- Research article
- Open Access
- Open Peer Review
Modes of e-Health delivery in secondary prevention programmes for patients with coronary artery disease: a systematic review
BMC Health Services Research volume 19, Article number: 364 (2019)
Electronic health (e-Health) interventions are emerging as an effective alternative model for improving secondary prevention of coronary artery disease (CAD). The aim of this study was to describe the effectiveness of different modes of delivery and components in e-Health secondary prevention programmes on adherence to treatment, modifiable CAD risk factors and psychosocial outcomes for patients with CAD.
A systematic review was carried out based on articles found in MEDLINE, CINAHL, and Embase. Studies evaluating secondary prevention e-Health programmes provided through mobile-Health (m-Health), web-based technology or a combination of m-Health and web-based technology were eligible. The main outcomes measured were adherence to treatment, modifiable CAD risk factors and psychosocial outcomes. The quality appraisal of the studies included was conducted using the Joanna Briggs Institute critical appraisal tool for RCT. The results were synthesised narratively.
A total of 4834 titles were identified and 1350 were screened for eligibility. After reviewing 123 articles in full, 24 RCTs including 3654 participants with CAD were included. Eight studies delivered secondary prevention programmes through m-Health, nine through web-based technology, and seven studies used a combination of m-Health and web-based technology. The majority of studies employed two or three secondary prevention components, of which health education was employed in 21 studies. The m-Health programmes reported positive effects on adherence to medication. Most studies evaluating web-based technology programmes alone or in combination with m-Health also utilised traditional CR, and reported improved modifiable CAD risk factors. The quality appraisal showed a moderate methodological quality of the studies.
Evidence exists that supports the use of e-Health interventions for improving secondary prevention of CAD. However, a comparison across studies highlighted a wide variability of components and outcomes within the different modes of delivery. High quality trials are needed to define the most efficient mode of delivery and components capable of addressing a favourable outcome for patients.
e-Health is an emerging field of medical informatics, referring to the organisation and delivery of health services and information using the Internet and related technologies , such as web-based technology and mobile health (m-Health) [2, 3]. The term e-Health characterises a way of working to improve direct healthcare such as monitoring clinical signs, delivering peer support, as well as health information and education, using information and communication technology . The European Society of Cardiology (ESC) recommends the use of e-Health sources to support remote clinical care and improve psychosocial health, diet and smoking cessation, in both primary and secondary prevention of coronary artery disease (CAD) [2,3,4]. Secondary prevention programmes, focusing on pharmacological and non-pharmacological treatment, play a pivotal role in decreasing modifiable CAD risk factors as well as improving adherence to treatment and quality of life . Despite the clinical benefits of secondary prevention, only a minority of patients achieve control of modifiable CAD risk factors . It is also known that adherence to treatment in patients with CAD is suboptimal . The reasons for non-adherence are complex and involve factors related to psychosocial factors and social support. Furthermore, secondary prevention programmes are underutilised . Only one-third of patients with CAD attend some form of secondary prevention programme . Geographical accessibility and shortcomings in the healthcare system are main issues for non-participation . This challenge underpins the need for the development of alternative modes of delivery of secondary prevention programmes, such as e-Health programmes where patients receiveaccess to resources at their discretion.
Among the alternative modes of e-Health secondary prevention programme delivery reviewed previously, the individual studies included have primarily focused on telehealth such as telephone interventions [8, 9] and internet technology including web-based and m-Health interventions [10, 11]. The most recent reviews brought some evidence of improved quality of life, diet and physical activity. However, due to the variations between key elements of the studies included, they could not give any implications for practice [10, 11]. A recent updated systematic review on telehealth interventions for patients with CAD, which included more diverse telehealth interventions such as internet and m-Health, reported improved cardiovascular risk factors. However, 60% of the studies included evaluated telephone interventions . To date, telephone interventions are some of the most studied tools of telemedicine interventions , but may require more resources than contemporary e-Health technology available. However, the use of e-Health in secondary prevention for CAD is a rapidly growing area and more evidence is therefore expected to become available .
Although, a number of components for secondary prevention programmes for CAD have been outlined and standardised in European guidelines and position statements [4, 14, 15], the e-Health secondary prevention programmes seem to differ substantially in their specific components. With regard to the feasibility of e-Health, one systematic review displayed that only 10% of the e-Health secondary prevention programmes included a multicore component approach .
To date, no systematic reviews focusing primarily of the effectiveness of m-health and web-based technology as modes of e-Health delivery and its secondary prevention components in patients with CAD have been published. To inform the design of further e-Health secondary prevention programmes, the aim of this systematic review was to describe the effectiveness of different modes of delivery and components for e-Health secondary prevention programmes on adherence to treatment, modifiable CAD risk factors and psychosocial outcomes for patients with CAD.
To minimise potential sources of bias this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . The protocol for this systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO)  (ID: 111927).
The studies selected had to meet the specific eligibility criteria presented in Table 1. Based on the definition of e-Health  and a position statement of the ESC , the eligibility criteria regarding the mode of e-Health secondary prevention programme delivery were defined a priori and categorised into: (i) m-Health comprising mobile phone applications and/or text messages, but excluding telephone calls (ii) web-based technology applicable for traditional desktop or laptop computers; and (iii) a combination of m-Health and web-based technology. The modes of e-Health delivery consist of screening, assessment, self-monitoring, self-management, social support, advice and education according to the secondary prevention components.
Based on standardised secondary prevention programme components defined by the British Association of Cardiovascular Prevention and Rehabilitation (BACPR)  and a position paper from the European Association for Cardiovascular Prevention and Rehabilitation (EACPR) , this review classified a priori the essential secondary prevention programme components into: (i) Physical activity and exercise management through exercise plans, supervision and counselling; (ii) Health education regarding medical risk management (blood pressure, lipids, glucose) and health behaviour change (dietary habit, weight management, tobacco cessation, psychological management, physical activity); (iii) Psychosocial management including counselling on emotional issues, stress management and facilitation of peer support; (iv) Self- monitoring of medical risk management (blood pressure, lipids, glucose) and health behaviour change (diet, weight, tobacco cessation, physical activity); and (v) Medical risk management including education and support on the use of cardioprotective medications and adherence.
The effectiveness of the eligible studies were described according to three broad categories of a priori defined outcome measures: (i) Adherence to treatment, defined as to what extent the patient follows a prescription (e.g. taking medication, following a diet, and/or executing lifestyle changes), that corresponds with agreed recommendations from a healthcare professional ; (ii) Modifiable CAD risk factors such as high blood pressure, high blood cholesterol levels, smoking, overweight, physical inactivity and poor diet [4, 5]; and (iii) Psychosocial outcomes, encompassing depression, anxiety, chronic stress, social support and quality of life .
The search strategy was designed to locate eligible studies published in English over a 15-year period (January 2003 to March 2018). The interval was chosen to reflect the time period in which the most relevant work in e-Health for patients with CAD was published . A team of clinical researchers in the CONCARDPCI-study and a PhD-prepared librarian (LBH) agreed upon a search strategy for MEDLINE (Additional file 1: Table S1.), which was then adapted for use in CINAHL and Embase. Articles identified through referenced and hand searches were considered for inclusion.
All selected titles and abstracts were scanned independently by two researchers (GB and TRP) using a checklist based on the eligibility criteria (Table 1) to identify papers for potential inclusion. Disagreements regarding the relevance of the abstract were resolved through discussion. The full-text version of the article was obtained and assessed when the abstract information was insufficient, or if one researcher found it necessary to see the full-text version. The full-text versions of the articles were obtained and assessed independently for eligibility by two researchers (GB and TRP). Any disagreements were resolved through discussion or by consulting a third researcher (TMN). The search process shown in the flow diagram was based on the PRISMA statement  (Fig. 1).
The pre-planned data extraction was imported by one researcher (GB) into an Excel spreadsheet. A second researcher (TRP) assessed the data extraction for accuracy. The Cochrane Consumers and Communication Review Group’s data extraction template  was used to capture all relevant information about the studies included. Study characteristics were extracted using the Template for Intervention Description and Replication (TIDieR) checklist .
The quality appraisal was systematically assessed by two independent researchers (GB and TRP) using the Joanna Briggs Institute (JBI) critical appraisal tool for RCTs. The tool contains 13 items that assess the methodological quality of the design, conduct and analysis of RCTs. The tool comprises four elemental answer choices (Yes, No, Unclear and Not Applicable) . If there was insufficient information to answer a question, unclear was recorded. In non-pharmacological trials, the blinding of participants and health professionals in relation to treatment assessment is not always possible . Therefore, criteria such as the blinding of participants and those who delivered the intervention were considered not applicable (NA) and the performance bias was scored at high risk in all the studies. The results of the quality appraisal assessment are reported in narrative form and in a table for each aspect relating to methodological quality (Table 2).
Describing and analysing studies
Due to large variations between the studies included in terms of key elements (secondary prevention programme components, content delivery, intensity, and outcomes measured between and within the three different modes of e-Health delivery), a meta-analysis was not performed . Further, the studies did not all address multiple risk factors. Using the narrative synthesis methodology, essential modes of delivery and components in e-Health secondary prevention programmes for the effectiveness of outcomes were identified and described. The ESRC methods programme for narrative synthesis was used to collate study findings into a coherent textual narrative, with descriptions of the differences in the studies’ characteristics . The narrative synthesis process was based on the conceptual framework of e-Health secondary prevention programmes, secondary prevention programme components and outcomes defined a priori as described in the section on eligibility criteria. A preliminary synthesis was developed by organising the findings from the studies included to describe patterns across the studies using a textual description. In parallel, the studies were grouped according to the conceptual framework to describe relationships between various modes of e-Health delivery, secondary prevention programme components and the outcome measures of the evaluated studies. Two researchers (GB and TRP) carried out the narrative synthesis process.
Characteristics of included studies
The trial selection process identified 4834 records, while 1350 studies were screened for possible inclusion and 123 full manuscripts were reviewed (Fig. 1). In total, 21 completed RCTs in 24 publications that fulfilled the eligibility criteria were included. The studies included were conducted in Asia [32, 39], Europe [26, 30, 31, 35, 40, 41, 43,44,45,46,47,48], North-America [29, 36,37,38, 49], and Oceania [27, 28, 33, 34, 42]. A total of 3654 participants (mean age 59.67) were recruited, with the sample size ranging from 46 to 710. Study characteristics are summarised in Table 3.
As regards the theoretical framework, this was described in 10 studies (12 publications); three applied a behavioural change technique [27, 28, 41], two applied the self-efficacy theory of Bandura [37, 38], two applied a patient-centred care approach [29, 31], while the others applied Internet-Based Cognitive Therapy (iCBT) , the Chronic Care Model , the Social Cognitive Theory , and the m-Health Development and Evaluation Framework  (Table 4).
Half (54%) of the studies identified in this review described the group of healthcare professionals delivering the e-Health secondary programme, including nurses [37, 38, 41, 43, 49], a nurse and a physician [31, 32], one nurse, exercise specialist and dietitian  cardiologist , a health professional who had coached cardiac patients for more than five years, supervised by a cardiologist , psychologist , exercise specialist , moderator , and a study coordinator  (Table 4).
Description of the e-Health interventions
Eight studies (n = 1513) utilised m-Health technology including text messages, smart phone applications, micro-letter applications and medication reminders to deliver CAD-related health education, recommendations, self-monitoring and medication risk management [27, 28, 30, 32, 37,38,39, 44] (Tables 3, 4 and 5). Seventy five percent of the studies comprised applications and text messages that provide health education and reminders for medical risk management and/or health behaviour change [27, 28, 30, 32, 37, 38] and one employed medication risk management only . One study only employed self-monitoring  (Tables 4 and 5, and Fig. 2). Although text messages were the most common to include, there was a diversity in the materials provided in all the studies. Two studies delivered the m-Health secondary prevention programme in addition to traditional exercise-based cardiac rehabilitation (CR) [27, 28], one in addition to traditional secondary prevention care  and clinical visits  (Table 4).
Overall, the studies reported secondary prevention programmes delivered by m-Health to be well accepted. In five studies, acceptability was assessed using a feedback questionnaire following completion of the intervention [27, 28, 30, 37, 38]. These studies demonstrated high patient satisfaction with the intervention, where 91% reported it to be useful , 88.6% found it easy to use [37, 38], and 97.5% would recommend the intervention to others in the same situation . One study assessed acceptability of the intervention through the number of sessions completed. Adherence to protocol was reported to be high with 83% completing more than 75% of the sessions . Three studies did not provide data on the acceptability of the intervention [27, 32, 39] (Table 3).
Effect of m-health interventions on outcomes
Adherence to treatment
Five studies assessed adherence to medications as the primary outcome [30, 32, 37,38,39]. There was a non-significant trend towards improved medication adherence in one study . Significant differences were found in four studies [30, 37,38,39]. One study reported that non-adherence scores were significantly lower in the intervention group compared with the control group when measured with an e-diary (16.6 vs 22.8, P = 0.025). However, no difference in self-reported adherence was found at the end of study visit as measured by the Medication Adherence Report Scale (MARS-5) . Regarding antiplatelet medications, two publications reported a higher number of doses taken (P = 0.003), correct doses taken (P = 0.047) and doses taken on schedule (P = 0.004) [37, 38]. Furthermore, one study demonstrated significantly higher medication adherence levels in the intervention group (P < 0.001) for patients with acute coronary syndrome as measured by the Morisky Medication Adherence Scale (MMAS-8)  (Tables 3, 4 and 5, and Fig. 2).
Modifiable CAD risk factors
Four studies utilising m-Health measured modifiable CAD risk factors. There was a diversity of risk factors assessed in the studies. Three studies assessed physical activity [27, 28, 30]. Of these, two studies assessed self-reported physical activity at baseline and follow-up by using the Global Physical Activity Questionnaire (GPAQ) [27, 28]. One study reported higher recreational physical activity (471 vs. 307 metabolic equivalent-min/week, P < 0.001), and higher work-related physical activity and lower sedentary times (494 vs. 587 min, P < 0.001) in favour of the intervention group . Further, one study reported that patients in the intervention group exercised more regularly compared with the control group (P < 0.001) . In one study, physical activity was registered in an e-diary. The use of an e-diary did not result in between-group differences in physical activity  (Tables 3, 4 and 5, and Fig. 2).
Of the three studies that assessed smoking cessation [28, 30, 44], one study reported significant differences between groups in favour of the intervention group (P < 0.001) . As regards the cardiovascular risk profile, one study reported that more patients in the intervention group achieved treatment goals for blood pressure (62.1% vs 42.9%, P = 0.012) and HbA1c (86.4% vs 54.2%, P = .0018). In addition, BMI was significantly lower in the intervention group (P = 0.005)  (Tables 3, 4 and 5, and Fig. 2).
Three studies utilising m-Health assessed psychosocial outcomes [30, 37, 44]. No significant differences were found for anxiety and depression , quality of life  or medication self-efficacy  (Tables 3, 4 and 5, and Fig. 2).
Nine studies (n = 1209) utilised web-based technologies, ranging from web-portals with written information, modules or tutorials regarding CAD-related risk factors, to moderated online discussion groups and chat sessions with health professionals [26, 36, 41, 43, 45,46,47,48,49] (Tables 3 and 4). Seven studies employed health education [26, 36, 41, 43, 45, 46, 49] in combination with physical activity and exercise management [36, 45], psychosocial-management [26, 41, 46], self-monitoring [26, 36, 41, 43, 45, 46, 49] and medication care management . One web-based technology programme employed physical activity and exercise management as a single component [47, 48] (Tables 3, 4 and 5, and Fig. 2). One web-based secondary prevention programme was delivered as an adjunct to usual care  (Table 4).
The completion rate of the interventions varied substantially. One study reported that 54% completed the introductory module. Furthermore, 0.9% (1/117) completed the recommended 14 steps within the 14-week treatment period . In four studies, completion rates varied from 43 to 83% [41, 45, 47, 48]. In one study, patient satisfaction was assessed through a semi-structured interview at the end of the programme. In this study, the intervention was perceived to be an accessible, convenient and effective way of delivering healthcare services . Further, one study reported high patient satisfaction, where the patients were ‘very satisfied’ with the programme and reported it to be ‘very helpful’ . One study reported that 152 patients logged in at a median of 56 times during the year. However, the monthly number of logins decreased during the intervention period . Data on acceptability was not reported in one study  (Table 3).
Effect of web-based interventions on outcomes
Modifiable CAD risk factors
Seven studies assessed a variety of modifiable CAD risk factors [36, 41, 43, 45, 46, 48, 49]. Six studies assessed physical activity [36, 41, 43, 45, 46, 49]. One study assessed improvement in physical capacity through changes in maximum time on the treadmill stress test (MTT). The intervention group reported a greater increase in MTT by 45.7 s (95% CI: 1.0, 90.5) compared whit usual care (P = 0.045) . Two studies [41, 45] assessed physical activity through changes in the average daily step count. In both studies, the intervention group significantly improved their daily step count. One study reported effects from the intervention at the six week follow-up (+ 497 steps), whereas the control group had decreased their number of steps (− 861 steps) (95% CI 263–2451, P = 0.02). However, no effects of the intervention were present at six months . Further, one study reported that the intervention group had significantly increased objectively measured (P = 0.023) and self-reported physical activity (P = 0.047) compared with the intervention group . Two studies utilised self-report alone to assess physical activity. No significant between-group differences were reported [46, 49] (Tables 3, 4 and 5, and Fig. 2).
In terms of the cardiovascular risk profile, systolic and diastolic blood pressure following completion of the programme were reported in two studies [41, 49]. One study reported no significant between-group differences , whereas one study reported a significant reduction in systolic blood pressure for the control group compared with the intervention group (ES = 0.68, 95% CI 2.99–13.91, P = 0.001) . No significant differences were reported in diastolic blood pressure . Three studies reported cholesterol levels [36, 48, 49]. In one study, intra-group analysis revealed a significant increase in high-density lipoproteins (HDL) in intervention group one (t = − 3.281, P = 0.017) . One study reported that total cholesterol and low density lipoprotein (LDL) levels were 7% (P = 0.026) and 12% (P = 0.022) lower, respectively. However, differences were no longer significant when adjusting for potential confounders . Further, one study found no significant between-group differences in cholesterol levels  (Tables 3, 4 and 5, and Fig. 2).
Five studies reported smoking status [36, 41, 45, 46, 49]. Of these, two studies reported smoking status at baseline [36, 41]. One study reported smoking status at baseline and 52 weeks. However, it is unclear whether the results are statistically significant . Two studies found no significant between-group differences [46, 49] (Tables 3, 4 and 5, and Fig. 2).
Two studies assessed weight reduction [41, 49]. Both studies reported significant differences with P = 0.003  and P = 0.02 . However, for one study, no effects of the intervention were present at 6-months follow-up . Three studies reported BMI [36, 48, 49]. One study reported a significant reduction in BMI (P = 0.003) . Further, one study found no significant differences in BMI , while one study reported the baseline characteristics of participants . Three studies assessed dietary habits [36, 41, 46]. One study reported a decrease in fat intake (ES = 0.30, 95% CI –6.12 to 1.80, P = 0.28) and an increase in fibre intake (ES = 0.29, 95% CI–2.23 to 8.53, P = 0.25) at six weeks. However, these differences were not present at 6-months follow-up . One study reported that participants in the intervention group had 1.6% kcal/day higher dietary protein and 1.4% kcal/day lower dietary saturated fat, P = 0.004 and P = 0.018 respectively. These differences remained statistically significant after adjusting for confounders, P = 0.003 and P = 0.018, respectively . One study found no significant between-group differences . One study assessed modifiable CAD risk factors through relative change in Framingham heart risk score at one year. A relative change of − 12% (− 22 to − 3%) in Framingham heart risk score for the intervention group compared with the usual care group was reported. Furthermore, a difference between groups was observed in low density lipoprotein cholesterol (− 0.3, − 0.5 to − 0.1, mmol/L) and smoking (− 7.7, − 14.9 to − 0.4%)  (Tables 3, 4 and 5, and Fig. 2).
Five studies assessed psychosocial outcomes, including anxiety and depression, quality of life and stress [26, 41, 45, 47, 49]. Only two studies demonstrated significant between-group differences in psychosocial outcomes [41, 45]. One study found significant benefits in the social quality of life score (95% CI 0.05–0.54, P = .02) at 6-month follow-up in favour of the intervention group . Similarly, another study reported higher emotional dimensions of heart disease health-related quality of life in the intervention group (P = 0.038) . One study reported a reduction in anxiety and depression scores over time in the total study sample (mean delta = − 5.1, P < .001), but no difference between the study groups at follow-up (beta = − 0.47, 95% CI − 1.95 to 1.00, P = 0.53). Furthermore, no effect of treatment was found on the secondary outcomes (severe depression, suicidal ideation, cardiac anxiety) at the follow-up . In two studies, no significant differences were found for quality of life or anxiety and depression [47, 49] (Tables 3, 4 and 5, and Fig. 2).
Combining m-health and web-based technology
Seven studies (n = 913) combined m-Health and web-based technology to deliver health education, self-monitoring and reporting systems, automated feedback from text messages or email, chat functions, and physical activity and exercise management [29, 31, 33,34,35, 40, 42] (Tables 3 and 4, and Fig. 2). Six studies employed health education [29, 33,34,35, 40, 42] in combination with: physical activity and exercise management [35, 40], psychosocial-management [29, 31, 33, 34] and self-monitoring [29, 33,34,35, 40, 42]. One study employed psychosocial management in combination with self-monitoring  (Tables 4 and 5, and Fig. 2). Five studies delivered a combination of m-Health and web-based secondary prevention programmes as an adjunct to traditional CR [29, 33,34,35, 40] and as an adjunct to a person-centred face-to-face care approach  (Table 4).
Overall, the acceptability of the combined m-Health and web-based technology programmes was good. One study demonstrated high patient satisfaction, where 95% reported to be ‘satisfied’ or ‘very satisfied’ with the programme . Similarly, two studies reported that 82–85% read some or all text messages [33, 34], and 79% of participants felt that the programme was of the right length . Completion rates of the interventions varied from 73%  to 80% . One study reported that 39% chose to use the intervention and continued to use it at least once after discharge from hospital . Data on acceptability was not reported in one study  (Table 3).
Effect of combining m-Health and web-based technology on outcomes
Adherence to treatment
Two studies assessed adherence to treatment, more specifically adherence to healthy lifestyle behaviours (primary outcome) and medications (secondary outcome) , and adherence to an exercise-based cardiac rehabilitation programme . For the primary outcome, one study reported a significant treatment effect in favour of the intervention at three months (AOR 2.55, 95% CI 1.12–5.84, P = 0.3), but not at six months (AOR 1.93, 95% CI 0.83–4.53, P = 0.13). For the secondary outcome, medication adherence scores as measured by MMAS-8 were significantly higher in the intervention group (mean difference: 0.58, 95% CI 0.19–0.97, P = 0.004) . One study reported that uptake was 1.3 times higher in the intervention group (80%) than in the control group (62%) (P < 0.005), the intervention group was 1.4 times more likely to adhere to the programme (RR 1.71; 95% CI 1.13–2.27, P < 0.005), and completion of the programme was 33% higher in the intervention group  (Tables 3, 4 and 5, and Fig. 2).
Modifiable CAD risk factors
All studies assessed modifiable CAD risk factors. However, there were a diversity of risk factors assessed in the studies. Six studies assessed physical activity [29, 31, 34, 35, 40, 42]. Of these, three studies assessed aerobic capacity (VO2 peak) as the primary outcome [34, 35, 40], and one study as the secondary outcome . Two studies reported a significant increase in VO2 peak in favour of the intervention group, P < 0.001 and P = 0.013, respectively [35, 40] whereas two studies reported no significant between-group differences (difference − 0.2 ml− 1kg min − 1, 95% CI: -1.1, 0.7: P = 0.65)  and P = 0.67 . However, significant improvements in self-reported leisure time physical activity (difference 110.2 min/week, 95% CI: _0.8, 221.3; P = 0.05) and walking (difference 151.4 min/ week, 95% CI: 27.6, 275.2; P = 0.02) at 24 weeks were reported in one study . One study used the six-minute walk test (6MWT) to assess physical capacity. Both intervention groups showed significant improvements in 6MWT from baseline to 6 weeks (TCR: 537 ± 86–584 ± 99 m; CAP-CR: 510 ± 77–570 ± 80 m), which was maintained at 6 months. However, between-group differences for changes in 6MWT were not significant at 6-month follow-up . One study reported no significant between-group differences regarding physical activity  (Tables 3, 4 and 5, and Fig. 2).
Three studies assessed weight reduction [29, 35, 40]. One study reported a significant difference in weight in favour of the intervention group (− 5.1 ± 6.5 kg vs. − 0.8 ± 3.8 kg, P = 0.02), which was mirrored in the results for BMI and waist circumference . Two studies reported no between-group differences regarding weight, BMI or waist circumference [35, 40] (Tables 3, 4 and 5, and Fig. 2).
Five studies reported systolic and diastolic blood pressure at the end of the programme [29, 33, 35, 40, 42]. One study reported significant improvements in diastolic blood pressure (P = 0.03), but not in systolic blood pressure (P = 0.4) . Four studies reported no significant between-group differences in systolic or diastolic blood pressure [29, 33, 35, 40].
Five studies reported HDL and LDL cholesterol levels [29, 33, 35, 40, 42]. However, no studies reported significant between-group differences for these parameters. Similarly, no significant between-group differences were reported regarding HbA1c [29, 35, 40].
One study reported a significant treatment effect in favour of the intervention group regarding adherence to recommended health guidelines at three months (AOR 2.55, 95% CI 1.12–5.84; P = 0.03), but not at six months (AOR 1.93, 95% CI 0.83–4.53; P = 0.13)  (Tables 3, 4 and 5, and Fig. 2).
Six studies assessed psychosocial outcomes, including anxiety and depression, self-efficacy, quality of life and stress [29, 31, 33,34,35, 42]. One study reported significant improvements in global health-related quality of life for the intervention group (P < 0.001). Between-group comparison confirmed that the intervention group improved more than the control group (U = 2407, z = 2.805, P = 0.01) . One study reported reductions in psychological distress as measured by Kessler 10 (K10) Psychological Distress Scale (median (IQR) 14.6 (13.4–16.0) to 12.6 (11.5–13.8)), as well as significant improvements in the EQ5D-Index for the intervention group at the end of the programme (six weeks). However, these differences were not significant at 6-month follow-up . One study reported a significant improvement in mean self-efficacy levels as measured by the General Self-Efficacy Scale (GSES) for the intervention group (P = 0.011) . Further, one study reported significantly higher quality of life scores as measured by the Dartmouth Quality of Life Index (P = 0.03). However, no significant differences were found regarding stress and depression . One study reported significant improvements in self-efficacy to be active (difference 6.2, 95% CI: 0.2,12.2; p = 0.04) and the general health domain of the SF-36 (difference 2.1, 95% CI: 0.1, 4.1; p = 0.03) . Furthermore, one study reported a negative effect for total hospital anxiety as the intervention group reported greater anxiety than the control group at six months (mean difference: 1.18, 95% CI 0.28–2.08, P = 0.01)  (Tables 3, 4 and 5, and Fig. 2).
On average, the studies included had a moderate methodological quality, with scores ranging from three to 13 (median 8) (Table 2). True randomisation was used for the assignment of participants to treatment groups in 21 studies. In three studies, the randomisation procedure was insufficiently reported (selection bias) [31, 39, 46]. The major risk of bias in the studies included was due to insufficuent blinding of participants, those who delivered the intervention (performance bias) and assessors (measurement bias). There was also a risk of allocation bias due to the treatment of interest. Half of the studies included in the review reported that participants were analysed in the groups to which they were randomised (attribution bias). The detailed quality appraisal of the studies included, systematically assessed by use of the JBI critical appraisal tool for RCTs , is given in Table 2.
The current systematic review identified, appraised and synthesised 24 publications that evaluated e-Health secondary prevention programmes and their effects on adherence to treatment, modifiable CAD risk factors and psychosocial outcomes for patients with CAD. The quality of these studies was moderate (median score of eight as measured by the JBI critical appraisal tool). Further, there was heterogeneity in terms of secondary prevention components, content delivery, intensity and outcomes measured between and within the three different modes of e-Health delivery.
In summary, web-based technology applicable for desktop or laptop computers was the most frequently used mode of e-Health delivery. Eight studies delivered m-Health secondary prevention programmes. Unlike that described in previous reviews [10, 11], seven studies combined web-based and m-Health technology. This finding shows a trend towards utilising all available technology when designing and implementing e-Health secondary prevention programmes for patients with CAD [10, 52]. Further, the acceptability of the combination of m-Health and web-based technology was high compared whit web-based technology alone, which varied substantially. However, the majority of the studies combining m-Health and web-based technology, delivered the programme as an adjunct to traditional CR and/or usual care [29, 31, 33,34,35, 40], which could have affected the acceptability in relation to the in-person approaches. Of those studies, all reported some level of effectiveness in at least one of three outcome categories defined a priori. A previous meta-analysis  reported that telehealth delivered in combination with traditional CR showed favourable changes in secondary prevention for patients with CAD of medium and long-term duration. However, many of those studies were telephone-based and the numbers of participants in traditional CR was moderate to low . Notably, another previous review that assessed evidence from an e-Health secondary prevention programme versus traditional centre-based CR for CAD, found no difference in outcomes between the two modes of delivery . These reviews recommended that e-Health could be offered to patients who cannot attend traditional CR  or as an adjunct . It has also been described that e-Health secondary prevention programmes would decrease non-participation and the dropout rate due to better adaptation to the patients’ needs and preferences . Therefore, based on knowledge of barriers associated with non-participation and dropout from traditional secondary prevention programmes, more research is needed to develop feasible modes of e-Health delivery which provide increased flexibility in relation to time and geographical location.
In terms of secondary prevention programme components, this review demonstrated that health education was the most frequently provided secondary prevention component. This differs from older reviews, which report that physical activity and exercise management were most frequently evaluated [8, 16]. This development is in line with the updated document from BACPR, which recognises that the educational component remains fundamental to all other secondary prevention programme components to increase self-management and healthy behaviour [15, 19]. Utilisation of e-Health technology in education delivery provides easier access and patients can self-pace through educational materials. However, patient-related barriers such as low electronic health literacy are of importance to the patients’ ability to apply knowledge, make appropriate decisions and achieve better self-management behaviour . None of the studies that included health education assessed in this review aimed to specifically address the electronic health literacy skills needed in an e-Health context, although, one web-based programme  reported positive improvement in self-efficacy, which is an indicator of health literacy . However, an m-Health secondary prevention programme based on self-efficacy theory reported non-significant improvement in medication self-efficacy. The intervention delivered consisted of medication reminders and health education through text messages . The lack of improvement may be associated with psychosocial factors and low socioeconomic status, which are reported as barriers for digital health adoption .
This review identified two web-based studies and four studies combining web-based and m-Health that report positive effects on psychosocial outcomes. It may be discussed whether psychosocial health management gets too little attention in the development of e-Health secondary prevention programmes. Despite this, it is well known that depression, anxiety and low quality of life are common among patients with CAD [4, 26] and psychosocial health is recommended as a standard component in secondary prevention programmes [4, 15]. Psychosocial factors cannot be underestimated in terms of their impact on medication adherence, behavioural change and self-management. One study that aimed to reduce negative emotions by providing advice about stress and anxiety management skills and social support  reported favourable changes in emotional and social quality of life. The study demonstrates that a web-based mode of psychosocial management and support delivery can be feasible in secondary prevention for CAD. Further, this review has identified viable tools, such as feedback messages, chat sessions and online discussion groups, that employ web-based technology to replace face-to-face meetings. To improve health outcomes, the e-Health system should be designed to foster effective interactions between patients and health professionals. However, little is known about whether social support offered through e-Health programmes has the same effect on self-management behaviour and psychosocial outcomes as traditional secondary prevention programmes .
In terms of medication adherence, a previous review identified seven positive RCTs published in the period 2012–2015 . This review identified an additional four studies that utilis m-Health interventions published after 2015 [27, 28, 30, 32], of which one study reported a positive trend for medication adherence . This finding shows a potential for using m-Health to improve medication adherence. According to studies providing a combination of m-Health and web-based technology, one study reported a positive short-term effect on medication adherence . It is surprising however, that studies combining m-Health and web-based technology did not utilise the technology available by providing medical risk management. This finding shows a need for more rigorous studies utilising the combination of different modes of e-Health technology available to improve medication adherence.
Within the mode of m-Health delivery positive effects were reported on physical activity [27, 28] and smoking cessation  in the TEXT ME trial. To our knowledge, no previous reviews evaluating the effect of m-Health secondary prevention programmes on cardiovascular diseases have reported studies demonstrating positive effects on smoking cessation [55, 56]. Unlike other m-Health interventions assessed, the TEXT ME trial was based on the behavioural change technique, which may be a determining factor for success. Interventions based on behavioural change theory tend to be more effective than those without a theoretical framework . Using principles from behaviour change theories in the development of m-Health secondary prevention programmes may increase the likelihood of success . A number of theoretical frameworks offer guidance on how to manage mechanisms of change in individual health-related behaviour patterns in the development of complex interventions. However, it is unclear whether and how the use of theory influences the effectiveness of e-Health interventions , and most studies assessed in this review did not clearly describe the underlying theoretical framework. Therefore, more studies that apply a theoretical framework that is made explicit in the reporting are needed. Several useful frameworks exist. For example, the UK Medical Research Council’s (MRC) revised framework  can be used to guide the complex e-Health secondary programme development. Another alternative could be the eHealth Enhanced Chronic Care Model (Model (eCCM). The eCCM is a further development of the Chronic Care Model (CCM) that included self-management support and clinical information systems as two of its key elements in order to provide technological skills, access to data, information and knowledge needed to improve health . Further, since it is proposed that behaviour change occurs when patients are well informed, highly motivated and have the skills necessary to perform self-management behaviour , e-Health literacy is of relevance. The e-Health literacy model aims to empower patients and enable them to fully participate in health decisions informed by different modes of e-Health delivery , and could be an alternative when developing health education as a component of e-Health secondary prevention programmes.
The strengths of this review include its systematic approach to data collection. The Cochrane Consumers and Communication Review Group’s data extraction template  was used to capture all relevant information about the studies included. Study characteristics were extracted using the TIDieR checklist . The quality appraisal was systematically assessed by two independent researchers (GB and TRP) using the JBI critical appraisal tool for RCTs . PRISMA guidelines  for reporting systematic reviews were used to minimise potential sources of bias. Articles identified through referenced and hand searches were also considered for inclusion. Nevertheless, the review was limited to empirical research published in the English language. There may also be unpublished theses or conference proceedings that were overlooked. Furthermore, as the literature search did not yield any results from Latin America or Sub-Saharan Africa, no studies from these countries were included in this review, thus reducing the generalisability of the results for these populations. However, this systematic review is merely the first step towards developing new and innovative e-Health interventions relating to follow-up care for patients with CAD.
This systematic review shows that evidence exists to support the use of e-Health secondary prevention programmes for patients with CAD. However, comparison across studies highlighted a wide variability of secondary prevention programme components and outcomes within the different modes of delivery. High quality trials are needed to define the most efficient mode of delivery and components capable of addressing a favourable outcome for patients.
Six minute walk test
Adjusted Odds Ratio
British Association of Cardiovascular Prevention and Rehabilitation
Body mass index
Coronary artery disease
Cumulative Index to Nursing and Allied Health
European Association for Cardiovascular Prevention and Rehabilitation
eHealth Enhanced Chronic Care Model
Euroqol 5 Dimensions Index
European Society of Cardiology
Global Physical Activity Questionnaire
General Self-Efficacy Scale
internet-Based Cognitive Therapy
Joanna Briggs Institute
Kessler 10 (K10) Psychological Distress Scale
Medication Adherence Report Scale
Morisky Medication Adherence Scale; MRC: UK Medical Research Council; MTT
Maximal time on the treadmill stress test
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
International Prospective Register of Systematic Reviews
Peak oxygen uptake
Randomized controlled trials
Short Form Health Survey 36
Template for Intervention Description and Replication
Pagliari C, Sloan D, Gregor P, Sullivan F, Detmer D, Kahan JP, Oortwijn W, MacGillivray S. What is eHealth (4): A scoping exercise to map the field. J Med Internet Res. 2005;7:1–e9.
Frederix I, Caiani EG, Dendale P, Anker S, Bax J, Bohm A, Cowie M, Crawford J, de Groot N, Dilaveris P, et al. ESC e-cardiology working group position paper: overcoming challenges in digital health implementation in cardiovascular medicine. Eur J Prev Cardiol. 2019. https://doi.org/10.1177/2047487319832394.
Cowie MR, Bax J, Bruining N, Cleland JG, Koehler F, Malik M, Pinto F, van der Velde E, Vardas P. e-health: A position statement of the European Society of Cardiology. Eur Heart J. 2016;37(1):63–6.
Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, Cooney MT, Corra U, Cosyns B, Deaton C, et al. 2016 European guidelines on cardiovascular disease prevention in clinical practice. Eur Heart J. 2016;37:2315–81.
Kotseva K, Wood D, De Bacquer D, De Backer G, Ryden L, Jennings C, Gyberg V, Amouyel P, Bruthans J, Castro Conde A, et al. EUROASPIRE IV: A European Society of Cardiology survey on the lifestyle, risk factor and therapeutic management of coronary patients from 24 European countries. Eur J Prev Cardiol. 2016;23(6):636–48.
Naderi SH, Bestwick JP, Wald DS. Adherence to drugs that prevent cardiovascular disease: meta-analysis on 376,162 patients. Am J Med. 2012;125(9):882–7.
Resurreccion DM, Moreno-Peral P, Gomez-Herranz M, Rubio-Valera M, Pastor L. Caldas de Almeida JM, Motrico E. factors associated with non-participation in and dropout from cardiac rehabilitation programmes: a systematic review of prospective cohort studies. Eur J Cardiovasc Nurs. 2019;18(1):38–47.
Huang K, Liu W, He D, Huang B, Xiao D, Peng Y, He Y, Hu H, Chen M, Huang D. Telehealth interventions versus center-based cardiac rehabilitation of coronary artery disease: a systematic review and meta-analysis. Eur J Prev Cardiol. 2015;22(8):959–71.
Neubeck L, Redfern J, Fernandez R, Briffa T, Bauman A, Freedman SB. Telehealth interventions for the secondary prevention of coronary heart disease: a systematic review. Eur J Cardiovasc Prev Rehabil. 2009;16(3):281–9.
Devi R, Singh SJ, Powell J, Fulton EA, Igbinedion E, Rees K. Internet-based interventions for the secondary prevention of coronary heart disease. Cochrane Database Syst Rev. 2015. https://doi.org/10.1002/14651858.CD009386.pub2.
Palacios J, Lee GA, Duaso M, Clifton A, Norman IJ, Richards D, Barley EA. Internet-delivered self-management support for improving coronary heart disease and self-management-related outcomes: a systematic review. J Cardiovasc Nurs. 2017;32(4):E9–e23.
Jin K, Khonsari S, Gallagher R, Gallagher P, Clark AM, Freedman B, Briffa T, Bauman A, Redfern J, Neubeck L. Telehealth interventions for the secondary prevention of coronary heart disease: a systematic review and meta-analysis. Eur J Cardiovasc Nurs. 2019. https://doi.org/10.1177/1474515119826510.
Wei J, Zheng H, Wang L, Wang Q, Wei F, Bai L. Effects of telephone call intervention on cardiovascular risk factors in T2DM: A meta-analysis. J Telemed Telecare. 2019;25(2):93–105.
Piepoli MF, Corra U, Benzer W, Bjarnason-Wehrens B, Dendale P, Gaita D, McGee H, Mendes M, Niebauer J, Zwisler AD, et al. Secondary prevention through cardiac rehabilitation: from knowledge to implementation. A position paper from the cardiac rehabilitation section of the European Association of Cardiovascular Prevention and Rehabilitation. Eur J Cardiovasc Prev Rehabil. 2010;17(1):1–17.
Cowie A, Buckley J, Doherty P, Furze G, Hayward J, Hinton S, Jones J, Speck L, Dalal H, Mills J. Standards and core components for cardiovascular disease prevention and rehabilitation. Heart. 2019;105(7):510–5.
Frederix I, Vanhees L, Dendale P, Goetschalckx K. A review of telerehabilitation for cardiac patients. J Telemed Telecare. 2015;21(1):45–53.
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg. 2010;8(5):336–41.
National institute for health research: International prospective register of systematic reviews (PROSPERO) https://www.crd.york.ac.uk/prospero/. Accessed 20 May 2018.
Buckley JPFG, Doherty P, Speck L, Connolly S, Hinton S, Jones JL, On behalf of BAPCR. The BACPR standards and core components for cardiovascular disease prevention and rehabilitation. 3nd ed; 2017. https://www.bacpr.com/resources/BACPR_Standards_and_Core_Components_2017.pdf.
Sabaté EE. Adherence to long-term therapies: evidence for action in. Geneva: World Health Organization; 2003.
Pedersen SS, von Kanel R, Tully PJ, Denollet J. Psychosocial perspectives in cardiovascular disease. Eur J Prev Cardiol. 2017;24(3_suppl):108–15.
Ryan R, Synnot A, Prictor M, Hill S. Cochrane Consumers and Communication Review Group. Data extraction template for included studies. In: Standard Protocol Text and Additional Guidance for Review Authors. Edited by Group CCaCR. https://cccrg.cochrane.org/author-resources:. Melbourne: La Trobe University; 2016.
Hoffmann TC, Glasziou PP, Boutron I, Milne R, Perera R, Moher D, Altman DG, Barbour V, Macdonald H, Johnston M, et al. Better reporting of interventions: template for intervention description and replication (TIDieR) checklist and guide. Gesundheitswesen. 2016;78(3):175–88.
Tufanaru C, Munn Z, Aromataris E, Campbell J, Hopp L. Chapter 3: Systematic reviews of effectiveness. In: Aromataris E, Munn Z (Editors). Joanna Briggs Institute Reviewer's Manual. The Joanna Briggs Institute; 2017. Available from https://reviewersmanual.joannabriggs.org/.
Polit DF, Gillespie BM, Griffin R. Deliberate ignorance: a systematic review of blinding in nursing clinical trials. Nurs Res. 2011;60(1):9–16.
Norlund F, Wallin E, Olsson EMG, Wallert J, Burell G, von Essen L, Held C. Internet-Based Cognitive Behavioral Therapy for Symptoms of Depression and Anxiety Among Patients With a Recent Myocardial Infarction: The U-CARE Heart Randomized Controlled Trial. J Med Internet Res. 2018;20(3):e88.
Thakkar J, Redfern J, Thiagalingam A, Chow CK. Patterns, predictors and effects of texting intervention on physical activity in CHD - insights from the TEXT ME randomized clinical trial. Eur J Prev Cardiol. 2016;23(17):1894–902.
Chow CK, Redfern J, Hillis GS, Thakkar J, Santo K, Hackett ML, Jan S, Graves N, de Keizer L, Barry T, 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):1255–63.
Widmer RJ, Allison TG, Lennon R, Lopez-Jimenez F, Lerman LO, Lerman A. Digital health intervention during cardiac rehabilitation: a randomized controlled trial. Am Heart J. 2017;188:65–72.
Johnston N, Bodegard J, Jerstrom S, Akesson J, Brorsson H, Alfredsson J, Albertsson PA, Karlsson JE, Varenhorst C. Effects of interactive patient smartphone support app on drug adherence and lifestyle changes in myocardial infarction patients: a randomized study. Am Heart J. 2016;178:85–94.
Wolf A, Fors A, Ulin K, Thorn J, Swedberg K, Ekman I. An eHealth Diary and Symptom-Tracking Tool Combined With Person-Centered Care for Improving Self-Efficacy After a Diagnosis of Acute Coronary Syndrome: A Substudy of a Randomized Controlled Trial. J Med Internet Res. 2016;18(2):e40.
Fang R, Li X. Electronic messaging support service programs improve adherence to lipid-lowering therapy among outpatients with coronary artery disease: an exploratory randomised control study. J Clin Nurs. 2016;25(5–6):664–71.
Pfaeffli Dale L, Whittaker R, Jiang Y, Stewart R, Rolleston A, Maddison R. Text Message and Internet Support for Coronary Heart Disease Self-Management: Results From the Text4Heart Randomized Controlled Trial. J Med Internet Res. 2015;17(10):e237.
Maddison R, Pfaeffli L, Whittaker R, Stewart R, Kerr A, Jiang Y, Kira G, Leung W, Dalleck L, Carter K, et al. A mobile phone intervention increases physical activity in people with cardiovascular disease: results from the HEART randomized controlled trial. Eur J Prev Cardiol. 2015;22(6):701–9.
Frederix I, Hansen D, Coninx K, Vandervoort P, Vandijck D, Hens N, Van Craenenbroeck E, Van Driessche N, Dendale P. Medium-Term Effectiveness of a Comprehensive Internet-Based and Patient-Specific Telerehabilitation Program With Text Messaging Support for Cardiac Patients: Randomized Controlled Trial. J Med Internet Res. 2015;17(7):e185.
Lear SA, Singer J, Banner-Lukaris D, Horvat D, Park JE, Bates J, Ignaszewski A. Improving access to cardiac rehabilitation using the internet: a randomized trial. Stud Health Technol Inform. 2015;209:58–66.
Park LG, Howie-Esquivel J, Whooley MA, Dracup K. Psychosocial factors and medication adherence among patients with coronary heart disease: a text messaging intervention. Eur J Cardiovasc Nurs. 2015;14(3):264–73.
Park LG, Howie-Esquivel J, Chung ML, Dracup K. A text messaging intervention to promote medication adherence for patients with coronary heart disease: a randomized controlled trial. Patient Educ Couns. 2014;94(2):261–8.
Khonsari S, Subramanian P, Chinna K, Latif LA, Ling LW, Gholami O. Effect of a reminder system using an automated short message service on medication adherence following acute coronary syndrome. Eur J Cardiovasc Nurs. 2015;14(2):170–9.
Frederix I, Van Driessche N, Hansen D, Berger J, Bonne K, Alders T, Dendale P. Increasing the medium-term clinical benefits of hospital-based cardiac rehabilitation by physical activity telemonitoring in coronary artery disease patients. Eur J Prev Cardiol. 2015;22(2):150–8.
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.
Varnfield M, Karunanithi M, Lee CK, Honeyman E, Arnold D, Ding H, Smith C, Walters DL. Smartphone-based home care model improved use of cardiac rehabilitation in postmyocardial infarction patients: results from a randomised controlled trial. Heart. 2014;100(22):1770–9.
Vernooij JW, Kaasjager HA, van der Graaf Y, Wierdsma J, Grandjean HM, Hovens MM, de Wit GA, Visseren FL. Internet based vascular risk factor management for patients with clinically manifest vascular disease: randomised controlled trial. Bmj. 2012;344:e3750.
Blasco A, Carmona M, Fernandez-Lozano I, Salvador CH, Pascual M, Sagredo PG, Somolinos R, Munoz A, Garcia-Lopez F, Escudier JM, et al. Evaluation of a telemedicine service for the secondary prevention of coronary artery disease. J Cardiopulm Rehabil Prev. 2012;32(1):25–31.
Reid RD, Morrin LI, Beaton LJ, Papadakis S, Kocourek J, McDonnell L, Slovinec D'Angelo ME, Tulloch H, Suskin N, Unsworth K, et al. Randomized trial of an internet-based computer-tailored expert system for physical activity in patients with heart disease. Eur J Prev Cardiol. 2012;19(6):1357–64.
Lindsay S, Smith S, Bellaby P, Baker R. The health impact of an online heart disease support group: a comparison of moderated versus unmoderated support. Health Educ Res. 2009;24(4):646–54.
Vieira A, Melo C, Machado J, Gabriel J. Virtual reality exercise on a home-based phase III cardiac rehabilitation program, effect on executive function, quality of life and depression, anxiety and stress: a randomized controlled trial. Disabil Rehabil Assist Technol. 2018;13(2):112–23.
Vieira A, Melo C, Noites ARS, Pinho S, Machado JP, Gabriel J, Mendes M. The effect of virtual reality on a home-based cardiac rehabilitation program on body composition, lipid profile and eating patterns: a randomized controlled trial. Eur J Intgr Med. 2017;9:69–78.
Southard BH, Southard DR, Nuckolls J. Clinical trial of an internet-based case management system for secondary prevention of heart disease. J Cardpulm Rehabil. 2003;23(5):341–8.
Higgins JPT, Green S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.1.0 [updated March 2011]. 2011.
Popay J, Roberts H, Sowden A, Petticrew M, Arai L, Rodgers M, Britten N. Guidance on the Conduct of Narrative Synthesis in Systematic Reviews. In: A Product from the ESRC Methods Programme, vol. 1: The ESRC Methods Programme; 2006.
Kotecha D, Chua WWL, Fabritz L, Hendriks J, Casadei B, Schotten U, Vardas P, Heidbuchel H, Dean V, Kirchhof P. European Society of Cardiology smartphone and tablet applications for patients with atrial fibrillation and their health care providers. Europace. 2018;20(2):225–33.
Huhta AM, Hirvonen N, Huotari ML. Health Literacy in Web-Based Health Information Environments: Systematic Review of Concepts, Definitions, and Operationalization for Measurement. J Med Internet Res. 2018;20(12):e10273.
Gee PM, Greenwood DA, Paterniti DA, Ward D, Miller LM. The eHealth Enhanced Chronic Care Model: a theory derivation approach. J Med Internet Res. 2015;17(4):e86.
Pfaeffli Dale L, Dobson R, Whittaker R, Maddison R. The effectiveness of mobile-health behaviour change interventions for cardiovascular disease self-management: a systematic review. Eur J Prev Cardiol. 2016;23(8):801–17.
Hamilton SJ, Mills B, Birch EM, Thompson SC. Smartphones in the secondary prevention of cardiovascular disease: a systematic review. BMC Cardiovasc Disord. 2018;18(1):25.
Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010;12(1):e4.
Beatty AL, Fukuoka Y, Whooley MA. Using mobile technology for cardiac rehabilitation: a review and framework for development and evaluation. J Am Heart Assoc. 2013;2(6):e000568.
Craig P, Dieppe P, Macintyre S, Michie S, Nazareth I, Petticrew M. Developing and evaluating complex interventions: the new Medical Research Council guidance. Bmj. 2008;337:a1655.
Berra K. Challenges of changing lifestyle to reduce risk for cardiovascular disease. J Cardiovasc Nurs. 2010;25(3):223–7.
The authors are grateful for the assistance provided by Marie Norekvål Hayes for the development of Fig. 2.
The study has been funded through a grant from Nord-Trøndelag Hospital Trust, Norway (grant no. 18/3742). A major grant from the Western Norway Health Authority supports the CONCARDPCI-study (grant no. 912184). The funding bodies had no role in the design of the study, the collection, analysis, and interpretation of data, or in writing the manuscript.
Availability of data and materials
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Brørs, G., Pettersen, T.R., Hansen, T.B. et al. Modes of e-Health delivery in secondary prevention programmes for patients with coronary artery disease: a systematic review. BMC Health Serv Res 19, 364 (2019) doi:10.1186/s12913-019-4106-1
- Coronary artery disease
- Secondary prevention programme
- Systematic review