A system for providing user insights based on heart rate variability, the system comprising: a wrist-worn device configured to obtain real-time physiological data including heart rate variability data from a user, obtain activity data from a user, and transmit the real-time physiological data and activity data; and a processor that is located in a server that is remote from the wrist-worn device, and configured to perform stages including: receiving the activity data; analyzing the activity data to determine whether the user is engaged in one of a plurality of stationary activities; in response to determining the user is engaged in one of the plurality of stationary activities, receiving and recording the real-time physiological data including the heart rate variability data; calculating a heart rate variability indicia, including: querying a database to obtain recorded heart rate variability data, referencing the recorded heart rate variability data to activity types and timing thresholds; determining whether a special type of activity is present based on analysis for the recorded heart rate variability data; if the special type of activity is not detected, a first noise removing algorithm is applied to calculate the heart rate variability indicia; and if the special type of activity is detected, a second noise removing algorithm is applied to calculate the heart rate variability indicia separately according to when body position or pose of the user was unchanged and after the body position or pose changed, wherein the second noise removing algorithm is different from the first noise removing algorithm; and transmitting a conclusion to the user that is derived at least in part from the heart rate variability indicia, wherein the conclusion indicates that the user has one of a variety of physiological characteristics. a wrist-worn device configured to obtain real-time physiological data including heart rate variability data from a user, obtain activity data from a user, and transmit the real-time physiological data and activity data; and a processor that is located in a server that is remote from the wrist-worn device, and configured to perform stages including: receiving the activity data; analyzing the activity data to determine whether the user is engaged in one of a plurality of stationary activities; in response to determining the user is engaged in one of the plurality of stationary activities, receiving and recording the real-time physiological data including the heart rate variability data; calculating a heart rate variability indicia, including: querying a database to obtain recorded heart rate variability data, referencing the recorded heart rate variability data to activity types and timing thresholds; determining whether a special type of activity is present based on analysis for the recorded heart rate variability data; if the special type of activity is not detected, a first noise removing algorithm is applied to calculate the heart rate variability indicia; and if the special type of activity is detected, a second noise removing algorithm is applied to calculate the heart rate variability indicia separately according to when body position or pose of the user was unchanged and after the body position or pose changed, wherein the second noise removing algorithm is different from the first noise removing algorithm; and transmitting a conclusion to the user that is derived at least in part from the heart rate variability indicia, wherein the conclusion indicates that the user has one of a variety of physiological characteristics. receiving the activity data; analyzing the activity data to determine whether the user is engaged in one of a plurality of stationary activities; in response to determining the user is engaged in one of the plurality of stationary activities, receiving and recording the real-time physiological data including the heart rate variability data; calculating a heart rate variability indicia, including: querying a database to obtain recorded heart rate variability data, referencing the recorded heart rate variability data to activity types and timing thresholds; determining whether a special type of activity is present based on analysis for the recorded heart rate variability data; if the special type of activity is not detected, a first noise removing algorithm is applied to calculate the heart rate variability indicia; and if the special type of activity is detected, a second noise removing algorithm is applied to calculate the heart rate variability indicia separately according to when body position or pose of the user was unchanged and after the body position or pose changed, wherein the second noise removing algorithm is different from the first noise removing algorithm; and transmitting a conclusion to the user that is derived at least in part from the heart rate variability indicia, wherein the conclusion indicates that the user has one of a variety of physiological characteristics.