Shattuck Dominick, Haile Liya T, Simmons Rebecca G
Institute for Reproductive Health at Georgetown University, Washington, DC, United States.
Department of Obstetrics & Gynecology, University of Utah, Salt Lake City, UT, United States.
JMIR Mhealth Uhealth. 2018 Apr 20;6(4):e99. doi: 10.2196/mhealth.9661.
Smartphone apps that provide women with information about their daily fertility status during their menstrual cycles can contribute to the contraceptive method mix. However, if these apps claim to help a user prevent pregnancy, they must undergo similar rigorous research required for other contraceptive methods. Georgetown University's Institute for Reproductive Health is conducting a prospective longitudinal efficacy trial on Dot (Dynamic Optimal Timing), an algorithm-based fertility app designed to help women prevent pregnancy.
The aim of this paper was to highlight decision points during the recruitment-enrollment process and the effect of modifications on enrollment numbers and demographics. Recruiting eligible research participants for a contraceptive efficacy study and enrolling an adequate number to statistically assess the effectiveness of Dot is critical. Recruiting and enrolling participants for the Dot study involved making decisions based on research and analytic data, constant process modification, and close monitoring and evaluation of the effect of these modifications.
Originally, the only option for women to enroll in the study was to do so over the phone with a study representative. On noticing low enrollment numbers, we examined the 7 steps from the time a woman received the recruitment message until she completed enrollment and made modifications accordingly. In modification 1, we added call-back and voicemail procedures to increase the number of completed calls. Modification 2 involved using a chat and instant message (IM) features to facilitate study enrollment. In modification 3, the process was fully automated to allow participants to enroll in the study without the aid of study representatives.
After these modifications were implemented, 719 women were enrolled in the study over a 6-month period. The majority of participants (494/719, 68.7%) were enrolled during modification 3, in which they had the option to enroll via phone, chat, or the fully automated process. Overall, 29.2% (210/719) of the participants were enrolled via a phone call, 19.9% (143/719) via chat/IM, and 50.9% (366/719) directly through the fully automated process. With respect to the demographic profile of our study sample, we found a significant statistical difference in education level across all modifications (P<.05) but not in age or race or ethnicity (P>.05).
Our findings show that agile and consistent modifications to the recruitment and enrollment process were necessary to yield an appropriate sample size. An automated process resulted in significantly higher enrollment rates than one that required phone interaction with study representatives. Although there were some differences in demographic characteristics of enrollees as the process was modified, in general, our study population is diverse and reflects the overall United States population in terms of race/ethnicity, age, and education. Additional research is proposed to identify how differences in mode of enrollment and demographic characteristics may affect participants' performance in the study.
ClinicalTrials.gov NCT02833922; http://clinicaltrials.gov/ct2/show/NCT02833922 (Archived by WebCite at http://www.webcitation.org/6yj5FHrBh).
能在月经周期为女性提供日常生育状态信息的智能手机应用程序有助于丰富避孕方法的种类。然而，如果这些应用程序宣称可帮助用户预防怀孕，那么它们必须像其他避孕方法一样经过严格的研究。乔治敦大学生殖健康研究所正在对Dot（动态最佳时机）进行一项前瞻性纵向疗效试验，Dot是一款基于算法的生育应用程序，旨在帮助女性预防怀孕。
本文旨在强调招募 - 入组过程中的决策点以及修改对入组人数和人口统计学特征的影响。为一项避孕效果研究招募符合条件的研究参与者并纳入足够数量的参与者以进行统计学评估Dot的有效性至关重要。为Dot研究招募和纳入参与者涉及基于研究和分析数据做出决策、持续修改流程以及密切监测和评估这些修改的效果。
最初，女性参与该研究的唯一方式是通过电话与研究代表联系。在注意到入组人数较少后，我们审视了从女性收到招募信息到完成入组的7个步骤，并相应地进行了修改。修改1中，我们增加了回电和语音邮件程序以增加完成通话的数量。修改2涉及使用聊天和即时消息（IM）功能以促进研究入组。修改3中，该过程完全自动化，允许参与者无需研究代表的协助即可参与研究。
在实施这些修改后，6个月内有719名女性参与了该研究。大多数参与者（494/719，68.7%）是在修改3期间入组的，在此期间她们可以选择通过电话、聊天或完全自动化流程入组。总体而言，29.2%（210/719）的参与者通过电话入组，19.9%（143/719）通过聊天/IM入组，50.9%（366/719）直接通过完全自动化流程入组。关于我们研究样本的人口统计学特征，我们发现所有修改在教育水平上存在显著统计学差异（P<.05），但在年龄、种族或民族方面无差异（P>.05）。
我们的研究结果表明，对招募和入组过程进行灵活且一致的修改对于获得合适的样本量是必要的。自动化流程导致的入组率显著高于需要与研究代表进行电话互动的流程。尽管随着流程的修改，入组者的人口统计学特征存在一些差异，但总体而言，我们的研究人群具有多样性，在种族/民族、年龄和教育方面反映了美国总体人口情况。建议进行更多研究以确定入组方式和人口统计学特征的差异如何可能影响参与者在研究中的表现。
ClinicalTrials.gov NCT02833922；http://clinicaltrials.gov/ct2/show/NCT02833922（由WebCite存档于http://www.webcitation.org/6yj5FHrBh）
