Evidence mapPaperPMID 41199295Full record

ArticleThe international journal of behavioral nutrition and physical activity2025

Identifying behaviour change techniques within precision health interventions that use continuous glucose monitoring: a secondary analysis of a scoping review.

Lauren Connell Bohlen, Jacob Crawshaw, Michelle R Jospe, Kelli M Richardson, Kristin J Konnyu, Susan M Schembre

Abstract readScoping Review
In one paragraph

Article in The international journal of behavioral nutrition and physical activity, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

6 authors.

Lauren Connell BohlenCenter for Health Promotion and Health Equity, Department of Behavioural and Social Sciences, Brown University School of Public Health, Providence, RI, USA.
Jacob CrawshawMethodological and Implementation Research Program, Ottawa Hospital Research Institute, Ottawa, ON, Canada.
Michelle R JospeDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, 2115 Wisconsin Avenue NW Suite 300, Washington, DC, 20007, USA.
Kelli M RichardsonDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, 2115 Wisconsin Avenue NW Suite 300, Washington, DC, 20007, USA.
Kristin J KonnyuCenter for Evidence Synthesis and Health, Department of Health Services Policy and Practice, Brown University School of Public Health, Providence, RI, USA.
Susan M SchembreDepartment of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, 2115 Wisconsin Avenue NW Suite 300, Washington, DC, 20007, USA. ss4731@georgetown.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundContinuous glucose monitoring (CGM) is increasingly being used within precision health interventions to motivate behaviour change. However, there is considerable variability and complexity in the design of behaviour change interventions that incorporate CGM-based biofeedback, making it challenging to disentangle the intervention components that are driving intervention effects. The objective of this review is to identify the behaviour change techniques and mechanisms of action commonly implemented alongside CGM-based biofeedback.

methodsWe conducted secondary analyses of a scoping review to identify health behaviour interventions (RCTs) that provided CGM-based biofeedback to promote behaviour change in adults. Two researchers applied the 93-item Behaviour Change Techniques (BCT) Taxonomy (v1) to independently code intervention content in all trial arms (i.e., intervention and comparison arms) dependent upon their targeted behaviour of CGM use, glucometer use, diet, physical activity, or medication adherence. BCTs were analysed individually and according to their corresponding category. We performed univariate linear regression analyses to examine whether the presence of individual BCTs and target behaviours influenced pre-post changes in HbA1c within CGM-based intervention arms.

resultsThirty-one RCTs comprising 35 intervention arms and 29 comparison arms were included. Theory was reported in 4 studies (13%), most commonly Self-Efficacy Theory. Mechanisms of action (MoAs) were specified in 5 studies (16%), typically targeting beliefs about capabilities. We identified 40 (of 93 possible) unique BCTs, with intervention arms employing an average of 7.1 BCTs (SD: 4.8) compared to 5.3 BCTs (SD: 4.3) in comparison arms. The most frequently implemented BCT categories in CGM-based biofeedback interventions were 'Feedback and monitoring' (n = 35/35, 100%), 'Shaping knowledge' (n = 28/35, 80%), and 'Social support' (n = 22/35, 63%). Commonly used BCTs supporting CGM use and promoting dietary and physical activity changes included 'Biofeedback' (n = 35/35; 100%), 'Instruction on how to perform the behaviour' (n = 19/35; 54%), and 'Credible source' (n = 14/35; 40%). Univariate linear regressions did not identify any individual BCTs or targeted behaviours that significantly moderated HbA1c outcomes.

conclusionsRCTs using CGM to change behaviour in adult populations include a range of BCTs, focusing predominantly on BCTs that support the implementation of CGM itself. Future research should examine whether BCTs operate through distinct MoAs when supporting CGM uptake and use versus when promoting broader health behaviour change in conjunction with CGM-based biofeedback.

Indexed as

Behavior TherapyBlood Glucose Self-MonitoringHealth BehaviorPrecision MedicineAdultBiofeedback, PsychologyBlood GlucoseContinuous Glucose MonitoringDiabetes Mellitus, Type 2ExerciseGlycated HemoglobinHumansBlood GlucoseGlycated HemoglobinBehavioural theoryBehaviour changeBehaviour change techniqueContinuous glucose monitoringDigital healthGlycaemic controlGlycated haemoglobinPrecision healthPrecision medicine

Identifiers

PMID41199295
PMCPMC12590819

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.