JMIR FORMATIVE RESEARCH

Roux de Bézieux et al

Original Paper

Medical Food Assessment Using a Smartphone App With
Continuous Glucose Monitoring Sensors: Proof-of-Concept Study
Hector Roux de Bézieux1,2,3, MA, MSc; James Bullard1, PhD; Orville Kolterman1, MD; Michael Souza1, BSc; Fanny
Perraudeau1, PhD
1

Pendulum Therapeutics, Inc, San Francisco, CA, United States

2

Division of Biostatistics, School of Public Health, University of California, Berkeley, Berkeley, CA, United States

3

Center for Computational Biology, University of California, Berkeley, CA, United States

Corresponding Author:
Fanny Perraudeau, PhD
Pendulum Therapeutics, Inc
933 20th Street
San Francisco, CA, 94107
United States
Phone: 1 650 276 6517
Email: fanny.perraudeau@pendulum.co

Abstract
Background: Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable newparadigms of clinical research. A new generation of continuous glucose monitoring (CGM) devices provides access to clinical-grade
measurement of interstitial glucose levels. Adoption of these sensors has become widespread for the management of type 1
diabetes and is accelerating in type 2 diabetes. In parallel, individuals are adopting health-related smartphone-based apps tomonitor and manage care.
Objective: We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinicalstudy measures of glucose levels without clinic visits.
Methods: Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical foodon glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint wasincremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided avisual diet log.
Results: All subjects completed the study and mastered CGM device usage. Over 40 days, 3000 data points on average per
subject were collected across three sensors. No adverse events were recorded, and subjects reported general satisfaction withsensor management, the study product, and the smartphone app, with an average self-reported satisfaction score of 8.25/10.
Despite a lack of sufficient power to achieve statistical significance, we demonstrated that we can detect meaningful changes in
the postprandial glucose response in real-world settings, pointing to the merits of larger studies in the future.
Conclusions: We have shown that CGM devices can provide a comprehensive picture of glucose control without clinic visits.
CGM device usage in conjunction with our custom smartphone app can lower the participation burden for subjects while reducing
study costs, and allows for robust integration of multiple valuable data types with glucose levels remotely.
Trial Registration: ClinicalTrials.gov NCT04424888; http://clinicaltrials.gov/ct2/show/NCT04424888.
(JMIR Form Res 2021;5(3):e20175) doi: 10.2196/20175
KEYWORDS
clinical trials; continuous glucose monitoring; lifestyle modification; mobile app; telemedicine; diabetes

Introduction
Diabetes is one of the most prevalent chronic diseases in theworld, impacting over 422 million people worldwide [1]. In thehttps://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

United States alone, type 2 diabetes (T2D) affected over 32
million individuals as of 2018 [2]. Western lifestyle, including
diet, has been shown to play a clear role in the development of
the disease [3-5]. Although a large and growing number of drugs
JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 1
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
have been approved for the treatment of T2D, no cure oruniversally effective pharmacological intervention yet exists.
By contrast, studies where sustained dietary and behavioral
changes were made (eg, caloric restriction) have shown profoundimprovements in glucose control in subjects who are able toadhere to the regimen [6]. For example, in a study conductedby Lean et al, 306 individuals with T2D were given an
850-calorie diet. While only 24% achieved the primary end goalof weight loss of ≥15 kg, 86% of subjects who achieved thegoal saw remission of their diabetes by 12 months [7]. Suchlow compliance, which is routinely observed in trials withdietary and behavioral changes, limits the interpretability of theintervention. In addition, rigorously accounting for such attrition
in a randomized controlled trial (RCT) can dramatically increase
the cost and complexity of conducting such studies, especiallyover long periods of time. As a consequence, rigorous trials of
feasible dietary changes in routine clinical practice settings are
frequently not performed, which in turn limits the body ofclinical evidence available to guide treatment [8,9]. Studies thatare less invasive in terms of the participants’ usual daily
activities may enable collection of endpoint measurements
matching the quality of traditional clinical study assessments
while decreasing dropouts and improving the tracking ofcompliance.
The growing use of mobile technologies, wearable biosensors,
and telemedicine in RCTs allows for closer patient monitoringand increased engagement while limiting overhead [10]. If asensor can provide high-quality measurements of relevant
clinical metrics, it may be leveraged to conduct robust trials
outside the traditional clinical trial setting. In the context ofdiabetes, continuous glucose monitoring (CGM) devices
represent a widely used type of sensor. A new generation ofdevices [11] has overcome earlier drawbacks of previous CGM
devices, including cost and the continuing need for calibration[12], and has gained regulatory approval [13-15]. Consequently,
the number of clinical trials using CGM devices to measure
primary or secondary endpoints has increased [16-22],
permitting new innovative approaches and clinical trial designs.
For example, in a previous report [23], 36 subjects were enrolled
remotely and received CGM devices via shipments. Instructions
on CGM application and usage were given remotely, and 34
subjects used the devices correctly more than 95% of the time.
Such recent results may enable and support the conduction ofrobust investigations outside the usual clinical trial setting.
Although CGM deployment in clinical research is now wellestablished, the advent of this new generation of glucose
monitors has led to a large increase in studies relying mostly orsolely on CGM data as primary outcome measures. Despite this,most studies do not fully leverage the potential of CGM devices,
as they are deployed only for specific time periods in the study,
for example, the first and last days of the trial [17-21], and when
conducting interventions and monitoring while attending aclinical center [24,25]. Furthermore, integrating and augmenting
the data provided by CGM devices with other data types, suchas food logs and activity data, are still relatively unexplored.
In this manuscript, we present the results from a 6-week,
double-blind, randomized, placebo-controlled, exploratory 2×2
https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

Roux de Bézieux et al
cross-over study (n=6). A custom smartphone app was used torecord food intake, exercise, and alcohol consumption andintegrate these data with CGM data to construct a visual dietlog in sync with concurrent glucose levels. In a concomitantstudy [26], we observed changes in postprandial glucose
response in patients with T2D who consumed a five-strain
synbiotic medical food manufactured by the study sponsor. The
postprandial glucose response was measured following astandardized meal-tolerance test (MTT) administered at theclinic. In parallel, we were also interested in following subjects
using the medical food while they pursue their daily activities
unencumbered by the disruptions introduced by required visits
to a research clinic. Thus, in this exploratory study, subjects
consumed the medical food, and the postprandial glucose
response was measured using CGM devices via aself-administered MTT at home. The aim of this study was todemonstrate the feasibility of measuring clinically relevant datafor research purposes using CGM devices and a smartphone
app while subjects pursue their normal daily activities.

Methods
Study Design
A double-blind, randomized, placebo-controlled cross-overdesign was used to compare responses to the following two
experimental interventions: a synbiotic medical food product,
which was provided by the study sponsor, in the form of threecapsules taken twice a day, and an inactive placebo present insimilar capsules (Multimedia Appendix 1). Subjects wore CGM(Freestyle Libre) devices through the entirety of the study
according to the manufacturer’s instructions for use. At baseline,
beginning/end of each period, and washout, subjects were asked
to collect a stool sample. The concentrations of the bacterial
strains, which were ingested in the medical food, in those stool
samples were estimated using quantitative polymerase chain
reaction (qPCR). Strain concentrations were averaged acrossthree replicates. One subject (Subject 4) did not provide stool
samples, so this part of the analysis was performed in fivesubjects. Anthropometric characteristics were also measured.
Throughout the study, subjects used a custom smartphone app
for the Android platform. This app was developed to facilitate
the real-time collection and integration of glucose levels, avisual food log, an alcohol log, and an exercise log, as well as
record scanning compliance (how often do subjects scan their
CGM devices) and study events (such as MTT). Through theapp, subjects could take pictures and annotate them to describe
their food consumption. They could also submit text annotations
without pictures to log events such as exercise. Screenshots ofthe mobile app can be found in Multimedia Appendix 2 andMultimedia Appendix 3.
The goal of this exploratory study was to determine how tocollect and analyze appropriate glucose metrics when conducting
a medical food study with CGM devices and a novel smartphoneapp. This includes measuring operating characteristics(sensitivity and specificity) and sources of variance for thosemetrics when determined with Freestyle Libre glucose sensors,
as well as identifying proper statistical tests of glucose metrics,
for example, to test for changes in areas under the curve (AUCs)
JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 2
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
for glucose derived from self-administered MTTs between active
and placebo groups. Additional endpoints of interest werechange in body mass, change in fecal probiotic strain
concentration, expected lifespan of CGM sensors, CGM sensor
scanning, photo logging of compliance rates, and usability
feedback.
Subjects were randomly assigned to the two study arms (study
product then placebo or vice versa). Subjects were assigned inpairs so that each arm contained the same number of subjects.
Study staff and subjects were blinded to the identity of the study
product. Randomization was performed by a statistician notdirectly involved in study conduct. Both periods lasted 13 to
17 days and were separated by a washout period of 3 to 6 days.
Details of the durations of the study periods for each subject
can be found in Multimedia Appendix 4.
This study was approved on March 13, 2018, by the Aspire
Institutional Review Board (IRB) (protocol number WB01-205).
Informed consent was obtained from each subject prior toparticipation. Since it was a prospective study, it was
retrospectively registered on ClinicalTrials.gov (registration
number NCT04424888).

Subject Enrollment and Characteristics
Since this was an exploratory study, the main inclusion criterion
was a minimum age of 17 years. Key exclusion criteria werepregnancy; continuous parenteral nutrition; and current use orplanned use of antibiotic, antifungal, antiparasitic, or antiviraltreatment during the study. Subjects were recruited by word of
mouth from members of the biotechnology research community
in San Francisco, California. A total of six subjects wereenrolled in the study. Subjects were remunerated in the form ofan Amazon gift card of US $100 for successful completion ofeach of the two study periods. Subjects had a run-in of 3 to 5

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

Roux de Bézieux et aldays before the first period (product/placebo) to familiarize
themselves with the CGM devices and demonstrate that they
could employ the devices as instructed. Subjects were shownhow to use their CGM devices and how to log their meals andexercise and take pictures of their food intake through thesmartphone app. Throughout the baseline period, they could
ask questions about CGM usage. All subjects mastered correct
use of the CGM devices and the smartphone app underobservation by the study coordinator at the clinic. This consistedof properly collecting 24 hours of data and demonstrating theability to take pictures and make annotations through the app.

Data Collection
The Freestyle Libre CGM devices used in this study provide
estimates of interstitial fluid glucose levels. At 15-minuteintervals, the estimates are recorded in the device and stored
for up to 8 hours. Whenever the CGM receiver is wanded overthe device, the 8 hours of data in the device at that time aredownloaded for storage in an external database. These data canbe used to display a subject’s glucose levels at various timeintervals. For a subject who started with the study productfollowed by washout (WS1), crossed over to placebo, and ended
with a second washout (WS2) (Figure 1A), for example, we canvisualize the glucose level throughout the study (Figure 1C) or
zoom in on a specific day (Figure 1D). Glucose levels as
estimated by the CGM device can be complemented by otherdata sources. A visual food log was constructed as subjects
logged pictures of their meals through the smartphone app. As
evidenced in Figure 1D, the recorded meal was associated witha spike in glucose levels. To facilitate interpretation of CGM
data, we developed a peak detection algorithm based on
methodology from Palshikar [27] that can automatically detect
glucose peaks (Multimedia Appendix 1).

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 3
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Roux de Bézieux et al

Figure 1. Glucose and dietary data collected for one subject. All data types collected for one subject. (A) Subject 1 starts in the active product arm (14days) and then moves to the placebo arm (14 days). (B) At baseline, the end of each period, and study end, the subject provides stool samples. Strainspresent in the product are detected at the end of the active product period and a little after (arrow mark). (C) Continuous glucose monitoring (CGM)
glucose levels are also tracked throughout the 6 weeks. (D) The values can be compared to the fingerstick measurements or used in coordination withpictures taken (here chicken wings at 7 PM) to detect meal-related glucose excursions. AMUC: Akkermansia muciniphila; BINF: Bifidobacterium
infantis; BSL: baseline; CBEI: Clostridium beijerinckii; CBUT: Clostridium butyricum; EHAL: Anaerobutyricum hallii; WS: washout.

To ensure that their CGM devices were appropriately inserted,
subjects also used conventional fingerstick devices (FreeStyleor CVS Health Blood Glucose Meter) to measure capillary
glucose levels several times during the study and reported thevalues through the app. Fingerstick and CGM measurements
https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

were in good agreement (Multimedia Appendix 5) and were
within the expected range [13].
At the end of the study, after unblinding, the clinical coordinatorconducted an interview with all subjects, excluding Subject 5.
JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 4
(page number not for citation purposes)

JMIR FORMATIVE RESEARCHThis interview was guided by a predefined template (Multimedia
Appendix 6). Questions focused on the following categories:
study design, product, CGM device, mobile app, behavior
changes, future studies, and closing remarks, and there was a
mix of open-ended and closed-ended questions. Subjects werealso asked if they would participate in another similar study andif they would recommend others to a study like this, on a scaleranging from 1 (no) to 10 (absolutely).
Finally, to assess the presence of our strains, stool samples werecollected throughout the study, and strain concentrations were
measured by qPCR of frozen fecal samples. qPCR analysis was
conducted in five of the six subjects who provided stool samples.
In Figure 1B, strain concentration information is overlaid witharm assignment. As expected, we only detected the presence ofthe strains after study product administration.

Measuring the Response to a Fasted MTT
The fasted MTT is a gold standard test to measure glucose
control in subjects. A standardized meal was to be consumed
in the morning after fasting for at least 6 hours, and subjects
were asked to ingest no additional calories for one additional
hour. All subjects ingested the same standardized meal asfollows: a Boost nutritional drink containing 45 g ofcarbohydrates including 22 g of sugars [28]. The MTT was
performed four times throughout the study by each subject (atthe start and end of both the placebo and product periods).
Subjects recorded when they ingested the Boost drink throughthe smartphone app, and corresponding glucose levels wereobtained from the CGM devices. The incremental AUC for
glucose levels was calculated for each time point and subject[29]. The baseline for the incremental AUC was defined as the
glucose level at t=0, when the Boost ingestion began, as
recorded by the CGM device.
The AUCs at the beginning and end of the placebo/productperiods were compared using ΔAUC as follows:
ΔAUC = AUCEnd – AUCBeginning(1)Finally, the cross-over design allowed the comparison of ΔAUC
between the placebo and active products (ΔΔAUC). We
performed a one-sided Wilcoxon signed-rank test to test thenull hypothesis (H0: ΔΔAUC ≥0 versus H1: ΔΔAUC <0).

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

Roux de Bézieux et al

Results
Subjects and Sensors
All six enrolled subjects completed the study. No adverse eventsor major deviations from the protocol were reported. All
hypoglycemic events (defined as excursion below 70 mg/dL
for at least two consecutive data points, ie, 30 minutes) aresummarized in Multimedia Appendix 7, according to therecommendations of Schnell et al [30]. In accordance with
manufacturer recommendations, CGM sensors were worn for
an average of 13 consecutive days. The first sensor was placed
under the supervision of the study coordinator. Subjects thenplaced their second and third sensors by themselves. Foursubjects (1, 3, 5, and 6) had to replace one sensor each after
only 10 days, mostly because the sensor was dislodged while
dressing. Multimedia Appendix 8 recapitulates a few
glucose-based subject-level metrics, according to the report by
Schnell et al [30].
During interviews, subjects reported that CGM usage was apositive experience (Multimedia Appendix 9). However, two
subjects inadvertently dislodged sensors because of clothingsnags. Two subjects also reported minor soreness shortly after
CGM implantation.

Fasted MTT Using CGM Devices
At four time points throughout the study, subjects performed
MTTs as described above. MTTs were performed just before
the start and at the end of the placebo/product periods. The
standardized MTT data were used to compare a potential change
in the glucose response for a fixed nutrient intake. If the study
product had the effect of improving a subject’s glucose
metabolism, a decrease in the peak glucose level and/or AUC
would be expected.
As shown in Figure 2, most subjects displayed an increase inglucose levels after taking the standardized meal. Eachindividual performed four MTTs (one at the beginning of eachperiod, one at the end of the placebo period, and one at the endof the product period). Since the CGM devices used in this study
generated a glucose measurement every 15 minutes, the curves
are quite smooth, which allowed a robust computation of the
AUC. The MTT data were obtained without additional subject
engagement other than ingesting the standardized meal at home.

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 5
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Roux de Bézieux et al

Figure 2. Results of the meal tolerance test (MTT). Glucose response to standardized meals for all six subjects in the study. Area under the curve values
for the beginning of placebo/product, end of placebo, and end of product periods are in gray, yellow, and green, respectively. The MTTs are annotatedby the subjects at time t=0 using the smartphone app. Glucose levels are normalized by the glucose level at t=0.

For Subjects 1, 2, and 4, the AUC decreased on ingesting theactive study product, and ΔAUCProduct was smaller than
ΔAUCPlacebo. For Subjects 3, 5, and 6, the AUC increased oningesting the product, and ΔAUCProduct was greater than
ΔAUCPlacebo (Table 1 and Multimedia Appendix 10). Owing to

the scale (n=6) and design of this pilot study, there is very
limited power. In addition, none of the subjects were diagnosed
with T2D, so we do not expect to find statistically significant
results. Accordingly, the one-sided Wilcoxon signed-rank test
had a P value of .28.

Table 1. Subjects ranked by baseline value of area under the glucose curve.

aSubjecta

Baseline AUCb (mg/dL/min)

ΔAUCPlacebo (mg/dL/min)

ΔAUCProduct (mg/dL/min)

ΔΔAUC (mg/dL/min)

6

265.3

1035.1

1683.2

648.1

3

737.0

1027.1

1598.3

571.2

5

834.8

102.8

552.8

450.0

4

1557.6427.7−933.0−1360.7

12084.2−221.6−1213.9−992.323931.81309.1−978.1−2287.2

For Subjects 1, 2, and 4, the AUC decreased when taking the product and ΔAUCProduct was smaller than ΔAUCPlacebo.

b

AUC: area under the curve.

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 6
(page number not for citation purposes)

JMIR FORMATIVE RESEARCHAs this was a small pilot study, we did not use any baseline
metric to stratify subjects based on their initial glucose control
levels. However, a post-hoc analysis revealed that baseline AUC
stratified the subjects into two clear and distinct groups (Table1). Interestingly, those groups exactly matched the responder(AUC decrease)/nonresponder (AUC increase) status. Baseline
AUC is an indication of the subject’s glucose control level atthe onset of the study. This is an interesting observation thatshould be explored in follow-up studies designed and powered
appropriately. Using the mean ΔΔAUC value of the three
responders as a reasonable effect size and considering thevariance of the ΔΔAUC value from all subjects, a future study
employing the same study design with 35 subjects is estimatedto have a power above 90%.

CGM Enables Novel Measures of Compliance
CGM devices can be utilized to perform MTTs in the subject’s
living environment instead of the artificial setting of a researchclinic. In this study, subjects were asked to use a custom Android
app to create a visual food diary and log workouts and the start
times of four standardized meal consumption tests. As such, weknow precisely when the MTT was conducted. Subjects wereasked to fast for 6 hours before taking the MTT, and Subject 4
seemed to have adhered to this. Indeed, the glucose level curveof this subject was rather flat for the 6 hours preceding the test.

Roux de Bézieux et alThen, right after ingesting the standardized meal, the glucose
level spiked in a characteristic manner before returning to thebaseline level after 2 hours (Figure 3A). On the other hand,
Subject 6’s response was quite different. For the test at the
beginning of the product period, Subject 6’s glucose level curve
did not stay flat for the 6 hours before the glucose challenge(Figure 3B). Instead, it spiked from 90 mg/dL to 130 mg/dL
and back over the course of 5 hours. Afterwards, glucose levels
stayed flat during the MTT. Several explanations are possible.
The MTT was performed at 7 AM, so the preceding peak mightreflect a dawn effect [31], poor quality of sleep, alcohol
consumption during the previous evening, or nighttime snacking.
The subject’s last recorded annotation before the Boost intake
was at 8.44 PM the previous evening, so there is no indicationof what could have caused this. Nonetheless, the fact that suchan event was detectable before the MTT was performed isvaluable information. Indeed, in such a case, it is easy to probe
the subject for additional details to understand potentiallyconfounding circumstances and ask the subject to take the MTT
again the following day. This would lower variability by
improving the quality of the MTT data, resulting in a more
precise estimate of the product effect. A single estimate offasting glucose at t=0, either via a fingerstick measurement ora blood draw as traditionally performed in clinical trials, would
have failed to detect this.

Figure 3. Continuous glucose monitoring (CGM) devices provide insight into compliance. (A) CGM data are concordant between when Subject 4
mentioned fasting/consuming the standardized meal for the glucose challenge and what was detected from the CGM device. (B) CGM data can also be
used to detect anomalies. There is no concordance between when Subject 6 recorded fasting/consumption of the standardized meal and what wasobserved from the CGM data. (C) Each subject is asked to obtain data at regular intervals and at least every 8 hours (red line), which is the limit for thesensor memory to store data.

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 7
(page number not for citation purposes)

JMIR FORMATIVE RESEARCHA CGM device could also serve as an easy and efficient device
for measuring engagement in the study since subjects neededto scan their devices regularly. In particular, since the devices
used in the study could only store 8 hours of data, consecutive
scans were not supposed to be more than 8 hours apart.
Measuring the time intervals between scans and comparing
them to the 8-hour mark provides a measure of the subject’s
compliance. In Figure 3C, time intervals between all consecutive
scans are displayed. As the study progressed, all but one subject
tended to let more time go between scans. This is consistent
with interviews where subjects reported fatigue as the study
progressed. Moreover, the number of scans with more than 8hours in between also increased. This expected study fatigue
affected subjects differently. While Subjects 3, 4, and 5 hadsmall increases in their median times between scans, Subject 2
actually saw a small decrease by the end of the study. Subject
1 went from being the most compliant to the least over thecourse of the study, whereas Subject 6 displayed a relative lackof compliance throughout the study. Access to these data in realtime could guide and target efforts to enhance compliance where
needed.
Subjects could also record pictures and annotations through thesmartphone app. The number of logs every day varied greatly
between subjects, where the average number of logs per day
ranged from 2.9 for Subject 4 to 9.73 for Subject 5, andthroughout the study, as the variance for the number of logs per
day was around 41% of the average. This reflects various
behaviors that are captured by the smartphone app and thatcomplement the CGM devices. For example, Subject 4 was oneof the most compliant in terms of scanning requirements but
logged the fewest number of meals. Study fatigue was also far
less apparent than that in scanning requirements, as the average
number of annotations per day decreased only slightly withtime, suggesting that engagement with the smartphone washttps://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

Roux de Bézieux et aleasier to maintain. However, compliance was also impacted bysocial norms, as reported in interviews. Subjects were less likely
to log pictures in social settings and thus annotated after thefact, although recent trends, such as posting pictures of meals
on social networks, have lowered the barrier.

Integrating CGM Data With the Smartphone App
With a measurement every 15 minutes for 6 weeks, the CGM
devices produced about 3000 data points per subject throughout
the study. An MTT has nine CGM measurements (ameasurement every 15 minutes for 2 hours), so all four MTTs
represented a total of 36 points per subject throughout the study(1% of the data collected). Here, we show that the CGM devices
do capture a trove of information that is not available in current
clinical designs without CGM devices.
One feature of interest in continuous monitoring of glucose
levels is glucose peaks. Those peaks are induced by a diverse
set of behaviors, mostly food consumption and, in some subjects,
exercise. Glucose peaks are characterized by their range andduration. Glucose levels of 140 mg/dL or above 2 hours after
a meal are indicative of a lack of glucose tolerance [32]. Using
our peak detection algorithm, we found that the subjects spentan average of 5 hours and 47 minutes of their day in glucose
peaks, representing roughly 25% of all the data collected,
whereas MTTs comprised only 1% of the data. We detected
between two and three peaks a day, consistent with a routine ofthree meals a day.
An example of the types of peaks detected by the algorithm canbe seen in Figure 4, where the results for March 20, 2018, for
one subject are shown. The algorithm detected three peaks
during this day, clearly differentiating between noisy
fluctuations of glucose levels and glucose peaks induced by ameal. While most peaks are easy to distinguish visually, theamount of data means that automated annotation is necessary.

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 8
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Roux de Bézieux et al

Figure 4. Continuous glucose monitoring (CGM) devices capture more data than the standard meal-tolerance test. (A) Zooming on a specific day
allows one to display an entire day of glucose estimates (March 20, 2018, for Subject 1). Peaks are detected with the in-house algorithm (Multimedia
Appendix 1) and colored in blue. Annotations logged in the smartphone app by the subject are marked with a dashed red vertical line and a picture logo.
(B-D) Pictures submitted by Subject 1 through the app on March 20, 2018.

Integrating the CGM data with the visual diet log provides richand insightful information. In particular, since the smartphone
app was used for both recording glucose levels and taking
pictures, we can precisely match the times of one with the other.
On March 20, 2018, the subject recorded three meal pictures(Figure 4B-D). The start of the first and second peaks matchedvery well with the pictures taken. Interestingly however, thethird picture (Figure 4D) and the third peak did not match. This
picture was recorded over an hour before the start of the next
glucose peak. The high fat content visible in the meal (Figure4D) is consistent with this delay.
The app was only available for Android-based devices, and forthis exploratory study, subjects were provided with compatible
smartphones. The need to carry a second smartphone was
highlighted as a burden in interviews, especially by users ofalternative smartphone operating systems. Development of ageneral app that can be used directly on a subject’s smartphonewould lessen the burden on subjects in a future study. Subjects
also mentioned several improvements to the design that would
facilitate their interactions with the app.

qPCR-Based Secondary Endpoints
At baseline, the beginning/end of each period, and washout,subjects were asked to provide a stool sample. Analysis of stool
samples was performed in five of the six subjects; one subject
did not provide stool samples. Using fecal qPCR, theconcentrations of the strains in the formulation were estimated.
In the results shown in Multimedia Appendix 11, strain
concentrations were averaged across three replicates. As
https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

expected, at baseline, during placebo, and during placebo
washout, the strains in the formulation were not detected. This
was true even when the placebo period followed the productperiod. Most strains were detected at the end of the productperiod in all subjects (Multimedia Appendix 11). For the
subjects with a washout duration of 1 to 3 days (Multimedia
Appendix 4), the strains in the formulation could still be
detected, although generally at lower levels, after the productwashout, suggesting that a washout period of 3 days is too short.
Additionally, for the subjects on the active product during period
1 (Subjects 1, 3, and 5), strains were not detected at the end of
the placebo period, suggesting that a washout duration between
5 days and 2 weeks is sufficient.

Code and Data Availability
All the codes and data necessary to reproduce the figures andtables in this paper are available at GitHub [33].

Discussion
Principal Findings
This manuscript describes a pilot study exploring the potentialof CGM devices with a smartphone app that collects additional
data as an avenue for measuring clinically relevant endpoints,
assessing compliance in realistic settings, and decreasing theburden of study participation while collecting valuable data thatcan easily be integrated with glucose levels. Using CGM
devices, it is possible to accurately measure important metrics,
such as the outcome of a fasted MTT, the AUC of meal-induced
JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 9
(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
glucose peaks, and the compliance to a fasting regimen. We
explored this framework in the context of a 6-week,
double-blind, placebo-controlled, 2×2 cross-over pilot study(n=6) comparing a twice daily synbiotic medical food designed
to improve glucose control. We demonstrated the promise of
CGM devices as a means to assess clinical outcomes ofnonpharmacological interventions in rigorous studies forresearch purposes.
Although accurate automatic detection of glucose peaks isstraightforward and can provide valuable data for the clinician
and individual, how to use those peaks as clinical endpoints ischallenging as the glucose peaks represent a response toreal-world behaviors and not to a prequantified study-wide
intervention. Commonly, diet logs are used to control for dietary
variation, but they have reporting bias and increase subject
burden considerably [34]. In this study, we tried to address theissues of subject burden and reporting bias by employing asmartphone app that allowed subjects to take pictures of theirmeals. This allows the creation of a visual diet log that is insync with the glucose levels measured with the CGM devices,which provides a rich and valuable source of data using asolution that is simpler to implement and less burdensome toparticipants. However, further work is required to bridge thegap between image data and actionable carbohydrate content
estimates. Variation in the regularity of eating habits or meal
content could potentially lead to higher between-subject
variance. Even for a within-subject contrast, the lack ofregularity in eating and exercise habits increases the varianceof the estimate of any treatment effect. Future studies could also
leverage those types of visual logs to monitor adherence to a
specific diet type (eg, high fiber diet). Complementing thephotos with more precise annotations could also improve
estimates. Smartphone apps that rely on drop-down menus
provide fair estimates of carbohydrate consumption [35,36] andgreatly outperform self-assessment [37].
On the other end of the spectrum, the MTT protocol employedin traditional clinical studies represents a very controlled setting.
This test yields a much more consistent measure, but it may notreflect the real-world effectiveness of an intervention as
accurately. As is expected and has been reported in various
studies [38,39], compliance decreases when subjects are
increasingly inconvenienced. MTTs, as required in this study,
necessitated consuming a specific shake, undergoing a 6-hour
fast, and monitoring for 2 to 3 hours following beverage
consumption. As this disrupts an individual’s daily routine, thenumber of observations per subject per week needs to be limited.
Focusing on breakfast-induced glucose peaks might represent
a middle ground. As can be seen in Multimedia Appendix 12,
all subjects, except Subject 5, had a high number of peaks at aconsistent time most mornings. This denotes a regular breakfast
routine at the subject level. This was confirmed in interviews,
where subjects reported having either a consistent weekday
breakfast or a rotation among a limited set of menus. Defining
a consistent breakfast for all subjects a few days a week would
provide a robust measure of the level of glucose control in arealistic setting with reduced variability.
We expect that future studies will leverage the novel capabilities
of CGM devices to provide real-time compliance data. For
https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

Roux de Bézieux et al
example, a reminder to subjects whenever compliance is
detected to be declining, in the form of a push notification or aphone call, could ensure that data capture remains sufficient,with the aim to improve the robustness of the inference. Subjects
unanimously said in the final interviews that reasonable
notifications would increase compliance, and such methods
have been successfully deployed in previous studies [40]. CGM
devices could also increase subjects’ engagement. In this study,
subjects were presented with an interactive dashboard
recapitulating their glucose and food behaviors (Figure 4).
Subjects were excited about this and spent time looking at theeffects of individual foods on their glucose levels. Presenting
an example dashboard to the subjects before baseline, with thepromise of more complete results at the end, could increase
engagement in the study and therefore compliance. While suchusage of CGM devices shows clear promise, care needs to betaken to ensure sound experimental design, including
maintaining subject and researcher blinding to the arm
assignment. In spite of these issues, proper usage of CGM
devices can provide just-in-time actionable measures ofcompliance.
The CGM devices deployed in this study measured
glucose-related metrics, but they can be complemented withother types of data collection depending on the setting. In this
exploratory study, stool samples were collected. While collectionof this type of data is feasible at home, the samples still need
to be delivered to a clinic for analysis. However, the difficulties
in collecting this type of data are limited by the fact that they
are not needed for all patients or in confirmatory studies. CGM
devices can also be used in coordination with other sensors (eg,smartwatches and mobility data from the smartphone) that could
be complementary with little added burden on the patient[41,42]. The general principles of compliance measurements
discussed above in the context of CGM devices are also
applicable to any other metric relying on a connected device.
Privacy concerns should also be addressed properly, as mostsubjects raised this issue during the interviews.

Limitations
Previous studies [43] have linked the regularity of CGM device
scanning to improvements in the “time in range” endpoint(glucose level between 70 mg/dL and 180 mg/dL). While we
did not observe it in this study given the general lack of glucose
intolerance in the study subjects, differences in engagement
between subjects could be linked to differences in outcomes inlarger studies. Controlling for adherence, directly measured
through scanning and recording annotations, could limit thisphenomenon and lead to less noisy estimates of the treatment
effect.
Given the scale of the study, a cross-over design was employed
as a means to guard against a number of potential confounders.
In particular, anecdotal observations while developing the app
suggested that individuals responded to the sensor in a distinct
and consistent manner. Coupled with the fact that the MTTs
were self-administered, a cross-over design seemed logical.
Despite these factors, cross-over designs can be of limited utility
in the presence of carryover or spillover effects. Indeed, strains
were detected at the beginning of the placebo period in some
JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 10(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
subjects crossing over from the active product period, indicating
the potential for carryover effects. No strains were detected at
the end of the placebo period for those same subjects, suggestingthat a longer washout period will be required in a future
cross-over study.

Roux de Bézieux et al

Conclusions
We have shown that CGM devices can provide high-quality
measurements of relevant endpoints and can enable granularmonitoring of subject compliance with low clinical overhead.
While this was a small pilot study, we observed a meaningful
effect of the study product in subjects with diminished initial
glucose control. A broader follow-up study, both in size andduration, is necessary to validate our very preliminary results.

Acknowledgments
We thank Suzanne Dufault, Nima Hejazi, and Ivana Malenica for insightful discussions on clinical trial designs. We gratefully
thank the participants of this study.
Editorial NoticeThis randomized study was only retrospectively registered, explained by authors with “Since it was a prospective trial, we did
not register it beforehand.”. The editor granted an exception from ICMJE rules mandating prospective registration of randomized
trials because the risk of bias appears low and the study was considered formative. However, readers are advised to carefully
assess the validity of any potential explicit or implicit claims related to primary outcomes or effectiveness, as retrospective
registration does not prevent authors from changing their outcome measures retrospectively.

Authors' Contributions
FP, JB, and OK designed the study. MS, FP, and OK conducted the study. HRdB and FP analyzed the data. HRdB wrote the firstdraft of the manuscript, and FP, JB, OK, and MS suggested edits.

Conflicts of Interest
All authors are employees and stock/stock option shareholders of Pendulum Therapeutics, Inc (formerly known as “Whole BiomeInc”). OK owns stock in GlySens, Inc, has stock options in ViaCyte, Inc, and is a consultant to NuSirt BioPharma, Adocia, Circius,
and NanoPrecision Medical.

Multimedia Appendix 1
Supplementary methods.
[PDF File (Adobe PDF File), 397 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Screenshot of the app.
[PDF File (Adobe PDF File), 39 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Screenshot of the app.
[PDF File (Adobe PDF File), 51 KB-Multimedia Appendix 3]

Multimedia Appendix 4
Number of days spent in each period. Each subject spent at least 2 weeks in each arm of the study. The washout period between
the two arms is at least 3 days.
[XLS File (Microsoft Excel File), 20 KB-Multimedia Appendix 4]

Multimedia Appendix 5
Difference between fingerstick values and continuous glucose monitoring (CGM) estimates. Each panel shows the fingerstickvalue logged through the mobile app (with a red dot) and the glucose levels as measured with the CGM devices. Values are inaccordance with expected values [14].
[PNG File , 1050 KB-Multimedia Appendix 5]

Multimedia Appendix 6
Interview guide.
https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 11(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Roux de Bézieux et al[PDF File (Adobe PDF File), 45 KB-Multimedia Appendix 6]

Multimedia Appendix 7
According to the recommendations of Schnell et al [30], we report all hypoglycemic events, defined as excursions below 70
mg/dL for at least two consecutive values.
[XLS File (Microsoft Excel File), 30 KB-Multimedia Appendix 7]

Multimedia Appendix 8
According to the recommendations of Schnell et al [30], we report four summary statistics at the subject level, measuring timeout of range (as number of continuous glucose monitoring scans per day), and glucose variability, reported as the average within-day
standard deviation of glucose levels and the standard deviation of the average daily glucose level.
[XLS File (Microsoft Excel File), 20 KB-Multimedia Appendix 8]

Multimedia Appendix 9Interview summary.
[PDF File (Adobe PDF File), 33 KB-Multimedia Appendix 9]

Multimedia Appendix 10
Area under the curve (AUC) during the self-administered meal-tolerance tests. Incremental AUCs are computed using the glucose
levels provided by the continuous glucose monitoring devices. Subjects recorded the time of the ingestion of the Boost drink
using the smartphone app. Incremental AUCs are rounded to the nearest unit.
[XLS File (Microsoft Excel File), 20 KB-Multimedia Appendix 10]

Multimedia Appendix 11
Fecal quantitative polymerase chain reaction measures of formulation strain concentration. Each subject, except for Subject 4,
provided stool samples. As expected, strains in the formulation were detected when the subjects were taking the formulation andnot detected when the subjects were not (ie, at baseline, during placebo, and during placebo washout). Some carryover is observed.
Strains are detectable during a few days following the product period.
[PNG File , 88 KB-Multimedia Appendix 11]

Multimedia Appendix 12
Times of the tops of the peaks during the day. Peaks are detected using the algorithm described in Multimedia Appendix 1. For
most subjects, a clear trend in the morning suggests a routine linked to breakfast.
[PNG File , 27 KB-Multimedia Appendix 12]

Multimedia Appendix 13
CONSORT-EHEALTH checklist (V 1.6.1).
[PDF File (Adobe PDF File), 1345 KB-Multimedia Appendix 13]

References
1.
2.

3.
4.

5.

World Health Organization. Global Report on Diabetes. Geneva: World Health Organization; 2016.
Centers for Disease Control and Prevention. National Diabetes Statistics Report: Estimates of Diabetes and Its Burden in
the United States. Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services;2020.
van Dam RM, Rimm EB, Willett WC, Stampfer MJ, Hu FB. Dietary patterns and risk for type 2 diabetes mellitus in U.S.
men. Ann Intern Med 2002 Feb 05;136(3):201-209. [doi: 10.7326/0003-4819-136-3-200202050-00008] [Medline: 11827496]
Cordain L, Eaton SB, Sebastian A, Mann N, Lindeberg S, Watkins BA, et al. Origins and evolution of the Western diet:health implications for the 21st century. Am J Clin Nutr 2005 Feb;81(2):341-354. [doi: 10.1093/ajcn.81.2.341] [Medline:
15699220]
Qi L, Cornelis MC, Zhang C, van Dam RM, Hu FB. Genetic predisposition, Western dietary pattern, and the risk of type2 diabetes in men. Am J Clin Nutr 2009 May;89(5):1453-1458 [FREE Full text] [doi: 10.3945/ajcn.2008.27249] [Medline:
19279076]

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 12(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
6.

7.

8.
9.

10.

11.
12.
13.

14.
15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

25.

Franz MJ, MacLeod J. Success of nutrition-therapy interventions in persons with type 2 diabetes: challenges and future
directions. Diabetes Metab Syndr Obes 2018;11:265-270 [FREE Full text] [doi: 10.2147/DMSO.S141952] [Medline:
29928137]
Lean ME, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, et al. Primary care-led weight management forremission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. The Lancet 2018 Feb 10;391(10120):541-551.
[doi: 10.1016/S0140-6736(17)33102-1] [Medline: 29221645]
Crichton GE, Howe PR, Buckley JD, Coates AM, Murphy KJ, Bryan J. Long-term dietary intervention trials: critical issues
and challenges. Trials 2012 Jul 20;13(1):111 [FREE Full text] [doi: 10.1186/1745-6215-13-111] [Medline: 22812577]
Lewis JD, Albenberg L, Lee D, Kratz M, Gottlieb K, Reinisch W. The Importance and Challenges of Dietary Intervention
Trials for Inflammatory Bowel Disease. Inflamm Bowel Dis 2017 Feb;23(2):181-191 [FREE Full text] [doi:10.1097/MIB.0000000000001009] [Medline: 28079619]
Coran P, Goldsack JC, Grandinetti CA, Bakker JP, Bolognese M, Dorsey ER, et al. Advancing the Use of Mobile
Technologies in Clinical Trials: Recommendations from the Clinical Trials Transformation Initiative. Digit Biomark
2019;3(3):145-154 [FREE Full text] [doi: 10.1159/000503957] [Medline: 32095773]
Waltz E. Sweet sensation. Nat Biotechnol 2019 Apr;37(4):340-344. [doi: 10.1038/s41587-019-0086-2] [Medline: 30940935]
Hirsch IB. History of Glucose Monitoring. In: Role of Continuous Glucose Monitoring in Diabetes Treatment. Arlington,
VA: American Diabetes Association; 2018.
Freestyle Libre 14 Day Flash Glucose Monitoring System. US Food & Drugs Administration. 2017. URL: https://www.
fda.gov/medical-devices/recently-approved-devices/freestyle-libre-14-day-flash-glucose-monitoring-system-p160030s017[accessed 2020-03-12]
Declarations of Conformity. Abbott. URL: https://www.diabetescare.abbott/declarations-of-conformity.html [accessed2020-03-25]
Murata T, Sakane N, Kato K, Tone A, Toyoda M. The Current Intermittent-Scanning CGM Device Situation in Japan:
Only Adjunctive Use to SMBG Is Approved and the Latest Health Insurance Coverage Details. J Diabetes Sci Technol
2018 May;12(3):729-730 [FREE Full text] [doi: 10.1177/1932296817749860] [Medline: 29281891]
Bergenstal RM, Rosenstock J, Bastyr EJ, Prince MJ, Qu Y, Jacober SJ. Lower glucose variability and hypoglycemia
measured by continuous glucose monitoring with novel long-acting insulin LY2605541 versus insulin glargine. Diabetes
Care 2014;37(3):659-665. [doi: 10.2337/dc12-2621] [Medline: 24198302]
Koyanagawa N, Miyoshi H, Ono K, Nakamura A, Cho KY, Yamamoto K, et al. Comparative effects of vildagliptin andsitagliptin determined by continuous glucose monitoring in patients with type 2 diabetes mellitus. Endocr J 2016 Aug
31;63(8):747-753 [FREE Full text] [doi: 10.1507/endocrj.EJ16-0266] [Medline: 27321385]
Jiménez-Domínguez G, Ble-Castillo JL, Aparicio-Trápala MA, Juárez-Rojop IE, Tovilla-Zárate CA, Ble-Castillo DJ, etal. Effects of Acute Ingestion of Native Banana Starch on Glycemic Response Evaluated by Continuous Glucose Monitoringin Obese and Lean Subjects. Int J Environ Res Public Health 2015 Jul 06;12(7):7491-7505 [FREE Full text] [doi:10.3390/ijerph120707491] [Medline: 26154657]
Nishimura R, Osonoi T, Kanada S, Jinnouchi H, Sugio K, Omiya H, et al. Effects of luseogliflozin, a sodium-glucose
co-transporter 2 inhibitor, on 24-h glucose variability assessed by continuous glucose monitoring in Japanese patients with
type 2 diabetes mellitus: a randomized, double-blind, placebo-controlled, crossover study. Diabetes Obes Metab 2015
Aug;17(8):800-804 [FREE Full text] [doi: 10.1111/dom.12481] [Medline: 25930989]
Jinnouchi H, Koyama M, Amano A, Takahashi Y, Yoshida A, Hieshima K, et al. Continuous Glucose Monitoring During
Basal-Bolus Therapy Using Insulin Glargine 300 U mL(-1) and Glargine 100 U mL (-1) in Japanese People with Type 1
Diabetes Mellitus: A Crossover Pilot Study. Diabetes Ther 2015 Jun;6(2):143-152 [FREE Full text] [doi:10.1007/s13300-015-0115-1] [Medline: 26055218]
Holst JJ, Buse JB, Rodbard HW, Linjawi S, Woo VC, Boesgaard TW, et al. IDegLira Improves Both Fasting and Postprandial
Glucose Control as Demonstrated Using Continuous Glucose Monitoring and a Standardized Meal Test. J Diabetes Sci
Technol 2015 Oct 06;10(2):389-397 [FREE Full text] [doi: 10.1177/1932296815610124] [Medline: 26443290]
Danne T, Philotheou A, Goldman D, Guo X, Ping L, Cali A, et al. A randomized trial comparing the rate ofhypoglycemia--assessed using continuous glucose monitoring--in 125 preschool children with type 1 diabetes treated withinsulin glargine or NPH insulin (the PRESCHOOL study). Pediatr Diabetes 2013 Dec;14(8):593-601 [FREE Full text] [doi:10.1111/pedi.12051] [Medline: 23730996]
Study Demonstrates Remote CGM Initiation Successful at Improving Diabetes Outcomes and Quality of Life. PRWeb.
URL: https://www.prweb.com/releases/study_demonstrates_remote_cgm_initiation_successful_at_improving_diabetes_outcomes_and_quality_of_life/prweb16940784.htm [accessed 2020-02-17]
Aberer F, Lichtenegger KM, Smajic E, Donsa K, Malle O, Samonigg J, et al. GlucoTab-guided insulin therapy using insulin
glargine U300 enables glycaemic control with low risk of hypoglycaemia in hospitalized patients with type 2 diabetes.
Diabetes Obes Metab 2019 Mar;21(3):584-591 [FREE Full text] [doi: 10.1111/dom.13559] [Medline: 30328252]
Reddy R, El Youssef J, Winters-Stone K, Branigan D, Leitschuh J, Castle J, et al. The effect of exercise on sleep in adults
with type 1 diabetes. Diabetes Obes Metab 2018 Feb;20(2):443-447 [FREE Full text] [doi: 10.1111/dom.13065] [Medline:
28718987]

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

Roux de Bézieux et al

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 13(page number not for citation purposes)

JMIR FORMATIVE RESEARCH
26.

27.
28.
29.

30.

31.

32.
33.
34.
35.

36.

37.

38.
39.
40.

41.

42.

43.

Roux de Bézieux et al

Perraudeau F, McMurdie P, Bullard J, Cheng A, Cutcliffe C, Deo A, et al. Improvements to postprandial glucose control
in subjects with type 2 diabetes: a multicenter, double blind, randomized placebo-controlled trial of a novel probiotic
formulation. BMJ Open Diabetes Res Care 2020 Jul;8(1) [FREE Full text] [doi: 10.1136/bmjdrc-2020-001319] [Medline:
32675291]
Girish P. Simple Algorithms for Peak Detection in Time-Series. ResearchGate. 2009. URL: https://www.researchgate.net/
publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series [accessed 2019-05-12]
BOOST® Original. Nestlé Health Science. URL: https://www.nestlehealthscience.com/brands/boost/boost-original [accessed
2019-11-20]
Potteiger J, Jacobsen D, Donnelly J. A comparison of methods for analyzing glucose and insulin areas under the curve
following nine months of exercise in overweight adults. Int J Obes Relat Metab Disord 2002 Jan 15;26(1):87-89. [doi:10.1038/sj.ijo.0801839] [Medline: 11791151]
Schnell O, Barnard K, Bergenstal R, Bosi E, Garg S, Guerci B, et al. Role of Continuous Glucose Monitoring in Clinical
Trials: Recommendations on Reporting. Diabetes Technol Ther 2017 Jul;19(7):391-399 [FREE Full text] [doi:10.1089/dia.2017.0054] [Medline: 28530490]
Schmidt MI, Hadji-Georgopoulos A, Rendell M, Margolis S, Kowarski A. The dawn phenomenon, an early morning glucose
rise: implications for diabetic intraday blood glucose variation. Diabetes Care 1981;4(6):579-585. [doi:10.2337/diacare.4.6.579] [Medline: 6751733]
Two-Hour Postprandial Glucose. University of Rochester Medical Center. URL: https://www.urmc.rochester.edu/
encyclopedia/content.aspx?contenttypeid=167&contentid=glucose_two_hour_postprandial [accessed 2021-02-20]
GitHub. URL: https://github.com/wholebiome/WB01-205 [accessed 2021-02-15]
Thompson F, Subar A. Dietary Assessment Methodology. In: Nutrition in the Prevention and Treatment of Disease. Bethesda,
MD: Elsevier; 2001:3-30.
Fallaize R, Zenun Franco R, Pasang J, Hwang F, Lovegrove JA. Popular Nutrition-Related Mobile Apps: An Agreement
Assessment Against a UK Reference Method. JMIR Mhealth Uhealth 2019 Feb 20;7(2):e9838 [FREE Full text] [doi:10.2196/mhealth.9838] [Medline: 30785409]
Ferrara G, Kim J, Lin S, Hua J, Seto E. A Focused Review of Smartphone Diet-Tracking Apps: Usability, Functionality,
Coherence With Behavior Change Theory, and Comparative Validity of Nutrient Intake and Energy Estimates. JMIR
Mhealth Uhealth 2019 May 17;7(5):e9232 [FREE Full text] [doi: 10.2196/mhealth.9232] [Medline: 31102369]
Rhyner D, Loher H, Dehais J, Anthimopoulos M, Shevchik S, Botwey RH, et al. Carbohydrate Estimation by a Mobile
Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study. J MedInternet Res 2016 May 11;18(5):e101 [FREE Full text] [doi: 10.2196/jmir.5567] [Medline: 27170498]
Spilker B. Methods of Assessing and Improving Patient Compliance in Clinical Trials. IRB: Ethics and Human Research
1992 May;14(3):1. [doi: 10.2307/3563718]
Claxton AJ, Cramer J, Pierce C. A systematic review of the associations between dose regimens and medication compliance.
Clin Ther 2001 Aug;23(8):1296-1310. [doi: 10.1016/s0149-2918(01)80109-0] [Medline: 11558866]
Haas K, Hayoz S, Maurer-Wiesner S. Effectiveness and Feasibility of a Remote Lifestyle Intervention by Dietitians for
Overweight and Obese Adults: Pilot Study. JMIR Mhealth Uhealth 2019 Apr 11;7(4):e12289 [FREE Full text] [doi:10.2196/12289] [Medline: 30973338]
Anguera JA, Jordan JT, Castaneda D, Gazzaley A, Areán PA. Conducting a fully mobile and randomised clinical trial for
depression: access, engagement and expense. BMJ Innov 2016 Jan;2(1):14-21 [FREE Full text] [doi:10.1136/bmjinnov-2015-000098] [Medline: 27019745]
Wahle F, Kowatsch T, Fleisch E, Rufer M, Weidt S. Mobile Sensing and Support for People With Depression: A Pilot Trialin the Wild. JMIR Mhealth Uhealth 2016 Sep 21;4(3):e111 [FREE Full text] [doi: 10.2196/mhealth.5960] [Medline:
27655245]
Dunn TC, Xu Y, Hayter G, Ajjan RA. Real-world flash glucose monitoring patterns and associations between self-monitoring
frequency and glycaemic measures: A European analysis of over 60 million glucose tests. Diabetes Res Clin Pract 2018
Mar;137:37-46 [FREE Full text] [doi: 10.1016/j.diabres.2017.12.015] [Medline: 29278709]

Abbreviations
AUC: area under the curve
CGM: continuous glucose monitoring
MTT: meal-tolerance test
qPCR: quantitative polymerase chain reaction
RCT: randomized controlled trial
T2D: type 2 diabetes

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 14(page number not for citation purposes)

JMIR FORMATIVE RESEARCH

Roux de Bézieux et al

Edited by G Eysenbach; submitted 12.05.20; peer-reviewed by C Reis, S Pit, A Teles; comments to author 17.09.20; revised version
received 22.10.20; accepted 24.01.21; published 04.03.21
Please cite as:Roux de Bézieux H, Bullard J, Kolterman O, Souza M, Perraudeau F
Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept StudyJMIR Form Res 2021;5(3):e20175
URL: https://formative.jmir.org/2021/3/e20175
doi: 10.2196/20175
PMID: 33661120©Hector Roux de Bézieux, James Bullard, Orville Kolterman, Michael Souza, Fanny Perraudeau. Originally published in JMIR
Formative Research (http://formative.jmir.org), 04.03.2021. This is an open-access article distributed under the terms of theCreative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The
complete bibliographic information, a link to the original publication on http://formative.jmir.org, as well as this copyright andlicense information must be included.

https://formative.jmir.org/2021/3/e20175

XSL• FO
RenderX

JMIR Form Res 2021 | vol. 5 | iss. 3 | e20175 | p. 15(page number not for citation purposes)