cycle
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.  more » « less
Award ID(s):
1914792
NSF-PAR ID:
10304023
Author(s) / Creator(s):
 ;  ;  ;  ;  
Date Published:
Journal Name:
Human Reproduction
Volume:
35
Issue:
10
ISSN:
0268-1161
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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    Abstract STUDY QUESTION

    Can we derive adequate models to predict the probability of conception among couples actively trying to conceive?

     SUMMARY ANSWER

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     WHAT IS KNOWN ALREADY

    Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59–64%).

     STUDY DESIGN, SIZE, DURATION

    Study participants were female, aged 21–45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013–2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry.

     PARTICIPANTS/MATERIALS, SETTING, METHODS

    On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III.

     MAIN RESULTS AND THE ROLE OF CHANCE

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     LIMITATIONS, REASONS FOR CAUTION

    Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study.

     WIDER IMPLICATIONS OF THE FINDINGS

    Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work.

     STUDY FUNDING/COMPETING INTEREST(S)

    The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests.

     TRIAL REGISTRATION NUMBER

    N/A.

     
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    Abstract STUDY QUESTION

    To what extent is male fatty acid intake associated with fecundability among couples planning pregnancy?

     SUMMARY ANSWER

    We observed weak positive associations of male dietary intakes of total and saturated fatty acids with fecundability; no other fatty acid subtypes were appreciably associated with fecundability.

     WHAT IS KNOWN ALREADY

    Male fatty acid intake has been associated with semen quality in previous studies. However, little is known about the extent to which male fatty acid intake is associated with fecundability among couples attempting spontaneous conception.

     STUDY DESIGN, SIZE, DURATION

    We conducted an internet-based preconception prospective cohort study of 697 couples who enrolled during 2015–2022. During 12 cycles of observation, 53 couples (7.6%) were lost to follow-up.

     PARTICIPANTS/MATERIALS, SETTING, METHODS

    Participants were residents of the USA or Canada, aged 21–45 years, and not using fertility treatment at enrollment. At baseline, male participants completed a food frequency questionnaire from which we estimated intakes of total fat and fatty acid subtypes. We ascertained time to pregnancy using questionnaires completed every 8 weeks by female participants until conception or up to 12 months. We used proportional probabilities regression models to estimate fecundability ratios (FRs) and 95% CIs for the associations of fat intakes with fecundability, adjusting for male and female partner characteristics. We used the multivariate nutrient density method to account for energy intake, allowing for interpretation of results as fat intake replacing carbohydrate intake. We conducted several sensitivity analyses to assess the potential for confounding, selection bias, and reverse causation.

     MAIN RESULTS AND THE ROLE OF CHANCE

    Among 697 couples, we observed 465 pregnancies during 2970 menstrual cycles of follow-up. The cumulative incidence of pregnancy during 12 cycles of follow-up after accounting for censoring was 76%. Intakes of total and saturated fatty acids were weakly, positively associated with fecundability. Fully adjusted FRs for quartiles of total fat intake were 1.32 (95% CI 1.01–1.71), 1.16 (95% CI 0.88–1.51), and 1.43 (95% CI 1.09–1.88) for the second, third, and fourth vs the first quartile, respectively. Fully adjusted FRs for saturated fatty acid intake were 1.21 (95% CI 0.94–1.55), 1.16 (95% CI 0.89–1.51), and 1.23 (95% CI 0.94–1.62) for the second, third, and fourth vs the first quartile, respectively. Intakes of monounsaturated, polyunsaturated, trans-, omega-3, and omega-6 fatty acids were not strongly associated with fecundability. Results were similar after adjustment for the female partner’s intakes of trans- and omega-3 fats.

     LIMITATIONS, REASONS FOR CAUTION

    Dietary intakes estimated from the food frequency questionnaire may be subject to non-differential misclassification, which is expected to bias results toward the null in the extreme categories when exposures are modeled as quartiles. There may be residual confounding by unmeasured dietary, lifestyle, or environmental factors. Sample size was limited, especially in subgroup analyses.

     WIDER IMPLICATIONS OF THE FINDINGS

    Our results do not support a strong causal effect of male fatty acid intakes on fecundability among couples attempting to conceive spontaneously. The weak positive associations we observed between male dietary fat intakes and fecundability may reflect a combination of causal associations, measurement error, chance, and residual confounding.

     STUDY FUNDING/COMPETING INTEREST(S)

    The study was funded by the National Institutes of Health, grant numbers R01HD086742 and R01HD105863. In the last 3 years, PRESTO has received in-kind donations from Swiss Precision Diagnostics (home pregnancy tests) and Kindara.com (fertility app). L.A.W. is a consultant for AbbVie, Inc. M.L.E. is an advisor to Sandstone, Ro, Underdog, Dadi, Hannah, Doveras, and VSeat. The other authors have no competing interests to report.

     TRIAL REGISTRATION NUMBER

    N/A.

     
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