Medical Food Assessment Using a Smartphone App With Continuous Glucose Monitoring Sensors: Proof-of-Concept Study

Abstract

Background: Novel wearable biosensors, ubiquitous smartphone ownership, and telemedicine are converging to enable new paradigms 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 to monitor and manage care.

Objective: We conducted a proof-of-concept study to investigate the potential of collecting robust, annotated, real-time clinical study measures of glucose levels without clinic visits.

Methods: Self-administered meal-tolerance tests were conducted to assess the impact of a proprietary synbiotic medical food on glucose control in a 6-week, double-blind, placebo-controlled, 2×2 cross-over pilot study (n=6). The primary endpoint was incremental glucose measured using Abbott Freestyle Libre CGM devices associated with a smartphone app that provided a visual 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 with sensor 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; https://ichgcp.net/clinical-trials-registry/NCT04424888.

Keywords: clinical trials; continuous glucose monitoring; diabetes; lifestyle modification; mobile app; telemedicine.

Conflict of interest statement

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

©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.

Figures

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 (14 days) 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. Strains present 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 with pictures 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.

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 annotated by the subjects at time t=0 using the smartphone app. Glucose levels are normalized by the glucose level at t=0.

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 was observed 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 the sensor memory to store data.

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.