US20110004085A1 - Performance reports associated with continuous sensor data from multiple analysis time periods - Google Patents

Performance reports associated with continuous sensor data from multiple analysis time periods Download PDF

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US20110004085A1
US20110004085A1 US12/770,618 US77061810A US2011004085A1 US 20110004085 A1 US20110004085 A1 US 20110004085A1 US 77061810 A US77061810 A US 77061810A US 2011004085 A1 US2011004085 A1 US 2011004085A1
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host
time period
analysis time
performance
blood glucose
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Michael R. Mensinger
Deborah M. Ruppert
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Dexcom Inc
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Dexcom Inc
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Assigned to DEXCOM, INC. reassignment DEXCOM, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: RUPPERT, DEBORAH M., MENSINGER, MICHAEL R.
Priority to US15/422,196 priority patent/US11437141B2/en
Priority to US17/817,457 priority patent/US20220392643A1/en
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0002—Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network
    • A61B5/0031—Implanted circuitry
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14503—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue invasive, e.g. introduced into the body by a catheter or needle or using implanted sensors
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14532—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring glucose, e.g. by tissue impedance measurement
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue
    • A61B5/14546—Measuring characteristics of blood in vivo, e.g. gas concentration or pH-value ; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid or cerebral tissue for measuring analytes not otherwise provided for, e.g. ions, cytochromes
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/486—Biofeedback
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
    • A61B5/6802—Sensor mounted on worn items
    • A61B5/681—Wristwatch-type devices
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7271—Specific aspects of physiological measurement analysis
    • A61B5/7275—Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/74—Details of notification to user or communication with user or patient; User input means
    • A61B5/742—Details of notification to user or communication with user or patient; User input means using visual displays
    • A61B5/743—Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2560/00—Constructional details of operational features of apparatus; Accessories for medical measuring apparatus
    • A61B2560/04—Constructional details of apparatus
    • A61B2560/0443—Modular apparatus
    • A61B2560/045—Modular apparatus with a separable interface unit, e.g. for communication

Definitions

  • Systems and methods for providing performance reports to persons with diabetes that generally indicate a measure of a glucose variability of the person over multiple analysis time periods are provided.
  • Diabetes mellitus is a disorder in which the pancreas cannot create sufficient insulin (Type I or insulin dependent) and/or in which insulin is not effective (Type 2 or non-insulin dependent).
  • Type I or insulin dependent in which the pancreas cannot create sufficient insulin
  • Type 2 or non-insulin dependent in which insulin is not effective
  • a hypoglycemic reaction low blood sugar may be induced by an inadvertent overdose of insulin, or after a normal dose of insulin or glucose-lowering agent accompanied by extraordinary exercise or insufficient food intake.
  • SMBG self-monitoring blood glucose
  • a method of analyzing continuous analyte sensor data associated with a host comprising: accessing a plurality of blood glucose readings associated with a host during two or more analysis time periods, wherein each blood glucose reading is indicative of a blood glucose level of the host at a respective time; and generating a performance report indicating a relationship between the plurality of blood glucose readings of the two or more analysis time periods, wherein the performance report is indicative of glucose control by the host over the two or more analysis time periods.
  • the performance report further indicates a relationship between the plurality of blood glucose readings of the two or more analysis time periods and target sensor data.
  • the method further comprises initiating transmission of appropriate clinical feedback to the host in response to determining that the relationship between the plurality of blood glucose readings meets criteria indicative of providing the host with clinical feedback.
  • the plurality of blood glucose readings comprises more than 72 blood glucose readings for each 24 hour period of an at least one week period.
  • the method further comprises transmitting the performance report to a caretaker computing device.
  • a method of analyzing continuous analyte sensor data associated with a host comprising: receiving from a user an indication of a first starting date of a first analysis time period; determining a first ending date of the first analysis time period by adding a predetermined time period to the first starting date; determining a second starting date of a second analysis time period; determining a second ending date of the second analysis time period by adding the predetermined time period to the second starting date; accessing a plurality of blood glucose levels associated with a host during the first analysis time period and the second analysis time period, wherein each blood glucose level is indicative of a blood glucose level of the host at respective times; determining one or more first statistical values associated with the first analysis time period based on each of the blood glucose levels of the host received during the first analysis time period; determining one or more second statistical values associated with the second analysis time period based on each of the blood glucose levels of the host received during
  • the second starting data of the second analysis time period is a day that immediately follows the first ending date.
  • the one or more first statistical values comprise a first mean glucose level of the blood glucose levels received during the first analysis time period.
  • the one or more second statistical values comprise a second mean glucose level of the blood glucose levels received during the second analysis time period.
  • the comparison indicator indicates at least a percentage difference between the first mean glucose level and second mean glucose level.
  • a method of analyzing continuous analyte sensor data associated with a host comprising: intermittently receiving analyte sensor data from one or more analyte sensors associated with a host at each of a plurality of sample times; determining a plurality of blood glucose levels of the host at each of the plurality of sample times, wherein the blood glucose levels are based on at least respective analyte sensor data; storing the plurality of blood glucose levels on a storage device; accessing at least the blood glucose levels associated with the host during a first analysis time period and a second analysis time period; determining a first mean glucose level of the host during the first analysis time period, wherein the first mean glucose level is based on each of the blood glucose levels of the host received during the first analysis time period; determining a second mean glucose level of the host during the second analysis time period, wherein the second mean glucose level is based on each of the blood glucose
  • the predetermined time period is selected from the group consisting of 1 week, 1 month, 2 months, 3 months, 4 months, 5 months, 6 months, 9 months, and 1 year.
  • the first analysis time period and the second analysis time period each comprise substantially a quarter of a year.
  • the predetermined time period is indicated by the user.
  • a computerized method of generating a performance report associated with continuous analyte sensor data configured for execution by a computing device having one or more processors, the method comprising: accessing historical sensor data associated with a host, wherein the historical sensor data comprises more than 72 blood glucose readings for each 24 hour period of an at least one week period; selecting blood glucose data associated with a first time period; determining a first statistical value that is indicative of blood glucose levels during the first time period; selecting blood glucose data associated with a second time period; determining a second statistical value that is indicative of blood glucose levels during the second time period; determining a performance indicator indicating a comparison between the first indicator and the second indicator; and generating a user interface for display on a computing device, the user interface including at least the performance indicator.
  • the first indicator and second indicator indicate one or more items selected from the group consisting of mean glucose levels over the respective time periods, standard deviation of glucose levels over the respective time periods, percentage of days in the respective time period in which the means glucose levels were within a predefined target range, and days within the respective time periods in which blood glucose data was received from the continuous analyte sensor.
  • a system for generating performance reports associated continuous analyte sensor data comprising: a computer-implemented user interface configured to receive performance report generation instructions from a user; a data storage module configured to store continuous analyte sensor data indicative of one or more analyte levels of a host; and a performance report module communicatively coupled to the user interface and the data storage module and configured to: (i) receive continuous sensor analyte data from the data storage module; (ii) determine a performance indication based on an analysis of the retrieved continuous analyte sensor data; (iii) generate a performance report based on performance report generation instructions received from the user interface and the performance indication; and (iv) transmit the generated performance report to the user interface, wherein the user interface is additionally configured to display the generated performance report.
  • system further comprises a continuous analyte sensor configured to generate the continuous sensor analyte data and transmit the continuous sensor analyte data to the data storage module.
  • system further comprises a computing device incorporating the user interface and performance report module.
  • the computing device includes functionality of a device selected from the group consisting of a mobile telephone, a personal digital assistant, a personal computer, a smart phone, a digital media player, an exercise monitoring device, and geographic position-tracking device.
  • the performance report generation instructions include a first analysis time period and a second analysis time period.
  • the performance report module is additionally configured to determine a first statistical value based on continuous sensor analyte data associated with the first analysis time period, and a second statistical value based on continuous sensor analyte data associates with the second analysis time period, wherein the performance indication is based on a comparison of the first statistical value and the second statistical value.
  • the performance report generation instructions include a first analysis time period and target sensor data.
  • the performance report module is additionally configured to determine a first statistical value based on continuous sensor analyte data associated with the first analysis time period, wherein the performance indication is based on a comparison of the first statistical value and the target sensor data.
  • the performance module is additionally configured to determine when a pre-defined criteria associated with the therapy recommendation is met and initiate display of the therapy recommendation on the user interface when the performance module determines that the pre-defined criteria is met.
  • FIG. 1 is a schematic diagram of a performance report system using a host computing device in accordance with one embodiment.
  • FIG. 2 is a schematic diagram of a performance report system using a mobile receiver in accordance with one embodiment.
  • FIG. 3 is a flow diagram illustrating a method of generating performance indicators associated with sample data of a host in accordance with one embodiment.
  • FIG. 4 is a flow diagram illustrating components of FIG. 3 , wherein specific exemplary data is shown communicated between the components.
  • FIGS. 5 , 6 A, 6 B, 7 - 8 illustrate exemplary performance reports that include statistical values and performance indicators in accordance with various embodiments.
  • FIG. 9 is an exemplary user interface in accordance with one embodiment.
  • FIG. 10 is a flowchart illustrating a method of generating performance reports indicating performance indicators between continuous sensor data from two or more analysis time periods in accordance with one embodiment.
  • FIG. 11 is a flowchart of a method of receiving user input for generating customized performance reports in accordance with one embodiment.
  • analyte as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a substance or chemical constituent in a biological fluid (for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine) that can be analyzed.
  • a biological fluid for example, blood, interstitial fluid, cerebral spinal fluid, lymph fluid or urine
  • Analytes can include naturally occurring substances, artificial substances, metabolites, and/or reaction products.
  • continuous analyte sensor is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a device that continuously or continually measures a concentration of an analyte, for example, at time intervals ranging from fractions of a second up to, for example, 1, 2, or 5 minutes, or longer.
  • the continuous analyte sensor is a glucose sensor such as described in U.S. Pat. No. 6,001,067, which is incorporated herein by reference in its entirety.
  • host as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a mammal, such as a human implanted with a device.
  • sensor data is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to any data associated with a sensor, such as a continuous analyte sensor.
  • Sensor data includes sensor readings, such as from a raw data stream, or simply data stream, of an analog or digital signal directly related to a measured analyte from an analyte sensor (or other signal received from another sensor), as well as calibrated and/or filtered raw data.
  • the sensor readings comprise digital data in “counts” converted by an A/D converter from an analog signal (e.g., voltage or amps) and each sensor reading includes one or more data points representative of a glucose concentration.
  • sensor reading refers generally to a digital representation of sensor data (e.g. a blood glucose level) at a particular time.
  • the term broadly encompasses a plurality of time spaced data points from a sensor, such as a from a substantially continuous glucose sensor, which comprises individual measurements taken at time intervals ranging from fractions of a second up to, e.g., 1, 2, or 5 minutes or longer.
  • the sensor data includes an integrated digital value representative of one or more data points averaged over a time period.
  • Sensor data may include calibrated data, smoothed data, filtered data, transformed data, and/or any other data associated with a sensor.
  • transformed sensor data is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to any data that is derived, either fully or in part, from sensor readings from one or more sensors. For example, sensor readings over a time period (e.g., 5 minutes) may be processed in order to generated transformed sensor data including one or more trend indicators (e.g., a 5 minute trend).
  • a time period e.g., 5 minutes
  • trend indicators e.g., a 5 minute trend
  • transformed data examples include filtered sensor data (e.g., one or more filtered analyte concentration values), calibrated sensor data (e.g., one or more calibrated analyte concentration values), rate of change information, trend information, rate of acceleration information, sensor diagnostic information, location information, alarm/alert information, calibration information, and/or the like.
  • calibration is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to a process of determining a relationship between sensor data and corresponding reference data, which can be used to convert sensor data into calibrated data (defined below).
  • calibration can be updated or recalibrated over time as changes in the relationship between the sensor data and reference data occur, for example, due to changes in sensitivity, baseline, transport, metabolism, and the like.
  • calibrated data and “calibrated data stream” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to data that has been transformed from its raw state to another state using a function, for example a conversion function, to provide a meaningful value to a user.
  • a function for example a conversion function
  • smoothed data and “filtered data” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to data that has been modified to make it smoother and more continuous and/or to remove or diminish outlying points, for example, by performing a moving average sensor readings.
  • data filters include FIR (finite impulse response), IIR (infinite impulse response), moving average filters, and the like.
  • smoothing and “filtering” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to a mathematical computation that attenuates or normalizes components of a signal, such as reducing noise errors in a raw data stream.
  • smoothing refers to modification of a data stream to make it smoother and more continuous or to remove or diminish outlying data points, for example, by performing a moving average a stream of sensor readings.
  • time period is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an amount of time including a single point in time and a path (for example, range of time) that extends from a first point in time to a second point in time.
  • measured analyte values is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an analyte value or set of analyte values for a time period for which analyte data has been measured by an analyte sensor.
  • the term is broad enough to include data from the analyte sensor before or after data processing in the sensor and/or receiver (for example, data smoothing, calibration, or the like).
  • estimated analyte values is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to an analyte value or set of analyte values, which have been algorithmically extrapolated from measured analyte values.
  • estimated analyte values are estimated for a time period during which no data exists.
  • estimated analyte values can also be estimated during a time period for which measured data exists, but is to be replaced by algorithmically extrapolated data due to a time lag in the measured data, for example.
  • alarm is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to audible, visual, or tactile signals that are triggered in response to detection of clinical risk to a patient.
  • hyperglycemic and hypoglycemic alarms are triggered when present or future clinical danger is assessed based on continuous analyte data.
  • target analyte values and “analyte value goal” as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to an analyte value or set of analyte values that are clinically acceptable.
  • a target analyte value is visually or audibly presented to a patient in order to aid in guiding the patient in understanding how they should avoid a clinically risky analyte concentration.
  • module as used herein is a broad term, and is to be given its ordinary and customary meaning to a person of ordinary skill in the art (and is not to be limited to a special or customized meaning), and refers without limitation to software, firmware, hardware, and any combination of these elements for performing the associated functions described herein. Additionally, for purpose of discussion, the various modules are described from time-to-time as discrete modules; however, as would be apparent to one of ordinary skill in the art, two or more modules may be combined to form a single module that performs the associated functions according embodiments.
  • computer program product “computer-readable medium”, and the like as used herein are broad terms, and are to be given their ordinary and customary meaning to a person of ordinary skill in the art (and are not to be limited to a special or customized meaning), and refer without limitation to media such as, memory storage devices, or storage unit.
  • These, and other forms of computer-readable media may be involved in storing one or more instructions for use by a processor to cause the processor to perform specified operations.
  • Such instructions generally referred to as “computer program code” (which may be grouped in the form of computer programs or other groupings), which when executed, enable a computing system to carry out specific functions in accordance with the computer program code.
  • Embodiments relate to use of analyte sensors that measure a concentration of analyte of interest or a substance indicative of the concentration or presence of the analyte.
  • Such analyte sensors may use any known method, including invasive, minimally invasive, and non-invasive sensing techniques, to provide an output signal indicative of the concentration of the analyte of interest.
  • an analyte sensor comprises a continuous analyte sensor, for example a subcutaneous, transdermal, or intravascular device.
  • a glucose sensor can take a plurality of intermittent or periodic measurements.
  • An analyte sensor can use any method of analyte-measurement, including enzymatic, chemical, physical, electrochemical, spectrophotometric, polarimetric, calorimetric, radiometric, or the like.
  • an analyte sensor can be any sensor capable of determining the level of any analyte in the body, for example glucose, oxygen, lactase, hormones, cholesterol, medicaments, viruses, or the like. It should be understood that the devices and methods described herein can be applied to any device capable of continually, continuously and/or intermittently detecting a concentration of analyte and providing an output signal that represents the concentration of that analyte.
  • an analyte sensor is an implantable glucose sensor, such as described with reference to U.S. Pat. No. 6,001,067 and co-pending U.S. Patent Application Publication No. 2005/0027463 entitled, “SYSTEM AND METHODS FOR PROCESSING ANALYTE SENSOR DATA,” filed Aug. 1, 2003 and published on Feb. 3, 2005, each of which is incorporated herein by reference in its entirety.
  • an analyte sensor is a transcutaneous glucose sensor, such as described with reference to U.S. Patent Publication No. 2005/0143635 entitled, “CALIBRATION TECHNIQUES FOR CONTINUOUS ANALYTE SENSOR,” filed on Dec. 3, 2004 and published on Jun. 30, 2005.
  • the continuous glucose sensor comprises a transcutaneous sensor such as described in U.S. Pat. No. 6,565,509 to Say et al., for example.
  • a continuous glucose sensor comprises a subcutaneous sensor such as described with reference to U.S. Pat. No. 6,579,690 to Bonnecaze et al. or U.S. Pat. No. 6,484,046 to Say et al., for example.
  • the continuous glucose sensor comprises a refillable subcutaneous sensor such as described with reference to U.S. Pat. No. 6,512,939 to Colvin et al., for example.
  • a continuous glucose sensor comprises an intravascular sensor such as described with reference to U.S. Pat. No.
  • the continuous glucose sensor comprises an intravascular sensor such as described with reference to U.S. Pat. No. 6,424,847 to Mastrototaro et al. All of the above patents and patent publications are incorporated by reference herein in their entireties.
  • FIG. 1 is a diagram illustrating a continuous analyte sensor 10 transmitting intermittent sensor readings 12 (including sensor readings 12 A, 12 B, 12 C, etc.) to a historical sensor data storage 30 .
  • the intermittent sensor readings 12 can each comprise sensor data, such as calibrated data, smoothed data, filtered data, and/or transformed data.
  • the intermittent sensor readings 12 include indications of one or more analyte levels of a host 40 (e.g. such as a blood glucose level of the host 40 ) and/or any quantity of transformed sensor data, including filtered and/or smoothed sensor data.
  • the intermittent sensor readings may be sensed and transmitted from the continuous analyte sensor 10 on a substantially regular schedule, such as every 3 minutes, 5 minutes, 10 minutes, 30 minutes, or hour, for example, or in response to certain triggers, such as the blood glucose level of the host 40 exceeding a threshold, or in response to a request sent from another device in communication with the analyte sensor.
  • the frequency of transmitting intermittent sensor readings 12 from the continuous analyte sensor 10 changes based on a current condition of the host 40 .
  • the continuous analyte sensor 10 may transmit intermittent sensor readings 12 at a higher frequency when the blood glucose level of the host 40 is outside of a preferred range (e.g., the host 40 is approaching hypoglycemia or hyperglycemia).
  • the historical sensor data storage 30 advantageously includes multiple sensor readings for the host 40 covering multiple 24-hour periods, such as from 72 sensor readings (an average of three readings per hour) to 288 sensor readings (an average of 12 readings per hour), or more, for each 24 hour period. This frequency of sensor readings advantageously allows more accurate determination of trends of the host over a period of several hours, days, weeks, or longer.
  • the intermittent sensor readings 12 are stored in the historical sensor data storage 30 so that the sensor data can be accessed by one or more computing devices, such as the host computing device 20 , in order to allow the computing devices to analyze the historical sensor data and calculate one or more performance indicators, such as comparison metrics that compare sensor data from two or more analysis time periods.
  • performance reports such as tables, charts, graphs, and/or any other user interface that illustrate differences and/or similarities in sensor data between analysis time periods may also be determined based on the historical sensor data stored on the historical sensor data storage 30 .
  • comparison of a mean blood glucose level of a host over a first one week period may be compared to a mean blood glucose level of the host over a second one week period in order to determine a general comparison of how the hosts blood glucose levels changed (overall) between the two one week periods.
  • Comparison of the performance indicators and/or comparison metrics e.g. the mean blood glucose levels for each of the two weeks
  • Performance reports may indicate glucose variability over and/or between more than two time periods, for example, a week-by-week comparison over a month's sensor data (e.g., 4 time periods). For example, a user may select a start and/or end date of an overall analysis time period for analysis (e.g., a last month or last quarter) and then select how many periods in which to divide the analysis time period.
  • the continuous analyte sensor 10 includes a communication interface (not shown) for enabling data to be transmitted and/or received over one or more data networks.
  • the data can be transmitted or received wirelessly, through the use of wires, or both.
  • Data transmitted to a receiving device can be first packetized and processed according to one or more standardized network protocols.
  • the communication may accommodate any wired or wireless protocol including, without limitation, USB, FireWire, Ethernet, Gigabit Ethernet, MoCA, radio frequency tuners, modems, ANT, WiFi, Blutooth, WiMax, and/or Infrared Data Association.
  • continuous analyte sensor 10 can transmit the intermittent sensor readings 12 A and 12 B via the communication interface to the historical sensor data storage 30 .
  • Historical sensor data storage 30 can include any type of module adapted to enable digital information, including the intermittent sensor readings 12 A and 12 B, to be stored, retained, and retrieved. Additionally, historical sensor data storage 30 may comprise any combination of volatile and non-volatile storage devices, including without limitation RAM, DRAM, SRAM, ROM, and/or flash memory. Note also that historical sensor data storage 30 may be organized in any number of architectural configurations, through the use of registers, memory caches, data buffers, main memory, mass storage, and/or removable media, for example. Furthermore, historical sensor data storage 30 need not be a physically separate device, but can reside with sensor 10 , host computer 20 , or other computing device in communication with sensor 10 or host computer 20 .
  • the host computing device 20 comprises any suitable electronic device for accessing the historical sensor data stored on the historical sensor data storage 30 , analyzing at least portions of the historical sensor data, and generating one or more comparison metrics and/or performance reports indicating a performance of the host as a comparison of sensor data from two different analysis time periods (to the host and/or other interested parties, such as a caretaker or medical advisor).
  • a comparison metrics and/or performance reports indicating a performance of the host as a comparison of sensor data from two different analysis time periods (to the host and/or other interested parties, such as a caretaker or medical advisor).
  • the host computing device 20 comprises a performance report module 22 that is configured for execution by the host computing device in order to access historical sensor data from a first and a second analysis time period, perform analysis (e.g., mathematical and/or statistical analysis) on the sensor data from each of the analysis time periods, and generate one or more performance indicators that indicate differences in the sample data from the two analysis time periods.
  • the performance report module 22 may be further configured to generate one or more performance reports, such as graphs, charts, tables, etc., that include and/or otherwise illustrate the determined performance indicators, which may be displayed on a display device of the host computing device 20 or other display device.
  • the analysis time periods may be any time period, such as one day, one week, two weeks, three weeks, one month, two months, three months, six months, one year, or any other time period.
  • the performance report module 22 provides the host 40 with reports that indicate changes in sensor data over various time periods.
  • FIG. 2 is a diagram illustrating a continuous analyte sensor 210 in communication with a mobile receiver 220 in accordance with one embodiment.
  • the mobile receiver 220 can be a portable receiver that is carried by a host 240 , for example.
  • the host 240 has diabetes and the mobile receiver 220 tracks a blood glucose level of the host 240 based on intermittent sensor readings 212 from the continuous analyte sensor 210 .
  • Continuous analyte sensor 210 can be the same sensor as analyte sensor 10 described with reference to FIG. 1 .
  • the mobile receiver 220 can be configured to be worn by the host 240 , such as on the wrist of the host 240 , or supported by a belt or clip on the host 240 .
  • the mobile receiver 220 can comprise a mobile electronic device that includes other functionality, such as a mobile telephone, personal digital assistant, personal computer, smart phone, digital media player, exercise monitoring device, geographic position-tracking device (e.g., GPS) or any other similar device.
  • mobile receiver 220 includes some or all of the functionality of the host computing device 20 described with reference to FIG. 1 .
  • the continuous analytes sensor 210 transmits intermittent sensor readings 212 to the mobile receiver 220 , which comprises historical sensor data storage 230 for storing sensor data (e.g. such as volatile and/or nonvolatile memory), including the received intermittent sensor readings 212 .
  • the mobile receiver 220 stores sensor data from the continuous analytes sensor 210 that may be analyzed in order to determine performance indicators and generate performance reports associated with host 240 .
  • historical sensor data storage 230 includes some or all of the functionality of the historical sensor data storage 30 described with reference to FIG. 1 .
  • the mobile receiver 220 can also comprises a performance report module 222 that is configured to determine one or more performance indicators indicating comparisons of sensor data from two or more analysis time periods and to generate one or more performance reports (e.g., user interfaces that are displayable on a display device) that illustrate certain of the performance indicators.
  • the exemplary mobile receiver 220 also includes a display 250 configured to depict the performance indicators and/or certain user interfaces generated by the performance report module 222 .
  • the mobile receiver 220 may include a telemetry module (or other transceiver) that is configured to receive and transmit sensor data and/or performance reports to and from other devices, such as one or more computing devices that may analyze the sensor data and generate performance reports and/or other reports associated with the sensor data. Such devices may transmit generated reports back to the mobile receiver 220 such that the reports can be depicted to be host 240 on the display 250 .
  • FIG. 3 is a flow diagram illustrating a method of generating performance indicators associated with sample data of a host in accordance with one embodiment.
  • continuous sensor readings from two analysis time periods such as sequential or nonsequential time periods, are selected for analysis.
  • the sample data from the two analysis time periods is then analyzed to generate one or more performance indicators, which may be displayed as part of one or more of multiple possible performance reports that generally indicate changes in the sensor data of the host from one analysis period to the next analysis period.
  • sample data from more than two analysis time periods may be selected and analyzed, such that performance indicators are generated from each of the more than two analysis time periods and performance reports that indicate changes in the sensor data and/or performance indicators over the more than two analysis time periods may be generated.
  • the historical sensor data storage 310 which can be data storage 30 or 230 , stores sensor data received from a continuous analyte sensor, such as sensor 10 or 210 .
  • the sensor data may span various time periods, such as weeks, months, years, or longer.
  • the historical sensor data may include sensor readings that are received as frequently as every minute, and thus can include multiple readings per hour.
  • continuous sensor data from analysis time period 320 and continuous sensor data from analysis time period 330 are both transmitted to a statistical analysis module 340 .
  • the statistical analysis module 340 can be configured to analyze the sensor data from each of the analysis time periods and generate one or more statistical values associated with the sensor data (e.g., the sensor readings) for each of the time periods.
  • the length of the analysis time periods may also vary. For example, in one embodiment, each of the analysis time periods is one week, while in another embodiment each of the analysis time periods is a quarter of a year. Other analysis time periods can be one or more hours, days, weeks, months, quarters of a year or years.
  • the statistical analysis module 340 which may comprise computer program code that is executed by a computing device, such as the host computing device 20 or the mobile receiver 220 ( FIG. 1 or FIG.
  • the statistical values for each analysis time period can vary, but generally include one or more indications of glucose variability during the selected time period.
  • the glucose variability may include first, second, or even third order indications of glucose variability.
  • Examples of blood glucose variability include a mean glucose level, a standard deviation of glucose levels during the analysis time period, a trend during the analysis time period, a percentage and/or quantity of days during the analysis time period in which the host was within a target glucose range, a percentage and/or quantity of days within the time period in which the host used the continuous analyte sensor for a minimum time, and any other related statistic.
  • the statistical values 350 , 360 associated with the two (or more) selected analysis time periods are received by a comparison module 370 that is configured to compare the statistical values in order to generate one or more performance indicators 380 , such as comparison metrics, that are indicative of relationships between the sensor data from the first and second analysis time periods.
  • the performance indicators 380 may include numbers, scores, percentages, trends, averages, grades, ranks, images, graphs, charts, and/or any other indicia that are indicative of differences and/or similarities between the statistical values 350 , 360 and/or sensor data 320 , 330 associated with respective first and second analysis time periods.
  • the performance indicators may also include comparison metrics, such as one or more overall trends in the improvement (or lack thereof) in a variety of areas for the two or more analysis time periods.
  • comparison metrics can be expressed, for example, by analyzing a predetermined, selectable, or changing, quantity of analysis time periods and generating a smoothed or filtered trend of the analysis times periods using one or more statistical parameters.
  • target sensor data is used instead of the continuous sensor data 320 or 330 .
  • Target sensor data can be indicative of a performance goal and can be provided by a caretaker, such as a physician of the host.
  • a physician may have a particular performance goal for a host and, accordingly may determine target glucose values for that host.
  • Target sensor data may be downloaded to the performance report module 22 , 222 ( FIGS. 1 and 2 ) and compared to actual data from the host from a previous analysis time period in order to gauge performance of the host with respect to the target performance goals set by the host's physician.
  • the continuous sensor data 320 may comprise sensor data of the host 40 , 240 from a previous one-month period and one month target data provided by the host's physician can be used instead of the continuous sensor data 330 .
  • the performance reports and comparison metrics that are generated show the relative performance of the host with respect to the targets provided by the host's physician.
  • target data provided by a caretaker can be used instead of sample data of a host for an analysis time period in order to achieve similar target comparison results.
  • multiple analysis time periods such as three or more, may be analyzed in generating performance reports using target data.
  • two analysis time periods comprising host sample data and one analysis time period comprising target data supplied by a physician may be analyzed in generating performance reports for the host.
  • performance indicators 380 include and/or initiate display of other information to the host, such as statistical, clinical, expert, and/or therapy recommendations/advice that may be aimed at improving overall glucose control, diabetes management, modified timing, and/or amount of pre-prandial insulin dose information, for example.
  • information can be associated with respective criteria, such as criteria associated with one or more statistical values, performance indicators, and/or ranges of these values.
  • criteria for a particular clinical/therapy recommendation are met, such as if a particular trend in glucose variability is met by a host, the particular clinical/therapy recommendation may be displayed (or otherwise provided, e.g., audibly spoken to the patient via a speaker on the receiver) to the host.
  • a caretaker such as a parent, guardian, or doctor, can define performance criteria that define when clinical therapy recommendations or other expert advice are provided to the host.
  • the host can be provided with expert advice, e.g., advice from a doctor of the host, via an avatar that is displayed on a display of the host's receiver in response to one or more of the host's performance indicators matching criteria associated with one or more performance criteria established by the host and/or a caretaker of the host, for example.
  • a caretaker e.g., doctor
  • can upload performance criteria e.g., including performance criteria that are applied to the one or more performance indicators in order to determine if the host has (or hasn't) met desired performance goals
  • performance criteria e.g., including performance criteria that are applied to the one or more performance indicators in order to determine if the host has (or hasn't) met desired performance goals
  • provide real-time feedback to the host via the receiver 220 and/or the computing device 20 as well as receive the performance information via upload of the information to the caretaker's computing device.
  • one or more performance reports that include the performance indicators 380 are generated for display to the host, such as via the host computing device 20 and/or the mobile receiver 220 of FIGS. 1 and 2 , for example.
  • the performance report module 22 , 222 ( FIGS. 1 , 2 , respectively) comprises the statistical analysis module 340 and/or comparison module 370 discussed with respect to FIG. 3 .
  • the host computing device 20 and/or the mobile receiver 220 may be configured to generate statistical values for each of multiple analysis time periods as well as performance indicators and/or performance reports (e.g., user interfaces) including the performance indicators.
  • FIG. 4 is a flow diagram illustrating the historical sensor data storage 310 , the statistical analysis module 340 , and the comparison module 370 of FIG. 3 , wherein specific exemplary data is shown communicated between the components.
  • analysis time period data 420 comprises sensor data including 72-300 sensor readings for each day from January to March of a particular year (e.g. 2008) and the analysis time period data 430 comprises sensor data including 72-300 sensor readings for each day from April to June of that same year (e.g., 2008).
  • the analysis time periods comprise consecutive quarters of a year.
  • the sensor data may include fewer or additional sensor readings for each 24 hour period.
  • a host may not use a continuous analytes sensor, or may not properly use a continuous analyte sensor (e.g., the sensor may not be used while on vacation or during exercise or other physical activities, and sensor readings may not be received when the battery of the receiver low or is dead, for example), such that no sensor readings are received.
  • sensor readings for one or more days (or more) within one of the first or second analysis time periods may include no (or very few) sensor readings. While lack of readings for such a short time period may not be ideal, the embodiments described herein can continue to provide statistical values, performance indicators, and/or performance reports notwithstanding the absence of sensor readings for certain such periods.
  • the statistical analysis module 340 determines a mean glucose level for each of the first and second analysis time periods.
  • mean glucose level 450 represents the sample readings during the January to March analysis time period and mean glucose level 460 represents the sample readings during the April to June analysis time period.
  • other statistical values may be generated by the statistical analysis module 340 .
  • the comparison module 370 receives the mean glucose levels 450 , 460 and determines a percentage difference between the quarterly mean glucose levels 450 , 460 . This percentage difference may be included in one or more performance reports as an indicator of performance of the host from one quarter to the next.
  • FIGS. 5-8 illustrate exemplary performance reports that include various statistical values and performance indicators in accordance with various embodiments.
  • FIG. 5 illustrates one exemplary performance report 500 that includes statistical values and performance indicators for each of two analysis time periods, which in this example are months.
  • the performance report 500 illustrates statistical values associated with a first analysis time period (from January 29 to February 27) in column 510 and statistical values associated with a second analysis period (from February 28 to March 27) in column 520 .
  • the performance report 500 also indicates performance indicators indicative of similarities between the statistical values in columns 510 and 520 in column 530 . For example, in a row 540 , the mean glucose levels of the sensor readings during each of the analysis time periods are provided in columns 510 , 520 , and a percentage change in the mean glucose levels between the two analysis time periods is indicated in column 530 of row 540 .
  • the performance report 500 also includes a column 535 that provides a graphical performance indicator that indicates whether a particular performance indicator is generally positive (e.g., indicated by a smiley face or similar graphic), negative (e.g., indicated by a warning indicator - as illustrated in FIG. 5 , a frowney face or similar graphic), or neutral (e.g., indicated by a neutral face or similar graphic).
  • row 542 indicates statistics for the standard deviation of sensor readings during the analysis time periods, wherein in the specific exemplary standard deviations have improved by 2% from the first analysis period to the second analysis period, as illustrated in column 530 of row 542 .
  • the illustrative performance report 500 also indicates in rows 544 , 546 , 548 , 550 , a percentage of days the host was in a hypoglycemia range, target range, high range, and hyperglycemia rage, respectively, and includes a change in the number of days that the host was in each of the ranges in column 530 of each respective row.
  • the performance report 500 also includes in row 560 a number of days the analyte sensor was used by this host in each of the two analysis time periods, as well as a change in the number of days the sensor was used by the host in column 530 of row 560 .
  • the performance indicators of column 535 may be based on a combination of more than one statistical value and/or performance indicator.
  • performance indicators may be provided as combinations of time spent in particular ranges, days/percentage of days that sensors are used, and/or any other performance indicators that are available in order to better provide the host, caretakers, and/or any other interested entity in suitable indicators of performance of the host.
  • FIG. 6A is an exemplary performance report 600 illustrating sensor readings for each of two analysis time periods in accordance with one embodiment.
  • two line graphs illustrate average blood glucose levels for each hour of a 24 hour period over the course of the relevant analysis time periods, which in this embodiment include a first analysis time period from January 29 to February 27 and a second analysis time period from February 28 to March 27.
  • FIG. 6B the same data that is illustrated in the line graphs of FIG. 6A is shown in table format, such that the average blood glucose levels for each one hour period during the relevant time periods are shown in each field.
  • the blood glucose level of 214 mg/dL indicates that during the first analysis time period from January 29 to February 27 the host had an average blood glucose level of 214 mg/dL during the hour from 12 am to 12:59 am (which may have included from 5-60 sensor readings per day during that time period).
  • the blood glucose level of 145 mg/dL indicates that during the time period from January 29 to February 27 the host had an average blood glucose level of 145 mg/dL during the hour from 1 am to 1:59 am.
  • Each of the fields of the table of FIG. 6B may be associated with multiple sample readings from multiple days, such as 2-20 sample readings per day, for example. In other embodiments, continuous sensor readings may be averaged over shorter or longer time periods, such as periods of 10 minutes, 30 minutes, or two hours, for example.
  • FIG. 7 is an exemplary performance report 700 illustrating a bar graph showing exemplary success metrics from each of the two compared analysis time periods.
  • bar graphs for each of the two analysis time periods are provided side-by-side for each of a number of statistical values. Thus, the viewer can easily distinguish a general performance of the host based on differences in the bar graphs for various statistical values.
  • the performance report illustrates bar graphs for each of the two analysis time periods including Alc % values 710 , mean glucose values 720 , standard deviation values 730 , percentage in target values 740 , and days sensor used values 750 . In other embodiments, additional or fewer statistical values may be included in a similar performance report.
  • FIG. 8 is an exemplary performance report 800 illustrating pie graphs showing percentage of time that a host was in each of a plurality of predefined blood glucose ranges during each of the two analysis time periods.
  • the predefined ranges include a hypoglycemia range, a target range, a higher range, and a hyperglycemia range.
  • the ranges may include fewer or additional ranges.
  • the ranges may be predefined by a software application and/or may be user-definable, such as by a host, caretaker of a host, or physician.
  • the performance report 800 may include a performance indicator, such as a comparison pie graph, that shows a difference between the percentage time in each of the ranges between the two comparison time periods.
  • a performance indicator such as a comparison pie graph
  • the graph may illustrate that the host decreased 4% from the first analysis period to the second analysis time period in the amount of time spent in the high range.
  • Alc % values are not indicated in the report 700 or report 800
  • a user can input Alc % values—via host computer 20 or mobile receiver 220 , for example.
  • performance report module 22 , 222 ( FIGS. 1 and 2 , respectively) can calculate estimated Alc % values based on historical sensor data stored in historical sensor storage 30 , 203 , for example.
  • an estimated Alc % can be calculated based on continuous glucose sensor readings.
  • the inputted and/or estimated Alc % values can then be illustrated in the performance reports 700 and 800 .
  • an estimated Alc % can be calculated using continuous analyte sensor readings.
  • an estimated Alc % can be calculated based on a magnitude and amount of time that a host's continuous glucose sensor readings fall outside of or inside a predetermined range during an analysis time period.
  • the estimated Alc % can be calculated by integrating the area bounded between a host's glucose level (based on the host's continuous glucose sensor readings during an analysis time period) and upper and/or lower levels of the predetermined range, while the host's glucose level falls outside of the predetermined range.
  • the Alc % value is based only on a time and magnitude that the host's glucose level, as indicated by the host's continuous glucose sensor readings, exceed a threshold.
  • SD standard deviation
  • % CV percentage coefficient of variation
  • Performance reports can also be based on or include a variety of other statistical information.
  • performance report module 22 , 222 can calculate a Glycemic Variability Index (GVI) and generate a performance report that indicates the calculated GVI.
  • GVI Glycemic Variability Index
  • the GVI can indicate a quality of glucose control over time based on continuous analyte sensor data.
  • the GVI is representative of a magnitude and amount of time that a host's glucose levels fall outside of or inside a predetermined range during an analysis time period.
  • the GVI can be calculated by integrating the area bounded between a host's glucose level over an analysis time period (based on the host's continuous glucose sensor readings) and upper and/or lower levels of the predetermined range, while the host's glucose level falls outside of the predetermined range.
  • the predetermined range can define a range of acceptable glucose concentration levels of the host.
  • the upper and lower thresholds of the range can be set and modified by the host or the host's caretaker or healthcare provider using host computing device 20 or mobile receiver 220 , for example.
  • SD standard deviation
  • % CV percentage coefficient of variation
  • IQR interquartile
  • the GVI can also be separated into different GVI components to provide a more comprehensive indication of a host's glucose control.
  • the GVI can include a total GVI component (GVI total ), a hyperglycemia GVI component (GVI hyper ) and a hypoglycemia component (GVI hypo ).
  • the GVI hyper component can be indicative of hyperglycemia control based entirely or substantially in part on sensor values that exceed an upper threshold of a predetermined range (i.e. a hyperglycemia threshold); for example, based on a time and magnitude of continuous sensor readings exceeding the upper threshold.
  • the GVI hypo component can be indicative of hypoglycemia control based entirely or partially on sensor values that fall below a lower threshold of a predetermined range (i.e.
  • a hypoglycemia threshold for example, based on a time and magnitude of continuous sensor readings falling below the lower threshold.
  • the GVI total can be a sum of the GVI hyper and GVI hypo components. A value for each of these components can be displayed on an electronic device, such as on computing device 20 or receiver 220 . In this manner, a user can obtain a better understanding of a host's glucose control because the user can obtain a measure over overall control (GVI total ) 1 , in addition to measures of control in hyperglycemia (GVI hyper ) and hypoglycemia (GVI hypo ) ranges.
  • FIG. 9 is an exemplary user interface 900 in accordance with one embodiment.
  • the user interface 900 can combine performance reports and/or other performance indicators, such as those described above, into a single user interface for display and interaction with a user.
  • the user interface 900 can be implemented using computer program code stored on a computing device used by a host to monitor and track blood glucose levels.
  • the performance report module 22 which may reside on the host computing device 20 and/or other suitable computing device, may comprise computer program code configured to analyze sensor historical data, generate one or more performance reports, and implement the user interface 900 of FIG. 9 .
  • the user interface 900 includes fields 910 and 920 for inputting respective first and second analysis time periods upon which statistical values, performance indicators, and/or performance reports are desired.
  • the user may input a start date of a first analysis period in the entry field 910 and a start date of a second analysis period in the entry field 920 .
  • the default analysis time period is set for one week and the start date of the second analysis period is defaulted to begin immediately after the end date of the first analysis period (i.e. defaulted to have consecutive analysis periods).
  • a computing device supporting user interface 900 e.g., device 20 of FIG.
  • the user 1 automatically calculates and displays on user interface 900 the end date of the first analysis period and the start date and end date of the second analysis period.
  • the user can also modify the second start date by inputting a different start date in the field 920 of the user interface 900 . In this way, a user can compare two analysis periods that are nonconsecutive. Additionally, if a different analysis time period is desired, a user can input a different analysis time period by selecting a desired analysis time period via drop down menu 930 .
  • the drop down menu 930 can allow the user to select one of a variety of analysis time periods, such as days, months, quarters, or years, or other custom time periods.
  • a computing device supporting user interface 900 can access relevant, associated historical sensor data (stored in storage 30 , for example) and generate statistical values, performance indicators, and performance reports for display to the host and/or others on a display of a computing device, such as host computing device 20 or mobile receiver 220 .
  • the user interface 900 of FIG. 9 includes many of the performance reports illustrated in FIGS. 5-8 , discussed above.
  • user interface 900 can be modified to display any of the other performance statistics (e.g., performance reports, performance indicators, and/or statistical values based on sensor readings) described herein.
  • more than two time analysis periods can be selected and displayed on user interface 900 .
  • the user interface 900 can be modified to display performance reports that compare multiple analysis time periods with target sensor data.
  • performance statistics generated by the computing device 20 and/or mobile receiver 220 may be transmitted to a physician's computing device, personal health records system, and/or other networked health record system.
  • the performance statistics can be transmitted with or without the underlying continuous sensor readings on which the performance statistics are based. In this manner, the need/desire for transmitting all of the continuous sensor readings to a remote device, e.g. a central storage device accessible by the host's physician, may be reduced, because the performance statistics can provide all of the information desired by the physician.
  • the physician may not be as interested in viewing, or may not need to view, the individual glucose readings generated from the host's continuous analyte sensor 10 , 210 ( FIGS. 1 and 2 ), because the performance report may provide all of the information needed by the physician.
  • a report module (not shown) generates a user interface that may be used by a caretaker, such as a doctor, in order to allow the caretaker access to sensor data from multiple hosts and, accordingly, to statistical values, performance indicators, and performance reports for multiple host. Accordingly, such a user interface may allow the physician to view comparisons of performance across multiple selectable population groups. For example, a user interface may be configured to allow a caretaker to select patients based on any one or more demographic (or other) criteria, such as age, gender, etc. Thus, the caretaker may compare the performance of all male patients between the ages of five and fifteen, comparing the period from December-March to July-August, and request a certain set of performance indicators and/or performance reports.
  • the physician may request that performance indicators indicating percentage of time in each of certain ranges and percentage of time the sensors are used are included in performance reports.
  • the report module provides the caretaker with a broader based performance report that may be used to provide therapy recommendations to patients, for example.
  • the user interface 900 is capable of automatically recognizing another other electronic device upon connection with the other device.
  • the connection can be either a wireless or wire connection.
  • PnP Plug and Play
  • UnP Universal Plug and Play
  • the user interface 900 incorporated into a hand-held mobile device, for example, can automatically install on a computing device, such as host computing device 20 or mobile receiver 220 , obtain historical sensor data and/or generate a performance report based on the historical sensor data, upon connection of the user interface 900 with another electric device.
  • FIG. 10 is a flowchart illustrating a method of generating performance reports indicating performance indicators between continuous sensor data from two or more analysis time periods in accordance with one embodiment.
  • the various tasks performed in connection with the method of FIG. 10 may be implemented by software, hardware, firmware, a computer-readable medium storing computer executable instructions for performing the process method, or any combination thereof.
  • the various tasks performed in connection with the method of FIG. 10 may be performed by any suitable computing device, such as the host computing device 20 of FIG. 1 , the mobile receiver 220 of FIG. 2 , and/or any other suitable computing device.
  • FIG. 10 may be described from time-to-time with respect to the host computing device 20 ; however, any reference to the host computing device 20 should be construed to cover any other suitable computing device, including mobile receiver 220 . Furthermore, a task associated with a block may be performed by one computing device while other tasks associated with the same block or other blocks may be performed by other computing device(s). Depending on the embodiment, the method of FIG. 10 may include fewer or additional blocks and the blocks may be performed in a different order than is described and illustrated in FIG. 10 .
  • the host computing device 20 accesses historical continuous sensor data for each of two (or more) analysis time periods.
  • the host computing device 20 may access historical sensor data storage 30 that stores continuous sensor readings for a period of weeks, months, years, or mores.
  • the historical sensor data can include multiple sensor readings per hour, such as more than 72 sensor readings per 24 hour period from a continuous analyte sensor.
  • the historical sensor data for the analysis time periods may be stored local to the host computing device 20 , e.g. on a hard drive or optical drive of the computing device, or may be accessed via one or more network connections, e.g., via a local area connections to a server that stores the data.
  • the host computing device 20 determines one or more statistical values for each of the analysis time periods.
  • the calculated statistical values may include, for example, one or more indications of glucose variability, such as mean glucose levels, standard deviation of glucose levels during the analysis time period, percentage and/or quantity of days during the analysis time period in which the host was within a target glucose range, a hyperglycemic range, a hypoglycemic range, and/or any other custom defined range, a percentage and/or quantity of days within the time period in which the host used the continuous analyte sensor for a minimum time, and any other related statistic.
  • one or more indications of glucose variability such as mean glucose levels, standard deviation of glucose levels during the analysis time period, percentage and/or quantity of days during the analysis time period in which the host was within a target glucose range, a hyperglycemic range, a hypoglycemic range, and/or any other custom defined range, a percentage and/or quantity of days within the time period in which the host used the continuous analyte sensor for a
  • the host computing device 20 determines one or more performance indicators that indicate differences and/or similarities between the statistical values and/or sensor data of the two or more analysis time periods.
  • the performance indicators may include comparison metrics, such as numbers, scores, percentages, trends, averages, grades, ranks, images, graphs, charts, and/or any other indicia that are indicative of differences and/or similarities between the statistical values deterring in block 1020 .
  • the host computing device generates one or more performance reports, such as interactive user interfaces, tables, charts, graphs, or any other reports that are illustrative of differences and/or similarities in the continuous sensor readings from the analysis time periods.
  • the performance reports may include reports such as those illustrated in FIGS. 5-9 , for example, and/or any other performance report.
  • the performance reports may then be displayed on a user interface of a computing device, such as the user interface 900 .
  • FIG. 11 is a flowchart of a method of receiving user input for generating customized performance reports, wherein the performance reports include performance indicators indicating comparisons between continuous sensor data from two or more analysis time periods, in accordance with one embodiment.
  • the various tasks performed in connection with the method of FIG. 11 may be implemented by software, hardware, firmware, a computer-readable medium storing computer executable instructions for performing the process method, or any combination thereof.
  • the various tasks performed in connection with the method of FIG. 10 may be performed by any suitable computing device, such as the host computing device 20 of FIG. 1 , the mobile receiver 220 of FIG. 2 , and/or any other suitable computing device.
  • FIG. 11 may be described from time-to-time with respect to the host computing device 20 ; however, any reference to the host computing device 20 should be construed to cover any other suitable computing device. Furthermore, a task associated with certain of the blocks may be performed by one computing device while other tasks associated with the same or other blocks may be performed by other computing device(s). Depending on the embodiment, the method of FIG. 11 may include fewer or additional blocks and the blocks may be performed in a different order than is described and illustrated in FIG. 11 .
  • a user such as the host 40 , doctor, or caretaker, provide the analysis time periods that are to be analyzed in performance reports.
  • a host executing the performance report module 22 on the host computing device 20 may be presented with a user interface, such as the user interface of FIG. 9 , wherein the host can select the analysis time period via one or more interface controls.
  • the user interface 900 includes interface controls that allow selection of a default analysis time periods (e.g., via drop down menu 930 ) and selection of a start date for the first analysis time period (e.g., in field 910 ), as well as a start date for the second analysis time period (e.g., in field 920 ).
  • selection of the analysis time periods is provided via a web accessible user interface.
  • the user optionally provides an indication of one or more performance indicators and/or performance reports of interest. For example, one user may want to see a performance report indicating percentage differences of times in which the host was in each of three target blood glucose ranges in two consecutive quarters, while another user may want to see a performance report indicating percentage differences in hours that sensor readings were received from one week to the next over a period of several weeks.
  • one or more default performance indicators are preselected and will be determined unless they are deselected by the user.
  • the historical sensor data associated with the host during the analysis time periods is accessed.
  • data may be accessed either local to the host computing device or remote to the host computing device via one or more network connections.
  • the historical sensor data for each of the selected analysis periods includes multiple sensor readings, such as from 72-288 sensor readings, per 24 hour period.
  • the host computing device determines one or more statistical values for each of the user-selected analysis time periods.
  • the calculated statistical values may include, for example, one or more of various indications of glucose variability.
  • one or more user-selected performance reports are generated including the user-selected performance indicators and/or other determined statistical values.
  • performance reports may be delivered to the user via a web interface, e.g., via a web browser, or via a stand-alone software application.
  • the performance reports illustrate differences and/or similarities in sensor data for extended periods of time (e.g., comparisons of consecutive weeks, months, or quarters).
  • firmware stored in a computer-readable storage medium and executed by a processor of computing device 20 or receiver 220 .
  • the firmware can also be stored and/or transported within any computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
  • a group of items linked with the conjunction ‘and’ should not be read as requiring that each and every one of those items be present in the grouping, but rather should be read as ‘and/of unless expressly stated otherwise.
  • a group of items linked with the conjunction ‘or’ should not be read as requiring mutual exclusivity among that group, but rather should be read as ‘and/or’ unless expressly stated otherwise.

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  • Primary Health Care (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Emergency Medicine (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
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US17/817,457 US20220392643A1 (en) 2009-04-30 2022-08-04 Performance reports associated with continuous sensor data from multiple analysis time periods

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EP4374790A2 (fr) 2024-05-29
EP2424435B1 (fr) 2021-06-02
EP3925533B1 (fr) 2024-04-10
US20220392643A1 (en) 2022-12-08
EP3925533A1 (fr) 2021-12-22
US11437141B2 (en) 2022-09-06
WO2010127169A2 (fr) 2010-11-04
WO2010127169A3 (fr) 2011-02-03
EP2424435A4 (fr) 2013-06-19
EP4374790A3 (fr) 2024-07-31
US20170143251A1 (en) 2017-05-25
EP2424435A2 (fr) 2012-03-07

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