Abstract: A method that includes accessing, from a database, a correlation between a first variable and a second variable; retrieving a list of suggested user behavior modifications for the correlation; selecting a user behavior modification from the list; presenting the selected user behavior modification to a computer device; receiving adherence data indicating a level of adherence by the user to the user behavior modification; and reevaluating the correlation between the first variable and the second variable based on the adherence data. The method may improve the determination of causality, versus merely correlation, between the variables. This determination may permit for improved recommendations to individuals to achieve a desired user outcome.

Abstract: Examples are disclosed that relate to providing healthcare-related information. One example provides a computing device comprising a logic machine and a storage machine holding instructions executable by the logic machine to receive an input of information regarding a health state of a user, obtain, based upon the information regarding the health state of the user, an inference of a possible health condition of the user, output a notification of the inference, the notification comprising a first representation of the inference, receive data representing a mechanism for authorizing a healthcare practitioner to access a second representation of the inference, and output a user-selectable control for triggering the mechanism. The instructions may be further executable to receive an input via the user-selectable control triggering the mechanism, and, in response, send authorization to provide the healthcare practitioner with access to the second representation of the inference.

Abstract: Examples are provided for a health monitoring computing system, including a data interface configured to receive appointment data and health tracking data, the health tracking data including biometric data measured by one or more biometric sensors worn by a user, health-scheduling logic configured to determine that the user is to see a healthcare professional within a threshold period of time based at least on computer analysis of the appointment data, and appointment-optimization logic configured to generate a list of diagnosis-relevant health information based at least on computer analysis of the health tracking data. The example health monitoring computing system also includes an output device configured to present the list to the user for editing prior to an appointment with the healthcare professional.

Abstract: Examples are disclosed herein that relate to modifying an analysis of personal behavior based on determining a subset of personal data to be non-routine. One example provides a computing device configured to receive personal data relating to personal behavior of a user, receive contextual data regarding the personal data, determine a subset of the personal data to be non-routine based upon the contextual data, and modify an analysis of personal behavior based upon the subset of the personal data determined to be non-routine.

Abstract: Examples are disclosed herein that relate to integrating health data and calendar data of one or more users and providing insights for a selected user to help the user accomplish an outcome of interest. The insights may be identified based on a group of cohorts determined to be similar to the selected user and/or used to predict a likelihood that the selected user will achieve an outcome of interest. Additional insights may be provided by monitoring an effect that following a recommendation has on the selected user achieving the outcome of interest. Recommendations and/or updates to recommendations may be provided based on the insights.

Abstract: One or more techniques and/or systems are provided for providing and/or applying privacy preferences for an entity. A multimedia device, such as a mobile phone, may capture multimedia content associated with an entity (e.g., a photo of a person at a restaurant). The multimedia device may identify a privacy preference for the entity. In an example, the person may be wearing a privacy object that may be recognized as corresponding to the privacy preference. In another example, a device associated with the person may emit a signal that may specify a privacy preference for the person and/or may be used by the multimedia device to identify the person (e.g., the identity may be used to query a service to obtain a privacy preference specified for the person). The privacy preference may be applied to the multimedia content (e.g., a no photography privacy preference, a no tagging privacy preference, etc.).