Effects of Short-term Mobile Application Use on Weight Reduction for Patients with Type 2 Diabetes

Diabetes self-management education and support delivered by mobile health (mHealth) interventions for adults with type 2 diabetes-A systematic review and meta-analysis

BACKGROUND

Diabetes self-management education (DSME) and support (DSME/S) delivered via mobile health (mHealth) is potentially cost-effective, if proven effective.

OBJECTIVES

To assess the effectiveness of DSME, DSMS or DSME/S delivered by mHealth interventions compared to usual care (UC) or attention placebo control (APC) in adults with type 2 diabetes.

METHODS

We searched (1) MEDLINE, (2) Embase, (3) PsycINFO, (4) Cochrane Central Register of Controlled Trials via the Cochrane Register of Studies Online, (5) ClinicalTrials.gov, and (6) World Health Organization International Clinical Trials Registry Platform from the year 2000 to January 31, 2023. We included RCTs comparing DSME/S delivered via mHealth versus UC or APC. Four authors independently selected trials, assessed risk of bias and extracted data. Primary outcome was HbA1c, outher outcomes secundairy. Meta-analysed with random-effects model was used.

RESULTS

We included 43 trials involving 9328 participants; sample sizes ranging from 20 to 1119. Pooled effects on HbA1c were for DSME: mean difference (MD) of -4 mmol/mol (-0.3%), 95% CI -6 mmol/mol (-0.6%) to -1 mmol/mol (-0.1); p = 0.002; DSMS MD -4 mmol/mol (-0.4%), 95% CI 7 mmol/mol (-0.6%) to -2 mmol/mol (-0.2); p < 0.001; and DSME/S MD of -2 mmol/mol (-0.2%) for HbA1c, 95% CI -3 mmol/mol (-0.3%) to -0 mmol/mol (-0.0%); p < 0.001. We found uncertain effects on other outcomes.

CONCLUSIONS

mHealth interventions delivering self management education with or without support to adults with type 2 diabetes appear to have a modest beneficial effect on HbA1c. Only a few trials investigated patient-reported outcomes.

Effect of Multimodal App-Based Interventions on Glycemic Control in Patients With Type 2 Diabetes: Systematic Review and Meta-Analysis

BACKGROUND

Digital technologies for type 2 diabetes mellitus (T2DM) care hold great potential to improve patients' health in the long term. Only a subset of telemedicine offerings are digital interventions that meet the criteria for prescribable digitale Gesundheitsanwendung (digital health apps; DiGAs) in Germany. Digital treatments further provide vast amounts of patient data that are important to generate evidence.

OBJECTIVE

This systematic review aims to analyze the efficacy of multimodal digital therapies that mainly meet the DiGA criteria for patients with T2DM and to elicit the potential of such therapies. This includes evidence from randomized controlled trials (RCTs) as well as from real-world data. The outcome of interest was a reduction in glycated hemoglobin (hemoglobin A1c [HbA1c]; long-term blood glucose measurements).

METHODS

A systematic literature search was conducted in the literature bases PubMed, LIVIVO, and Cochrane, based on the predefined PICO (Population; Intervention; Control; Outcome) scheme. Identified studies were assessed for risk of bias, pragmatism, and overall quality of evidence. Meta-analyses were conducted for between group differences using RCTs only, and for within-group differences using RCTs and non-RCTs, to examine the effect of the interventions on HbA1c.

RESULTS

In total, 795 records were identified, of which 24 were eligible for this systematic review and 23 studies were eligible for the meta-analysis. The results of the meta-analyses showed significant and clinically relevant reductions in HbA1c in patients with T2DM. Regarding the between-group difference for HbA1c reduction, the pooled effect of the RCTs showed a reduction of -0.36% (95% CI -0.59% to -0.14%; P<.001), favoring app-based interventions. The average mean within-group reduction in HbA1c was -0.79 (95% CI -1.02 to -0.55), with no significant difference between RCTs (-0.69, 95% CI -1.13 to -0.24) and non-RCTs (-0.87, 95% CI -1.16 to -0.57; P<.01, differences between RCTs and RCTs P=.44). A pragmatism rating showed that both study types were on average (very) pragmatic, that is, close to usual care. However, the overall quality of evidence was low to very low.

CONCLUSIONS

This systematic review shows that digital therapies that mainly meet the DiGA criteria can effectively improve HbA1c in patients with T2DM. The integration of digital health care into usual care holds great potential and should be considered as a complementary option to usual care in the future.

TRIAL REGISTRATION

PROSPERO CRD42023440203; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=440203.

User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review

BACKGROUND

There are numerous mobile health (mHealth) interventions for treatment adherence and self-management; yet, little is known about user engagement or interaction with these technologies.

OBJECTIVE

This systematic review aimed to answer the following questions: (1) How is user engagement defined and measured in studies of mHealth interventions to promote adherence to prescribed medical or health regimens or self-management among people living with a health condition? (2) To what degree are patients engaging with these mHealth interventions? (3) What is the association between user engagement with mHealth interventions and adherence or self-management outcomes? (4) How often is user engagement a research end point?

METHODS

Scientific database (Ovid MEDLINE, Embase, Web of Science, PsycINFO, and CINAHL) search results (2016-2021) were screened for inclusion and exclusion criteria. Data were extracted in a standardized electronic form. No risk-of-bias assessment was conducted because this review aimed to characterize user engagement measurement rather than certainty in primary study results. The results were synthesized descriptively and thematically.

RESULTS

A total of 292 studies were included for data extraction. The median number of participants per study was 77 (IQR 34-164). Most of the mHealth interventions were evaluated in nonrandomized studies (157/292, 53.8%), involved people with diabetes (51/292, 17.5%), targeted medication adherence (98/292, 33.6%), and comprised apps (220/292, 75.3%). The principal findings were as follows: (1) >60 unique terms were used to define user engagement; "use" (102/292, 34.9%) and "engagement" (94/292, 32.2%) were the most common; (2) a total of 11 distinct user engagement measurement approaches were identified; the use of objective user log-in data from an app or web portal (160/292, 54.8%) was the most common; (3) although engagement was inconsistently evaluated, most of the studies (99/195, 50.8%) reported >1 level of engagement due to the use of multiple measurement methods or analyses, decreased engagement across time (76/99, 77%), and results and conclusions suggesting that higher engagement was associated with positive adherence or self-management (60/103, 58.3%); and (4) user engagement was a research end point in only 19.2% (56/292) of the studies.

CONCLUSIONS

The results revealed major limitations in the literature reviewed, including significant variability in how user engagement is defined, a tendency to rely on user log-in data over other measurements, and critical gaps in how user engagement is evaluated (infrequently evaluated over time or in relation to adherence or self-management outcomes and rarely considered a research end point). Recommendations are outlined in response to our findings with the goal of improving research rigor in this area.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022289693; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022289693.

Digital therapeutics in the clinic

Digital therapeutics are emerging as a new form of therapeutic interventions. Unlike conventional therapeutics, digital therapeutics deliver interventions directly to patients using an evidence-based, clinically evaluated software to treat, manage, or prevent diseases. Digital therapeutics manifest in diverse forms such as web-based applications, mobile applications on smart devices, virtual reality, and video games. As its own product category for FDA approval, digital therapeutics can function as stand-alone treatments or in combination with conventional therapeutics to improve adherence and/or efficacy. Here, we review the clinical landscape of digital therapeutics. We summarize FDA-approved products and their clinical use, overview >300 ongoing clinical trials, and discuss challenges for their clinical translation and strategies to overcome the same.

Non-pharmacologic treatment for obesity

Background: The prevalence of obesity has been rapidly increasing in the Korean population. Obesity is a well-known risk factor for various chronic diseases, including diabetes mellitus, hypertension, dyslipidemia, atherosclerosis, chronic kidney disease, degenerative arthritis, and autoimmune diseases. It also increases the risks of different malignancies, gall bladder disease, and pancreatitis.Current Concepts: Lifestyle intervention assisted by frequent behavioral therapy is crucial despite the modest amount of weight loss achieved. Energy intake restriction combined with increased physical activity can not only facilitate weight loss but also improve metabolic health. Furthermore, this combination can help maintain weight reduction during and after lifestyle interventions. Energy intake restriction with a daily deficit of 500–1,000 kcal and physical activity including aerobic exercise for 150 minutes or more per week and resistance training 2–4 times a week are generally recommended for obesity management.Discussion and Conclusion: Comprehensive lifestyle intervention should be individualized and supported by a multidisciplinary team. A long-term behavioral intervention is necessary for success in obesity treatment.