By: DaKysha Moore, Ph.D., MHS, MS ; Elijah O. Onsomu, Ph.D., MPH, MS ; André Griffin
Introduction
Between 2015 and 2019, over 10 million women in the United States were diagnosed with infertility (Nugent & Chandra, 2024), although its varied etiology affects both sexes (Centers for Disease Control and Prevention [CDC], 2024). Causes among women range from ovulatory dysfunction and endometriosis to tubal blockage (Walker & Tobler, 2022). However, among men, hormonal imbalance (Leslie, Soon-Sutton, & Khan, 2024), problems with sperm production, and/or sexually transmitted infections (Mayo Clinic, n.d.) might be the reasons for infertility.
In the United States, slightly over 2% of all babies are born through ART (U.S. DHHS, 2024). Couples navigating infertility but desiring parenthood may try in vitro fertilization (IVF), one type of assisted reproductive technology (ART) in which eggs and sperm are combined in a lab to create embryos (Gutiérrez & Goosman, 2022). The embryo is then transferred to the uterus of the woman who will carry the baby. The success of transfer can only be measured by a pregnancy test, usually a blood test, some 12 to 15 days later, often called “the two-week wait” (Gutiérrez & Goosman, 2022). It is a time of anxiety for most couples (Lindberg, 2021), and in 2022, more than 90,000 U.S. babies were born using IVF (ASRM, n.d.). This paper explores messages about the two-week wait conveyed by videos on social media, specifically, YouTube.
People often seek information about their reproductive health experiences on the internet or social media (Chee et al., 2023; Lim et al., 2022). Many women consult platforms like YouTube to seek information about endometriosis (Lee et al., 2022; McGough et al., 2025) and breast cancer (Brar et al., 2022; Chai & Ingledew, 2023; Morena et al., 2025). George et al. (2023) found that slightly more than 70% of 404 Black women surveyed used social media platforms to obtain information about pregnancy. Another study found pregnant women search the internet for health information ranging from nutrition to the effects of their medications (Sayakhot & Carolan-Olah, 2016). Participants in a study by Bjelke et al. (2016) considered the internet an important tool, not only for finding pregnancy information, but also for reading about women in similar health situations. Participants also had at least one concern about the information discovered. Previous studies have examined the quality of messages about women’s health on social media platforms, such as YouTube. The purpose of the study is to explore the type of information disseminated on YouTube about the two-week wait after an embryo transfer.
Methodology
Data and data collection
The researchers typed in the keywords “IVF” and “two-week wait” on the YouTube search bar and, because the study was exploratory, focused on the first 20 videos that fit the inclusion criteria: (a) 10 minutes or less in length, (b) were in English, and (c) were posted within the last five years. Videos listed as “short,” or under a minute, were excluded.
Content analysis is often used to help researchers understand patterns of information presented in videos or text (Krippendorff, 2019). Riffe et al. (2019) define it as follows:
Quantitative content analysis is the systematic and replicable examination of symbols of communication, which have been assigned numeric values according to valid measurement rules, and the analysis of relationships involving those values using statistical methods, to describe the communication, draw inferences about its meaning, or infer from the communication to its context, both of production and consumption (p. 23).
Based on this definition and common practice, the researchers developed a codebook with 14 variables, adapted from previous work (Moore & Onsomu, 2025, see Table 1). The variables were divided into two sections, with seven variables in each section. The first section was coded for seven variables based on video and platform elements, such as the length of the video, the timeframe for posting it, likes, dislikes, views, subscriber information, and number of posted comments. The second section focused on the substance of the information, such as its source and its discussion of pregnancy symptoms, things to do during the two-week wait, mental and physical health, implantation bleeding, and taking a pregnancy test.
Table 1Codebook Variables and Definitions
| Variables | Definitions |
| Video length | Duration of the video in seconds. |
| Years | The year the video was uploaded. |
| Likes | The number of likes posted for the video. |
| Dislikes | The number of dislikes posted for the video. |
| Views | The number of views of the video during the analysis. |
| Subscriber | The number of people who subscribe to the channel. |
| Comments | The number of comments posted about the video. |
| Source of information | The type of person or organization giving the information during the video. |
| Pregnancy symptoms | Comments regarding the possible pregnancy symptoms during the two-week wait. |
| Things to do | Comments regarding the suggested dos and don’ts during the two-week wait. |
| Mental health | Conversations about taking care of one’s mental and emotional health during the two-week wait. |
| Physical health | Conversations about taking care of one’s physical health during the two-week wait, including information about exercise and nutrition. |
| Implantation bleeding | Conversations about possible bleeding during the two-week wait. |
| Pregnancy test | Comments regarding taking a pregnancy test during the two-week wait. |
Data analysis
One researcher was responsible for coding all the videos, and, to help ensure intercoder reliability and overall validity, a second researcher was trained on coding the data and was responsible for coding five videos (25%) of the total analyzed (Mao, 2017). While coding, both researchers took notes on the main comments, images, and viewpoints of the videos. After coding, the researchers discussed their findings. If they disagreed about a given code, they discussed their interpretations until they came to an agreement. Microsoft Excel (IBM Corp., 2023) was used to code all the videos, and all data were analyzed using IBM SPSS Statistics (Version 29.0). Statistical analyses included descriptive statistics for both continuous and nominal variables, as well as Spearman’s rank-order correlation to examine associations between variables.
Findings
Analysis of the 20 videos, which focused on the two-week period after embryo transfer, revealed that the average video length was nearly six minutes (M = 340.3 seconds, or 5.67 minutes). On average, videos had been posted 2.8 years prior to analysis, with the majority posted more than three years earlier (n = 12, 60%). The average number of likes per video was 358, with a range of 0 to 3,200. No videos received dislikes. The average number of comments was 36, and there were a total of 179 comments across all of the videos combined. Views peaked at over 25,001 (n = 8, 40%) then dropped to under 1,000 (n = 6, 30%). The number of subscribers to the video uploaders’ channels exceeded 25,001 for 45% of the videos (n =9), followed by 30% (n = 6) with subscriber counts between 1,001 and 5,000. The analysis also showed that the majority of videos (n = 13, 65%) featured information presented by individuals in the health professions, while 20% (n = 4) of the videos included women sharing personal experiences during the two-week wait (see Tables 2 and 3).
Table 2Continuous Variables
| Characteristics | Mean | Std. Dev. | Min. | Max. |
| Likes | 358 | 742.9 | 0 | 3200 |
| Comments | 36 | 53.18 | 0 | 179 |
| Dislikes | 0 | 0 | 0 | 0 |
| Time of video in seconds | 340.3 | 166.46 | 91 | 581 |
Table 3Nominal Variables
| Characteristics | n | % |
| Years since the video was posted | ||
| Less than one year | 3 | 15 |
| One year | 2 | 10 |
| Two years | 3 | 15 |
| Three years | 5 | 25 |
| Four years | 2 | 10 |
| Five years | 5 | 25 |
| Views | ||
| Less than 1000 | 6 | 30 |
| 1001-5000 | 3 | 15 |
| 5001-10000 | 2 | 10 |
| 10001-25000 | 1 | 5 |
| Greater than 25001 | 8 | 40 |
| Number of subscribers to the video site | ||
| Less than 1000 | 1 | 5 |
| 1001-5000 | 6 | 30 |
| 5001-10000 | 3 | 15 |
| 10001-25000 | 1 | 5 |
| Greater than 25001 | 9 | 45 |
| Source of information | ||
| News | 0 | 0 |
| Patient experience | 4 | 20 |
| Health professional | 13 | 65 |
| Other | 3 | 15 |
In relation to the study’s focus on two-week wait narratives, 45% of the videos (n = 9) provided information about possible pregnancy symptoms. The majority (55%, n = 11) did not suggest activities to engage in during the two-week timeframe. Additionally, 65% of the videos (n = 13) addressed mental health, while only 25% (n = 5) discussed physical health. Finally, most of the narratives did not mention implantation bleeding (80%, n = 16) or pregnancy tests (65%, n = 13, see Table 4).
Table 4 Two-Week Wait Narrative
| Wait Narratives | n | % |
| Pregnancy Symptoms | ||
| Yes | 9 | 45 |
| No | 11 | 55 |
| Things to do | ||
| Yes | 9 | 45 |
| No | 11 | 55 |
| Mental Health | ||
| Yes | 13 | 65 |
| No | 7 | 35 |
| Physical Health | ||
| Yes | 5 | 25 |
| No | 15 | 75 |
| Implantation Bleeding | ||
| Yes | 4 | 20 |
| No | 16 | 80 |
| Pregnancy Test | ||
| Yes | 7 | 35 |
| No | 13 | 65 |
The Spearman’s rank-order correlation showed a very strong, positive relationship between comments and likes (rs = 0.86, p < 0.001), with comments explaining 73.96% of the variance in likes (R2 = 0.7396). There was a weak positive relationship between likes and length of videos (rs = 0.25, p = 0.291), with likes explaining 6.15% of the variance in length of the video in seconds (R2 = 0.0615). Also, there was a very weak relationship between comments and length of videos in seconds (rs = 0.19, p = 0.433), with comments explaining 3.46% of the variance (R2 = 0.0346; see Table 5).
Table 5 Spearman’s Rank-order Correlation
| Likes | Comments | Seconds | |||
| Spearman’s rho | Likes | Correlation Coefficient | 1.000 | .860** | .248 |
| Sig. (2-tailed) | <.001 | .291 | |||
| N | 20 | 20 | 20 | ||
| Comments | Correlation Coefficient | .860** | 1.000 | .186 | |
| Sig. (2-tailed) | <.001 | .433 | |||
| N | 20 | 20 | 20 | ||
| Seconds | Correlation Coefficient | .248 | .186 | 1.000 | |
| Sig. (2-tailed) | .291 | .433 | |||
| N | 20 | 20 | 20 | ||
| **. Correlation is significant at the 0.01 level (2-tailed). | |||||
Discussion
The analysis shows an interest in the topic, as 40% of the videos had more than 25,000 views, as compared to the less than 1,000 views for almost all videos posted on YouTube (Schwab, 2024). One video had approximately 289,000 views. The average length of the videos was more than 5 minutes, which is not uncommon. The average length of videos analyzed by Cheng et al. (2008) was approximately 600 seconds. Furthermore, most of the videos analyzed in this study were posted between three and five years ago; however, approximately 25% were posted within the past two years, indicating that people continue to upload content on this topic. Among the videos in this study, a very strong positive relationship between comments and likes was observed (rs = 0.86, p < 0.001) with comments accounting for 73.96% of the variance in likes.
The majority of the videos featured a health professional (65%), including physicians, nurses, and dieticians. Some tried to explain physical symptoms that might arise during the two-week wait while maintaining a conversational tone to engage viewers. Atef et al. (2023) similarly found that physicians who used YouTube tried to make sure they appeared both knowledgeable and approachable.
The information disseminated seemed general (basic information about the two-week wait); most of the videos did not discuss pregnancy testing or implantation bleeding. When specific symptoms were mentioned, they were typically described in relation to medications taken during the two-week wait. According to Bilbao et al. (2024), medications, such as progesterone, can cause symptoms similar to pregnancy, and a positive pregnancy test does not necessarily align with symptoms. Another study that focused on IVF information on TikTok found a lack of health education (Peipert et al., 2023).
Mental health was a frequent topic across the videos, with many encouraging self-care practices such as seeking support from family and friends and managing stress. Women may experience heightened anxiety during the interval between fertility treatment and pregnancy testing (Boivin & Lancastle, 2010). Consistent with prior findings that women often share personal health narratives on YouTube (Lee et al., 2020; 2022), and 20% of the videos in this study featured women discussing their personal experiences during the two-week wait.
Limitations and Recommendations for Future Research
This study excluded videos longer than 10 minutes in duration. Future research should examine the type of information, including the tone and accuracy, regarding the messages on YouTube about the two-week wait. It should also include videos considered “short” on YouTube, because there were videos about the two-week wait in this category that were not part of the analysis. Finally, future analysis could be expanded to other popular social media platforms to gain a broader understanding of how two-week wait experiences are shared online.
Conclusion
The current study highlights the role of YouTube as a platform for dialogue between health professions and prospective mothers during the two-week wait following embryo transfer. While this study examined the characteristics and content of the videos, it did not assess the overall quality or accuracy of the information presented. The findings may be useful to health educators and communication specialists seeking to support individuals navigating the two-week wait by leveraging popular social media platforms. Based on the number of views across the sample, findings signal a desire to learn more about the two-weeks after embryo transfer and the need for more information and support.
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