Abstract: Methods, systems, and devices for detection of medical conditions using respiration rate data are described. A method may include receiving physiological data associated with a user, the physiological data being continuously collected via a wearable device associated with the user, determining a set of respiration rate values for the user over a time interval based on the physiological data, and determining one or more respiration rate parameters associated with a change of the set of respiration rate values over the time interval. The method may further include determining one or more condition risk metrics associated with one or more medical conditions based on the one or more respiration rate parameters, where the one or more condition risk metrics associated with a relative probability that the user is associated with a respective medical condition.

Abstract: Methods, systems, and devices for miscarriage identification are described. A system may be configured to receive physiological data associated with a user that is pregnant and collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values. The system may then identify that the temperature values are lower than a pregnancy baseline of temperature values for the user and detect an indication of an early pregnancy loss of the user. The system may generate a message for display on a graphical user interface on a user device that indicates the indication of the early pregnancy loss.

Abstract: Methods, systems, and devices for menopause detection and treatment are described. A system may be configured to receive physiological data associated with a user from a wearable device, and identify one or more physiological indications of a hot flash experienced by the user based on the physiological data satisfying one or more thresholds. Additionally, the system may be configured to determine a metabolic efficiency metric associated with the user based on the received physiological data, and determine a menopause metric for the user based on the metabolic efficiency metric, where the menopause metric is associated with a relative probability that the user will experience menopausal symptoms. The system may then cause a graphical user interface (GUI) of a user device to display information associated with the identified hot flash and/or one or more messages associated with the menopause metric.

Abstract: Methods, systems, and devices for pregnancy complication identification and prediction are described. A system may be configured to receive physiological data associated with a user that is pregnant and collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values. The system may then identify that the temperature values deviate from a pregnancy baseline of temperature values for the user and detect an indication of one or more pregnancy complications of the user. The system may generate a message for display on a graphical user interface on a user device that indicates the indication of the one or more pregnancy complications.

Abstract: Methods, systems, and devices for pregnancy detection are described. A system may be configured to receive physiological data collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values taken over the plurality of days. The system may then identify temperature elevations in the time series of the temperature values relative to a temperature baseline for the user and detect an indication of a pregnancy of the user based on the identified temperature elevations. The indication of the pregnancy may be detectable from the identified temperature elevations prior to being detectable from a threshold increase in hormone elevations relative to a hormone baseline of the user. The system may cause the graphical user interface to display the detected indication of the pregnancy.

Abstract: Methods, systems, and devices for miscarriage identification are described. A system may be configured to receive physiological data associated with a user that is pregnant and collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values. The system may then identify that the temperature values are lower than a pregnancy baseline of temperature values for the user and detect an indication of an early pregnancy loss of the user. The system may generate a message for display on a graphical user interface on a user device that indicates the indication of the early pregnancy loss.

Abstract: Methods, systems, and devices for labor onset and birth identification and prediction are described. A system may be configured to receive physiological data associated with a user that is pregnant and collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values. The system may then calculate a pregnancy baseline temperature slope for the user and identify that the temperature slope deviates from the pregnancy baseline temperature slope for the user. The system may detect an indication of a labor onset of the user and generate a message for display on a graphical user interface on a user device that indicates the indication of the labor onset.

Abstract: Methods, systems, and devices for a pregnancy mode configuration are described. In some cases, the system may receive physiological data associated with a user from a wearable device and provide a first set of targets associated with one or more physiological metrics and a first set of messages associated with the one or more physiological metrics based on the received physiological data. The system may receive an indication of the user being pregnant. In some cases, the system may identify a trigger to transition from the first operational mode to a second operational mode of the application based on receiving the indication. The system may provide a second set of targets associated with the one or more physiological metrics and a second set of messages associated with the one or more physiological metrics that are adjusted for pregnancy based on the second operational mode.

Abstract: Methods, systems, and devices for anovulatory cycle detection are described. A system may be configured to receive physiological data collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values taken over the plurality of days where the time series includes a plurality of ovulatory cycles for the user. The system may then identify the plurality of ovulatory cycles based on positive slopes in the time series and identify an indication of an anovulatory cycle based on deviations in the time series. The system may generate a message for display on a graphical user interface on a user device that indicates the indication of the identified anovulatory cycle.

Abstract: Methods, systems, and devices for fertility prediction are described. A system may be configured to receive physiological data collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values taken over the plurality of days where the time series includes a plurality of menstrual cycles. The system may then determine a plurality of menstrual cycle length parameters associated with the menstrual cycles and determine a user fertility prediction based on the menstrual cycle length parameters. The system may generate a message for display on a graphical user interface that indicates the determined user fertility prediction.

Abstract: Methods, systems, and devices for menstrual cycle phase identifier are described. A system may be configured to receive physiological data collected over a plurality of days, where the physiological data includes at least temperature data. Additionally, the system may be configured to determine a time series of temperature values taken over the plurality of days where the time series includes a plurality of menstrual cycles for the user. The system may then identify morphological features in the time series and identify menstrual cycle phases in the time series based on the morphological features. The system may cause the graphical user interface to display the identified menstrual cycle phases.

Abstract: Methods, systems, and devices for activity classification are described. A system may receive physiological data associated with a user via a wearable device, where the physiological data includes at least motion data. The system may identify an activity segment during which the user is engaged in a physical activity based on the motion data, where the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment. The system may generate activity classification data associated with the activity segment based on the activity segment data, the activity classification data including a set of classified activity types and corresponding confidence values. The system may then cause a graphical user interface (GUI) of a user device to display the activity segment data and at least one classified activity type of the set of classified activity types.

Abstract: Methods, systems, and devices for detection of medical conditions using respiration rate data are described. A method may include receiving physiological data associated with a user, the physiological data being continuously collected via a wearable device associated with the user, determining a set of respiration rate values for the user over a time interval based on the physiological data, and determining one or more respiration rate parameters associated with a change of the set of respiration rate values over the time interval. The method may further include determining one or more condition risk metrics associated with one or more medical conditions based on the one or more respiration rate parameters, where the one or more condition risk metrics associated with a relative probability that the user is associated with a respective medical condition.