U.S. patent application number 17/155252 was filed with the patent office on 2021-05-13 for systems and methods for analysis of insulin regimen adherence data.
The applicant listed for this patent is Novo Nordisk A/S. Invention is credited to Tinna Bjoerk Aradottir, Henrik Bengtsson, Pete Brockmeier.
Application Number | 20210142877 17/155252 |
Document ID | / |
Family ID | 1000005355438 |
Filed Date | 2021-05-13 |
![](/patent/app/20210142877/US20210142877A1-20210513\US20210142877A1-2021051)
United States Patent
Application |
20210142877 |
Kind Code |
A1 |
Bengtsson; Henrik ; et
al. |
May 13, 2021 |
SYSTEMS AND METHODS FOR ANALYSIS OF INSULIN REGIMEN ADHERENCE
DATA
Abstract
Systems and methods are provided for monitoring adherence to an
insulin medicament dosage regimen for a subject. A data set
comprising a plurality of metabolic events the subject engaged in
within a period of time is obtained. Each respective metabolic
event comprises a timestamp of the event and a characterization
that is one of insulin regimen adherent and insulin regimen
nonadherent. A plurality of primary adherence values is calculated,
each respective adherence value representing a corresponding time
window in a plurality of time windows within the period of time.
Each time window is of a same first fixed duration. Each respective
adherence value is computed by dividing a number of insulin regimen
adherent events by a total number of events that have timestamps in
the time window corresponding to the respective adherence value.
The adherence values across the period of time are communicated
thereby monitoring adherence to the insulin regimen.
Inventors: |
Bengtsson; Henrik;
(Taastrup, DK) ; Aradottir; Tinna Bjoerk;
(Copenhagen, DK) ; Brockmeier; Pete; (Copenhagen
V, DK) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Novo Nordisk A/S |
Bagsvaerd |
|
DK |
|
|
Family ID: |
1000005355438 |
Appl. No.: |
17/155252 |
Filed: |
January 22, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16309251 |
Dec 12, 2018 |
10930382 |
|
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PCT/EP2017/065383 |
Jun 22, 2017 |
|
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17155252 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/0004 20130101;
A61B 5/4839 20130101; G16H 40/63 20180101; A61B 5/4866 20130101;
A61B 5/14532 20130101; A61B 5/7282 20130101; A61B 5/4833 20130101;
A61B 5/742 20130101; G16H 20/10 20180101; G16H 50/20 20180101 |
International
Class: |
G16H 20/10 20060101
G16H020/10; A61B 5/145 20060101 A61B005/145; A61B 5/00 20060101
A61B005/00; G16H 50/20 20060101 G16H050/20; G16H 40/63 20060101
G16H040/63 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 30, 2016 |
EP |
16177082.1 |
Claims
1. A device for monitoring adherence to a prescribed insulin
medicament dosage regimen for a subject over time, wherein the
device comprises one or more processors and a memory, the memory
storing instructions that, when executed by the one or more
processors, perform a method of: obtaining a first data set, the
first data set comprising a plurality of metabolic events the
subject engaged in, wherein the plurality of metabolic events is
within a first period of time and each respective metabolic event
in the plurality of metabolic events comprises (i) a timestamp of
the respective metabolic event and (ii) a first characterization
that is one of insulin regimen adherent and insulin regimen
nonadherent, computing a plurality of primary adherence values,
wherein each respective primary adherence value in the plurality of
primary adherence values represents a corresponding primary time
window in a plurality of primary time windows within the first
period of time, each primary time window is of a same first fixed
duration, and each respective primary adherence value in the
plurality of primary adherence values is computed by dividing a
number of insulin regimen adherent metabolic events by a total
number of metabolic events in the plurality of metabolic events
that have timestamps in the primary time window corresponding to
the respective primary adherence value; and communicating the
plurality of primary adherence values across the first period of
time thereby monitoring adherence to the prescribed insulin
medicament dosage regimen for the subject over time, wherein:
computing a plurality of secondary adherence values, wherein each
respective secondary adherence value in the plurality of secondary
adherence values represents a corresponding secondary time window
in a plurality of contemporaneously overlapping secondary time
windows within the first period of time, each respective secondary
adherence value in the plurality of secondary adherence values is
computed by dividing a number of metabolic events that are insulin
regimen adherent by a total number of metabolic events in the
plurality of metabolic events that have timestamps in the secondary
time window corresponding to the respective secondary adherence
value, and each secondary time window in at least a subset of the
secondary time windows in the plurality of secondary time windows
is of longer duration than the first fixed duration; and wherein
the communicating comprises communicating a superposition of the
plurality of primary adherence values and the plurality of
secondary adherence values across the first period of time, and
wherein: each metabolic event in the plurality of metabolic events
is a fasting event and the insulin medicament dosage regimen is a
basal insulin medicament dosage regimen, or each metabolic event in
the plurality of metabolic events is a meal event and the insulin
medicament dosage regimen is a bolus insulin medicament dosage
regimen.
2. The device of claim 1, the method further comprising:
identifying a trend in adherence to the prescribed insulin
medicament dosage regimen for the subject as a drop off in the
plurality of primary adherence values or the plurality of secondary
adherence values below a first threshold adherence value for at
least a second threshold amount of time; and reducing amounts of
insulin medicament dosage in the insulin medicament dosage regimen
for the subject when the trend is identified.
3. The device of claim 1, wherein the first fixed duration is a
week, and each respective secondary time window in the plurality of
secondary time windows represents three months in the first time
period.
4. The device of claim 3, wherein the first fixed duration is a
day, and each respective secondary time window in the plurality of
secondary time windows represents a running average from the
beginning of the first time period.
5. The device of claim 1, the method further comprising: obtaining
an HbA1c lookup table that includes a calculated HbA1c increase as
a function of adherence values in the plurality of primary
adherence values; and communicating an indication of which
respective primary adherence values in the plurality of primary
adherence values cause the calculated HbA1c increase to be over a
threshold value according to the HbA1c lookup table.
6. The device of claim 1, wherein each secondary time window is of
a same second fixed duration that is greater than the first fixed
duration.
7. The device of claim 1, wherein respective metabolic events in
the plurality of metabolic events that occur earlier than a set
cutoff time are down-weighted relative to respective metabolic
events in the plurality of metabolic events that occur after the
set cutoff time in each respective primary time window.
8. The device of claim 1, wherein respective metabolic events in
the plurality of metabolic events are down-weighted as a linear
function of time in each respective primary time window.
9. The device of claim 1, wherein the device is a mobile device
that includes a display and the communicating includes presenting
the superposition on the display.
10. The device of claim 1, the method further comprising: obtaining
a second data set, the second data set comprising a plurality of
autonomous glucose measurements of the subject and, for each
respective autonomous glucose measurement in the plurality of
autonomous glucose measurements, a glucose measurement timestamp
representing when the respective measurement was made; and the
communicating provides the plurality of autonomous glucose
measurements temporally matched to the plurality of primary
adherence values over time within the first period of time.
11. The device of claim 10, the device further comprising a
wireless receiver, and wherein the second data set is obtained
wirelessly from a glucose sensor affixed to the subject.
12. A method of monitoring adherence to a prescribed insulin
medicament dosage regimen for a subject over time, the method
comprising: obtaining a first data set, the first data set
comprising a plurality of metabolic events the subject engaged in,
wherein the plurality of metabolic events are within a first period
of time and each respective metabolic event in the plurality of
metabolic events comprises (i) a timestamp of the respective
metabolic event and (ii) a first characterization that is one of
insulin regimen adherent and insulin regimen nonadherent, computing
a plurality of primary adherence values, wherein each respective
primary adherence value in the plurality of primary adherence
values represents a corresponding primary time window in a
plurality of primary time windows within the first period of time,
each primary time window is of a same first fixed duration, and
each respective primary adherence value in the plurality of primary
adherence values is computed by dividing a number of insulin
regimen adherent metabolic events that have timestamps in the
primary time window corresponding to the respective primary
adherence value by a total number of metabolic events in the
plurality of metabolic events that have timestamps in the primary
time window corresponding to the respective primary adherence
value; and communicating the plurality of primary adherence values
across the first period of time thereby monitoring adherence to the
prescribed insulin medicament dosage regimen for the subject over
time, computing a plurality of secondary adherence values, wherein
each respective secondary adherence value in the plurality of
secondary adherence values represents a corresponding secondary
time window in a plurality of contemporaneously overlapping
secondary time windows within the first period of time, each
respective secondary adherence value in the plurality of secondary
adherence values is computed by dividing a number of metabolic
events that are insulin regimen adherent by a total number of
metabolic events in the plurality of metabolic events that have
timestamps in the secondary time window corresponding to the
respective secondary adherence value, and each secondary time
window in at least a subset of the secondary time windows in the
plurality of secondary time windows is of longer duration than the
first fixed duration; and wherein the communicating comprises
communicating a superposition of the plurality of primary adherence
values and the plurality of secondary adherence values across the
first period of time, and wherein: each metabolic event in the
plurality of metabolic events is a fasting event and the insulin
medicament dosage regimen is a basal insulin medicament dosage
regimen, or each metabolic event in the plurality of metabolic
events is a meal event and the insulin medicament dosage regimen is
a bolus insulin medicament dosage regimen.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. application Ser.
No. 16/309,251, filed Dec. 12, 2018 which is a 35 U.S.C. .sctn. 371
National Stage application of International Application
PCT/EP2017/065383 (published as WO 2018/001854), filed Jun. 22,
2017, which claims priority to European Patent Application
16177082.1, filed Jun. 30, 2016, the contents of all above-named
applications are incorporated herein by reference.
TECHNICAL FIELD
[0002] The present disclosure relates generally to systems and
methods for assisting patients and health care practitioners in
monitoring adherence to prescribed insulin medicament dosage
regimens and for suggesting which improvements to regimen adherence
will favorably affect glucose levels.
BACKGROUND
[0003] Type 2 diabetes mellitus is characterized by progressive
disruption of normal physiologic insulin secretion. In healthy
individuals, basal insulin secretion by pancreatic .beta. cells
occurs continuously to maintain steady glucose levels for extended
periods between meals. Also in healthy individuals, there is
prandial secretion in which insulin is rapidly released in an
initial first-phase spike in response to a meal, followed by
prolonged insulin secretion that returns to basal levels after 2-3
hours.
[0004] Insulin is a hormone that binds to insulin receptors to
lower blood glucose by facilitating cellular uptake of glucose,
amino acids, and fatty acids into skeletal muscle and fat and by
inhibiting the output of glucose from the liver. In normal healthy
individuals, physiologic basal and prandial insulin secretions
maintain euglycemia, which affects fasting plasma glucose and
postprandial plasma glucose concentrations. Basal and prandial
insulin secretion is impaired in Type 2 diabetes and early
post-meal response is absent. To address these adverse events,
patients with Type 2 diabetes are provided with insulin treatment
regimens. Patients with Type 1 diabetes are also provided with
insulin treatment regimens.
[0005] Some diabetic patients only need a basal insulin treatment
regimen to make up for deficiencies in pancreatic .beta. cells
insulin secretion. Some patients need both basal insulin treatment
and bolus insulin treatment. Thus, patients that require both basal
insulin treatment and bolus insulin treatment take a periodic basal
insulin medicament treatment, for instance once or twice a day, as
well as one or more bolus insulin medicament treatments with
meals.
[0006] The goal of these insulin treatment regimens is to achieve
steady glucose levels. The success of an insulin treatment regimen
in a subject can be deduced by taking continuous glucose level
measurements of a subject or by measuring HbA1c levels. The term
"HbA1c" refers to glycated haemoglobin. It develops when
haemoglobin, a protein within red blood cells that carries oxygen
throughout the body, joins with glucose in the blood, thus becoming
"glycated." By measuring glycated haemoglobin (HbA1c), health care
practitioners are able to get an overall picture of average glucose
levels over a period of weeks/months. For people with diabetes, the
higher the HbA1c, the greater the risk of developing
diabetes-related complications
[0007] Insulin treatment regimen nonadherence is a barrier for
diabetes patients to reaching suitable HbA1c goals. Insulin regimen
adherence is typically defined as the degree to which a patient
correctly follows medical advice (e.g., a standing insulin regimen
for a subject comprising at least a basal insulin medicament dosage
regimen), but can also be, for example, consistency in diet and
exercise. The reasons for nonadherence are many and different. One
reason for nonadherence is poor health literacy and comprehension
of treatment. Patients fail to understand glucose measurement
results, lack positive feedback when adherent, or feel a lack of
urgency. Another reason for nonadherence is the fear of side
effects. For instance, the fear of hypoglycemia if the patient
strictly adheres to the standing insulin regimen. Yet another
reason for nonadherence is the hassle and time-consuming aspect of
conventional standing insulin regimens, which often entail
home-logging data and frequent injections and glucose measurements.
Still another reason for nonadherence is an inability to pinpoint
the source of nonadherence that is the actual source of the adverse
effect on stable glucose levels.
[0008] International Publication Number WO 2012/152295 A2 to
Insulin Medical Ltd. optimizes insulin absorption by using one or
more sensors and actuators configured to provide data relating to a
user's meal status, meal timing, the timing of administered drug,
drug dose, drug type, the logging of user activity, and the
analysis thereof. For instance, WO 2012/152295 A2 discloses a
device that may be placed over an injection site or an injection
port to treat the tissue at the injection site, while collecting
information on the injected drug at the time of injections with an
option to provide feedback to the user, such as alerts on missed
injections. WO 2012/152295 A2 further discloses using meal data and
other subject data, such as the activity of the subject, to
facilitate mapping the subject activity relative to injection
events and optionally meal events to provide for fine control of
the systemic metabolic process of glucose and insulin and therefore
minimize occurrence of post prandial hyperglycemic and hypoglycemic
events. However, WO 2012/152295 A2 fails to provide satisfactory
ways to determine and quantify the effects of insulin regimen
adherence, or lack thereof, on the health of a subject (e.g.,
glucose levels of the subject) or to provide guidance on what forms
of insulin regimen adherence would benefit a subject. In essence,
WO 2012/152295 A2 fails to provide satisfactory ways to pinpoint
precisely what forms of regimen nonadherence are most adversely
affecting glucose levels. Moreover, generally, WO 2012/152295 A2
fails to provide overall feedback on the subject's adherence to an
insulin medicament regimen. Further, the meal detection in WO
2012/152295 A2 is not based upon autonomous glucose measurements
and thus the reliability of the meal detection in WO 2012/152295 A2
is uncertain.
[0009] International Publication Number WO 2014/037365 A1 to Roche
Diagnostics GMBH describes methods and apparatuses for analyzing
blood glucose data and events, and, in particular, to computer
implemented methods for visualizing correlations between blood
glucose data and events associated with the blood glucose data such
as meals. However, WO 2014/037365 A1 fails to disclose any
categorization of meals in terms of insulin regimen adherence.
Further, WO 2014/037365 A1 A2 fails to provide satisfactory ways in
which to determine and quantify the effects of insulin regimen
adherence, or lack thereof, on the health of a subject or to
provide guidance on what forms of insulin regimen adherence would
benefit a subject.
[0010] US Publication Number US 2015/0006462 A1 describes a system
for managing a patient's medical adherence, wherein the system is
adapted for performing a method comprising receiving data related
to a patient, the data including information related to a
prescribed medication regimen having one or more medications,
patient behavior data, a respective literacy level associated with
each of the one or more medications. The method further comprises
calculating a compliance to dosage and a compliance to time for
each of the one or more medications based on the received data.
Compliance to dosage, can for example be calculated as if a patient
was prescribed 10 units of a medication in a day and took only 8
units, the respective compliance to disease is obtained by dividing
the amount of units actually consumed by the prescribed units. In
this instance it would be 0.8. Compliance to time, can for example
be calculated as follows. For ten dosages prescribed throughout the
day, the Boolean values may be utilized to calculate an overall
value for the day. For example, if 8 out of 10 actual consumption
times for a particular medication complied with the prescribed
dosage times, then the actual consumption time would be assigned a
"1" for those 8 instances and a "0" would be assigned for the other
two instances. Accordingly a compliance value of 0.8 may be
calculated for the compliance to time for that particular medicine
by dividing the overall Boolean value with the total instances. The
method further comprises calculating a drug adherence count
associated with each of the one or more medications by summing at
least two of the compliance to dosage, compliance to time and the
respective literacy level associated with each of the one or more
medications. The literacy level is a metric to assess the
familiarity of a patient with a prescribed regimen and its
medications, and it may be impacted based on occurrence of a
condition based on lack of medical adherence by a patient, e.g.,
effects based on user behaviour, such as lowering of blood sugar
level due to missing medication. The method further comprises
determining a daily medication adherence value and a daily
medication adherence baseline value, and a threshold based on the
ratio between the two values. The threshold can be used to
determine whether an intervention is required. However, US
2015/006462 A1 fails to disclose how to automatically obtain
metabolic events that are related to the prescribed insulin
medicament dosage regimen, and thereby fails to systematically
monitor adherence for a subject engaged in such metabolic events as
a part of the daily routines. In fact US 2015/0006462 A1 suggests a
generic method for any medicament, where it is assumed that the
prescribed dose events are independent of the users behavior, e.g.,
10 units or 10 doses are taken during a day or at prescribed times.
Such a method would fail to track adherence, where the number of
bolus injection events may vary due to user behavior, e.g., the
user have more meals than expected. In general, US 2015/006462 A1
does not solve the problem of how to systematically allow tracking
of adherence based on well defined reference points in time, and is
limited to track adherence within the boundaries specified by
periods, where the beginning of the period and the end of the
period is pre-defined in relation to the structure of a calendar,
e.g., 10 units during a day.
[0011] A drawback of adherence algorithms based on calendar periods
can be explained by considering an example for a basal insulin
dosage regimen specifying 1 bolus injection per day, in combination
with an adherence tracking algorithm based on a calendar day of 24
hours. The calendar day starts at midnight. On the first day of the
example basal insulin is injected at 23:00 PM, on the second day
basal insulin is omitted, but on the third day basal insulin is
injected at 00:30 AM, and 23:00 PM. In that case an adherence
algorithm based on a 24 hours calendar day would characterize day 1
as in-adherence but day 2 and 3 as nonadherent. Three injections
were applied with some degree of regularity, but only 1 out of
three days were categorized in adherence. Although US 2015/006462
suggests that adherence can be a function of a time delay, this
functional relation is only possible, if at well defined reference
time is established, as is the case in the described example where
insulin is to be injected a 2 PM with an expectation of a meal to
be consumed at 2:30. However, as mentioned previously user
behaviour does not always follow expectations and there can be
drawbacks associated with the use of expectations for reference
times, and as also mentioned there can be drawbacks associated with
only using calendar days to establish a measure of basal
adherence.
[0012] International Publication Number WO 2010/149388 A2 to Roche
Diagnostics GMBH describes a method of measuring adherence to
following or achieving prescribed therapy steps to achieve stated
target goals for improved chronic disease self-management. The
method comprises defining a plurality of adherence units, each
adherence unit containing a plurality of rules governing activities
which need to be accomplished in order to complete the prescribed
therapy steps; collecting data when the activities are accomplished
specifying a time window of interest in the collected data;
determining total number of adherence units in the collected data
which fall within the specified time window of interest; counting
each of the adherence units in the specified time window of
interest as an adhered unit when the collected data indicates the
accomplished activities were in accordance to the rules;
determining adherence as a percentage of the count for the adhered
units to the total number of adherence units for the specified time
window; and providing at least one of the determined adherence
percentage and adherence count for the specified time window. The
publication further describes, a computer program, when running on
a processing device, instructing the processing device to collect
the data regarding an individual's activities per the prescribed
(i.e., inputted and selected) protocol(s). The information
regarding each activity is captured by the processing device by the
computer program instructing the processing device to prompt the
individual via the user interface or other suitable output hardware
and to accept user inputs providing the information. The computer
program then stores the inputted information in a memory of the
processing device as collected data. In one embodiment, the
computer program annotates the collected data regarding the
protocol and/or activity, such as with a timestamp of start and
completion, contextual information, and other relevant quantified
and subjective data. Recording of the activity and managing the
associated information via the above mentioned data collection
processes enables such data to be analyzed in order to provide an
assessment of an individuals adherence level. In particular, via
the data collection processes, the data information and
associations are captured within the memory of the processing
device (or a database) such that the recorded sequence of
activities has no ambiguity. The collected data is then utilized in
later steps for extracting relevant subsets of data, applying
adherence rules, and providing a number either as a ratio or in
percentage format or an equivalent which indicates the extent to
which adherence is accomplished. Even though an activity unit is
generally of finite duration, the start of activity is considered
as the absolute time for the activity unit 16. For example, a
breakfast activity time is the time at which the breakfast activity
unit is initiated. If the breakfast activity consists of a number
of activity steps, such as for example, estimating carbohydrates in
the breakfast meal, followed by measuring blood glucose (bG),
followed by computation of insulin dose, followed by eating of the
breakfast meal, followed by a 2-hour post-prandial measuring of bG,
then the breakfast activity is timed as per preference or choice
for marking the activity as preferably suggested by physician, so
for example when the individual starts the estimation of the
carbohydrate in the breakfast. As appears, WO 2010/149388 A2 relies
on collecting data by prompting a user, and the collected data,
therefore, comprises user input activities. WO 2010/149388 does not
solve the problem of measuring adherence of a metabolic activity
relevant to the prescribed regimen in situations where a user
forgets to input when prompted, is unable to answer when prompted
or for some reason inputs wrong data to the memory when prompted,
and it does not solve the problem of directly monitoring adherence
based on a metabolic activity that a user has engaged in, and not
merely intends to engage in, or have engaged in a while ago. In
other words the timing between user input activities and the
metabolic activity relevant for monitoring adherence is subject to
uncertainty. Given the above background, what is needed in the art
are systems and methods that provide satisfactory ways to pinpoint
what forms of regimen nonadherence are adversely affecting glucose
levels in diabetic patients.
[0013] The object of the present disclosure is to provide systems
and methods for reliably monitoring and communicating insulin
regimen adherence and to pinpoint what forms of regimen
nonadherence are adversely affecting glucose levels in diabetic
patients.
SUMMARY
[0014] In the disclosure of the present invention, embodiments and
aspects will be described, which will address one or more of the
above objects or which will address objects apparent from the below
disclosure as well as from the description of exemplary
embodiments.
[0015] The present disclosure addresses the above-identified need
in the art by providing methods and apparatus for assisting
patients and health care practitioners in managing insulin delivery
to diabetic patients. Using the systems and method of the present
disclosure, patients or health care practitioners can determine
what form of regimen nonadherence most adversely affects glucose
levels. For instance, using the systems and methods of the present
disclosure, the effect of noncompliant meals, fasting events, bolus
injections, or basal injections on glucose levels is ascertained
and can be used to pinpoint what forms of regimen nonadherence are
adversely affecting glucose levels.
[0016] Thus, the present disclosure relates to the computation,
processing, and visualization of prescribed insulin medicament
dosage regimen adherence data that provides a patient and/or a
health care practitioner with the ability to monitor adherence to
insulin treatment and thereby the ability to pinpoint to which
degree and how adherence affects the regimen treatment results.
[0017] In one aspect of the present disclosure, systems and methods
are provided for monitoring adherence to an insulin medicament
dosage regimen for a subject. A data set comprising a plurality of
metabolic events (e.g., periods of fasting, meals, etc.) the
subject engaged in within a period of time (e.g., the past week,
the past two weeks, the past month, etc.) is obtained. Each
respective metabolic event comprises a timestamp of the event and a
characterization that is one of insulin regimen adherent and
insulin regimen nonadherent. A plurality of primary adherence
values is calculated. Each respective primary adherence value
represents a corresponding primary time window in a plurality of
primary time windows within the period of time. Each such primary
time window is of a same first fixed duration (e.g. 24 hours). Each
respective primary adherence value is computed by dividing a number
of insulin regimen adherent events that have timestamps in the
primary time window corresponding to the respective primary
adherence value by a total number of events that have timestamps in
the primary time window corresponding to the respective primary
adherence value. The primary adherence values across the period of
time are communicated (e.g., displayed on a screen, sent to a
remote server, input into a regimen analysis program) thereby
monitoring adherence to the insulin regimen.
[0018] Hereby is provided a system and method which establishes
adherence monitoring based on metabolic events, which the subject
actually engaged in, and thereby eliminates the risk of user
behaviour not always follows expectations. The system and the
method solves the problem of how to systematically allow tracking
of adherence based on well defined reference points in time. As the
data set only comprises metabolic events that the subject engaged
in, the system and the method does not rely on input on a user
response, and it thereby solves the problem of prior art. As the
data set comprises timestamps for each metabolic event, which the
subject engaged in the adherence is monitored with a high degree of
uncertainty. The use of data comprising metabolic events that the
subject actually engaged in for the purpose of monitoring adherence
has not been previously used or described, nor has the importance
of using such data in order to minimize uncertainty of the
monitored adherence.
[0019] In a further aspect, the timestamp of the metabolic event is
derived from autonomously timestamped measurements of an indicator
of the metabolic event.
[0020] In a further aspect, the timestamp of the metabolic event is
derived from autonomous timestamped glucose measurements, wherein
the glucose measurements is an indicator of the metabolic event,
i.e., the glucose measurement is a measurement of the glucose
concentration in the blood stream.
[0021] In a further aspect, the timestamp of the metabolic event is
derived from autonomous timestamped glucagon, lipids or amino acids
measurements, wherein the glucagon, lipids or amino acid
measurements are indicators of the metabolic event, i.e., the
measurements are measurements of the concentration of the
respective molecules in the blood stream.
[0022] In a further aspect, autonomous measurements are
measurements obtained by a measuring device, wherein the measuring
is undertaken or carried on without outside control of a user.
[0023] Hereby is provided data that do not rely on input controlled
by the subject or an operator of the device.
[0024] In a further aspect, autonomous measurements are
measurements obtained by a device measuring at a specified or a
variable frequency.
[0025] In a further aspect, the method further comprises: computing
a plurality of secondary adherence values (236), wherein each
respective secondary adherence value in the plurality of secondary
adherence values represents a corresponding secondary time window
in a plurality of contemporaneously overlapping secondary time
windows within the first period of time, each respective secondary
adherence value in the plurality of secondary adherence values is
computed by dividing a number of metabolic events that are insulin
regimen adherent by a total number of metabolic events in the
plurality of metabolic events that have timestamps in the secondary
time window corresponding to the respective secondary adherence
value, and each secondary time window in at least a subset of the
secondary time windows in the plurality of secondary time windows
is of longer duration than the first fixed duration; and wherein
the communicating comprises communicating a superposition of the
plurality of primary adherence values and the plurality of
secondary adherence values across the first period of time.
[0026] In a further aspect, the method further comprises:
identifying a trend in adherence to the prescribed insulin
medicament dosage regimen for the subject as a drop off in the
plurality of primary adherence values or the plurality of secondary
adherence values below a first threshold adherence value for at
least a second threshold amount of time; and reducing amounts of
insulin medicament dosage in the insulin medicament dosage regimen
for the subject when the trend is identified.
[0027] In a further aspect, each metabolic event in the plurality
of metabolic events is a fasting event and the insulin medicament
dosage regimen is a basal insulin medicament dosage regimen.
[0028] In a further aspect, the first fixed duration is a week, and
each respective secondary time window in the plurality of secondary
time windows represents three months in the first time period.
[0029] In a further aspect, each metabolic event in the plurality
of metabolic events is a meal event and the insulin medicament
dosage regimen is a bolus insulin medicament dosage regimen.
[0030] In a further aspect, the first fixed duration is a day, and
each respective secondary time window in the plurality of secondary
time windows represents a running average from the beginning of the
first time period.
[0031] In a further aspect, the method further comprises: obtaining
an HbA1c lookup table that includes a calculated HbA1c increase as
a function of adherence values in the plurality of primary
adherence values; and communicating an indication of which
respective primary adherence values in the plurality of primary
adherence values cause the calculated HbA1c increase to be over a
threshold value according to the HbA1c lookup table.
[0032] In a further aspect, each secondary time window is of a same
second fixed duration that is greater than the first fixed
duration.
[0033] In a further aspect, the respective metabolic events in the
plurality of metabolic events that occur earlier than a set cutoff
time are down-weighted relative to respective metabolic events in
the plurality of metabolic events that occur after the set cutoff
time in each respective primary time window.
[0034] In a further aspect, the respective metabolic events in the
plurality of metabolic events are down-weighted as a linear
function of time in each respective primary time window.
[0035] In a further aspect, the device is a mobile device that
includes a display (282) and the communicating includes presenting
the superposition on the display.
[0036] In a further aspect, the method further comprises: obtaining
a second data set, the second data set comprising a plurality of
autonomous glucose measurements of the subject and, for each
respective autonomous glucose measurement in the plurality of
autonomous glucose measurements, a glucose measurement timestamp
representing when the respective measurement was made.
[0037] In a further aspect, the communicating provides the
plurality of autonomous glucose measurements temporally matched to
the plurality of primary adherence values over time within the
first period of time.
[0038] In a further aspect, the device further comprising a
wireless receiver, and wherein the second data set is obtained
wirelessly from a glucose sensor affixed to the subject.
[0039] In a further aspect, the method comprises: obtaining a third
data set from one or more insulin pens used by the subject to apply
the insulin medicament dosage regimen, the third data set comprises
a plurality of insulin medicament records, each insulin medicament
record in the plurality of medicament records comprising: (i) a
respective insulin medicament injection event including an amount
of insulin medicament injected into the subject using a respective
insulin pen in the one or more insulin pens and (ii) a
corresponding electronic timestamp that is automatically generated
by the respective insulin pen upon occurrence of the respective
insulin medicament injection event; identifying the plurality of
metabolic events using the plurality of autonomous glucose
measurements of the subject and the respective timestamps in the
second data set; applying a first characterization to each
respective metabolic event in the plurality of metabolic events,
wherein the first characterization is one of insulin regimen
adherent and insulin regimen nonadherent, a respective metabolic
event is deemed basal regimen adherent when the second data set
includes one or more medicament records that establish, on a
temporal and quantitative basis, adherence with the insulin
medicament dosage regimen during the respective metabolic event,
and a respective metabolic event is deemed insulin regimen
nonadherent when the second data set fails to include one or more
medicament records that establish, on a temporal and quantitative
basis, adherence with the insulin medicament dosage regimen.
[0040] In a further aspect, the method comprises: obtaining a third
data set from one or more insulin pens used by the subject to apply
the insulin medicament dosage regimen, the third data set comprises
a plurality of insulin medicament records, each insulin medicament
record in the plurality of medicament records comprising: (i) a
respective insulin medicament injection event including an amount
of insulin medicament injected into the subject using a respective
insulin pen in the one or more insulin pens and (ii) a
corresponding electronic timestamp that is automatically generated
by the respective insulin pen upon occurrence of the respective
insulin medicament injection event; identifying the plurality of
fasting events using the plurality of autonomous glucose
measurements of the subject and the respective timestamps in the
second data set; applying a first characterization to each
respective fasting event in the plurality of fasting events,
wherein the first characterization is one of insulin regimen
adherent and insulin regimen nonadherent, a respective fasting
event is deemed basal regimen adherent when the second data set
includes one or more medicament records that establish, on a
temporal and quantitative basis, adherence with the insulin
medicament dosage regimen during the respective fasting event, and
a respective fasting event is deemed insulin regimen nonadherent
when the second data set fails to include one or more medicament
records that establish, on a temporal and quantitative basis,
adherence with the insulin medicament dosage regimen during the
respective fasting event.
[0041] In a further aspect the medicament record further comprises
a type of insulin medicament, and wherein, a respective fasting
event is deemed insulin regimen adherent when one or more
medicament records in the plurality of medicament records further
indicates in the third data set, on a type of insulin medicament
basis, adherence with the standing insulin medicament dosage
regimen during the respective fasting event, and a respective
fasting event is deemed insulin regimen nonadherent when the
plurality of medicament records in the third data set further fails
to indicate adherence, on a type of insulin medicament basis with
the insulin medicament dosage regimen during the respective fasting
period.
[0042] In a further aspect the insulin regimen adherent is defined
basal regimen adherent, and insulin regiment nonadherent is defined
basal regimen nonadherent.
[0043] In a further aspect, the method comprises: obtaining a third
data set from one or more insulin pens used by the subject to apply
the insulin medicament regimen, the third data set comprises a
plurality of insulin medicament records, each insulin medicament
record in the plurality of medicament records comprising: (i) a
respective insulin medicament injection event including an amount
of insulin medicament injected into the subject using a respective
insulin pen in the one or more insulin pens and (ii) a
corresponding electronic timestamp that is automatically generated
by the respective insulin pen upon occurrence of the respective
insulin medicament injection event; the method further comprises
identifying a plurality of meal events using the plurality of
autonomous glucose measurements and the corresponding timestamps in
the second data set; applying a second characterization to each
respective meal event in the plurality of meal events, wherein the
second characterization is one of insulin regimen adherent and
insulin regimen nonadherent, a respective meal event is deemed
insulin regimen adherent when one or more medicament records in the
plurality of medicament records indicates in the third data set, on
a temporal basis, a quantitative basis, adherence with the insulin
medicament dosage regimen during the respective meal, and a
respective meal is deemed insulin regimen nonadherent when the
plurality of medicament records in the third data set fails to
indicate adherence, on a temporal basis, and a quantitative basis
with the insulin medicament dosage regimen during the respective
meal.
[0044] In a further aspect the medicament record further comprises
a type of insulin medicament, and wherein, a respective meal event
is deemed insulin regimen adherent when one or more medicament
records in the plurality of medicament records further indicates in
the third data set, on a type of insulin medicament basis,
adherence with the insulin medicament dosage regimen during the
respective meal, and a respective meal is deemed insulin regimen
nonadherent when the plurality of medicament records in the third
data set further fails to indicate adherence, on a type of insulin
medicament basis with the insulin medicament dosage regimen during
the respective meal.
[0045] In a further aspect the insulin regimen adherent is defined
as bolus regimen adherent, and insulin regiment nonadherent is
defined as bolus regimen nonadherent.
[0046] In a further aspect, the metabolic events are automatically
obtained from measurement relating to a body function indicating a
metabolic event like chewing or swallowing. Depending on the
intensity chewing or swallowing may be an indication of a meal
event.
[0047] In a further aspect, the metabolic events are inherently
timestamped, i.e., the timestamp of the metabolic event is a direct
consequence of the occurrence of the metabolic event and the
timestamp is acquired in response to this occurrence.
[0048] Hereby is provided a system ensuring that adherence is
monitored with respect to metabolic events that the subject has
engaged in, and as the metabolic event is timestamped there is
provided a well defined reference in time, allowing the
characterization of adherence to utilize the timestamp.
[0049] In a further aspect, the timestamp relating to a respective
metabolic event is used as a starting point for examining whether
the metabolic event is insulin regimen adherent or insulin regimen
nonadherent.
[0050] In a further aspect, wherein the metabolic events are
fasting event, the fasting events are identified using the
autonomous timestamped glucose measurements of the subject.
[0051] In a further aspect, wherein the metabolic events are meal
events, the meal events are identified using the autonomous
timestamped glucose measurements.
[0052] In a further aspect, metabolic events can be a metabolic
event defined in the medicament regimen, which can be automatically
identified from a device continuously measuring an indicator of an
event relating to a metabolic state of the subject, whereby the
device allows the metabolic event to be timestamped and to be
characterized with respect to the medicament regimen as regimen
adherent or regimen nonadherent. For example, a metabolic event
defined according to the medicament regimen could be a meal event,
wherein the medicament regimen determines that bolus insulin should
be administered based on glucose measurements relating to this
event, or it could be a fasting event, wherein the medicament
regimen determines that basal insulin should be administered based
on glucose measurements relating to this event.
[0053] In some embodiments the characterization of a metabolic
event as insulin regimen adherent can be determined as a degree or
percentage of insulin regimen adherent depending on the estimated
glycemic effect of taking a dose later than recommended according
to the insulin medicament dosage regimen or taking an amount of
dose below or above a recommended dose.
[0054] In another aspect of the present disclosure, a computer
program is provided comprising instructions that, when executed by
one or more processors, perform a method comprising: [0055]
obtaining a first data set, the first data set comprising a
plurality of metabolic events the subject engaged in, wherein the
plurality of metabolic events are within a first period of time and
each respective metabolic event in the plurality of metabolic
events comprises (i) a timestamp of the respective metabolic event
and (ii) a first characterization that is one of insulin regimen
adherent and insulin regimen nonadherent, [0056] computing a
plurality of primary adherence values, wherein [0057] each
respective primary adherence value in the plurality of primary
adherence values represents a corresponding primary time window in
a plurality of primary time windows within the first period of
time, [0058] each primary time window is of a same first fixed
duration, and [0059] each respective primary adherence value in the
plurality of primary adherence values is computed by dividing a
number of insulin regimen adherent metabolic events that have
timestamps in the primary time window corresponding to the
respective primary adherence value by a total number of metabolic
events in the plurality of metabolic events that have timestamps in
the primary time window corresponding to the respective primary
adherence value; and [0060] communicating the plurality of primary
adherence values across the first period of time thereby monitoring
adherence to the prescribed insulin medicament dosage regimen for
the subject over time.
[0061] In a further aspect is provided a computer-readable data
carrier having stored thereon the computer program.
BRIEF DESCRIPTION OF THE DRAWINGS
[0062] FIG. 1 illustrates an exemplary system topology that
includes a regimen adherence monitor device for monitoring
adherence to a prescribed insulin medicament dosage regimen for a
subject over time, a regimen adherence assessor device for
analyzing and preparing regimen adherence data, one or more glucose
sensors that measure glucose data from the subject, and one or more
insulin pens or pumps that are used by the subject to inject
insulin medicaments in accordance with the prescribed insulin
medicament dosage regimen, where the above-identified components
are interconnected, optionally through a communications network, in
accordance with an embodiment of the present disclosure.
[0063] FIG. 2 illustrates a device for monitoring adherence to a
prescribed insulin medicament dosage regimen for a subject over
time in accordance with an embodiment of the present
disclosure.
[0064] FIG. 3 illustrates a device for monitoring adherence to a
prescribed insulin medicament dosage regimen for a subject over
time in accordance with another embodiment of the present
disclosure.
[0065] FIGS. 4A, 4B, and 4C collectively provide a flow chart of
processes and features of a device for monitoring adherence to a
prescribed insulin medicament dosage regimen for a subject over
time in accordance with various embodiments of the present
disclosure.
[0066] FIG. 5 illustrates an example integrated system of connected
insulin pen(s), continuous glucose monitor(s), memory and a
processor for performing algorithmic categorization of autonomous
glucose data in accordance with an embodiment of the present
disclosure.
[0067] FIG. 6 illustrates the temporal relationship between primary
adherence values, primary time windows, metabolic events, secondary
adherence values and secondary time windows in accordance with an
embodiment of the present disclosure.
[0068] FIG. 7 illustrates the communication of a superposition of
primary adherence values and secondary adherence values across a
first period of time in accordance with an embodiment of the
present disclosure.
[0069] FIG. 8A illustrates the communication of a superposition of
primary adherence values and secondary adherence values across a
first period of time, where each of the primary adherence values
represents weekly basal adherence, and each of the secondary
adherence values is a running unweighted average adherence over a
number of weeks, in accordance with an embodiment of the present
disclosure.
[0070] FIG. 8B illustrates the communication of a superposition of
primary adherence values and secondary adherence values across a
first period of time, where each of the primary adherence values
represents weekly basal adherence, and each of the secondary
adherence values is a running average adherence, weighted linearly
with time over a number of weeks, in accordance with an embodiment
of the present disclosure.
[0071] FIG. 8C illustrates the communication of a superposition of
primary adherence values and secondary adherence values across a
first period of time, where each of the primary adherence values
represents weekly basal adherence, and each of the secondary
adherence values is a running average adherence, weighted such that
the past four weeks are weighted 100% and weeks prior to the past
four weeks are weighted fifty percent, in accordance with an
embodiment of the present disclosure.
[0072] FIG. 9 illustrates an example of how the adherence effect on
HbA1c, for bolus regimen adherence data, is communicated in
accordance with an embodiment of the present disclosure.
[0073] FIG. 10 illustrates an example of how the adherence effect
on HbA1c, for basal regimen adherence data, is communicated in
accordance with an embodiment of the present disclosure.
[0074] FIG. 11 illustrates an example of how bolus adherence is
communicated in accordance with another embodiment of the present
disclosure.
[0075] FIG. 12 illustrates an example of how basal adherence is
communicated in accordance with another embodiment of the present
disclosure.
[0076] FIG. 13 illustrates the temporal relationship between
primary adherence values, primary time windows, metabolic events,
secondary adherence values and secondary time windows in an
embodiment where each primary time window has a duration of one
day, and each respective secondary time window represents a running
average from the beginning of a first time period in accordance
with an embodiment of the present disclosure.
[0077] FIG. 14 illustrates the temporal relationship between
primary adherence values, primary time windows, metabolic events,
secondary adherence values and secondary time windows where each
primary time window has a duration of one week, each secondary time
window has a duration of two weeks, and metabolic events that occur
earlier than a set cutoff time are down-weighted relative to
metabolic events that occur after the set cutoff time in the
secondary time windows in accordance with an embodiment of the
present disclosure.
[0078] FIG. 15 illustrates an algorithm for characterizing
metabolic events in accordance with an embodiment of the present
disclosure.
[0079] Like reference numerals refer to corresponding parts
throughout the several views of the drawings.
DETAILED DESCRIPTION
[0080] The present disclosure relies upon the acquisition of data
regarding a plurality of metabolic events, such as fasting events
or meals, a subject engaged in over a period of time. For each such
metabolic event, the data includes a timestamp and a
characterization of the metabolic event that is either insulin
regimen adherent or insulin regimen nonadherent. FIG. 1 illustrates
an example of an integrated system 502 for the acquisition of such
data, and FIG. 5 provides more details of such a system 502. The
integrated system 502 includes one or more connected insulin pens
or pumps 104, one or more continuous glucose monitors 102, memory
506, and a processor (not shown) for performing algorithmic
categorization of autonomous glucose data of a subject.
[0081] A metabolic event is an event relating to metabolism, which
is the sum of the processes in the buildup and destruction of
protoplasm, e.g., the chemical changes in living cells by which
energy is provided for vital processes and activities and new
material is assimilated, i.e., utilized as nourishment.
[0082] The metabolism in a living body can be defined in different
states: an absorptive state, or fed state, occurs after a meal when
the body is digesting food and absorbing nutrients. Digestion
begins the moment food enters the mouth, as the food is broken down
into its constituent parts to be absorbed through the intestine.
The digestion of carbohydrates begins in the mouth, whereas the
digestion of proteins and fats begins in the stomach and small
intestine. The constituent parts of these carbohydrates, fats, and
proteins are transported across the intestinal wall and enter the
bloodstream (sugars and amino acids) or the lymphatic system
(fats). From the intestines, these systems transport them to the
liver, adipose tissue, or muscle cells that will process and use,
or store, the energy. In the absorptive state glucose, lipids and
amino acids enter the blood stream and insulin may be released
(depending on the other conditions like the state and type of
diabetes). The postabsorptive state, or the fasting state, occurs
when the food has been digested, absorbed, and stored. You commonly
fast overnight, but skipping meals during the day puts your body in
the postabsorptive state as well. During this state, the body must
rely initially on stored glycogen. Glucose levels in the blood
begin to drop as it is absorbed and used by the cells. In response
to the decrease in glucose, insulin levels also drop. Glycogen and
triglyceride storage slows. However, due to the demands of the
tissues and organs, blood glucose levels must be maintained in the
normal range of 80-120 mg/dL. In response to a drop in blood
glucose concentration, the hormone glucagon is released from the
alpha cells of the pancreas. Glucagon acts upon the liver cells,
where it inhibits the synthesis of glycogen and stimulates the
breakdown of stored glycogen back into glucose. This glucose is
released from the liver to be used by the peripheral tissues and
the brain. As a result, blood glucose levels begin to rise.
Gluconeogenesis will also begin in the liver to replace the glucose
that has been used by the peripheral tissues. Further information
can be found in OpenStax College, Anatomy and Physiology. OpenStax
CNX.
http://cnx.org/contents/14fb4ad7-39a1-4eee-ab6e-3ef2482e3e22@8.81.
[0083] A metabolic event may therefore relate to an event where a
certain metabolic state occurs, and the occurrence may be detected
by measuring the concentration of an indicator of the event. The
metabolic event will be an indicator of the type of state, and the
progress of the state, and an indicator of a metabolic event can be
the concentration of glucose, glucagon, lipids and amino acids in
the blood stream. Other hormones may also be useful for determining
events relating to metabolism like cortisol and adrenaline.
[0084] Autonomous measurements or autonomous data are measurements
or data obtained by a device measuring at a specified or a variable
frequency, wherein the measuring is undertaken or carried on
without outside control, e.g., when the device is operating in a
measurement mode the measuring can be performed without control
from the a subject using the device. With the integrated system
502, autonomous timestamped glucose measurements of the subject are
obtained 520. Also, data from the one or more insulin pens and/or
pumps used to apply a prescribed insulin regimen to the subject is
obtained 540 as a plurality of records. Each record comprises a
timestamped event specifying an amount of injected (or pumped)
insulin medicament that the subject received as part of the
prescribed insulin medicament dosage regimen. Fasting events are
identified using the autonomous timestamped glucose measurements of
the subject. Optionally, meal events are also identified using the
autonomous timestamped glucose measurements 502. In this way, the
glucose measurements are filtered 504 and stored in non-transitory
memory 506.
[0085] A metabolic event is characterized as adherent or
nonadherent. A metabolic event is adherent when one or more records
from the one or more connected insulin pens or pumps 104 temporally
and quantitatively establish adherence with the prescribed insulin
medicament regimen. Conversely, a metabolic event is characterized
as nonadherent when none of the records from the one or more
connected insulin pens or pumps 104 temporally and quantitatively
establish adherence with the prescribed basal insulin medicament
regimen.
[0086] Each fasting event is characterized as adherent or
nonadherent 508. A fasting event is adherent when one or more
records from the one or more connected insulin pens or pumps 104
temporally and quantitatively establish adherence with the
prescribed basal insulin medicament regimen during the fasting
event. Conversely, a fasting event is characterized as nonadherent
when none of the records from the one or more connected insulin
pens or pumps 104 temporally and quantitatively establish adherence
with the prescribed basal insulin medicament regimen.
[0087] A respective meal is deemed bolus regimen adherent when one
or more medicament records indicates, on a temporal basis, a
quantitative basis and a type of insulin medicament basis,
adherence with a prescribed bolus insulin medicament dosage regimen
during the respective meal. Conversely, a respective meal is deemed
bolus regimen nonadherent when the plurality of medicament records
fails to indicate adherence, on a temporal basis, a quantitative
basis, and a type of insulin medicament basis, with the prescribed
bolus insulin medicament dosage regimen during the respective
meal.
[0088] This filtered and cataloged glucose data is analyzed and
visualized in accordance with the methods of the present disclosure
510. Such visualization enables the subject or health care
practitioner to see the effect of insulin regimen adherence on
critical subject markers such as blood glucose levels and HbA1c
levels.
[0089] Reference will now be made in detail to embodiments,
examples of which are illustrated in the accompanying drawings. In
the following detailed description, numerous specific details are
set forth in order to provide a thorough understanding of the
present disclosure. However, it will be apparent to one of ordinary
skill in the art that the present disclosure may be practiced
without these specific details. In other instances, well-known
methods, procedures, components, circuits, and networks have not
been described in detail so as not to unnecessarily obscure aspects
of the embodiments.
[0090] It will also be understood that, although the terms first,
second, etc. may be used herein to describe various elements, these
elements should not be limited by these terms. These terms are only
used to distinguish one element from another. For example, a first
subject could be termed a second subject, and, similarly, a second
subject could be termed a first subject, without departing from the
scope of the present disclosure. The first subject and the second
subject are both subjects, but they are not the same subject.
Furthermore, the terms "subject" and "user" are used
interchangeably herein.
[0091] The terminology used in the present disclosure is for the
purpose of describing particular embodiments only and is not
intended to be limiting of the invention. As used in the
description of the invention and the appended claims, the singular
forms "a", "an" and "the" are intended to include the plural forms
as well, unless the context clearly indicates otherwise. It will
also be understood that the term "and/or" as used herein refers to
and encompasses any and all possible combinations of one or more of
the associated listed items. It will be further understood that the
terms "comprises" and/or "comprising," when used in this
specification, specify the presence of stated features, integers,
steps, operations, elements, and/or components, but do not preclude
the presence or addition of one or more other features, integers,
steps, operations, elements, components, and/or groups thereof.
[0092] As used herein, the term "if" may be construed to mean
"when" or "upon" or "in response to determining" or "in response to
detecting," depending on the context. Similarly, the phrase "if it
is determined" or "if [a stated condition or event] is detected"
may be construed to mean "upon determining" or "in response to
determining" or "upon detecting [the stated condition or event]" or
"in response to detecting [the stated condition or event],"
depending on the context.
[0093] A detailed description of a system 48 for monitoring
adherence to a prescribed insulin medicament dosage regimen 206 for
a subject over time in accordance with the present disclosure is
described in conjunction with FIGS. 1 through 3. As such, FIGS. 1
through 3 collectively illustrate the topology of the system in
accordance with the present disclosure. In the topology, there is a
device for monitoring adherence to a prescribed insulin medicament
dosage regimen ("monitor device 250") (FIGS. 1, 2, and 3), a device
for assessing regimen adherence ("adherence device 200"), one or
more glucose sensors 102 associated with the subject (FIG. 1), and
one or more insulin pens or pumps 104 for injecting insulin
medicaments into the subject (FIG. 1). Throughout the present
disclosure, the adherence device 200 and the monitor device 250
will be referenced as separate devices solely for purposes of
clarity. That is, the disclosed functionality of the adherence
device 200 and the disclosed functionality of the monitor device
250 are contained in separate devices as illustrated in FIG. 1.
However, it will be appreciated that, in fact, in some embodiments,
the disclosed functionality of the adherence device 200 and the
disclosed functionality of the monitor device 250 are contained in
a single device.
[0094] Referring to FIG. 1, the monitor device 250 monitors
adherence to an insulin medicament dosage regimen prescribed to a
subject. To do this, the adherence device 200, which is in
electrical communication with the monitor device 250, receives
autonomous glucose measurements originating from one or more
glucose sensors 102 attached to a subject on an ongoing basis.
Further, the adherence device 200 receives insulin medicament
injection data from one or more insulin pens and/or pumps 104 used
by the subject to inject insulin medicaments. In some embodiments,
the adherence device 200 receives such data directly from the
glucose sensor(s) 102 and insulin pens and/or pumps 104 used by the
subject. For instance, in some embodiments the adherence device 200
receives this data wirelessly through radio-frequency signals. In
some embodiments such signals are in accordance with an 802.11
(WiFi), Bluetooth, or Zigbee standard. In some embodiments, the
adherence device 200 receives such data directly, characterizes
metabolic events within the data as regimen adherent or regimen
nonadherent, and passes the characterized data to the monitor
device 250. In some embodiments the glucose sensor 102 and/or
insulin pen/pump includes and RFID tag and communicates to
adherence device 200 and/or the monitor device 250 using RFID
communication.
[0095] In some embodiments, the adherence device 200 and/or the
monitor device 250 is not proximate to the subject and/or does not
have wireless capabilities or such wireless capabilities are not
used for the purpose of acquiring glucose data and insulin
medicament injection data. In such embodiments, a communication
network 106 may be used to communicate glucose measurements from
the glucose sensor 102 to the adherence device 200 and from the one
or more insulin pens or pumps 104 to the adherence device 200.
[0096] Examples of networks 106 include, but are not limited to,
the World Wide Web (WWW), an intranet and/or a wireless network,
such as a cellular telephone network, a wireless local area network
(LAN) and/or a metropolitan area network (MAN), and other devices
by wireless communication. The wireless communication optionally
uses any of a plurality of communications standards, protocols and
technologies, including but not limited to Global System for Mobile
Communications (GSM), Enhanced Data GSM Environment (EDGE),
high-speed downlink packet access (HSDPA), high-speed uplink packet
access (HSDPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+,
Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field
communication (NFC), wideband code division multiple access
(W-CDMA), code division multiple access (CDMA), time division
multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g.,
IEEE 802.11a, IEEE 802.11ac, IEEE 802.11ax, IEEE 802.11b, IEEE
802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP),
Wi-MAX, a protocol for e-mail (e.g., Internet message access
protocol (IMAP) and/or post office protocol (POP)), instant
messaging (e.g., extensible messaging and presence protocol (XMPP),
Session Initiation Protocol for Instant Messaging and Presence
Leveraging Extensions (SIMPLE), Instant Messaging and Presence
Service (IMPS)), and/or Short Message Service (SMS), or any other
suitable communication protocol, including communication protocols
not yet developed as of the filing date of the present
disclosure.
[0097] In some embodiments, there is a single glucose sensor
attached to the subject and the adherence device 200 and/or the
monitor device 250 is part of the glucose sensor 102. That is, in
some embodiments, the adherence device 200 and/or the monitor
device 250 and the glucose sensor 102 are a single device.
[0098] In some embodiments the adherence device 200 and/or the
monitor device 250 is part of an insulin pen or pump 104. That is,
in some embodiments, the adherence device 200 and/or the monitor
device 250 and an insulin pen or pump 104 are a single device.
[0099] Of course, other topologies of system 48 are possible. For
instance, rather than relying on a communications network 106, the
one or more glucose sensors 102 and the one or more insulin pens
and/or pumps 104 may wirelessly transmit information directly to
the adherence device 200 and/or monitor device 250. Further, the
adherence device 200 and/or the monitor device 250 may constitute a
portable electronic device, a server computer, or in fact
constitute several computers that are linked together in a network
or be a virtual machine in a cloud computing context. As such, the
exemplary topology shown in FIG. 1 merely serves to describe the
features of an embodiment of the present disclosure in a manner
that will be readily understood to one of skill in the art.
[0100] Referring to FIG. 2, in typical embodiments, the monitor
device 250 comprises one or more computers. For purposes of
illustration in FIG. 2, the monitor device 250 is represented as a
single computer that includes all of the functionality for
monitoring adherence to a prescribed insulin medicament dosage
regimen. However, the disclosure is not so limited. The
functionality for monitoring adherence to a prescribed insulin
medicament dosage regimen may be spread across any number of
networked computers and/or reside on each of several networked
computers and/or by hosted on one or more virtual machines at a
remote location accessible across the communications network 106.
One of skill in the art will appreciate that a wide array of
different computer topologies are possible for the application and
all such topologies are within the scope of the present
disclosure.
[0101] Turning to FIG. 2 with the foregoing in mind, an exemplary
monitor device 250 for monitoring adherence to a prescribed insulin
medicament dosage regimen comprises one or more processing units
(CPU's) 274, a network or other communications interface 284, a
memory 192 (e.g., random access memory), one or more magnetic disk
storage and/or persistent devices 290 optionally accessed by one or
more controllers 288, one or more communication busses 212 for
interconnecting the aforementioned components, and a power supply
276 for powering the aforementioned components. Data in memory 192
can be seamlessly shared with non-volatile memory 290 using known
computing techniques such as caching. Memory 192 and/or memory 290
can include mass storage that is remotely located with respect to
the central processing unit(s) 274. In other words, some data
stored in memory 192 and/or memory 290 may in fact be hosted on
computers that are external to the monitor device 250 but that can
be electronically accessed by the monitor device 250 over an
Internet, intranet, or other form of network or electronic cable
(illustrated as element 106 in FIG. 2) using network interface
284.
[0102] The memory 192 of the monitor device 250 for monitoring
adherence to a prescribed insulin medicament dosage for a subject
stores: [0103] an operating system 202 that includes procedures for
handling various basic system services; [0104] an insulin regimen
monitoring module 204; [0105] a prescribed insulin medicament
dosage regimen 206 for a subject, the prescribed insulin medicament
dosage regimen comprising a basal insulin medicament dosage regimen
208 and, optionally in some embodiments, a bolus insulin medicament
dosage regimen 214; [0106] a first data set 220, the first data set
representing a first period of time 222 and comprising a plurality
of metabolic events the subject engaged in during this first period
of time and, for each respective metabolic event 224 in the
plurality of metabolic events, a timestamp 226 representing when
the respective metabolic event occurred as well as a
characterization 228 of the respective metabolic event; [0107] a
plurality of primary adherence values 230 for the subject, each
respective primary adherence value 232 in the plurality of primary
adherence values representing a corresponding primary time window
234 in a plurality of primary time windows within the first period
of time; [0108] an optional plurality of secondary adherence values
236 for the subject; [0109] an optional HbA1c lookup table; and
[0110] an optional second data set for the subject.
[0111] In some embodiments, the insulin regimen monitoring module
204 is accessible within any browser (phone, tablet,
laptop/desktop). In some embodiments the insulin regimen monitoring
module 204 runs on native device frameworks, and is available for
download onto the monitor device 250 running an operating system
202 such as Android or iOS.
[0112] In some implementations, one or more of the above identified
data elements or modules of the monitor device 250 for monitoring
adherence to a prescribed insulin medicament dosage regimen for a
subject over time are stored in one or more of the previously
described memory devices, and correspond to a set of instructions
for performing a function described above. The above-identified
data, modules or programs (e.g., sets of instructions) need not be
implemented as separate software programs, procedures or modules,
and thus various subsets of these modules may be combined or
otherwise re-arranged in various implementations. In some
implementations, the memory 192 and/or 290 optionally stores a
subset of the modules and data structures identified above.
Furthermore, in some embodiments the memory 192 and/or 290 stores
additional modules and data structures not described above.
[0113] In some embodiments, a monitor device 250 for monitoring
adherence to a prescribed insulin medicament dosage regimen 206 for
a subject over time is a smart phone (e.g., an iPHONE), laptop,
tablet computer, desktop computer, or other form of electronic
device (e.g., a gaming console). In some embodiments, the monitor
device 250 is not mobile. In some embodiments, the monitor device
250 is mobile.
[0114] FIG. 3 provides a further description of a specific
embodiment of a monitor device 250 that can be used with the
instant disclosure. The monitor device 250 illustrated in FIG. 3
has one or more processing units (CPU's) 274, peripherals interface
370, memory controller 368, a network or other communications
interface 284, a memory 192 (e.g., random access memory), a user
interface 278, the user interface 278 including a display 282 and
input 280 (e.g., keyboard, keypad, touch screen), an optional
accelerometer 317, an optional GPS 319, optional audio circuitry
372, an optional speaker 360, an optional microphone 362, one or
more optional intensity sensors 364 for detecting intensity of
contacts on the monitor device 250 (e.g., a touch-sensitive surface
such as a touch-sensitive display system 282 of the monitor device
250), an optional input/output (I/O) subsystem 366, one or more
optional optical sensors 372, one or more communication busses 212
for interconnecting the aforementioned components, and a power
system 276 for powering the aforementioned components.
[0115] In some embodiments, the input 280 is a touch-sensitive
display, such as a touch-sensitive surface. In some embodiments,
the user interface 278 includes one or more soft keyboard
embodiments. The soft keyboard embodiments may include standard
(QWERTY) and/or non-standard configurations of symbols on the
displayed icons.
[0116] The monitor device 250 illustrated in FIG. 3 optionally
includes, in addition to accelerometer(s) 317, a magnetometer (not
shown) and a GPS 319 (or GLONASS or other global navigation system)
receiver for obtaining information concerning the location and
orientation (e.g., portrait or landscape) of the monitor device 250
and/or for determining an amount of physical exertion by the
subject.
[0117] It should be appreciated that the monitor device 250
illustrated in FIG. 3 is only one example of a multifunction device
that may be used for monitoring adherence to a prescribed insulin
medicament dosage regimen 206 for a subject over time, and that the
monitor device 250 optionally has more or fewer components than
shown, optionally combines two or more components, or optionally
has a different configuration or arrangement of the components. The
various components shown in FIG. 3 are implemented in hardware,
software, firmware, or a combination thereof, including one or more
signal processing and/or application specific integrated
circuits.
[0118] Memory 192 of the monitor device 250 illustrated in FIG. 3
optionally includes high-speed random access memory and optionally
also includes non-volatile memory, such as one or more magnetic
disk storage devices, flash memory devices, or other non-volatile
solid-state memory devices. Access to memory 192 by other
components of the monitor device 250, such as CPU(s) 274 is,
optionally, controlled by the memory controller 368.
[0119] The peripherals interface 370 can be used to couple input
and output peripherals of the device to CPU(s) 274 and memory 192.
The one or more processors 274 run or execute various software
programs and/or sets of instructions stored in memory 192, such as
the insulin regimen monitoring module 204, to perform various
functions for the monitoring device 250 and to process data.
[0120] In some embodiments, the peripherals interface 370, CPU(s)
274, and memory controller 368 are, optionally, implemented on a
single chip. In some other embodiments, they are, optionally,
implemented on separate chips.
[0121] RF (radio frequency) circuitry of network interface 284
receives and sends RF signals, also called electromagnetic signals.
In some embodiments, prescribed insulin medicament dosage regimen,
first data set 220, HbA1c lookup table 238, and/or second data set
240 is received using this RF circuitry from one or more devices
such as a glucose sensor 102 associated with a subject an insulin
pen or pump 104 associated with the subject and/or the adherence
device 200. In some embodiments, the RF circuitry 108 converts
electrical signals to/from electromagnetic signals and communicates
with communications networks and other communications devices,
glucose sensors 102, and insulin pens or pumps 104 and/or the
adherence device 200 via the electromagnetic signals. RF circuitry
284 optionally includes well-known circuitry for performing these
functions, including but not limited to an antenna system, an RF
transceiver, one or more amplifiers, a tuner, one or more
oscillators, a digital signal processor, a CODEC chipset, a
subscriber identity module (SIM) card, memory, and so forth. RF
circuitry 284 optionally communicates with the communication
network 106. In some embodiments, the circuitry 284 does not
include RF circuitry and, in fact, is connected to the network 106
through one or more hard wires (e.g., an optical cable, a coaxial
cable, or the like).
[0122] In some embodiments, audio circuitry 372, optional speaker
360, and optional microphone 362 provide an audio interface between
the subject and the monitor device 250. The audio circuitry 372
receives audio data from peripherals interface 370, converts the
audio data to electrical signals, and transmits the electrical
signals to speaker 360. Speaker 360 converts the electrical signals
to human-audible sound waves. Audio circuitry 372 also receives
electrical signals converted by the microphone 362 from sound
waves. Audio circuitry 372 converts the electrical signal to audio
data and transmits the audio data to peripherals interface 370 for
processing. Audio data is, optionally, retrieved from and/or
transmitted to memory 192 and/or RF circuitry 284 by peripherals
interface 370.
[0123] In some embodiments, the power supply 276 optionally
includes a power management system, one or more power sources
(e.g., battery, alternating current (AC)), a recharging system, a
power failure detection circuit, a power converter or inverter, a
power status indicator (e.g., a light-emitting diode (LED)) and any
other components associated with the generation, management and
distribution of power in portable devices.
[0124] In some embodiments, the monitor device 250 optionally also
includes one or more optical sensors 372. The optical sensor(s) 372
optionally include charge-coupled device (CCD) or complementary
metal-oxide semiconductor (CMOS) phototransistors. The optical
sensor(s) 372 receive light from the environment, projected through
one or more lens, and converts the light to data representing an
image. The optical sensor(s) 372 optionally capture still images
and/or video. In some embodiments, an optical sensor is located on
the back of the monitor device 250, opposite the display 282 on the
front of the device 250, so that the input 280 is enabled for use
as a viewfinder for still and/or video image acquisition. In some
embodiments, another optical sensor 372 is located on the front of
the monitor device 250 so that the subject's image is obtained
(e.g., to verify the health or condition of the subject, to
determine the physical activity level of the subject, or to help
diagnose a subject's condition remotely, etc.).
[0125] As illustrated in FIG. 3, a monitor device 250 preferably
comprises an operating system 202 that includes procedures for
handling various basic system services. The operating system 202
(e.g., iOS, DARWIN, RTXC, LINUX, UNIX, OS X, WINDOWS, or an
embedded operating system such as VxWorks) includes various
software components and/or drivers for controlling and managing
general system tasks (e.g., memory management, storage device
control, power management, etc.) and facilitates communication
between various hardware and software components.
[0126] In some embodiments the monitor device 250 is a smart phone.
In other embodiments, the monitor device 250 is not a smart phone
but rather is a tablet computer, desktop computer, emergency
vehicle computer, or other form or wired or wireless networked
device. In some embodiments, the monitor device 250 has any or all
of the circuitry, hardware components, and software components
found in the monitor device 250 depicted in FIG. 2 or 3. In the
interest of brevity and clarity, only a few of the possible
components of the monitor device 250 are shown in order to better
emphasize the additional software modules that are installed on the
monitor device 250.
[0127] While the system 48 disclosed in FIG. 1 can work standalone,
in some embodiments it can also be linked with electronic medical
records to exchange information in any way.
[0128] Now that details of a system 48 for monitoring adherence to
a prescribed insulin medicament dosage regimen 206 for a subject
over time been disclosed, details regarding a flow chart of
processes and features of the system, in accordance with an
embodiment of the present disclosure, are disclosed with reference
to FIGS. 4A through 4C. In some embodiments, such processes and
features of the system are carried out by the insulin regimen
monitoring module 204 illustrated in FIGS. 2 and 3.
[0129] Block 402. With reference to block 402 of FIG. 4A, the goal
of insulin therapy in subjects with either type 1 diabetes mellitus
or type 2 diabetes mellitus is to match as closely as possible
normal physiologic insulin secretion to control fasting and
postprandial plasma glucose. This is done with a prescribed insulin
medicament dosage regimen 206 for the subject. One aspect of the
present disclosure provides a monitoring device 250 for monitoring
adherence to a prescribed insulin medicament dosage regimen 206 for
a subject over time. In the present disclosure, the prescribed
insulin medicament dosage regimen comprises a basal insulin
medicament dosage regimen 208. The monitoring device comprises one
or more processors 274 and a memory 192/290. The memory stores
instructions that, when executed by the one or more processors,
perform a method. In the method, a first data set 220 is
obtained.
[0130] The first data set comprises a plurality of metabolic events
in which the subject engaged. The plurality of metabolic events is
within a first period of time 222. In varying embodiments, the
first period of time is one day or more, three days or more, five
days or more, ten days or more, one month or more, two months or
more, three months or more or five months or more. Each respective
metabolic event 224 in the plurality of metabolic events comprises
(i) a timestamp 226 of the respective metabolic event and (ii) a
first characterization 228 that is one of insulin regimen adherent
and insulin regimen nonadherent.
[0131] In some embodiments each metabolic event 224 in the first
data set 220 has one or more characterizations 228 set forth in
Table 1.
TABLE-US-00001 TABLE 1 Exemplary characterizations 228 of metabolic
events 224. Category A A1 In Bolus Adherence A2 Out of Bolus
Adherence Category B B1 In Basal Adherence B2 Out of Basal
Adherence Category C C1 In timing adherence C2 Out of timing
adherence Category D D1 In dose size adherence D2 Out of dose size
adherence
[0132] Using the characterizations set forth in Table 1, the same
period of time can contain metabolic events with different labels.
For instance, a whole day can contain a metabolic event (fasting
event) marked as out of basal adherence, B2, but three metabolic
events (meal events) within that day can be labelled in bolus
adherence, A1. FIG. 15 illustrates an algorithm for characterizing
a metabolic event, wherein the example is a fasting event, and
wherein the relevant period of time defined by the regimen is one
day. The characterization is provided in accordance with the
categories of Table 1. In such embodiments, continuously marked
periods, e.g. primary period being a day, contains a fasting event
marked with B2 or a meal events marked with A1, are referred to as
characterized metabolic events. As another example, consider the
case where three fasting events within each of the first three days
of a period of one week are marked as in 100% basal adherence (e.g.
in basal and timing adherence B1, C1), two fasting events within
each of the two following days are marked as in 50% basal adherence
(e.g. in basal adherence but out of timing adherence), and two
fasting events within the last two days are marked as in 0% basal
adherence (e.g. out of basal adherence and out of timing adherence
B2, C2). In the case where a fasting event is characterized and
marked as in basal and timing adherence the event can as an example
be defined as 100% insulin regimen adherent, in the case where the
metabolic event is marked in basal adherence, but out of timing
adherence the event can as an example be defined as 50% insulin
regimen adherent, this could be a different percentage, based on
estimated effect of taking a dose later than recommended. In the
case where the fasting event is out of basal adherence the event is
0% insulin regimen adherent corresponding to insulin regimen
nonadherent. The number of insulin regimen adherent metabolic
events in the example is thus 3+2*50%+2*0%. In this example the
past week's adherence (primary adherence value 232 for the primary
time window 234 of the past week) is thus:
Past .times. .times. 7 .times. .times. days ' .times. .times.
adherence = 3 + 0 , 5 * 2 7 = 4 7 = 57 .times. % ##EQU00001##
[0133] In other embodiments, such characterizations are imposed by
considering metabolic events to be fasting events or meal events
and characterizing each fasting event or meal event for insulin
medicament regimen adherence.
[0134] In some embodiments, metabolic events can be a metabolic
events defined in the medicament regimen, which can be
automatically identified from a device continuously measuring an
indicator of an event relating, wherein the event is relating to a
metabolic state of the subject, whereby the device allows the
metabolic event to be timestamped and to be characterized with
respect to the medicament regimen as regimen adherent or regimen
nonadherent. For example, a metabolic event defined according to
the medicament regimen could be a meal event, wherein the
medicament regimen determines that bolus insulin should be
administered based on glucose measurements relating to this event,
or it could be a fasting event, wherein the medicament regimen
determines that basal insulin should be administered based on
glucose measurements relating to this event.
[0135] In some embodiments, metabolic events (e.g., meal events,
fasting events, etc.) incurred by the subject are identified
without reliance on records kept by the subject. For instance, in
some embodiments a second data set 240 comprising autonomous
glucose measurements 242 of the subject from one or more glucose
sensors 102 is obtained. Each such autonomous glucose measurement
242 is timestamped with a glucose measurement timestamp 244 to
represent when the respective measurement was made.
[0136] The FREESTYLE LIBRE CGM by ABBOTT ("LIBRE") is an example of
a glucose sensor that may be used as a glucose sensor 102. The
LIBRE allows calibration-free glucose measurements with an on-skin
coin-sized sensor, which can send up to eight hours of data to a
reader device (e.g., the adherence device 200 and/or the monitor
device 250) via near field communications, when brought close
together. The LIBRE can be worn for fourteen days in all daily life
activities. In some embodiments, autonomous glucose measurements
are taken from the subject at an interval rate of 5 minutes or
less, 3 minutes or less, or 1 minute or less. In some cases the
sampling rate may vary during sampling. Example 1 below illustrates
how such autonomous glucose measurements are used to both identify
metabolic events and to characterize each of them as insulin
regimen adherent or insulin regimen nonadherent.
[0137] Block 404. Referring to block 404 of FIG. 4A, the process
continues with the computation of a plurality of primary adherence
values 230. Each respective primary adherence value 232 in the
plurality of primary adherence values represents a corresponding
primary time window 232 in a plurality of primary time windows
within the first period of time. Each primary time window is of a
same first fixed duration. FIG. 6 illustrates. In FIG. 6, the first
period of time 222 is illustrated as a timeline. Each primary time
window 234, and its corresponding primary adherence value 232, is
allocated an equal portion of this timeline.
[0138] Each respective primary adherence value 232 in the plurality
of primary adherence values is computed by dividing a number of
insulin regimen adherent metabolic events by a total number of
metabolic events in the plurality of metabolic events that have
timestamps in the primary time window 234 corresponding to the
respective primary adherence value 232. For example, consider the
primary time window 234-1 of FIG. 6 in which there are two insulin
regimen adherent metabolic events (224-1 and 224-3) and one insulin
regimen nonadherent metabolic event for a total of three metabolic
events 224 in the primary time window 234-1. In this example, the
primary adherence value 232-1 is computed by dividing the number of
insulin regimen adherent metabolic events in the corresponding
primary time window 234-1 (two, 224-1 and 224-3) by the total
number of metabolic events that have timestamps in the
corresponding primary time window (three, 224-1, 224-2, and 224-3),
that is dividing "2" by "3." It will be appreciated that the
process of dividing a number of insulin regimen adherent metabolic
events by a total number of metabolic events in the plurality of
metabolic events can be done any number of ways and all such ways
are encompassed by the phrase "dividing a number of insulin regimen
adherent metabolic events by a total number of metabolic events in
the plurality of metabolic events." For instance, the division can
be effectuated by, in fact, multiplying a number of insulin regimen
adherent metabolic events by the inverse of the total number of
metabolic events in the plurality of metabolic events (e.g., in the
example above, by computing (2*( 1/3)).
[0139] Block 406. Referring to block 404 of FIG. 4A, the plurality
of primary adherence values 230 is communicated across the first
period of time 222 thereby monitoring adherence to the prescribed
insulin medicament dosage regimen for the subject over time. FIG. 7
illustrates. In FIG. 7, each adherence values 230 in the plurality
of primary adherence values is communicated as a data point on the
line 702.
[0140] In one such embodiment, the metabolic events 224 are meal
events, each primary time window 234 is one day, and the plurality
of primary adherence values 232 of line 702 represent daily bolus
adherence.
[0141] In other embodiment, the metabolic events 224 are meal
events, each primary time window 234 is 4 hours, 8 hours, 12 hours,
24 hours, 48 hours, 72 hours or four days, and the plurality of
primary adherence values 232 of line 702 represent bolus
adherence.
[0142] As FIG. 7 further illustrates, in some embodiments,
secondary adherence values 236 are plotted along a line 704 along
with the primary adherence values on the line 702. For instance, as
illustrated in block 408 of FIG. 4A, in some embodiments, a
plurality of secondary adherence values 236 is computed. Each
respective secondary adherence value in the plurality of secondary
adherence values represents a corresponding secondary time window
244 in a plurality of contemporaneously overlapping secondary time
windows within the first period of time. FIG. 6 illustrates.
[0143] In FIG. 6, each secondary time window 244, and its
corresponding secondary adherence value 244, is allocated a portion
of the first period of time. However, each respective secondary
time window 244 in the plurality of secondary time windows 244 is
contemporaneously overlapping at least one other secondary time
window 244 in the plurality of secondary time windows 244 along
this the time line. Each respective secondary adherence value 236
in the plurality of secondary adherence values is computed by
dividing a number of metabolic events 224 that are insulin regimen
adherent by a total number of metabolic events in the plurality of
metabolic events that have timestamps in the secondary time window
corresponding to the respective secondary adherence value. For
example, consider the secondary time window 244-1 of FIG. 6 in
which there are three insulin regimen adherent metabolic events
(224-1, 224-3, and 224-4) and three insulin regimen nonadherent
metabolic events (224-2, 224-5 and 224-6) for a total of six
metabolic events 224 in the secondary time window 244-1. In this
example, the secondary adherence value 236-1 is computed by
dividing the number of insulin regimen adherent metabolic events in
the corresponding secondary time window 236-1 (three, 224-1, 224-3,
and 224-3) by the total number of metabolic events that have
timestamps in the corresponding secondary time window (six, 224-1
through -6), that is dividing "3" by "6." It will be appreciated
that the process of dividing a number of insulin regimen adherent
metabolic events by a total number of metabolic events in the
plurality of metabolic events can be done any number of ways and
all such ways are encompassed by the phrase "dividing a number of
insulin regimen adherent metabolic events by a total number of
metabolic events in the plurality of metabolic events." For
instance, the division can be effectuated by, in fact, multiplying
a number of insulin regimen adherent metabolic events by the
inverse of the total number of metabolic events in the plurality of
metabolic events (e.g., in the example above, by computing
(3*(1/6)).
[0144] As illustrated in FIG. 6, each secondary time window 244 in
at least a subset of the secondary time windows in the plurality of
secondary time windows is of longer duration than the first fixed
duration that sets the duration of the primary time window. In FIG.
6, the secondary time windows are each the same length and are
exactly twice as long as the primary time windows 234. In various
other embodiments, the secondary time windows are each the same
length and are exactly three times as long as, exactly four times
as long as, exactly five times as long as, exactly six times as
long as, exactly seven times as long as, exactly eight times as
long as, exactly nine times as long as, or exactly ten times as
long as the primary time windows 234.
[0145] In some embodiments each secondary time window 244 is one
week long and each respective secondary time window 244 overlaps
another respective secondary time window 244 by six days. In some
embodiments each secondary time window 244 is one week long and
each respective secondary time window 244 overlaps another
respective secondary time window 244 by one day, two days, three
days, four days, five days, or six days. In some embodiments each
secondary time window 244 is one week long or longer and each
respective secondary time window 244 does not overlap any other
secondary time window 244.
[0146] In some embodiments, each respective secondary time window
244 exhibits fifty percent temporal overlap with another secondary
time window in the plurality of time windows, with respect to the
length of time of the respective secondary time window 244, as
illustrated in FIG. 6. In some embodiments, each respective
secondary time window 244 exhibits less than ten percent temporal
overlap with another secondary time window in the plurality of time
windows, with respect to the length of time of the respective
secondary time window 244. In some embodiments, each respective
secondary time window 244 exhibits between ten percent and thirty
percent temporal overlap with another secondary time window in the
plurality of time windows with respect to the length of time of the
respective secondary time window 244. In some embodiments, each
respective secondary time window 244 exhibits between thirty
percent and sixty percent temporal overlap with another secondary
time window in the plurality of time windows with respect to the
length of time of the respective secondary time window 244. In some
embodiments, each respective secondary time window 244 exhibits
between sixty percent and ninety percent temporal overlap with
another secondary time window in the plurality of time windows with
respect to the length of time of the respective secondary time
window 244.
[0147] As illustrated in FIG. 7, in embodiments where both primary
and secondary adherence values are computed, the communicating step
comprises communicating a superposition of the plurality of primary
adherence values and the plurality of secondary adherence values
across the first period of time. In FIG. 7, the plurality of
primary adherence values is communicated as line 602 whereas the
plurality of secondary adherence values is communicated as line
704. In some embodiments, calculated primary adherence values 232
and/or secondary adherence values 244 are scaled so that they fall
into a range other than their native range. Thus, in some
embodiments, the native range of the calculated primary adherence
values 232 and/or secondary adherence values 244 is zero to 1, but
they are then uniformly scaled to zero to 100, zero to 1000, or any
other suitable scale. Such scaling acts independently of any
downweighting of metabolic events 224.
[0148] In some embodiments, the adherence device 250 allows a
subject to add and mark events manually. In some such embodiments,
the adherence device 250 suggests categories for the subject to
choose from, e.g. events such as meals, insulin and glucose
measurements, sleeping periods, periods of physical activity, sick
days. In some embodiments, these events are marked with a specific
category name, which is then used to identify causes of poor
glycemic control and provide improved treatment transparency. For
instance, in some embodiments this is accomplished by temporally
superimposing these additional events onto the primary adherence
values and/or secondary adherence values and displaying the
superposition on the display of the monitor device 250. In some
embodiments, these additional events are detected by a wearable
device.
[0149] Referring to block 410 of FIG. 4A, in some embodiments, each
secondary time window 244 is of a same second fixed duration that
is greater than the first fixed duration of the primary time
windows. This is illustrated in FIG. 6 where it can be seen that
the secondary time window 244 are a longer fixed duration than the
primary time windows 234. FIG. 11 illustrates an example of how
bolus adherence is communicated in accordance with an embodiment of
the present disclosure, in which line 1102 is the primary adherence
values and line 1104 is the secondary adherence values. FIG. 12
illustrates an example of how basal adherence is communicated in
accordance with an embodiment of the present disclosure, in which
line 1202 is the primary adherence values and line 1204 is the
secondary adherence values.
[0150] Referring to block 412 of FIG. 4B, in some embodiments, each
metabolic event 224 in the plurality of metabolic events is a
fasting event and the prescribed insulin medicament dosage regimen
206 is a basal insulin medicament dosage regimen 208. In some such
embodiments, the first fixed duration of each primary time window
234 is a week, and each respective secondary time window in the
plurality of secondary time windows represents three months in the
first time period. Communication of primary adherence values 232
and secondary adherence values 242 in accordance with such
embodiments is illustrated in FIG. 8A, where the primary adherence
values 232 are reported as line 802 and the secondary adherence
values 242 are reported as line 804. The communication of regimen
adherence data in this way is highly advantageous, because the user
can ascertain not only the weekly basal adherence from line 802,
but also get a more time averaged perspective of regimen adherence
from line 804.
[0151] Referring to block 416 of FIG. 4B, in some embodiments, each
metabolic event 224 in the plurality of metabolic events is a meal
event and the insulin medicament dosage regimen 206 is a bolus
insulin medicament dosage regimen 214. Referring to block 418 of
FIG. 4B, in some such embodiments, the first fixed duration of each
primary time window 234 is a day, and each respective secondary
time window 244 in the plurality of secondary time windows
represents a running average from the beginning of the first time
period. FIG. 13 illustrates. In FIG. 13, each primary time window
234 is a day, and each respective secondary time window 244 in the
plurality of secondary time windows represents a running average
from the beginning of the first time period. Thus, for example the
secondary adherence value 236-P of FIG. 13 is calculated by
dividing the number of regimen compliant metabolic events in the
first period of time 222 {224-1, 224-3, 224-4, 224-7, 224-8, 224-9,
224-11, 224-12, 224-13, 224-14, . . . , 224-(Q-2), 224-(Q-1),
224-Q} by the total number of regimen metabolic events in the first
time period (Q).
[0152] Referring to block 420 of FIG. 4B, in some embodiments,
respective metabolic events 224 in the plurality of metabolic
events that occur earlier than a set cutoff time are down-weighted
relative to respective metabolic events in the plurality of
metabolic events that occur after the set cutoff time in each
respective primary time window.
[0153] In some embodiments, the application of the set cutoff time
is applied to the computation the primary adherence values 232 and
not the secondary adherence values 242.
[0154] In some embodiments, the application of the set cutoff time
is applied to the computation of both the primary adherence values
232 and the secondary adherence values 242.
[0155] In some embodiments, the application of the set cutoff time
is applied only to the computation of the secondary adherence
values 242, not the primary adherence values 232. In such
embodiments, the primary adherence values 232 are computed as
described above whereas the secondary adherence values 242 are
computed by taking the cutoff time into consideration. To
illustrate, referring to FIG. 6, when the set cutoff time is one
week and the first fixed duration of the primary time window is one
week, whereas the secondary time window 244 is set for two weeks,
such downweighting will only affect computation of the secondary
adherence values, not the primary adherence values. This is because
the cutoff time is applied retrospectively from the end of each
primary and/or secondary time window. FIG. 14 illustrates. In the
example illustrated in FIG. 14, the secondary time window 244-2 has
a duration of two weeks, the primary time window has a duration of
one week, and the set cutoff time is one week, applied
retrospectively from the end of time window 244-2 as illustrated by
arrow 1406. Thus, in computation of the secondary adherence value
236-2, metabolic events 224-4 through 224-6 are downweighted
relative to metabolic events 224-7 and 224-8. Without the
downweighting, the secondary adherence value is computed as the
division of the number of adherent metabolic events (3: 224-4,
224-5, and 224-7) by the total number of metabolic events (5: 224-4
through 224-8) or 3/5. With the set cutoff time of one week,
metabolic events in week 1, within box 1402 of FIG. 14, are
downweighted relative to the metabolic events in week 2, within box
1404 of FIG. 14.
[0156] Downweighting is particularly useful in the example of FIG.
13, where each respective secondary time window 244 in the
plurality of secondary time windows represents a running average
from the beginning of the first time period. In such embodiments,
it is useful to downweight, or even disregard, older metabolic
events when computing the secondary adherence values. This could be
done by a linear function of time, a non-linear function of time,
or a memory cut-off where data older than a specific time period
are completely eliminated. In one example, linear weighing of
adherence data when calculating a 12 week secondary adherence value
could be done by the following calculation:
12 .times. .times. week .times. .times. linear .times. .times.
adherence = 1 w .times. i = 1 12 .times. w 1 .function. ( Weekly
.times. .times. adherence ) i ##EQU00002##
[0157] where,
w _ = { w 1 , w 2 .times. .times. , w 12 } = { 12 12 , 11 12 , 10
12 , .times. .times. , 1 12 } . ##EQU00003##
[0158] An example of this is illustrated for the secondary
adherence values plotted as line 806 in FIG. 8B using as input the
secondary time windows illustrated in FIG. 13, whereas as line 802
is weekly primary adherence values.
[0159] An example of a non-linear weighting could be, for example,
weighing the metabolic events in the last four weeks 100%, the four
previous weeks 50%, and disregarding all metabolic events older
than eight weeks when computing a respective adherence value. Such
an example is illustrated for the secondary adherence values
plotted as line 808 in FIG. 8C using as input the secondary time
windows illustrated in FIG. 13, whereas as line 802 is weekly
primary adherence values.
[0160] The extent that metabolic events that are occur prior to a
set cutoff time used for downweighting is application dependent. In
some embodiments, such metabolic events are uniformly downweighted
by a predetermined amount between zero and 99 percent, such as
fifty percent. Thus, in an example of FIG. 14 where the downweight
is fifty percent, metabolic events 224-4 through 224-6 are
downweighted by fifty percent and the secondary adherence value
236-2 is computed as (0.5+1.0+1.0)/(0.5+0.5+0.5+1+1) for the
division of metabolic events 224-4, 224-7 and 224-8 by metabolic
events 224-4 through 224-8. In an example of FIG. 14 where the
downweight is seventy five percent, metabolic events 224-4 through
224-6 are downweighted by seventy five percent and the secondary
adherence value 236-2 is computed as
(0.25+1.0+1.0)/(0.25+0.25+0.25+1+1) for the division of metabolic
events 224-4, 224-7 and 224-8 by metabolic events 224-4 through
224-8. In an example of FIG. 14 where the downweight is ninety
percent, metabolic events 224-4 through 224-6 are downweighted by
ninety percent and the secondary adherence value 236-2 is computed
as (0.10+1.0+1.0)/(0.10 +0.10+0.10+1+1) for the division of
metabolic events 224-4, 224-7 and 224-8 by metabolic events 224-4
through 224-8.
[0161] In some embodiments, metabolic events occurring before the
set cutoff time are downweighted as a function of time, so that
events occurring earlier in time than later events are downweighted
more. Referring to FIG. 14 in such an example, metabolic event
224-4 would be downweighted more than metabolic event 224-5 which,
in turn, would be downweighted more than metabolic event 224-5,
whereas metabolic events 224-7 and 224-8 would each have full
value.
[0162] Referring to block 422 of FIG. 4B, in some embodiments,
respective metabolic events in the plurality of metabolic events
are down-weighted as a linear function of time in each respective
primary time window. For instance, referring to FIG. 6, metabolic
event 224-1 would be downweighted more than metabolic event 224-2
which, in turn, would be downweighted more than metabolic event
224-3 and the extent of such downweighting would be a linear
function of time. In some embodiments, respective metabolic events
in the plurality of metabolic events are down-weighted as a linear
function of time in each respective primary time window and/or
secondary time window.
[0163] Referring to block 424 of FIG. 4B, in some embodiments, the
first plurality of adherence values or the second plurality of
adherence values are analyzed for a drop off in adherence. In some
such embodiments, such a trend is identified as a drop off below a
first threshold adherence value for at least a second threshold
amount of time. FIG. 10 illustrates a drop off below a first
threshold adherence value for at least a second threshold amount of
time.
[0164] In some such embodiments, when such a trend is identified,
insulin medicament dosage in the insulin medicament dosage regimen
for the subject is reduced. In other words, when it is determined
that the subject is not adhering to the insulin regimen, the
insulin regimen is shifted to a more conservative regimen to
protect the subject from adverse events. Values for the first
threshold adherence value and the second threshold amount of time
are highly application dependent and will depend on a number of
factors such as health care practitioner judgment, stage of the
underlying diabetic condition, additional complications in the
subject's health, and type of insulin medicaments the subject is
taking. In general the second threshold amount of time is on the
order of weeks or months to ensure that a change in treatment
regimen is warranted.
[0165] Referring to block 426 of FIG. 4B, in some embodiments a
HbA1c lookup table 238 is obtained that includes a calculated HbA1c
increase as a function of adherence values in the first plurality
of adherence values. For instance, the methods disclosed in Poulsen
and Randlov, 2009, "How Much Do Forgotten Insulin Injections Matter
to Hemoglobin A1c in People with Diabetes? A Simulation Study,"
Journal of Diabetes Science and Technology March; 2(2):229-235,
which is hereby incorporated by reference are used determine how a
missed dose affects HbA1c level. The referenced study estimates
that forgetting 2.1 bolus injections per week results in an
approximate 0.3 to 0.4 percent increase in HbA1c, and forgetting
the same amount of basal injections (assuming a dosage regimen
requiring 2 basals per day) results in a 0.2 to 0.3 percent rise in
HbA1c levels. Furthermore, the reference estimate that forgetting
39% of the basal or bolus injections results in as much as a 1.8
percent increase in HbA1c. Such numbers can be used to estimate how
much nonadherence is affecting the patient's treatment outcome.
FIG. 9 illustrates the example of forgetting 2.1 bolus insulin
injections for week according to the following calculations:
Boluses .times. .times. to .times. .times. tak .times. e .times. :
.times. .times. 7 .times. .times. days week * 3 .times. .times.
.times. boluses day = 21 .times. .times. boluses week ##EQU00004##
[0166] Forgetting 2.1 boluses/week corresponds to: 18.9/21=90%
adherence.
[0167] Hence, adherence around 90% increases HbA1c about 0.3-0.4%,
and adherence around 60% results in 1.8% increase in HbA1c. The
same can be done for basal adherence.
[0168] Further, an indication of which of the primary adherence
values in the plurality of primary adherence values cause the
calculated HbA1c increase to be over a threshold value according to
the HbA1c lookup table is communicated. FIG. 9 illustrates this
feature for adherence to a bolus insulin medicament dosage regimen,
where the HbA1c lookup table 238 indicates that a bolus regimen
adherence of between 90 percent and 100 percent (e.g., missing less
than 2 bolus injections per week) can cause an increase in about
0.1% HbA1c 906 for the subject, a bolus regimen adherence of
between 82 percent and 90 percent (e.g., missing 2-3 bolus
injections per week) can cause an increase in about 0.3% to 0.4%
HbA1c 908 for the subject, and a bolus regimen adherence of between
60 percent and 82 percent (e.g., missing more than three bolus
injections per week) can cause an increase in about 1.8% HbA1c 910
for the subject. From FIG. 9 it can be seen which metabolic events
are responsible for which increases in HbA1c. For instance, the
metabolic events occurring when lines 902 and/or 904 fall into zone
910 are causing an increase of 1.8 percent HbA1c.
[0169] FIG. 9 expresses adherence in terms of missed bolus
injections per week. However, bolus injections are temporally
matched to metabolic events (e.g., meal events) in order to provide
a characterization 228 to such metabolic events in some embodiments
of the first data set 210. Thus, in some such embodiments,
adherence is equivalently expressed in terms of percentage of
noncompliant metabolic events 224.
[0170] FIG. 10 illustrates the feature for adherence to a basal
insulin medicament dosage regimen 208, where the HbA1c lookup table
238 indicates that a basal regimen adherence of between 88 percent
and 100 percent (e.g., missing less than one basal injection per
week) can cause an increase in about 0.1% HbA1c 1010 for the
subject, a basal regimen adherence of between 62 percent and 88
percent (e.g., missing 1-2 basal injections per week) can cause an
increase in about 0.2% to 0.3% HbA1c 1012 for the subject, and a
basal regimen adherence of between 50 percent and 62 percent (e.g.,
missing three basal injections per week) can cause an increase in
about 1.8% HbA1c 1012 for the subject. From FIG. 10 it can be seen
which metabolic events are responsible for which increases in
HbA1c. For instance, the metabolic events occurring when lines 1002
and/or 1004 fall into zone 1012 are causing an increase of 0.2 to
0.3 percent HbA1c.
[0171] FIG. 10 expresses adherence in terms of missed basal
injections per week. However, because basal injections are
temporally matched to metabolic events (e.g., fasting events) in
order to provide a characterization 228 to such metabolic events in
some embodiments of the first data set 210, in some such
embodiments, adherence is equivalently expressed in terms of
percentage of noncompliant metabolic events 224.
[0172] Referring to block 428 of FIG. 4C, advantageously, the
plurality of primary adherence values across the first period of
time and/or the superposition of the plurality of primary adherence
values and the plurality of secondary adherence values across the
first period of time can be communicated to a monitor device 250
that is mobile to thereby monitor adherence to a prescribed insulin
medicament dosage regimen for the subject over time. Thus, in some
embodiments, monitor device 250 is a mobile device that includes a
display 208 and the communication of the adherence values includes
presenting them on the display. FIGS. 11 and 12 illustrate this
feature for bolus adherence and basal adherence respectively.
[0173] Referring to block 430 of FIG. 4C, in some embodiments a
second data set 240 is obtained.
[0174] The second data set comprises a plurality of autonomous
glucose measurements of the subject and, for each respective
autonomous glucose measurement 242 in the plurality of autonomous
glucose measurements, a timestamp 244 representing when the
respective measurement was made. In some such embodiments, these
glucose measurements are temporally matched to the plurality of
primary adherence values and/or primary secondary adherence values
over time within the first period of time and the glucose
measurements superimposed onto the primary adherence values and/or
primary secondary adherence values are displayed on the monitor
device 250 so that the subject can get a sense of how adherence
affects glucose values in real time. Referring to block 432 of FIG.
4C, in some such embodiments, the adherence device 250 comprises a
wireless receiver 284 to receive the second data set wirelessly
from a glucose sensor 102 affixed to the subject.
Example 1
[0175] Use of autonomous glucose measurements to identify metabolic
events and to characterize them as insulin regimen adherent or
insulin regimen nonadherent. Block 402 above described how a second
data set 240 comprising a plurality of glucose measurements is
obtained autonomously in some embodiments. In this example, in
addition to the autonomous glucose measurements, insulin
administration events are obtained in the form of insulin
medicament records from one or more insulin pens and/or pumps 104
used by the subject to apply the prescribed insulin regimen. These
insulin medicament records may be in any format, and in fact may be
spread across multiple files or data structures. As such, in some
embodiments, the instant disclosure leverages the recent advances
of insulin administration pens, which have become "smart" in the
sense that they can remember the timing and the amount of insulin
medicament administered in the past. One example of such an insulin
pen 104 is the NovoPen 5. Such pens assists patients in logging
doses and prevent double dosing. It is contemplated that insulin
pens will be able to send and receive insulin medicament dose
volume and timing, thus allowing the integration of continuous
glucose monitors 102, insulin pens 104 and the algorithms of the
present disclosure. As such, insulin medicament records from one or
more insulin pens 104 and/or pumps is contemplated, including the
wireless acquisition of such data from the one or more insulin pens
104.
[0176] In some embodiments, each insulin medicament record
comprises: (i) a respective insulin medicament injection event
including an amount of insulin medicament injected into the subject
using a respective insulin pen in the one or more insulin pens and
(ii) a corresponding electronic timestamp that is automatically
generated by the respective insulin pen 104 upon occurrence of the
respective insulin medicament injection event.
[0177] In some embodiments, a plurality of fasting events, which is
one form of metabolic event 224, are identified using the
autonomous glucose measurements 242 of the subject and their
associated glucose measurement timestamps 244 in the second data
set 240. Glucose measurements during fasting events are of
importance for measuring basal glucose levels, i.e, the glucose
level related to the basal injection events. The basal glucose
level allows evaluation of the effect of the basal injection
event.
[0178] There are a number of methods for detecting a fasting event
using autonomous glucose measurements 242 from a glucose monitor
102. For instance, in some embodiments a first fasting event (in
the plurality of fasting events) is identified in a first time
period (e.g., a period of 24 hours) encompassed by the plurality of
autonomous glucose measurements by first computing a moving period
of variance 4 across the plurality of autonomous glucose
measurements, where:
.sigma. k 2 = ( 1 M .times. i = k - M k .times. ( G i - G _ ) ) 2
##EQU00005##
[0179] and where, G is the i.sup.th glucose measurement in the
portion k of the plurality of glucose measurements, M is a number
of glucose measurements in the plurality of glucose measurements
and represents a contiguous predetermined time span, G is the mean
of the M glucose measurements selected from the plurality of
glucose measurements, and k is within the first time period. As an
example, the glucose measurements may span several days or weeks,
with autonomous glucose measurements taken every five minutes. A
first time period k (e.g., one day) within this overall time span
is selected and thus the portion k of the plurality of measurements
is examined for a period of minimum variance. The first fasting
period is deemed to be the period of minimum variance
min k .times. .sigma. k 2 ##EQU00006##
within the rust time period. Next, the process is repeated with
portion k of the plurality of glucose measurements by examining the
next portion k of the plurality of glucose measurements for another
period of minimum variance thereby assigning another fasting
period. Repetition of this method through all portions k of the
plurality of glucose measurements is used to build the plurality of
fasting periods.
[0180] Once the fasting events are identified, by the method
described above or any other method, a first characterization 228
is applied to each respective fasting event in the plurality of
identified fasting events. Thus, for each respective fasting event
there is a first characterization 228 for the respective fasting
event. The first characterization is one of insulin regimen
adherent and insulin regimen nonadherent. More specifically, here,
the first characterization is one of basal insulin regimen adherent
and basal insulin regimen nonadherent.
[0181] A respective fasting event is deemed basal insulin regimen
adherent when the acquired one or more medicament records
establish, on a temporal and quantitative basis, adherence with the
prescribed basal insulin medicament dosage regimen during the
respective fasting event. A respective fasting event is deemed
basal regimen nonadherent when the acquired one or more medicament
records do not include one or more medicament records that
establish, on a temporal and quantitative basis, adherence with the
prescribed basal insulin medicament dosage regimen during the
respective fasting event. In some embodiments the basal regimen 208
specifies that a basal dose of long acting insulin medicament 210
is to be taken during each respective epoch 212 in a plurality of
epochs and that a respective fasting event is deemed basal insulin
medicament regimen 208 nonadherent when there are no medicament
records for the epoch 212 associated with the respective fasting
event. In various embodiments, each epoch in the plurality of
epochs is two days or less, one day or less, or 12 hours or less
418. Thus, consider the case where the second data set 240 is used
to identify a fasting period and the prescribed basal insulin
medicament dosage regimen 208 specifies to take dosage A of a long
acting insulin medicament 210 every 24 hours. In this example,
therefore, the epoch is one day (24 hours). The fasting event is
inherently timestamped because it is derived from a period of
minimum variance in timestamped glucose measurements, or by other
forms of analysis of the timestamped autonomous glucose
measurements. Thus the timestamp, or period of fasting, represented
by a respective fasting event is used as a starting point for
examining whether the fasting event is basal insulin medicament
regimen adherent. For instance, if the period of fasting associated
with the respective timestamp is 6:00 AM on Tuesday, May 17, what
is sought in the medicament injection records is evidence that the
subject took dosage A of the long acting insulin medicament in the
24 hour period (the epoch) leading up to 6:00 AM on Tuesday, May 17
(and not more or less of the prescribed dosage). If the subject
took the prescribed dosage of the long acting insulin medicament
during this epoch, the respective fasting event (and/or the basal
injection event and/or the glucose measurements during this time)
is deemed basal regimen adherent. If the subject did not take the
prescribed dosage of the long acting insulin medicament 210 during
this epoch 212 (or took more than the prescribed dosage of the long
acting insulin medicament during this period), the respective
fasting event (and/or the basal injection event and/or the glucose
measurements during this time) is deemed basal regimen
nonadherent.
[0182] While the use of the fasting event to retrospectively
determine whether a basal injection event is basal insulin
medicament regimen adherent has been described, the present
disclosure is not so limited. In some embodiments, the epoch is
defined by the basal insulin medicament regimen and, so long as the
subject took the amount of basal insulin required by the basal
regimen during the epoch (and not more), even if after the fasting
event, the fasting event will be deemed basal insulin medicament
regimen adherent. For instance, if the epoch is one day beginning
each day at just after midnight (in other words the basal regimen
specifies one or more basal insulin medicament dosages to be taken
each day, and further defines a day as beginning and ending at
midnight), and the fasting event occurs at noon, the fasting event
will be deemed compliant provided that the subject takes the basal
injections prescribed for the day at some point during the day.
[0183] In some embodiments a fasting event is not detected during
an epoch when, in fact, the basal insulin medicament regimen
specifies that a basal insulin injection event must occur. Thus,
the basal injection should be taken according to the prescribed
basal insulin medicament regimen 208. According to the above use
case, this epoch would not have a basal adherence categorization
for failure to find a fasting event. In some such embodiments,
because the basal insulin medicament dosage regimen 208 is known, a
determination as to the adherence (of the glucose measurement
during the epoch in question and/or the basal injection event in
the epoch) is based on the basal insulin medicament regimen itself
and the injection event data, and thus does not require detecting
the fasting period from the injection event data. As another
example, if the basal insulin medicament regimen is once weekly
basal injection, the exemplary procedure would look for a basal
injection within a seven day window even if a fasting event is not
found.
[0184] In some embodiments, the prescribed insulin medicament
dosage regimen 206 further comprises a bolus insulin medicament
dosage regimen 214 in addition to or instead of the basal insulin
medicament dosage regimen 208.
[0185] In embodiments where the subject is taking more than one
insulin medication type, each respective insulin medicament
injection event in the plurality of medicament records provides a
respective type of insulin medicament injected into the subject
from one of (i) a long acting insulin medicament and (ii) a short
acting insulin medicament. Typically, the long acting insulin
medicament is for a basal insulin medicament dosage regimen 208
whereas the short acting insulin medicament is for a bolus insulin
medicament dosage regimen 214.
[0186] Thus, advantageously, the instant disclosure can also make
use of the bolus insulin medicament injection events, when such
events are available, to provide an additional type of categorized
metabolic event 224 in the first data set 220. In some such
embodiments, the bolus insulin medicament injection events are made
use of in the following way. A plurality of meal events are
identified using the plurality of autonomous glucose measurements
242 and the corresponding timestamps 244 in the second data set 240
using a meal detection algorithm. If no meal is detected, the
process ends. If a meal is detected then a characterization is
applied to the respective meal event. In this way, a plurality of
meal events, with each respective meal event including a
characterization that is one of "bolus regimen adherent" and "bolus
regimen nonadherent" is acquired. Such information can then be used
in the systems and methods of the present disclosure, where each
meal is considered a metabolic event 224 and the characterization
of such meals as "bolus regimen adherent" and "bolus regimen
nonadherent" is the characterization 228 of the metabolic
event.
[0187] In some embodiments, a respective meal is deemed bolus
regimen adherent when one or more medicament records in the
plurality of medicament records indicates, on a temporal basis, a
quantitative basis and a type of insulin medicament basis,
adherence with the bolus insulin medicament dosage regimen 214
during the respective meal. In some embodiments, a respective meal
is deemed bolus regimen nonadherent when the plurality of
medicament records fails to indicate adherence, on a temporal
basis, a quantitative basis, and a type of insulin medicament
basis, with the standing bolus insulin medicament dosage regimen
during the respective meal 608. For instance, consider the case
where the standing bolus insulin medicament dosage regimen
specifies that dosage A of insulin medicament B is to be taken up
30 minutes before a respective meal and that a certain meal that
occurred at 7:00 AM on Tuesday, May 17. It will be appreciated that
dosage A may be a function of the anticipated size or type of meal.
What is sought in the medicament records is evidence that the
subject took dosage A of insulin medicament B in the 30 minutes
leading up to 7:00 AM on Tuesday, May 17 (and not more or less of
the prescribed dosage). If the subject took the prescribed dosage A
of the insulin medicament B during the 30 minutes leading up to the
respective meal, the respective meal (and/or the bolus
administration(s) and/or the glucose measurements during this time)
is deemed bolus regimen adherent. If the subject did not take the
prescribed dosage A of the insulin medicament B during the 30
minutes leading up to the respective meal (or took more than the
prescribed dosage A of the insulin medicament B during this
period), the respective meal (and/or the bolus administration
and/or the glucose measurements during this time) is deemed bolus
regimen nonadherent. The time period of 30 minutes here is
exemplary, in other embodiments the time is shorter or longer
(e.g., between 15 minutes to 2 hours prior to the meal and/or is
dependent upon the type of insulin medicament prescribed).
[0188] In some embodiments the characterization of a metabolic
event as insulin regimen adherent can be determined as a degree or
percentage of insulin regimen adherent depending on the estimated
glycemic effect of taking a dose later than recommended or taking
an amount of dose below or above a recommended dose.
[0189] In some embodiments, a plurality of feed-forward events are
acquired and used to help characterize metabolic events. In some
embodiments, each respective feed-forward event represents an
instance where the subject has indicated they are having or are
about to have a meal. In such embodiments, the plurality of meal
events determined using the autonomous glucose measurements 242 are
verified against the plurality of feed-forward events by either
removing any respective meal event in the plurality of meal events
that fails to temporally match any feed-forward event in the
plurality of feed-forward events.
[0190] In some embodiments, the bolus insulin medicament dosage
regimen 214 specifies that the short acting insulin medicament is
to be taken up to a predetermined amount of time prior to a meal.
In some such embodiments, a respective meal is deemed bolus regimen
nonadherent when there is no insulin medicament record of the short
acting insulin medicament type having an electronic timestamp up to
the predetermined amount of time prior to the respective meal. In
some such embodiments, the predetermined amount of time is thirty
minutes or less, twenty minutes or less, or fifteen minutes or
less.
[0191] In some embodiments, the long acting insulin medicament
consists of a single insulin medicament having a duration of action
that is between 12 and 24 hours or a mixture of insulin medicaments
that collectively have a duration of action that is between 12 and
24 hours.
[0192] Examples of such long acting insulin medicaments include,
but are not limited to Insulin Degludec (developed by NOVO NORDISK
under the brand name Tresiba), NPH (Schmid, 2007, "New options in
insulin therapy. J Pediatria (Rio J). 83(Suppl 5):S146-S155),
Glargine (LANTUS, Mar. 2, 2007, insulin glargine [rDNA origin]
injection, [prescribing information], Bridgewater, N.J.:
Sanofi-Aventis), and Determir (Plank et al., 2005, "A double-blind,
randomized, dose-response study investigating the pharmacodynamic
and pharmacokinetic properties of the long-acting insulin analog
detemir," Diabetes Care 28:1107-1112).
[0193] In some embodiments, the short acting insulin medicament
consists of a single insulin medicament having a duration of action
that is between three to eight hours or a mixture of insulin
medicaments that collectively have a duration of action that is
between three to eight hours. Examples of such short acting insulin
medicaments include, but are not limited, to Lispro (HUMALOG, May
18,2001, insulin lispro [rDNA origin] injection, [prescribing
information], Indianapolis, Ind.: Eli Lilly and Company), Aspart
(NOVOLOG, July 2011, insulin aspart [rDNA origin] injection,
[prescribing information], Princeton, N.J., NOVO NORDISK Inc.,
July, 2011), Glulisine (Helms Kelley, 2009, "Insulin glulisine: an
evaluation of its pharmacodynamic properties and clinical
application," Ann Pharmacother 43:658-668), and Regular (Gerich,
2002, "Novel insulins: expanding options in diabetes management,"
Am J Med. 113:308-316).
[0194] In some embodiments, the identification of the plurality of
meal events from the autonomous glucose measurements 242 in the
second data set 240 is performed by computing: (i) a first model
comprising a backward difference estimate of glucose rate of change
using the plurality of autonomous glucose measurements, (ii) a
second model comprising a backward difference estimate of glucose
rate of change based on Kalman filtered estimates of glucose using
the plurality of autonomous glucose measurements, (iii) a third
model comprising a Kalman filtered estimate of glucose and Kalman
filtered estimate of rate of change (ROC) of glucose based on the
plurality of autonomous glucose measurements, and/or (iv) a fourth
model comprising a Kalman filtered estimate of rate of change of
ROC of glucose based on the plurality of autonomous glucose
measurements. In some such embodiments, the first model, the second
model, the third model and the fourth model are each computed
across the plurality of autonomous glucose measurements and each
respective meal event in the plurality of meal events is identified
at an instance where at least three of the four models indicate a
meal event. For further disclosure on such meal event detection,
see Dassau et al., 2008, "Detection of a Meal Using Continuous
Glucose Monitoring," Diabetes Care 31, pp. 295-300, which is hereby
incorporated by reference. See also, Cameron et al., 2009,
"Probabilistic Evolving Meal Detection and Estimation of Meal Total
Glucose Appearance," Journal of Diabetes Science and Technology
3(5), pp. 1022-1030, which is hereby incorporated by reference.
Example 2
[0195] The following example works through how primary adherence
values 232 are reported for various basal insulin medicament dosage
regimens 208. First, consider a prescribed basal insulin dosage
medicament regimen 208 consisting of two basal injections per
twenty four hours. In some embodiments, the duration of the primary
time window 234 is set at 24 hours, though in other embodiments the
duration of the primary time window 234 is set at some multiple of
24 hours, such as every two days, every three days, and so forth.
If the duration of the primary time window 234 is set to 24 hours,
the number of metabolic events 224 in a given primary time window
234 is one, a single fasting event. If two basal injections are
registered in a given primary time window 234, the injections are
deemed adherent and/or the fasting event is deemed adherent. If one
or zero injections are registered, the fasting event is
non-adherent. In this example, each primary adherence value 232 for
the basal insulin medicament dosage regimen adherence is
categorized based on fasting events. There is a single fasting
event per day, and each such fasting event is independently
categorized.
[0196] Thus, in the scenario of one fasting event per day, a dosage
regimen of two basal injections per day, and a primary time window
234 length of 1 day, each fasting event is categorized as either
regimen adherent or regimen nonadherent based on whether there have
been two injections in the past 24 hours. In accordance with the
present disclosure, the first data set 220 provides a stream of
time stamped metabolic events {M.sub.1, . . . , M.sub.N}, over a
first period of time. This stream of events is used to compute
respective primary adherence values 232 for corresponding primary
time windows 234, with each such primary adherence value 232
representing a corresponding primary time window 234 in the first
period of time 222 represented by the first data set 220. There is
no requirement that the length of primary time window be dictated
by the basal dosing regimen. That is, the duration of the primary
window can be a day, a week, a month, or three months. When the
duration of the primary time window is chosen to be a day, then the
primary adherence value 232 for the primary time window 234 will be
one of two values: 0 (the fasting event of that day is regimen
nonadherent) or 1 (the fasting event of that day was regimen
adherent). That is, the computed primary adherence value 232 for
the primary time window 234 will be a number that is zero or
one.
[0197] In some embodiments primary adherence values 232 and/or
secondary adherence values 244 are scaled so that they fall into a
range other than zero to 1, such as zero to 100, zero to 1000 or
any other suitable scale.
[0198] In the scenario of a single fasting event per day, a dosage
regimen of two basal injections per day, and a primary time window
length of 1 week, each fasting event is categorized as either
regimen adherent or regimen nonadherent based on whether there have
been two injections in the past 24 hours. With the primary time
window 234 duration set at one week, then the primary adherence
value will be one of eight values: 0/7 (regimen nonadherent on all
seven days), 1/7 (one fasting event in one day was regimen
adherent, six other days were regimen nonadherent), 2/7 (fasting
event in each of two days are regimen adherent, five other days
were regimen nonadherent), . . . , 7/7 (regimen adherent on all
seven days). Thus, the primary adherence value is one of 8 values
that range between zero and one. In some embodiments these primary
adherence values 232 are then scaled so that they fall into a range
other than zero to 1, such as zero to 100, zero to 1000 or any
other suitable scale.
[0199] Next, consider a basal insulin medicament dosage regimen 208
that specifies one basal injection per week. There is one fasting
event per day, and each such fasting event is categorized
independently. Each respective fasting event is categorized as
regimen adherent or regimen nonadherent based on whether there has
been an injection in the week prior to the respective fasting
event. Thus, a stream of time stamped metabolic events {M.sub.1, .
. . , M.sub.N}, over a first period of time, is obtained in the
form of the first data set 220. This stream of events is then used
to compute a plurality of primary adherence values, with each such
primary adherence value representing a primary time window 234 in
the first period. There is no requirement that the duration of the
primary time window 234 be dictated by the basal dosing regimen.
That is, here, the duration of the primary time window can be a
week, a month, three months, or any other suitable period of time.
In fact, the duration of the primary time window could be one day,
since each metabolic event (here, a fasting event) is independently
categorized.
[0200] If the duration of the primary time window 234 is set to 24
hours, then each primary adherence value 234 for respective primary
time window 234 will be one of two values: "0" (the fasting event
of that day was regimen nonadherent) or "1" (the fasting event of
that day was regimen adherent). That is, the computed primary
adherence value 234 for the corresponding primary time window 234
will be a number that ranges between zero and one. In some
embodiments, the duration of the primary time window 234 will be
chosen to be one week or longer for more of a gradient between zero
and one. This primary adherence value could be multiplied by the
number of metabolic events in the primary time window or some other
scalar for purposes of conveying information regarding the status
of the subject's health.
Example 3
[0201] The following example works through how primary adherence
values 232 are reported for various bolus insulin medicament dosage
regimens 214. In particular, consider a prescribed bolus regimen
that is one bolus injection for each ingested meal and the duration
of the primary time window 234 is four hours. Here, bolus regimen
adherence is determined based upon metabolic events that are meal
events. There may be three meal events per day, and each such
metabolic event is separately characterized.
[0202] In the case of three meal events per day and a regimen that
specifies a bolus injection before each meal, each meal event is
characterized as either regimen adherent or regimen nonadherent
based on whether there has been a bolus injection prior to that
meal within a predetermined amount of time. Thus, a stream of time
stamped metabolic events {M.sub.1, . . . , M.sub.N} over a first
period of time, is obtained in the form of the first data set 220.
This stream of events is then used to compute a plurality of
primary adherence values, with each such primary adherence value
232 representing a corresponding primary time window 234 in the
first period of time 222. There is no requirement that the duration
of the primary time window 234 be dictated by the timing of
injection events in the bolus dosing regimen. That is, the duration
of the primary time window can be a day, a week, a month, or three
months, or any other suitable amount of time. If the duration of
the primary time window 234 is chosen to be one day, then the
primary adherence value 232 for a corresponding primary time window
will be one of four values: 0 (all three meal events of that day
were regimen nonadherent), 1/3 (one of the three meal events of
that day was regimen nonadherent, and the other two were regimen
adherent), 2/3 (two of the three meal events of that day were
regimen nonadherent, and the other one was regimen adherent), 3/3
(all three meal events of that day were regimen adherent). That is,
the computed primary adherence value for the primary time window
will be a number that ranges between zero and one. This primary
adherence value can be multiplied by the total number of metabolic
events in the primary time window to obtain 3*0 (all three meal
events of that day were regimen nonadherent), 3*1/3 (one of the
three meal events of that day was regimen nonadherent, and the
other two were regimen adherent), 3*2/3 (two of the three meal
events of that day were regimen nonadherent, and the other one was
regimen adherent), 3*3/3 (all three meal events of that day were
regimen adherent). In this instance, the computed primary adherence
value for the primary time window will be a number that ranges
between zero and three. If, on the other hand, the primary window
is chosen to be a week, then the primary adherence value 232 will
be one of 22 values (that range between zero and one (before
multiplication against total metabolic events or some other
scalar).
[0203] In some embodiments, no bolus for a particular meal is
required by the bolus insulin medicament dosage regimen and thus
that meal is adherent even though there was no bolus prior to the
meal. For instance, some bolus regimens only assume a bolus for
dinner and not for breakfast and lunch. Therefore a detected lunch
meal event but no corresponding bolus would be classified as in
adherence.
REFERENCES CITED AND ALTERNATIVE EMBODIMENTS
[0204] All references cited herein are incorporated herein by
reference in their entirety and for all purposes to the same extent
as if each individual publication or patent or patent application
was specifically and individually indicated to be incorporated by
reference in its entirety for all purposes.
[0205] The present invention can be implemented as a computer
program product that comprises a computer program mechanism
embedded in a nontransitory computer readable storage medium. For
instance, the computer program product could contain the program
modules shown in any combination of FIG. 1, 2, or 3 and/or
described in FIG. 4. These program modules can be stored on a
CD-ROM, DVD, magnetic disk storage product, or any other
non-transitory computer readable data or program storage
product.
[0206] Many modifications and variations of this invention can be
made without departing from its spirit and scope, as will be
apparent to those skilled in the art. The specific embodiments
described herein are offered by way of example only. The
embodiments were chosen and described in order to best explain the
principles of the invention and its practical applications, to
thereby enable others skilled in the art to best utilize the
invention and various embodiments with various modifications as are
suited to the particular use contemplated. The invention is to be
limited only by the terms of the appended claims, along with the
full scope of equivalents to which such claims are entitled.
* * * * *
References