Mobile Phone Apps for the Prevention of Unintended Pregnancy: A Systematic Review and Content Analysis
Bibliographic record
Abstract
BACKGROUND: Over 50% of pregnancies in the United States are unintended, meaning that the pregnancy is mistimed, unplanned, or unwanted. Unintended pregnancy increases health risks for mother and child, leads to high economic costs for society, and increases social disparities. Mobile phone ownership is rapidly increasing, providing opportunities to reach at-risk populations with reproductive health information and tailored unintended pregnancy prevention interventions through mobile phone apps. However, apps that offer support for unintended pregnancy prevention remain unevaluated. OBJECTIVE: To identify, describe, and evaluate mobile phone apps that purport to help users prevent unintended pregnancy. METHODS: We conducted an extensive search of the Apple iTunes and Android Google Play stores for apps that explicitly included or advertised pregnancy prevention or decision-making support in the context of fertility information/tracking, birth control reminders, contraceptive information, pregnancy decision-making, abortion information or counseling, sexual communication/negotiation, and pregnancy tests. We excluded apps that targeted medical professionals or that cost more than US $1.99. Eligible apps were downloaded and categorized by primary purpose. Data extraction was performed on a minimum of 143 attributes in 3 domains: (1) pregnancy prevention best practices, (2) contraceptive methods and clinical services, and (3) user interface. Apps were assigned points for their inclusion of features overall and for pregnancy prevention best practices and contraceptive information. RESULTS: Our search identified 6805 app descriptions in iTunes and Google Play. Of these, 218 unique apps met inclusion criteria and were included in the review. Apps were grouped into 9 categories: fertility trackers (n=72), centers and resources (n=38), birth control reminders (n=35), general sexual and reproductive health (SRH) information (n=17), SRH information targeted specifically to young adults (YA) (n=16), contraceptive information (n=15), service or condom locators (n=12), pregnancy tests (n=10), and games (n=3). Twelve apps scored at least 50 points (out of 94) for overall number of features and at least 15 points (out of 21) for contraceptive information and pregnancy prevention best practices. Overall, 41% of apps did not mention any modern contraceptive methods and 23% mentioned only 1 method. Of apps that did mention a modern contraceptive method, fewer than 50% of these apps provided information on how to use it. YA SRH apps had the highest percentage of pregnancy prevention best practices in each app. Demographic and interface evaluation found that most apps (72%) did not target any race and only 10% explicitly targeted youth. Communication interface features were present in fewer than 50% of apps. CONCLUSIONS: This review identified several useful, evidence-based apps that support the prevention of unintended pregnancy. However, most apps miss opportunities to provide users with valuable information, interactive decision aids, and evidence-based interventions for unintended pregnancy prevention. Further, some apps in this space may increase the likelihood of unintended pregnancy due to the low effectiveness of the contraceptive methods promoted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".