The Impact of a Mobile Diabetes Health Intervention on Diabetes Distress and Depression Among Adults: Secondary Analysis of a Cluster Randomized Controlled Trial

Quinn, Charlene C.; Swasey, Krystal; Crabbé, J.; Shardell, Michelle; Terrin, Michael L.; Barr, Erik; Gruber‐Baldini, Ann L.

doi:10.2196/mhealth.8910

Cited by 29 publications

(17 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our program of studies [ 11 , 15 , 17 , 18 , 23 ], including the analysis reported here, demonstrates that a mobile phone diabetes technology achieved a clinically significant change in BG control and that important service utilization increased (pharmacy) or decreased (physician office visits). These findings may help persons with T2D diabetes engage with health service providers and participate in decisions to receive services, lab tests, and prescribed medications recommended by treatment guidelines.…”

Section: Discussionmentioning

confidence: 81%

“…In general, patients were not depressed or distressed by their diabetes, with 79% (73/92) reporting minimal to mild depression and an average diabetes distress scale score of 2.6: <2: little distress and ≥3: high distress [ 18 ]. Participants’ total cholesterol levels were desirable, low-density lipoprotein levels were near ideal: UC group: 105.1 (SD 31.8) mg/dl; CO group: 102.3 (SD 25.9) mg/dl; CPP group: 94.6 (SD 30.6) mg/dl; and CPDS group: 112.4 (SD 30.4) mg/dl).…”

Section: Resultsmentioning

confidence: 99%

“…This improvement is important because a reduction of 0.5%-1.0% in HbA 1c is considered clinically significant to reduce the risk of comorbid conditions; the FDA recognizes a 0.4% improvement as clinically significant for the prescription antihyperglycemic medications [ 12 - 14 ]. In addition, 5 related substudies evaluating the MDIS impact on physician prescribing behavior, differences by participant age, depression, diabetes distress, and a mixed methods analysis of patient engagement reported modest benefits [ 15 - 18 ]. The purpose of this a priori -identified secondary data analysis [ 10 ] was to determine whether a mobile phone intervention impacted the utilization of health services identified in commercial insurance administrative claims data and whether changes in health service utilization explained HbA 1c change over time.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An mHealth Diabetes Intervention for Glucose Control: Health Care Utilization Analysis

Quinn

Swasey

Torain

et al. 2018

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

BackgroundType 2 diabetes (T2D) is a major chronic condition requiring management through lifestyle changes and recommended health service visits. Mobile health (mHealth) is a promising tool to encourage self-management, but few studies have investigated the impact of mHealth on health care utilization.ObjectiveThe objective of this analysis was to determine the change in 2-year health service utilization and whether utilization explained a 1.9% absolute decrease in glycated hemoglobin (HbA1c) over 1-year in the Mobile Diabetes Intervention Study (MDIS).MethodsWe used commercial claims data from 2006 to 2010 linked to enrolled patients’ medical chart data in 26 primary care practices in Maryland, USA. Secondary claims data analyses were available for 56% (92/163) of participants. In the primary MDIS study, physician practices were recruited and randomized to usual care and 1 of 3 increasingly complex interventions. Patients followed physician randomization assignment. The main variables in the analysis included health service utilization by type of service and change in HbA1c. The claims data was aggregated into 12 categories of utilization to assess change in 2-year health service usage, comparing rates of usage pre- and posttrial. We also examined whether utilization explained the 1.9% decrease in HbA1c over 1 year in the MDIS cluster randomized clinical trial.ResultsA significant group by time effect was observed in physician office visits, general practitioner visits, other outpatient services, prescription medications, and podiatrist visits. Physician office visits (P=.01) and general practitioner visits (P=.02) both decreased for all intervention groups during the study period, whereas prescription claims (P<.001) increased. The frequency of other outpatient services (P=.001) and podiatrist visits (P=.04) decreased for the control group and least complex intervention group but increased for the 2 most complex intervention groups. No significant effects of utilization were observed to explain the clinically significant change in HbA1c.ConclusionsClaims data analyses identified patterns of utilization relevant to mHealth interventions. Findings may encourage patients and health providers to discuss the utilization of treatment-recommended services, lab tests, and prescribed medications.Trial RegistrationClinicalTrials.gov NCT01107015; https://clinicaltrials.gov/ct2/show/NCT01107015 (Archived by Webcite at http://www.webcitation.org/72XgTaxIj)

show abstract

Section: Discussionmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An mHealth Diabetes Intervention for Glucose Control: Health Care Utilization Analysis

Quinn

Swasey

Torain

et al. 2018

JMIR Mhealth Uhealth

Self Cite

View full text Add to dashboard Cite

show abstract

“…The decreased utilization for depression correlates with one of the main findings of the Lorig et al study that indicated that patients’ self-reported depression was reduced [ 29 ]. This may be evidence that the structure or content of the intervention was more successful in decreasing distress and depression associated with diabetes management than the mobile health app studied by Quinn et al [ 36 ]. Furthermore, intervention cohort reported increased aerobic activity in the original clinical study, which may correlate with improvements seen in claims for depression and musculoskeletal disorders [ 29 ].…”

Section: Discussionmentioning

confidence: 96%

Evaluation of a Diabetes Self-Management Program: Claims Analysis on Comorbid Illnesses, Health Care Utilization, and Cost

Turner¹,

Ma²,

Lorig³

et al. 2018

J Med Internet Res

View full text Add to dashboard Cite

BackgroundAn estimated 30.3 million Americans have diabetes mellitus. The US Department of Health and Human Services created national objectives via its Healthy People 2020 initiative to improve the quality of life for people who either have or are at risk for diabetes mellitus, and hence, lower the personal and national economic burden of this debilitating chronic disease. Diabetes self-management education interventions are a primary focus of this initiative.ObjectiveThe aim of this study was to evaluate the impact of the Better Choices Better Health Diabetes (BCBH-D) self-management program on comorbid illness related to diabetes mellitus, health care utilization, and cost.MethodsA propensity score matched two-group, pre-post design was used for this study. Retrospective administrative medical and pharmacy claims data from the HealthCore Integrated Research Environment were used for outcome variables. The intervention cohort included diabetes mellitus patients who were recruited to a diabetes self-management program. Control cohort subjects were identified from the HealthCore Integrated Research Environment by at least two diabetes-associated claims (International Classification of Diseases-Ninth Revision, ICD-9 250.xx) within 2 years before the program launch date (October 1, 2011-September 30, 2013) but did not participate in BCBH-D. Controls were matched to cases in a 3:1 propensity score match. Outcome measures included pre- and postintervention all-cause and diabetes-related utilization and costs. Cost outcomes are reported as least squares means. Repeated measures analyses (generalized estimating equation approach) were conducted for utilization, comorbid conditions, and costs.ResultsThe program participants who were identified in HealthCore Integrated Research Environment claims (N=558) were matched to a control cohort of 1669 patients. Following the intervention, the self-management cohort experienced significant reductions for diabetes mellitus–associated comorbid conditions, with the postintervention disease burden being significantly lower (mean 1.6 [SD 1.6]) compared with the control cohort (mean 2.1 [SD 1.7]; P=.001). Postintervention all-cause utilization was decreased in the intervention cohort compared with controls with −40/1000 emergency department visits vs +70/1000; P=.004 and −5780 outpatient visits per 1000 vs −290/1000; P=.001. Unadjusted total all-cause medical cost was decreased by US $2207 in the intervention cohort compared with a US $338 decrease in the controls; P=.001. After adjustment for other variables through structural equation analysis, the direct effect of the BCBH-D was –US $815 (P=.049).ConclusionsPatients in the BCBH-D program experienced reduced all-cause health care utilization and costs. Direct cost savings were US $815. Although encouraging, given the complexity of the patient population, further study is needed to cross-validate the results.

show abstract

“…57 Ongoing patient education and family engagement is also vital—in presence of shortage of health providers, digital and mobile health technology can be used to convey and reinforce information to patients and caregivers. 87 – 89 Cost-effective health education should penetrate the targeted populations in remote areas with low health resources and among people, with poor education. 90 Active involvement of youth in health education is recommended as they have the most access to technology and may serve to influence their communities towards better health practices.…”

Section: Assessment Of Ncd Burden In Low- and Middle-income Countriesmentioning

confidence: 99%

<p>Systemic Solutions for Addressing Non-Communicable Diseases in Low- and Middle-Income Countries</p>

Ganju¹,

Goulart²,

Majumdar³

et al. 2020

JMDH

View full text Add to dashboard Cite

Non-communicable diseases (NCDs) have been on the rise in low-and middleincome countries (LMICs) over the last few decades and represent a significant healthcare concern. Over 85% of "premature" deaths worldwide due to NCDs occur in the LMICs. NCDs are an economic burden on these countries, increasing their healthcare expenditure. However, targeting NCDs in LMICs is challenging due to evolving health systems and an emphasis on acute illness. The major issues include limitations with universal health coverage, regulations, funding, distribution and availability of the healthcare workforce, and availability of health data. Experts from across the health sector in LMICs formed a Think Tank to understand and examine the issues, and to offer potential opportunities that may address the rising burden of NCDs in these countries. This review presents the evidence and posits pragmatic solutions to combat NCDs.

show abstract

The Impact of a Mobile Diabetes Health Intervention on Diabetes Distress and Depression Among Adults: Secondary Analysis of a Cluster Randomized Controlled Trial

Cited by 29 publications

References 51 publications

An mHealth Diabetes Intervention for Glucose Control: Health Care Utilization Analysis

An mHealth Diabetes Intervention for Glucose Control: Health Care Utilization Analysis

Evaluation of a Diabetes Self-Management Program: Claims Analysis on Comorbid Illnesses, Health Care Utilization, and Cost

<p>Systemic Solutions for Addressing Non-Communicable Diseases in Low- and Middle-Income Countries</p>

Contact Info

Product

Resources

About