Keywords
patient self-care - smartphone - usability testing - survey - diabetes
Background and Significance
Background and Significance
Type 1 diabetes (T1D) is a complex disease that affects 1.2 million people in the
United States and has an estimated worldwide prevalence of 5 to 10% of all cases of
diabetes.[1]
[2] T1D is a chronic condition in which the pancreas does not produce insulin. The lack
of insulin results in elevated blood glucose (BG). Effective treatment requires insulin
injections before meals and throughout the day both to maintain BG as well as to offset
above-target glucose levels. The goal of glycemic management is to maintain BG levels
in a desired target range; avoiding high blood sugars (hyperglycemia) and low blood
sugars (hypoglycemia). Poor glycemic control can lead to devastating complications
including organ damage and increased mortality from heart disease.[3]
[4] Patients who consistently engage in self-management behaviors (SMBs) and are adherent
to standard treatment protocols are more likely to have better glycemic control.[5] Examples of SMBs include monitoring BG, recording carbohydrate intake, administering
insulin boluses (i.e., one large dose, generally between 1 and 15 units) to compensate
for carbohydrate intake, and exercising.[6] Tools such as insulin bolus calculators embedded into glucose meters and insulin
pumps are designed to aid patients in making accurate insulin dosing decisions. However,
even when these tools are available it is still difficult for patients to meet glycemic
goals and little is understood about how patients include bolus calculators into their
self-care beyond measuring frequency of use.[7]
Investigators have primarily studied self-reported data from surveys and interviews
to typify SMB.[5]
[8]
[9] It would be invaluable to understanding therapeutic and behavioral adherence if
we could discern what the patient was thinking at a decision point, e.g. at the beginning
of a meal or exercise routine, and the compensation techniques (CTs) employed at that
moment to self-manage BG. While administering insulin is a SMB, in this study the
behaviors that the patient engages in at the point of consuming a meal or alcohol,
or engaging in exercise, are considered CTs. Patients can employ one or more CTs at
the time of an event which includes: doing nothing, consuming a snack, removing the
pump, adjusting the basal rate (e.g., small dose of insulin released over time such
as 1 unit per hour), administering an insulin bolus, or changing the bolus waveform
(e.g. normal to square).
There is an abundance of smartphone applications (apps) that track exercise and nutrition
for the general population, as well as apps that target patients with diabetes to
facilitate the tracking of BG and medications.[10] Insulin pumps automatically gather glucose readings and insulin-related data, but
provide limited functionalities (e.g., patients only enter carbohydrates when they
desire an insulin bolus suggestion from the bolus calculator) or limited tracking
capabilities for other activities that impact BG, such as exercise or alcohol. Previously,
the authors contrasted data collected from semistructured interviews against data
collected by insulin pumps, glucose meters, continuous glucose monitors, and paper
logs to understand the SMBs and CTs of adults with T1D with respect to alcohol, exercise,
and meals.[11]
[12] A limitation of that study was that participants did not always have the paper logs
readily available to record SMBs related to alcohol, food, and exercise. Replacing
the paper logs for a smartphone app could increase the convenience for participants
as they log behaviors which may improve the accuracy of the logs. To our knowledge,
there are no apps for patients with T1D that allow them to track SMBs and CTs related
to daily activities (e.g. eating a snack or disconnecting from the pump to compensate
for exercise).[10]
[13]
[14] Thus, we have limited insights into the cognitive processes associated with the
management of their diabetes.
Objectives
The objectives of this study were (1) use a patient-centered approach to design iDECIDE,
a smartphone app that gathers real-time diabetes SMBs and CTs related to meals, alcohol
intake, and exercise, and (2) contrast patient's self-reported behaviors on a survey
against self-tracked SMBs and CTs captured with iDECIDE.
Methods
Institutional Review Board approvals (#1878, #3516 and #3689) were granted for the
three phases of the usability testing and deployment of iDECIDE.
Design Phase
A literature search was conducted to gather information on the devices and software
commercially available in the United States for T1D management. We engaged in discussions
with endocrinologists and members of a diabetes care team regarding self-management
challenges faced by patients with T1D. We participated in a simulated training session
with a certified diabetes nurse educator that included hands-on experience with insulin
pumps, glucose meters, and other related devices and software. Based what was learned
regarding the impact that exercise, alcohol, and food have on BG control and T1D SMBs,
iDECIDE's interfaces and functionalities were developed to achieve the following goals:
(1) set up the user profile, such as define target BG, (2) set up SMB goals, such
as exercise every day for 20 minutes, (3) self-track food and alcohol intake plans,
including serving size, type, and carbohydrate content, (4) self-track exercise plans,
describing duration and intensity, (5) self-track techniques used to compensate for
food and alcohol intake and exercise, such as bolus insulin before food intake, and
(6) support researchers' need to remotely access app data synchronized to cloud storage.
iDECIDE First Usability Study
iDECIDE was first built by the research team as a native Android app with Android
Studio, the official integrated development environment for Android apps, and installed
on a smartphone ([Fig. 1]).[15] We conducted a preliminary usability study with five healthy adult participants
who did not have T1D, which was considered an adequate sample size to discover most
problems for novel software.[16] They were given a brief introduction to diabetes and presented with a fictitious
T1D patient profile. Participants were given instructions to think aloud as they interacted
with the app on the provided smartphone.[17] Participants were allowed to explore the app for approximately 5 minutes and then,
one at a time, were given 7 tasks to complete. Two of the tasks were specific to self-tracking
meals, alcohol, and exercise activities, while other tasks were related to setting
up the user's profile or setting goals. The usability testing was recorded with Morae,
a video capture and analysis tool, and the screencast of the smartphone was captured
with Droid@Screen.[18]
[19] Upon completion of the tasks, the participants were given a usability survey, which
was modified from the System Usability Scale (SUS).[20] The audio-video recordings were analyzed and annotated with Morae. Errors and comments
were grouped into themes and the time to complete tasks was measured.
Fig. 1 Screenshots of the app from the first usability test. Participants could self-track
(A) a meal's carbohydrates and current blood glucose, (B) the number and type of alcoholic beverages, and (C) the duration and intensity of exercise. Design changes that resulted from the study
included: removing the slider bars for data entry, changing check boxes into a pull-down
menu, and providing additional features for tracking meals, alcohol, and exercise
which required the design of additional icons.
iDECIDE Second Usability Study
The feedback from the first usability study was used by the research team to improve
the interfaces and functionality of iDECIDE. A prototype was built using Justinmind,
a prototyping tool for Web and mobile apps, and tested by patients with T1D[21] ([Fig. 2]).
Fig. 2 Screenshots of the app from the second usability test. Participants could self-track
(A) a meal item's serving size, number of fruit and vegetable servings, associated carbohydrates,
and current blood glucose; (B) the number and type of alcoholic beverage, associated carbohydrates, and current
blood glucose; and (C) the duration, intensity, start time, and current blood glucose of exercise. Following
this study, the entry of meals and alcohol was streamlined from two interfaces down
to just one interface and users could access the United States Department of Agriculture
nutrition database. Additionally, users could track the compensation techniques employed
at the time of meals and exercise.
Although these six participants were adult patients with T1D, they were provided a
fictitious T1D profile, similar to the first usability study. They were instructed
to think aloud as they interacted with the app during the 5-minute exploration phase
and while completing 7 tasks, one at a time. As before, two of the tasks were specific
to self-tracking meals, alcohol, and exercise. Other tasks dealt with app functionalities
related to the user's profile and setting goals. After completing the tasks, the participants
were given the same usability survey. Morae was used to record the usability sessions
and to analyze the recordings.
Self-Management Survey Design
The survey content was informed by results from semistructured interviews and analysis
of corresponding diabetes device-recorded data collected from patients with T1D.[11]
[12] The objective of the survey was to provide more insight regarding (1) insulin pump
usage behaviors, (2) reported SMBs and CTs for meals, exercise, and alcohol, and (3)
perceptions of the effects of exercise and alcohol on BG control. The survey consisted
of a combination of 11 structured, 14 semistructured, and 8 free-text questions (see
[Supplementary Material:] iDECIDE Recruitment Survey, available in the online version). Questions that inquired
about specific techniques participants employed when compensating for meals, alcohol
consumption, and when engaging in exercise were used for the analysis described in
the “Comparison of Survey Responses versus Self-Tracking iDECIDE Logs” section. The
questions did not ask about the frequency of any behaviors, but rather inquired how
they currently self-manage in certain situations. Many of the CTs were automatically
gathered from structured survey questions, while additional CTs were identified by
manual review of free-text portions of the survey. The self-management survey was
administered electronically in the clinic prior to the pilot testing of the iDECIDE
app.
iDECIDE Pilot Testing
Fourteen adult patients with T1D on insulin pump therapy were recruited to participate
in the study. There were exclusion criteria for portions of study reported in Groat
et al[22] that limited the recruitment to patients using an insulin pump from a specific manufacturer.
After consenting, participants were administered the self-management survey in an
electronic format at the clinic. The app, which was developed by the research team,
was then installed on their personal smartphone. Participants were instructed how
to use the app. For the following 30 days the participants were asked to use iDECIDE
to track meals, exercise activity, and alcohol intake. For each reported event, participants
recorded one or more CTs ([Fig. 3]) which were later used for comparison against the survey (the “Comparison of Survey
Responses versus Self-Tracking iDECIDE Logs” section). The logs were synchronized
to a secure cloud-based server. At the conclusion of the study, 9 of the 14 responded
to a Web link after completing the pilot test to take an exit survey on the usability
of iDECIDE.
Fig. 3 Screenshots of the app from the 30-day pilot study. Participants could (A) self-track carbohydrate content of meals and alcoholic beverages, (B) self-track the start time, duration, and intensity of exercise, and (C) indicate the compensation techniques employed at the time of a meal, alcohol, or
exercise activity. Following the pilot study, the app was designed for the Apple iOS
platform and was also updated to provide advanced meal and alcohol tracking features.
Comparison of Survey Responses versus Self-Tracking iDECIDE Logs
Responses related to CTs from the self-management survey and self-tracking logs from
the pilot test of iDECIDE were quantified. We examined concordance between survey
responses and self-tracking logs. The CTs reported by each participant from the survey
were noted separately for meals, alcohol, and exercise. In the case of meals, the
total number of meals reported by the participant was used to determine the percentage
of meals for which there were one or more matches with the survey. A concordance of
100% indicates that every meal reported by the participant with iDECIDE had at least
one CT that matched one of the CTs reported on the survey for that event type.
Results
iDECIDE: First Usability Study
Five graduate students were recruited and reported an average of 5.5 years of smartphone
ownership. The average number of errors and time to complete the two tasks related
to self-tracking meals, alcohol, and exercise are shown in [Table 1]. Each task took an average of approximately 2 minutes to complete, with 2.0 errors
occurring when tracking a meal and 3.4 errors occurring when tracking exercise. As
the audio-video recordings were analyzed, usability issues emerged that were related
to self-tracking, such as lack of flexibility for entering nutritional information
for meals and difficulty viewing the numeric values beneath the slider bars ([Table 1]). The average subjective usability rating from the SUS questionnaire was 76.4, which
can be interpreted that the app was good and acceptable, where a score of 100 indicates
the best imaginable and acceptable.[23]
Table 1
Average duration in minutes, number of errors, and usability issues identified for
each self-tracking task with the iDECIDE app
|
Task
|
Duration (min)
|
# of Errors
|
Usability issue
|
|
First usability study
|
|
Self-track meal with
alcoholic beverage
|
1.95
(0.74)
|
2.0
(0.71)
|
Icons confusing,
functionality missing,
slider bar inconvenient
|
|
Self-track exercise
|
2.24
(0.76)
|
3.4
(1.52)
|
Functionality missing,
slider bar inconvenient
|
|
Second usability study
|
|
Self-track meal with
nonalcoholic beverage
|
2.56
(0.53)
|
1.3
(0.5)
|
Goals versus plans
confusing
|
|
Self-track exercise
|
1.35
(0.74)
|
0.3
(0.5)
|
Goals versus plans
confusing
|
Note: All values are reported as mean and standard deviation (SD).
This preliminary study was instrumental in the iterative design of the app. Following
this study and prior to conducting the second usability study, slider bars were removed
and check boxes ([Fig. 1B]) were replaced with pull-down menus ([Fig. 2B]). More granularity was added to the tracking of meals, alcohol, and exercise which
required the creation of additional icons, such as a measuring cup to represent serving
size ([Fig. 2B]). A food description became part of logging meals and alcohol and more than one
food item could be logged at one time ([Figs. 2A] and [B]), for example, a single meal entry could consist of soup, breadsticks, and a beverage.
Specifying a start time for exercise was added and more than one exercise segment
could be tracked such that a single exercise event could consist of 10 minutes of
light exercise at 7:00 a.m. followed by 20 minutes of moderate exercise at 7:10 a.m.
iDECIDE Second Usability Study
Six adults with T1D receiving care from an outpatient endocrinology department were
recruited. The average years of smartphone ownership was 6.5. The average time to
complete each task related to self-tracking of meals, alcohol, and exercise and the
number of errors per task are presented in [Table 1]. Usability issues related to the self-tracking tasks are also listed in [Table 1]. The time to track a meal took longer than in the first usability study, and this
can be attributed to the additional meal tracking features that were implemented.
There were fewer errors for tracking exercise and alcohol in the second usability
study, suggesting that the design changes were impactful. The main problem was the
confusion participants had differentiating between short-term plans (e.g., drink one
cocktail tonight) and goals (e.g., drink less than two units of alcohol every weekend).
The average subjective usability rating from the SUS questionnaire was 79.9, which
indicates the app was good and acceptable.
There were two problems identified in the second usability testing which required
design changes that were implemented prior to the pilot test. First, to resolve issues
related to differentiating plans from goals, the app's references to recording plans
([Figs. 2A] and [B]) were replaced with the notion of logs ([Figs. 3A] and [B]). Second, the process for tracking meals and alcohol was streamlined into one interface
([Fig. 3A]) instead of two separate interfaces ([Figs. 2A] and [B]). Due to the prototype environment, carbohydrate counting assistance was not implemented
during the usability study but was included in the pilot-test of the app. Conversely,
providing a current BG reading was part of the second usability study ([Figs. 2A]–[C]) but not the pilot study ([Figs. 3A] and [B]). The United States Department of Agriculture (USDA) nutrition database was accessible
via text search within the app and populated the food description, serving size, and
carbohydrates of meals and alcohol ([Fig. 3A]). Also implemented in the pilot test was the addition of self-tracking CTs for meals,
alcohol, and exercise ([Fig. 3C]).
iDECIDE Pilot Test
Fourteen adults with T1D on insulin pump therapy were recruited and completed the
survey on SMBs. One participant withdrew from the study. Nine participants responded
to the usability exit survey.
On the exit survey, participants were asked to rate several features of the iDECIDE
app with a 1 to 5 Likert scale rating, with 5 being a positive rating. The average
rating across all questions was 3.8. The responses on the survey were generally positive
with most participants noting deficiencies with self-tracking meals. Users desired
specific functionalities for logging meals, such as maintaining a list of favorites
and more flexible searching capabilities for accessing the nutritional content of
food from restaurants and grocers while the interface for tracking exercise was well
received. The app interfaces in [Fig. 4] depict how the desired functionalities were implemented. Users can use text searching
to access: the USDA nutrition database, grocery data, and restaurant food data from
the Nutritionix database, and in the case of alcohol, an internal database contains
carbohydrate information for common drinks ([Fig. 4A]).[24]
[25] A meal review page is provided which allows users to view all the components of
the meal and to document the meal with a photo ([Fig. 4B]). Unchanged from the pilot test, users indicate how, if at all, they are compensating
for the meal, alcohol, or exercise ([Fig. 4C]). Users can build a list of favorite foods to quickly populate the food description
and carbohydrate content ([Fig. 4D]). Alternately, barcode scanning can be used to search grocery foods and their nutritional
content ([Fig. 4E]).[25] Relatively unchanged is the interface for self-tracking exercise ([Fig. 4F]).
Fig. 4 Resulting app interfaces for the Apple platform based on user feedback includes the
following functionalities for self-tracking meals: (A) carbohydrate counting assistance with textbox search function to access restaurant,
grocery Nutritionix databases, and the United States Department of Agriculture food
nutrition database, and an internal database of common alcoholic beverages; (B) blood glucose readings that sync from a HealthKit connected meter, meal component
breakdown, and meal documentation with a photo; (C) compensation techniques employed for meals, alcohol, and exercise events; (D) favorite foods list defined by the user; and (E) barcode scanning to access grocery food data. The interface (F) for tracking exercise duration, intensity and start time was relatively unchanged.
Comparison of Survey Responses versus iDECIDE Self-Tracking Logs
The survey helped to identify CTs for exercise and alcohol, along with reasons for
employing those. Also, the survey identified factors that participants said affected
BG control with regard to exercise and alcohol. Most importantly, the study confirmed
the considerable inconsistency patients have about incorporating exercise and alcohol
into decisions about SMBs. More details on the survey responses can be found at Groat
et al.[26] The comparison between the survey response and the iDECIDE self-tracking logs is
detailed below.
Carbohydrates
Thirteen participants logged 1,710 meal events and 1,204 CTs with the iDECIDE app.
The frequency of CTs self-tracked with the app for meals were: the pump's bolus suggestion
(64.2%), participant's bolus choice (14.7%), no compensation (16.5%), square wave
bolus adjustment (3.9%), basal adjustment (0.4%), or other (0.3%).
On average (standard deviation), there was 77% (25) concordance of the 1,204 meal
events, where CTs the participants logged with the app matched to those that they
had indicated on the survey. In the other 23% (25) of the meal events, participants'
logged CTs differed from those reported in the survey ([Fig. 5A]).
Fig. 5 Comparison of number of participants and their survey responses against self-tracking
with the app for: (A) meals, (B) alcohol, and (C) exercise. Compensation techniques were: basal adjustment (basal), square waveform
bolus adjustment (square), participant's bolus choice (my bolus), pump's bolus suggestions
(pump bolus), consume a snack (snack), disconnect from the pump (no pump), other technique
(other), and no compensation (none).
Alcohol
There were 10 participants that self-tracked alcohol intake with the app. Participants
logged 86 alcohol events and 76 CTs. The CTs logged by the participants with the app
when consuming alcohol were: following the pump's bolus suggestion (44.9%), no compensation
(34.6%), participant's own bolus choice (15.4%), square wave bolus adjustment (2.6%),
basal adjustment (1.3%), or other (1.3%).
Two of the CTs from the survey options did not match exactly with the selections available
on the app. For the analysis of alcohol behaviors, we mapped the category “insulin
bolus” from the survey to the app's choices of “pump's bolus suggestion” and “my bolus
choice.” “Eating a snack” from the survey was mapped to the app option “other technique.”
The inconsistency between the survey and the logs was higher for alcohol tracking
than with meal tracking. There was very low and variable concordance of 35.4% (35)
for the 86 alcohol events where participants' log entries matched their survey responses.
Four participants reported on the survey that they did not compensate for alcohol
in any fashion, but there were seven participants that self-tracked no compensation
with the app ([Fig. 5B]).
Exercise
Thirteen participants logged a total of 181 exercise events along with 166 CTs. The
CTs self-tracked with the app were: no compensation (71.1%), disconnecting from the
pump (18.1%), snack intake (4.8%), basal adjustment (4.2%), my bolus choice (1.2%),
or other (0.6%).
The concordance for exercise had high variability and was found to be 46% (41) for
the 181 exercise events. Eight participants reported adjusting the basal rate to account
for exercise on the survey, but only 2 participants self-tracked this technique in
the app. Also, in the survey, 11 participants reported eating a snack while exercising,
while only 4 participants self-tracked this technique with the app. [Fig. 5C] depicts the discrepancies between survey responses and logs from the app regarding
exercise.
Discussion
Most studies that aim to understand T1D SMBs rely on self-reported data gathered from
surveys or interviews.[5]
[8]
[26]
[27]
[28]
[29]
[30]
[31] While these studies are informative, they are subject to recall error (i.e., inaccurately
remembering and reporting behaviors) and social desirability (i.e., overreport favorable
behavior and underreport poor behavior).[28]
[32] By using devices to facilitate real-time self-tracking, we can circumvent those
limitations and gain additional insight into behavioral patterns. There are few freely
available apps that support the tracking of BG, insulin, meals, and exercise.[13] Although there are many reviews that attempt to assess the current state of mobile
applications used in randomized clinical trials of T1D, to the best of our knowledge
there are no apps or diabetes devices that collect data on SMBs and CTs in real-time
and at the level of granularity that has been done with iDECIDE for this study.[14]
The self-management survey and 30-day pilot test of iDECIDE is a continuation of previous
work by the authors,[11]
[12] where we replaced semistructured interviews with a more detailed survey and paper-based
logs with a smartphone app for self-tracking meals, alcohol, and exercise, and for
reporting CTs. When comparing behaviors from the survey and the app, we found a high
level of concordance when compensating for meals and a high level of discordance and
variability for alcohol and exercise. A limitation of the survey was asking participants
about their current SMBs and not inquiring about their frequency. Also, the survey
was administered before self-tracking with iDECIDE, and it is possible that concordance
levels could have been higher if the survey was administered after pilot testing iDECIDE
as participants may have reacted to the self-tracking by changing behaviors. Nonetheless,
the results from this study and the aforementioned studies indicate that patients
may misreport behaviors, particularly on surveys, but more importantly they indicate
a need for developing better educational tools and programs to improve patients' ability
to understand the effects of lifestyle choices, such as exercise and alcohol, which
may lead to better glucose control.
Out of the 1,000+ diabetes apps available for download, it is unknown how many undergo
usability testing, and it is unknown if patients or clinicians were involved during
the design phases.[33]
[34] The users that participated in the various studies described herein had substantial
experience with smartphones and were highly educated and this may have influenced
their desires for more features to be included with the app, which contradicts research
that indicate fewer features improve the usability of diabetes apps.[35] Further testing with users that have less smartphone experience or who do not use
insulin pumps is needed to ensure that the advanced features of iDECIDE do not hinder
usability for all patients with T1D. Based on two usability studies and user feedback
from the 30-day pilot test, several new features were included for tracking meals
and assisting with carbohydrate counting: carbohydrate content of common alcoholic
beverages, barcode and text search access to food databases (e.g., USDA, restaurants,
and grocery), user-generated favorite foods list, and photograph documentation of
meals ([Fig. 4]). iDECIDE also seamlessly shares data with Apple's HealthKit, including glucose
readings, exercise, nutrition, and body weight.[36] Furthermore, the data locally stored by iDECIDE is also synchronized with cloud
services and can be remotely accessed through a password-protected Web-based interface.
Although this study did not assess the effectiveness of the app in improving outcomes
nor did we address the need for better educational tools, this study and others have
found that adherence to expected behaviors varies greatly and that undesirable behaviors
frequently occur.[5]
[11]
[12]
[26]
[27] Current behavioral interventions are based on the “one size fits all” approach which
standardizes diabetes education.[37] Group diabetes education has been compared against individualized instruction, but
those studies did not use patient-reported data to influence individualized instruction.[38] Initially, our goal in designing iDECIDE was to improve postprandial BG by accounting
for meals, exercise, and alcohol.[22] We realized that iDECIDE could easily be extended to gather real-time data that
would help us better understand behaviors at the time decisions are made. With iDECIDE
as a tool to measure CTs, we are now conducting a pilot study to identify unique self-care
profiles based on SMBs and CTs logged in real-time. Individualized data-driven diabetes
education will then be delivered which will be followed by self-tracking with iDECIDE
for a second time to measure any changes in behaviors.
Conclusion
This study contrasted diabetes self-care behaviors of adults with T1D as self-reported
on a survey against logs generated by self-tracking in real-time with the iDECIDE
app. The study found inconsistencies between CTs reported by surveys and CTs documented
with the app. Clinicians should use caution when interpreting patient's survey responses
regarding exercise and alcohol behaviors when assessing adherence. The insights gathered
from this study reveal the need for improved education and decision support tools
to help patients on insulin therapy integrate alcohol and exercise behaviors as they
self-manage BG. iDECIDE is currently being used in a pilot study to determine the
effectiveness of data-driven educational interventions in improving SMBs.
Clinical Relevant Statement
Clinical Relevant Statement
A user-friendly, smartphone app that empowers T1D patients to track in real-time their lifestyle choices and diabetes self-management behaviors
(SMBs) and compensation techniques (CTs) related to food, alcohol, and exercise was
designed and pilot tested.
Inconsistencies between SMBs and CTs reported on a survey and documented with the
app we developed indicate that patients may misreport both types of behaviors. Clinicians should use caution when making diabetes care decisions that require interpreting
SMBs and CTs related to alcohol and exercise reported by patients through surveys.
Multiple Choice Question
When contrasting self-reported versus real-time diabetes compensation techniques (CTs)
as captured by a survey and a smartphone app, which behaviors were found to be highly
(more than 70%) consistent?
-
Exercise behaviors
-
Sleep behaviors
-
Meal behaviors
-
Alcohol behaviors
Correct Answer: The correct answer is option c. Comparisons of survey responses and iDECIDE logs
showed high concordance of 77% for CTs related to meals, but low concordance of 35%
and 46% for alcohol and exercise events, respectively. Sleep behaviors were not quantified.
