Does Diabetes Distress Influence Clinical Response to an mHealth Diabetes Self-Management Education and Support Intervention?
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ABSTRACT: Purpose
The purpose of this study was to examine whether baseline levels of diabetes distress (DD) impacted clinical benefit from a mobile health (mHealth) diabetes self-management education and support (DSME/S) intervention ("Dulce Digital").Methods
This secondary analysis included the full sample of 126 Hispanic adults (mean age = 48.43 years, SD = 9.80) with type 2 diabetes and glycosylated hemoglobin A1C >7.5% enrolled from a Federally Qualified Health Center in a randomized, nonblinded clinical trial that compared Dulce Digital to usual care. Dulce Digital participants received educational/motivational, medication reminders, and blood glucose monitoring prompt text messages over 6 months.Results
Baseline levels of DD prospectively moderated the effect of Dulce Digital (vs usual care) on glycemic control over 6 months, such that Dulce Digital participants with higher DD experienced relatively greater benefit from the intervention. The effect of the intervention on A1C change was 178% larger among individuals experiencing moderate/high versus no/low DD.Conclusions
Although research has found DD to be associated with poorer self-management and clinical outcomes, individuals already distressed about their diabetes may benefit from a lower-burden mHealth DSME/S approach.
SUBMITTER: Clark TL
PROVIDER: S-EPMC8344137 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Patients with diabetes and poorly controlled hypertension are at increased risk for adverse renal and cardiovascular outcomes. Identifying these patients early and addressing modifiable risk factors is central to delaying renal complications such as diabetic kidney disease. Mobile health (mHealth), a relatively inexpensive and easily scalable technology, can facilitate patient-centered care and promote engagement in self-management, particularly for patients of lower socioeconomic status. Thus, mHealth may be a cost-effective way to deliver self-management education and support. OBJECTIVE:This feasibility study aimed to build a population management program by identifying patients with diabetes and poorly controlled hypertension who were at risk for adverse renal outcomes and evaluate a multifactorial intervention to address medication self-management. We recruited patients from a federally qualified health center (FQHC) in an underserved, diverse county in the southeastern United States. METHODS:Patients were identified via electronic health record. Inclusion criteria were age between 18 and 75 years, diagnosis of type 2 diabetes, poorly controlled hypertension over the last 12 months (mean clinic systolic blood pressure [SBP] ?140 mm Hg and/or diastolic blood pressure [DBP] ?90 mm Hg), access to a mobile phone, and ability to receive text messages and emails. The intervention consisted of monthly telephone calls for 6 months by a case manager and weekly, one-way informational text messages. Engagement was defined as the number of phone calls completed during the intervention; individuals who completed 4 or more calls were considered engaged. The primary outcome was change in SBP at the conclusion of the intervention. RESULTS:Of the 141 patients enrolled, 84.0% (118/141) of patients completed 1 or more phone calls and had follow-up SBP measurements for analysis. These patients were on average 56.9 years of age, predominately female (73/118, 61.9%), and nonwhite by self-report (103/118, 87.3%). The proportion of participants with poor baseline SBP control (50/118, 42.4%) did not change significantly at study completion (53/118, 44.9%) (P=.64). Participants who completed 4 or more phone calls (98/118, 83.1%) did not experience a statistically significant decrease in SBP when compared to those who completed fewer calls. CONCLUSION:We did not reduce uncontrolled hypertension even among the more highly engaged. However, 83% of a predominately minority and low-income population completed at least 67% of the multimodal mHealth intervention. Findings suggest that combining an automated electronic health record system to identify at-risk patients with a tailored mHealth protocol can provide education to this population. While this intervention was insufficient to effect behavioral change resulting in better hypertension control, it does suggest that this FQHC population will engage in low-cost population health applications with a potentially promising impact. TRIAL REGISTRATION:ClinicalTrials.gov NCT02418091; https://clinicaltrials.gov/ct2/show/NCT02418091 (Archived by WebCite at http://www.webcitation.org/76RBvacVU).
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Project description:BACKGROUND:Despite the plethora of evidence on mHealth interventions for patient education, there is a lack of information regarding their structures and delivery strategies. OBJECTIVE:This review aimed to investigate the structures and strategies of patient education programs delivered through smartphone apps for people with diverse conditions and illnesses. We also examined the aim of educational interventions in terms of health promotion, disease prevention, and illness management. METHODS:We searched PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, and PsycINFO for peer-reviewed papers that reported patient educational interventions using mobile apps and published from 2006 to 2016. We explored various determinants of educational interventions, including the content, mode of delivery, interactivity with health care providers, theoretical basis, duration, and follow-up. The reporting quality of studies was evaluated according to the mHealth evidence and reporting assessment criteria. RESULTS:In this study, 15 papers met the inclusion criteria and were reviewed. The studies mainly focused on the use of mHealth educational interventions for chronic disease management, and the main format for delivering interventions was text. Of the 15 studies, 6 were randomized controlled trials (RCTs), which have shown statistically significant effects on patients' health outcomes, including patients' engagement level, hemoglobin A1c, weight loss, and depression. Although the results of RCTs were mostly positive, we were unable to identify any specific effective structure and strategy for mHealth educational interventions owing to the poor reporting quality and heterogeneity of the interventions. CONCLUSIONS:Evidence on mHealth interventions for patient education published in peer-reviewed journals demonstrates that current reporting on essential mHealth criteria is insufficient for assessing, understanding, and replicating mHealth interventions. There is a lack of theory or conceptual framework for the development of mHealth interventions for patient education. Therefore, further research is required to determine the optimal structure, strategies, and delivery methods of mHealth educational interventions.
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Project description:IntroductionDiabetes self-management education (DSME) programs that engage the families of patients with diabetes have shown to be effective in improving diabetes-related outcomes of the patients. The health effects of these "family models" of DSME on participating family members are rarely studied. Opportunity exists for the participating family members to benefit from the healthy lifestyle recommendations offered through such programs.MethodUsing data from a randomized controlled trial to assess the effect of family DSME compared to standard DSME among Marshallese adults with Type 2 diabetes, this study examined baseline to 12-month changes in A1c, body mass index (BMI), food consumption, and physical activity among participating family members, comparing outcomes of family members based on attended at least 1 (n = 98) versus attended no (n = 44) DSME sessions.ResultsOverall, family member attendance was low. There were no differences in the level of change from baseline to 12 months for A1c, BMI, food consumption, and physical activity between groups. After controlling for attendance and sociodemographic measures, lowering of BMI was the only significant predictor of not having an A1c level indicative of diabetes at 12 months.DiscussionFuture research on family DSME should consider ways to improve family member attendance; have them set their own health improvement goals; and integrate healthy lifestyle education, such as healthy eating and being physically active, along with the DSME core content to create an added benefit of diabetes prevention for participating family members. The limitations of this study and recommendations for future research are provided. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
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Project description:Background:Self-management is an important aspect of long-term HIV treatment. Mobile technologies offer the potential to efficiently deliver interventions to facilitate HIV self-management. The last comprehensive review of such mHealth interventions was conducted in 2011. Given the rapidly evolving field, a need was identified for an updated review of the literature. Objective:The study aimed to describe and evaluate current evidence-based mHealth interventions to support self-management in HIV. Method:Eight online databases (Medline, Scopus, Embase, PsycINFO, Cochrane, Global Health CAB, IEEE explore, Web of Science) were systematically searched for papers describing and evaluating mHealth HIV self-management interventions. Reference lists of relevant papers were also searched. Data on intervention content and evaluation methodology were extracted and appraised by two researchers. Results:41 papers were identified evaluating 28 interventions. The majority of these interventions (n=20, 71%) had a single focus of either improving adherence (n=16), increasing engagement in care (n=3) or supporting smoking cessation (n=1), while just 8 (29%) were more complex self-management interventions, targeting a range of health-related behaviours. Interventions were predominantly delivered through SMS messaging. They significantly impacted on a range of outcomes including adherence, viral load, mental health and social support. Conclusion:Since the last major review of mHealth interventions in HIV, there has been a shift from exploratory acceptability/feasibility studies to impact evaluations. While overall the interventions impacted on a range of outcomes, they were generally limited in scope, failing to encompass many functions identified as desirable by people living with HIV. Participant incentives may limit the generalizability of findings.
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Project description:Examine influences of diabetes-specific social support (D-SS) and depressive symptoms on glycemic control over time, among adults randomized to a diabetes self-management education and support (DSME/S) intervention or usual care.Data were from 108 African-American and Latino participants in a 6-month intervention trial. Multivariable linear regression models assessed associations between baseline D-SS from family and friends and depressive symptoms with changes in HbA1c. We then examined whether baseline D-SS or depression moderated intervention-associated effects on HbA1c.Higher baseline D-SS was associated with larger improvements in HbA1c (adjusted ?HbA1c -0.39% for each +1-point D-SS, p=0.02), independent of intervention-associated HbA1c decreases. Baseline depressive symptoms had no significant association with subsequent HbA1c change. Neither D-SS nor depression moderated intervention-associated effects on HbA1c.Diabetes self-management education and support programs have potential to improve glycemic control for participants starting with varying levels of social support and depressive symptoms. Participants starting with more support for diabetes management from family and friends improved HbA1c significantly more over 6 months than those with less support, independent of additional significant DSME/S intervention-associated HbA1c improvements. Social support from family and friends may improve glycemic control in ways additive to DSME/S.
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Project description:Self-management is crucial to successful glycemic control in patients with diabetes, yet it requires patients to initiate and sustain complicated behavioral changes. Support programs can improve glycemic control, but may be expensive to implement. We report here an analysis of the costs of a successful telephone-based self-management support program delivered by lay health educators utilizing a municipal health department A1c registry, and relate them to near-term effectiveness.Costs of implementation were assessed by micro-costing of all resources used. Per-capita costs and cost-effectiveness ratios from the perspective of the service provider are estimated for net A1c reduction, and percentages of patients achieving A1c reductions of 0.5 and 1.0 percentage points. One-way sensitivity analyses of key cost elements, and a Monte Carlo sensitivity analysis are reported.The telephone intervention was provided to 443 people at a net cost of $187.61 each. Each percentage point of net A1c reduction was achieved at a cost of $464.41. Labor costs were the largest component of costs, and cost-effectiveness was most sensitive to the wages paid to the health educators.Effective telephone-based self-management support for people in poor diabetes control can be delivered by health educators at moderate cost relative to the gains achieved. The costs of doing so are most sensitive to the prevailing wage for the health educators.
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Project description:BackgroundThe aim of this work was to develop a National Evaluation Framework to facilitate the standardization of delivery, quality, reporting, and evaluation of diabetes education and support programs delivered throughout Australia through the National Diabetes Services Scheme (NDSS). The NDSS is funded by the Australian Government, and provides access to diabetes information, education, support, and subsidized product across diverse settings in each state and territory of Australia through seven independent service-providers. This article reports the approach undertaken to develop the Framework.MethodsA participatory approach was undertaken, focused on adopting nationally consistent outcomes and indicators, nominating objectives and measurement tools, specifying evaluation processes, and developing quality standards. Existing programs were classified based on related, overarching indicators enabling the adoption of a tiered system of evaluation.ResultsTwo outcomes (i.e., improved clinical, reduced cost) and four indicators (i.e., improved knowledge and understanding, self-management, self-determination, psychosocial adjustment) were adopted from the Eigenmann and Colagiuri national consensus position statement for diabetes education. This allowed for the identification of objectives (i.e., improved empowerment, reduced distress, autonomy supportive program delivery, consumer satisfaction) and related measurement instruments. Programs were categorized as comprehensive, topic-specific, or basic education, with comprehensive programs allocated to receive the highest-level of evaluation. Eight quality standards were developed, with existing programs tested against those standards. Based on the results of testing, two comprehensive (OzDAFNE for people with type 1 diabetes, DESMOND for people with type 2 diabetes), and eight topic-specific (CarbSmart, ShopSmart, MonitorSmart, FootSmart, MedSmart, Living with Insulin, Insulin Pump Workshop, Ready Set Go - Let's Move) structured diabetes self-management education and support programs were nominated for national delivery.ConclusionsThe National Evaluation Framework has facilitated consistency of program quality, delivery, and evaluation of programs delivered by multiple service providers across diverse contexts. The Framework could be applied by other service providers who facilitate multiple diabetes education and support programs and could be adapted for use in other chronic disease populations where education and support are indicated.
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