Abdominal adiposity and daily step counts as determinants of glycemic control in a cohort of patients with type 2 diabetes mellitus

StepAdd: A personalized mHealth intervention based on social cognitive theory to increase physical activity among type 2 diabetes patients

OBJECTIVE

Investigate the preliminary efficacy and feasibility of a personalized mobile health (mHealth) intervention based on social cognitive theory (SCT) to promote physical activity among type 2 diabetes patients via self-monitoring, goal setting, and automatic feedback.

METHODS

We conducted a pilot study involving 33 type 2 diabetes patients attending Mitsui Memorial Hospital in Japan using a pre-post evaluation design over 12 weeks. Participants measured daily step count, body weight, and blood pressure at home, with the measurements synchronized with the StepAdd application (app) automatically. Participants used the app to review daily results, update personalized step goals, identify individualized barriers to achieving the step goals, find coping strategies to overcome each barrier, and implement these strategies, thereby building effective coping skills to meet the goals. Pharmacists examined the usage of the app and provided coaching on lifestyle modifications. Ultimately, patients established skills to enhance diabetes self-care by using the app.

RESULTS

Daily step count increased dramatically with high statistical significance (p < 0.0001), from a mean of 5436 steps/day to 10,150 steps/day, an 86.7 % increase. HbA1c (p = 0.0001) and BMI (p = 0.0038) also improved. Diabetes self-care in diet, exercise, and foot care as well as self-management behavior, self-regulation, and self-efficacy in achieving daily step goals showed significant improvements. The retention rate of the study was very high, at 97.0 % (n = 32).

CONCLUSIONS

A personalized smartphone-based mHealth intervention based on SCT is feasible and effective at promoting physical activity among type 2 diabetes patients. The methodology of the intervention could be readily applied to other patient populations.

A randomized controlled trial of mobile health intervention in patients with heart failure and diabetes

BACKGROUND

Mobile health (mHealth) platforms can affect health behaviors but have not been rigorously tested in randomized trials.

OBJECTIVES

We sought to evaluate the effectiveness of a pragmatic mHealth intervention in patients with HF and DM Methods: We conducted a multicenter randomized trial in 187 patients with both HF and DM to assessing a mHealth intervention to improve physical activity and medication adherence compared to usual care. The primary endpoint was change in mean daily step count from baseline through 3 months. Other outcomes included medication adherence, health related quality of life, and metabolomic profiling.

RESULTS

The mHealth group had an increase in daily step count of 151 steps/day at 3 months whereas the usual care group had a decline of 162 steps/day (LS-mean between-group difference = 313 steps/day; 95% CI: 8, 619, p = 0.044). Medication adherence measured using the Voils Adherence Questionnaire did not change from baseline to 3 months (LS-mean change -0.08 in mHealth vs. -0.15 in usual care, p = 0.47). The mHealth group had an improvement in Kansas City Cardiomyopathy Questionnaire Overall Summary Score (KCCQ-OSS) compared to the usual care group (LS-mean difference = 5.5 points, 95% CI: 1.4, 9.6, p = 0.009). Thirteen metabolites, primarily medium- and long-chain acylcarnitines, changed differently between treatment groups from baseline to 3 months (p < 0.05).

CONCLUSIONS

In patients with HF and DM, a 3-month mHealth intervention significantly improved daily physical activity, health related quality of life and metabolomic markers of cardiovascular health, but not medication adherence.

CLINICALTRIALS

gov Identifier: NCT02918175 Condensed Abstract: Heart failure (HF) and diabetes (DM) have overlapping biologic and behavioral risk factors. We conducted a multicenter randomized, clinical trial in 187 patients with both HF (regardless of ejection fraction) and DM to assess whether a mHealth intervention could improve physical activity and medication adherence. The mHealth group had an increase in mean daily step count and quality of life but not medication adherence. Medium- and long-chain acylcarnitines changed differently between treatment groups from baseline to 3 months (p < 0.05). These data have important implications for designing effective lifestyle interventions in HF and DM.

mHealth app using machine learning to increase physical activity in diabetes and depression: clinical trial protocol for the DIAMANTE Study

INTRODUCTION

Depression and diabetes are highly disabling diseases with a high prevalence and high rate of comorbidity, particularly in low-income ethnic minority patients. Though comorbidity increases the risk of adverse outcomes and mortality, most clinical interventions target these diseases separately. Increasing physical activity might be effective to simultaneously lower depressive symptoms and improve glycaemic control. Self-management apps are a cost-effective, scalable and easy access treatment to increase physical activity. However, cutting-edge technological applications often do not reach vulnerable populations and are not tailored to an individual's behaviour and characteristics. Tailoring of interventions using machine learning methods likely increases the effectiveness of the intervention.

METHODS AND ANALYSIS

In a three-arm randomised controlled trial, we will examine the effect of a text-messaging smartphone application to encourage physical activity in low-income ethnic minority patients with comorbid diabetes and depression. The adaptive intervention group receives messages chosen from different messaging banks by a reinforcement learning algorithm. The uniform random intervention group receives the same messages, but chosen from the messaging banks with equal probabilities. The control group receives a weekly mood message. We aim to recruit 276 adults from primary care clinics aged 18-75 years who have been diagnosed with current diabetes and show elevated depressive symptoms (Patient Health Questionnaire depression scale-8 (PHQ-8) >5). We will compare passively collected daily step counts, self-report PHQ-8 and most recent haemoglobin A1c from medical records at baseline and at intervention completion at 6-month follow-up.

ETHICS AND DISSEMINATION

The Institutional Review Board at the University of California San Francisco approved this study (IRB: 17-22608). We plan to submit manuscripts describing our user-designed methods and testing of the adaptive learning algorithm and will submit the results of the trial for publication in peer-reviewed journals and presentations at (inter)-national scientific meetings.

TRIAL REGISTRATION NUMBER

NCT03490253; pre-results.

Determinants of poor glycemic control among adult patients with type 2 diabetes mellitus in Jimma University Medical Center, Jimma zone, south west Ethiopia: a case control study

OBJECTIVE AND BACKGROUND

In 2015 approximately 5.0 million people were estimated to have died from diabetes. Poor glycemic control is the most determinant of diabetes-related complication and death. The percentage of patients whose blood glucose level are not well controlled remains high yet. The aim of this study is to identify the determinants of poor glycemic control at the diabetes clinic of the Jimma University Medical Center from April 01 to June 30/2017.

METHODS

Facility-based case-control study design was conducted on patients with type 2 diabetes mellitus on follow-up at the diabetes clinic of Jimma University medical center. The consecutive sampling technique was employed and data were collected from April to June 2017. The data were entered using Epidata manager version 4.0.2 and exported to SPSS Version 21 for analysis. Logistic regression analysis was performed and variables with the p-value of less than 0.05 were considered as statistically significant determinants of poor glycemic control.

RESULT

The study was conducted on 410 patients, of which 228 males and 182 females. The determinants of poor glycemic control were comorbidities [Adjusted odd ratio(AOR) = 2.56, 95%CI = 1.10-5.96], lack of self-monitoring blood glucose [AOR = 3.44,95%CI = 1.33-8.94], total cholesterol level of 200 mg/dl or more [AOR = 3.62, 95%CI = 1.46-8.97], diabetes duration of greater than 7 years [AOR = 3.08, 95%CI = 1.33-7.16], physical activity of three or less than three days [AOR = 4.79, 95%CI = 1.70-13.53], waist to hip ratio of 0.9 or greater for male and 0.85 or greater for female [AOR = 3.52, 95%CI = 1.23-10.11], being on metformin plus insulin [AOR = 9.22, 95%CI = 2.90-29.35] and being on insulin [AOR = 4.48, 95%CI = 1.52-13.16].

CONCLUSION

Lack of Self-monitoring blood glucose, presence of comorbidities, duration of diabetes mellitus, physical activity of three or less than three days, total cholesterol of 200 mg/dl or more, waist to hip ratio of 0.9 or greater for male and 0.85 or greater for female, and types of antidiabetic medication were the independent predictors of poor glycemic control. Effort should be made towards reducing these factors by the concerned body.

Utilizing mobile technologies to improve physical activity and medication adherence in patients with heart failure and diabetes mellitus: Rationale and design of the TARGET-HF-DM Trial

Heart failure (HF) and diabetes mellitus (DM) are major public health issues that place significant burden on patients and health care systems. Patients with both HF and DM are at higher risk of adverse cardiovascular and HF outcomes than those with either disease in isolation. Different antihyperglycemic medications (even within the same medication class) have conflicting results of benefit or harm in patients with established and incident HF. Recent data highlight the importance of a renewed focus on optimal pharmacotherapy for this population with DM and HF (or at risk for HF). Both HF and DM require major lifestyle modification for optimal management, in terms of both optimizing health behaviors (eg, physical activity, diet) and adherence to complex medical and self-care regimens. Mobile health (mHealth) technologies (eg, apps, wearables) are widely available in the community and may play a role in optimizing the health status of patients; however, there is limited and conflicting information on whether such technologies are actually beneficial in at-risk populations. In this article, we summarize current strategies, including mobile health interventions, to improve physical activity levels, drug adherence, and outcomes in patients with DM, HF, or both and describe the design and rationale for the Technologies to improve drug Adherence and Reinforce Guideline based Exercise Targets in patients with heart Failure and Diabetes Mellitus trial, which is designed to test the efficacy of using mHealth technology to improve health behaviors and outcomes in this high-risk population.

Steps, moderate-to-vigorous physical activity, and cardiometabolic profiles

The relative benefits of meeting the current moderate-to-vigorous intensity physical activity (MVPA) and active step count recommendations are unknown. Using robust linear regressions, we compared cardiometabolic marker differences (blood pressure, lipid levels, Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), hemoglobin A1C, C-reactive protein (CRP), and body mass index (BMI)) across MVPA (150min/week) and step (10,000 steps/day) thresholds and between step categories (low active: 5000 to 7499, somewhat active: 7500 to 9999, and active: ≥10,000 steps/day vs. inactive: <5000 steps/day) in approximately 6000 Canadian adults (41.5years, SD 14.9). Differences across MVPA and step thresholds were similar but additional benefits were observed for BMI and A1C for the MVPA target (i.e., above vs. below 150min/week MVPA: -1.02kg/m2 (95% Confidence Interval [CI] -1.25 to -0.80) and -0.04% (95% CI -0.06 to -0.02); above vs. below ≥10,000 steps/day: -0.40kg/m2 (95% CI -0.63 to -0.16) and 0.01% (95% CI -0.01 to 0.03)). In terms of steps categories, the greatest incremental improvement was achieved at the somewhat active threshold (e.g., somewhat active vs. inactive: -0.90kg/m2, 95% CI -1.28 to -0.53; low active vs. inactive: -0.36kg/m2, 95% CI -0.73 to 0.02). Additional benefits beyond the 10,000 step/day threshold were limited (e.g., -0.93kg/m2, 95% CI -1.30 to -0.57). Given that most benefits to markers of cardiometabolic health were at the ≥7500 step/day threshold and that there was some additional benefit across the 150min/week MVPA threshold compared to a 10,000 steps/day threshold, we suggest aiming for ≥7500 steps/day and then advancing to a 150min/MVPA goal.

Habitual physical activity is associated with improved anthropometric and androgenic profile in PCOS: a cross-sectional study

PURPOSE

To examine the effect of habitual physical activity (PA) on the metabolic and hormonal profiles of women with polycystic ovary syndrome.

MATERIALS AND METHODS

Anthropometric, metabolic and hormonal assessment and determination of habitual PA levels with a digital pedometer were evaluated in 84 women with PCOS and 67 age- and body mass index (BMI)-matched controls. PA status was defined according to number of steps (≥7500 steps, active, or <7500 steps, sedentary).

RESULTS

BMI was lower in active women from both groups. Active PCOS women presented lower waist circumference (WC) and lipid accumulation product (LAP) values versus sedentary PCOS women. In the control group, active women also had lower WC, lower values for fasting and 120-min insulin, and lower LAP than sedentary controls. In the PCOS group, androgen levels were lower in active versus sedentary women (p = 0.001). In the control group, free androgen index (FAI) was also lower in active versus sedentary women (p = 0.018). Homeostasis model assessment of insulin resistance and 2000 daily step increments were independent predictors of FAI. Each 2000 daily step increment was associated with a decrease of 1.07 in FAI.

CONCLUSIONS

Habitual PA was associated with a better anthropometric and androgenic profile in PCOS.

Effects of Tailored Health Education Program on Overweight Elementary School Students' Obesity-Related Lifestyle: A School-Based Interventional Study

OBJECTIVES

Overweight and obesity are regarded as one of the most serious health and nutrition issues worldwide. This is immediately recognizable in both children and adolescents. In this study, we aimed to determine the effects of tailored education on lifestyle modification in elementary school students in Isfahan.

METHODS

In a quasi-experimental study, two elementary schools in Isfahan District 3, Iran, were randomly selected to determine the intervention and comparison groups. Subsequently, 32 students from each school, 64 in total, were chosen arbitrarily and included in the study. They filled out the standardized questionnaire of physical activity and dietary behaviors before the intervention. Following one and four months' educational intervention, they were asked to complete the questionnaires once more.

RESULTS

The average general lifestyle score, one and four months after education, was significantly different between the groups (p < 0.001). However, this difference was not significant before the intervention 
(p = 0.660). Furthermore, the average lifestyle scores related to nutrition and physical activity one and four months after education showed a significant difference between the groups (p < 0.001).

CONCLUSIONS

Providing overweight elementary school students with education about a healthy lifestyle improves their general lifestyle and reduces weight. Therefore, it is recommended that such interventions are applied to prevent complications associated with being overweight in children.

Correlates of sitting time in adults with type 2 diabetes

BACKGROUND

Studies suggest a relationship between sitting time and cardiovascular disease mortality. Our aim was to identify socio-demographic, contextual, and clinical (e.g., body composition, diabetes duration) correlates of self-reported sitting time among adults with type 2 diabetes, a clinical population at high risk for cardiovascular disease. We sought to determine if there was an inverse relationship between sitting and step counts in a diabetes cohort in whom we had previously identified low step counts with further lowering in fall/winter.

METHODS

The cohort included 198 adults (54 % men; age 60.0 SD 11.5 years; Body mass index 30.4 SD 5.6 kg/m(2)) (Montréal, Canada). Socio-demographic, contextual and clinical factors were assessed using standardized questionnaires and step counts with a pedometer over 14 days (concealed viewing windows). Total sitting time was estimated once per season (up to 4 times per year at -month intervals) using the International Physical Activity Questionnaire-Short version. Potential sitting time correlates were evaluated using Bayesian longitudinal hierarchical linear regression models in participants with sitting time data (n = 191).

RESULTS

The average sitting time was 308 (SD 161) minutes/day without variation across seasons. Sitting time correlates were being an immigrant (56 fewer minutes/day spent sitting compared to non- immigrants, 95 % credible interval, CrI: -100, -11) and having a university degree (55 more minutes/day spent sitting compared to those without a university degree, 95 % CrI: 10, 100) after adjustment for potential correlates observed in univariate analyses (sex, age, job status, waist circumference, depressed mood, steps). Correlation between sitting and steps, adjusted for age and sex, was -0.144 (95 % CI: -0.280, 0.002).

CONCLUSION

There was low correlation between sitting time and step counts. Therefore, high sitting time and low step counts are behaviours that may need to be independently targeted. Interventions to reduce sitting time in adults with type 2 diabetes may need to target non-immigrants and those with a university degree.

A call for caution and transparency in the calculation of land use mix: measurement bias in the estimation of associations between land use mix and physical activity

There is evidence that land use mix based on the Shannon (1948) entropy formula may be misspecified in some studies. The aim of this study was to quantify the bias arising from this misspecification. Spatial coordinates were obtained from Statistics Canada for 9348 unique point locations. Five hundred-metre polygon-based network buffers were drawn around each coordinate (ArcGIS 10.1). Land use mix was calculated for each buffer using the true and misspecified land use mix formulas. Linear regression models were used to estimate the associations between a simulated dataset of daily steps and the true and misspecified measures. Misspecification of the land use mix formula resulted in a systematic underestimation of the true association by 26.4% (95% CI 25.8-27.0%). To minimize measurement bias in future studies, researchers are encouraged to use a constant definition of N in the denominator of the Shannon entropy formula.

Step counter use in type 2 diabetes: a meta-analysis of randomized controlled trials

BACKGROUND

While step counter use has become popular among type 2 diabetes (T2D) patients, its effectiveness in increasing physical activity (PA) and improving glycemic control has been poorly defined. The aim of this meta-analysis of randomized controlled trials (RCTs) was to evaluate the association of step counter use with PA and glycemic control in T2D patients.

METHODS

Articles were identified by searches of PubMed, Web of Science and Cochrane Library from January 1994 to June 2013. RCTs in the English language were included, if they had assessed the effectiveness of step counters as motivating and monitoring tools in T2D patients, with reported changes in steps per day (steps/d) or glycosylated hemoglobin A1c (HbA1c), or both. Data were independently collected by 2 authors and overall estimates were made by a random-effects model.

RESULTS

Of the 551 articles retrieved, 11 RCTs were included. Step counter use significantly increased PA by 1,822 steps/d (7 studies, 861 participants; 95% confidence interval (CI): 751 to 2,894 steps/d) in patients with T2D. Step counter use with a PA goal showed a bigger increase in PA (weighted mean difference (WMD) 3,200 steps/d, 95% CI: 2,053 to 4,347 steps/d) than without (WMD 598 steps/d, 95% CI: -65 to 1,260 steps/d). Further subgroup analysis suggested step counter use with a self-set PA goal (WMD 2,816 steps/d, 95% CI: 1,288 to 4,344 steps/d) made no difference in increasing PA from a 10,000 steps/d goal (WMD 3,820 steps/d, 95% CI: 2,702 to 4,938 steps/d). However, no significant HbA1c change was observed by step counter use (10 studies, 1,423 participants; WMD 0.02%, 95% CI: -0.08% to 0.13%), either with (WMD 0.04%, 95% CI: -0.21% to 0.30%) or without a PA goal (WMD 0.01%, 95% CI: -0.10% to 0.13%).

CONCLUSIONS

Step counter use is associated with a significant increase in PA in patients with T2D. However, evidence regarding its effect in improving glycemic control remains insufficient.

TRIAL REGISTRATION

PROSPERO CRD42013005236.

Step Monitoring to improve ARTERial health (SMARTER) through step count prescription in type 2 diabetes and hypertension: trial design and methods

Background

With increasing numbers of type 2 diabetes (DM2) and hypertension patients, there is a pressing need for effective, time-efficient and sustainable strategies to help physicians support their patients to achieve higher physical activity levels. SMARTER will determine whether physician-delivered step count prescriptions reduce arterial stiffness over a one-year period, compared with usual care, in sedentary overweight/obese adults with DM2/hypertension.

Design

Randomized, allocation-concealed, assessor-blind, multisite clinical trial. The primary outcome is change in arterial stiffness over one year. The secondary outcomes include changes in physical activity, individual vascular risk factors, medication use, and anthropometric parameters. Assessments are at baseline and one year.

Methods

Participants are sedentary/low active adults with 25 ≤ BMI < 40 kg/m² followed for DM2/hypertension by a collaborating physician. The active arm uses pedometers to track daily step counts and review logs with their physicians at 3 to 4-month intervals. A written step count prescription is provided at each visit, aiming to increase counts by ≥3,000 steps/day over one year, with an individualized rate increase. The control arm visits physicians at the same frequency and receives advice to engage in physical activity 30-60 minutes/day. SMARTER will enroll 364 individuals to detect a 10 ± 5% difference in arterial stiffness change between arms. Arterial stiffness is assessed noninvasively with carotid femoral pulse wave velocity using applanation tonometry.

Discussion

The importance of SMARTER lies not simply in the use of pedometer-based monitoring but also on its integration into a prescription-based intervention delivered by the treating physician. Equally important is the measurement of impact of this approach on a summative indicator of arterial health, arterial stiffness. If effectiveness is demonstrated, this strategy has strong potential for widespread uptake and implementation, given that it is well-aligned with the structure of current clinical practice.

Trial registration

ClinicalTrials.gov (NCT01475201)

Age at type 2 diabetes onset and glycaemic control: results from the National Health and Nutrition Examination Survey (NHANES) 2005-2010

AIMS/HYPOTHESIS

We tested the hypothesis that age younger than 65 years at type 2 diabetes diagnosis is associated with worse subsequent glycaemic control.

METHODS

A cross-sectional analysis of data from participants in the 2005-2010 National Health and Nutrition Examination Survey was performed. For adults with self-reported diabetes, we dichotomised age at diabetes diagnosis as younger (<65 years) vs older (≥ 65 years). The primary outcome of interest was HbA1c >9.0% (75 mmol/mol). Secondary outcomes were HbA1c >8.0% (64 mmol/mol) and >7.0% (53 mmol/mol). We used multivariable logistic regression for analysis.

RESULTS

Among 1,438 adults with diabetes, a higher proportion of those <65 years at diagnosis compared with those ≥ 65 at diagnosis had an HbA1c >9.0% (14.4% vs 2.5%, p  <  0.001). After adjustment for sex, race/ethnicity, education, income, insurance, usual source of care, hyperglycaemia medication, duration of diabetes, family history, BMI and waist circumference, age <65 years at diagnosis remained significantly associated with greater odds of HbA1c >9.0% (OR 3.22, 95% CI 1.54, 6.72), HbA1c >8.0% (OR 2.72, 95% CI 1.43, 5.16) and HbA1c >7.0% (OR 1.92, 95% CI 1.18, 3.11). The younger group reported fewer comorbidities, but were less likely to report good health (OR 0.54, 95% CI 0.36, 0.83).

CONCLUSIONS/INTERPRETATION

Younger age at type 2 diabetes diagnosis is significantly associated with worse subsequent glycaemic control. Because patients who are younger at diagnosis have fewer competing comorbidities and complications, safe, aggressive, individualised treatment could benefit this higher-risk group.

Neighborhood walkability: field validation of geographic information system measures

BACKGROUND

Given the health benefits of walking, there is interest in understanding how physical environments favor walking. Although GIS-derived measures of land-use mix, street connectivity, and residential density are commonly combined into indices to assess how conducive neighborhoods are to walking, field validation of these measures is limited.

PURPOSE

To assess the relationship between audit- and GIS-derived measures of overall neighborhood walkability and between objective (audit- and GIS-derived) and participant-reported measures of walkability.

METHODS

Walkability assessments were conducted in 2009. Street-level audits were conducted using a modified version of the Pedestrian Environmental Data Scan. GIS analyses were used to derive land-use mix, street connectivity, and residential density. Participant perceptions were assessed using a self-administered questionnaire. Audit, GIS, and participant-reported indices of walkability were calculated. Spearman correlation coefficients were used to assess the relationships between measures. All analyses were conducted in 2012.

RESULTS

The correlation between audit- and GIS-derived measures of overall walkability was high (R=0.7 [95% CI=0.6, 0.8]); the correlations between objective (audit and GIS-derived) and participant-reported measures were low (R=0.2 [95% CI=0.06, 0.3]; R=0.2 [95% CI=0.04, 0.3], respectively). For comparable audit and participant-reported items, correlations were higher for items that appeared more objective (e.g., sidewalk presence, R=0.4 [95% CI=0.3, 0.5], versus safety, R=0.1 [95% CI=0.003, 0.3]).

CONCLUSIONS

The GIS-derived measure of walkability correlated well with the in-field audit, suggesting that it is reasonable to use GIS-derived measures in place of more labor-intensive audits. Interestingly, neither audit- nor GIS-derived measures correlated well with participants' perceptions of walkability.

Effects of meal preparation training on body weight, glycemia, and blood pressure: results of a phase 2 trial in type 2 diabetes

BACKGROUND

Modest reductions in weight and small increases in step- related activity (e.g., walking) can improve glycemic and blood pressure control in type 2 diabetes mellitus (DM2). We examined changes in these parameters following training in time- efficient preparation of balanced, low- energy meals combined with pedometer- based step count monitoring.

METHODS

Seventy- two adults with DM2 were enrolled in a 24- week program (i.e., 15 three- hour group sessions). They prepared meals under a chef's supervision, and discussed eating behaviours/nutrition with a registered dietitian. They maintained a record of pedometer- assessed step counts. We evaluated changes from baseline to 24 weeks in terms of weight, step counts, hemoglobin A1c (HbA1c, glycemic control), blood pressure, and eating control ability (Weight Efficacy Lifestyle WEL Questionnaire). 53 participants (73.6%) completed assessments.

RESULTS

There were improvements in eating control (11.2 point WEL score change, 95% CI 4.7 to 17.8), step counts (mean change 869 steps/day, 95% CI 198 to 1,540), weight (mean change -2.2%; 95% CI -3.6 to -0.8), and HbA1c (mean change -0.3% HbA1c, 95% CI -0.6 to -0.1), as well as suggestion of systolic blood pressure reduction (mean change -3.5 mm Hg, 95% CI -7.8 to 0.9). Findings were not attributable to medication changes. In linear regression models (adjusted for age, sex, ethnicity, insulin use, season), a -2.5% weight change was associated with a -0.3% HbA1c change (95% CI -0.4 to -0.2) and a -3.5% systolic blood pressure change (95% CI -5.5 to -1.4).

CONCLUSIONS

In this 'proof of concept' study, persistence with the program led to improvements in eating and physical activity habits, glycemia reductions, and suggestion of blood pressure lowering effects. The strategy thus merits further study and development to expand the range of options for vascular risk reduction in DM2.