Effectiveness of behavioral change techniques employed in eHealth interventions designed to improve glycemic control in persons with poorly controlled type 2 diabetes: a systematic review and meta-analysis protocol

Development and psychometric evaluation of a new brief scale to measure eHealth literacy in people with type 2 diabetes

Background

The internet has become a major source of health information, and obtaining appropriate information requires various abilities and skills, labeled as electronic health literacy (eHealth literacy). The existing instruments for measuring eHealth literacy are outdated because they were developed during the Web 1.0 era, or not sufficiently sensitive for people with a specific condition or disease because they were designed to assess eHealth literacy over a broad range for a general population. Approximately one in ten adults worldwide live with diabetes. Health professionals have a responsibility to identify patients with low eHealth literacy to prevent them from obtaining misleading internet diabetes information.

Aims

The aims were to develop a condition-specific eHealth literacy scale for diabetes and to evaluate its psychometric properties among people with type 2 diabetes.

Methods

An instrument development design was used. This study recruited 453 people diagnosed with type 2 diabetes at the outpatient clinics of hospitals in 2021. Psychometric properties (internal consistency, measurement invariance, and content, structural, convergent, and known-groups validities) were analyzed.

Results

An expert panel assessed content validity. Exploratory factor analysis, exploratory graph analysis, and confirmatory factor analysis (CFA) for structural validity yielded a two-factor solution (CFI = 0.977, SRMR = 0.029, RMSEA = 0.077). Cronbach’s alpha and omega values were excellent for each factor (0.87–0.94). Multigroup CFA yielded configural and metric measurement invariance across the gender, age, and glycemic control status groups. Convergent validity with a comparator instrument to measure health literacy was supported by a moderate correlation, and known-groups validity determined using groups with different internet-use frequencies was satisfied with a high effect size.

Conclusion

A new condition-specific eHealth literacy scale for people with type 2 diabetes was developed, comprising 10 items. The scale exhibited good psychometric properties; however, test–retest reliability must be determined for the stability of the scale and cross-cultural validity is required among different languages. The brief scale has the merits of being feasible to use in busy clinical practice and being less burdensome to respondents. The scale can be applied in clinical trials of internet-based diabetes interventions for assessing the eHealth literacy of respondents.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12912-022-01062-2.

Relationships Between Mobile eHealth Literacy, Diabetes Self-care, and Glycemic Outcomes in Taiwanese Patients With Type 2 Diabetes: Cross-sectional Study

Background

Understanding how people with diabetes seek online health information and use health applications is important to ensure these electronic tools are successfully supporting patient self-care. Furthermore, identifying the relationship between patient mobile eHealth literacy (mobile eHL) and diabetes outcomes can have far-reaching utility, for example, in the design of targeted interventions to address mobile eHL limitations. However, only limited studies have explored the impact of mobile eHL in a population with diabetes.

Objective

This study aims to present data about online information-seeking behavior and mobile health (mHealth) app usage, investigate the factors related to mobile eHL in Taiwanese patients with type 2 diabetes, and flesh out the relationship between eHealth literacy (eHL), mobile health literacy (mHL), and health outcomes.

Methods

Subjects were recruited from January 2017 to December 2017 in the outpatient departments of 3 hospitals in Taiwan. A total of 249 Taiwanese patients with diabetes voluntarily completed a cross-sectional survey assessing sociodemographic characteristics; diabetes status; knowledge and skills of computers, the internet, and mobile apps; mobile eHL; and patient outcomes (self-care behaviors, self-rated health, HbA_1c). Structural equation modeling analyses examined the model fit of mobile eHL scores and the interrelationships between latent constructs and observable variables.

Results

Of the 249 patients with diabetes, 67% (164/249) reported they had searched for online diabetes information. The participants with smartphones had owned them for an average of 6.5 years and used them for an average of 4.5 (SD 3.81) hours per day. Only 1.6% (4/249) of the patients used health apps. Some demographic factors affecting mobile eHL included age, education, and duration of type 2 diabetes. Mobile eHL was related to self-care behaviors as well as knowledge and skills of computers, the internet, and mobile technology, but only had a weak, indirect effect on self-rated health. The final model had adequate goodness-of-fit indexes: chi-square (83)=149.572, P<.001; comparative fit index (CFI)=0.925; root mean square of approximation (RMSEA)=0.057 (90% CI 004-006); chi-square/df=1.082. Mobile eHL had a weak, indirect effect on self-rated health through the variables of knowledge with skills.

Conclusions

Our study reveals that although people with diabetes who rated their health conditions as moderate were confident in using mobile eHealth and technology, few adopted these tools in their daily lives. The study found that mobile eHL had a direct effect on self-care behavior as well as knowledge and skills of computers, the internet, and mobile technology, and had an indirect effect on health outcomes (glycemic control and self-rated health status). Information about this population's experiences and the role mobile eHL plays in them can spur necessary mobile eHealth patient education.

The correlation between glucose fluctuation from self-monitored blood glucose and the major adverse cardiac events in diabetic patients with acute coronary syndrome during a 6-month follow-up by WeChat application

Background This study aimed to investigate the correlation between glucose fluctuation from self-monitored blood glucose (SMBG) and the major adverse cardiac events (MACE) in diabetic patients with acute coronary syndrome (ACS) during a 6-month follow-up period using the WeChat application. Methods From November 2016 to June 2017, 262 patients with ACS were discharged in a stable condition and completed a 6-month follow-up period. SMBG was recorded using the WeChat application. The patients were divided to a high glucose fluctuation group (H group; n=92) and a low glucose fluctuation group (L group; n=170). The 6-month incidence of MACE, lost-to-follow-up rate and satisfaction rate were measured through the WeChat follow-up. Results MACE occurred in 17.4% of patients in the H group and in 8.2% of patients in the L group (p=0.04). Multivariable analysis suggested that high glucose fluctuation conferred an 87% risk increment of MACE in the 6-month follow-up period (odds ratio: 2.1, 95% confidence interval 1.95-4.85; p=0.03). The lost-to-follow-up rate was lower and the satisfaction rate was higher in the patients using the WeChat application during follow-up than those of the regular outpatient follow-up during the same period (p<0.05). Conclusions The trial demonstrates that higher glucose fluctuation from SMBG after discharge was correlated with a higher incidence of MACE in diabetic patients with ACS. WeChat follow-up might have the potential to promote a good physician-patient relationship.

Comparison of three meta-analytic methods using data from digital interventions on type 2 diabetes

AIMS

Pooling the effect sizes of randomized controlled trials (RCTs) from continuous outcomes, such as glycated hemoglobin level (HbA1c), is an important method in evidence syntheses. However, due to challenges related to baseline imbalances and pre/post correlations, simple analysis of change scores (SACS) and simple analysis of final values (SAFV) meta-analyses result in under- or overestimation of effect estimates. This study was aimed to compare pooled effect sizes estimated by Analysis of Covariance (ANCOVA), SACS, and SAFV meta-analyses, using the example of RCTs of digital interventions with HbA1c as the main outcome.

MATERIALS AND METHODS

Three databases were systematically searched for RCTs published from 1993 through June 2017. Two reviewers independently assessed titles and abstracts using predefined eligibility criteria, assessed study quality, and extracted data, with disagreements resolved by arbitration from a third reviewer.

RESULTS

ANCOVA, SACS, and SAFV resulted in pooled HbA1c mean differences of -0.39% (95% CI: [-0.51, -0.26]), -0.39% (95% CI: [-0.51, -0.26]), and -0.34% (95% CI: [-0.48-0.19]), respectively. Removing studies with both high baseline imbalance (≥±0.2%) and pre/post correlation of ≥±0.6 resulted in a mean difference of -0.39% (95% CI: [-0.53, -0.26]), -0.40% (95% CI: [-0.54, -0.26]), and -0.33% (95% CI: [-0.48, -0.18]) with ANCOVA, SACS, and SAFV meta-analyses, respectively. Substantial heterogeneity was noted. Egger's test for funnel plot symmetry did not indicate evidence of publication bias for all methods.

CONCLUSION

By all meta-analytic methods, digital interventions appear effective in reducing HbA1c in type 2 diabetes. The effort to adjust for baseline imbalance and pre/post correlation using ANCOVA relies on the level of detail reported from individual studies. Reporting detailed summary data and, ideally, access to individual patient data of intervention trials are essential.

Effectiveness of Digital Interventions for Improving Glycemic Control in Persons with Poorly Controlled Type 2 Diabetes: A Systematic Review, Meta-analysis, and Meta-regression Analysis

BACKGROUND

Digital interventions may assist patients with type 2 diabetes in improving glycemic control. We aimed to synthesize effect sizes of digital interventions on glycated hemoglobin (HbA1c) levels and to identify effective features of digital interventions targeting patients with poorly controlled type 2 diabetes.

MATERIALS AND METHODS

MEDLINE, ISI Web of Science, and PsycINFO were searched for randomized controlled trials (RCTs) comparing the effects of digital interventions with usual care. Two reviewers independently assessed studies for eligibility and determined study quality, using the Cochrane Risk of Bias Assessment Tool. The Behavioral Change Technique Taxonomy V1 (BCTTv1) was used to identify BCTs used in interventions. Mean HbA1c differences were pooled using analysis of covariance to adjust for baseline differences and pre-post correlations. To examine effective intervention features and to evaluate differences in effect sizes across groups, meta-regression and subgroup analyses were performed.

RESULTS

Twenty-three arms of 21 RCTs were included in the meta-analysis (n = 3787 patients, 52.6% in intervention arms). The mean HbA1c baseline differences ranged from -0.2% to 0.64%. The pooled mean HbA1c change was statistically significant (-0.39 {95% CI: [-0.51 to -0.26]} with substantial heterogeneity [I² statistic, 80.8%]) and a significant HbA1c reduction was noted for web-based interventions. A baseline HbA1c level above 7.5%, β = -0.44 (95% CI: [-0.81 to -0.06]), the BCTs "problem solving," β = -1.30 (95% CI: [-2.05 to -0.54]), and "self-monitoring outcomes of behavior," β = -1.21 (95% CI: [-1.95 to -0.46]) were significantly associated with reduced HbA1c levels.

CONCLUSIONS

Digital interventions appear effective for reducing HbA1c levels in patients with poorly controlled type 2 diabetes.