Study Methods

Publications under ‘Study Methods’ explain and describe the components and populations of both PRESTO and our Danish study “Snart Gravid”

Wise LA, Rothman KJ, Mikkelsen EM, Stanford JB, Wesselink AK, McKinnon C, Gruschow SM, Horgan CE, Wiley AS, Hahn KA, Sørensen HT, Hatch EE. Design and conduct of an internet-based preconception cohort study in North America: Pregnancy Study Online. Paediatr Perinat Epidemiol. 2015; 29(4): 360-71.

This paper summarizes the design of the Boston University Pregnancy Study Online (PRESTO). In the first two years (2013-2015), 2,421 women and 693 male partners in the US and Canada enrolled in PRESTO.

Mikkelsen EM, Hatch EE, Wise LA, Rothman KJ, Riis A, Sørensen HT. Cohort profile: the Danish Web-based Pregnancy Planning Study—‘Snart Gravid’. Int J Epidemiol 2009; 38(4): 938-43.

This article describes the Danish Web-based Pregnancy Planning Study (“Snart Gravid”).

Rothman KJ, Mikkelsen EM, Riis A, Sørensen HT, Wise LA, Hatch EE. Randomized trial of questionnaire length. Epidemiology 2009; 20(1): 154.

In the Danish Web-based Pregnancy Planning Study (“Snart Gravid”), we randomly assigned participants to receive a longer or shorter version of the same questionnaire at enrollment. Enrollment rates were similar for both versions.

Huybrecht KF, Mikkelsen EM, Christensen T, Riis AH, Hatch EE, Wise LA, Sørensen HT, Rothman KJ. A successful implementation of e-epidemiology: the Danish pregnancy planning study ‘Snart-Gravid’. Eur J Epidemiol 2010; 25(5): 297-304.

After the first year of the Danish Web-based Pregnancy Planning Study (“Snart Gravid”), we evaluated the feasibility and cost-effectiveness of the study. We found that the study was successful in recruiting and retaining participants, and it was cost-effective.

Mikkelsen EM, Mainidal HT. [Pilot testing of an internet based pregnancy planning study “”.] Klin Sygepleje 2011; 25(1): 57-66.

We describe the first year of the Danish Web-based Pregnancy Planning Study (“Snart Gravid”) in detail. This article is in Danish.

Radin RG, Rothman KJ, Hatch EE, Mikkelsen EM, Sørensen HT, Riis AH, Fox MP, Wise LA. Maternal recall error in retrospectively reported time-to-pregnancy: an assessment and bias analysis. Paediatr Perinat Epidemiol 2015; 29(6): 576-88.

We compared the accuracy of reported time to pregnancy by reporting every 2 months while trying to conceive vs. by recall during the first trimester of pregnancy. The two methods had similar results.

Hatch EE, Hahn KA, Wise LA, Mikkelsen EM, Jumar R, Fox MP, Brooks DR, Riis AH, Sørensen HT, Rothman KJ. Evaluation of selection bias in an internet-based study of pregnancy planners. Epidemiology 2016; 27(1): 98-104.

We analyzed the associations between several risk factors and pregnancy outcomes in the Snart Gravid cohort and among all births in Denmark, using data from the National Danish Birth Registry. Similar associations were found, suggesting that selection bias is not likely to be a major problem in Snart Gravid.

Knudsen VK, Hatch EE, Cueto H, Tucker KL, Wise L, Christensen T, Mikkelsen EM. Relative validity of a semi-quantitative, web-based FFQ used in the ‘Snart Forældre’ cohort – a Danish study of diet and fertility. Public Health Nutr 2016; 19(6): 1027-34.

We compared measurement of diet and nutritional intake using a web-based questionnaire vs. a four-day food diary. The web-based questionnaire performed well.

Christensen T, Riis AH, Hatch EE, Wise LA, Nielsen MG, Rothman KJ, Sørensen HT, Mikkelsen EM. Costs and efficiency of online and offline recruitment methods: a web-based cohort study. J Med Internet Res 2017; 19(3): e58.

We compared the costs and efficiency of several methods of recruiting participants to the Danish Web-based Pregnancy Planning Study. Over 80% of participants were enrolled via various online recruitment methods, which had a lower average cost per participant compared with “offline” methods such as flyers and press releases.

Harville EW, Mishra GD, Yeung E, Mumford SL, Schisterman EF, Jukic AM, Hatch EE, Mikkelsen EM, Jiang H, Ehrenthal DB, Porucznik CA, Stanford JB, Wen SW, Harvey A, Symons Downs D, Yajnik C, Santillan D, Santillan M, McElrath TF, Woo JG, Urbina EM, Chavarro JE, Sotres-Alvarez D, Bazzano L, Zhang J, Steiner A, Gunderson EP, Wise LA. The Preconception Period analysis of Risks and Exposures Influencing health and Development (PrePARED) consortium. Paediatr Perinat Epidemiol 2019; 33(6): 490-502.

This article describes the Preconception Period analysis of Risks and Exposures Influencing health and Development (PrePARED) consortium. Through the PrePARED consortium, PRESTO will work together with several other studies of preconception health to answer important research questions related to fertility and pregnancy.

Wise LA, Wang TR, Willis SK, Wesselink AK, Rothman KJ, Hatch EE. Effect of a home pregnancy test intervention on cohort retention and pregnancy detection: a randomized trial. Am J Epidemiol 2020; 189(8): 773-778.

In a randomized study, we sent home pregnancy tests to 50% of participants with ≤6 cycles of pregnancy attempt time. Receiving a home pregnancy test made participants more likely to stay in the study, but did not influence their fecundability or timing of pregnancy detection.

Wise LA, Wang TR, Wesselink AK, Willis SK, Chaiyasarikul A, Levinson JS, Rothman KJ, Hatch EE, Savitz DA. Accuracy of self-reported birth outcomes relative to birth certificate data in an Internet-based prospective cohort study. Paediatr Perinat Epidemiol. 2021 Sep;35(5):590-595. doi: 10.1111/ppe.12769. Epub 2021 May 6. PMID: 33956369; PMCID: PMC8380669.

We linked PRESTO participant data on birth outcomes to birth certificate data. Self- reported data on gestational age at delivery (in weeks) and birth weight (in grams) from PRESTO participants was shown to be highly accurate compared with what was listed on their birth certificate.

Yland JJ, Wang T, Zad Z, et al. PrYland, J. J., Wang, T., Zad, Z., Willis, S. K., Wang, T. R., Wesselink, A. K., Jiang, T., Hatch, E. E., Wise, L. A., & Paschalidis, I. C. (2022). Predictive models of pregnancy based on data from a preconception cohort study. Human reproduction (Oxford, England)37(3), 565–576.edictive models of pregnancy based on data from a preconception cohort study. Hum Reprod. 2022;37(3):565-576.

PRESTO researchers used machine learning methods to develop models that can predict pregnancy based on lifestyle, medical, and reproductive characteristics. Important predictors included female age, male and female BMI, a history of childbearing, and pre-pregnancy use of multivitamins or folate supplements.