Unpacking the Lived Experience of Collaborative Pregnancy Tracking

In some non-Western settings, researchers point to greater levels of stigma surrounding non-pregnant male stakeholders’ involvement with pregnancy than in the U.S., inhibiting pregnant people from getting the desired support. For example, in some African regions, male partners who offer care for their pregnant partners are often stigmatized as “weak men” , and pregnant people are proud of giving birth without support from their spouses [1, 68]. In some Asian countries, such as India, pregnant people may overwhelmingly rely on their mothers for informational and emotional support rather than spouses [12, 95]. While we focus on the lived experiences of collaborative pregnancy tracking in the U.S. context, we expect many of the tensions we unpack carry over and are perhaps exacerbated by other cultural norms.

In recent years, HCI has repeatedly argued for the importance of studying and designing technology to support and empower women’s physical, mental, and reproductive health [6, 61], since their health & wellness needs have been historically overlooked due to systemic discrimination around women’s access to health and education [5, 37]. Mirroring academia’s interest in supporting women’s health, the FemTech (female-oriented technologies [7]) industry is similarly experiencing remarkable growth, and is predicted to be a $50 billion industry by 2025 [37, 53, 116]. Numerous studies and technologies have begun to explore and support various aspects of women’s health, such as pregnancy [50, 85], new motherhood [31, 91], menstruation [35, 107], menopause [63], fertility [43], and intimate care [114]. Existing works have highlighted multiple benefits of technology in supporting women’s health, such as helping make sense of data, raising health awareness, and signaling potential risks [35, 44].

Despite technology’s advantages, research has also surfaced various tensions, drawbacks, and potential risks associated with technology for women’s health. For example, prior works have highlighted a need for collaboration as many aspects of women’s health need social support, such as fertility and pregnancy, while existing technology is primarily designed to support solo experiences [42, 87, 114, 115]. A core challenge in designing social or collaborative experiences to support women’s health pertains to privacy, as women’s health data is often viewed as highly sensitive that could disclose intimate and detailed information about their everyday lives [76]. However, current studies that examine privacy concerns related to technology for women’s health mainly focus on app practices around sharing data with third parties without users’ consent or the potential to share data with governments or other authorities for legal needs [11, 30, 75]. Less is known about people’s concerns when supporting women’s health in their in-person or social settings such as disclosure concerns among friends, families, or other peers. Some prior work suggests that most users do not have privacy concerns or knowledge about how their data related to women’s health is being collected and used by technology [18, 37, 98]. However, political issues around women’s bodily autonomy, such as the overturning of Roe versus Wade, have led to greater concern around how women’s sensitive health data could be abused [30, 75]. Besides concerns around privacy and bodily autonomy, research also surfaces that women’s health is often stigmatized and regarded as taboo, resulting in women being judged or feeling ashamed when talking about their health issues or using relevant technology in public [4, 25, 115, 116].

Prior works’ examining people’s experiences with technology for women’s health as well as its benefits and drawbacks are mainly situated in a broad societal context, such as workplaces, politics, and culture [30, 63, 103, 114, 116]. This work expands on this understanding by exploring concerns and tensions when pregnant people collaborate with their close social networks around tracking and managing pregnancy.

The HCI and CSCW community have delved into multiple facets of how technology can support pregnancy. For example, fertility apps aim to help people get pregnant by predicting ovulation and providing insights about people’s fertility status [42, 43]. Social media and online communities provide a way for pregnant people to receive social support, such as getting emotional support to help cope with negative outcomes like pregnancy complications and loss, and learning from others’ experiences [9, 50, 94]. Online search engines help pregnant people find useful information about how to manage pregnancy [46, 62].

Pregnancy tracking technology aims to support pregnant people in achieving healthy pregnancy outcomes by enabling them to monitor data on their health and wellbeing (e.g., maternal health, mood, diet, and physical activity) and identify patterns and gain insights [29, 41, 84]. Some also provide detailed information about fetal development, such as fetal size or changes in fetal capability (e.g., new senses like hearing, behaviors like kicking) [47]. Existing technology support rarely considers non-pregnant stakeholders’ needs and roles during pregnancy [70], even though they also play a pivotal part [26, 40, 64]. For example, a few studies examine the user experience of pregnancy tracking apps, but mostly concentrate on understanding how existing technology aids or hinders pregnant people [8, 13, 87]. In terms of social interactions during pregnancy, the existing design space primarily focuses on supporting pregnant people in getting support from healthcare providers, peers, and local communities [20, 50, 87, 118]. The understanding of how pregnant people and their close social networks, such as family members and close friends, mutually support each other is much more limited. This work extends the current literature by understanding how pregnant people and non-pregnant stakeholders collaborate around tracking pregnancy in everyday lives.

The field of Personal Informatics (PI) stems from people’s increasing use of self-monitoring technology in everyday lives [36, 66, 97]. While the concept of PI centers around people seeking self-understanding on their own, studies find people’s self-tracking behaviors are heavily intertwined with social interactions [23, 73, 97]. Social practices in PI can be mainly categorized into three forms: socially sharing one’s data [23, 67, 69], personal data being tracked by others [45, 59, 117], and co-tracking behaviors [79, 82, 90].

Prior work suggests that people socially share their personal data via tracking tools’ internal social features (e.g., built-in social platforms, rankings, and leaderboards) or external social platforms (e.g., Twitter, Facebook, Instagram) to get emotional support, celebrate achievements, seek information, compete with others, and to be held accountable [23, 49, 69]. Social sharing behaviors also frequently happen in clinical and personal health settings, where patients self-monitor complex health conditions and share data with clinicians for them to view and make medical decisions [24, 56, 99]. While trackers can socially share their data with diverse groups of people including like-minded strangers, close social networks, or medical professionals, these people usually are passive data recipients who are not actively involved in the tracking experience [23, 24, 33]. They may react to the data by giving advice on what or how to track, or may help people further make sense of their data [23, 33]. However, they typically do not participate in the person’s process of tracking data.

In other circumstances, people may have others track data about them on their behalf. This largely happens when people are often preoccupied, such as dealing with a serious health condition, and they therefore turn to caregivers to offload the responsibility of tracking and understanding necessary data [59, 78, 117]. Studies also show that when caregivers directly track patients’ data, it helps enhance their understanding of and fosters empathy toward the patient’s behaviors and health conditions [59, 117]. In family settings, parents sometimes track on behalf of their children when they cannot track on their own [59, 100].

Mishra et al. coin “collaborative tracking” to refer to when multiple people track an individual’s health and care throughout all stages, meaning they participate in preparing to track, collecting the data, through reflecting and acting on that data [79]. The practice of collaborative tracking has largely been studied in family or clinical settings, involving patients, caregivers, and clinicians. Perspectives around family informatics, or tracking in families, exhibit similar practices wherein family members collaborate around all stages of tracking [90, 102]. A frequent recommendation for supporting collaborative tracking is to offer all stakeholders equal data access and control, such as granting access to a patient portal or enabling everyone involved in the collaboration to contribute data [38, 79, 90, 100]. This approach allows each stakeholder to contribute their perspectives toward a tracking event, since they might experience it differently (e.g., a patient and a caregiver may have different interpretations of tracked data) [14, 79, 82]. It also helps reduce the burden of data work on a single individual, such as the patient [82] or a particular parent [72, 90]. Some also suggest treating caregivers as proxies (e.g., taking over the role of collecting and making sense of data) rather than supporting actors, empowering their agency to meet care recipients’ health needs better [14, 45].

Across forms of social practice, tensions often arise when self-tracking behaviors are extended to interpersonal practices. For example, trackers frequently have impression management concerns, such as worrying what they are sharing is not of interest to others [23, 34, 69]. When multiple people participate in tracking, they may have conflicting views about how to interpret data or what actions to take [79, 102]. Research frequently points to concerns around the disclosure of private information, particularly when more detailed data about a person’s lifestyle is shared and disclosed [32, 82, 89]. When others are monitoring a person’s data or making decisions on their behalf, people sometimes feel that their bodily autonomy is violated [21, 82, 102].

This work examines social practices of PI in the case of pregnancy since its social dynamics diverge from the ones examined in the common social tracking settings. In these conventional collaborative tracking scenarios, the need for collaboration usually arises because whoever needs to be tracked is either incapable of tracking or it would be difficult or burdensome to do so, such as in cases of younger children or people with serious health conditions [78, 82, 90, 117]. However, in pregnancy, pregnant people typically have full capacity to track, and prior discussions on the topic primarily perceive non-pregnant stakeholders as passive data recipients [85, 86, 87]. Examining how people practically collaborate around pregnancy in their everyday situations can shed light on how people approach collaboration when multiple stakeholders are capable contributors, but where societal and technological norms suggest differing levels of participation.

We recruited two sets of participants for this study: pregnant people and non-pregnant stakeholders. For potential pregnant participants, we asked them to verify that they were either currently pregnant or have had a pregnancy within the past two years. This choice was to ensure that the pregnancy was recent enough that they could recall details about their collaborative management and tracking practices. Also, we opted for a two-year range rather than a one-year or shorter was to minimize participation demands on people taking care of a recent newborn. We similarly required non-pregnant stakeholders to verify that they were either currently supporting someone close to them through pregnancy or had done so in the past two years. We did not restrict the nature of the relationship that non-pregnant stakeholders had with the pregnant person beyond being a close tie. All participants were also required to be 18 or older and currently reside in the U.S.

We mainly recruited our participants through ResearchMatch, a nonprofit program sponsored by the National Institutes of Health (NIH) which helps connect volunteers with researchers across the U.S. [96]. ResearchMatch enables researchers to search for targeted volunteers by setting up demographic and health condition criteria. We described our study as one that primarily focuses on parenting or pregnancy, enabling us to reach out to broad pregnancy-relevant stakeholders. We further set up filters for specific pregnancy and parenting statuses to find potential pregnant participants, using filters provided by ResearchMatch such as pregnant, pregnant-planned, or parenting caregiver status. We contacted potential volunteers who fit our criteria with a general recruitment message, providing a link to our demographic survey on Qualtrics to collect the demographic information and further screen for eligibility. We also posted our recruitment message on other online platforms and social media, such as Reddit, parenting Discord servers, Twitter, and public Facebook groups.

Table 1 describes the pregnant participants’ demographics with certain information aggregated to protect participant anonymity. Pregnant participants’ ages ranged from 28 to 38 (mean=33, sd=3.19). While we did not have gender criteria for pregnant participants, all of the pregnant participants were Female. About half (N=6) of the participants had been pregnant once, the other half had twice or more (max=5). Most (N=10) of our pregnant participants had at least one pregnancy-relevant health complication, with anxiety or depression (N=7) being the most common one. Most of our pregnant participants identified themselves as White, with one as Hispanic or Latino and one as Asian. Two participants self-identified as bisexual while the others were heterosexual. All the participants were either married or in a domestic relationship. Our participants were generally highly educated (all had a bachelor’s degree, and eight had a graduate degree) and affluent, with the median annual household income being $100,000 or more.

As for participants who supported a pregnant person (Table 2), Six were non-pregnant partners, two were siblings, two were mothers of pregnant people, and one was a close friend. Their ages ranged from 23 to 75 (mean=42.82, sd= 17.93). Most of these participants frequently interacted with the pregnant person, with nine reporting talking with the pregnant person about the pregnancy more than three times per week. Most of our participants were Male, with three Female and one Non-binary. More than half reported themselves as White, with two as Black or African-American, one as Asian, and two as Multiracial. One participant self-identified as bisexual and one as gay or lesbian, and the remainder were heterosexual. Participants who were non-pregnant stakeholders also were generally highly educated, with 10 having a bachelor’s degree, and six having a graduate degree. The median annual household income of these participants fell between $60,000 to $79,999.

Our interviews sought to understand how stakeholders approached tracking pregnancy, with a particular focus on their collaborative practices and any tensions or challenges which may have resulted from jointly tracking. All interviews were semi-structured in nature, following up with participants as they described their collaborative tracking practices and challenges. We asked different interview questions with pregnant people and non-pregnant stakeholders. During interviews, we explicitly asked participants about whether and/or how other stakeholders were involved in their pregnancy, probing into what others did and their perspectives about others’ involvement. This approach aimed to help develop a comprehensive understanding from multiple stakeholders’ perspectives.

Interviews with pregnant people contained three parts, seeking to understand how they benefited from and experienced challenges with collaborative tracking. We first sought to understand how the pregnancy was supported by their close social network in general, asking questions like “How has anyone, such as your partner, family members, close friends, and even colleagues, been involved in your pregnancy?” We then examined their experiences with self-tracking, asking questions like “Did you use any tool or method, such as paper journals, digital notebook, excel, software, mobile apps, or even just your memory, to memorize, journal, record, track, or manage any pregnancy-relevant data?” Finally, we dove into how pregnant people’s close social relationships participated in pregnancy tracking by asking questions like “How had anyone, such as your partner, family member, close friends, and even colleagues, been involved in your journaling or tracking toward pregnancy?”, “Do you wish that the other people were more involved or less involved in your tracking around pregnancy? Why?”, and “What are the benefits and challenges of involving other people in your tracking toward pregnancy?”

Interviews with non-pregnant stakeholders had two major sections related to their experiences with collaborative pregnancy tracking. We first asked the participants to reflect on how they supported their pregnant person’s pregnancy in general, asking questions such as “How have you been involved in [pregnant person]’s pregnancy?” and “Do you wish you could be more involved or less involved in [pregnant person]’s pregnancy? Why?” We then explored how the participant was involved in tracking around the pregnancy by asking questions like “Do you know if [pregnant person] is using any tool or method, such as paper journals, digital notebook, excel, software, mobile apps, or even just the memory, to memorize, journal, record, track, or manage any pregnancy-relevant data?”, “What has it been like to be involved in any forms in [pregnant person]’s tracking toward pregnancy?”, “Do you wish you could be more involved or less involved in [pregnant person]’s tracking toward pregnancy?”, and “What are the benefits and challenges of being involved in [pregnant person]’s tracking toward pregnancy?” We also inquired about how or whether other non-pregnant stakeholders were involved with the pregnancy. For example, we asked N5, a non-pregnant partner and the father of two children, about how the elder child was involved in and responded to his wife’s second pregnancy.

We conducted all the interviews via Zoom and video recorded all the sessions. The first author transcribed all the interviews. The interviews were about one hour on average, with little difference between the stakeholder groups. We compensated our participants each with $25.

Our qualitative approach drew influence from an inductive approach to thematic analysis [17]. The first and second authors familiarized themselves with the first ten transcripts and opened-coded them to identify interesting takeaways relative to our research questions. They regularly compared insights and discussed findings with the rest of the research team, resulting in some preliminary insights. We found that participants had different methods and forms of collaborating around pregnancy, and faced various challenges in doing so. We decided to focus on more deeply understanding these collaborative tracking practices. Drawing from these insights, the first author built a codebook centering around different tracking formats and the associated benefits and tensions. The first and second authors used the preliminary codebook to go through the first ten transcripts again, adding or re-organizing the codebook as necessary. Following a comparison of the two authors’ codebooks and discussion involving all authors, the first author then generated a formal codebook with three parent codes (tracking goals, tracking practices, collaboration factors) and 11 child codes (goals, self-tracking, social share, co-track, temporality, privacy, pregnancy contexts, interaction concerns, data control/bodily autonomy, individual background, other factors). The first author then coded all the interviews by using the formal codebook. Based on these codes, we surfaced three themes articulating why people collaboratively track (RQ1), how they do so (RQ2), and the challenges and tensions they face (RQ3).

We refer to participants with pregnancy experience as PXX and participants who supported a pregnant person with NXX.

Our participants were all involved in tracking different pregnancies, which had the benefit of increasing the range of experiences we were able to draw insight from. We designed our interviews to enable participants to provide insightful descriptions of how they collaborated with others to monitor pregnancy, and we utilized follow-up questions to delve into the intricacies of their collaboration. However, by not recruiting stakeholders who were involved in tracking the same pregnancy, we missed out on some nuanced dynamics between and among stakeholders. For example, when a participant surfaced a tension surrounding tracking pregnancy collaboratively, we were unable to follow up on how that tension was experienced by other stakeholders. We were also reliant on each individual’s description of their involvement in collaboratively tracking. We posit that the difficulties with recruiting participants as pairs may speak to the challenging nature of collaboratively tracking pregnancy. We take it as a signal of how pregnancy has profound impacts on people’s everyday lives and social configurations, underscoring the necessity for researchers and designers to contemplate technology’s potential role in addressing and mitigating collaboration challenges. While we used follow-up questions to prompt participants’ experiences around pregnancy, people whose experiences were tied to a pregnancy within the past two years might still have some difficulties in accurately recalling details than those currently pregnant or recently gave birth. In addition, our non-stakeholder participants only included one LGBTQ+ partner (N11). This demographic may interact differently with the gender norms around pregnancy responsibilities that might influence collaborative tracking practices. Overall, we see opportunities for follow-up research to more deeply examine collaborative pregnancy tracking in specific stakeholder relationships to better understand their technology needs.

Participants generally described having healthy and supportive relationships with the social contacts they discussed. Questions around sharing tracked information and collaborative tracking become more challenging when relationships are more contentious. Future work might be worth exploring the collaborative tracking experience (or lack thereof) in the face of more complex social dynamics such as for single expectant mothers, those who lack family support, or have specific strained relationships they need to navigate when deciding how to track and share.

Our participants generally had a high education level and a relatively high income level, which limits the generalizability of our findings. While we aimed for our participants to cover more of the socioeconomic spectrum, ResearchMatch’s focus on health-related studies may have influenced the demographic composition. For example, researchers or healthcare professionals often registered themselves as volunteers, which may lead to participants skewed toward individuals with higher education, affluence, or enhanced technological proficiency. Challenges and tensions around collaborative tracking might be different when examining people with relatively low socioeconomic status, since lived constraints (e.g., the labor required to collaboratively track) might interfere with people’s ability to participate in collaborative tracking. It is therefore worth studying how people with low socioeconomic status (e.g., people with a relatively low education and income level) collaborate around pregnancy tracking, exploring how socioeconomic factors influence collaborative tracking. About two-thirds of our participants were White, suggesting a need for future studies to focus on racial minorities since they often encounter socioeconomic and educational disparities and experience challenges in pregnancy care support from the U.S. healthcare system. In addition, our pregnant participants’ ages ranged from 28 to 38, with an average age of approximately 33 years old. As of 2019, the U.S. Census Bureau reports that the median age of U.S. women giving birth is 30 [81], suggesting that our participants were approximately the same age as the typical for the country. However, we acknowledge an absence of younger participants (e.g., 18 to 27 and teen pregnancies) or older participants (e.g., 40+), which may also impact the study findings as these groups may exhibit radically different social interactions and needs surrounding pregnancy. For instance, a teenage expectant mother might have limited support from peers who do not have experience with pregnancy, therefore having to mostly rely on their parents or family members [65]. On the other hand, the older group might have more nuanced tracking needs, potentially necessitating support from non-pregnant stakeholders, as pregnancy-related complications are more common in this demographic [71].

Findings revealed three primary reasons why pregnant people and non-pregnant stakeholders collaboratively tracked pregnancy: bonding with each other and seeking social support, managing pregnancy data jointly, and promoting a healthy pregnancy together.

Participants’ strategies for sharing their pregnancy-related data with others for support largely resembled social sharing practices discussed in prior works [23, 69]. Pregnant people often took charge of tracking, sharing pregnancy-relevant data with non-pregnant stakeholders to bond with others or seek advice. For example, N7’s pregnant sister was the first to get pregnant in their family, and she frequently shared her pregnancy progress in the family’s group chat: “She shares [pregnancy] information with us often, like when we found out the gender of the baby. Like her doctor appointments, when she’s having one, she’ll say I’ll have the doctor’s appointment tomorrow. Then we’ll ask afterward: ‘How’s everything going with you and the baby?’.” We did not observe any instances where pregnant participants largely had others tracking their pregnancy on their behalf, which has been observed in other domains [14, 117].

We primarily focus on describing participants’ experiences collaborating around pregnancy tracking. Our interviews surfaced two ways in which pregnant people and non-pregnant stakeholders collaboratively managed pregnancy tracking: dividing tracking responsibilities within a pregnancy to be in charge of different types of data, and jointly tracking around the same type of information.

While pregnant people and non-pregnant stakeholders were often able to collaborate around tracking and making sense of pregnancy-related data, they faced some difficulties in doing so effectively. We identified several challenges and tensions that shaped people’s collaborative pregnancy tracking practices: privacy concerns, managing stakeholders’ opinions and feelings, level of trust in different stakeholders’ tracking capabilities, varied interests in tracking, and desire to occasionally disengage.

When prior work has introduced and advocated for collaborative tracking in health domains, it has largely suggested treating stakeholders involved as a collective unit, with equal opportunity and unrestricted access to collect data and review it [38, 77, 79, 90], or as proxies for patients [14, 45]. These approaches can be practical in some circumstances, such as managing serious diseases or complex health conditions, as restricting personal tracking to whomever the data is about can make it more challenging for other stakeholders to assist or share in decision-making. While our findings continue to suggest that tracking benefits from being joint, it also requires more careful consideration of the downsides surrounding the expectation that everyone will and wants to participate in all tracking stages , especially in the context of socio-cultural norms around gender norms and parenting roles during pregnancy. Several tensions surfaced in this study, such as stakeholders’ varied tracking interests and a lack of trust in non-pregnant stakeholders’ tracking capabilities, might be rooted in gendered norms and expectations of family roles that narrowly assign pregnancy-relevant responsibility and capability to pregnant people [28, 39, 43, 54, 112]. For example, in our study, some pregnant participants had to self-monitor specific data types due to non-pregnant male stakeholders showing little interest in pregnancy management, resulting in pregnant participants’ co-tracking needs being unmet. Technology that encourages equal pregnancy participation, without considering gender norms and parenting roles’ potential influences on stakeholders’ responsibilities and interests, might not be effective at facilitating and sustaining different stakeholders’ distinctive involvement. Designing collaborative technology aligning with traditional gendered norms would risk perpetuating stereotyped and even misogynistic designs, placing a disproportionate burden of data tracking and management on pregnant people. However, a challenge in designing technology that avoids reinforcing stereotypes is how it might be used in light of national policies around pregnancy. For example, some countries offer extended parental leave for mothers as part of family bonding, often starting before birth [58]. These policies might impact who has more time to manage pregnancy, which might complicate efforts to design collaborative pregnancy tracking technology with equal parenting roles in mind.

Prior research has suggested that women can face challenges with protecting their bodily autonomy when using health technology [4, 7, 116]. Our work shows that trying to collaborate around pregnancy tracking can sometimes put pregnant people’s bodily autonomy in tension with the opportunity for others to support them in tracking pregnancy. While our results indicate that non-pregnant participants are willing to help with various aspects of tracking pregnancy, their participation sometimes conflicts with pregnant people’s control over tracking and interpreting their pregnancy data. For example, involving others when interpreting pregnancy data collaboratively might result in getting unsolicited suggestions from those with strong opinions about pregnancy. This aligns with prior works’ concerns about the potential for women’s sensitive data to be used to restrict decision-making [2, 7, 74]. In addition, some pregnant participants express that tracking independently sometimes empowers them to manage and make decisions about pregnancy, leading to their tendencies to share pregnancy progress with non-pregnant stakeholders rather than co-tracking with them. Therefore by preserving autonomy, pregnant people accept that they may take on more of the responsibility and labor of monitoring the pregnancy, which may result in greater tracking burdens and prevent them from getting desired support around pregnancy.

To protect pregnant people’s bodily autonomy, all participants we talked to felt that pregnant people should have data control within a collaborative tracking practice, such as the authority to decide who they want to involve and what data others could access. However, considering some pregnant people’s desire to track independently, providing full control over data control and tracking preferences might lead them to refuse non-pregnant stakeholders’ involvement which might negatively influence their relationships with other stakeholders. This is particularly noteworthy in that pregnancy is often perceived as a “crisis” that reconfigures stakeholders’ social relationships, and tensions around tracking of pregnancy could further drive a wedge between stakeholders [26, 40, 64]. This may further impact their ability to get support, or create obstacles around ensuring the health of the pregnant people and the fetus(es).

Moreover, women’s health is often stigmatized and regarded as taboo, leading people to feel hesitance or shame when interacting with others regarding aspects of their health [25, 115, 116]. To mitigate stigma and support women in getting desired support, a few works explore opportunities for technology to promote collaboration around women’s health [55, 103, 114]. A potential concern is that in pregnancy, rejecting non-pregnant stakeholders’ participation may exacerbate pregnant people’s experienced or perceived stigmas associated with pregnancy and their bodies.

Supporting collaborative pregnancy tracking therefore requires balancing between protecting pregnant people’s bodily autonomy, such as ensuring their data control, and supporting non-pregnant stakeholders’ sharing in the responsibility of collecting and making sense of pregnancy-relevant data. We also see value in offering flexibility in tracking approaches, allowing pregnant people to choose among self-tracking, social sharing, and collaborative tracking. Meanwhile, designs would benefit from helping stakeholders better communicate and negotiate their expectations around involvement with pregnancy tracking.

Noted, supporting unequal tracking poses questions around who is involved in tracking pregnancy, what data to involve non-pregnant stakeholders with, and what collaboration responsibilities non-pregnant stakeholders undertake. Should these approaches be implemented, there is a need for designs to help stakeholders make informed choices around these topics. For example, early in pregnancy, tracking technology could help surface typical data that people often track and guide them through deciding who will be responsible for entry. Configurable profiles, such as from more distant stakeholders, could help indicate what data they are interested in being aware of. Even if pregnant people choose more restrictive settings around intimate data, surfacing interests could lead the stakeholders to have conversations about what they feel comfortable disclosing.

While technology can play a role in mitigating stereotyped socio-cultural norms around pregnancy, it is worth considering how to design such features in light of deeply ingrained cultural perceptions in more patriarchal or sensitive cultural settings. For example, our suggestions to emphasize the benefit of non-pregnant partners’ participation may not work effectively in cultures that stigmatize “caring" non-pregnant partners or prioritize the needs of partners over those of pregnant people. Further work is needed in conjunction with policymakers, local community health workers, and educators to examine how technology can be leveraged to raise people’s awareness about some biased norms around pregnancy, mitigating cultural barriers that neglect pregnant people’s bodily autonomy and fostering a supportive environment for pregnant people. We see the design of culturally relevant collaborative pregnancy tracking technology as potentially indicative of the underlying norms, but unlikely to singlehandedly reshape those norms.