Effect of Remote Health Interventions on Blood Pressure Control and Quality of Life for Hypertension Self-management: A systematic review and meta-analysis

Evaluation of Internet-based interventions

Article information

Res Community Public Health Nurs. 2025;36(1):150-164

Publication date (electronic) : 2025 March 31

doi : https://doi.org/10.12799/rcphn.2024.00570

YingMei Yuan ¹

, MeiLing Song^,²

¹Assistant Professor, College of Nursing, Dali University, Yunnan, China

²Assistant Professor, College of Nursing, Daegu Health College, Daegu, Korea

Corresponding author: MeiLing Song College of Nursing, Daegu Health College, 15, Yeongsong-ro, Buk-gu, Daegu 41453, Korea Tel: +82-53-250-1464 Fax: +82-53-320-1470 E-mail: spring830128@dhc.ac.kr

Received 2024 May 21; Revised 2025 February 23; Accepted 2025 February 24.

Abstract

Objective

To evaluate the effect of remote health interventions on self-management of hypertension.

Methods

We systematically searched the literature for studies published in English in PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Central Register of Controlled Trials. The database was used to search for relevant studies with full text and evaluate the remote health interventions for hypertension self-management versus usual care for hypertension. RevMan 5.4 was used for data analysis.

Results

A total of 19 studies eventually met our inclusion criteria. The results showed that the remote health interventions group could significantly reduce the levels of SBP (MD=5.67, 95% CI=4.12-7.22, p<.001) and DBP (MD=1.88, 95% CI=1.16- 2.60, p<.001), compared with usual care group, it also significantly improving the patient's quality of life (SMD=0.84, 95% CI=0.32- 1.37, p=.002), reduce waist circumference (MD=2.39, 95% CI=0.35-4.44, p=.020) and BMI (MD=0.49, 95% CI=0.06-0.91, p=.020), and significantly increasing the physical activity of patients (SMD=0.19, 95% CI=0.06- 0.31, p=.004). No obvious publication bias was found in this meta-analysis.

Conclusion

This study showed that remote health interventions for self-management can significantly improve patients’ quality of life with hypertension and better BP control than usual care. Further studies could be assess the long-term clinical effectiveness and economic evaluation of remote health interventions for self-management.

Keywords: Health; Self-Management; Hypertension; Meta-Analysis

Introduction

Hypertension is one of the main risk factors for cardiovascular disease, and cardiovascular disease is the main cause of death caused by non-communicable diseases [1]. With the rapid development of the social economy, the acceleration of population aging, and the change in traditional eating habits and lifestyle, the incidence rate of hypertension worldwide has increased significantly [1]. At present, it is estimated that more than 1.5 billion people worldwide suffer from hypertension [2]. Although the treatment strategy for hypertension has been improved in recent decades, the research shows that the treatment rate and control rate of hypertension are not ideal, only 41.4% and 13.8%, respectively [3].

Reducing the blood pressure (BP) level of patients can reduce the incidence of stroke[4] and cardiovascular events [5], significantly improve the quality of life of patients and effectively reduce the burden of diseases [6]. Research shows that the most effective way to prevent and treat hypertension is community management [7,8]. In 2014, the United States formulated the clinical practice guide for community hypertension management in the United States to guide the practice of grass-roots community doctors on hypertension management, and the management effect is obvious [9]. Canada's community health service system is also very mature; with the cooperation of family doctors, nursing doctors, and other professionals as the model, a special fund has been established to support and improve the quality of health services, which has played a great role in the community management of hypertension [10].

Self-management is derived from the field of psycho-behavioral therapy, and it is gradually recognized in practice that it is conducive to disease control [11,12]. Self-management can include a wide range of behaviors, such as the ability of individuals to manage physical, psychological, and lifestyle behaviors related to their chronic diseases and the appropriate use of health care [13]. Self-management of hypertension, including self-titration and behavioral intervention, has been proven to be effective [13]. In addition, self-management of hypertension can include focusing on improving adherence to diet, weight loss, increasing physical activity, quitting smoking, and moderate drinking [14].

In recent years, with the continuous development and popularization of the Internet and mobile communication networks, information technology is more and more applied to blood pressure management [15-17]. Medical staff can use several methods, including Internet and mobile communication network platforms, telephone follow-up, short message service (SMS), and network education, to carry out remote health interventions for patients with hypertension. The research shows that good results have been achieved [18-20].

Several meta-analyses were also conducted to analyze the effects based on these individual studies [21-24]. However, to our knowledge, no meta-analysis has focused on the effectiveness of remote health interventions in integrated self-management factors, such as weight control and lifestyle behavior changes (salt intake, fruit and vegetable intake, high-fat and high-sugar food intake, and physical activity). Thus, it is necessary to identify the effect of remote health interventions on integrated self-management factors and blood pressure. At present, many studies have compared remote health interventions with usual care to evaluate their effect [25-27]. This study conducted a meta-analysis of the relevant randomized controlled trials (RCTs) to evaluate the impact of remote health interventions on blood pressure control, quality of life, and changes in weight and behavioral change in patients with hypertension.

Methods

Study design

This study was a systematic review and meta-analysis conducted to integrate and analyze the results of studies that conducted remote health interventions for patients with hypertension.

Criteria of literature selection

This study was conducted following the Cochrane Association’s Handbook of Systematic Reviews [28] and the reporting guidelines for systematic reviews proposed by the Preferred Reporting Items for systematic reviews and Meta-analysis groups [29]. To select literature, we selected key questions that reviewed the effect of the remote health interventions on the self-management of patients with hypertension and then conducted an electronic database search according to selection and exclusion criteria.

1. Inclusion criteria

For inclusion in our research, the studies were required to meet the following criteria:

1) Participants: Patients with hypertension [diastolic BP (DBP) ≥90 mmHg or systolic BP (SBP) ≥140 mmHg.]

2) Intervention: The targeted interventions were remote health interventions, which were checking and guiding patients' self-management for their hypertension through the remote health interventions. The type of remote health interventions included a website, email, application, short message service (SMS), and telephone calls. The telephone calls were conducted automatically or by trained researchers. The automated calls were maintained on a server and interfaced with local telephone systems via session initiation protocol lines and voice-over or an automated modern device that was connected to the sphygmomanometer and plugged into a normal telephone socket like an answerphone.

3) Comparisons: Hypertension patients who received usual care.

4) Outcomes: The primary outcomes were SBP, DBP, and quality of life. The secondary outcomes were weight, Body Mass Index (BMI), and waist circumstance.

5) Type of study design: Randomized controlled trials (RCTs).

2. Exclusion criteria

The exclusion criteria the reviewers agreed upon were as follows:

1) The outcomes of interest were not reported or impossible to use.

2) Review, abstract, duplicate publication.

Literature Search and selection

1. Literature search

A systematic search database of PubMed, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), and Cochrane Central Register of Controlled Trials was systematically searched and searched for the literature published before July 2023. In this study, additional hand searches were not conducted. The search was performed by a combination of the following search terms using the Boolean operators “OR” and/or “AND”: the retrieval formula in English for the subject is "blood pressure" OR "hypertension," and the Chinese retrieval formula is "血压" OR "高血压." The retrieval formula for intervention methods in English is ("Internet" OR "network-based") AND "self-management," and the Chinese retrieval formula is ("互联网" OR "基于网络") AND "自我管理."

2. Study selection

Two researchers independently reviewed all studies included in the analysis and reviewed the studies according to inclusion and exclusion criteria. If there were any discrepancies during the selection process, a third reviewer was requested to ask for their opinions to make the decision.

During the review process, duplicate literature was first removed from the literature retrieved through the database, and the title and abstract were reviewed to confirm whether the study met the literature inclusion criteria. If it was difficult to determine which studies met the inclusion or exclusion criteria based on the title or abstract alone, the full text was referred to decide whether to select the relevant literature. Finally, information was extracted from selected studies for baseline characteristics such as the first author’s name, age and country, intervention time, intervention mode, and the number of patients in each group.

3. Quality Assessment

For assessing the quality of each eligible article, the revised Cochrane Risk of Bias Tool (ROB2) [30] was used, which was one of the most useful scales for evaluating the quality of randomized studies. ROB2 is structured into a fixed set of bias domains, focusing on different aspects of trial design, conduct, and reporting. Within each domain, a series of questions aim to elicit information about features of the trial that are relevant to the risk of bias. An algorithm based on answers to the signaling questions generates a proposed judgment about the risk of bias arising from each domain. Judgment can be a 'Low' or 'High' risk of bias or can express 'Some concerns'.

Statistical analysis

Meta-analysis was performed using Review Manager 5.4, which Cochrane provided. Continuous variables were evaluated by the mean difference (MD) or standard mean difference (SMD), and dichotomous variables were evaluated by the relative risk (RR). For the Continuous variables, MD was used for analysis when measurements were unified, and when measurements were non-unified, the SMD was used for analysis [31,32]. The variables of quality of life and physical activity were evaluated by SMD, while the variables of SBP, DBP, weight, waist circumference, BMI, and fruit and vegetable intake were evaluated by MD. The heterogeneity of the data was assessed using I² values. Previous research reported that if I²≤50%, the fixed effect model was used for meta-analysis [32]; however, considering the small number of studies (not more than five), the random effect model was used for meta-analysis [33-35]. The causes of heterogeneity were further assessed using a sensitivity analysis in which the sequential omission of individual studies was performed to analyze the influence of a single study on the overall detection rate. Sensitivity analysis was conducted only for SBP and DBP based on the evidence that there were more than 10 meaningful papers for subgroup analysis [36,37]. If more than 10 studies were included in the meta-analysis, the data were evaluated for publication bias by viewing the symmetry of the funnel plot and using the Egger test.

Ethical considerations

This study was approved by the institutional review board of Dali University in China (IRB No. MECDU-201803-20).

Results

Search Process

A total of 1489 studies were identified by the screening electronic search strategy. After the removal of duplicate files, 1432 articles were screened to determine whether they were eligible. After the screening based on the titles and abstracts, 1264 articles were excluded. After careful reading of the full text, 168 studies were excluded because of the study design and insufficient data presented. Thus, 19 studies met the criteria for inclusion in the present meta-analysis [16,27,36-53]. Figure 1 shows the details of our literature search and selection process.

Figure 1.

Flow Diagram of Study Selection Process

Characteristics of Included Studies

The basic features included in the study are shown in Table 1. In total, 5735 patients were included, of which 2665 were in the intervention group and 3070 were in the control group. The intervention time was extended from 6 weeks to 12 months. The mode of the intervention included a website, email, application, short message service (SMS), and telephone calls. Among the studies, all studies were published in the English language and came from 9 different countries and regions.

Table 1.

Characteristics of Studies included in the Systematic Review and Meta-Analysis

Results of Quality Assessment

The methodological quality was assessed with the Cochrane bias risk assessment tool. Among the 19 articles, for risk of bias arising from random processes, 18 (94.7%) of the studies were at “low risk of bias,” and one study showed “unclear risk of bias.” In the risk of bias due to allocation concealment, 17 (89.5%) of the studies were at “low risk of bias,” and 2 (10.5%) of the studies showed “unclear risk of bias.” In risk of bias due to blinding of participants and personnel, outcome assessment, and risk of bias by other sources, 16 (84.2%) of the studies were at “low risk of bias,” and 3 (15.8%) of the studies showed “unclear risk of bias.” There was no “high risk of bias” reported in the list above. In risk of bias due to incomplete outcome data, 14 (73.7%) of the studies were at “low risk of bias,” 4 (21.1%) of the studies were at “unclear risk of bias,” and 1 (5.3%) study reported “high risk of bias.” In risk of bias due to selective reporting, 10 (52.6%) of the studies were at “low risk of bias,” 7 (36.8%) of the studies were at “unclear risk of bias,” and 2 (10.5%) of the studies were at “high risk of bias” (Figure 2). A summary of the risk of bias assessment for each study is shown in Figure 3.

Figure 2.

Quality Assessment of included Studies: Low Risk (green), Unclear (yellow), and High Risk (red)

Figure 3.

Graph of the Risk of Bias Summary

The Effectiveness of Remote Health Interventions

The effectiveness of remote health interventions was evaluated by dividing it into primary and secondary outcomes. The primary outcomes were SBP, DBP, and quality of life indicators. Secondary outcomes included weight control and lifestyle behavior changes, such as salt intake, fruits and vegetables intake, high-fat and high-sugar foods intake, and physical activity.

1. Primary outcomes

SBP was reported in all studies except one. Because significant heterogeneity was found among the included studies (I²=93%, p<.001), the random effects model was used to evaluate the SBP reduction. The pooled results showed that the test group had a higher SBP reduction than the control group (MD=5.67, 95% CI=4.12-7.22, p<.001) (Figure 4).

Figure 4.

Forest Plot Evaluating the Outcomes of Systolic Blood Pressure(SBP) Reduction

A total of seventeen studies reported DBP. The pooled studies were heterogeneous (I²=86%, p<.001); thus, a random effects model was conducted. The pooled analysis showed that the test group had a better improvement in DBP reduction than the control group (MD=1.88, 95% CI=1.16-2.60, p<.001) (Figure 6).

Figure 6.

Forest Plot Evaluating the Outcomes of Diastolic Blood Pressure(DBP) Reduction

Further sensitivity analysis was performed to determine potential sources of heterogeneity and demonstrated that the overall statistical significance did not change when any of the studies were omitted, indicating that the results are relatively stable (Figure 5, 7).

Figure 5.

Meta-Analysis Estimates, Given Name of the Study is Omitted (For Systolic Blood Pressure)

Figure 7.

Meta-analysis Estimates, Given Name of the Study is Omitted (For Diastolic Blood Pressure)

In the evaluation of the quality-of-life improvement between the intervention group and control group, four articles were included. The quality of life was measured using the standardized EuroQol-5 dimensions-5-level (EQ-5D-5L) questionnaire, which was developed for measuring five dimensions (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression) [52]. The pooled studies showed significant heterogeneity among studies (I²=98%, p<.001); thus, a random effect model was used for analysis. The forest plot showed that the test group had a better improvement in quality of life than the control group (SMD=0.84, 95% CI=0.32-1.37, p=.002) (Figure 8).

Figure 8.

Forest Plot Evaluating the Outcomes of Quality of Life Improvement

2. Secondary outcomes

In terms of weight control, 3 indexes involving weight, waist circumference, and body mass index (BMI) contributed to the analysis. Five studies reported weight, and the pooled studies were homogeneous (I²=0%, p=.820), however, because of the small number of studies, a random effect model was used for analysis [33-35]. There was no significant weight reduction between the test group and control group (MD=0.99, 95% CI=–0.18-2.16, p=.100) (Figure 9A), Four studies had data on waist circumference, and the pooled studies were heterogeneous (I²=63%, p=.040). Thus, a random effect model was used for analysis, and the test group had a significant waist circumference reduction than the control group (MD=2.39, 95% CI=0.35-4.44, p=.020) (Figure 9B). Four studies assessed the BMI difference between the two groups, and the pooled studies were homogeneous (I²=0%, p=.830), however, because of the small number of studies, a random effect model was used for analysis[33-35], and the test group showed a greater BMI reduction than the control group (MD=0.49, 95% CI=0.06-0.91, p=.020) (Figure 9C).

Figure 9.

Forest Plot Evaluating the Outcomes of Weight Control. (A) Weight, (B) Waist Circumference, (C)Body Mass Index (BMI)

Regarding lifestyle behavior changes, 4 indexes involving salt intake, fruit and vegetable intake, high-fat and high-sugar food intake, and physical activity were reported, while only one study reported salt intake and high-fat and high-sugar food intake, we just performed a meta-analysis on indexes of fruit and vegetable intake and physical activity. The pooled results are illustrated in Table 2. The pooled studies of fruit and vegetable intake were homogeneous (I²=0%, p=.950), however, because of the small number of studies, a random effect model was used for analysis [33-35], and there was no significant difference between the two groups regarding fruit and vegetable intake (MD=0.09, 95% CI=0.00-0.18, p=.060). Similarly, the pooled studies of physical activity were homogeneous (I²=8%, p=.340), however, a random effect model was used for analysis, and a significant difference in physical activity was found between the two groups (SMD=0.19, 95% CI=0.06-0.31, p=.004).

Table 2.

Meta-Analysis of Lifestyle Behavior Changes

Publication Bias

A funnel plot was performed to qualitatively evaluate the publication bias for SBP and DBP, the shape of the funnel plot showed some evidence of symmetry (Figure 10), and the Egger test was nonsignificant (SBP p=0.72; DBP p=.660), which indicated that no obvious publication bias existed in this meta-analysis.

Figure 10.

Funnel plot for Systolic Blood Pressure(SBP) and Diastolic Blood Pressure (DBP) reduction

Discussion

The innovative integration of traditional BP monitoring equipment and mobile Internet technologies such as "big data", "cloud computing", and "Internet of things" provides new models and tools for hypertension management, which aims to improve the self-management level of patients with hypertension, and the improvement of the self-management level of patients with hypertension is finally reflected in the control of blood pressure [54-56]. The purpose of this meta-analysis was to evaluate previous studies based on remote health interventions to support patients' self-management of interventions for hypertension and to evaluate the impact on BP control and reduction, quality of life, living habits, etc.

Our research showed that after several weeks to one year of intervention, compared with usual care, the remote health interventions provided decision support for the self-management of patients with hypertension. Such website-based education platforms can facilitate self-management by offering structured learning modules on hypertension control, and email-based communication can play a crucial role in maintaining regular follow-ups with patients and offering personalized feedback on blood pressure readings, medication adherence reminders, and lifestyle recommendations. The application supported self-management by integrating functionalities such as blood pressure tracking, combined with the smartphone system, and sending a self-care message to the patients. SMS and automatic telephone calls can serve as an efficient tool for delivering brief, targeted messages that reinforce self-care behaviors. By leveraging these diverse media channels, remote health interventions effectively empowered patients in self-managing their hypertension, ultimately leading to better control of blood pressure, improved quality of life, reduced waist circumference and BMI, and physical activities.

For remote health interventions to effectively reduce the BP level, this study found that most of the studies reported that remote health interventions were better than usual care in terms of BP control, which was consistent with the results of similar studies [21]. For the self-management-related factors, although we extracted just a small number of studies that evaluated the changes of the behavior factors, such as reduced waist circumference, BMI, and physical activity, however, it showed significant results. It was supported by meta-analysis studies for metabolic diseases, which also reported that remote health interventions could significantly improve exercise habits and body weight [57]. For improving the quality of life, although in our study we just extracted four studies to evaluate the effectiveness, it is also supported by many previous meta-analysis studies for chronic diseases such as pain, metabolic diseases, type 2 diabetes, inflammatory bowel disease, and so on [57-60].

This study found that the intervention effect of the remote health interventions was better than usual care in terms of BP control, quality of life, weight control, and physical activity. The reasons may be: First, the intervention mode of community hypertension self-management is based on remote health interventions are more novel than conventional self-management by integrating website, application, email, SMS, and automatic telephone calls that can continuously manage the patients, and can increase patient’s knowledge, and provide enhanced support according to patient’s different conditions and needs [61]. Second, remote health interventions can enhance communication and support. Such as communication using email, informing patients using SMS and automatic telephone calls was the carrier that facilitated communication between health managers and patients, ensuring that patients receive timely and personalized guidance, promoting the exchange of experience in self-management and disease control, and enhancing the confidence of patients in implementing self-management activities [36,38,41,43,48,49,52,62,63]. Third, remote health interventions utilize real-time data monitoring, allowing healthcare providers to track patients’ progress consistently. Monitoring combined with applications can track the levels of BP daily, medication intake reminders, and interactive health coaching [39,47]. Fourth, remote health interventions can enhance patients’ knowledge. Website-based education can provide structured and evidence-based learning resources, allowing patients to understand hypertension management comprehensively. Information provided by website education empowers patients with knowledge of lifestyle modifications, dietary recommendations, and self-monitoring techniques, fostering better adherence to treatment plans [38,46,50,63,64].

Moreover, today, with the rapid development of information technology, traditional medicine, and health care are gradually adopting innovative remote health interventions and formats. These interventions and formats utilize technologies, such as the internet, mobile internet, big data, and cloud computing [18,19,20,45,53,55,63]. By integrating these technologies with medical business management, remote chronic disease management networks are being established. This enables the creation of an interactive doctor-patient information service platform [37,42,53]. Additionally, research is being conducted to develop quality assurance standards for remote chronic disease self-management services. Furthermore, an innovative chronic disease management business is emerging through mobile Internet-based remote health services. This approach aims to serve by offering both online and offline integrated continuous healthcare services [52,55]. Ultimately, it fosters a new model of individualized chronic disease health management, centered on community residents. Thus, in the future, it will be essential to assess the effectiveness of advanced remote health technologies that enhance self-management interventions. Moreover, verifying their efficacy using various indicators will be crucial for ensuring their reliability and impact [65].

There were some limitations to this review. First, the longest intervention time of each study was one year. Therefore, we failed to further evaluate the impact of the remote health interventions on long-term outcome indicators of patients with hypertension, such as cardiovascular events. Second, whether the remote health interventions for hypertension can be popularized depends on the evaluation of health economics. Some literature lacks outcome indicators such as medical expenses and fails to conduct a cost-benefit evaluation, which weakens the evidence strength of this system evaluation. Third, in this study, the trials included in the meta-analysis of quality of life, waist circumference, and BMI, physical activities are in a limited count (not over 10 trials), so it needs more trials that investigate the effect on these variables and also the meta-analysis results in this study cannot firmly generate. Fourth, in this study, when selecting the literature for review, we searched for a database that is easy to access. We have not included the grey literature and literature from some of the databases like CINAH; thus, it has the limitation that has not considered the publication bias thoroughly. Fifth, before conducting this study, we had not completed the protocol registration process, so some biases may not have been verified, and it needs to be considered when interpreting the results.

Conclusions

In conclusion, the effect of remote health interventions on hypertension self-management was better than usual care, which can effectively reduce patients' BP, improve quality of life, control weight, and increase physical activity. We believe that under the impetus of remote health interventions integration of tradition and innovation, the innovative service of cardiovascular disease management based on remote health will have greater room for development. Although the long-term effectiveness and economic evaluation of the remote health interventions were still uncertain, the remote health interventions may be beneficial to patients based on the current research results.

Notes

Conflict of interest

No conflict of interest has been declared by all authors.

Funding

This work has supported by the Department of Science and Technology, Yunnan, China(No. 2018FH001-078).

Authors’ contributions

YingMei Yuan contributed to conceptualization, data curation, formal analysis, funding acquisition, methodology, project administration, visualization, writing - original draft, investigation, resources, software, and validation. MeiLing Song contributed to data curation, methodology, writing - review & editing, investigation, resources, software, supervision, and validation.

Data availability

Please contact the corresponding author for data availability.

Acknowledgments

None.

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Study	Country	Study	Groups		No. of patients		Age^†		Mode of intervention	Intervention time
Study	Country	design	Intervention	Control	Intervention	Control	Intervention	Control	Mode of intervention	Intervention time
Carrasco 2008	Spain	RCT	Short-messages-based interaction with telemedicine-based system	Routine visits	131	142	62.10±11.90	62.80±12.50	SMS	6 months
Green 2008	US	RCT	Home BP monitoring and secure Web services training	Usual care	258	261	58.60±8.50	59.30±8.60	Website	12 months
Park 2009	South Korea	RCT	Health education by internet server system	Usual treatment	28	21	53.20±6.90	54.60±11.10	Website	8 weeks
Yoo 2009	South Korea	RCT	Ubiquitous Chronic Disease Care system using cellular phones and the internet	Usual treatment	57	54	57.00±9.10	59.40±8.40	Website	3 months
McManus 2010	UK	RCT	Telemonitoring of home BP	Usual care	234	246	66.60±8.80	66.20±8.80	Website/calls	6, 12 months
Logan 2012	Canada	RCT	Telemonitoring Self-Care Support System	Usual care	55	55	62.70±7.80	63.1±9.00	Application	12 months
Nolan 2012	Canada	RCT	Internet-based strategic transdisciplinary approach to risk reduction and treatment	Usual care	97	227	55.7 (54.30-57.00)	56.7 (55.70-57.70)	Email	4 months
Park 2012	South Korea	RCT	Recommendations on diet and exercise by SMS and internet	Usual treatment	34	33	55.80±5.70	57.60±5.50	SMS/website	12 weeks
Piette 2012	US	RCT	Automated monitoring and behavior change telephone calls sent from a server	Usual care	89	92	58.00±1.30	57.10±1.10	Calls	6 weeks
Ralston 2014	US	RCT	Home BP monitor and web-based pharmacist care	Usual care	186	197	59.80±8.60	59.80±8.30	Website	12 months
Kim 2016	US	RCT	Web-based disease management program and app for monitoring and education	Standard management	52	43	57.50±8.60	57.70±8.70	Website/application	6 months
Milani 2017	US	RCT	Home-based digital-medicine BP program	Usual care	156	400	68.00±10.00	68.00±10.00	Website	3 months
Rubinstein 2016	Argentina	RCT	Mobile phone-based health interventions	Usual care	316	321	43.60±8.40	43.20±8.40	SMS/calls	6, 12 months
Nolan 2018	US	RCT	E-Counseling used multimedia and interactive tools for self-care	Usual care	100	97	57.2 (56, 59)	58.00 (56, 60)	Email	4, 12 months
Jahan 2020	Bangladesh	RCT	SMS text messaging and health education	Usual care	209	211	46.40±8.30	47.80±8.60	SMS	5 months
Kao 2019	Taiwan	RCT	Web-based self-titration program	Usual care	111	111	62.07±9.77	63.40±8.80	Website	3, 6 months
Lisón 2020	Spain	RCT	Self-administered online intervention program	Usual care	55	50	54.90±8.30	51.40±9.30	Website	3 months
Zhai 2020	China	RCT	SMS text messaging and pharmacy student–led consultations	Usual care	192	192	68.50±7.90	69.40±9.70	SMS	3 months
McManus 2021	UK	RCT	Home and online management and evaluation of BP	Usual care	305	317	65.20±10.30	66.70±10.20	Email	6，12 months

Outcome	No. of studies	Effects model	MD or SMD (95% CI)	p	I²
Salt intake	1^†
Fruits and vegetables intake	2	Random	0.09 (0.00, 0.18)	.060	0
High-fat and high-sugar foods intake	1^†
Physical activity	3	Random	0.19 (0.06, 0.31)	.004	8