Pregnancy is a commonly occurring medical event. Women who are pregnant may experience pregnancy-related symptoms and complications. However, there is a relative lack of multi-dimensional data on large populations of pregnant patients.

The Study Investigators aim to derive novel insights and deeper understanding of maternal physiology and pathology through the analysis of an unprecedented breadth and depth of data collected from connected devices (i.e., wearables, smart home scale, mobile apps, etc.), additional virtual study assessments and support calls, and information derived from standard of care clinical visits. They will share these insights to empower patients to better care for themselves.

The Investigators hope to know how leveraging the data collected from connected devices in addition to information obtained from routine clinical care helps researchers and clinicians better understand pregnancy related symptoms, conditions, and complications.

During pregnancy a woman may experience symptoms that are specific to being pregnant, including nausea, fatigue, shortness of breath, insomnia etc. to much more complicated and serious symptoms. While pregnancy is a commonly occurring medical event that poses health risks to the pregnant woman and fetus, there is limited research on how to prevent and treat symptoms before they become higher risk complications. Utilizing mHealth technology for the collection of objective and subjective measurements and the integration of passive data (from connected devices) will increase understanding of pregnancy and subsequent complications and symptoms as indicative or predictive of particular outcomes.

In order to mitigate the risks of pregnancy, pregnant women are monitored closely and frequently through periodic in-clinic visits with their clinician. However, little is known about the progression of symptoms and measurements between clinic visits as continuous data is not collected as part of clinical practice.

Symptom trajectories have been historically characterized by sporadic visible data, insufficient to identify transition points. Visible data points are episodic and may (or may not) be captured by monthly clinical assessments during pregnancy, but invisible data points can be captured and more clearly defined through the use of longitudinal, passive data collection by wearing and connecting devices.

The Study Investigators aim to detect individual symptom transitions and shift trajectories of health to those which cannot be confined to the standard office clinical visit. They have selected devices which may help track the symptoms including The Oura Ring, the Garmin Venu Sq and the Bodyport scale. The study will follow women anticipating becoming pregnant and those pregnant up to and including 15 weeks.

Inclusion Criteria:

• Pregnant (up to and including 15 weeks) (Pregnancy cohort)
• Anticipating to be pregnant (Pre-pregnancy cohort)
• 18+ years (Pregnancy cohort)
• 18-40 years (Pre-pregnancy cohort)
• Has a personal cell phone that is an iPhone SE or newer or an Android device, version 6.0 or newer, and is willing to upgrade to the most recent operating system and use their phone for study (This includes a willingness to download and use the study applications and sync their phone with the necessary study devices)
• Has an unshared email address
• Proficient in English language

Exclusion Criteria:

• Prisoner
• Unable to read or understand the study materials
• Does not have a permanent address
• Intention to terminate pregnancy at the time of enrolment

Pregnant woman or anticipating to be pregnant.

The pre-pregnancy cohort involves tracking symptoms in women anticipating to become pregnant. The pregnancy cohort involves tracking symptoms during pregnancy

Under the 4YouandMe open source model, we will make all data, findings, digital health applications and algorithms available in the public domain. Accordingly, de-identified data produced from this project will be shared broadly with qualified researchers through Sage Bionetworks Synapse Only data from consenting participants will be shared through Sage Bionetworks Synapse and this will not include video diary data, relative location data or social media data. Additionally, source code for the developed app will be made available as open source software on GitHub so it can be evolved for future work by others. Our coalition partners that will have access to all coded data include Vector, 4YouandMe, Evidation Health and Sema4, while our collaboration partners (Bodyport and Cambridge Cognition) will have access to subsets of the coded study data not including video diary data, relative location data, or social media data.

Internal researchers will have access to all coded data during the full duration of the study. Consented participants' coded data will be available in the Synapse at Sage Bionetworks for selected researchers to access indefinitely, one year after study completion.

We will combine the coded study data from all the study participants. Subsets of the coded study data will be made accessible to researchers according to a tiered permission: Internal Researchers (Vector Institute, Evidation Health, and Sema4) will have access to all coded data during the full duration of the study. Collaboration Partners (CamCog and Bodyport) may be given access to a subset of coded data streams, with the exclusion of the video diary, any location data, and any social network data, quarterly during the data collection period. Each partner will additionally have the data collected by their respective system (e.g. Garmin will access data collected off the Garmin device). The research queries being conducted on the coded data is limited to the study of emesis, gait, cognition, preeclampsia, depression/anxiety, edema, sleep/stress and social wellbeing.