A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking. Li, K., Urteaga, I., Shea, A., Vitzthum, V. J., Wiggins, C. H., & Elhadad, N. Journal of the American Medical Informatics Association, 29(1):3 – 11, 09, 2021. ![A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 12 downloads The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.
@Article{j-Li2021,
author = {Kathy Li and I{\~n}igo Urteaga and Amanda Shea and Virginia J. Vitzthum and Chris H. Wiggins and Noémie Elhadad},
journal = {Journal of the American Medical Informatics Association},
title = {A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking},
year = {2021},
issn = {1527-974X},
month = {09},
number = {1},
pages = {3 -- 11},
volume = {29},
abstract = {The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.},
doi = {10.1093/jamia/ocab182},
url = {https://doi.org/10.1093/jamia/ocab182},
}
Downloads: 12
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H.","Elhadad, N."],"bibdata":{"bibtype":"article","type":"article","author":[{"firstnames":["Kathy"],"propositions":[],"lastnames":["Li"],"suffixes":[]},{"firstnames":["Iñigo"],"propositions":[],"lastnames":["Urteaga"],"suffixes":[]},{"firstnames":["Amanda"],"propositions":[],"lastnames":["Shea"],"suffixes":[]},{"firstnames":["Virginia","J."],"propositions":[],"lastnames":["Vitzthum"],"suffixes":[]},{"firstnames":["Chris","H."],"propositions":[],"lastnames":["Wiggins"],"suffixes":[]},{"firstnames":["Noémie"],"propositions":[],"lastnames":["Elhadad"],"suffixes":[]}],"journal":"Journal of the American Medical Informatics Association","title":"A predictive model for next cycle start date that accounts for adherence in menstrual self-tracking","year":"2021","issn":"1527-974X","month":"09","number":"1","pages":"3 – 11","volume":"29","abstract":"The study sought to build predictive models of next menstrual cycle start date based on mobile health self-tracked cycle data. Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. 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Because app users may skip tracking, disentangling physiological patterns of menstruation from tracking behaviors is necessary for the development of predictive models.We use data from a popular menstrual tracker (186 000 menstruators with over 2 million tracked cycles) to learn a predictive model, which (1) accounts explicitly for self-tracking adherence; (2) updates predictions as a given cycle evolves, allowing for interpretable insight into how these predictions change over time; and (3) enables modeling of an individual's cycle length history while incorporating population-level information.Compared with 5 baselines (mean, median, convolutional neural network, recurrent neural network, and long short-term memory network), the model yields better predictions and consistently outperforms them as the cycle evolves. The model also provides predictions of skipped tracking probabilities.Mobile health apps such as menstrual trackers provide a rich source of self-tracked observations, but these data have questionable reliability, as they hinge on user adherence to the app. By taking a machine learning approach to modeling self-tracked cycle lengths, we can separate true cycle behavior from user adherence, allowing for more informed predictions and insights into the underlying observed data structure.Disentangling physiological patterns of menstruation from adherence allows for accurate and informative predictions of menstrual cycle start date and is necessary for mobile tracking apps. The proposed predictive model can support app users in being more aware of their self-tracking behavior and in better understanding their cycle dynamics.},\n doi = {10.1093/jamia/ocab182},\n url = {https://doi.org/10.1093/jamia/ocab182},\n}\n\n","author_short":["Li, K.","Urteaga, I.","Shea, A.","Vitzthum, V. 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