JMIR J Med Internet Res Journal of Medical Internet Research 1438-8871 JMIR Publications Inc. Toronto, Canada v18i3e55 26968879 10.2196/jmir.5218 Original Paper Original Paper Web-Based Interventions Targeting Cardiovascular Risk Factors in Middle-Aged and Older People: A Systematic Review and Meta-Analysis Eysenbach Gunther Green Beverly Anand Sonia Burdorf Alex Widmer Rj Beishuizen Cathrien RL MSc, MD 1 http://orcid.org/0000-0003-0199-0723 Stephan Blossom CM PhD 2 http://orcid.org/0000-0002-1235-360X van Gool Willem A MD, PhD 1 http://orcid.org/0000-0003-3174-8233 Brayne Carol MD 3 http://orcid.org/0000-0001-5307-663X Peters Ron JG MD, PhD 4 http://orcid.org/0000-0002-3642-5022 Andrieu Sandrine MD, PhD 5 http://orcid.org/0000-0002-1142-770X Kivipelto Miia MD, PhD 6 http://orcid.org/0000-0003-0992-3875 Soininen Hilkka MD, PhD 7 http://orcid.org/0000-0002-2785-9937 Busschers Wim B MSc 8 http://orcid.org/0000-0002-3393-9511 Moll van Charante Eric P MD, PhD 8 http://orcid.org/0000-0002-1489-5218 Richard Edo MD, PhD 1
Academic Medical Center Department of Neurology University of Amsterdam PO Box 22660 Amsterdam, 1100 DD Netherlands 31 20 5663446 31 20 5669290 e.richard@amc.uva.nl
9 http://orcid.org/0000-0002-7250-3390 1 Academic Medical Center Department of Neurology University of Amsterdam Amsterdam Netherlands 2 Institute of Health and Society Newcastle University Institute for Ageing Newcastle University Newcastle upon Tyne United Kingdom 3 Cambridge Institute of Public Health Department of Public Health and Primary Care University of Cambridge Cambridge United Kingdom 4 Academic Medical Center Department of Cardiology University of Amsterdam Amsterdam Netherlands 5 Inserm U1027 Department of Epidemiology and Public Health Toulouse University Hospital Toulouse France 6 Aging Research Center Alzheimer Disease Research Center Karolinska Institutet Stockholm Sweden 7 Department of Neurology University of Eastern Finland and Kuopio University Hospital Kuopio Finland 8 Academic Medical Center Department of General Practice University of Amsterdam Amsterdam Netherlands 9 Department of Neurology Radboud University Medical Center Nijmegen Netherlands Corresponding Author: Edo Richard e.richard@amc.uva.nl 03 2016 11 03 2016 18 3 e55 12 10 2015 22 11 2015 18 12 2015 17 1 2016 ©Cathrien RL Beishuizen, Blossom CM Stephan, Willem A van Gool, Carol Brayne, Ron JG Peters, Sandrine Andrieu, Miia Kivipelto, Hilkka Soininen, Wim B Busschers, Eric P Moll van Charante, Edo Richard. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.03.2016. 2016

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 Background

Web-based interventions can improve single cardiovascular risk factors in adult populations. In view of global aging and the associated increasing burden of cardiovascular disease, older people form an important target population as well.

 Objective

In this systematic review and meta-analysis, we evaluated whether Web-based interventions for cardiovascular risk factor management reduce the risk of cardiovascular disease in older people.

 Methods

Embase, Medline, Cochrane and CINAHL were systematically searched from January 1995 to November 2014. Search terms included cardiovascular risk factors and diseases (specified), Web-based interventions (and synonyms) and randomized controlled trial. Two authors independently performed study selection, data-extraction and risk of bias assessment. In a meta-analysis, outcomes regarding treatment effects on cardiovascular risk factors (blood pressure, glycated hemoglobin A1c (HbA1C), low-density lipoprotein (LDL) cholesterol, smoking status, weight and physical inactivity) and incident cardiovascular disease were pooled with random effects models.

 Results

A total of 57 studies (N=19,862) fulfilled eligibility criteria and 47 studies contributed to the meta-analysis. A significant reduction in systolic blood pressure (mean difference –2.66 mmHg, 95% CI –3.81 to –1.52), diastolic blood pressure (mean difference –1.26 mmHg, 95% CI –1.92 to –0.60), HbA1c level (mean difference –0.13%, 95% CI –0.22 to –0.05), LDL cholesterol level (mean difference –2.18 mg/dL, 95% CI –3.96 to –0.41), weight (mean difference –1.34 kg, 95% CI –1.91 to –0.77), and an increase of physical activity (standardized mean difference 0.25, 95% CI 0.10-0.39) in the Web-based intervention group was found. The observed effects were more pronounced in studies with short (<12 months) follow-up and studies that combined the Internet application with human support (blended care). No difference in incident cardiovascular disease was found between groups (6 studies).

 Conclusions

Web-based interventions have the potential to improve the cardiovascular risk profile of older people, but the effects are modest and decline with time. Currently, there is insufficient evidence for an effect on incident cardiovascular disease. A focus on long-term effects, clinical endpoints, and strategies to increase sustainability of treatment effects is recommended for future studies.

 eHealth cardiovascular disease prevention older people aging systematic review meta-analysis Introduction

The field of eHealth is expanding the potential of contemporary medicine [1]. Global aging and its associated burden of cardiovascular disease may expand the scope for innovative Internet interventions [2,3]. Current cardiovascular risk management programs in primary care will become too expensive and, although they are highly effective in research settings [4-6], their effectiveness is markedly lower in daily life [7]. This evidence-practice gap has several causes [8]. Adherence to life-long lifestyle and medication regimens is a serious challenge, illustrated by long-term adherence rates in chronic diseases that average as low as 50% [9,10]. Web-based interventions are cheap, have a wide reach, and they enable self-management [11]. This renders Web-based interventions potentially powerful and scalable tools to enhance sustained adherence in cardiovascular risk management [12].

Older people form an important target population because cardiovascular risk reduction appears effective until old age [13-16]. In 2012, 42% of European people aged between 55 and 74 years used the Internet and this number is increasing [17]. Meta-analyses showed that Web-based interventions targeting single cardiovascular risk factors can induce improvements in adult populations [18-21]. However, optimal cardiovascular prevention and risk management practice, as affirmed by the European Society of Cardiology [22] and the American Heart Association [23], requires targeting the complete cardiovascular risk profile. This is particularly applicable for older people, who often have multiple risk factors or already suffered a cardiovascular event. A comprehensive approach would increase the value of Web-based interventions for daily practice. Currently, little is known about the effectiveness of Web-based interventions in older people.

In this systematic review and meta-analysis, we aim to answer the question whether Web-based interventions for cardiovascular risk factor management reduce cardiovascular risk and disease in older people.

 Methods Search Strategy and Selection of Eligible Studies

We performed a systematic literature search for randomized controlled trials (RCT) on Web-based interventions in older people targeting one or more cardiovascular risk factors and/or disease. Methods were predefined in a research protocol using the PRISMA checklist and the Systematic Reviews Guidelines of the Center of Reviews and Dissemination (Multimedia Appendix 1). We defined Web-based interventions as Web-based participant-centered treatment or prevention programs delivered via the Internet and interacting with the participant in a tailored fashion [24,25]. Internet had to be the main medium through which the intervention was delivered, but other media (phone, face-to-face) could be included too. We excluded the following eHealth interventions: telemonitoring, telemedicine, and mobile phone-mediated interventions. The target of the intervention had to be one or more cardiovascular risk factors and/or cardiovascular disease. Thus, we included interventions for both primary and secondary prevention of cardiovascular disease [22]. The target population had to have a mean age of 50 years or older and could have a mixed level of cardiovascular risk (one or more cardiovascular risk factors or established cardiovascular disease).

Main outcomes of interest were incident cardiovascular disease (myocardial infarction, angina pectoris, heart failure, stroke or transient ischemic attack, and peripheral arterial disease), cardiovascular mortality and overall mortality, and changes in cardiovascular risk factors including blood pressure (BP), glycated hemoglobin A1c (HbA1c), low-density lipoprotein (LDL) cholesterol, smoking status, weight, level of physical exercise, or a composite cardiovascular risk score.

We performed a comprehensive literature search in the EMBASE, Medline, CINAHL, and Cochrane databases from 1995 onward (because the Internet was not widely available before then). Key search terms were cardiovascular risk factors and diseases (separate diseases and risk factors specified), terms related to aspects of cardiovascular risk management (eg, diet, exercise, BP control), Web-based interventions (including all definitions and synonyms), and RCT/review/meta-analysis. The search was last updated on November 3, 2014 by CRB. The comprehensive search strategy is provided in Multimedia Appendix 2. Studies were included if (1) they were on Web-based interventions targeting cardiovascular risk factors and/or disease, (2) study design was a RCT, (3) at least 50 patients were included, (4) mean age was at least 50 years, (5) the duration of the intervention was 4 or more weeks and follow-up was 3 or more months, (6) at least one of the outcomes of our interest was reported, and (7) language was English. Study selection was performed by two independent researchers (CRB and BS) by means of screening of titles and abstracts, and thereafter reading full texts on the basis of the inclusion criteria. If two publications described the same trial, the paper that reported the primary outcomes of the trial was included. Disagreements were resolved by discussion or by a third investigator (ER). We assessed reviews and meta-analyses encountered with our search strategy to check for additional relevant articles.

 Data Extraction

Two reviewers (BS and CRB) extracted data using a predefined data extraction form (Multimedia Appendix 3) for half of the included articles and checked each other’s results. Extracted information included study characteristics, patient baseline characteristics, characteristics of the intervention and control conditions, and available data on clinical and intermediate outcomes. For BP, glucose control, weight, lipids, and physical activity level, we extracted all baseline and follow-up levels, change scores or mean differences. Corresponding authors were contacted if needed. We used an adapted Cochrane Risk of Bias Tool to evaluate randomization procedures, representativeness of study populations, blinding of outcome assessors (blinding of participants was usually not possible due to study design), completeness of outcome data, and completeness of reporting.

 Meta-Analysis

For categorical variables, we calculated odds ratios with 95% confidence intervals. We estimated pooled odds ratios with Mantel-Haenszel random-effects models. For continuous outcomes, mean differences or standardized mean differences (Hedges’ g effect sizes) with 95% confidence intervals were calculated. We estimated pooled effects with DerSimonian and Laird random-effects models. All HbA1c values were converted to percentages. All LDL cholesterol values were converted to mg/dL. All weight values were converted to kg. For level of physical activity, which was assessed with various instruments, we calculated standardized mean differences and 95% confidence intervals. If mean differences or standardized mean differences were reported, we included them directly in the pooled analyses. If not, we calculated change scores (difference between baseline and follow-up within group) or values assessed at follow-up. If values were measured at multiple time points, we used the values recorded at the last follow-up contact.

For studies with multiple arms, we included only one intervention arm in the meta-analysis in order not to create “unit-of-analysis” error by double counting the control group. Where possible, we selected the Internet-only intervention arm. No data were imputed.

We estimated pooled effects for all single cardiovascular risk factors. To address the overall question of efficacy of Web-based interventions for cardiovascular risk factor management, we evaluated the effect on cardiovascular composite scores, clinical outcomes (cardiovascular morbidity and mortality), and pooled the standardized primary outcomes of all studies. We used the primary outcomes as defined by the authors of the studies.

Funnel plots were inspected to assess for potential publication bias. Statistical heterogeneity was assessed using Q and I2 tests. We explored reasons for heterogeneity by jackknife analysis and subgroup analyses. We assessed the following factors in subgroup analyses: study duration (predefined, short term [<12 months] versus long term [≥12 months]), type of cardiovascular prevention (primary versus secondary) [22], and type of intervention (Internet only or “blended” [Internet application combined with human support]). Subgroup analyses were performed on the studies used for the analysis on primary outcomes only. The latter subgroup analysis (on type of intervention) consisted of two separate analyses, one to evaluate the Internet-only interventions versus the control conditions and one to evaluate the blended interventions versus control conditions. In case a study tested both types of interventions with a multiple-arm design, the appropriate arm was included for each analysis. In addition, we performed a mixed effects meta-regression using the unrestricted maximum likelihood method to explore the association between study duration and effect size (standardized primary outcome). Last, we performed sensitivity analyses for the different domains of the risk-of-bias assessment by repeating the analysis on standardized primary outcomes in subgroups of studies with low risk of bias versus studies with an unclear or high risk of bias. For this analysis, we wanted to include all studies that contributed to one of the meta-analyses. Therefore, we complemented the sample of studies with defined primary outcomes that were cardiovascular risk factors of interest with studies that had not defined their primary outcome. If there was no defined primary outcome, we used the cardiovascular risk factor that was targeted most directly in the intervention studied. We used Review Manager 5.2 to draw the risk-of-bias assessment figure and to calculate standard deviations or 95% confidence intervals in cases where only standard errors were available in the original data. We used Microsoft Office Excel version 10, SPSS version 20, and Comprehensive Meta Analysis version 2.2.064 for the statistical analyses.

 Results Study Selection

The search yielded 5251 papers after removal of duplicates. We did not identify additional studies by searching reference lists. After screening of titles and abstracts, 462 papers remained. Review of these full texts resulted in 57 RCTs (corresponding with 84 papers) that fulfilled the selection criteria and were included in the systematic review. We contacted 16 authors to request additional data: nine authors responded and three authors complied with our request. Out of this final selection, 47 studies could be included in the meta-analysis (see Figure 1 for PRISMA flowchart).

Prisma flowchart illustrating literature search.

 Study Characteristics

The 57 RCTs included 19,862 individuals (Tables 1-5). Study sample size ranged from 61 to 2140 participants. Median study duration was 9 months (interquartile range [IQR] 6, range 3-60 months). The mean dropout rate was 15% (range 0%-62%). The mean age of the study populations ranged from 50 to 71 years. In only 7 studies were all participants older than 50 years of age. All participants had an increased risk of cardiovascular disease: 46 studies conducted primary prevention (control of cardiovascular risk factors or diabetes) and 11 studies conducted secondary prevention. In 41 studies, the intervention targeted a single cardiovascular risk factor; in 16 studies, multiple risk factors were addressed. We found no studies on interventions for smoking cessation meeting our inclusion criteria. In most studies, the primary outcome was change in a specific cardiovascular risk factor targeted by the intervention. Sixteen studies reported on clinical outcomes including new cardiovascular events [26-31] and mortality rates [29-41] as a part of adverse event monitoring. All interventions included lifestyle education and were participant-centered. Forty-four studies stimulated self-management by means of goal setting and self-monitoring. Half of interventions were stand-alone Internet platforms and the other half were “blended” (ie, the platforms were supported by a nurse or another health care professional). Intervention usage was reported by 22 studies. The median percentage of participants logging in to the intervention platform was 72% (range 33%-100%).

 Quality Assessment

Methodological quality of the included studies varied (Multimedia Appendix 4). Most studies adequately described the randomization and allocation concealment procedures. Due to the nature of the interventions, none of the studies had a double-blind design. In 20 studies, outcome assessors were blinded [27,29,30,32,34-40,42,52,54,57,59,61,64,67,81], in 19 studies blinding was not mentioned or unclear [28,31,33,41,43,44,46,48,53,56,60,63,65,69,71,74,76,77,80], and in 18 studies outcome assessors were not blinded [26,45,47,49-51,55,58,62,66,68,70,72,73,75,78,79,82].

 Effect of Web-Based Interventions on Single Risk Factors

Of the 57 studies included in the systemic review, 47 studies [26-32,34-42,44-53,55-60,62,64-68,70,73,74,76-79,81,82] provided sufficient information to be included in the meta-analysis. The mean age of the study populations of these 46 studies had the same range as the complete sample of 57 studies.

Systolic and Diastolic Blood Pressure

The pooled analysis showed a significant reduction in both systolic and diastolic BP favoring the intervention (26 studies; n=7720; Figures 2 and 3). For systolic BP, the weighted mean difference was –2.66 mmHg (95% CI –3.81 to –1.52; I2=53%). For diastolic BP, the weighted mean difference was –1.26 mmHg (95% CI –1.92 to –0.60; I2=46%).

Characteristics of the studies included for the systematic review: interventions targeting diabetes.a

Study Setting and study length Participants Age (years), mean (SD) Sex (% female) Intervention Control Primary; secondary outcomes
Bond 2010 [42] 2-arm RCT; USA; 6 m 62 people with DM via university/veteran clinic 67.2 (6.0) 45 Website: education, self-monitoring (glucose, exercise, weight, BP, medication), forum; nurse support (email, chat) Standard diabetes care HbA1c, BP, weight, total cholesterol, HDL cholesterol
IDEATEL 2000-2010 [34] 2-arm RCT; USA; 60 m 1665 Medicare recipients with DM 70.9 (6.7) 63 Online home telemedicine unit: nurse support (video chat), Web portal for self-monitoring (glucose, BP), education Standard diabetes care HbA1c, systolic BP, diastolic BP, total cholesterol, LDL cholesterol
D-net 2001 [43] 4-arm RCT; USA; 10 m 320 people with DM2, Internet, from 16 GPs 59 (9.2) 53 Website: (1) Self-management (glucose), coach support; (2) education, forum; (3) 1 and 2 combinedb (4) Information on medical and lifestyle aspects of diabetes Not defined; behavioral, biological, and psychosocial outcomes
My path 2010 [44] 3-arm RCT; USA; 12 m 463 Medicare recipients with DM2, BMI ≥25 kg/m2 or ≥1 CV risk factor, Internet 58.4 (9.2) 50 (1) Website for computer-assisted self-management(CASM): goal setting, monitoring (HbA1c, BP, cholesterol), forum, education;b,c (2) CASM+ social support (coach, group sessions)b,c (3) Computer-based health risk appraisal, no key features of CASM Behavior changes in diet, physical activity, medication adherence
My care team 2005 [45] 2-arm RCT; USA; 12 m 104 people with DM, HbA1c ≥9.0% via veteran clinic 63.5 (7.0) 0.5 Website: self-management (glucose, BP), education, reminders (phone); care manager support DM self-management training, usual care HbA1c and BP at 3, 6, 9, and 12 m
Mobile DM 2011 [32] 4-arm cluster RCT; USA; 12 m 26 physician practices with 163 people with DM and HbA1c ≥7.5% 52.8 (8.1) 50 (2) Self-management via website + mobile phone, patient informs doctor;b (3) 2 + doctor access to data; (4) 3 + advice from doctorc (1) Care as usual Change in HbA1c over 1 year
Avdal 2011 [46] 2-arm RCT; Turkey; 6 m 122 people with DM2, Internet from clinic 51 (7.3) 51 Website: review risk profile, messaging to researcher, daily glucose monitoring Education and usual care HbA1c, attendance rates at outpatient clinic
Cho 2006 [47] 2-arm RCT; South Korea; 30 m 80 people with DM, Internet from clinic 53 (9) 39 Website: monitoring (glucose, medication, BP, weight, lifestyle), nurse feedback, medication alterations Conventional note-keeping record system HbA1c and HbA1c fluctuation index
Lorig 2010 [48] 3-arm RCT; USA; 6 m 761 people with DM2, Internet 54.3 (9.9) 73 Self-management website with peer support: lessons, action plans, bulletin board, messaging Care as usual HbA1C level at 6 and 18 months
Grant 2008 [49] 2-arm cluster RCT; USA; 12 m 244 people with DM, HbA1c >7.0% from 11 primary clinics 56.1 (11.6) 49 Online personal health record: education, diabetes care plan, agenda, messaging, prescription refills Access to general website Patient Gateway Changes in HbA1c, BP, and LDL cholesterol
McMahon 2012 [50] 3-arm RCT; USA; 12 m 151 people with DM, HbA1c>8.5% from veteran health services 60.2 (10.8) 5 (1) Self-monitoring via phone (BP, glucose); (2) website: self-monitoring (BP, glucose), education, support by care managersb,c (3) Website with links to other DM websites; usual care Change in HbA1c and BP over time
Ralston 2009 [51] 2-arm RCT; USA; 12 m 83 people with DM2, HbA1c≥7.0% and Internet from clinic: 65% with 2 CV risk factors 57.3 (—) 52 Electronic medical record: self-monitoring (glucose, exercise, diet, medication), support by care manager, usual care visits Usual care visits Change in HbA1c
Kwon 2004 [52] 2-arm RCT; South Korea; 3 m 110 people with DM2, Internet from clinic: 27% hypertension 54.1 (9.1) 33 Website: self-monitoring (glucose), reminders, professor/nurse/dietician-support Monthly visit to diabetes specialist HbA1c
EMPOWER-D 2013 [39] 2-arm RCT; USA; 12 m 415 people with DM and HbA1c ≥7.5% from clinic 53.7 (10.2) 40 Online health record: risk estimation, self-monitoring (glucose, diet, exercise, BP), nurse support, own doctor informed Usual care HbA1c at 12 m
REDEEM 2013 [53] 3-arm RCT; USA; 12 m 392 people with DM2, Internet from community centers 56.1 (9.6) 54 (1) CASM website: goal setting; self-monitoring (HbA1c, BP, cholesterol); 8 phone calls;b (2) Computer-assisted self-management + problem solving treatment (CASP): CASM + 8 sessions problem solving Computer health risk appraisal, education, same phone calls as intervention Diabetes distress; HbA1c, physical activity, medication compliance

a Abbreviations: BP: blood pressure; CASM: computer-assisted self-management; CASP: computer-assisted self-management + problem solving treatment; CV: cardiovascular; DM: diabetes mellitus; DM2: type 2 diabetes mellitus; GP: general practitioner; HbA1c; glycated hemoglobin A1c; HDL: high-density lipoprotein; LDL: low-density lipoprotein.

b For studies with more than 2 arms, this arm was used for all analyses.

c For studies with more than 2 arms, this arm was used for the subgroup analysis on blended interventions.

Effect on systolic blood pressure (26 studies).

Characteristics of the studies included for the systematic review: interventions targeting blood pressure.a

Study Setting and study length Participants Age (years), mean (SD) Sex (% female) Intervention Control Primary; secondary outcomes
e-BP 2008 [29] 3-arm RCT; USA; 12 m 778 people with Internet, hypertension, from GPs: 61.1% obese 59.1 (8.5) 52 (1) Website: BP self-monitoring;b (2) 1 + pharmacist supportc General website: personal medical record Change in diastolic, systolic and mean BP
Nolan 2012 [54] 2-arm RCT; Canada; 4 m 387 people with hypertension via website: 41% obese 56.5 (7.4) 59 BP action plan website: assessing motivational readiness, advice, feedback, education E-newsletters Change in diastolic and systolic BP, and pulse pressure
Bove 2013 [55] 2-arm RCT; USA; 6 m 241 people with elevated BP from 2 clinics 59.6 (13.6) 65 Website + telephone system: education, self-monitoring (BP, weight, exercise), online nurse support, doctor informed Provision of data from initial assessment, usual care Proportion of participants with controlled BP at 6 m
Madsen 2008 [56] 2-arm RCT; Denmark; 6 m 236 people with hypertension from 10 GPs 55.9 (11.7) 50 Website: self-monitoring (BP), feedback from own doctor by email Usual care Change in ambulatory systolic BP -at 6 m
Magid 2013 [57] 2-arm RCT; USA; 6 m 348 people with hypertension from 10 clinics 60 (11) 40 Written educational material, website: self-monitoring (BP), pharmacist support, doctor informed, reminders Written education material, usual care Proportion of participants with controlled BP at 6 m
McKinstry 2013 [38] 2-arm RCT; Scotland; 6 m 401 people with hypertension from 20 GPs 60.7 (11.2) 40 Telemonitoring unit + website: self-monitoring (BP), feedback from own doctor Usual care Mean ambulatory BP at 6 m
Thiboutot 2013 [58] 2-arm cluster RCT; USA; 12 m 500 patients with elevated BP from 54 GPs 60.5 (11.9) 58 Website: self-monitoring (BP, medication), feedback, reminders Different prevention website (eg, breast screening) BP control at 12 m

a Abbreviations: BP: blood pressure; GP: general practitioner.

b For studies with more than 2 arms, this arm was used for all analyses.

c For studies with more than 2 arms, this arm was used for the subgroup analysis on blended interventions.

Characteristics of the studies included for the systematic review: interventions targeting weight loss and weight loss maintenance.a

Study Setting and study length Participants Age (years), mean (SD) Sex (% female) Intervention Control Primary; secondary outcomes
Weight loss
Appel 2011 [35] 3-arm RCT; USA; 24 m 415 people with obesity, ≥1 CV risk factor, Internet from 6 primary clinics 54 (10.2) 64 (1) Website + mobile coach support: education, self-monitoring (weight, diet, exercise), reminders, doctor informed;b,c (2) 1 + in-person support 1 (or 2) meetings with coach; brochure with websites for weight loss Change in weight from baseline to 24 m
Bennett 2012 [28] 2-arm RCT; USA; 24 m 365 obese people with hypertension from 3 clinics 54.6 (10.9) 69 Website/interactive voice response system: self-monitoring weight, setting, coach support (phone), group sessions, education Self-help booklet Change in weight at 24 m
Bennett 2010 [59] 2-arm RCT; USA; 3 m 101 obese people with hypertension, Internet from clinic 54.4 (8.1) 48 Website: goal setting, self-monitoring, behavioral skills education, forum, coach support (online, phone, face-to-face) Folder on healthy weight, usual care Change in weight at 12 weeks
Kraschnewsky 2011 [60] 2-arm RCT; USA; 3 m 100 overweight people, Internet via flyers/Internet 50.3 (10.9) 70 Website: target body weight, monitoring, behavioral tips, videos, weight loss plan, tailored feedback, reminders Wait list, people got access to website after 12 weeks Weight loss
Webberd 2008 [61] 2-arm RCT; USA; 4 m 66 women, BMI 25-40, Internet from advertisements 50.0 (9.9) 100 Website: weight loss tips, lessons, message board, self-monitoring (weight, diet), chat sessions All features of intervention except for online chat sessions Not defined; weight, BMI, diet, exercise
E-LITE 2013 [36] 3-arm RCT; USA; 15 m 241 people with a BMI ≥25, metabolic syndrome from 1 clinic 52.9 (10.6) 47 (1) Website + 12 lifestyle classes;c (2) website: self-monitoring (weight, exercise), messaging, DVD with lifestyle classesb Usual care Change in BMI from baseline to 15 m
POWER 2014 [62] 4-arm RCT; UK; 12 m 179 people with BMI ≥30 kg/m2 or ≥28 kg/m2 + CV risk factors from 5 GPs 51.2 (13.1) 66 (1) Website: 12 self-management sessions monitoring (weight), nurse support (email);b,c (2) 1 + 3 nurse contacts; (3) 1 + 7 nurse contacts Usual care Weight at 12 m
Weight loss maintenance
Stop Regain 2008 [41] 3-arm RCT; USA; 18 m 314 people with 10% weight loss in 2 years, via advertisements 51 (10) 81 (1) Website: self-monitoring, email counseling, experts chat;b (2) face-to-face: self-monitoring via phone, weekly group sessions (3) Newsletters Weight gain at 18 m
WLM 2008 [40] 2-phase 3-arm RCT; USA; 30 m 1032 people with ≥4 kg previous weight loss, hypertension, Internet via university/ medicare 55.6 (8.7) 63 (1) Website: goal setting, action plans, self-monitoring (weight, PA, diet), education, bulletin board, reminders, support (email/phone);b (2) personal contact (phone +face-to-face) Printed lifestyle guidelines, 1 visit with coach Change in weight

a Abbreviations: BMI: body mass index; CV: cardiovascular; GP: general practitioner; PA: physical activity.

b For studies with more than 2 arms, this arm was used for all analyses.

c For studies with more than 2 arms, this arm was used for the subgroup analysis on blended interventions.

d Control arm consists of same interactive Internet platform as intervention arm.

Characteristics of the studies included for the systematic review: interventions targeting physical activity and cholesterol.a

Study Setting and study length Participants Age (years), mean (SD) Sex (% female) Intervention Control Primary; secondary outcomes
Physical activity
Richardsonb 2010 [63] 2-arm RCT; USA; 4 m 324 patients from clinic: 12% CHD, 20% DM2, 62% BMI >30 52.0 (11.4) 65 Website as control + online community forum Website: pedometer, tailored feedback Change in average daily step count, patient attrition
Reid 2011 [30] 2-arm RCT; Canada; 12 m 223 patients with a recent CHD event, Internet via 2 cardiac centers 56.4 (9.0) 16 Website: tutorials, exercise plans, self-monitoring, specialist support Usual care, education booklet Mean steps per day
Ferney 2009 [64] 2-arm RCT; Australia; 6 m 106 inactive residents: 58% overweight 52.0 (4.6) 72 Website: behavioral strategies, goal setting, self-monitoring, advice, bulletin board, news Website with minimal interactivity Not defined; physical activity, website use
Active after 55 2013 [65] 2-arm RCT; USA; 3 m 405 sedentary people with Internet via senior centers/websites 60.3 (4.9) 69 Website: education, goal setting, exercise planning, 11 online exercise lessons, self-monitoring, reminders No access to the intervention Not defined; physical activity, BMI
HEART 2014 [37] 2-arm RCT; New Zealand; 6 m 171 people with stable CHD, Internet from 2 hospitals 60.2 (9.2) 19 Exercise prescription, behavioral strategies, Website: videos, self-monitoring (exercise), education, reminders Usual care Change in peak oxygen uptake from baseline to 6 m
Philips Direct Life 2013 [66] 2-arm RCT; Netherlands; 3 m 235 inactive people with Internet through local media 64.8 (2.9) 41 Website: goal setting, self-monitoring (exercise), e-coach feedback Waitlist control Change in physical activity
Suboc 2014 [67] 3-arm RCT; USA; 3 m 114 sedentary people through media and Internet 63.0 (7.0) 34 (1) Pedometer; (2) website + pedometer: exercise strategies, goal setting, self-monitoring (exercise) feedback, forumc No intervention Endothelial function; vascular stiffness, step count, exercise
Peels 2013 [68] 5-arm cluster RCT; Netherlands; 12 m 2140 people from 6 municipal regions, ±50% overweight 63.2 (8.4) 51 (1) Printed feedback report; (2) 1 + local exercise tips; (3) Web-based feedback report; (4) 3 + local exercise tipsc Waitlist control Physical activity
Cholesterol
Blochb 2006 [69] 3-arm RCT; USA; 6 m 171 employees with increased cholesterol, DM or CHD 54.8 (9.4) — (1) Website + financial reward; (2) website + 4 classes, nurse support (phone) Website, 10-year CVD score, monitoring, goals, tailored info LDL cholesterol change at 6 m
Live well 2013 [70] 2-arm RCT; USA; 3 m 61 people with LDL cholesterol ≥3.37 mmol/L, Internet from primary clinics 52.0 (12.8) 75 Web-based rate-your-plate assessment, written educational material, Website: goal setting, self-monitoring, reminders Web-based rate-your-plate assessment Not defined; cholesterol, weight, Framingham risk score

a Abbreviations: BMI: body mass index; CHD: coronary heart disease; CVD: cardiovascular disease; DM: diabetes mellitus; DM2: type 2 diabetes mellitus; LDL: low-density lipoprotein.

b Control arm consists of same interactive Internet platform as intervention arm.

c For studies with more than 2 arms, this arm was used for all analyses.

Characteristics of the studies included for the systematic review: interventions targeting multiple risk factors.a

Study Setting and study length Participants Age (years), mean (SD) Sex (% female) Intervention Control Primary; secondary outcomes
Lindsay 2008 [71] 2-arm RCT; UK; 6 m 108 heart patients living in deprived areas 62.9 (6.0) 33 eHealth portal: glossary, education, local community links, discussion forum No access to the eHealth portal Not defined; behavior change (exercise, smoking, diet)
Heartcare II 2010 [72] 2-arm cluster RCT; USA; 30 m 282 patients with chronic heart disease needing nursing care 64.0 (12.7) 39 Personal health record: education, monitoring, communication, goal setting, email, bulletin board Usual care as the home care agencies use to provide Satisfaction with nursing care
Hughes 2011 [73] 3-arm RCT; USA; 12 m 423 senior university employees with Internet, 32% overweight, 46% obese 51.0 (7.0) 82 (1) Coach for Web-based risk assessment, lifestyle plan, email, phone or in-person contact;c (2) website: risk profile assessment, advice, goal setting, action planningb Printed list of health promotion programs Not defined; diet, exercise, weight
Southard 2003 [26] 2-arm RCT; USA; 6 m 104 patients with CHD or heart failure from 10 hospitals, 200 GPs, adverts 62.3 (10.6) 25 Website + nurse: education, self-monitoring, discussion group, links contact (email, phone or mail), dietician Usual care Not defined; weight, exercise, BP, lipid profile, new CV events
Winett 2007 [74] 3-arm cluster RCT; USA; 16 m 14 churches with 1071 members: 57% overweight, 60% sedentary 51.4 (15.7) 67 (1) Website: education, goal setting, pedometer;b (2) 1 + pulpit supportc Waitlist condition Nutrition improvement, physical activity
Vernooij 2012 [27] 2-arm RCT; Netherlands; 12 m 330 patients with CVD, 2 risk factors, Internet via 2 hospitals 59.9 (8.4) 25 Website: risk profile, self-monitoring (BP, cholesterol), treatment goal, nurse support, news, medication changes Usual care by specialist or GP, receiving baseline risk profile Relative change in Framingham heart risk score after 1 year
Verheijden 2004 [75] 2-arm RCT; Canada; 8 m 146 people with increased CV risk, Internet from 14 GPs 63.0 (10.5) 45 Website: tailored information, diet tool, bulletin board Usual care Not defined; BMI, BP, lipid profile
Ross 2004 [33] 2-arm RCT; USA; 12 m 107 patients with heart failure, Internet via clinic 56.0 (-) 23 Online medical record (clinical notes, laboratory reports, test results), education, nurse support Usual care Change in self-efficacy domain
Bove 2011 [76] 2-arm RCT; USA; 12 m 465 people with CVD risk >10% via community, clinics, churches 61.0 (10.0) 46 Online telemedicine system: laboratory and medication review, self-monitoring (BP, weight, pedometer), feedback, education, own doctor involved 4-months meetings with nurse: review data from logbooks Reduction in Framingham 10-year CVD risk score
Keyserling 2014 [31] 2-arm RCT; USA; 12 m 385 people with CHD risk score ≥10% but no CVD from 5 GPs 62.0 (7.8) 48 Website: CHD risk calculator, advice, education, action planning, goal setting. Same CHD risk calculator, but in-person and by phone Framingham 10-year CHD risk score at 4 and 12 m
Zullig 2014 [77] 2-arm RCT; USA; 3 m 96 people with CVD or DM from primary clinics 36.1 (12.2) 67 CVD risk assessment, website: 6 modules with risk assessments, goal setting, education Printed information on CVD Not defined; Framingham 10-year CVD risk score, BMI, smoking status, systolic BP
Activate your Heart 2014 [78] 2-arm RCT; UK; 6 m 95 people with stable angina, Internet from 9 GPs 66.2 (9.2) 25 Website: CVD risk assessment, education, goal setting, self-monitoring, email/chat with experts Usual care with GP Change in step count at 6 weeks and 6 m
e-Care 2014 [79] 2-arm RCT USA 6m 101 people with BMI >26, elevated BP via electronic health records 56.9 (7.0) 42 Website + dietician: CVD risk assessment, goal setting, action planning, self-monitoring (weight, BP, physical activity, diet) Usual care, printed report for patient and doctor Change in systolic BP, weight and 10-year CVD risk score
Greene 2012 [80] 2-arm RCT; USA; 6 m 513 employees + families 45% overweight and 48% obese 60% older than 50 years 79 Printed lifestyle guide, website: online social network, self-monitoring (weight, exercise), goal setting, feedback Printed lifestyle guide Not defined; physical activity, weight, lipid profile
Holbrook 2009 [81] 2-arm cluster RCT; Canada; 12 m 46 GPs with 511 people with DM, ≥1 CV risk factor 60.7 (12.5) 49 Personal Web-based profile overview for DM/CVRM care, automated telephone reminders, summary for doctor, doctor involved Usual care Composite score for process of care
Diabetes in Check 2014 [82] 2-arm RCT; Australia; 9 m 436 people with DM, Internet via DM network 58.2 (10.3) 48 Website: self-monitoring (exercise) goal setting, education, discussion board General website with home page and contacts page only Not defined; physical activity, BMI

a Abbreviations: BMI: body mass index; BP: blood pressure; CHD: coronary heart disease; CV: cardiovascular; CVD: cardiovascular disease; CVRM: cardiovascular risk management; DM: diabetes mellitus; GP: general practitioner.

b For studies with more than 2 arms, this arm was used for all analyses.

c For studies with more than 2 arms, this arm was used for the subgroup analysis on blended interventions.

Effect on diastolic blood pressure (26 studies).

 Glycated Hemoglobin A1c

A significant reduction in HbA1c level favoring the intervention among patients with type 2 diabetes mellitus was found (21 studies; n=6518; Figure 4). The weighted mean difference for HbA1c was –0.13% (95% CI –0.22 to –0.05; I2=74%). The jackknife procedure did not reveal one particular study responsible for high heterogeneity.

Effect on glycated hemoglobin (21 studies).

 Weight

Fifteen studies tested interventions for weight loss and two studies tested interventions for maintenance of weight loss. The pooled analysis (17 studies; n=3713; Figure 5) showed a significant reduction in weight favoring the intervention (weighted mean difference –1.34 kg, 95% CI –1.91 to –0.77; I2=61%). A sensitivity analysis leaving out the two studies on weight loss maintenance resulted in a similar effect size and level of heterogeneity. The jackknife procedure identified three studies contributing considerably to heterogeneity [35,42,59].

Effect on weight (17 studies).

 Low-Density Lipoprotein Cholesterol

A small but significant reduction in LDL cholesterol favoring the intervention was found (17 studies; n=5035; Figure 6; weighted mean difference –2.18 mg/dL, 95% CI –3.96 to –0.41; I2=44%).

Effect on low-density lipoprotein cholesterol (17 studies).

 Physical Activity

Fourteen studies (n=4444; Figure 7) reported the effect on physical activity. Eight studies used self-reported physical activity levels in minutes per week, five studies used daily step counts obtained from pedometers, and one study measured physical activity with accelerometers. Because of the differences in measurement instruments, we calculated standardized mean differences. A small significant difference in increase of physical activity levels was found in favor of the intervention (weighted standardized mean difference 0.25, 95% CI 0.10-0.39; I2=81%), but heterogeneity was high. The jackknife procedure identified one study [65] driving a substantial part of heterogeneity; without this study, I2 was 68%.

 Effect of Web-Based Interventions on Overall Cardiovascular Risk Profile, Cardiovascular Morbidity, and Mortality Cardiovascular Composite Scores

Nine studies (n=2321; Figure 8) reported a cardiovascular composite score. Five studies reported the Framingham 10-year cardiovascular disease risk score, three studies reported the Framingham 10-year coronary heart disease risk score, and one study reported a clinical composite score based on number of cardiovascular risk factors on target (BP, HbA1c, body mass index, LDL cholesterol, physical activity, albuminuria, foot ulcers, and smoking). Because of the differences between the composite scores, we calculated standardized mean differences. A small significant improvement of the cardiovascular composite scores was found (weighted standardized mean difference –0.10, 95% CI –0.18 to –0.02; I2=0%).

 General Effect of Web-Based Interventions on Cardiovascular Risk Factors

Finally, we pooled the primary outcomes of the 37 studies (n=11,021; Figure 9) that defined a primary outcome (systolic BP: 7 studies; HbA1c: 13 studies; weight: 8 studies; physical activity: 6 studies; cardiovascular composite score: 3 studies). The weighted standardized mean difference was –0.24 (95% CI –0.31 to –0.16; I2=69%) in favor of the intervention. The jackknife procedure revealed that one study [57] somewhat influenced the heterogeneity; without this study, heterogeneity dropped to 64%. The funnel plot (Multimedia Appendix 5) indicated that small studies reporting large effects might be overrepresented. The Egger’s test confirmed that the funnel plot was not symmetrical (P=.01).

Effect on physical activity (14 studies).

Effect on cardiovascular composite scores (9 studies).

General effect on primary outcomes (37 studies).

 Cardiovascular Morbidity and Total Mortality

Six studies (n=1904; 1 short-term and 5 long-term studies) reported on cardiovascular event rates. The mean length of the studies was 13 months (range 6-24 months). The pooled analysis showed no difference in rate between groups (pooled OR 0.75, 95% CI 0.39-1.42; I2=27%; Figure 10). Total mortality rates were reported in 13 studies; in five studies, no deaths occurred and in the other eight studies, there were no differences between groups.

Effect on cardiovascular event rates (6 studies).

Association between study duration and effect size (Hedges' g). One outlier study (Ideatel) was removed from analysis.

 Subgroup Analyses

Results are summarized in Table 6. Within the analysis of pooled primary outcomes, the intervention effect was more pronounced in the short-term studies (15 studies; n=2934; standardized mean difference –0.43, 95% CI –0.57 to –0.29; I2=69%) than in the long-term studies (22 studies; n=8087; standardized mean difference –0.12, 95% CI –0.19 to –0.06; I2=41%). The same pattern was found for all other outcomes except for LDL cholesterol (Multimedia Appendix 6). There were no substantial differences in effect size between studies on primary prevention versus secondary prevention. To further explore the studies targeting primary prevention, we compared studies with populations of relatively low age (not all participants older than 50 years, n=29) with studies with populations of older age (all participants older than 50 years, n=4). The pooled effect size was larger for the studies with older participants (Hedges’ g=–0.30) than for the studies with relatively younger participants (Hedges’ g=–0.23), but the confidence intervals overlapped largely. We repeated the analysis of pooled primary outcomes on the sample of studies testing an Internet-only and a blended intervention. The intervention effect was more pronounced in the sample of blended studies (26 studies; n=7538; standardized mean difference –0.33, 95% CI –0.43 to –0.22; I2=78%) compared to the sample of Internet-only studies (14 studies; n=4280; standardized mean difference –0.15, 95% CI –0.23 to –0.07; I2=40%).

Subgroup analyses within the analysis of standardized primary outcomes.

Subgroup N of studies Hedges’ g 95% CI I2
Duration a
Short (<12 months) 15 –0.43 –0.57, –0.29 69%
Long (≥12 months) 22 –0.12 –0.19, –0.06 41%
Type of prevention a
Primary (including diabetes control) 33 –0.25 –0.32, –0.17 72%
Secondary 4 –0.20 –0.34, –0.06 0%
Primary prevention: age subgroups b
Not all older than 50 years 29 –0.23 –0.33, –0.14 72%
All older than 50 years 4 –0.30 –0.51, –0.09 80%
Internet only vs controlc 14 –0.15 –0.23, –0.07 40%
Blended vs controld 26 –0.33 –0.43, –0.22 79%

a Subgroup analysis performed in the sample of studies that was used for the analysis of primary outcomes.

b Subgroup analysis performed on the sample of studies that targeted primary prevention (including diabetes control).

c Subgroup analysis performed on the sample of studies that evaluated an Internet-only intervention. In case a study tested multiple arms, the appropriate arm was included in the analysis.

d Subgroup analysis performed on the sample of studies that evaluated a blended intervention. In case a study tested multiple arms, the appropriate arm was included in the analysis.

 Meta-Regression

Because of the fairly consistent finding that treatment effects were higher in short-term studies than in long-term studies, we performed a mixed effects meta-regression to explore the association between study duration and effect size. The effect size seemed to become smaller in studies with longer follow-up, although the association was not significant (Hedges’ g=–0.321+0.006*months; P=.07). After removal of one outlier study [34] that had a very long follow-up (5 years), the effect size significantly decreased over time in studies lasting 3 to 32 months (Hedges’ g=–0.415+ 0.015*months; P=.008; Figure. 11).

 Sensitivity Analyses for the Risk-of-Bias Assessment

We performed sensitivity analyses for each of the six domains of bias assessed with the adapted Cochrane Risk of Bias Tool by comparing the standardized primary outcomes of the studies with the low risk and unknown/high risk of bias (Multimedia Appendix 7). There were no significant differences in pooled effect sizes in any of the domains except for the domain random sequence generation, in which the pooled effect was significantly larger in the subgroup of studies with unknown/high risk of bias.

 Discussion

In this systematic review and meta-analysis, we found for people with elevated cardiovascular risk, Web-based interventions lead to improvement of systolic and diastolic BP, HbA1c, weight, LDL cholesterol, physical activity levels, and cardiovascular risk composite scores. Only seven studies included participants all aged 50 years or older. Therefore, our conclusions apply for the population in middle age and beyond. Effects were more pronounced over the short term (study duration <12 months) and in studies that tested a blended intervention (combination of an Internet application and human support). We found no evidence for an effect on incident cardiovascular disease.

Our findings on single cardiovascular risk factors are consistent with conclusions of other meta-analyses in younger adult populations [19-21]. We found a significant reduction in systolic BP of 2.66 mmHg. A reduction of 3 mmHg in systolic BP can lead to an 8% reduction in annual stroke mortality rate and a 5% reduction in annual coronary heart disease mortality rate [83]. We found a reduction of LDL cholesterol of 2.18 mg/dL (converted=0.06 mmol/L). A reduction of 0.5 mmol/L in LDL cholesterol for at least 2 years can lead to a reduction in coronary heart disease events of 20% [6]. Theoretically, assuming a linear relation, a reduction of 0.06 mmol/L could lead to a 2.4% reduction of coronary heart disease events. Thus, the effects on Internet interventions on BP reduction and, to a lesser extent, LDL cholesterol reduction, can be clinically relevant at the population level if reductions are maintained. In addition, we evaluated the effect on the complete cardiovascular risk profile and prevention of cardiovascular disease, which has not been performed before. One other systematic review without meta-analysis that evaluated Internet interventions for lifestyle change in older people reported that interventions with multiple components are more effective than interventions with a single component [84].

We found that the beneficial effects of Web-based interventions decline over time and effects are larger when interventions are combined with human support. Decreasing adherence over time was reported in several studies included in our meta-analysis and could be an important contributor to the decreasing effect over time. We were unable to formally test this because information on adherence and engagement was only reported by 22 studies and definitions varied widely. The identified effect moderators are not specific to Web-based interventions for cardiovascular risk factors [85,86]. Maintenance of behavioral change is notoriously complex and best achieved in longer studies with intensive interventions, more face-to-face, and more follow-up contacts. However, such interventions lead to high attrition rates, probably reflecting selection of the most motivated participants [87]. A careful balance should be sought between effectiveness and implementability when designing cardiovascular risk management interventions, whether or not an Internet-based approach is used.

Our results do not show a beneficial effect of Web-based interventions on incident cardiovascular disease. Although the declining effect over time could play a role, more likely explanations for these findings are the limited follow-up time of the studies to detect these outcomes (mean length of the studies was 13 months) and the fact that these outcomes were not the primary focus of these studies. Because of the latter, data collection may not have been systematic and adjudication of the data by an independent committee may be lacking. Therefore, we cannot draw strong conclusions from these findings.

The results of this study should be interpreted with caution because of several limitations. The methodological quality of the studies was fair, but none of the studies was double blind, rendering them prone to performance bias. Only 20 studies had a blinded outcome assessment, so detection bias may also be present. Because the sensitivity analyses for the risk-of-bias assessment did not reveal significant differences between the low risk and unknown/high risk-of-bias subgroups, except for the domain of random sequence generation, we think that our findings have not been largely affected by these potential sources of bias. Another limitation is the substantial heterogeneity in several of the meta-analyses that is, in part, explained by two effect modifiers: study duration and intervention type. Patient groups with a higher burden have a larger window of opportunity for improvement potentially resulting in larger intervention effects [88], which could also have contributed to heterogeneity. We could not draw firm conclusions on the difference between primary and secondary prevention, because only four studies on secondary prevention were included in this analysis. Last, there is a potential for publication bias and small study bias. Most of the studies with small sample sizes reported large effects and similar studies with null findings did not appear in the funnel plots (Multimedia Appendix 5).

Strengths of our study are the comprehensive search strategy, the quantitative meta-analysis, and the assessment of the effect of Web-based interventions for all cardiovascular risk factors using both intermediate and clinical outcomes. Our search strategy was comprehensive because we used a broad definition of Web-based interventions and only excluded telemedicine and mobile phone interventions. It was not always possible to set Web-based interventions apart from telemedicine and mobile phone interventions. As long as the Web-based program was the main component of the intervention, we judged the study eligible for our systematic review. By pooling the effect sizes on all different cardiovascular risk factors, we aimed to assess the overall effect of an Internet-based approach for people with increased risk of cardiovascular disease. This approach provides insight into the overall potential of Internet-based interventions in this field. Although basic computer literacy as an inclusion criterion probably led to selection of participants with a relatively high socioeconomic status, several studies included in the meta-analysis focused on people from medically underserved areas. Therefore, the external validity of the results might be acceptable and may be generalizable to middle-aged to older primary care populations with an increased risk of cardiovascular disease.

Our results show that Web-based interventions can be effective in improving the cardiovascular risk factor profile of middle-aged and older people, but effects are modest and can only have clinical relevance on the population level if sustained over time. Considering the current interest and focus on eHealth by policy makers, funding agencies, and a myriad of research and patient organizations [89,90], it is important to evaluate the actual evidence base objectively. Unrealistic expectations of the effectiveness of Web-based interventions obscure the true challenges that have to be overcome first, including testing interventions that were designed specifically for older people, improving methodological robustness of studies, and improving sustainability of effects. On the macro level, trials can assess sustainability by prolonging follow-up, recording clinical events, and measuring surrogate cardiovascular outcomes (eg, BP, cholesterol levels, and weight) at multiple time points (eg, at 6, 12, 24, and 36 months). On the micro level, adherence should be evaluated by studying intervention usage through time with standardized evaluation methods. Sustainability is of particular importance because long-term effects are required for primary and secondary prevention to truly contribute to the prevention of cardiovascular disease. Web-based interventions combined with human support are more promising than Internet-only interventions.

 Multimedia Appendix 1

Research protocol.

 Multimedia Appendix 2

Comprehensive search strategy.

 Multimedia Appendix 3

Data-extraction form.

 Multimedia Appendix 4

Summary of the risk of bias assessment.

 Multimedia Appendix 5

Funnel plots.

 Multimedia Appendix 6

Subgroup analysis: study-duration.

 Multimedia Appendix 7

Risk of bias assessment: sensitivity analyses for the domains of risk of bias.

 Abbreviations BP

blood pressure

 HbA1c

glycated hemoglobin A1c

 IQR

interquartile range

 LDL

low-density lipoprotein

 PA

physical activity

 RCT

randomized controlled trial

We thank R Spijker, medical librarian at the Academic Medical Centre of Amsterdam, for his assistance in designing and conducting the search and M Siervo, lecturer in Nutrition and Ageing at the Institute for Ageing and Health, Newcastle University, for his advice on our statistical analysis plan. We also thank the study authors (LA Volk, CC Quinn, and JD Ralston) who provided additional data. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 305654. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

CRB had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. ER, EMvC, WvG, CB, MK, SA, and HS conceived the study. CRB, BS, and ER wrote the study protocol and analysis plan. CRB and BS collected and extracted the data, with support from ER and EMvC. CRB performed the statistical analysis, supported by BS, ER, WB, and WvG. CRB and ER drafted the manuscript and all authors critically revised the manuscript for important intellectual content.

None declared.

 Meier CA Fitzgerald MC Smith JM eHealth: extending, enhancing, and evolving health care Annu Rev Biomed Eng 2013 15 359 382 10.1146/annurev-bioeng-071812-152350 23683088 World Health Organization National Institute on Aging National Institutes of Health Global Health and Aging 2011 2016-02-22 Geneva World Health Organization http://www.who.int/ageing/publications/global_health.pdf [No authors listed] An epidemic of risk factors for cardiovascular disease Lancet 2011 02 12 377 9765 527 10.1016/S0140-6736(11)60182-7 21315931 S0140-6736(11)60182-7 Chow CK Jolly S Rao-Melacini P Fox KA Anand SS Yusuf S Association of diet, exercise, and smoking modification with risk of early cardiovascular events after acute coronary syndromes Circulation 2010 02 16 121 6 750 758 10.1161/CIRCULATIONAHA.109.891523 20124123 CIRCULATIONAHA.109.891523 Law MR Morris JK Wald NJ Use of blood pressure lowering drugs in the prevention of cardiovascular disease: meta-analysis of 147 randomised trials in the context of expectations from prospective epidemiological studies BMJ 2009 338 b1665 19454737 PMC2684577 Law MR Wald NJ Rudnicka AR Quantifying effect of statins on low density lipoprotein cholesterol, ischaemic heart disease, and stroke: systematic review and meta-analysis BMJ 2003 06 28 326 7404 1423 10.1136/bmj.326.7404.1423 12829554 326/7404/1423 PMC162260 Kotseva K Wood D De Backer G De Bacquer D Pyörälä K Keil U EUROASPIRE III: a survey on the lifestyle, risk factors and use of cardioprotective drug therapies in coronary patients from 22 European countries Eur J Cardiovasc Prev Rehabil 2009 04 16 2 121 137 10.1097/HJR.0b013e3283294b1d 19287307 Nieuwlaat R Schwalm J Khatib R Yusuf S Why are we failing to implement effective therapies in cardiovascular disease? Eur Heart J 2013 05 34 17 1262 1269 10.1093/eurheartj/ehs481 23376448 ehs481 Al Hamid A Ghaleb M Aljadhey H Aslanpour Z A systematic review of qualitative research on the contributory factors leading to medicine-related problems from the perspectives of adult patients with cardiovascular diseases and diabetes mellitus BMJ Open 2014 4 9 e005992 10.1136/bmjopen-2014-005992 25239295 bmjopen-2014-005992 PMC4170202 Sabaté E Adherence to Long-Term Therapies: Evidence for Action 2003 Geneva World Health Organization Griffiths F Lindenmeyer A Powell J Lowe P Thorogood M Why are health care interventions delivered over the internet? A systematic review of the published literature J Med Internet Res 2006 8 2 e10 10.2196/jmir.8.2.e10 16867965 v8i2e10 PMC1550698 McManus RJ Mant J Bray EP Holder R Jones MI Greenfield S Kaambwa B Banting M Bryan S Little P Williams B Hobbs FD Telemonitoring and self-management in the control of hypertension (TASMINH2): a randomised controlled trial Lancet 2010 07 17 376 9736 163 172 10.1016/S0140-6736(10)60964-6 20619448 S0140-6736(10)60964-6 Briasoulis A Agarwal V Tousoulis D Stefanadis C Effects of antihypertensive treatment in patients over 65 years of age: a meta-analysis of randomised controlled studies Heart 2014 02 100 4 317 323 10.1136/heartjnl-2013-304111 23813846 heartjnl-2013-304111 Beckett NS Peters R Fletcher AE Staessen JA Liu L Dumitrascu D Stoyanovsky V Antikainen RL Nikitin Y Anderson C Belhani A Forette F Rajkumar C Thijs L Banya W Bulpitt CJ Treatment of hypertension in patients 80 years of age or older N Engl J Med 2008 05 1 358 18 1887 1898 10.1056/NEJMoa0801369 18378519 NEJMoa0801369 Peters R Beckett N Forette F Tuomilehto J Clarke R Ritchie C Waldman A Walton I Poulter R Ma S Comsa M Burch L Fletcher A Bulpitt C Incident dementia and blood pressure lowering in the Hypertension in the Very Elderly Trial cognitive function assessment (HYVET-COG): a double-blind, placebo controlled trial Lancet Neurol 2008 08 7 8 683 689 10.1016/S1474-4422(08)70143-1 18614402 S1474-4422(08)70143-1 Ritchie K Carrière I Ritchie CW Berr C Artero S Ancelin M Designing prevention programmes to reduce incidence of dementia: prospective cohort study of modifiable risk factors BMJ 2010 341 c3885 20688841 PMC2917002 Seybert H Eurostat: Industry, Trade and Services 2012 12 13 2016-02-22 Internet use in households and by individuals in 2012http://ec.europa.eu/eurostat/documents/3433488/5585460/KS-SF-12-050-EN.PDF/39000dab-e2b7-49b2-bc4b-6aad0bf01279 Civljak M Stead LF Hartmann-Boyce J Sheikh A Car J Internet-based interventions for smoking cessation Cochrane Database Syst Rev 2013 7 CD007078 10.1002/14651858.CD007078.pub4 23839868 Liu S Dunford SD Leung YW Brooks D Thomas SG Eysenbach G Nolan RP Reducing blood pressure with Internet-based interventions: a meta-analysis Can J Cardiol 2013 05 29 5 613 621 10.1016/j.cjca.2013.02.007 23618507 S0828-282X(13)00075-5 Wieland LS Falzon L Sciamanna CN Trudeau KJ Brodney S Schwartz JE Davidson KW Interactive computer-based interventions for weight loss or weight maintenance in overweight or obese people Cochrane Database Syst Rev 2012 8 CD007675 10.1002/14651858.CD007675.pub2 22895964 PMC3996838 Pal K Eastwood SV Michie S Farmer AJ Barnard ML Peacock R Wood B Inniss JD Murray E Computer-based diabetes self-management interventions for adults with type 2 diabetes mellitus Cochrane Database Syst Rev 2013 3 CD008776 10.1002/14651858.CD008776.pub2 23543567 Fifth Joint Task Force of the European Society of Cardiology European Association of Echocardiography European Association of Percutaneous Cardiovascular Interventions European Heart Rhythm Association Heart Failure Association European Association for Cardiovascular Prevention & Rehabilitation European Atherosclerosis Society International Society of Behavioural Medicine European Stroke Organisation European Society of Hypertension European Association for the Study of Diabetes European Society of General Practice/Family Medicine International Diabetes Federation Europe European Heart Network European Guidelines on cardiovascular disease prevention in clinical practice (version 2012): the Fifth Joint Task Force of the European Society of Cardiology and Other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of nine societies and by invited experts) Eur J Prev Cardiol 2012 08 19 4 585 667 10.1177/2047487312450228 22763626 2047487312450228 Pearson TA Palaniappan LP Artinian NT Carnethon MR Criqui MH Daniels SR Fonarow GC Fortmann SP Franklin BA Galloway JM Goff DC Heath GW Frank AT Kris-Etherton PM Labarthe DR Murabito JM Sacco RL Sasson C Turner MB American Heart Association Council on Epidemiology and Prevention American Heart Association Guide for Improving Cardiovascular Health at the Community Level, 2013 update: a scientific statement for public health practitioners, healthcare providers, and health policy makers Circulation 2013 04 23 127 16 1730 1753 10.1161/CIR.0b013e31828f8a94 23519758 CIR.0b013e31828f8a94 Ritterband LM Thorndike F Internet interventions or patient education web sites? J Med Internet Res 2006 8 3 e18; author reply e19 10.2196/jmir.8.3.e18 17032634 v8i3e18 PMC2018829 Bennett GG Glasgow RE The delivery of public health interventions via the Internet: actualizing their potential Annu Rev Public Health 2009 30 273 292 10.1146/annurev.publhealth.031308.100235 19296777 Southard B Southard D Nuckolls J Clinical trial of an internet-based case management system for secondary prevention of heart disease J Cardiopulm Rehabi 2003 23 5 341 348 14512778 Vernooij W Kaasjager H Van Der Graaf Y Wierdsma J Grandjean H Hovens M Internet based vascular risk factor management for patients with clinically manifest vascular disease: randomised controlled trial BMJ (Online) 2012 344 7863 e3750 22692651 Bennett G Warner E Glasgow R Askew S Goldman J Ritzwoller D Emmons KM Rosner BA Colditz GA Be Fit‚ Be Well Study Investigators Obesity treatment for socioeconomically disadvantaged patients in primary care practice Arch Intern Med 2012 04 9 172 7 565 574 10.1001/archinternmed.2012.1 22412073 archinternmed.2012.1 PMC3609656 Green B Cook A Ralston J Fishman P Catz S Carlson J Carrell D Tyll L Larson EB Thompson RS Effectiveness of home blood pressure monitoring, Web communication, and pharmacist care on hypertension control: a randomized controlled trial JAMA 2008 06 25 299 24 2857 2867 10.1001/jama.299.24.2857 18577730 299/24/2857 PMC2715866 Reid R Morrin L Beaton L Papadakis S Kocourek J McDonnell L Slovinec DM Tulloch H Suskin N Unsworth K Blanchard C Pipe AL Randomized trial of an internet-based computer-tailored expert system for physical activity in patients with heart disease Eur J Prev Cardiol 2012 12 19 6 1357 1364 10.1177/1741826711422988 21903744 1741826711422988 Keyserling TC Sheridan SL Draeger LB Finkelstein EA Gizlice Z Kruger E Johnston LF Sloane PD Samuel-Hodge C Evenson KR Gross MD Donahue KE Pignone MP Vu MB Steinbacher EA Weiner BJ Bangdiwala SI Ammerman AS A comparison of live counseling with a web-based lifestyle and medication intervention to reduce coronary heart disease risk: a randomized clinical trial JAMA Intern Med 2014 07 174 7 1144 1157 10.1001/jamainternmed.2014.1984 24861959 1876677 PMC4142754 Quinn C Shardell M Terrin M Barr E Ballew S Gruber-Baldini A Cluster-randomized trial of a mobile phone personalized behavioral intervention for blood glucose control Diabetes Care 2011 09 34 9 1934 1942 10.2337/dc11-0366 21788632 dc11-0366 PMC3161305 Ross S Moore L Earnest M Wittevrongel L Lin C Providing a web-based online medical record with electronic communication capabilities to patients with congestive heart failure: randomized trial J Med Internet Res 2004 05 14 6 2 e12 10.2196/jmir.6.2.e12 15249261 v6e12 PMC1550594 Shea S Weinstock R Teresi J Palmas W Starren J Cimino J Lai AM Field L Morin PC Goland R Izquierdo RE Ebner S Silver S Petkova E Kong J Eimicke JP IDEATel Consortium A randomized trial comparing telemedicine case management with usual care in older, ethnically diverse, medically underserved patients with diabetes mellitus: 5 year results of the IDEATel study J Am Med Inform Assoc 2009 16 4 446 456 10.1197/jamia.M3157 19390093 M3157 PMC2705246 Appel L Clark J Yeh H Wang N Coughlin J Daumit G Miller ER Dalcin A Jerome GJ Geller S Noronha G Pozefsky T Charleston J Reynolds JB Durkin N Rubin RR Louis TA Brancati FL Comparative effectiveness of weight-loss interventions in clinical practice N Engl J Med 2011 11 24 365 21 1959 1968 10.1056/NEJMoa1108660 22085317 PMC4074540 Ma J Yank V Xiao L Lavori PW Wilson SR Rosas LG Stafford RS Translating the Diabetes Prevention Program lifestyle intervention for weight loss into primary care: a randomized trial JAMA Intern Med 2013 01 28 173 2 113 121 10.1001/2013.jamainternmed.987 23229846 1485081 PMC3856315 Maddison R Pfaeffli L Whittaker R Stewart R Kerr A Jiang Y Kira G Leung W Dalleck L Carter K Rawstorn J A mobile phone intervention increases physical activity in people with cardiovascular disease: Results from the HEART randomized controlled trial Eur J Prev Cardiol 2015 06 22 6 701 709 10.1177/2047487314535076 24817694 2047487314535076 McKinstry B Hanley J Wild S Pagliari C Paterson M Lewis S Sheikh A Krishan A Stoddart A Padfield P Telemonitoring based service redesign for the management of uncontrolled hypertension: multicentre randomised controlled trial BMJ 2013 346 f3030 23709583 PMC3663293 Tang PC Overhage JM Chan AS Brown NL Aghighi B Entwistle MP Hui SL Hyde SM Klieman LH Mitchell CJ Perkins AJ Qureshi LS Waltimyer TA Winters LJ Young CY Online disease management of diabetes: engaging and motivating patients online with enhanced resources-diabetes (EMPOWER-D), a randomized controlled trial J Am Med Inform Assoc 2013 05 1 20 3 526 534 10.1136/amiajnl-2012-001263 23171659 amiajnl-2012-001263 PMC3628059 Svetkey L Stevens V Brantley P Appel L Hollis J Loria C Vollmer WM Gullion CM Funk K Smith P Samuel-Hodge C Myers V Lien LF Laferriere D Kennedy B Jerome GJ Heinith F Harsha DW Evans P Erlinger TP Dalcin AT Coughlin J Charleston J Champagne CM Bauck A Ard JD Aicher K Weight Loss Maintenance Collaborative Research Group Comparison of strategies for sustaining weight loss: the weight loss maintenance randomized controlled trial JAMA 2008 03 12 299 10 1139 1148 10.1001/jama.299.10.1139 18334689 299/10/1139 Wing R Tate D Gorin A Raynor H Fava J A self-regulation program for maintenance of weight loss N Engl J Med 2006 10 12 355 15 1563 1571 10.1056/NEJMoa061883 17035649 355/15/1563 Bond G Burr R Wolf F Price M McCurry S Teri L The effects of a web-based intervention on the physical outcomes associated with diabetes among adults age 60 and older: a randomized trial Diabetes Technol Ther 2007 02 9 1 52 59 10.1089/dia.2006.0057 17316098 PMC2748064 Glasgow R Boles S McKay H Feil E Barrera JM The D-Net diabetes self-management program: long-term implementation, outcomes, and generalization results Prev Med 2003 04 36 4 410 419 12649049 S0091743502000567 Glasgow R Kurz D King D Dickman J Faber A Halterman E Woolley T Toobert DJ Strycker LA Estabrooks PA Osuna D Ritzwoller D Twelve-month outcomes of an Internet-based diabetes self-management support program Patient Educ Couns 2012 04 87 1 81 92 10.1016/j.pec.2011.07.024 21924576 S0738-3991(11)00391-0 PMC3253192 McMahon G Gomes H Hickson HS Hu T Levine B Conlin P Web-based care management in patients with poorly controlled diabetes Diabetes Care 2005 07 28 7 1624 1629 15983311 28/7/1624 PMC1262644 Avdal E Kizilci S Demirel N The effects of web-based diabetes education on diabetes care results: a randomized control study Comput Inform Nurs 2011 02 29 2 101 106 10.1097/NCN.0b013e3181fcbdc6 21099675 Cho J Chang S Kwon H Choi Y Ko S Moon S Yoo SJ Song KH Son HS Kim HS Lee WC Cha BY Son HY Yoon KH Long-term effect of the Internet-based glucose monitoring system on HbA1c reduction and glucose stability: a 30-month follow-up study for diabetes management with a ubiquitous medical care system Diabetes Care 2006 12 29 12 2625 2631 10.2337/dc05-2371 17130195 29/12/2625 Lorig K Ritter P Laurent D Plant K Green M Jernigan VB Case Siobhan Online diabetes self-management program: a randomized study Diabetes Care 2010 06 33 6 1275 1281 10.2337/dc09-2153 20299481 dc09-2153 PMC2875437 Grant R Wald J Schnipper J Gandhi T Poon E Orav E Williams DH Volk L Middleton B Practice-linked online personal health records for type 2 diabetes mellitus: a randomized controlled trial Arch Intern Med 2008 09 8 168 16 1776 1782 10.1001/archinte.168.16.1776 18779465 168/16/1776 PMC3829635 McMahon G Fonda S Gomes H Alexis G Conlin P A randomized comparison of online- and telephone-based care management with internet training alone in adult patients with poorly controlled type 2 diabetes Diabetes Technol Ther 2012 11 14 11 1060 1067 10.1089/dia.2012.0137 22953754 PMC3482842 Ralston J Hirsch I Hoath J Mullen M Cheadle A Goldberg H Web-based collaborative care for type 2 diabetes: a pilot randomized trial Diabetes Care 2009 02 32 2 234 239 10.2337/dc08-1220 19017773 dc08-1220 PMC2628685 Kwon H Cho J Kim Hee-Soo Song Bok-Re Ko Seung-Hyun Lee Jung-Min Kim Sung-Rae Chang Sang-Ah Kim Hee-Seung Cha Bong-Yun Lee Kwang-Woo Son Ho-Young Lee Jin-Hee Lee Won-Chul Yoon Kun-Ho Establishment of blood glucose monitoring system using the internet Diabetes Care 2004 02 27 2 478 483 14747232 Fisher L Hessler D Glasgow RE Arean PA Masharani U Naranjo D Strycker LA REDEEM: a pragmatic trial to reduce diabetes distress Diabetes Care 2013 09 36 9 2551 2558 10.2337/dc12-2493 23735726 dc12-2493 PMC3747867 Nolan R Liu S Shoemaker J Hachinski V Lynn H Mikulis D Wennberg RA Moy LM Zbib A Therapeutic benefit of internet-based lifestyle counselling for hypertension Can J Cardiol 2012 05 28 3 390 396 10.1016/j.cjca.2012.02.012 22498181 S0828-282X(12)00106-7 Bove AA Homko CJ Santamore WP Kashem M Kerper M Elliott DJ Managing hypertension in urban underserved subjects using telemedicine--a clinical trial Am Heart J 2013 04 165 4 615 621 10.1016/j.ahj.2013.01.004 23537980 S0002-8703(13)00048-3 Madsen LB Kirkegaard P Pedersen EB Blood pressure control during telemonitoring of home blood pressure. A randomized controlled trial during 6 months Blood Press 2008 17 2 78 86 10.1080/08037050801915468 18568696 791848828 Magid DJ Olson KL Billups SJ Wagner NM Lyons EE Kroner BA A pharmacist-led, American Heart Association Heart360 Web-enabled home blood pressure monitoring program Circ Cardiovasc Qual Outcomes 2013 03 1 6 2 157 163 10.1161/CIRCOUTCOMES.112.968172 23463811 CIRCOUTCOMES.112.968172 Thiboutot J Sciamanna CN Falkner B Kephart DK Stuckey HL Adelman AM Curry WJ Lehman EB Effects of a web-based patient activation intervention to overcome clinical inertia on blood pressure control: cluster randomized controlled trial J Med Internet Res 2013 15 9 e158 10.2196/jmir.2298 24004475 v15i9e158 PMC3785979 Bennett G Herring S Puleo E Stein E Emmons K Gillman M Web-based weight loss in primary care: a randomized controlled trial Obesity (Silver Spring) 2010 02 18 2 308 313 10.1038/oby.2009.242 19696764 oby2009242 PMC3710670 Kraschnewski J Stuckey H Rovniak L Lehman E Reddy M Poger J Kephart DK Coups EJ Sciamanna CN Efficacy of a weight-loss website based on positive deviance. A randomized trial Am J Prev Med 2011 12 41 6 610 614 10.1016/j.amepre.2011.08.012 22099238 S0749-3797(11)00611-8 Webber KH Tate DF Michael BJ A randomized comparison of two motivationally enhanced Internet behavioral weight loss programs Behav Res Ther 2008 09 46 9 1090 1095 10.1016/j.brat.2008.06.008 18675402 S0005-7967(08)00118-6 Yardley L Ware LJ Smith ER Williams S Bradbury KJ Arden-Close EJ Mullee MA Moore MV Peacock JL Lean ME Margetts BM Byrne CD Hobbs RF Little P Randomised controlled feasibility trial of a web-based weight management intervention with nurse support for obese patients in primary care Int J Behav Nutr Phys Act 2014 11 67 10.1186/1479-5868-11-67 24886516 1479-5868-11-67 PMC4045942 Richardson C Buis L Janney A Goodrich D Sen A Hess M Mehari Kathleen S Fortlage Laurie A Resnick Paul J Zikmund-Fisher Brian J Strecher Victor J Piette John D An online community improves adherence in an internet-mediated walking program. Part 1: results of a randomized controlled trial J Med Internet Res 2010 12 4 e71 10.2196/jmir.1338 21169160 v12i4e71 PMC3056526 Ferney S Marshall A Eakin E Owen N Randomized trial of a neighborhood environment-focused physical activity website intervention Prev Med 2009 02 48 2 144 150 10.1016/j.ypmed.2008.10.022 19028519 S0091-7435(08)00574-4 Irvine AB Gelatt VA Seeley JR Macfarlane P Gau JM Web-based intervention to promote physical activity by sedentary older adults: randomized controlled trial J Med Internet Res 2013 15 2 e19 10.2196/jmir.2158 23470322 v15i2e19 PMC3636271 Wijsman CA Westendorp RG Verhagen EA Catt M Slagboom PE de Craen AJ Broekhuizen K van Mechelen MW van Heemst HD van der Ouderaa F Mooijaart SP Effects of a web-based intervention on physical activity and metabolism in older adults: randomized controlled trial J Med Internet Res 2013 15 11 e233 10.2196/jmir.2843 24195965 v15i11e233 PMC3841355 Suboc Tisha B Strath Scott J Dharmashankar Kodlipet Coulliard Allison Miller Nora Wang Jingli Tanner Michael J Widlansky Michael E Relative importance of step count, intensity, and duration on physical activity's impact on vascular structure and function in previously sedentary older adults J Am Heart Assoc 2014 3 1 e000702 10.1161/JAHA.113.000702 24572255 jah3457 PMC3959701 Peels DA Bolman C Golsteijn RH de Vries H Mudde AN van Stralen MM Lechner L Long-term efficacy of a printed or a Web-based tailored physical activity intervention among older adults Int J Behav Nutr Phys Act 2013 10 104 10.1186/1479-5868-10-104 24228862 1479-5868-10-104 PMC3766084 Bloch MJ Armstrong DS Dettling L Hardy A Caterino K Barrie S Partners in lowering cholesterol: comparison of a multidisciplinary educational program, monetary incentives, or usual care in the treatment of dyslipidemia identified among employees J Occup Environ Med 2006 07 48 7 675 681 10.1097/01.jom.0000205997.18143.6c 16832224 00043764-200607000-00010 Kulick D Langer RD Ashley JM Gans KM Schlauch K Feller C Live well: a practical and effective low-intensity dietary counseling intervention for use in primary care patients with dyslipidemia--a randomized controlled pilot trial BMC Fam Pract 2013 14 59 10.1186/1471-2296-14-59 23663789 1471-2296-14-59 PMC3662581 Lindsay S Bellaby P Smith S Baker R Enabling healthy choices: is ICT the highway to health improvement? Health (London) 2008 07 12 3 313 331 10.1177/1363459308090051 18579630 12/3/313 Brennan PF Casper GR Burke LJ Johnson KA Brown R Valdez RS Sebern M Perez OA Sturgeon B Technology-enhanced practice for patients with chronic cardiac disease: home implementation and evaluation Heart Lung 2010 39 6 Suppl S34 546 10.1016/j.hrtlng.2010.09.003 21092830 S0147-9563(10)00359-6 PMC3033014 Hughes S Seymour R Campbell R Shaw J Fabiyi C Sokas R Comparison of two health-promotion programs for older workers Am J Public Health 2011 05 101 5 883 890 10.2105/AJPH.2010.300082 21421955 AJPH.2010.300082 PMC3076396 Winett R Anderson E Wojcik J Winett S Bowden T Guide to health: nutrition and physical activity outcomes of a group-randomized trial of an Internet-based intervention in churches Ann Behav Med 2007 06 33 3 251 261 10.1080/08836610701358045 17600452 Verheijden M Bakx J Akkermans R van den Hoogen H Godwin N Rosser W van Staveren W van Weel C Web-based targeted nutrition counselling and social support for patients at increased cardiovascular risk in general practice: randomized controlled trial J Med Internet Res 2004 12 16 6 4 e44 10.2196/jmir.6.4.e44 15631968 v6e44 PMC1550618 Bove A Santamore W Homko C Kashem A Cross R McConnell T Shirk Gail Menapace Francis Reducing cardiovascular disease risk in medically underserved urban and rural communities Am Heart J 2011 02 161 2 351 359 10.1016/j.ahj.2010.11.008 21315219 S0002-8703(10)01049-5 Zullig LL Sanders LL Shaw RJ McCant F Danus S Bosworth HB A randomised controlled trial of providing personalised cardiovascular risk information to modify health behaviour J Telemed Telecare 2014 04 20 3 147 152 10.1177/1357633X14528446 24647384 1357633X14528446 Devi R Powell J Singh S A web-based program improves physical activity outcomes in a primary care angina population: randomized controlled trial J Med Internet Res 2014 16 9 e186 10.2196/jmir.3340 25217464 v16i9e186 PMC4180351 Green BB Anderson ML Cook AJ Catz S Fishman PA McClure JB Reid RJ e-Care for heart wellness: a feasibility trial to decrease blood pressure and cardiovascular risk Am J Prev Med 2014 04 46 4 368 377 10.1016/j.amepre.2013.11.009 24650839 S0749-3797(13)00630-2 PMC3978093 Greene J Sacks R Piniewski B Kil D Hahn JS The impact of an online social network with wireless monitoring devices on physical activity and weight loss J Prim Care Community Health 2013 07 1 4 3 189 194 10.1177/2150131912469546 23799706 2150131912469546 Holbrook A Thabane L Keshavjee K Dolovich L Bernstein B Chan D Troyan Sue Foster Gary Gerstein Hertzel Individualized electronic decision support and reminders to improve diabetes care in the community: COMPETE II randomized trial CMAJ 2009 07 7 181 1-2 37 44 10.1503/cmaj.081272 19581618 181/1-2/37 PMC2704409 Jennings CA Vandelanotte C Caperchione CM Mummery WK Effectiveness of a web-based physical activity intervention for adults with Type 2 diabetes-a randomised controlled trial Prev Med 2014 03 60 33 40 10.1016/j.ypmed.2013.12.011 24345601 S0091-7435(13)00465-9 Appel LJ Brands MW Daniels SR Karanja N Elmer PJ Sacks FM American HA Dietary approaches to prevent and treat hypertension: a scientific statement from the American Heart Association Hypertension 2006 02 47 2 296 308 10.1161/01.HYP.0000202568.01167.B6 16434724 47/2/296 Aalbers T Baars MA Rikkert MG Characteristics of effective Internet-mediated interventions to change lifestyle in people aged 50 and older: a systematic review Ageing Res Rev 2011 09 10 4 487 497 10.1016/j.arr.2011.05.001 21628005 S1568-1637(11)00045-6 Schwingshackl L Dias S Hoffmann G Impact of long-term lifestyle programmes on weight loss and cardiovascular risk factors in overweight/obese participants: a systematic review and network meta-analysis Syst Rev 2014 3 130 10.1186/2046-4053-3-130 25358395 2046-4053-3-130 PMC4227972 Mellen PB Gao SK Vitolins MZ Goff DC Deteriorating dietary habits among adults with hypertension: DASH dietary accordance, NHANES 1988-1994 and 1999-2004 Arch Intern Med 2008 02 11 168 3 308 314 10.1001/archinternmed.2007.119 18268173 168/3/308 Fjeldsoe B Neuhaus M Winkler E Eakin E Systematic review of maintenance of behavior change following physical activity and dietary interventions Health Psychol 2011 01 30 1 99 109 10.1037/a0021974 21299298 2011-02060-010 Ebrahim S Taylor F Ward K Beswick A Burke M Davey SG Multiple risk factor interventions for primary prevention of coronary heart disease Cochrane Database Syst Rev 2011 1 CD001561 10.1002/14651858.CD001561.pub3 21249647 Fifty-eighth World Health Assembly, Geneva, 16-25 May 2005 2005 2015-10-08 Geneva World Health Organization Resolutions and decisions: eHealth (WHA58/2005/REC/1)http://www.who.int/healthacademy/media/WHA58-28-en.pdf Kroes N European Commission 2014 05 12 Shaping a healthier, stronger futurehttp://europa.eu/rapid/press-release_SPEECH-14-369_en.htm