{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T03:16:05Z","timestamp":1794280565869,"version":"4.9.0"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Behav Nutr Phys Act"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Continuous 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Thirty-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 \u2018\n                      <jats:italic>Feedback and monitoring<\/jats:italic>\n                      \u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200935\/35, 100%), \u2018\n                      <jats:italic>Shaping knowledg<\/jats:italic>\n                      e\u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200928\/35, 80%), and \u2018\n                      <jats:italic>Social support<\/jats:italic>\n                      \u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200922\/35, 63%). Commonly used BCTs supporting CGM use and promoting dietary and physical activity changes included \u2018\n                      <jats:italic>Biofeedback<\/jats:italic>\n                      \u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200935\/35; 100%), \u2018\n                      <jats:italic>Instruction on how to perform the behaviour<\/jats:italic>\n                      \u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200919\/35; 54%), and \u2018\n                      <jats:italic>Credible source<\/jats:italic>\n                      \u2019 (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200914\/35; 40%). Univariate linear regressions did not identify any individual BCTs or targeted behaviours that significantly moderated HbA1c outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>RCTs 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12966-025-01833-5","type":"journal-article","created":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T09:18:14Z","timestamp":1762420694000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Identifying behaviour change techniques within precision health interventions that use continuous glucose monitoring: a secondary analysis of a scoping review"],"prefix":"10.1186","volume":"22","author":[{"given":"Lauren Connell","family":"Bohlen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Crawshaw","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michelle R.","family":"Jospe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kelli M.","family":"Richardson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin J.","family":"Konnyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan M.","family":"Schembre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"issue":"6202","key":"1833_CR1","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.1126\/science.1259809","volume":"345","author":"DA Chokshi","year":"2014","unstructured":"Chokshi DA, Farley TA. 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