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Review
. 2024 Jul 10;21(1):74.
doi: 10.1186/s12966-024-01622-6.

Leveraging continuous glucose monitoring as a catalyst for behaviour change: a scoping review

Michelle R Jospe #  1 , Kelli M Richardson #  2 , Ahlam A Saleh  3 , Lauren C Bohlen  4 , Jacob Crawshaw  5 , Yue Liao  6 , Kristin Konnyu  7 , Susan M Schembre  8
Affiliations
Review

Leveraging continuous glucose monitoring as a catalyst for behaviour change: a scoping review

Michelle R Jospe et al. Int J Behav Nutr Phys Act. .

Abstract

Background: Amidst the escalating prevalence of glucose-related chronic diseases, the advancements, potential uses, and growing accessibility of continuous glucose monitors (CGM) have piqued the interest of healthcare providers, consumers, and health behaviour researchers. Yet, there is a paucity of literature characterising the use of CGM in behavioural intervention research. This scoping review aims to describe _targeted populations, health behaviours, health-related outcomes, and CGM protocols in randomised controlled trials (RCTs) that employed CGM to support health behaviour change.

Methods: We searched Ovid MEDLINE, Elsevier Embase, Cochrane Central Register of Controlled Trials, EBSCOhost PsycINFO, and ProQuest Dissertations & Theses Global from inception to January 2024 for RCTs of behavioural interventions conducted in adults that incorporated CGM-based biological feedback. Citation searching was also performed. The review protocol was registered ( https://doi.org/10.17605/OSF.IO/SJREA ).

Findings: Collectively, 5389 citations were obtained from databases and citation searching, 3995 articles were screened, and 31 were deemed eligible and included in the review. Most studies (n = 20/31, 65%) included adults with type 2 diabetes and reported HbA1c as an outcome (n = 29/31, 94%). CGM was most commonly used in interventions to _target changes in diet (n = 27/31, 87%) and/or physical activity (n = 16/31, 52%). 42% (n = 13/31) of studies provided prospective CGM-based guidance on diet or activity, while 61% (n = 19/31) included retrospective CGM-based guidance. CGM data was typically unblinded (n = 24/31, 77%) and CGM-based biological feedback was most often provided through the CGM and two-way communication (n = 12/31, 39%). Communication typically occurred in-person (n = 13/31, 42%) once per CGM wear (n = 13/31; 42%).

Conclusions: This scoping review reveals a predominant focus on diabetes in CGM-based interventions, pointing out a research gap in its wider application for behaviour change. Future research should expand the evidence base to support the use of CGM as a behaviour change tool and establish best practices for its implementation.

Trial registration: doi.org/10.17605/OSF.IO/SJREA.

Keywords: Behaviour change; Biomarkers; Blood glucose self-monitoring; Continuous glucose monitoring; Feedback.

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Conflict of interest statement

MRJ reports ongoing consultation to ZOE. KMR reports ongoing consultation to WeightWatchers International, Inc. SMS reports consultation (unpaid) for Viocare. All other authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR)
Fig. 2
Fig. 2
Overview of CGM-based health behaviour RCTs: study duration, _targeted population, and number of sensor days (2006–2024). This figure illustrates that CGM-based health behaviour RCTs are increasing in frequency, duration, and number of days participants were asked to wear CGM sensors from 2006 to 2024. Since 2020, the _target population has started to include participants without diabetes
Fig. 3
Fig. 3
Delivery of CGM-based biological feedback in behaviour change interventions l (N = 31). This figure illustrates how studies delivered CGM-based biological feedback. The size of the band indicates the number of studies. “CGM blinding” describes whether CGM data were visible (unblinded) or were not visible (blinded) to a study participant in real-time during the CGM wear period(s). “Mode”, “Channel”, “Frequency”, and “Timing” are specific to how CGM-based biological feedback was communicated. “Frequency” was calculated by the number of one- or two-way feedback sessions divided by the number of sensors worn. “Unclear” was used when the study protocols did not provide related information. From this figure we can see that the plurality of studies used unblinded CGM, with device and two-way communication, which was usually in-person, at a frequency of 1 communication session per CGM sensor, which was provided after CGM wear
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