HK1221627B - Reminder, classification, and pattern identification systems and methods for handheld diabetes management devices - Google Patents

Description

Alerting, classification, and pattern recognition systems and methods for handheld diabetes management devices

Technical Field

The present disclosure relates to handheld medical devices, and more particularly to systems and methods for reminding a user to take a blood glucose (bG) measurement using a handheld bG measurement device, identifying the type of bG measurement, and identifying trends in bG measurements.

Background Art

People with diabetes have difficulty regulating their blood sugar levels. Consequently, many of these individuals carry specialized electronic meters (known as glucometers) that allow them to regularly measure their blood sugar levels and take appropriate action, such as administering insulin. These individuals may also carry portable communication devices, such as mobile phones, personal digital assistants, tablet computers, or similar devices. People often rely on their portable communication devices as their primary means of planning, scheduling, and communicating with others. Consequently, most portable communication devices are equipped with sophisticated software that provides a user-friendly means of viewing and entering data.

The user interface of a handheld diabetes management device, including a blood glucose meter, may be limited to limit complications associated with operating the diabetes management device. A need exists for a handheld diabetes management device that has a limited user interface while also having easily user-configurable reminders for taking blood glucose (bG) measurements, automatically classifying bG measurements into specific types of bG measurements, and identifying and notifying the user of trends in stored bG measurements, e.g., based on the classification.

The background description provided herein is for the purpose of generally presenting the context of the present disclosure. The work of the presently cited inventors (to the extent it is described in this background section) and aspects of the description that were not otherwise prior art at the time of filing are neither explicitly nor implicitly admitted as prior art against the present disclosure.

Summary of the Invention

In a feature, a handheld diabetes management device for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The handheld diabetes management device includes a blood glucose (BG) measurement engine that measures the BG level in a blood sample input to the handheld diabetes management device; a display; a clock that tracks the current date and time; a processor; and memory including code executed by the processor. The code is configured to: measure the BG level in a blood sample received by the handheld diabetes management device; store the BG level and the time the blood sample was received in the memory; store a classification of the blood sample, wherein the classification is one of: a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; select a group of stored BG levels that has one of the classifications; and generate an indicator of identification of a pattern in the selected group of BG levels when a BG evaluation parameter calculated based on the selected BG level is greater than a first threshold indicating a high BG level and less than a second threshold indicating a low BG level. The code is further for, in response to a user request to display patterns identified in the bG level, generating a list of identified patterns, the list including patterns in the selected bG level; prioritizing the list based on predetermined prioritization criteria; and displaying the list on the display.

In a feature, a method for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The method includes: measuring a blood glucose (BG) level in a blood sample received by a handheld diabetes management device; storing the BG level and the time the blood sample was received in a memory; storing a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; selecting a group of stored BG levels having one of the classifications; and generating an indicator of identification of a pattern in the selected group of BG levels when a BG evaluation parameter calculated based on the selected BG level is one of greater than a first threshold indicating a high BG level and less than a second threshold indicating a low BG level. The method further includes, in response to a user request to display the identified patterns in the BG levels: generating a list of the identified patterns, the list including the pattern in the selected BG level; prioritizing the list based on predetermined prioritization criteria; and displaying the list on a display.

In a feature, a handheld diabetes management device for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The handheld diabetes management device includes: a blood glucose (BG) measurement engine that measures the BG level in a blood sample input to the handheld diabetes management device; a display; a clock that tracks the current date and time; a processor; and memory including code executed by the processor. The code is configured to: store the BG level and the time the blood sample was received in the memory; store a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; in response to receiving the blood sample, select a group of stored BG levels that have the blood sample classification and were received within a predetermined period prior to receiving the blood sample; calculate a BG evaluation parameter based on the selected BG level; evaluate the BG evaluation parameter relative to a first predetermined criterion, the first predetermined criterion including a first threshold value indicating a high BG level or a low BG level; and selectively display an indication of the identification of a pattern in the selected BG level when the BG evaluation parameter is greater than or less than the first threshold value.

In a feature, a method for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The method includes: measuring a blood glucose (BG) level in a blood sample received by a handheld diabetes management device; storing the BG level and the time the blood sample was received in a memory; storing the classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; and, in response to receiving the blood sample, selecting a group of stored BG levels that have the classification of the blood sample and were received within a predetermined period prior to receiving the blood sample. The method further includes: calculating a BG evaluation parameter based on the selected BG level; evaluating the BG evaluation parameter with respect to a first predetermined criterion, the first predetermined criterion including a first threshold value indicating a high BG level or a low BG level; selectively displaying an indication of identification of a pattern in the selected BG level when the BG evaluation parameter is greater than or less than the first threshold value; and removing the indication from the display only in response to receiving a predetermined user input indicating confirmation of the presence of the pattern.

Further areas of applicability of the present disclosure will become apparent from the detailed description provided hereinafter.It should be understood that the detailed description and specific examples are intended for purposes of illustration only and are not intended to limit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be more fully understood based on the detailed description and accompanying drawings, in which:

FIG1 shows a patient and a healthcare professional along with various devices that can be used to help the patient monitor and manage their health;

Figure 2 shows a patient with a continuous glucose monitor (CGM), an ambulatory durable insulin infusion pump, an ambulatory non-durable insulin infusion pump, and a blood glucose (bG) management device;

FIG3 illustrates a diabetes care system that can be used to manage diabetes;

FIG4 is a high-level diagram of an example implementation of a diabetes management device;

FIG5 includes a functional block diagram of an example implementation of a diabetes management device;

6-7 include example illustrations of various data stored in memory;

FIG8 includes a flow chart depicting an example method of generating a reminder for an event to measure bG;

9 includes a flow chart depicting an example method for generating a reminder to measure bG after a meal;

FIG10 includes an example method for generating a reminder to measure bG after providing a low blood glucose bG measurement;

11A includes a flow chart depicting an example method of notifying a user that a diabetes management device is capable of providing reminders for an event;

11B includes a flow chart depicting an example method of notifying a user that a diabetes management device is capable of performing pattern recognition;

FIG12 includes a flow chart depicting an example method for identifying and displaying patterns in bG sample data;

13 includes a flow chart depicting an example method for displaying a reminder of the presence of a pattern at an event before the event occurs based on identification of a pattern associated with a daily event;

14 includes a flow chart depicting an example method of displaying previously identified patterns in bG sample data.

DETAILED DESCRIPTION

1 , a patient 100 with diabetes and a healthcare professional 102 are shown in a clinical setting. The patient 100 with diabetes may be diagnosed with metabolic syndrome, prediabetes, type 1 diabetes, type 2 diabetes, gestational diabetes, etc. Healthcare providers for diabetes are diverse and include nurses, nurse practitioners, physicians, endocrinologists, and others, and are collectively referred to as healthcare professionals.

During a healthcare consultation, patient 100 typically shares various data with healthcare professional 102, including blood glucose (BG) measurements, continuous glucose monitoring data, the amount and type of insulin administered, the amount of food and beverages consumed, exercise schedule, health status, and other lifestyle information. Healthcare professional 102 may obtain additional data specific to patient 100, such as measurements of HbA1C, cholesterol levels, plasma glucose, triglycerides, blood pressure, and weight. This data may be recorded manually or electronically on a handheld diabetes management device 104 (e.g., a handheld BG monitoring device), diabetes analysis software executed on a personal computer (PC) 106, and/or a web-based diabetes analysis website. Healthcare professional 102 may use the diabetes analysis software and/or web-based diabetes analysis website to manually or electronically analyze the patient data. After analyzing the data and reviewing how effective the previously prescribed treatment was and how well the patient 100 adhered to the previously prescribed treatment, healthcare professional 102 may decide whether to modify the treatment prescribed for patient 100.

2 , a patient 100 may use a continuous glucose monitor (CGM) 200, an ambulatory, durable insulin infusion pump 204, or an ambulatory, non-durable insulin infusion pump 202 (collectively, insulin pump 204), and a diabetes management device 104. The CGM 200 may use a subcutaneous sensor to sense and monitor the amount of glucose (e.g., glucose concentration) of the patient 100. The CGM 200 communicates the glucose measurement to the diabetes management device 104.

The diabetes management device 104 performs various tasks, including measuring and recording bG measurements, determining the amount of insulin to be administered to the patient 100 via the insulin pump 204, receiving user input via a user interface, archiving data, performing structured bG testing, etc. The diabetes management device 104 may send instructions to the insulin pump 204, and the insulin pump 204 may selectively deliver insulin to the patient 100. The insulin may be delivered in the form of a pre-meal bolus, a correction bolus, a basal dose, etc.

3 , there is shown a diabetes management system 300 that may be used by a patient 100 and/or a healthcare professional 102. The system 300 may include one or more of the following: a diabetes management device 104, a CGM 200, an insulin pump 204, a mobile device 302, diabetes management software executing on a computer 106, and one or more other healthcare devices 304.

The diabetes management device 104 can be configured as a system "hub" and communicate with one or more other devices of the system 300. The insulin pump 204, the mobile device 302, or another suitable device can alternatively serve as the system hub. Communication between the various devices in the system 300 can be performed using a wireless interface (e.g., Bluetooth) and/or a wired interface (e.g., USB). The communication protocol used by these devices can include a protocol compliant with the IEEE 11073 standard extended using the guidelines provided by the ContinuaHealth Alliance Design Guidelines. Further, health care record systems such as Microsoft HealthVault™ and Google Health™ can be used by the patient 100 and the health care professional 102 to exchange information.

Referring now to FIG. 4 , a high-level diagram of an example embodiment of a (handheld) diabetes management device 402 is presented. Among other components, diabetes management device 402 includes a housing 404, a display 408, and a bG test strip port 420. Diabetes management device 402 may optionally include a bG test strip cartridge (not shown). The bG test strip cartridge may hold a plurality of bG test strips, such as bG test strip 416.

The diabetes management device 402 also includes user interface switches/buttons, such as an up button 424, a down button 428, a back button 432, and an enter button 436. The user interface switches/buttons may also include other buttons or switches, such as an on/off switch and/or one or more other switches/buttons or other types of control devices that the patient may use to control the functions/operations of the diabetes management device 402.

A bG test strip 416 can be inserted into the bG test strip port 420 by a user. The bG test strip 416 is shown inserted into the bG test strip port 420 in the example of FIG. 4 , and is shown not yet inserted into the bG test strip port 420 in the example of FIG. 5 . The display 408 of the diabetes management device 402 can be a non-touch screen display, such as a dot matrix display. Various information can be selectively displayed on the display 408. For example, when a bG measurement is taken in response to the insertion of the bG test strip, the bG measurement can be displayed on the display 408.

5 , a functional block diagram of an example implementation of a diabetes management device 402 is presented. The diabetes management device 402 includes a processor module (e.g., a microprocessor-based subsystem) 504 that is capable of receiving information from a bG measurement engine 508.

The bG measurement engine 508 reads (measures) the bG level of a blood sample present on a bG test strip inserted into the bG test strip port 420. For example, the bG measurement engine 508 measures the bG level of the bG test strip 416 when the bG test strip 416 is inserted into the bG test strip port 420. The bG measurement engine 508 can be positioned adjacent to the bG test strip port 420. The bG measurement engine 508 generates bG sample data 516 based on its reading of the blood sample present on the bG test strip. The bG sample data 516 includes, among other things, data indicating the bG level of the bodily fluid sample on the bG test strip.

The bG measurement engine 508 can also generate the bG sample data 516 to include the date and time when the bG test strip 416 was read. In other words, the bG measurement engine 508 can include a timestamp with the bG sample data 516. In various embodiments, the processor module 504 can selectively timestamp the bG sample data 516. The clock 518 can track the current day, date, and time. The timestamp can include data indicating the date and time, and can also include the day.

The processor module 504 can receive user input and output information to the user via one or more user input/output (I/O) devices 514, such as buttons 424-436 and the display 408. One or more I/O interfaces, such as the I/O interface 524, facilitate communication between the user I/O devices 514 and the processor module 504. The I/O interfaces can also facilitate communication between the processor module 504 and one or more communication modules, such as the communication module 536. The communication module 536 can include a wireless transceiver and communicate (transmit and receive) wirelessly via one or more antennas.

The diabetes management device 402 includes tangible computer-readable media, such as a memory and/or one or more other suitable tangible computer-readable media. Various data, such as bG sample data 540 and other types of data, can be stored in the memory 532, as discussed further below. The bG sample data 540 can include bG sample data generated by the bG measurement engine 508, such as the bG sample data 516. The bG sample data 540 can also include other types of data related to bG samples.

Regular bG measurements taken at approximately the same time(s) and/or around the same daily event(s) can yield better results in bG management than randomly taken bG measurements. For example, a bG management strategy generated based on bG measurements taken at the same daily event(s) may be more accurate than a bG management strategy generated based on various bG measurements taken under different conditions throughout the day. This is because a user's bG levels vary continuously and change with food intake, exercise, stress, etc. Measurements taken at or around the same daily event(s) can be used to provide better recommendations about what actions to take before and after those daily event(s).

Timing data 544 includes times for daily events and times/periods for other events. FIG6 includes an example illustration of the content of timing data 544. Referring now to FIG5 and FIG6, daily events may include, for example, waking up/getting up, breakfast, lunch, dinner, and bedtime. Timing data 544 thus includes data indicating a wake-up time 604 for waking up/getting up, a breakfast time 608 for breakfast, a lunch time 612 for lunch, a dinner time 616 for dinner, and a bed time 620 for bedtime.

Other events can include, for example, post-meal bG measurements and hypoglycemic tracking bG measurements. The timing data 544 therefore includes data indicating a post-meal period 624 for post-meal bG measurements and a hypoglycemic tracking period 628 for hypoglycemic tracking bG measurements. In various embodiments, the timing data 544 can include a post-meal period for breakfast, a post-meal period for lunch, and a post-meal period for dinner. A bG measurement can be considered hypoglycemic when its bG level is less than a predetermined hypoglycemic bG value. A hypoglycemic tracking bG measurement can refer to a measurement taken after a bG measurement that is considered hypoglycemic.

The wake time 604, breakfast time 608, lunch time 612, dinner time 616, bed time 620, post-meal period 624, and hypoglycemia tracking period 628 are predetermined values and may be set, for example, by the manufacturer of the diabetes management device 402. For example only, the wake time 604 may be set to approximately 7:00 AM, the breakfast time 608 may be set to approximately 8:00 AM, the lunch time 612 may be set to approximately 12:00 PM, the dinner time 616 may be set to approximately 5:00 PM, the bed time 620 may be set to approximately 9:00 PM, the post-meal period 624 may be set to approximately 2 hours, and the hypoglycemia tracking period 628 may be set to approximately 15 minutes.

The wake time 604, breakfast time 608, lunch time 612, dinner time 616, bed time 620, post-meal period 624, and/or hypoglycemia tracking period 628 can be adjusted by the user, such as via one or more buttons 424-436. The wake time 604, breakfast time 608, lunch time 612, dinner time 616, bed time 620, post-meal period 624, and/or hypoglycemia tracking period 628 can be adjusted, for example, to more accurately reflect when those events occur in the user's daily life.

The diabetes management device 402 can remind the user to enter a bG measurement for one or more events. For example only, the processor module 504 can display a reminder to enter a bG measurement on the display 408 and/or generate one or more other alerts (e.g., audible, visual, and/or tactile) to remind the user to enter a bG measurement for an event.

Reminder data 548 includes data indicating whether the user is to be reminded for each event. FIG7 includes an example illustration of the contents of reminder data 548. Referring now to FIG5-7, reminder data 548 includes wake-up enable/disable data 704 associated with/related to wake-up time 604. Wake-up enable/disable data 704 indicates whether the user is to be reminded to enter a bG measurement at wake-up time 604.

Reminder data 548 also includes breakfast enable/disable data 708 associated with/related to breakfast time 608. Breakfast enable/disable data 708 indicates whether the user is reminded to enter a bG measurement before eating breakfast at breakfast time 608. Lunch enable/disable data 712 is associated with/related to lunch time 612. Lunch enable/disable data 712 indicates whether the user is reminded to enter a bG measurement before eating lunch at lunch time 612. Dinner enable/disable data 716 is associated with/related to dinner time 616. Dinner enable/disable data 716 indicates whether the user is reminded to enter a bG measurement before eating dinner at dinner time 616. Sleep enable/disable data 720 is associated with/related to sleep time 620. Sleep enable/disable data 720 indicates whether the user is reminded to enter a bG measurement at sleep time 620.

Post-meal enable/disable data 724 is associated with/relates to the post-meal period 624. The post-meal enable/disable data 724 indicates whether the user is prompted to enter a bG measurement during the post-meal period 624 following receipt of a bG measurement result for a given meal (such as breakfast, lunch, or dinner) (or a time associated with a given meal). Hypoglycemia tracking enable/disable data 728 is associated with/relates to the hypoglycemia tracking period 628. The hypoglycemia tracking enable/disable data 728 indicates whether the user is prompted to enter a bG measurement during the hypoglycemia tracking period 628 following receipt of a bG measurement result that is considered hypoglycemia.

The waking enable/disable data 704, the breakfast enable/disable data 708, the lunch enable/disable data 712, the dinner enable/disable data 716, the sleep enable/disable data 720, the post-meal enable/disable data 724, and the hypoglycemia tracking enable/disable data 728 are predetermined values and may be set, for example, by the manufacturer of the diabetes management device 402. However, the waking enable/disable data 704, the breakfast enable/disable data 708, the lunch enable/disable data 712, the dinner enable/disable data 716, the sleep enable/disable data 720, the post-meal enable/disable data 724, and the hypoglycemia tracking enable/disable data 728 may be adjusted by a user, such as via one or more of the buttons 424-436.

Each of the wake-up enable/disable data 704, breakfast enable/disable data 708, lunch enable/disable data 712, dinner enable/disable data 716, sleep enable/disable data 720, post-meal enable/disable data 724, and low blood glucose tracking enable/disable data 728 may include other data, such as a timestamp of when the segment enable/disable data was set to disable generation of an associated reminder.

The processor module 504 selectively generates a reminder to input a bG measurement for the event at an associated time (as indicated in the timing data 544) based on whether the reminder for the event is enabled or disabled, respectively (as indicated in the reminder data 548). For example, when the wake enable/disable data 704 is set to enable the wake reminder, the processor module 504 generates a reminder to provide a wake bG measurement at the wake time 604.

When the breakfast enable/disable data 708 is set to enable a breakfast reminder, the processor module 504 generates a reminder to provide a bG measurement at the breakfast time 608. When the lunch enable/disable data 712 is set to enable a lunch reminder, the processor module 504 generates a reminder to provide a bG measurement at the lunch time 612. When the dinner enable/disable data 716 is set to enable a dinner reminder, the processor module 504 generates a reminder to provide a bG measurement at the dinner time 616. When the sleep enable/disable data 720 is set to enable a sleep reminder, the processor module 504 generates a reminder to provide a bG measurement at the sleep time 620.

When the post-meal enable/disable data 724 is set to enable a post-meal reminder, the processor module 504 generates a reminder to provide a bG measurement during the post-meal period 624 after one of the times 608-616 for the meal or after receiving a bG measurement result for the meal. When the hypoglycemia tracking enable/disable data 728 is set to enable a reminder to track a hypoglycemia bG measurement, the processor module 504 generates a reminder to provide a bG measurement during the hypoglycemia tracking period 628 after receiving a hypoglycemia bG measurement result. As described above, the reminder may include, for example, a reminder to enter a bG measurement displayed on the display 408 and/or one or more other alarms (e.g., audible, visual, and/or tactile) that remind the user to enter a bG measurement.

FIG8 is a flow chart depicting an example method for generating a reminder to measure bG for an event. While waking up will be discussed as an event, FIG8 is also applicable to generating a reminder to measure bG for other events, such as breakfast, lunch, dinner, and sleep. At 804, the processor module 504 may determine whether to enable the reminder to wake up. The processor module 504 may determine whether to enable the reminder to wake up based on the wake up enable/disable data 704. If 804 is true, control may proceed to 808. If 804 is false, control may end.

At 808, the processor module 504 retrieves the wake-up time 604 and the current time, and the processor module 504 determines whether the current time is the same as the wake-up time 604. If 808 is false, control may proceed to 809. If 808 is true, the processor module 504 generates a reminder to measure bG levels at 812, and control may end. As discussed above, the reminder may include, for example, displaying a reminder to measure bG levels on the display 408 and/or generating one or more other suitable types of reminders, such as audio, visual, and/or tactile alarms.

At 809, the processor module 504 determines whether a bG sample for the wake-up time 604 has been provided. If 809 is true, the processor module 504 refrains from generating a reminder to measure the bG level for the wake-up time 604 at 810, and control may end. If 809 is false, control may end. Although control is shown and discussed as ending, FIG8 may illustrate a control loop, and the control loop may be executed at a predetermined rate.

FIG9 is a flow chart depicting an example method for generating a reminder to measure bG after a meal (i.e., post-meal). At 904, the processor module 504 may determine whether to enable the reminder for post-meal bG measurement. The processor module 504 may determine whether to enable the reminder for post-meal bG measurement based on the post-meal enable/disable data 724. If 904 is true, control may proceed to 908. If 904 is false, control may end.

At 908, the processor module 504 may determine whether the period between the time the meal started and the current time is greater than the post-meal period 624. The current time may be stored as the time the meal started when, for example, the user inputs an indicator of the start of the meal, such as via one or more of the user input devices 514 (e.g., buttons 424-436). Additionally or alternatively, the processor module 504 may determine at 908 whether the period between the time of the bG measurement provided for the meal (the pre-meal measurement) and the current time is greater than the post-meal period 624. If 908 is false, control may proceed to 909. If 908 is true, the processor module 504 may generate a reminder to measure bG levels at 912, and control may end.

At 909, the processor module 504 determines whether a bG sample has been provided after a meal. If 909 is true, the processor module 504 refrains from generating a reminder to measure bG levels after the meal at 910, and control may end. If 909 is false, control may end. Although control is shown and discussed as ending, FIG9 may illustrate a control loop, and the control loop may be executed at a predetermined rate.

FIG10 includes an example method for generating a reminder to measure bG after providing a low blood glucose (bG) measurement. At 1008, the processor module 504 receives the bG level of a blood sample present on a bG test strip. At 1012, the processor module 504 determines whether the bG level of the blood sample is below a predetermined low blood glucose (bG) level. If 1012 is true, control may proceed to 1014. If 1012 is false, control may end.

At 1014, the processor module 504 can determine whether to enable a reminder for a follow-up measurement after a low blood glucose bG measurement. The processor module 504 can determine whether to enable a reminder for a follow-up measurement after a low blood glucose bG measurement based on the low blood glucose tracking enable/disable data 728. If 1014 is true, control can proceed to 1016. If 1014 is false, control can end.

At 1016, the processor module 504 may reset the timer. The processor module 504 may increment the timer at 1020. The timer tracks the period since the low blood glucose bG sample was received. At 1024, the processor module 504 determines whether the timer is greater than the low blood glucose tracking period 628. If 1024 is false, control may transfer to 1025. If 1024 is true, the processor module 504 may generate a reminder to measure bG at 1028, and control may end.

At 1025, the processor module 504 determines whether a bG sample has been provided after a hypoglycemic bG measurement. If 1025 is true, the processor module 504 refrains from generating a reminder to measure a bG level after the hypoglycemic measurement at 1026, and control may end. Of course, if the bG level is also hypoglycemic, the process may continue (e.g., returning to 1016). If 1025 is false, control may transfer to 1020. Although control is shown and discussed as ending, FIG10 may illustrate a control loop, and the control loop may be executed at a predetermined rate.

Prompting the user to regularly provide bG measurements can help the user provide bG measurements more consistently. More regularly provided bG measurements can be better analyzed for patterns/trends, and more regularly provided bG measurements can be used to more accurately provide recommendations about actions to take at various times of the day to stabilize the user's bG levels.

5 , the processor module 504 can analyze the reminder data 548 to determine whether to disable the reminder. When the period between when the reminder was disabled is greater than a predetermined notification period, the processor module 504 can selectively display a notification on the display 408. The notification can indicate that the diabetes management device 402 is capable of generating reminders for the event and prompt the user to provide input regarding whether to enable or disable reminders for the event.

The processor module 504 selectively updates the data 704-728 based on user input and, if the user chooses to continue disabling reminders, updates the data 704-728 based on the current date and time.For example only, the predetermined notification period may be approximately 90 days or another suitable period.

FIG11A is a flow chart illustrating an example method for notifying a user that the diabetes management device 402 is capable of providing a reminder for an event. At 1104, the processor module 504 may determine whether to disable the reminder. The processor module 504 may determine whether to disable the reminder based on the reminder data 548. If 1104 is true, control may proceed to 1108. If 1104 is false, control may end.

At 1108, the processor module 504 can determine whether the period between the date and time when the reminder was disabled and the current date and time is greater than the predetermined notification period. If 1108 is false, control can end. If 1108 is true, control can continue with 1112.

At 1112, the processor module 504 generates a notification that the diabetes management device 402 can remind the user to measure their bG level for each event. The notification can include, for example, a visual notification on the display 408. The processor module 504 can also ask the user at 1112 whether the user desires to enable reminders for the event.

At 1116, the processor module 504 may determine whether the user's response to the query indicates that the user desires to enable the reminder. If 1116 is true (indicating that the user desires to enable the reminder), the processor module 504 may update the reminder data 548 at 1120 to enable the reminder for the event, and control may end. If 1116 is false (indicating that the user desires to keep the reminder disabled), the processor module 504 may store the current date and time at 1124, and control may end. If this is not the first time the reminder has been enabled, the notification may be generated again in the future (a predetermined notification period after the stored date and time). Although control is shown and discussed as ending, FIG. 11A may illustrate a control loop, and the control loop may be executed at a predetermined rate.

FIG11B is a flow chart depicting an example method for notifying a user that the diabetes management device 402 is capable of performing pattern recognition in bG samples. At 1154, the processor module 504 may determine whether to disable pattern recognition. The user may enable and disable pattern recognition via one or more user input devices 514. Pattern recognition is described in further detail below. The processor module 504 may determine whether to disable pattern recognition based on the pattern data 560. If 1154 is true, control may proceed to 1158. If 1154 is false, control may end.

At 1158, the processor module 504 may determine whether the period between the date and time when pattern recognition was disabled and the current date and time is greater than a predetermined notification period. If 1158 is false, control may end. If 1158 is true, control may continue with 1162.

At 1162, the processor module 504 generates a notification that the diabetes management device 402 is capable of performing pattern recognition. The notification may, for example, include a visual notification on the display 408. The processor module 504 may also query the user at 1162 as to whether the user desires to enable pattern recognition.

At 1166, the processor module 504 may determine whether the user's response to the query indicates that the user desires to enable pattern recognition. If 1166 is true (indicating that the user desires to enable pattern recognition), the processor module 504 may update the pattern data 560 at 1170 to enable pattern recognition, and control may end. If 1166 is false (indicating that the user desires to keep pattern recognition disabled), the processor module 504 may store the current date and time at 1174, and control may end. If this is not the first time pattern recognition is enabled, a notification may be generated again in the future (a predetermined notification period after the stored date and time). Although control is shown and discussed as ending, FIG. 11B may illustrate a control loop, and the control loop may be executed at a predetermined rate.

Referring back to FIG. 5 , the processor module 504 further selectively classifies the bG sample data 516 into one of the following: awake measurements, pre-breakfast measurements, post-breakfast measurements, pre-lunch measurements, post-lunch measurements, pre-dinner measurements, post-dinner measurements, or bedtime measurements. Awake measurements may also be referred to as fasting measurements. However, fasting measurements may also include measurements taken when there is a period of fasting preceding the measurement. The processor module 504 classifies the bG sample data 516 based on a timestamp included in the bG sample data 516 and timing data 544 associated with the event. The processor module 504 may update the bG sample data 516 to include an indicator of the classification or may store the indicator of the classification and associate the indicator with the bG sample data 516.

The processor module 504 performs this process for each bG sample measured by the bG measurement engine 508. The processor module 504 can select bG sample data associated with one or more events based on the classification, as discussed below. The processor module 504 uses the classification rule data 552 to determine the classification for each bG sample. The classification rule data 552 includes data indicating what data to use and rules for how to classify the bG sample data.

For example, the processor module 504 may classify the bG sample data 516 as a wake measurement when the timestamp of the bG sample data 516 is within a predetermined period before the wake time 604 or within a predetermined time period around the wake time 604. The processor module 504 may classify the bG sample data 516 as a sleep time measurement when the timestamp of the bG sample data 516 is within a predetermined period after the sleep time 620 or within a predetermined time period around the sleep time 620.

When the timestamp of the bG sample data 516 is within a predetermined time window around one of the times 608-616 associated with the meal, the processor module 504 may classify the bG sample data 516 as a pre-meal measurement of the meal. For example, when the timestamp of the bG sample data 516 is within a predetermined time window around the breakfast time 608, the processor module 504 may classify the bG sample data 516 as a pre-breakfast measurement. When the timestamp of the bG sample data 516 is within a predetermined time window around the lunch time 612, the processor module 504 may classify the bG sample data 516 as a pre-lunch measurement. When the timestamp of the bG sample data 516 is within a predetermined time window around the dinner time 616, the processor module 504 may classify the bG sample data 516 as a pre-dinner measurement.

When the timestamp of the bG sample data 516 is within the post-meal period 624 after the timestamp of another piece of bG sample data classified as a pre-meal sample of a meal, the processor module 504 may classify the bG sample data 516 as a post-meal measurement of the meal. For example, when the timestamp of the bG sample data 516 is within the post-meal period 624 after the timestamp of the second piece of bG sample data classified as a pre-breakfast measurement, the processor module 504 may classify the bG sample data 516 as a post-breakfast measurement. When the timestamp of the bG sample data 516 is within the post-meal period 624 after the timestamp of the second piece of bG sample data classified as a pre-lunch measurement, the processor module 504 may classify the bG sample data 516 as a post-lunch measurement. When the timestamp of the bG sample data 516 is within the post-meal period 624 after the timestamp of the second piece of bG sample data classified as a pre-dinner measurement, the processor module 504 may classify the bG sample data 516 as a post-dinner measurement.

The processor module 504 may prompt the user to select one of a plurality of different categories for the bG sample data, and set the category for the bG sample data based on the user input. The processor module 504 may also selectively update the category determined for the bG sample data based on the user input indicating one of the categories. In this manner, the user may overwrite the category determined for the bG sample data.

The automatic classification of the bG sample data performed by the processor module 504 can ensure that the bG sample data is classified without the user providing a classification. The automatic classification of the bG sample data performed by the processor module 504 can also provide for less frequent user classifications.

The processor module 504 can determine a classification for the bG sample data 516 and store an indicator of the classification before displaying the bG level of the bG sample data 516. For example, the processor module 504 can determine the classification and store the indicator before the blood sample is applied to the test strip or after the bG level is determined but before the bG level is displayed. Classifying the bG sample data 516 and storing the indicator before displaying the bG level may make testing more sanitary because the user may not need to self-classify the bG sample data at a time when the user may or may not have blood on a finger to provide the blood sample.

The processor module 504 selectively performs statistical analysis functions based on the stored bG sample data 540. For example, when a user requests the execution of statistical analysis, the processor module 504 may perform the statistical analysis and display one or more results of the statistical analysis on the display 408.

The processor module 504 performs statistical analysis using the statistical data 556. The statistical data 556 includes data indicating which bG levels stored in the memory 532 to use and rules for performing the statistical analysis. The statistical analysis can be performed regardless of the classification of the stored bG levels used or using stored bG levels selected for one or more classifications.

The processor module 504 can, for example, determine an average of the stored bG levels. Additionally or alternatively, the processor module 504 can determine a standard deviation of the stored bG levels. Additionally or alternatively, the processor module 504 can determine a ratio or percentage of stored bG levels that are less than a predetermined low blood glucose level, a ratio or percentage of stored bG levels that are greater than a predetermined low blood glucose level, and/or a ratio or percentage of stored bG levels that are between an upper target bG level and a lower target bG level. Additionally or alternatively, the processor module 504 can determine a frequency of low blood glucose bG samples and/or a frequency of high blood glucose bG samples based on the stored bG levels.

The processor module 504 can also selectively display more detailed information for the stored bG levels used to perform the statistical analysis on the display 408. For example, the processor module 504 can selectively display the time and date, bG level, and classification of the bG sample data used in performing the statistical analysis. The processor module 504 can display more detailed information, for example, in response to user input requesting the display of more detailed information.

The processor module 504 also selectively performs pattern recognition functions based on the stored bG sample data 540. The processor module 504 may, for example, perform pattern recognition whenever a bG measurement is provided.

The processor module 504 performs pattern recognition using the pattern data 560. The pattern data 560 includes data indicating which bG sample data stored in the memory 532 to use (select) and rules for identifying patterns in the stored bG sample data. For example, pattern recognition can be performed using stored bG sample data having the same classification as the most recently received bG measurement result.

The processor module 504 selects stored bG sample data that is categorized in the same manner as the most recently received bG measurement result. The processor module 504 may, for example, select stored bG sample data that has the same category as the most recently received bG measurement result and was received within a recent predetermined period (e.g., 3-7 days). As an example of selection, when a pre-lunch sample is received, the processor module 504 may select bG sample data for blood samples categorized as pre-lunch samples received within the past 7 days.

The processor module 504 determines whether a pattern exists based on whether the selected bG sample data meets predetermined pattern criteria. For example, if more than a first predetermined number (e.g., at least three or four, when using a 7-day period) of segments of selected bG sample data (selected due to having the pre-meal classification) have bG levels greater than a first predetermined bG value, the processor module 504 may determine that a high pre-meal bG pattern exists. A meal can be breakfast, lunch, dinner, or optionally, a fourth meal, such as a snack. If more than a second predetermined number (e.g., at least two, when using a 7-day period) of segments of selected bG sample data (selected due to having the pre-meal classification) have bG levels less than a second predetermined bG value, the processor module 504 may determine that a low pre-meal bG pattern exists. As with the high pre-meal bG pattern, for the low pre-meal bG pattern, a meal can be breakfast, lunch, dinner, or optionally, a fourth meal. The first predetermined bG value is greater than the second predetermined bG value. When more than a first predetermined number (e.g., at least 3 or 4 when using a 7-day period) of selected bG sample data (selected due to having this pre-meal classification) of segments have bG levels greater than a third predetermined bG value, the processor module 504 may determine that a high post-meal bG pattern exists. A meal may be breakfast, lunch, dinner, or optionally, a fourth meal, such as a snack. When more than a second predetermined number (e.g., at least 2 when using a 7-day period) of selected bG sample data (selected due to having this pre-meal classification) of segments have bG levels less than a fourth predetermined bG value, the processor module 504 may determine that a low post-meal bG pattern exists. As with the high post-meal bG pattern, for the low post-meal bG pattern, a meal may be breakfast, lunch, dinner, or optionally, a fourth meal. The third predetermined bG value is greater than the predetermined fourth predetermined bG value. When a pattern is identified, the processor module 504 stores an indicator of the identified pattern and selected bG sample data in the memory 532 , such as in pattern data 560 , based on which bG pattern is identified.

The processor module 504 selectively displays the identified pattern on the display 408. When a pattern is identified based on the selected bG sample data, the processor module 504 displays an indication of the pattern on the display 408. For example, the processor module 504 may display an indication that a high or low pre- or post-meal pattern exists when a high or low pre- or post-meal pattern is identified.

The processor module 504 may also prompt the user to input confirmation of the identified pattern and view details of the bG sample data based on which pattern was identified. In response to the user input indicating confirmation of the identified pattern, the processor module 504 displays details of the selected bG sample data based on which pattern was identified. The details may include, for example, the date and time of the sample measurement, the bG level, the associated target bG level, and other suitable details. The processor module 504 may display details, such as a segment of bG sample data at a certain time.

12 includes a flow chart depicting an example method for identifying and displaying patterns in bG sample data. At 1202, the diabetes management device 402 receives a bG sample. The processor module 504 can determine a classification for the bG sample or a classification of the bG sample can be set based on user input indicating a classification.

At 1204, the processor module 504 selects a set of stored bG sample data. The processor module 504 can select the set of stored bG sample data based on, for example, a classification, a timestamp, and/or other suitable parameters. For example, the processor module 504 can select stored bG sample data having the same classification as the bG sample received (at 1202) and received within a predetermined period (e.g., 7 days) in the past. For clarity, it should be noted that the bG sample data associated with the bG sample received at 1202 is one of the selected segments of bG sample data.

At 1208, the processor module 504 determines whether a pattern exists based on the selected set of stored bG sample data. If 1208 is true, the processor module 504 stores an indicator of the identified pattern in the memory 532 and proceeds to 1216. If 1208 is false, control can end. At 1216, the processor module 504 displays the identified pattern on the display 408. The processor module 504 also prompts the user to provide input indicating confirmation of the identified pattern at 1216.

At 1220, the processor module 504 determines whether a user input has been received indicating confirmation that the identified pattern exists. If 1220 is false, control returns to 1216, and the processor module 504 continues to display the identified pattern. If 1220 is true, the processor module 504 may proceed to 1222. At 1222, the processor module 504 selectively displays details of the selected set of stored bG sample data, such as date and time, bG level, etc. The processor module 504 then allows the user to clear the identified pattern from the display 408 at 1224, and control may end.

5 , when the processor module 504 identifies the presence of a pattern based on stored bG sample data having a classification, the processor module 504 can identify daily events preceding the event associated with the classification. For example, when the processor module 504 identifies the presence of a high or low bG pattern based on the pre-lunch bG sample, the processor module can identify breakfast as the preceding event.

The next time a bG sample for a previous event is provided, the processor module 504 displays an indicator of the identified pattern at the event associated with the classification. For example, if a pattern is identified at breakfast following lunch, the processor module 504 may display an indicator that a high or low bG pattern was identified at the previous lunch. In the above example, the high or low bG pattern may be attributable to meal consumption at breakfast. Providing an indication of the pattern identified at breakfast may help the user adjust their breakfast to avoid having high or low pre-lunch bG levels at a later lunch.

FIG13 includes a flow chart depicting an example method for displaying a reminder of the presence of a pattern at an event preceding an everyday event based on identifying a pattern associated with the event. At 1304, the processor module 504 determines whether a pattern exists based on a selected set of stored bG sample data, as discussed above in conjunction with FIG12. As discussed above, the selected set of bG sample data has a classification and is associated with an everyday event, such as lunch. If 1304 is true, control proceeds to 1308. If 1304 is false, control may end.

At 1308, the processor module 504 determines the daily event preceding the daily event associated with the selected set of bG sample data based on which pattern was identified. For example, if the selected set of bG sample data is associated with lunch, the processor module 504 may determine that the previous daily event was breakfast. At 1312, the processor module 504 sets a flag or other indicator to indicate the presence of the identified pattern at the next instance of the previous daily event. For example, if the selected set of bG sample data is associated with lunch, the processor module 504 may set a flag or other indicator to indicate the presence of the identified pattern at the next breakfast following the lunch at which the pattern was identified.

At 1316, the processor module 504 may determine whether a next occurrence of the previous daily event (e.g., the next breakfast) exists. For example, the processor module 504 may determine whether a next occurrence of the previous daily event exists at a predetermined time associated with the previous event and/or based on user input indicating the next occurrence of the previous daily event. If 1316 is false, control may remain at 1316. If 1316 is true, control may proceed to 1318. At 1318, the display order of the identified modes may be prioritized. For example, the processor module 504 may prioritize low bG modes over high bG modes, prioritize identified modes based on the daily order of events (e.g., breakfast mode before lunch mode, etc.), and/or prioritize newer modes over older modes.

The processor module 504 displays an indicator of the identified pattern(s) associated with the next event at 1320, and control may end. For example, at the next breakfast after identifying a high or low pre-lunch bG pattern, the processor module 504 may display the presence of a high or low pre-lunch bG pattern. The user may then diligently adjust their breakfast intake to reduce or increase their pre-lunch bG level for the following lunch.

Referring back to FIG5 , the processor module 504 may also display previously identified patterns in response to a user input indicating a desire to view previously identified patterns. For example, the processor module 504 may generate a list of currently active patterns identified within a recent predetermined period and display the list of identified patterns. The predetermined period may be, for example, 7 days or another suitable period.

Based on user input selecting one of the modes from the list, the processor module 504 may display details of the stored bG sample data under the selected mode. The processor module 504 may also prioritize the order in which the identified modes are displayed. For example, the processor module 504 may display low bG modes before high bG modes, display modes for earlier daily events before modes for later daily events (e.g., breakfast modes before lunch modes), and/or display newer modes before older modes.

FIG14 includes a flow chart depicting an example method for displaying previously identified patterns in bG sample data. At 1402, the processor module 504 can determine whether user input requesting display of previously identified patterns has been received. If 1402 is true, control proceeds to 1404. If 1402 is false, control can end.

At 1404, the processor module 504 identifies previously identified patterns. For example, the processor module 504 may identify patterns identified within the last predetermined period, such as 3 months, 6 months, 9 months, 1 year, or another suitable period. At 1408, the processor module 504 prioritizes the previously identified patterns for display.

At 1412, the processor module 504 displays a list of identified patterns on the display 408 according to the priority order. Based on user input selecting one of the patterns from the list, the processor module 504 can display details of the bG sample data stored under the selected one of the patterns. The processor module 504 can display, for example, the time and date of the following bG sample, the bG level of the following bG sample, and other suitable data.

In a feature, a method for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The method includes: measuring a blood glucose (BG) level in a blood sample received by a handheld diabetes management device; storing the BG level and the time the blood sample was received in a memory; storing a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; and, in response to receiving the blood sample, selecting a group of stored BG levels that have the classification and were received within a predetermined period prior to receiving the blood sample. The method further includes: calculating a BG evaluation parameter based on the selected BG level; evaluating the BG evaluation parameter with respect to a first predetermined criterion, the first predetermined criterion including a first threshold value indicating a high BG level or a low BG level; selectively displaying an indication of identification of a pattern in the selected BG level when the BG evaluation parameter is greater than or less than the first threshold value; and removing the indication from the display only in response to receiving a predetermined user input indicating confirmation of the presence of the pattern.

In further features, the method further comprises storing the classification based on user input indicating the classification.

In still further features, the method further comprises, when reminder data associated with one of the categories is enabled, displaying a reminder to provide a blood sample at a predetermined time associated with one of the categories.

In still further features, the method further comprises updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

In further features, the method further comprises determining a classification for the blood sample based on a time of receipt of the blood sample.

In still further features, the method further comprises determining the classification of the blood sample as a fasting sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with a fasting sample.

In still further features, the method further comprises determining the classification of the blood sample as a pre-breakfast sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with breakfast.

In further features, the method further comprises determining the classification of the blood sample as a post-breakfast sample when the receipt time of the blood sample is within a predetermined period after the receipt time of a second blood sample classified as a pre-breakfast sample.

In still further features, the method further comprises determining the classification of the blood sample as a post-breakfast sample when the receipt time of the blood sample is within a predetermined period after a second receipt time of the user input indicating the start of breakfast.

In still further features, the method further comprises determining the classification of the blood sample as a pre-lunch sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with lunch.

In further features, the method further comprises determining the classification of the blood sample as a post-lunch sample when the receipt time of the blood sample is within a predetermined period after the second receipt time of a second blood sample classified as a pre-lunch sample.

In still further features, the method further comprises determining the classification of the blood sample as a post-lunch sample when the receipt time of the blood sample is within a predetermined period after a second receipt time of the user input indicating the start of lunch.

In still further features, the method further comprises determining the classification of the blood sample as a pre-dinner sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with dinner.

In further features, the method further comprises determining the classification of the blood sample as a post-dinner sample when the receipt time of the blood sample is within a predetermined period after the second receipt time of a second blood sample classified as a pre-dinner sample.

In still further features, the method further comprises determining the classification of the blood sample as a post-dinner sample when the receipt time of the blood sample is within a predetermined period after a second receipt time of the user input indicating the start of dinner.

In still further features, the method further comprises determining the classification of the blood sample as a sleep time sample when a timestamp of the blood sample is within a predetermined time window around a predetermined time associated with the sleep time sample.

In further features, the method further comprises: displaying the bG level of the blood sample; and prior to displaying the bG level, determining a classification of the blood sample.

In still further features, the method further includes determining a first daily event that precedes a second daily event associated with the classification; and displaying an indication of a pattern in bG levels at selected times associated with the first daily event during a day after receiving the blood sample.

In a feature, a handheld diabetes management device for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The handheld diabetes management device includes: a blood glucose (BG) measurement engine that measures the BG level in a blood sample input to the handheld diabetes management device; a display; a clock that tracks the current date and time; a processor; and a memory including code executed by the processor. The code is configured to: store the BG level and the time the blood sample was received in the memory; store a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; in response to receiving the blood sample, select a group of stored BG levels that have the blood sample classification and were received within a predetermined period prior to receiving the blood sample; calculate a BG evaluation parameter based on the selected BG level; evaluate the BG evaluation parameter relative to a first predetermined criterion, the first predetermined criterion including a first threshold value indicating a high BG level or a low BG level; and selectively display an indication of the identification of a pattern in the selected BG level when the BG evaluation parameter is greater than or less than the first threshold value.

In further features, the memory further comprises code for determining a classification for the blood sample based on a time of receipt of the blood sample.

In still further features, the memory further comprises code for storing a classification for the blood sample based on user input indicating the classification.

In still further features, the memory further comprises code for: displaying the bG level of the blood sample; and prior to displaying the bG level, determining a classification of the blood sample.

In further features, the memory further comprises code for displaying a reminder to provide a blood sample at a predetermined time associated with one of the categories when the reminder data associated with the one of the categories is enabled.

In still further features, the memory further comprises code for updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

In still further features, the memory further includes code for determining a first daily event preceding a second daily event associated with the classification; and displaying an indication of a pattern in bG levels at selected times associated with the first daily event during a day after receiving the blood sample.

In a feature, a method for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The method includes: measuring a blood glucose (BG) level in a blood sample received by a handheld diabetes management device; storing the BG level and the time the blood sample was received in a memory; storing a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; selecting a group of stored BG levels having one of the classifications; and generating an indicator of identification of a pattern in the selected group of BG levels when a BG evaluation parameter calculated based on the selected BG level is one of greater than a first threshold indicating a high BG level and less than a second threshold indicating a low BG level. The method further includes, in response to a user request to display the pattern identified in the BG levels: generating a list of identified patterns including the pattern in the selected BG level; prioritizing the list based on predetermined prioritization criteria; and displaying the list on a display.

In further features, the method further comprises storing the classification based on user input indicating the classification.

In still further features, the method further comprises, when reminder data associated with one of the categories is enabled, displaying a reminder to provide a blood sample at a predetermined time associated with the one of the categories.

In still further features, the method further comprises updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

In further features, the method further comprises determining a classification for the blood sample based on a time of receipt of the blood sample.

In still further features, the method further comprises determining the classification of the blood sample as a fasting sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with a fasting sample.

In still further features, the method further comprises determining the classification of the blood sample as a pre-breakfast sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with breakfast.

In further features, the method further comprises determining the classification of the blood sample as a post-breakfast sample when the receipt time of the blood sample is within a predetermined period after the second receipt time of a second blood sample classified as a pre-breakfast sample.

In still further features, the method further comprises determining the classification of the blood sample as a post-breakfast sample when the receipt time of the blood sample is within a predetermined period after a second receipt time of the user input indicating the start of breakfast.

In still further features, the method further comprises determining the classification of the blood sample as a pre-lunch sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with lunch.

In further features, the method further comprises determining the classification of the blood sample as a post-lunch sample when the receipt time of the blood sample is within a predetermined period after the second receipt time of a second blood sample classified as a pre-lunch sample.

In still further features, the method further comprises determining the classification of the blood sample as a post-lunch sample when the receipt time of the blood sample is within a predetermined period after a second receipt time of the user input indicating the start of lunch.

In still further features, the method further comprises determining the classification of the blood sample as a pre-dinner sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with dinner.

In further features, the method further comprises determining the classification of the blood sample as a post-dinner sample when the receipt time of the blood sample is within a predetermined period after the second receipt time of a second blood sample classified as a pre-dinner sample.

In still further features, the method further comprises determining the classification of the blood sample as a post-dinner sample when the receipt time of the blood sample is within a predetermined period after a second receipt time of the user input indicating the start of dinner.

In still further features, the method further comprises determining the classification of the blood sample as a sleep time sample when a timestamp of the blood sample is within a predetermined time window around a predetermined time associated with the sleep time sample.

In further features, the method further comprises: displaying the bG level of the blood sample; and prior to displaying the bG level, determining a classification of the blood sample.

In still further features, the method further includes determining a first daily event that precedes a second daily event associated with the classification; and displaying an indication of a pattern in selected bG levels at times associated with the first daily event during a day after receiving the blood sample.

In a feature, a handheld diabetes management device for classifying blood samples and identifying medically relevant patterns in a group of blood samples is disclosed. The handheld diabetes management device includes a blood glucose (BG) measurement engine that measures the BG level in a blood sample input to the handheld diabetes management device; a display; a clock that tracks the current date and time; a processor; and a memory containing code executed by the processor. The code is configured to: measure the BG level in a blood sample received by the handheld diabetes management device; store the BG level and the time the blood sample was received in the memory; store a classification of the blood sample, wherein the classification is one of: a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; select a group of stored BG levels that has one of the classifications; and generate an indicator of identification of a pattern in the selected group of BG levels when a BG evaluation parameter calculated based on the selected BG level is one of: greater than a first threshold indicating a high BG level and less than a second threshold indicating a low BG level. The code is further for, in response to a user request to display the patterns identified in the bG level: generating a list of identified patterns including the pattern in the selected bG level; prioritizing the list based on predetermined prioritization criteria; and displaying the list on the display.

In further features, the memory further comprises code for determining a classification for the blood sample based on a time of receipt of the blood sample.

In still further features, the memory further comprises code for storing a classification for the blood sample based on user input indicating the classification.

In still further features, the memory further comprises code for: displaying the bG level of the blood sample; and prior to displaying the bG level, determining a classification of the blood sample.

In further features, the memory further comprises code for displaying a reminder to provide a blood sample at a predetermined time associated with one of the categories when reminder data associated with the one of the categories is enabled.

In still further features, the memory further comprises code for updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

In still further features, the memory further includes code for determining a first daily event preceding a second daily event associated with the classification; and displaying an indication of a pattern in the selected bG levels at times associated with the first daily event during a day after receiving the blood sample.

The foregoing description is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or use. The broad teachings of this disclosure can be implemented in various forms. Therefore, although this disclosure includes specific examples, the true scope of this disclosure should not be so limited, as other modifications will become apparent upon study of the drawings, the specification, and the appended claims. As used herein, the phrase at least one of A, B, and C should be construed to mean a logical (A or B or C), using a non-exclusive logical OR. It should be understood that one or more steps within a method may be performed in a different order (or concurrently) without altering the principles of this disclosure.

In this application (including the definitions below), the term "circuit" may be used instead of "module." The term "module" may refer to, be part of, or include: an application-specific integrated circuit (ASIC); a digital, analog, or mixed analog/digital discrete circuit; a digital, analog, or mixed analog/digital integrated circuit; a combinational logic circuit; a field-programmable gate array (FPGA); a (shared, dedicated, or group) processor that executes code; a (shared, dedicated, or group) memory that stores code executed by the processor; other suitable hardware components that provide the described functionality; or a combination of some or all of the above, such as a system on a chip.

The term code as used above may include software, firmware and/or microcode, and may refer to programs, routines, functions, classes, and/or objects. The term shared processor encompasses a single processor that executes some or all code from multiple modules. The term group processor encompasses a processor that is combined with additional processors to execute some or all code from one or more modules. The term shared memory encompasses a single memory that stores some or all code from multiple modules. The term group memory encompasses memory that is combined with additional memory to store some or all code from one or more modules. The term memory may be a subset of the term computer-readable medium. The term computer-readable medium does not encompass transient electrical and electromagnetic signals that propagate through the medium, and may therefore be considered tangible and non-transitory. Non-limiting examples of non-transitory tangible computer-readable media include non-volatile memory, volatile memory, magnetic storage devices, and optical storage devices.

The apparatus and methods described herein may be implemented in part or in whole by one or more computer programs executed by one or more processors. The computer programs include processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer programs may also include and/or rely on stored data.

In the following, some embodiments of the present invention are described:

1. A method for classifying a blood sample and identifying a medically relevant pattern in a group of blood samples, the method comprising: measuring a blood glucose (bG) level in a blood sample received by a handheld diabetes management device; storing the bG level and a time of receipt of the blood sample in a memory; storing a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; in response to receiving the blood sample, selecting a group of stored bG levels having the classification of blood samples and received within a predetermined period prior to receiving the blood sample; calculating a bG evaluation parameter based on the selected bG levels; evaluating the bG evaluation parameter with respect to a first predetermined criterion, the first predetermined criterion comprising a first threshold value indicating a high bG level or a low bG level; selectively displaying an indication of identification of a pattern in the selected bG levels when the bG evaluation parameter is greater than or less than the first threshold value; and removing the indication from the display only in response to receiving a predetermined user input indicating confirmation that a pattern exists.

2. The method of embodiment 1 further comprises storing the categories based on user input indicating the categories.

3. The method of embodiment 1 further comprises, when reminder data associated with one of the categories is enabled, displaying a reminder to provide a blood sample at a predetermined time associated with the one of the categories.

4. The method of Example 3 further comprises updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

5. The method of embodiment 1 further comprises determining a category for the blood sample based on a time of receipt of the blood sample.

6. The method of embodiment 5 further comprises: determining that the classification of the blood sample is a fasting sample when the receiving time of the blood sample is within a predetermined time window around a predetermined time associated with a fasting sample.

7. The method of embodiment 5 further comprises: determining the classification of the blood sample as a pre-breakfast sample when the receiving time of the blood sample is within a predetermined time window around a predetermined time associated with breakfast.

8. The method of embodiment 7 further comprises: determining the classification of the blood sample as a post-breakfast sample when the reception time of the blood sample is within a predetermined period after the second reception time of the second blood sample classified as a pre-breakfast sample.

9. The method of embodiment 5 further comprises: determining the classification of the blood sample as a post-breakfast sample when the reception time of the blood sample is within a predetermined period after the second reception time of the user input indicating the start of breakfast.

10. The method of embodiment 5 further comprises: determining the classification of the blood sample as a pre-lunch sample when the blood sample is received at a time within a predetermined time window around a predetermined time associated with lunch.

11. The method of embodiment 10 further comprises determining the classification of the blood sample as a post-lunch sample when the reception time of the blood sample is within a predetermined period after the second reception time of the second blood sample classified as a pre-lunch sample.

12. The method of embodiment 5 further comprises: determining the classification of the blood sample as a post-lunch sample when the reception time of the blood sample is within a predetermined period after the second reception time of the user input indicating the start of lunch.

13. The method of embodiment 5 further comprises: determining the classification of the blood sample as a pre-dinner sample when the blood sample is received at a time within a predetermined time window around a predetermined time associated with dinner.

14. The method of embodiment 13 further comprises determining the classification of the blood sample as a post-dinner sample when the reception time of the blood sample is within a predetermined period after the second reception time of the second blood sample classified as a pre-dinner sample.

15. The method of embodiment 5 further comprises: determining the classification of the blood sample as a post-dinner sample when the reception time of the blood sample is within a predetermined period after the second reception time of the user input indicating the start of dinner.

16. The method of embodiment 5 further comprises: determining that the classification of the blood sample is a sleep time sample when the time stamp of the blood sample is within a predetermined time window around a predetermined time associated with the sleep time sample.

17. The method of Example 1 further comprises: displaying the bG level of the blood sample; and determining the classification of the blood sample before displaying the bG level.

18. The method of embodiment 1 further comprises: determining a first daily event preceding a second daily event associated with the category; and displaying an indication of a pattern in selected bG levels at times associated with the first daily event during a day after receiving the blood sample.

19. A handheld diabetes management device for classifying a blood sample and identifying medically relevant patterns in a group of blood samples, the handheld diabetes management device comprising: a blood glucose (bG) measurement engine that measures the bG level in a blood sample input to the handheld diabetes management device; a display; a clock that tracks the current date and time; a processor; and a memory including code executed by the processor to: store in the memory the bG level and the time the blood sample was received; and store a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample.

In response to receiving a blood sample, selecting a set of stored bG levels having the classification of the blood sample and received within a predetermined period prior to receiving the blood sample; calculating a bG evaluation parameter based on the selected bG levels; evaluating the bG evaluation parameter with respect to a first predetermined criterion, the first predetermined criterion comprising a first threshold value indicating a high bG level or a low bG level; and selectively displaying an indication of an identification of a pattern in the selected bG levels when the bG evaluation parameter is greater than or less than the first threshold value.

20. The handheld diabetes management device of embodiment 19, wherein the memory further comprises code for determining a classification for the blood sample based on a time of receipt of the blood sample.

21. The handheld diabetes management device of embodiment 19, wherein the memory further comprises code for storing a classification for the blood sample based on user input indicating the classification.

22. The handheld diabetes management device of embodiment 19, wherein the memory further comprises code for: displaying the bG level of the blood sample; and determining a classification of the blood sample before displaying the bG level.

23. The handheld diabetes management device of embodiment 19, wherein the memory further comprises code for displaying a reminder to provide a blood sample at a predetermined time associated with one of the categories when the reminder data associated with the one of the categories is enabled.

24. The handheld diabetes management device of embodiment 23, wherein the memory further comprises code for updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

25. The handheld diabetes management device of embodiment 19, wherein the memory further comprises code for: determining a first daily event preceding a second daily event associated with the classification; and displaying an indication of a pattern in selected bG levels at times associated with the first daily event during a day after receiving the blood sample.

26. A method for classifying blood samples and identifying medically relevant patterns in a group of blood samples, the method comprising: measuring a blood glucose (bG) level in a blood sample received by a handheld diabetes management device; storing the bG level and a time of receipt of the blood sample in a memory; storing a classification of the blood sample, wherein the classification is one of a fasting sample, a pre-breakfast sample, a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; selecting a group of stored bG levels having one of the classifications; generating an indicator of identification of a pattern in the selected group of bG levels when a bG evaluation parameter calculated based on the selected bG level is one of greater than a first threshold indicating a high bG level and less than a second threshold indicating a low bG level; in response to a user request to display the pattern identified in the bG levels: generating a list of patterns including the identified pattern in the selected bG level; prioritizing the list based on predetermined prioritization criteria; and displaying the list on a display.

27. The method of embodiment 26 further comprises storing the categories based on user input indicating the categories.

28. The method of embodiment 26 further comprises, when reminder data associated with one of the categories is enabled, displaying a reminder to provide a blood sample at a predetermined time associated with the one of the categories.

29. The method of embodiment 28 further comprises updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

30. The method of embodiment 26 further comprising determining a classification for the blood sample based on a time of receipt of the blood sample.

31. The method of embodiment 30 further comprises determining the classification of the blood sample as a fasting sample when the time of receipt of the blood sample is within a predetermined time window around a predetermined time associated with a fasting sample.

32. The method of embodiment 30 further comprises determining the classification of the blood sample as a pre-breakfast sample when the blood sample is received at a time within a predetermined time window around a predetermined time associated with breakfast.

33. The method of embodiment 32 further comprises determining the classification of the blood sample as a post-breakfast sample when the receiving time of the blood sample is within a predetermined period after the second receiving time of the second blood sample classified as a pre-breakfast sample.

34. The method of embodiment 30 further comprises determining the classification of the blood sample as a post-breakfast sample when the reception time of the blood sample is within a predetermined period after the second reception time of the user input indicating the start of breakfast.

35. The method of embodiment 30 further comprises determining the classification of the blood sample as a pre-lunch sample when the blood sample is received at a time within a predetermined time window around a predetermined time associated with lunch.

36. The method of embodiment 35 further comprises determining the classification of the blood sample as a post-lunch sample when the reception time of the blood sample is within a predetermined period after the second reception time of the second blood sample classified as a pre-lunch sample.

37. The method of embodiment 30 further comprises determining the classification of the blood sample as a post-lunch sample when the reception time of the blood sample is within a predetermined period after the second reception time of the user input indicating the start of lunch.

38. The method of embodiment 30 further comprises determining the classification of the blood sample as a pre-dinner sample when the blood sample is received within a predetermined time window around a predetermined time associated with dinner.

39. The method of embodiment 38 further comprises determining the classification of the blood sample as a post-dinner sample when the reception time of the blood sample is within a predetermined period after the second reception time of the second blood sample classified as a pre-dinner sample.

40. The method of embodiment 30 further comprises determining the classification of the blood sample as a post-dinner sample when the reception time of the blood sample is within a predetermined period after the second reception time of the user input indicating the start of dinner.

41. The method of embodiment 30 further comprises determining the classification of the blood sample as a sleep time sample when the time stamp of the blood sample is within a predetermined time window around a predetermined time associated with the sleep time sample.

42. The method of embodiment 26 further comprising: displaying the bG level of the blood sample; and determining the classification of the blood sample before displaying the bG level.

43. The method of Example 26 further comprises: determining a first daily event preceding a second daily event associated with the category; and displaying an indication of a pattern in selected bG levels at times associated with the first daily event during a day after receiving the blood sample.

44. A handheld diabetes management device for classifying blood samples and identifying medically relevant patterns in a group of blood samples, the handheld diabetes management device comprising: a blood glucose (bG) measurement engine that measures the bG level in a blood sample input to the handheld diabetes management device; a display; a clock that tracks the current date and time; a processor; and a memory including code executed by the processor, the code for: measuring the blood glucose (bG) level in a blood sample received by the handheld diabetes management device; storing the bG level and the time the blood sample was received in the memory; storing the classification of the blood sample, wherein the classification is a fasting sample, a pre-breakfast sample, one of a post-breakfast sample, a pre-lunch sample, a post-lunch sample, a pre-dinner sample, a post-dinner sample, and a bedtime sample; selecting a set of stored bG levels having one of the classifications; generating an indicator of recognition of a pattern in the selected set of bG levels when a bG evaluation parameter calculated based on the selected bG level is one of greater than a first threshold indicating a high bG level and less than a second threshold indicating a low bG level; in response to a user request to display the pattern recognized in the bG levels: generating a list of patterns including the recognition of the pattern in the selected bG level; prioritizing the list based on a predetermined priority criterion; and displaying the list on a display.

45. The handheld diabetes management device of embodiment 44, wherein the memory further comprises code for determining a classification for the blood sample based on a time of receipt of the blood sample.

46. The handheld diabetes management device of embodiment 44, wherein the memory further comprises code for storing a classification for the blood sample based on user input indicating the classification.

47. The handheld diabetes management device of embodiment 44, wherein the memory further comprises code for: displaying the bG level of the blood sample; and determining a classification of the blood sample before displaying the bG level.

48. The handheld diabetes management device of embodiment 44, wherein the memory further comprises code for displaying a reminder to provide a blood sample at a predetermined time associated with one of the categories when the reminder data associated with the one of the categories is enabled.

49. The handheld diabetes management device of embodiment 48, wherein the memory further comprises code for updating the reminder data based on user input indicating whether to display a reminder to provide a blood sample at a predetermined time.

50. The handheld diabetes management device of embodiment 44, wherein the memory is further configured to provide code for: determining a first daily event preceding a second daily event associated with the classification; and displaying an indication of a pattern in selected bG levels at times associated with the first daily event during a day after receiving the blood sample.

Claims (25)

1. A handheld diabetes management device for classifying blood samples and identifying medically relevant patterns in a group of blood samples, the handheld diabetes management device comprising: A blood glucose measurement engine measures the blood glucose level in a blood sample input into a handheld diabetes management device; a display screen is also included. A clock that tracks the current date and time; a processor; and a memory including code executed by the processor to configure the handheld diabetes management device to: Measure blood glucose levels in a blood sample received by a handheld diabetes management device; The blood glucose level and the blood sample reception time are stored in memory; Blood sample classification is determined based on the time of receipt. The memory stores the classification of blood samples, wherein the classification is one of fasting sample, pre-breakfast sample, post-breakfast sample, pre-lunch sample, post-lunch sample, pre-dinner sample, post-dinner sample, and sleep time sample; In response to receiving a blood sample, select the category containing the blood sample and a set of stored blood glucose levels received within a predetermined period prior to receiving the blood sample; Calculate blood glucose assessment parameters based on the selected blood glucose level; Regarding the evaluation of blood glucose assessment parameters by the first predetermined standard, the first predetermined standard includes a first threshold indicating a high or low blood glucose level; When the blood glucose evaluation parameter is greater than or less than a first threshold, an indication of pattern recognition in the selected blood glucose level is selectively displayed on the display; and The indication is removed from the display only in response to a predetermined user input confirming the presence of the indication mode. 2. The handheld diabetes management device of claim 1, wherein the memory further includes code such that the handheld diabetes management device is configured to store a classification for a blood sample based on user input instructing the classification. 3. The handheld diabetes management device of claim 1 or 2, wherein the memory further includes code that configures the handheld diabetes management device to: Displays the blood glucose level of the blood sample; and The blood sample is classified before displaying blood glucose levels. 4. The handheld diabetes management device of claim 1 or 2, wherein the memory further includes code such that the handheld diabetes management device is configured to display a reminder for providing a blood sample at a predetermined time associated with the one in the category when the reminder data associated with the one in the category is enabled. 5. The handheld diabetes management device of claim 4, wherein the memory further includes code that configures the handheld diabetes management device to update reminder data based on user input indicating whether a reminder to provide a blood sample at a predetermined time is displayed. 6. The handheld diabetes management device of claim 5, wherein the memory further includes code that configures the handheld diabetes management device to: The first daily event preceding the second daily event associated with the classification; and An indication of patterns in selected blood glucose levels at times associated with the first daily event during the day following receipt of a blood sample. 7. The handheld diabetes management device of claim 1 or 2, wherein the memory further includes code that configures the handheld diabetes management device to: When the blood glucose assessment parameter calculated based on the selected blood glucose level is greater than one of a first threshold indicating a high blood glucose level and less than a second threshold indicating a low blood glucose level, an indicator for identifying patterns in the selected set of blood glucose levels is generated; and in order to display the identified patterns in the blood glucose levels, a list of identified patterns including patterns in the selected blood glucose levels is generated; the list is arranged in priority according to a predetermined priority criterion; and the list is displayed on a display. 8. A method for classifying blood samples and identifying medically relevant patterns in a group of blood samples, the method comprising: Measure blood glucose levels in a blood sample received by a handheld diabetes management device; The blood glucose level and the blood sample reception time are stored in memory; Blood sample classification is determined based on the time of receipt. The memory stores the classification of blood samples, wherein the classification is one of fasting sample, pre-breakfast sample, post-breakfast sample, pre-lunch sample, post-lunch sample, pre-dinner sample, post-dinner sample, and sleep time sample; In response to receiving a blood sample, select the category containing the blood sample and a set of stored blood glucose levels received within a predetermined period prior to receiving the blood sample; Calculate blood glucose assessment parameters based on the selected blood glucose level; Regarding the evaluation of blood glucose assessment parameters by the first predetermined standard, the first predetermined standard includes a first threshold indicating a high or low blood glucose level; When the blood glucose assessment parameter is greater than or less than a first threshold, indicators of pattern recognition in the selected blood glucose level are selectively displayed; and The indicator is removed from the display only in response to a pre-defined user input confirming the presence of the indicator mode. 9. The method of claim 8, further comprising storing the classification based on user input indicating the classification. 10. The method of claim 8 or 9, further comprising, when an alert associated with one of the categories is enabled, displaying an alert for providing a blood sample at a predetermined time associated with said one of the categories. 11. The method of claim 10, further comprising updating reminder data based on user input indicating whether a reminder to provide a blood sample at a predetermined time is displayed. 12. The method of claim 8 or 9, further comprising determining the classification of the blood sample based on the time of receipt of the blood sample. 13. The method of claim 12, further comprising: determining that the blood sample is a fasting sample when the time of receipt of the blood sample is within a predetermined time window near a predetermined time associated with a fasting sample. 14. The method of claim 12, further comprising: determining that the blood sample is a pre-breakfast sample when the blood sample is received within a predetermined time window near a predetermined time associated with breakfast. 15. The method of claim 14, further comprising: determining that the blood sample is a post-breakfast sample when the blood sample is received within a predetermined period after the second receipt time of a second blood sample classified as a pre-breakfast sample. 16. The method of claim 12, further comprising: determining that the blood sample is a post-breakfast sample when the blood sample is received within a predetermined period after a second reception time input by a user indicating the start of breakfast. 17. The method of claim 12, further comprising: determining that the blood sample is a pre-lunch sample when the blood sample is received within a predetermined time window near a predetermined time associated with lunch. 18. The method of claim 17, further comprising: determining that the blood sample is a post-lunch sample when the blood sample is received within a predetermined period after the second receipt time of the second blood sample classified as a pre-lunch sample. 19. The method of claim 12, further comprising: determining that the blood sample is a post-lunch sample when the blood sample is received within a predetermined period after a second reception time indicated by a user inputting a start time for lunch. 20. The method of claim 12, further comprising: determining that the blood sample is classified as a pre-dinner sample when the time of receipt of the blood sample is within a predetermined time window near a predetermined time associated with dinner. 21. The method of claim 20, further comprising: determining that the blood sample is a post-dinner sample when the blood sample is received within a predetermined period after the second receipt time of a second blood sample classified as a pre-dinner sample. 22. The method of claim 12, further comprising: determining that the blood sample is a post-dinner sample when the blood sample is received within a predetermined period after a second reception time input by a user indicating the start of dinner. 23. The method of claim 12, further comprising: determining that the blood sample is a sleep time sample when the time of receipt of the blood sample is within a predetermined time window near a predetermined time associated with a sleep time sample. 24. The method of any one of claims 8, 9, 11 and 13-23, further comprising: Displays the blood glucose level of the blood sample; and The blood sample is classified before displaying blood glucose levels. 25. The method of claim 12, further comprising: The first daily event preceding the second daily event associated with the classification; and An indication of patterns in selected blood glucose levels at times associated with the first daily event during the day following receipt of a blood sample.