The Accuracy of Websites and Cellular Phone Applications in Predicting the Fertile Window [12G]

Setton, Robert A.; Tierney, Christina; Tsai, Tony

doi:10.1097/01.aog.0000483896.97718.ee

Cited by 3 publications

(3 citation statements)

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“…Coding was conducted using a combination of an inductive approach based on the grounded theory [22] and a deductive approach [24]. Two coders (GT and KD) met to review categories and agree on a codebook [25] that was applied to all the transcripts thematically using Dedoose Version 7.0.23 software -42% of all transcripts were double coded. The lead authors (KN and RH) reviewed all the emergent themes with the coders to ensure accuracy and consistency of interpretation.…”

Section: Discussionmentioning

confidence: 99%

Delivering safer conception services to HIV serodiscordant couples in Kenya: perspectives from healthcare providers and HIV serodiscordant couples

Ngure

Kimemia

Dew

et al. 2017

Journal of the International AIDS Society

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Introduction: For HIV serodiscordant couples in resource-limited settings, pregnancy is common despite the risk of sexual and/or perinatal HIV transmission. Some safer conception strategies to reduce HIV transmission during pregnancy attempts are available but often not used for reasons including knowledge, accessibility, preference and others. We sought to understand Kenyan health providers’ and HIV serodiscordant couples’ perspectives and experiences with safer conception.Methods: Between August 2015 and March 2016, we conducted key informant interviews (KIIs) with health providers from public and private HIV care and fertility clinics and in-depth interviews (IDIs) and focus group discussions (FGDs) with HIV serodiscordant couples participating in an open-label study of integrated pre-exposure prophylaxis (PrEP) and antiretroviral therapy (ART) for HIV prevention (the Partners Demonstration Project). An inductive analytic approach identified a number of themes related to experiences with and perceptions of safer conception strategies.Results: We conducted 20 KIIs with health providers, and 21 IDIs and 4 FGDs with HIV serodiscordant couples. HIV clinic providers frequently discussed timed condomless sex and antiretroviral medications while providers at private fertility care centres were more comfortable recommending medically assisted reproduction. Couples experienced with ART and PrEP reported that they were comfortable using these strategies to reduce HIV risk when attempting pregnancy. Timed condomless sex in conjunction with ART and PrEP was a preferred strategy, often owing to them being available for free in public and research clinics, as well as most widely known; however, couples often held inaccurate knowledge of how to identify days with peak fertility in the upcoming menstrual cycle.Conclusions: Antiretroviral-based HIV prevention is acceptable and accessible to meet the growing demand for safer conception services in Kenya, since medically assisted interventions are currently cost prohibitive. Cross-disciplinary training for health providers would expand confidence in all prevention options and foster the tailoring of counselling to couples’ preferences.

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Section: Discussionmentioning

confidence: 99%

Delivering safer conception services to HIV serodiscordant couples in Kenya: perspectives from healthcare providers and HIV serodiscordant couples

Ngure

Kimemia

Dew

et al. 2017

Journal of the International AIDS Society

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“…There is a need for the development of an appropriate model for one-step-ahead forecast periods.In the last few years, the usage of web systems and apps to track periods and predict the fertile window has become popular [29,8]. For instance, in the manuscript [21], the authors built a hybrid predictive model based on autoregressive-moving-average (ARMA) and linear mixed effect model (LMM) for forecasting menstrual cycle length in athletes.…”

Section: Related Workmentioning

confidence: 99%

Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach

Rego

2023

Preprint

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A proper forecast of the menstrual cycle is meaningful for women's health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important events in a woman's life, such as family planning. In this work, we explored the use of machine learning techniques to predict regular and irregular menstrual cycles. We implemented some time series forecasting algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term Memory Network. Moreover, we generated synthetic data to achieve our purposes. The results showed that it is possible to accurately predict the onset and duration of menstrual cycles using machine learning techniques.

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“…There is a need for the development of an appropriate model for one-step-ahead forecast periods. In the last few years, the usage of web systems and apps to track periods and predict the fertile window has become popular [22], [23]. For instance, in the manuscript [24], the authors built a hybrid predictive model based on autoregressive-moving-average (ARMA) and linear mixed effect model (LMM) for forecasting menstrual cycle length in athletes.…”

Section: Related Workmentioning

confidence: 99%

Time Series Forecasting Approach for Predictive Modeling of Next Menstrual Cycle Length

Oliveira,

Cavalcante,

Rego

2023

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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A proper forecast of the menstrual cycle is meaningful for women’s health, as it allows individuals to take preventive actions to minimize cycle-associated discomforts. In addition, precise prediction can be useful for planning important events in a woman’s life, such as family planning. In this work, we explored the use of machine learning techniques to predict regular and irregular menstrual cycles. We implemented some time series forecasting algorithm approaches, such as AutoRegressive Integrated Moving Average, Huber Regression, Lasso Regression, Orthogonal Matching Pursuit, and Long Short-Term Memory Network. Moreover, we generated synthetic data to achieve our purposes. The results showed that it is possible to accurately predict the onset and duration of menstrual cycles using machine learning techniques.

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The Accuracy of Websites and Cellular Phone Applications in Predicting the Fertile Window [12G]

Cited by 3 publications

References 0 publications

Delivering safer conception services to HIV serodiscordant couples in Kenya: perspectives from healthcare providers and HIV serodiscordant couples

Delivering safer conception services to HIV serodiscordant couples in Kenya: perspectives from healthcare providers and HIV serodiscordant couples

Predictive Modeling of Menstrual Cycle Length: A Time Series Forecasting Approach

Time Series Forecasting Approach for Predictive Modeling of Next Menstrual Cycle Length

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