Design and methods of "diaBEAT-it!": a hybrid preference/randomized control trial design using the RE-AIM framework

A randomized controlled trial to test the effectiveness of two technology-enhanced diabetes prevention programs in primary care: The DiaBEAT-it study

Objective

To evaluate the effectiveness of two technology-enhanced interventions for diabetes prevention among adults at risk for developing diabetes in a primary care setting.

Methods

The DiaBEAT-it study employed a hybrid 2-group preference (Choice) and 3-group randomized controlled (RCT) design. This paper presents weight related primary outcomes of the RCT arm. Patients from Southwest Virginia were identified through the Carilion Clinic electronic health records. Eligible participants (18 and older, BMI ≥ 25, no Type 2 Diabetes) were randomized to either Choice (n = 264) or RCT (n = 334). RCT individuals were further randomized to one of three groups: (1) a 2-h small group class to help patients develop a personal action plan to prevent diabetes (SC, n = 117); (2) a 2-h small group class plus automated telephone calls using an interactive voice response system (IVR) to help participants initiate weight loss through a healthful diet and regular physical activity (Class/IVR, n = 110); or (3) a DVD with same content as the class plus the same IVR calls over a period of 12 months (DVD/IVR, n = 107).

Results

Of the 334 participants that were randomized, 232 (69%) had study measured weights at 6 months, 221 (66%) at 12 months, and 208 (62%) at 18 months. Class/IVR participants were less likely to complete weight measures than SC or DVD/IVR. Intention to treat analyses, controlling for gender, race, age and baseline BMI, showed that DVD/IVR and Class/IVR led to reductions in BMI at 6 (DVD/IVR -0.94, p < 0.001; Class/IVR -0.70, p < 0.01), 12 (DVD/IVR -0.88, p < 0.001; Class/IVR-0.82, p < 0.001) and 18 (DVD/IVR -0.78, p < 0.001; Class/IVR -0.58, p < 0.01) months. All three groups showed a significant number of participants losing at least 5% of their body weight at 12 months (DVD/IVR 26.87%; Class/IVR 21.62%; SC 16.85%). When comparing groups, DVD/IVR were significantly more likely to decrease BMI at 6 months (p < 0.05) and maintain the reduction at 18 months (p < 0.05) when compared to SC. There were no differences between the other groups.

Conclusions

The DiaBEAT-it interventions show promise in responding to the need for scalable, effective methods to manage obesity and prevent diabetes in primary care settings that do not over burden primary care clinics and providers.

Registration

https://clinicaltrials.gov/ct2/show/NCT02162901, identifier: NCT02162901.

Reach Outcomes and Costs of Different Physician Referral Strategies for a Weight Management Program Among Rural Primary Care Patients: Type 3 Hybrid Effectiveness-Implementation Trial

BACKGROUND

Rural residents are at high risk for obesity; however, little resources exist to address this disproportional burden of disease. Primary care may provide an opportunity to connect primary care patients with overweight and obesity to effective weight management programming.

OBJECTIVE

The purpose of this study is to examine the utility of different physician referral and engagement processes for improving the reach of an evidence-based and technology-delivered weight management program with counseling support for rural primary care patients.

METHODS

A total of 5 rural primary care physicians were randomly assigned a sequence of four referral strategies: point-of-care (POC) referral with active telephone follow-up (ATF); POC referral, no ATF; a population health registry-derived letter referral with ATF; and letter referral, no ATF. For registry-derived referrals, physicians screened a list of patients with BMI ≥25 and approved patients for participation to receive a personalized referral letter via mail.

RESULTS

Out of a potential 991 referrals, 573 (57.8%) referrals were made over 16 weeks, and 98 (9.9%) patients were enrolled in the program (58/98, 59.2% female). Differences based on letter (485/991, 48.9%) versus POC (506/991, 51.1%) referrals were identified for completion (100% vs 7%; P<.001) and for proportion screened (36% vs 12%; P<.001) but not for proportion enrolled (12% vs 8%; P=.10). Patients receiving ATF were more likely to be screened (47% vs 7%; P<.001) and enrolled (15% vs 7%; P<.001) than those not receiving ATF. On the basis of the number of referrals made in each condition, we found variations in the proportion and number of enrollees (POC with ATF: 27/190, 50%; POC no ATF: 14/316, 41%; letter ATF: 30/199; 15.1%; letter no ATF: 27/286, 9.4%). Across all conditions, participants were representative of the racial and ethnic characteristics of the region (60% female, P=.15; 94% White individuals, P=.60; 94% non-Hispanic, P=.19). Recruitment costs totaled US $6192, and the overall recruitment cost per enrolled participant was US $63. Cost per enrolled participant ranged from POC with ATF (US $47), registry-derived letter without ATF (US $52), and POC without ATF (US $56) to registry-derived letter with ATF (US $91).

CONCLUSIONS

Letter referral with ATF appears to be the best option for enrolling a large number of patients in a digitally delivered weight management program; however, POC with ATF and letters without ATF yielded similar numbers at a lower cost. The best referral option is likely dependent on the best fit with clinical resources.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03690557; http://clinicaltrials.gov/ct2/show/NCT03690557.

Communicating personalized risk of diabetes and offering weight reduction program choice: Recruitment, participation, and outcomes

OBJECTIVE

Low patient recruitment into diabetes prevention programs is a challenge. The primary aim of this study was to demonstrate that an increased recruitment rate can be achieved by communicating personalized risk of progression to type 2 diabetes, estimating risk reduction with weight loss, and offering program choice. Secondary aims included program participation rate, weight loss, and short-term decreased diabetes risk.

METHODS

In this single-arm study, persons with prediabetes from 3 primary care sites received a letter that communicated their personalized risk of progression to diabetes within 3-years, estimated risk reduction with 5, 10, 15 % weight loss, reported in pounds, and offered a choice of 5 free, 6-month, programs. A one-sided test was used to compare the recruitment rate against the maximum expected rate of (10 %).

RESULTS

Recruitment response rate was 25.3 % (81/328, 95 % CI=[20.0 %, 29.4 %]) which was significantly higher than expected (p < 0.0001). Overall, 65 % of participants completed >75 % of contacts. BMI, HbA1c, and diabetes risk (all p < 0.0001) improved at 6 months; BMI (p < 0.0001) and HbA1c (p < 0.05) improved at 12 months.

CONCLUSION

Recruitment response rate was better than expected.

PRACTICE IMPLICATIONS

Communicating personalized risk and reduction estimates with a choice of programs resulted in favorable outcomes, sustained at 1-year.

Examining the feasibility and characteristics of realistic weight management support for patients: Focus groups with rural, micropolitan, and metropolitan primary care providers

The purpose of this investigation was to understand perspectives of physicians, nurses, and staff regarding the feasibility of implementing an evidence-based weight management program to support primary care practice. An exploratory aim was to examine differences in responses based on the clinic location. Ten focus groups were conducted with primary care staff from rural, micropolitan, and metropolitan clinics. The Promoting Action on Research in Health Services (PARIHS) framework was used to inform the interview guide. Transcripts were reviewed to identify common themes among PARIHS constructs (evidence, context, and facilitation). Presence of comorbidities (e.g., diabetes, hypertension) were typical prompts for provider-led discussions about patient weight. Metropolitan clinics reported the availability of health coaching, diabetes education, or dietician consultation, but no clinic reported offering a comprehensive weight management program. Participants agreed it is possible to implement a weight management program through primary care, but cited potential facilitation challenges such as costs, clinic resources, and individual patient barriers. More enthusiasm arose for a referral program with patient tracking. Program characteristics such as proven efficacy, individual tailoring, program accessibility, and patient feedback to the providers were desired. Rural focus group participants reported unique barriers (lack of local resources) and facilitators (more flexibility in practice changes) to weight management when compared to metropolitan and micropolitan focus groups. Primary care staff are interested in weight management solutions for their patients and would prefer an evidence-based program to which they could refer patients, receive feedback on patient progress, and sustainably include as part of their regular services.

Leveraging implementation science to improve implementation outcomes in precision medicine

BACKGROUND AND PURPOSE

Introduction of omics technologies in clinical practice means increased use of validated biomarkers, through precision medicine (PM). Although implementation science (IS) affords an array of theoretical approaches that can potentially explain PM intervention uptake, their relevance and applicability in PM implementation has not been empirically tested. This article identifies and examines existing implementation frameworks for their applicability in PM, demonstrating how different IS theories can be used to generate testable implementation hypotheses in PM.

METHODS

A three-step methodology was employed to search and select implementation models: a scoping search in Google Scholar produced 15 commonly used models in healthcare; a systematic search in PUBMED and Web of Science using the names of each model as keywords in search strings produced 290 publications for screening and abstraction; finally, a citation frequency search in the 3 databases produced most cited models that were included in the narrative synthesis.

RESULTS

Main concepts and constructs associated with each of the 15 models were identified. Four most cited frameworks in healthcare were: REAIM, CFIR, PRISM and PARiHS. Corresponding constructs were mapped and examined for potential congruence to PM. A generalized PM implementation conceptual framework was developed showing how omics biomarker uptake relates to their evidence base, patient and provider engagement and Big data capabilities of involved organizations.

CONCLUSION

We demonstrated how implementation complexities in PM can be addressed by explicit use of implementation theories. The work here may provide a reference for further research of empirically testing and refining the identified implementation constructs.

Influence of Personality on mHealth Use in Patients with Diabetes: Prospective Pilot Study

Background

Mobile technology for health (mHealth) interventions are increasingly being used to help improve self-management among patients with diabetes; however, these interventions have not been adopted by a large number of patients and often have high dropout rates. Patient personality characteristics may play a critical role in app adoption and active utilization, but few studies have focused on addressing this question.

Objective

This study aims to address a gap in understanding of the relationship between personality traits and mHealth treatment for patients with diabetes. We tested the role of the five-factor model of personality traits (openness to experience, conscientiousness, extraversion, agreeableness, and neuroticism) in mHealth adoption preference and active utilization.

Methods

We developed an mHealth app (DiaSocial) aimed to encourage diabetes self-management. We recruited 98 patients with diabetes—each patient freely chose whether to receive the standard care or the mHealth app intervention. Patient demographic information and patient personality characteristics were assessed at baseline. App usage data were collected to measure user utilization of the app. Patient health outcomes were assessed with lab measures of glycated hemoglobin (HbA_1c level). Logistic regression models and linear regression were employed to explore factors predicting the relationship between mHealth use (adoption and active utilization) and changes in health outcome.

Results

Of 98 study participants, 46 (47%) downloaded and used the app. Relatively younger patients with diabetes were 9% more likely to try and use the app (P=.02, odds ratio [OR] 0.91, 95% CI 0.85-0.98) than older patients with diabetes were. Extraversion was negatively associated with adoption of the mHealth app (P=.04, OR 0.71, 95% CI 0.51-0.98), and openness to experience was positively associated with adoption of the app (P=.03, OR 1.73, 95% CI 1.07-2.80). Gender (P=.43, OR 0.66, 95% CI 0.23-1.88), education (senior: P=.99, OR 1.00, 95% CI 0.32-3.11; higher: P=.21, OR 2.51, 95% CI 0.59-10.66), and baseline HbA_1c level (P=.36, OR 0.79, 95% CI 0.47-1.31) were not associated with app adoption. Among those who adopted the app, a low education level (senior versus primary P=.003; higher versus primary P=.03) and a high level of openness to experience (P=.048, OR 2.01, 95% CI 1.01-4.00) were associated with active app utilization. Active users showed a significantly greater decrease in HbA_1c level than other users (ΔHbA_1c=−0.64, P=.05).

Conclusions

This is one of the first studies to investigate how different personality traits influence the adoption and active utilization of an mHealth app among patients with diabetes. The research findings suggest that personality is a factor that should be considered when trying to identify patients who would benefit the most from apps for diabetes management.

Effectiveness of DVD vs. group-initiated diabetes prevention on information uptake for high & low health literacy participants

OBJECTIVE

This study evaluates the effectiveness of technology versus in-person, group-initiated diabetes prevention to enhance comprehension of learning objectives between patients with differing health literacy (HL).

METHODS

Evidence-based content through either a DVD (n = 217) or in-person, group class (n = 225) to initiate the intervention. A teach-back call was used to assess comprehension of, and reinforce, learning objectives. Chi-squared was used to determine differences between conditions (DVD vs Class) and HL levels (High n = 361 vs. Low n = 81) and regression analyses were used to examine relationships.

RESULTS

DVD participants performed significantly better across teach back questions (15.4 ± 2.5 v. 14.8 ± 2.6, p < 0.01), demonstrated comprehension in fewer teach-back rounds (1.9 ± 0.7 v. 2.1 ± 0.7, p < 0.01), and answered more questions correctly on the first try (4.2 ± 1.6 v. 3.4 ± 1.8, p < 0.01). Models for HL levels and modality by HL level were statistically significant (p < 0.01) favoring the DVD.

CONCLUSION

Initiating a diabetes prevention program with the use of a DVD appears to be a superior option to in-person, class sessions. Teach-back and teach-to-goal strategies enables participants of both high and low health literacy levels to receive and confirm mastery of diabetes prevention objectives.

PRACTICE IMPLICATIONS

A teach-back call may improve information uptake increasing the likelihood of health behavior uptake.

A Pragmatic Application of the RE-AIM Framework for Evaluating the Implementation of Physical Activity as a Standard of Care in Health Systems

Introduction

Exercise is Medicine (EIM) is an initiative that seeks to integrate physical activity assessment, prescription, and patient referral as a standard in patient care. Methods to assess this integration have lagged behind its implementation.

Purpose and Objectives

The purpose of this work is to provide a pragmatic framework to guide health care systems in assessing the implementation and impact of EIM.

Evaluation Methods

A working group of experts from health care, public health, and implementation science convened to develop an evaluation model based on the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. The working group aimed to provide pragmatic guidance on operationalizing EIM across the different RE-AIM dimensions based on data typically available in health care settings.

Results

The Reach of EIM can be determined by the number and proportion of patients that were screened for physical inactivity, received brief counseling and/or a physical activity prescription, and were referred to physical activity resources. Effectiveness can be assessed through self-reported changes in physical activity, cardiometabolic biometric factors, incidence/burden of chronic disease, as well as health care utilization and costs. Adoption includes assessing the number and representativeness of health care settings that adopt any component of EIM, and Implementation involves assessing the extent to which health care teams implement EIM in their clinic. Finally, Maintenance involves assessing the long-term effectiveness (patient level) and sustained implementation (clinic level) of EIM in a given health care setting.

Implications for Public Health

The availability of a standardized, pragmatic, evaluation framework is critical in determining the impact of implementing EIM as a standard of care across health care systems.

Automated telephone communication systems for preventive healthcare and management of long-term conditions

BACKGROUND

Automated telephone communication systems (ATCS) can deliver voice messages and collect health-related information from patients using either their telephone's touch-tone keypad or voice recognition software. ATCS can supplement or replace telephone contact between health professionals and patients. There are four different types of ATCS: unidirectional (one-way, non-interactive voice communication), interactive voice response (IVR) systems, ATCS with additional functions such as access to an expert to request advice (ATCS Plus) and multimodal ATCS, where the calls are delivered as part of a multicomponent intervention.

OBJECTIVES

To assess the effects of ATCS for preventing disease and managing long-term conditions on behavioural change, clinical, process, cognitive, patient-centred and adverse outcomes.

SEARCH METHODS

We searched 10 electronic databases (the Cochrane Central Register of Controlled Trials; MEDLINE; Embase; PsycINFO; CINAHL; Global Health; WHOLIS; LILACS; Web of Science; and ASSIA); three grey literature sources (Dissertation Abstracts, Index to Theses, Australasian Digital Theses); and two trial registries (www.controlled-trials.com; www.clinicaltrials.gov) for papers published between 1980 and June 2015.

SELECTION CRITERIA

Randomised, cluster- and quasi-randomised trials, interrupted time series and controlled before-and-after studies comparing ATCS interventions, with any control or another ATCS type were eligible for inclusion. Studies in all settings, for all consumers/carers, in any preventive healthcare or long term condition management role were eligible.

DATA COLLECTION AND ANALYSIS

We used standard Cochrane methods to select and extract data and to appraise eligible studies.

MAIN RESULTS

We included 132 trials (N = 4,669,689). Studies spanned across several clinical areas, assessing many comparisons based on evaluation of different ATCS types and variable comparison groups. Forty-one studies evaluated ATCS for delivering preventive healthcare, 84 for managing long-term conditions, and seven studies for appointment reminders. We downgraded our certainty in the evidence primarily because of the risk of bias for many outcomes. We judged the risk of bias arising from allocation processes to be low for just over half the studies and unclear for the remainder. We considered most studies to be at unclear risk of performance or detection bias due to blinding, while only 16% of studies were at low risk. We generally judged the risk of bias due to missing data and selective outcome reporting to be unclear.For preventive healthcare, ATCS (ATCS Plus, IVR, unidirectional) probably increase immunisation uptake in children (risk ratio (RR) 1.25, 95% confidence interval (CI) 1.18 to 1.32; 5 studies, N = 10,454; moderate certainty) and to a lesser extent in adolescents (RR 1.06, 95% CI 1.02 to 1.11; 2 studies, N = 5725; moderate certainty). The effects of ATCS in adults are unclear (RR 2.18, 95% CI 0.53 to 9.02; 2 studies, N = 1743; very low certainty).For screening, multimodal ATCS increase uptake of screening for breast cancer (RR 2.17, 95% CI 1.55 to 3.04; 2 studies, N = 462; high certainty) and colorectal cancer (CRC) (RR 2.19, 95% CI 1.88 to 2.55; 3 studies, N = 1013; high certainty) versus usual care. It may also increase osteoporosis screening. ATCS Plus interventions probably slightly increase cervical cancer screening (moderate certainty), but effects on osteoporosis screening are uncertain. IVR systems probably increase CRC screening at 6 months (RR 1.36, 95% CI 1.25 to 1.48; 2 studies, N = 16,915; moderate certainty) but not at 9 to 12 months, with probably little or no effect of IVR (RR 1.05, 95% CI 0.99, 1.11; 2 studies, 2599 participants; moderate certainty) or unidirectional ATCS on breast cancer screening.Appointment reminders delivered through IVR or unidirectional ATCS may improve attendance rates compared with no calls (low certainty). For long-term management, medication or laboratory test adherence provided the most general evidence across conditions (25 studies, data not combined). Multimodal ATCS versus usual care showed conflicting effects (positive and uncertain) on medication adherence. ATCS Plus probably slightly (versus control; moderate certainty) or probably (versus usual care; moderate certainty) improves medication adherence but may have little effect on adherence to tests (versus control). IVR probably slightly improves medication adherence versus control (moderate certainty). Compared with usual care, IVR probably improves test adherence and slightly increases medication adherence up to six months but has little or no effect at longer time points (moderate certainty). Unidirectional ATCS, compared with control, may have little effect or slightly improve medication adherence (low certainty). The evidence suggested little or no consistent effect of any ATCS type on clinical outcomes (blood pressure control, blood lipids, asthma control, therapeutic coverage) related to adherence, but only a small number of studies contributed clinical outcome data.The above results focus on areas with the most general findings across conditions. In condition-specific areas, the effects of ATCS varied, including by the type of ATCS intervention in use.Multimodal ATCS probably decrease both cancer pain and chronic pain as well as depression (moderate certainty), but other ATCS types were less effective. Depending on the type of intervention, ATCS may have small effects on outcomes for physical activity, weight management, alcohol consumption, and diabetes mellitus. ATCS have little or no effect on outcomes related to heart failure, hypertension, mental health or smoking cessation, and there is insufficient evidence to determine their effects for preventing alcohol/substance misuse or managing illicit drug addiction, asthma, chronic obstructive pulmonary disease, HIV/AIDS, hypercholesterolaemia, obstructive sleep apnoea, spinal cord dysfunction or psychological stress in carers.Only four trials (3%) reported adverse events, and it was unclear whether these were related to the interventions.

AUTHORS' CONCLUSIONS

ATCS interventions can change patients' health behaviours, improve clinical outcomes and increase healthcare uptake with positive effects in several important areas including immunisation, screening, appointment attendance, and adherence to medications or tests. The decision to integrate ATCS interventions in routine healthcare delivery should reflect variations in the certainty of the evidence available and the size of effects across different conditions, together with the varied nature of ATCS interventions assessed. Future research should investigate both the content of ATCS interventions and the mode of delivery; users' experiences, particularly with regard to acceptability; and clarify which ATCS types are most effective and cost-effective.

Mitochondrial Epigenetic Changes Link to Increased Diabetes Risk and Early-Stage Prediabetes Indicator

Type 2 diabetes (T2D) is characterized by mitochondrial derangement and oxidative stress. With no known cure for T2D, it is critical to identify mitochondrial biomarkers for early diagnosis of prediabetes and disease prevention. Here we examined 87 participants on the diagnosis power of fasting glucose (FG) and hemoglobin A1c levels and investigated their interactions with mitochondrial DNA methylation. FG and A1c led to discordant diagnostic results irrespective of increased body mass index (BMI), underscoring the need of new biomarkers for prediabetes diagnosis. Mitochondrial DNA methylation levels were not correlated with late-stage (impaired FG or A1c) but significantly with early-stage (impaired insulin sensitivity) events. Quartiles of BMI suggested that mitochondrial DNA methylation increased drastically from Q1 (20 < BMI < 24.9, lean) to Q2 (30 < BMI < 34.9, obese), but marginally from Q2 to Q3 (35 < BMI < 39.9, severely obese) and from Q3 to Q4 (BMI > 40, morbidly obese). A significant change was also observed from Q1 to Q2 in HOMA insulin sensitivity but not in A1c or FG. Thus, mitochondrial epigenetic changes link to increased diabetes risk and the indicator of early-stage prediabetes. Further larger-scale studies to examine the potential of mitochondrial epigenetic marker in prediabetes diagnosis will be of critical importance for T2D prevention.

An Evaluation Model for a Multidisciplinary Chronic Pelvic Pain Clinic: Application of the RE-AIM Framework

OBJECTIVE

Chronic pelvic pain (CPP) is a prevalent, debilitating, and costly condition. Although national guidelines and empiric evidence support the use of a multidisciplinary model of care for such patients, such clinics are uncommon in Canada. The BC Women's Centre for Pelvic Pain and Endometriosis was created to respond to this need, and there is interest in this model of care's impact on the burden of disease in British Columbia. We sought to create an approach to its evaluation using the RE-AIM (Reach, Efficacy, Adoption, Implementation, Maintenance) evaluation framework to assess the impact of the care model and to guide clinical decision-making and policy.

METHODS

The RE-AIM evaluation framework was applied to consider the different dimensions of impact of the BC Centre. The proposed measures, data sources, and data management strategies for this mixed-methods approach were identified.

RESULTS

The five dimensions of impact were considered at individual and organizational levels, and corresponding indicators were proposed to enable integration into existing data infrastructure to facilitate collection and early program evaluation.

CONCLUSION

The RE-AIM framework can be applied to the evaluation of a multidisciplinary chronic pelvic pain clinic. This will allow better assessment of the impact of innovative models of care for women with chronic pelvic pain.

Insulin resistance is associated with epigenetic and genetic regulation of mitochondrial DNA in obese humans

Background

Mitochondrial alterations have been observed in subjects with metabolic disorders such as obesity and diabetes. Studies on animal models and cell cultures suggest aberrant glucose and lipid levels, and impaired insulin signaling might lead to mitochondrial changes. However, the molecular mechanism underlying mitochondrial aberrance remains largely unexplored in human subjects.

Results

Here we show that the mitochondrial DNA copy number (mtDNAn) was significantly reduced (6.9-fold lower, p < 0.001) in the leukocytes from obese humans (BMI >30). The reduction of mtDNAn was strongly associated with insulin resistance (HOMA-IR: −0.703, p < 0.05; fasting insulin level: −0.015, p < 0.05); by contrast, the correlation between fasting glucose or lipid levels and mtDNAn was not significant. Epigenetic study of the displacement loop (D-loop) region of mitochondrial genome, which controls the replication and transcription of the mitochondrial DNA as well as organization of the mitochondrial nucleoid, revealed a dramatic increase of DNA methylation in obese (5.2-fold higher vs. lean subjects, p < 0.05) and insulin-resistant (4.6-fold higher vs. insulin-sensitive subjects, p < 0.05) individuals.

Conclusions

The reduction of mtDNAn in obese human subjects is associated with insulin resistance and may arise from increased D-loop methylation, suggesting an insulin signaling-epigenetic-genetic axis in mitochondrial regulation.

Electronic supplementary material

The online version of this article (doi:10.1186/s13148-015-0093-1) contains supplementary material, which is available to authorized users.