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Factors Used by Mobile Applications to Predict Female Fertility Status and Their Reported Effectiveness: A Scoping
Elaine E Saugar1, Sabine Katsoulos1, Hyun-Su Kim2
1Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA.
Abstract:
Family planning, whether for pregnancy prevention or conception, is of pivotal importance to women of reproductive age. As hormonally driven methods, such as oral contraceptive pills, are widely used but have numerous side effects, women often seek alternative non-hormonal, non-invasive options, including fertility-tracking mobile applications (apps). However, the effectiveness of these apps as a method of contraception and conception planning has not been extensively vetted. The goal of this scoping review is to identify the various factors used by apps marketed as a method of contraception and/or family planning to predict a woman's fertility status, as well as their documented effectiveness. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guidelines, a literature search was performed in CINAHL, MEDLINE, and Alt HealthWatch databases for articles published between October 1, 2017, and October 4, 2022. Quality assessment of eligible full-text articles was conducted using the Joanna Briggs Institute critical appraisal tools. A total of 629 articles were screened. Overall, 596 articles were excluded and the remaining 33 articles underwent full-text review. Seven articles were included in the final analysis, yielding data on the following five apps: Natural Cycles, Ava Fertility, Clearblue Connected, Ovia Fertility, and Dynamic Optimal Timing (DOT). Data supporting the effectiveness of these apps is limited. All apps provided predictions on fertility status throughout a woman's menstrual cycle using proprietary algorithms, biometric data, and self-reported menstrual cycle data. Further research, particularly independent research following a randomized controlled design, on the efficacy of these apps is needed to produce more robust results.
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