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Figure 1.
CONSORT Diagram
CONSORT Diagram

CVD indicates cardiovascular disease; DHI, digital health intervention; and MI, myocardial infarction.

Figure 2.
Overall Myocardial Infarction (MI) Risk Score Change
Overall Myocardial Infarction (MI) Risk Score Change

The MI risk score reduction (95% CI) from baseline to final visit in the digital health intervention (DHI) (n = 152) and control (n = 159) groups is shown. The intervention effect on the final MI risk scores, adjusted for the baseline scores, was tested using analysis of covariance.

Table 1.  
Baseline Demographicsa
Baseline Demographicsa
Table 2.  
MI Risk Score and Risk Factors by Study Group at Baseline and 12-Month Visitsa
MI Risk Score and Risk Factors by Study Group at Baseline and 12-Month Visitsa
1.
Anand  SS, Yusuf  S, Vuksan  V,  et al.  Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE).  Lancet. 2000;356(9226):279-284.PubMedGoogle ScholarCrossref
2.
Joshi  P, Islam  S, Pais  P,  et al.  Risk factors for early myocardial infarction in South Asians compared with individuals in other countries.  JAMA. 2007;297(3):286-294.PubMedGoogle ScholarCrossref
3.
Rana  A, de Souza  RJ, Kandasamy  S, Lear  SA, Anand  SS.  Cardiovascular risk among South Asians living in Canada: a systematic review and meta-analysis.  CMAJ Open. 2014;2(3):E183-E191.PubMedGoogle ScholarCrossref
4.
Chiu  M, Austin  PC, Manuel  DG, Tu  JV.  Cardiovascular risk factor profiles of recent immigrants vs long-term residents of Ontario: a multi-ethnic study.  Can J Cardiol. 2012;28(1):20-26.PubMedGoogle ScholarCrossref
5.
Gaede  P, Lund-Andersen  H, Parving  HH, Pedersen  O.  Effect of a multifactorial intervention on mortality in type 2 diabetes.  N Engl J Med. 2008;358(6):580-591.PubMedGoogle ScholarCrossref
6.
Estruch  R, Ros  E, Salas-Salvadó  J,  et al; PREDIMED Study Investigators.  Primary prevention of cardiovascular disease with a Mediterranean diet [published correction appears in N Engl J Med. 2014;370(9):886].  N Engl J Med. 2013;368(14):1279-1290.PubMedGoogle ScholarCrossref
7.
Ebrahim  S, Taylor  F, Ward  K, Beswick  A, Burke  M, Davey Smith  G.  Multiple risk factor interventions for primary prevention of coronary heart disease.  Cochrane Database Syst Rev. 2011;(1):CD001561.PubMedGoogle Scholar
8.
Castro  FG, Barrera  M  Jr, Holleran Steiker  LK.  Issues and challenges in the design of culturally adapted evidence-based interventions.  Annu Rev Clin Psychol. 2010;6:213-239.PubMedGoogle ScholarCrossref
9.
Widmer  RJ, Collins  NM, Collins  CS, West  CP, Lerman  LO, Lerman  A.  Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis.  Mayo Clin Proc. 2015;90(4):469-480.PubMedGoogle ScholarCrossref
10.
Marteau  TM, French  DP, Griffin  SJ,  et al.  Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours.  Cochrane Database Syst Rev. 2010;(10):CD007275.PubMedGoogle Scholar
11.
Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group.  Recommendations from the EGAPP Working Group: genomic profiling to assess cardiovascular risk to improve cardiovascular health.  Genet Med. 2010;12(12):839-843.PubMedGoogle Scholar
12.
McGorrian  C, Yusuf  S, Islam  S,  et al; INTERHEART Investigators.  Estimating modifiable coronary heart disease risk in multiple regions of the world: the INTERHEART Modifiable Risk Score.  Eur Heart J. 2011;32(5):581-589.PubMedGoogle ScholarCrossref
13.
Prochaska  JO, DiClemente  CC.  Stages and processes of self-change of smoking: toward an integrative model of change.  J Consult Clin Psychol. 1983;51(3):390-395.PubMedGoogle ScholarCrossref
14.
Samaan  Z, Schulze  KM, Middleton  C,  et al.  South Asian Heart Risk Assessment (SAHARA): randomized controlled trial design and pilot study.  JMIR Res Protoc. 2013;2(2):e33.PubMedGoogle ScholarCrossref
15.
Sullivan  LM, Massaro  JM, D’Agostino  RB  Sr.  Presentation of multivariate data for clinical use: the Framingham Study risk score functions.  Stat Med. 2004;23(10):1631-1660.PubMedGoogle ScholarCrossref
16.
Van Breukelen  GJ.  ANCOVA versus change from baseline: more power in randomized studies, more bias in nonrandomized studies [published correction appears in J Clin Epidemiol. 2006;59(12):1334].  J Clin Epidemiol. 2006;59(9):920-925.PubMedGoogle ScholarCrossref
17.
Lovibond  SH, Birrell  PC, Langeluddecke  P.  Changing coronary heart disease risk-factor status: the effects of three behavioral programs.  J Behav Med. 1986;9(5):415-437.PubMedGoogle ScholarCrossref
18.
Wing  RR, Bolin  P, Brancati  FL,  et al; Look AHEAD Research Group.  Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes.  N Engl J Med. 2013;369(2):145-154.PubMedGoogle ScholarCrossref
19.
Maruthur  NM, Wang  NY, Appel  LJ.  Lifestyle interventions reduce coronary heart disease risk: results from the PREMIER Trial.  Circulation. 2009;119(15):2026-2031.PubMedGoogle ScholarCrossref
20.
Appel  LJ, Clark  JM, Yeh  HC,  et al.  Comparative effectiveness of weight-loss interventions in clinical practice.  N Engl J Med. 2011;365(21):1959-1968.PubMedGoogle ScholarCrossref
21.
Vroege  DP, Wijsman  CA, Broekhuizen  K,  et al.  Dose-response effects of a Web-based physical activity program on body composition and metabolic health in inactive older adults: additional analyses of a randomized controlled trial.  J Med Internet Res. 2014;16(12):e265.PubMedGoogle ScholarCrossref
22.
Sheridan  SL, Draeger  LB, Pignone  MP,  et al.  A randomized trial of an intervention to improve use and adherence to effective coronary heart disease prevention strategies.  BMC Health Serv Res. 2011;11:331.PubMedGoogle ScholarCrossref
23.
Chow  CK, Redfern  J, Hillis  GS,  et al.  Effect of lifestyle-focused text messaging on risk factor modification in patients with coronary heart disease: a randomized clinical trial.  JAMA. 2015;314(12):1255-1263.PubMedGoogle ScholarCrossref
24.
Arkadianos  I, Valdes  AM, Marinos  E, Florou  A, Gill  RD, Grimaldi  KA.  Improved weight management using genetic information to personalize a calorie controlled diet.  Nutr J. 2007;6:29.PubMedGoogle ScholarCrossref
25.
Cho  AH, Killeya-Jones  LA, Suchindran  S,  et al.  Preliminary outcomes of genetic risk testing in primary care for common DNA variants associated with type 2 diabetes.  J Gen Intern Med. 2012;27:S278.Google Scholar
Brief Report
August 2016

A Digital Health Intervention to Lower Cardiovascular Risk: A Randomized Clinical Trial

Author Affiliations
  • 1Department of Medicine, McMaster University, Hamilton, Ontario, Canada
  • 2Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada
  • 3Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada
  • 4Department of Psychiatry and Behavioral Sciences, McMaster University, Hamilton, Ontario, Canada
  • 5Ted Rogers School of Management, Ryerson University, Toronto, Ontario, Canada
  • 6Department of Psychology, York University, Toronto, Ontario, Canada
  • 7Institute for Clinical Evaluative Sciences Central, Toronto, Ontario, Canada
  • 8Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
  • 9Faculty of Health Science, Simon Fraser University, Vancouver, British Columbia, Canada
JAMA Cardiol. 2016;1(5):601-606. doi:10.1001/jamacardio.2016.1035
Abstract

Importance  South Asian individuals have a high burden of premature myocardial infarction (MI).

Objectives  To test whether a digital health intervention (DHI) designed to change diet and physical activity improves MI risk among a South Asian population.

Design, Setting, and Participants  This single-blind, community-based, randomized clinical trial with 1-year follow-up was performed among South Asian men and women 30 years or older and living in Ontario and British Columbia who were free of cardiovascular disease. Data analysis was by intention to treat. Data were collected from June 3, 2012, to October 27, 2013. Final follow-up was completed on December 2, 2014, and data were analyzed from April 2, 2015, to February 29, 2016.

Interventions  Participants were randomized 1:1 to the DHI or control condition. The goal-setting DHI used emails or text messages and focused on improving diet and physical activity that was tailored to the participant’s self-reported stage of change.

Main Outcomes and Measures  The change in an MI risk score from baseline to 1 year was the primary outcome. Secondary outcomes included the change in each objectively measured component of the MI risk score (ie, blood pressure, waist to hip ratio, hemoglobin A1c level, and the ratio of apolipoprotein B to apolipoprotein A). Genetic risk for MI was determined by counting the 9p21 risk alleles; results were provided to each participant at baseline.

Results  A total of 343 South Asian men and women (178 men [51.9%]; mean [SD] age, 50.6 [11.4] years) who were free of cardiovascular disease were randomized to the control condition (n = 174) or the DHI (n = 169). The mean (SD) MI risk score was 13.3 (6.6) at baseline. No significant difference was found in the change in MI score after 1 year between the DHI and control groups (−0.27; 95% CI, −1.12 to 0.58; P = .53) after adjusting for baseline scores, and no difference was found in the fully adjusted model (−0.39; 95% CI, −1.24 to 0.45; P = .36). No association between knowledge of the genetic risk status at baseline and the change in MI risk score was found (0.19; 95% CI, −0.40 to 0.78; P = .53).

Conclusions and Relevance  Among South Asian individuals, a DHI was not associated with a reduction in MI risk score after 12 months and was not influenced by knowledge of genetic risk status.

Trial Registration  clinicaltrials.gov Identifier: NCT01841398

Introduction

People who originate from the Indian subcontinent, known as South Asians, have an increased risk for premature myocardial infarction (MI) compared with white individuals.1-4 Few interventions have been designed and tested to lower the risk for MI in this high-risk ethnic group.

Health behavior change interventions are effective in reducing risk factors, MI, and mortality in high-risk groups.5-7 Despite the benefit of such interventions, uptake of these strategies by certain populations has been limited.8 With advances in technology, behavioral interventions can be delivered to high-risk populations using email, web-based strategies, mobile phone applications, and text messages.9

Genetic information has also been used as a motivator to change health behavior.10 The most robust single-nucleotide polymorphism for MI, the 9p21 variant, has the highest effect size (relative risk, 1.2-1.3) compared with other MI single-nucleotide polymorphisms. Whether knowledge of 9p21 status influences health behavior change is uncertain.11 The objectives of this study were to determine (1) whether a digital health intervention (DHI) designed to change dietary habits and physical activity improves the MI risk score during a 12-month study period and (2) whether knowledge of 9p21 genetic risk influences changes in behavior and the MI risk score.

Box Section Ref ID

Key Points

  • Question What is the effectiveness of a digital health intervention (DHI) focused on improving diet and physical activity via messaging in reducing risk for myocardial infarction (MI) among a South Asian population living in Canada?

  • Findings In this single-blind randomized clinical trial, no difference was found in the change in the MI risk score after 1 year between the DHI and control groups.

  • Meaning Among healthy South Asian individuals receiving MI and genetic risk scores, a DHI using email and text messaging was not effective in reducing the MI risk score after 12 months.

Methods
Study Design

The South Asian Heart Risk Assessment (SAHARA) trial was a single-blind randomized clinical trial that compared a DHI and usual care (control condition). The study was approved by the research ethics boards of McMaster University (June 6, 2009) and the University of British Columbia (July 11, 2012), and the trial protocol is available in Supplement 1. All participants provided written informed consent.

Participants

Women and men 30 years or older who were South Asian (ie, ancestors originated from the Indian subcontinent) with an email address were eligible and recruited from the Toronto and Vancouver sites. Individuals with prior coronary artery disease or stroke or with a family member already participating in the SAHARA trial were excluded.

Baseline Data Collection

Data were collected from June 3, 2012, to October 27, 2013. The MI risk score components included age, sex, brief dietary and physical activity questions, tobacco exposure, psychosocial stress, blood pressure, waist and hip circumference, and levels of apolipoprotein A and B and hemoglobin A1c12 (eTable 1 in Supplement 2). Questions about stage of change were asked to assess participant’s motivation to make health behavior changes in dietary intake and physical activity (eTable 2 in Supplement 2), and a 30-mL nonfasting blood sample was collected.13 Blood samples were processed and then shipped to the core laboratory in Hamilton, Ontario, Canada.

Randomization

A computerized program randomly assigned all eligible participants to the DHI or control condition using 1:1 blocked randomization within centers, stratified by province. All participants were sent their treatment allocation and MI risk score by email within 4 weeks (eMethods 1 and 2 in Supplement 2). All communication was in English.

DHI and Control Condition

Two main health behaviors—dietary intake and physical activity—were targeted for 6 months each. Participants randomized to DHI received the following 2 types of messages: stages of change–oriented motivational messages, which supported confidence in behavior change, sent by email every 2 weeks and health tips focused on diet and physical activity sent by email or text messages (participant’s choice) every week (eTables 3 and 4 in Supplement 2). Participants were also encouraged to access the SAHARA website for South Asian–specific prevention advice (http://www.saharaproject.ca). Participants randomized to the control condition were encouraged to access the SAHARA website. All participants were contacted to schedule clinical assessments at 6 months and 1 year after randomization and received gift card incentives for completing their visits.

Outcomes

The primary outcome of the trial was the change in the MI risk score after 1 year. All components of the MI risk score were measured in participants at baseline and at 12 months of follow-up. The secondary outcomes included the change in each objectively measured component of the MI risk score.

Statistical Considerations

We hypothesized that the MI risk score would be lower in the DHI participants compared with the control participants after 1 year. With the mean (SD) MI risk score from the pilot study (13.0 [5.8]),14 we anticipated a 25% risk score reduction in DHI participants and a 10% reduction among controls after 1 year.15 Thus, the sample size required to detect a 15% relative difference in mean score with 90% power that took into account nonadherence to the intervention and a dropout rate of 20% was 320 participants (160 per group).

Statistical Analysis

Follow-up was completed on December 2, 2014, and data were assessed from April 2, 2015, to February 29, 2016. The principal investigators, statistician, and personnel performing the MI risk score measurements (S.S.A., Z.S., C.M., J.I., K.M.S., B.R.S., G.P., J.B., and S.A.L.) were blind to participants’ treatment allocations. The primary analysis compared the MI risk scores at the end of the study period in the DHI and control groups adjusted for the baseline score using analysis of covariance.16 The genetic risk status (GRS) was tested as a main effect in a similar analysis of covariance model. We used an intention to treat analysis and validated our outcomes with sensitivity analyses. P < .05 was considered significant.

Results

Three hundred fifty-four men and women underwent screening for eligibility, with 343 randomized (178 men [51.9%]; mean [SD] age, 50.6 [11.4] years). Reasons for the 11 exclusions are outlined in Figure 1. One hundred sixty-nine participants were randomized to the DHI and 174 to the control condition. One hundred fifty-two participants (89.9%) in the DHI group and 159 controls (91.4%) had the full MI risk score repeated at 12 months. Approximately two-thirds of participants reported actively trying to reduce consumption of high-calorie foods and exercising for 20 minutes 3 times per week (Table 1).

Risk Factor Profile

The baseline MI risk score was 13.3 (6.6), representing a moderate risk, and 248 participants (72.3%) had 1 or 2 risk alleles for the 9p21 variant (Table 1). The DHI group received motivational messages by email and health tips based on their preference (email, 156 [92.3%]; text message, 13 [7.7%]). The median number of motivational messages sent to the DHI group was 26 (interquartile range, 24-28) and the median number of health tips was 54 (interquartile range, 53-60) compared with zero sent to the control group during 12 months.

Changes in Risk Score and Risk Factors

The MI risk score decreased from 13.3 to 12.3 in the intervention group and from 13.3 to 12.6 in the control group. The relative change between intervention participants and controls was not significant (−0.27; 95% CI, −1.12 to 0.58; P = .53) (Figure 2) and remained nonsignificant in the adjusted model, which included the prespecified covariates (−0.39; 95% CI, −1.24 to 0.45; P = .36). No difference between the intervention and control participants was observed in the sensitivity analysis among participants with high adherence (−0.02; 95% CI, −1.05 to 1.01; P = .97). Furthermore, no changes in the measured components of the risk score occurred between baseline and the end of the study (Table 2).

GRS and Risk Recall

We found no association between the baseline GRS on the MI risk score at 12 months (0.19; 95% CI, −0.40 to 0.78; P = .53) and no interaction between the GRS and the DHI (−0.61; 95% CI, −1.79 to 0.57; P = .31). All participants received their MI risk score and GRS by email. Only 102 (30.6%) and 78 (23.1%) of 337 participants correctly recalled their calculated MI risk score and GRS, and we found no differences between the risk score recall by the DHI and control groups.

Discussion

After provision of the MI risk score and GRS, a DHI using motivational messages and health tips to optimize dietary intake and physical activity was not effective in reducing the MI risk score among South Asian individuals. Knowledge of the GRS was not a motivator for behavior change.

The intervention did not reduce the MI score by the predicted 15% relative difference, and the observed difference was only 2.4%. The intervention failed for several reasons. First, despite DHI participants receiving a high number of emails or text messages compared with controls (ie, 80 vs 0) during the 12 months of the study, more face-to-face contacts may be required to bring about behavioral change. Prior intensive counseling interventions have been successful in reducing cardiovascular outcomes17 and cardiovascular risk factors,18,19 whereas other interventions with less intensive communication have not.7 Prior DHI trials in primary prevention have shown mixed results.20-22 This finding is in contrast to the recent “Text-me” trial in adults with proven coronary disease23 that used frequent text messaging (ie, 4 times per week), with positive change in MI risk factors that were observed after 6 months. Our messaging was unidirectional and not interactive, and we are unable to determine whether health messages were opened and read. A greater understanding of the recipient context for receiving electronic messages (ie, a computer vs a smartphone screen) and the optimal frequency and time of day for sending electronic messages is required before the initiation of future DHI trials in this group.

Second, 222 participants (64.7%) reported they were already exercising regularly (including walking), and 246 (71.7%) reported consistently avoiding high-calorie foods. Thus, the DHI may have failed because most of the participants were already actively trying to make lifestyle changes, and little additional change was possible with a DHI.

Third, we anticipated that knowledge of the MI risk score and GRS would be additional motivators to change. However, more than two-thirds of participants did not correctly recall their MI risk score or GRS category at the end of the intervention. Prior randomized trials24,25 in which a genetic variant was evaluated as a motivator to change have also shown a minimal effect on cardiometabolic risk factors.

The strengths of our trial included our extensive pretesting and development of a culturally tailored DHI, incorporation of the stage-of-change model, and conduct of a pilot study. A limitation of our trial was its relatively modest size, although we were adequately powered to detect the hypothesized MI risk score reduction.

Conclusions

Among South Asian adults living in Canada, a DHI using email and text messages that included genetic risk information was not associated with a significant reduction in MI risk score after 12 months. Future trials should consider using more frequent text messaging and have bidirectional communication with participants.

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Article Information

Corresponding Author: Sonia S. Anand, MD, PhD, FRCPC, Department of Medicine, Population Health Research Institute, Hamilton Health Sciences, McMaster University, 1280 Main Street W, MDCL Room 3204, Hamilton, ON L8S 4K1, Canada (anands@mcmaster.ca).

Accepted for Publication: March 28, 2016.

Published Online: May 18, 2016. doi:10.1001/jamacardio.2016.1035.

Author Contributions: Dr Anand had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Anand, Samaan, Middleton, Irvine, Desai, Pare, Lear.

Acquisition, analysis, or interpretation of data: Anand, Samaan, Irvine, Desai, Schulze, Hussain, Shah, Pare, Beyene, Lear.

Drafting of the manuscript: Anand, Samaan, Lear.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Anand, Schulze, Beyene.

Obtained funding: Anand, Samaan, Irvine, Pare, Beyene, Lear.

Administrative, technical, or material support: Samaan, Irvine, Desai, Sothiratnam, Hussain, Pare.

Study supervision: Anand, Samaan, Lear.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: This study was supported by grant MOP-123309 from Canadian Institutes of Health Research and by the Canadian Network and Center for Trials Internationally.

Role of the Funder/Sponsor: The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Group Members: The South Asian Heart Risk Assessment (SAHARA) investigators include the following: Scott A. Lear, PhD, Faculty of Health Science, Simon Fraser University, Vancouver, British Columbia, Canada; Andrew Mente, PhD, Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada; Catherine Middleton, MBA, PhD, Ted Rogers School of Management, Ryerson University, Toronto, Ontario, Canada; Zubin Punthakee, MD, MSc, Department of Medicine, McMaster University; Baiju R. Shah, MD, PhD, Institute for Clinical Evaluative Sciences Central, Toronto, Ontario, Canada; Shofiqul Islam, MSc, Population Health Research Institute, McMaster University and Hamilton Health Sciences; Zainab Samaan, MBChB, MSc, DMMD, PhD, MRCPsych, Department of Psychiatry and Behavioral Sciences, McMaster University; Jane Irvine, DPhil, Department of Psychology, York University, Toronto, Ontario, Canada; Guillaume Pare, MD, MSc, Department of Pathology and Molecular Medicine, McMaster University; Joseph Beyene, PhD, Department of Clinical Epidemiology and Biostatistics, McMaster University; Phillip Joseph, MD, MSc, Population Health Research Institute, McMaster University and Hamilton Health Sciences; and Sonia S. Anand, MD, PhD, FRCPC, Department of Medicine, McMaster University.

Additional Contributions: We thank all the participants in the SAHARA trial. B. Doobay, MD, Vishnu Mandir Temple, Richmond Hill, Ontario, Canada, members of Sringeri Vidya Bharati Foundation, Toronto, Ontario, Canada, Vedic Cultural Centre and Arya Samaj, Markham, Ontario, Canada, Brampton Hindu Sabha, Brampton, Ontario, Canada, Sampradaya Dance Academy, Mississauga, Ontario, Canada, Ross Street Gurudwara, Vancouver, British Colombia, Canada, Akali Singh Gurudwara, Surrey, British Colombia, Canada, Dashmesh Darbar, Surrey, Laxmi Narayan Mandir, Surrey, and Simon Fraser University, Vancouver, British Columbia, Canada, assisted with recruitment from within their respective organizations. Zahra Sohani, PhD, Phillip Joseph, MD, and Andrew Mente, PhD, Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada, assisted at Temple visits. None of these contributors received any compensation for these roles.

References
1.
Anand  SS, Yusuf  S, Vuksan  V,  et al.  Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE).  Lancet. 2000;356(9226):279-284.PubMedGoogle ScholarCrossref
2.
Joshi  P, Islam  S, Pais  P,  et al.  Risk factors for early myocardial infarction in South Asians compared with individuals in other countries.  JAMA. 2007;297(3):286-294.PubMedGoogle ScholarCrossref
3.
Rana  A, de Souza  RJ, Kandasamy  S, Lear  SA, Anand  SS.  Cardiovascular risk among South Asians living in Canada: a systematic review and meta-analysis.  CMAJ Open. 2014;2(3):E183-E191.PubMedGoogle ScholarCrossref
4.
Chiu  M, Austin  PC, Manuel  DG, Tu  JV.  Cardiovascular risk factor profiles of recent immigrants vs long-term residents of Ontario: a multi-ethnic study.  Can J Cardiol. 2012;28(1):20-26.PubMedGoogle ScholarCrossref
5.
Gaede  P, Lund-Andersen  H, Parving  HH, Pedersen  O.  Effect of a multifactorial intervention on mortality in type 2 diabetes.  N Engl J Med. 2008;358(6):580-591.PubMedGoogle ScholarCrossref
6.
Estruch  R, Ros  E, Salas-Salvadó  J,  et al; PREDIMED Study Investigators.  Primary prevention of cardiovascular disease with a Mediterranean diet [published correction appears in N Engl J Med. 2014;370(9):886].  N Engl J Med. 2013;368(14):1279-1290.PubMedGoogle ScholarCrossref
7.
Ebrahim  S, Taylor  F, Ward  K, Beswick  A, Burke  M, Davey Smith  G.  Multiple risk factor interventions for primary prevention of coronary heart disease.  Cochrane Database Syst Rev. 2011;(1):CD001561.PubMedGoogle Scholar
8.
Castro  FG, Barrera  M  Jr, Holleran Steiker  LK.  Issues and challenges in the design of culturally adapted evidence-based interventions.  Annu Rev Clin Psychol. 2010;6:213-239.PubMedGoogle ScholarCrossref
9.
Widmer  RJ, Collins  NM, Collins  CS, West  CP, Lerman  LO, Lerman  A.  Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis.  Mayo Clin Proc. 2015;90(4):469-480.PubMedGoogle ScholarCrossref
10.
Marteau  TM, French  DP, Griffin  SJ,  et al.  Effects of communicating DNA-based disease risk estimates on risk-reducing behaviours.  Cochrane Database Syst Rev. 2010;(10):CD007275.PubMedGoogle Scholar
11.
Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group.  Recommendations from the EGAPP Working Group: genomic profiling to assess cardiovascular risk to improve cardiovascular health.  Genet Med. 2010;12(12):839-843.PubMedGoogle Scholar
12.
McGorrian  C, Yusuf  S, Islam  S,  et al; INTERHEART Investigators.  Estimating modifiable coronary heart disease risk in multiple regions of the world: the INTERHEART Modifiable Risk Score.  Eur Heart J. 2011;32(5):581-589.PubMedGoogle ScholarCrossref
13.
Prochaska  JO, DiClemente  CC.  Stages and processes of self-change of smoking: toward an integrative model of change.  J Consult Clin Psychol. 1983;51(3):390-395.PubMedGoogle ScholarCrossref
14.
Samaan  Z, Schulze  KM, Middleton  C,  et al.  South Asian Heart Risk Assessment (SAHARA): randomized controlled trial design and pilot study.  JMIR Res Protoc. 2013;2(2):e33.PubMedGoogle ScholarCrossref
15.
Sullivan  LM, Massaro  JM, D’Agostino  RB  Sr.  Presentation of multivariate data for clinical use: the Framingham Study risk score functions.  Stat Med. 2004;23(10):1631-1660.PubMedGoogle ScholarCrossref
16.
Van Breukelen  GJ.  ANCOVA versus change from baseline: more power in randomized studies, more bias in nonrandomized studies [published correction appears in J Clin Epidemiol. 2006;59(12):1334].  J Clin Epidemiol. 2006;59(9):920-925.PubMedGoogle ScholarCrossref
17.
Lovibond  SH, Birrell  PC, Langeluddecke  P.  Changing coronary heart disease risk-factor status: the effects of three behavioral programs.  J Behav Med. 1986;9(5):415-437.PubMedGoogle ScholarCrossref
18.
Wing  RR, Bolin  P, Brancati  FL,  et al; Look AHEAD Research Group.  Cardiovascular effects of intensive lifestyle intervention in type 2 diabetes.  N Engl J Med. 2013;369(2):145-154.PubMedGoogle ScholarCrossref
19.
Maruthur  NM, Wang  NY, Appel  LJ.  Lifestyle interventions reduce coronary heart disease risk: results from the PREMIER Trial.  Circulation. 2009;119(15):2026-2031.PubMedGoogle ScholarCrossref
20.
Appel  LJ, Clark  JM, Yeh  HC,  et al.  Comparative effectiveness of weight-loss interventions in clinical practice.  N Engl J Med. 2011;365(21):1959-1968.PubMedGoogle ScholarCrossref
21.
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