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Iranian Journal of Public Health logoLink to Iranian Journal of Public Health
. 2022 Nov;51(11):2388–2403. doi: 10.18502/ijph.v51i11.11157

Quality of Life in Heart Patients Receiving Telerehabilitation: An Overview with Meta-Analyses

Mahdieh Arian 1, Ali Valinejadi 2, Mohsen Soleimani 3,*
PMCID: PMC9745418  PMID: 36561264

Abstract

Background:

This overview is conducted to evaluate the effect of telerehabilitation on Health-Related Quality of Life (HRQOL) in patients with cardiovascular diseases (CVDs).

Methods:

A comprehensive search was performed through the [MeSH] keywords (heart diseases, coronary disease, coronary artery disease, myocardial infarction, coronary artery bypass, heart failure, cardiac rehabilitation and telemedicine) until January 20, 2021 in databases of Science Direct, Medline/PubMed, Web of Science, Scopus, ProQuest, Google Scholar and Cochrane library. Finally, 20 reviews were entered into the analysis.

Results:

The results of meta-analyses showed that receiving telerehabilitation program by telemedicine method has a positive effect on the physical dimension and changing the mental status of patients following this intervention depends on age so that the use of these technologies in heart patients with younger ages promotes mental status. On the other hand, increasing the duration of the intervention 18 months or more affects the physical dimension and 12 months or more affects promoting overall HRQOL. Among the various types of Telemedicine methods, telephone support has a greater effect on promoting the physical dimension.

Conclusion:

The ability to use virtual technology is less at older ages, so age conditions of patients should be considered in choosing this type of intervention. The living place of the people and the level of access to advanced care, seem to play an important role in changing outcomes and choosing this type of intervention because the main purpose of telerehabilitation is to provide treatment care in areas with low access levels.

Keywords: Telerehabilitation, Telemedicine, Rehabilitations, Cardiovascular disease

Introduction

Cardiovascular disease (CVD) is the main cause of death worldwide that includes almost one-third of deaths (1). According to high mortality in CVD and especially heat failure (HF), there is a risk of decreasing in health-related quality of life (HRQOL) in these patients (2, 3). Cardiac rehabilitation (CR) and secondary prevention have 10 main cores including patient assessment, nutrition counseling, weight management, blood pressure management, lipid management, diabetes management, tobacco cessation, psychological management, physical activity counseling, and exercise training (4).

Numerous studies have been conducted on the use of technology in CR programs, while the effect of these programs on HRQOL requires investigation (5, 6). Some studies compared the effect of telerehabilitation-based interventions on HRQOL in patients with CVDs, which showed conflicting results. The use of a smartphone-based interactive patient support tool after 6 months had no significant effect on HRQOL (7). Telerehabilitation had different effects on quality of life. HRQOL in chronic heart disease (CHD) patients increased slightly after 12 weeks in the telerehabilitation group compared to the control group. But after 24 weeks, it increased in the control group and decreased in the remote rehabilitation group (8). No significant increase has been occurred in a 90-day follow-up in HF patients using Mobile Web-Based Telemonitoring-MWBT (9). While HRQOL and all its dimensions have been significantly increased in HF patients using Mobile Phone-Based Telemonitoring-MPBT after 6 months (10).

In a meta-analysis, telemedicine had no effect on improving the physical and psychological dimensions of quality of life in patients with heart failure (HF) but increased the overall quality of life (6). In addition, the positive effects of telephone support and telemonitoring on the quality of life of heart failure patients were identified in six studies (5).

Although there were a significant number of systematic reviews examining the effect of telerehabilitation on various outcomes in cardiac patients, most of them do not have the same primary studies despite having similar inclusion criteria. Therefore, researchers decided to use an overview to analyze and meta-analyze studies to find the effect of telerehabilitation on HRQOL of patients with CVDs.

Materials and Methods

Search method

A comprehensive and regular search was performed through the [MeSH] keywords (heart diseases or coronary disease or coronary artery disease or myocardial infarction or coronary artery bypass or heart failure and cardiac rehabilitation and telemedicine) by two reviewers until January 20, 2021 without language restrictions in the: Science Direct, Medline/PubMed, Web of Science, Scopus, ProQuest, Google Scholar, and Cochrane library. The reporting items were used for Preferred Reporting Items for Systematic Reviews and Meta-Analyses-PRISMA (11) and a comprehensive evidence map of an overview of systematic reviews (12, 13) to perform the present review.

Eligible criteria

All articles that were conducted on people over 18 years of age were selected. Studies implemented in non-CVDs were excluded; those combining cancer with non-CVDs diseases were excluded. Eligible interventions were virtual cardiac rehabilitation programs. Eligible cardiac rehabilitation program interventions had to have been offered via “telemedicine”. Comparators were routine, standard, and non-virtual cardiac rehabilitation programs Outcomes included HRQOL. The studies as systematic review or meta-analysis were eligible.

Selection procedure

The search and screening process was performed by two reviewers. In case of contradiction in the results of each screening stage, the views of the third person or discussion were used to achieve the result. Finally, after evaluating the quality, 20 reviews that reported HRQOL entered the analysis (5, 6, 14–31) (Fig. 1).

Fig. 1:

Fig. 1:

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PRISMA Flow Diagram

Quality of included reviews

To assess the risk of bias in systematic reviews, ROBIS-Risk of Bias in Systematic Reviews was reviewed. This tool examines the risk of bias in systematic reviews in four key areas:1) criteria for qualifying study, 2) identifying and selecting studies, 3) evaluating and collecting data, 4) synthesis, and findings. For each question in each domain, information about possible systematic review constraints is provided, which leads to the judgments about concerns in that domain with criteria low, high, or indefinite. Evaluators in the final decision report the risk of bias in general, with signaling questions and supportive information on the low, high, or uncertain risk of bias (32). Two authors independently evaluated the quality of systematic reviews and agreed in case of the dispute through discussion (Fig. 2). Review manager 5.3 was used to draw the risk of bias summary and risk of bias graph.

Fig. 2:

Fig. 2:

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Review authors' judgements about each risk of bias item presented as percentages across all included studies, a-Risk of bias summary, b-Risk of bias graph

Quality of included studies within reviews

The quality of the initial studies was investigated with the CONSORT checklist, which includes 25 items to evaluate six sections of title, abstract, introduction, cases and methods, results and discussion, and other information. Each of the articles gained number one in case of pointing to the items in the checklist, and number zero in case of non-pointing. The highest and lowest score that each article could gain was 37 to zero. Finally, 51 studies with appropriate quality were analyzed (33).

Analyses

Overall HRQOL is the sum of mental, and physical dimensions. If the results were reported in a study with two questionnaires, their results were used for comparison. If a study reported the average of the components of each dimension of a questionnaire separately, first the average of each dimension was determined and then the overall HRQOL was obtained by summing the dimensions of each questionnaire. The meta-analysis was performed separately for each dimension of HRQOL. The Q statistic, the I2 index, and the standardized mean difference-SMD were used to evaluate the heterogeneity of the studies, and the heterogeneity was evaluated with the Q statistic (34). I2 index was used due to its accuracy and the amount less than 50% indicates less variance between studies and a fixed-effect model was used. Otherwise, the I–V heterogeneity method was used (35). According to the different questionnaires, SMD effect size was used to aggregate using hedges g (36). Cohen's thresholds were used to interpret the effect size (37). Analyses were performed with review manager 5.3 and comprehensive meta-analysis software.

Results

Study Characteristics

Of 20 selected reviews published, 51 initial studies that measured HRQOL were selected. Details of the initial studies are listed in Table 1. A total of 12,449 people participated in 51 studies published between 2000 and 2021, of which 7,948 were men. The intervention consisted of 6,544 people with mean age 8.1±66.11 and control group consisted of 5905 people with average age 8.1±66.81. The men were 4,293 in the intervention group and 3,655 in the control group. Most of the studies 41.4% were conducted in the USA. In 41(74.5%) studies, participants had HF or CHF. In 51 studies, 58 tools were used. Most instruments that measured HRQOL were MLHFQ 22 (37.9%), SF-36 15 (25.9%), EQ-5D 8 (13.8%), SF-12 6 (10.3%), KCCQ2 (3.4%). Mac-new 2 (3.4%), GHQ1 (1.7%), QOL Heart disease1 (1.7%). QOL Darthmouth1 (1.7%).

Table 1:

Basic characteristics of the included studies in the meta-analysis

First author, year Country Intervention Control follow up (mounts) Intervention (n) Control (n) Disease Type of study Male Control (n) Male Intervention Questionnaire
Ades, 2000 (38) USA Home based monitoring UC 3 83 50 CHD No RCT 45 63 SF-36
Angermann, 2012(39) Germany Heart net care-HNC UC 6 352 363 HF RC 257 248 SF-36
Antonicelli, 2008(40) Italy Home telemonitoring Standard 12 28 29 CHF RCT 19 16 SF-36
Arthur, 2002(41) Canada Telephone monitoring UC 6 120 122 CABG RCT 96 101 SF-36
Artinian, 2003(42) USA Web-based monitoring UC 3 9 9 CHF No RCT - - MLHFQ
Barth, 2001(43) USA Telephone calls UC 2 17 17 CHF RCT 6 10 MLHFQ
Benatar, 2003(44) USA Nurse telemonitoring UC 12 108 108 HF RCT 41 39 MLHFQ
Blum, 2014(45) USA Telemonitoring UC 12 81 75 HF RCT 54 57 MLHFQ-SF-36
Blum, 2006(46) USA Home telemonitoring UC 12 64 51 HF RCT - - MLHFQ-SF-36
Boyne, 2013(47) Singapore Telemonitoring UC 3-6-12 197 185 HF RCT 111 115 EQ-5D
Copeland, 2010(48) USA Telephone support UC 12 220 238 CHF RCT - - SF-36
Dalal, 2007(49) UK Telephone support UC 9 60 44 MI RCT 35 49 Mac-new
Dar, 2009(50) UK Telemonitoring UC 6 91 91 HF RCT 59 62 MLHFQ-SF-36
de Lusignan, 2001(51) UK Home telemonitoring UC 12 20 20 CHF RCT GHQ
Delaney, 2013(52) USA Telemonitoring UC 3 46 47 HF RCT 14 14 MLHFQ
DeWalt, 2006(53) USA Telephone support UC 12 59 64 HF RCT 26 34 MLHFQ
Dunagan, 2005 (54) USA Telephone support UC 12 45 75 HF RCT 35 31 MLHFQ-SF-12
Ferrante, 2010(55) Argentina Telephone support UC 36 760 758 HF RCT 522 551 MLHFQ
Frederix, 2015(56) Belgium Telerehabilitation-SMS UC 6 69 70 HF RCT 55 59 Heart QOL
Goldberg, 2003(57) USA Telemonitoring Standard 6 138 142 HF RCT 93 96 MLHFQ-SF-12
Hagglund, 2015(58) Sweden tablet UC 3 32 40 HF RCT 18 12 KCCQ
Gesica, 2005(59) Argentina Telephone support UC 20 760 758 HF RCT 522 552 MLHFQ
Jerant, 2003(60) USA Telecare UC 2 13-12 12 HF RCT 6 6-5 MLHFQ
Johnston, 2016(7) Sweden Smartphone UC 6 86 80 MI RCT 63 71 EQ-5D
Kasper, 2002(61) USA Telephone support UC 6 102 98 HF RCT 55 66 MLHFQ
Koehler, 2011(62) Germany Telemonitoring UC 1-3-6-9-12-24 345 356 HF RCT 292 285 SF-36
Konstam, 2011(2) Island Home monitoring UC 1–3 44 44 HF RCT 30 26 MLHFQ
Körtke, 2005(63) Telemonitoring UC 6–12 100 70 SVD RCT 67 90 SF-36
LaFramboise, 2003(64) Omaba Telephone support UC 2 26-21-20 23 HF - 2 4-3-2 SF-36
Maddison, 2019(8) Newzeland Telerehabilitation-remote UC 3–6 82 80 HF RCT 70 69 EQ-5D
Madigan, 2013(65) USA Telemonitoring UC 6 54 45 HF RCT 18 24 KCCQ
Oerkild, 2011(66) Denmark Telephone support UC 12 36 39 CVD RCT 26 19 SF-12
Piotrowicz, 2010(67) Poland Home based telemonitoring Standard 2 75 56 HF RCT 53 64 SF-36
Piotrowicz, 2015(68) Poland Telerehabilitation Standard 2 75 56 HF RCT 53 64 SF-36
Piotrowicz, 2014(69) Poland Home based Telemonitoring Standard 2 75 32 HF RCT 31 64 SF-36
Ramachandran, 2007(70) India Telephone support UC 25 25 HF RCT 19 20 SF-36
Reid, 2012(71) Canada Internet based UC 6 115 108 CVD RCT 93 95 Macnew
Riegel, 2006(3) USA Telephone case management UC 1-3-6 69 65 HF RCT 33 29 EQ-5D
Schwarz, 2008(72) USA Telemonitoring UC 3 51 51 HF RCT 20 29 MLHFQ
Seto, 2012(10) USA Mobile phone Standard 6 50 50 HF RCT 38 41 MLHFQ
Sisk, 2006(73) USA Telephone support UC 12 203 203 HF RCT MLHFQ-SF-12
Smith, 2005(74) USA Telephone support Monitoring UC 1-6-12-18 356
354		 359 HF RCT 257 253
247		 SF-36
Stromberg, 2006(75) Sweden CD Ram Standard 6 82 72 HF RCT 55 54 EQ-5D
Tomita, 2008(76) USA Internet based UC 6 16 24 HF 9 4 MLHFQ
Varnfield, 2014(77) Australia Smartphone based UC 6 53 41 MI RCT 48 34 EQ-5D
Wade, 2011(78) USA Telemonitoring UC 164 152 HF RCT 81 84 SF-12
Wakefield, 2008(79) USA Telephone support UC 3–6 45–52 49 HF RCT 48 47–51 MLHFQ
Widmer, 2015(80) USA Digital health intervention UC 3 25 19 CVD 17 19 QOL Dartmouth
Woodend, 2008(81) Ontario Telephone support UC 1-3-12 62
62		 59
66		 CVD RCT 41
52		 46
48		 MLHFQ
Wootton, 2009(82) UK Telephone support UC 12 214 195 CHF RCT 127 154 EQ-5D-SF-12
Zan, 2015(9) USA Tele-Web based UC 3 21 20 HF - 14 15 MLHFQ
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Overall QoL

Analysis of 47 studies without considering moderator analyses did not show a significant effect of telemedicine compared to normal care on overall HRQOL in CVD patients (SMD:0.02, 95% CI: −0.03, 0.06, P=0.42, I2=49%) (Fig. 3).

Fig. 3:

Fig. 3:

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Impact of telemedicine on overall HRQOL VS usual care

Mental QoL

Analysis of 35 studies without considering moderator analyses did not show a significant effect of telemedicine compared to usual care on overall mental in CVD patients (SMD: −0.05, 95% CI: −0.17,0.08, I2=87%) (Figs. 4, 5). After the removal of two studies, Barth and Copeland (43, 48), heterogeneity decreased, but the effect of telemedicine overall mental was not significant compared to usual care (SMD: −0.05, 95% CI: −0.07,0.09, I2=30.3%).

Fig. 4:

Fig. 4:

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Impact of telemedicine on overall mental vs usual care

Fig. 5:

Fig. 5:

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Impact of telemedicine on overall physical vs usual care

Physical QoL

Analysis of 32 studies without considering moderator analyses showed a significant effect of telemedicine compared to usual care on overall physical in CVD patients (SMD: 0.08, 95% CI: 0.01, 0.16, I2=60 %) (Fig. 4). Analysis of overall physical by separating the type of questionnaire showed that telemedicine showed a significant effect only on the aggregation of 4 studies that used the SF-12 questionnaire (SMD: 0.19, 95% CI:0.06, 0.33, I2=0%).

Moderator analyses

The results of moderator analyses show that there was a significant positive effect on overall HRQOL compared to usual care in 14 studies with follow up 12 months or more (SMD: 0.045, df=13 P=0.02). Analysis of the type of intervention showed that m-health had a negative and significant effect on overall HRQOL compared to usual care (SMD: −0.33, df=1, P=0.01). Moderator analyses showed that telemedicine with follow up (18 months or more) had a significant positive effect on overall physical compared to usual care (SMD: 0.13, df=1, P=0.002) while with follow up less than 18, (SMD: 0.08, df=29, P=0.08), this effect was not significant. Overall physical analysis based on telemedicine type showed that telephone support has a significant positive effect on overall physical compared to usual care (SMD: 0.16, df=12, P=0.04) while in Net care interventions (SMD: 0.09, df=3, P=0.11), telemonitoring (SMD: 0.041, df=13, P=0.22), it was not significant compared to usual care.

Meta-regression results

Effects of mean age on the effect size of telemedicine on overall mental includes 34 studies (β= −0.09, P =0.007). Therefore, it can be concluded that by increasing one unit of the average age of participants, the effect size of overall mental reduces in 34 studies (Fig. 6).

Fig. 6:

Fig. 6:

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Meta-regression bubble plot for the effect of age on mental

Publication bias assessment

Publication bias was examined with Egger test (overall HRQOL P=0.24, overall mental P=0.72, overall physical P=0.25). Moreover, graphical funnel plots were symmetrical in most zones and did not show bias (Fig. 7).

Fig. 7:

Fig. 7:

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Funnel plot for the estimation of publication bias. Physical and mental and overall HRQOL

Discussion

The findings revealed that telemedicine has a positive and significant effect compared to usual care in promoting physical dimension, while it does not have a significant effect on promoting overall HRQOL and mental. The previous 20 reviews reported HRQOL, of which 14 reviews reported that the effect of technology, including telemedicine, was significantly effective and positive compared to usual care on HRQOL (5, 6, 14, 16, 21, 23, 27, 28, 30, 31, 83, 84).

In this study, telemedicine had a positive effect on the physical dimension; on the other hand, increasing the duration of intervention and telephone support has increased the effect of the intervention on the physical dimension. A systematic review also showed that telephone support structure had a positive and significant effect on physical dimension and HRQOL overall (84). Telehome monitoring significantly reduced the number of hospital readmissions for patients with angina and improved quality of life and physical functioning in patients with heart failure or angina (81). It seems that the effect of telephone support on the physical dimension may be due to the continuous support provided in it, which allows early detection of complications or progress of the disease.

Moderator analyses showed that increasing duration of implementation 12 months or more, the positive effect of telemedicine on overall HRQOL is determined. By increasing the duration of implementation 18 months and more than 18 months, the positive effect of telemedicine on the physical dimension is determined. A systematic review showed that telemedicine did not have a significant effect on physical and mental health, but it significantly affects overall HRQOL. Telemedicine interventions after 52 weeks of follow-up had a greater effect on HRQOL. This effect over a long period of time could be related to more support that has created (6). While it was found that telerehabilitation did not have a significant effect on HRQOL patients 24 weeks after the intervention (8). Additional education through a computer-based program for 6 months had no significant effect on the promotion of psychological problems, but it had a positive and significant effect on overall HRQOL and the physical dimension. According to their study, factors such as gender and age of patients and cardiac condition of patients are among the factors affecting the results of the study (75). The difference in the accuracy of the tools could be a factor in the lack of effect of telemedicine on quality of life dimensions (29). It seems that the HRQOL study tool to be influential in the results of the study. The effects of home telemonitoring on SF-36 vitality subscale was significant (one month after intervention P=0.022, three months later P=0.017, and one year later P=0.009) (81). However, in Wakefield's study, this effect was not significant throughout the study (79).

In this study, meta-regression results showed that there was a significant and very strong negative relationship between overall mental and people's age. One of the most important hypotheses in this regard is technophobia in elderly patients. Older people may have used the internet or technology or a smartphone for fewer years, and this issue leads to fear and distrust of telemedicine. Therefore, the trust of older patients in online and remote counseling to improve and enhance mental conditions is not acceptable and does not implement (85). In another hypothesis, it seems that isolation and less expression of emotions are more in the aging process and this issue prevents older people to receive appropriate counseling to promote their mental dimension. On the other hand, teaching people how to use technology at older ages is less and they do not implement what they are asked to do alone and without dependence (31).

Conclusion

Among the types of telemedicine methods, telephone support has a greater effect on promoting the physical dimension. Telemedicine can provide close monitoring on the status of cardiac patients. It is effective in improving physiological conditions, but better planning is important based on the age of the patients to improve the mental status of patients. One of the limitations of the study was that in the initial studies, the living place of the people in terms of geography, rural and urban and the level of access to advanced care was not specified. This mediator seems to play an important role in changing outcomes because the main purpose of telerehabilitation is to provide treatment care in areas with low access levels. Therefore, it is recommended that initial studies report these cases when recording data.

Data Availability

The data used to support the findings of this study are included within the study and supplementary file.

Journalism Ethics considerations

Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors.

Acknowledgements

Semnan University of Medical Sciences supported this study financially (Code: IR.SEMUMS.REC.1398.042).

Abbreviation words and full phrases

CVD

Cardio Vascular Disease

CR

Cardiac Rehabilitation

CHD

Coronary Heart Disease

HF

Heart Failure

CABG

Coronary Artery Bypass Surgery

MI

Myocardial Infarction

CHF

Congestive Heart Failure

HRQL

Health-Related Quality of Life

MLHFQ

Minnesota Living with Heart failure Questionnaire

SF-36

36-Item Short Form Health Survey

SF-12

12-Item Short From Survey

EQ-5D

Euro Quality of Life Five-Dimensional

KCCQ

Kansas City Cardiomyopathy Questionnaire

GHQ

General Health Questionnaire

AMSTAR

Measurement Tool to Assess Reviews

ROBIS

Risk of Bias in Systematic Reviews

RCT

Randomized Clinical Trial

PRISMA

Preferred Reporting items for Systematic Reviews and Meta-Analyses

MeSH

Medical Subject Headings

CONSORT

Consolidated Standards of Reporting Trials

PICO

Population, Intervention, Comparison, and Outcome

SMD

Standardized Mean Difference

ES

Effect Size

CIs

Confidence Intervals

CMA

Comprehensive Meta-Analysis Software

Mhealth

Mobile health

MWBT

Mobile Web-Based Telemonitoring

MPBT

Mobile Phone-Based Telemonitoring

Footnotes

Conflicts of interest

The authors declare that they have no conflicts of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data used to support the findings of this study are included within the study and supplementary file.

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