Effectiveness of Targeted Insulin-Adherence Interventions for Glycemic Control Using Predictive Analytics Among Patients With Type 2 Diabetes

Lauffenburger, Julie C.; Lewey, Jennifer; Jan, Saira; Makanji, Sagar S; Ferro, Christina A.; Krumme, Alexis A.; Lee, Jessica; Ghazinouri, Roya; Choudhry, Niteesh K.

doi:10.1001/jamanetworkopen.2019.0657

Cited by 20 publications

(35 citation statements)

References 32 publications

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“…(TARGIT-Diabetes) pragmatic trial, 12,13 which began enrollment in July 2016 and completed follow-up in October 2017. The parent TARGIT-Diabetes trial is registered with ClinicalTrials.gov.…”

Section: Intervention To Reach Glycemic Control With Insulin Therapy mentioning

confidence: 99%

Association of Potentially Modifiable Diabetes Care Factors With Glycemic Control in Patients With Insulin-Treated Type 2 Diabetes

et al. 2020

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IMPORTANCE Numerous factors are associated with the ability of patients with type 2 diabetes to achieve optimal glycemic control. However, many of these factors are not modifiable by quality improvement interventions. In contrast, the structure of how diabetes care is delivered, such as whether patients visit an endocrinologist or how prescriptions are filled, is potentially modifiable, yet its associations with glycemic control have not been rigorously evaluated. OBJECTIVE To investigate the association of diabetes care delivery with glycemic control in patients with type 2 diabetes using insulin. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study used baseline claims and laboratory insurer data within a large pragmatic trial to identify individuals with type 2 diabetes using insulin with data for at least 1 hemoglobin A 1c (HbA 1c) test result from before trial randomization (July 1, 2014, to October 5, 2016) and for key nonmodifiable patient factors as well as diabetes care delivery and behavioral factors measured before the HbA 1c test. Analyses were conducted from February 4, 2017, to November 13, 2018. MAIN OUTCOMES AND MEASURES Multivariable modified Poisson regression was used to evaluate the independent associations of nonmodifiable patient factors and potentially modifiable diabetes care delivery and patient behavioral factors with achieving adequate diabetes control (ie, HbA 1c level <8%). The extent of measured variation explained in glycemic control by these factors was also explored using pseudo R 2 and C statistics. RESULTS Of 1423 patients included, 565 (39.7%) were women, and the mean (SD) age was 56.4 (9.0) years. In total, 690 (48.5%) had HbA 1c levels less than 8%. Age (relative risk [RR] per 1-unit

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Section: Intervention To Reach Glycemic Control With Insulin Therapy mentioning

confidence: 99%

Association of Potentially Modifiable Diabetes Care Factors With Glycemic Control in Patients With Insulin-Treated Type 2 Diabetes

et al. 2020

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“…Overall, the combined results for HbA1c values were not significant (p=0.12) and a standard mean difference of -0.14 (95% CI -0.32, 0.04). Out of the 10 trials, only 2 completely crossed the line of null effect which was Huang et al [26] (2015) mean difference (MD) -1.33 (95% CI -2.03, -0.63) and Lauffenburger et al, [27] MD -0.14 (95% CI -0.23, -0.05) with the latter having the highest weighting of 28.8%. The remaining 8 trials all had confidence intervals (CI) that crossed the line of null effect which shows that the study result no significant differences between the intervention and usual care groups.…”

Section: Diabetes Clinical Outcomementioning

confidence: 94%

“…All 10 studies investigated diabetic control measured through HbA1c (%) values which are displayed in Figure 3. Only 6 of these studies [26][27][28][29][30][31] explored T2DM, with the remaining 4 exploring both T1DM and T2DM [32][33][34][35][36]. For this reason, a sub-group analysis was conducted and separated those two sets of studies to determine if that could have been a cause of increased heterogeneity.…”

Section: Diabetes Clinical Outcomementioning

confidence: 99%

“…This is not reflected in the forest plots produced from RevMan© as a higher score in the intervention group indicates usual care favored result. For this reason, the results were reversed to showcase a more accurate representation of the results which showed 3 studies favoring intervention [27][28][29] with only one favoring the usual care group [30].…”

Section: Depression Clinical Outcomesmentioning

confidence: 99%

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Systematic Review of Psychological and Educational Interventions Used to Improving Adherence in Diabetes and Depression Patients

Bashir¹,

Morrissey²,

Ball³

2021

Arch. Pharm. Pract

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Diabetes is a life-long chronic condition that is an established risk factor for the development of comorbid depression and possibly affecting medication adherence. Psychological and educational interventions are reported efficacious by the National Institute for Health and Care Excellence (NICE) in treating depression associated with a comorbid condition. As depression is associated with low adherence rates to treatment regimens, improving depression outcomes could improve adherence and therefore glycaemic control. A search strategy was conducted on search engines Google Scholar, PubMed, and Cochrane library for clinical trials. A total of 10 Randomised controlled trials (RCTs) were identified which investigated the effects of psychological and educational interventions on Type 2 diabetic patients with comorbid depression. Outcomes measured were depression and glycaemic control. Evidence from the 10 RCTs with 5759 participants suggests that psychological and educational intervention improved depression outcomes substantially and glycaemic control to an extent. Depression outcomes results were significant: standard mean deviation (SMD) was -0.39 (95% CI -0.62, -0.15); I 2 = 81%) p<0.001. Diabetes outcomes were not seen to be significant, SMD was -0.14 (95% CI -0.32, 0.03); I 2 = 44%) p=0.12. Psychological and educational interventions are effective in improving depression symptoms significantly and may assist in glycaemic control. Further research is required using larger sample sizes that could be generalized and representative of the whole population.

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“…The participants were also shown 15 example text messages to respond to toward the end of the interview to further elicit perspectives on the framing of information. These text messages have been used in prior text messaging-based interventions for diabetes (Multimedia Appendix 1, Table S2) [25,26].…”

Section: Qualitative Interviewsmentioning

confidence: 99%

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study

Lauffenburger¹,

Barlev²,

Sears³

et al. 2021

J Med Internet Res

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Background Individuals with diabetes need regular support to help them manage their diabetes on their own, ideally delivered via mechanisms that they already use, such as their mobile phones. One reason for the modest effectiveness of prior technology-based interventions may be that the patient perspective has been insufficiently incorporated. Objective This study aims to understand patients’ preferences for mobile health (mHealth) technology and how that technology can be integrated into patients’ routines, especially with regard to medication use. Methods We conducted semistructured qualitative individual interviews with patients with type 2 diabetes from an urban health care system to elicit and explore their perspectives on diabetes medication–taking behaviors, daily patterns of using mobile technology, use of mHealth technology for diabetes care, acceptability of text messages to support medication adherence, and preferred framing of information within text messages to support diabetes care. The interviews were digitally recorded and transcribed. The data were analyzed using codes developed by the study team to generate themes, with representative quotations selected as illustrations. Results We conducted interviews with 20 participants, of whom 12 (60%) were female and 9 (45%) were White; in addition, the participants’ mean glycated hemoglobin A1c control was 7.8 (SD 1.1). Overall, 5 key themes were identified: patients try to incorporate cues into their routines to help them with consistent medication taking; many patients leverage some form of technology as a cue to support adherence to medication taking and diabetes self-management behaviors; patients value simplicity and integration of technology solutions used for diabetes care, managing medications, and communicating with health care providers; some patients express reluctance to rely on mobile technology for these diabetes care behaviors; and patients believe they prefer positively framed communication, but communication preferences are highly individualized. Conclusions The participants expressed some hesitation about using mobile technology in supporting diabetes self-management but have largely incorporated it or are open to incorporating it as a cue to make medication taking more automatic and less burdensome. When using technology to support diabetes self-management, participants exhibited individualized preferences, but overall, they preferred simple and positively framed communication. mHealth interventions may be improved by focusing on integrating them easily into daily routines and increasing the customization of content.

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Effectiveness of Targeted Insulin-Adherence Interventions for Glycemic Control Using Predictive Analytics Among Patients With Type 2 Diabetes

Cited by 20 publications

References 32 publications

Association of Potentially Modifiable Diabetes Care Factors With Glycemic Control in Patients With Insulin-Treated Type 2 Diabetes

Association of Potentially Modifiable Diabetes Care Factors With Glycemic Control in Patients With Insulin-Treated Type 2 Diabetes

Systematic Review of Psychological and Educational Interventions Used to Improving Adherence in Diabetes and Depression Patients

Preferences for mHealth Technology and Text Messaging Communication in Patients With Type 2 Diabetes: Qualitative Interview Study

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