This review narratively synthesized the outcome of medication adherence and self-management behavior ( Multimedia Appendix 2 ). A total of 7 articles reported statistically significant improvement in medication adherence in intervention groups [ 32 , 34 , 39 , 41 , 49 - 51 ]. Five studies suggested that mHealth interventions improved medication adherence, despite nonsignificant outcomes [ 33 , 35 , 37 , 41 , 44 ]. Morisky Medication Adherence Scale was used in 6 studies [ 35 , 37 , 39 , 42 , 50 , 51 ]. As a result, Bove et al [ 33 ] reported that there is no association between medication adherence and BP control, that is, an improvement in adherence did not necessarily lead to better BP control. Of the 9 articles that focused on the behavioral change of self-management, all reported positive effects either through physical activities or through a healthier diet. Adverse events reported in studies, such as medication side-effect and cardiovascular event, were unrelated to self-management and were evenly distributed across intervention and control groups. An exception to this was McKinstry et al [ 37 ], who found 3 patients became anxious as a result of self-monitoring. Of which, 6 studies conducted qualitative research about satisfaction related to the intervention [ 31 , 32 , 35 , 37 , 47 , 50 ]. All showed high levels of satisfaction. Patients and physicians were keen continuing to practice mHealth.
A total of 6 articles measured the cost of mHealth ( Multimedia Appendix 2 ) [ 37 , 42 , 44 , 46 , 52 ]. In cost-saving analyses, 2 reported that the cost of mHealth interventions was higher than control [ 37 , 42 ]. Two studies found the cost of mHealth interventions was lower than that of control [ 44 , 47 ]. The measurements of expenditure varied between study settings. The main cost was from monitoring, mobile phone use, connection charges, and cost of nurse support. However, Davidson et al [ 44 ] adjusted the BP control effect into the cost analysis, which means that patients with controlled BP after receiving the experimental treatment saved the extra cost of further treatment. This showed an overall health care cost saving of over US $20,000 between intervention and control groups.
The overall risk of bias was relatively high, because no study was absolutely free of bias. Eight articles were rated as low risk for selection bias [ 30 , 35 - 38 , 48 , 53 ]. Others were judged to be unclear as the procedure of the sequence generation was not classified [ 31 - 34 , 38 - 46 , 50 - 52 ]. A total of 13 articles did not describe the methods of random allocation [ 31 , 33 , 38 - 41 , 43 - 45 , 47 , 50 - 52 ]. The risk of detection bias was high in 4 articles [ 37 , 38 , 43 , 46 ], as these studies were unmasked to outcome assessors; 13 studies were rated as unclear, as there was an insufficient illustration of whether they blinded the outcome investigators [ 32 - 35 , 41 , 44 - 48 , 50 , 52 ]; 2 studies were double-blind trials in which participants were blinded to treatment conditions [ 36 , 53 ]. The control groups consisted of sending different messages compared with the intervention groups. Four articles reported no missing data from the baseline to the endpoint [ 47 , 49 , 51 , 52 ]. Low risk of attrition bias was found in 8 studies [ 43 , 44 , 46 , 47 , 49 , 51 - 53 ]. They reported that less than 5% of participants withdrew from the follow-up were analyses on an intention-to-treat basis for the missing data. Four studies had a high risk of attrition bias as the rate of dropout was over 15% in each study [ 30 , 33 , 40 , 48 ]. 92% (22/24) of the total studies were defined as low risk of reporting bias because all outcomes included in the protocol were reported in the results [ 30 - 39 , 41 - 43 , 45 - 53 ]. Missing pre-specified outcomes occurred in 2 articles resulting poor clarity in reporting bias [ 40 , 44 ]. In addition, funding bias was considered. Two articles mentioned that OMRON, which makes sphygmomanometers, including for home use, donated the BP device [ 31 , 49 ]. This was identified as a funding bias. Figure 5 describes the total risk of bias in the 24 studies. The funnel plot of the comparison of SBP and DBP did not show any extreme asymmetry and outliers, which suggests no significant publication bias.
Results of the risk of bias analysis.
According to the essential checklist of mHealth, a total of 16 items should be reported in the articles. The details of each item are demonstrated in Table 2 . An average of 55% (9/16) of the 16 items was mentioned in each study, from the lowest to the highest proportion, 25% (4/16) and 81% (13/16), respectively. Among the essential items, technology platform, intervention delivery, and intervention content were reported in all 24 articles. Only 6 articles introduced the availability of infrastructure, which can support technology operations in the study site [ 30 , 31 , 34 , 41 , 43 , 47 ]. The rate of reporting of cost assessment was also low (6/24, 25%). The usability of the content testing, communication, and the technical solution to meet the target population were described in 8 studies [ 35 , 38 , 39 , 41 , 44 , 45 , 47 , 50 ], as well as the reporting rate of user feedback [ 30 , 31 , 35 , 37 , 38 , 42 , 47 , 50 ].
Rate of reporting of each item in the Mobile Health Evidence Reporting and Assessment essential criteria checklist (N=24).
Items | Report rate, n (%) |
Infrastructure | 6 (25) |
Technology platform | 24 (100) |
Interoperability | 11 (46) |
Intervention delivery | 24 (100) |
Intervention content | 24 (100) |
Content testing | 8 (33) |
User feedback | 8 (33) |
Access of individual participants | 11 (46) |
Cost assessment | 6 (25) |
Adopting input | 11 (46) |
Limitation for delivery at scale | 13 (54) |
Contextual adaptability | 5 (23) |
Replicability | 18 (75) |
Data security | 12 (50) |
Compliance with guideline | 14 (58) |
Fidelity of the intervention | 10 (42) |
This systematic review found 24 RCTs with 8933 adult patients with hypertension, which met the criteria to assess the effectiveness of mHealth-enabled interventions in supporting self-management. According to this meta-analysis, mHealth interventions resulted in better BP control, with a significant decrease of SBP and DBP by 3.78 mm Hg and 2.19 mm Hg, respectively, compared with usual care. All 24 studies showed a greater decrease in mHealth intervention groups than the control groups. Findings of this review confirmed that self-management education through mHealth was effective in increasing patients’ knowledge of hypertension and a healthy lifestyle, medication management, and self-efficacy.
Outcomes of the economic evaluations were inconsistent across the studies. A total of 2 articles reported the negative outcomes of cost focused on direct costs [ 37 , 42 ]. The cost of mobile technology was shown as relatively high in rural areas. In contrast, the cost of health professionals’ time in consulting in urban areas was higher than that in the rural. Thus, cost became an inevitable element when considering barriers and facilitators.
All interventions were conducted via mobile technologies. A total of 3 elements may have contributed to the effectiveness of self-management: First, the high intensity of medication reminders. Most studies focused on medication adherence adopted weekly automated alerts and educational or motivational messages. This increased exposure to interventions, which is impossible in routine care. Brennan et al [ 41 ] conducted a comparison between the different intensities of messages and showed that a higher frequency of SMS text messages achieved better medication adherence. However, previous research has shown that reported high-dose reminders would result in response fatigue [ 54 ].
Second, user-driven designs were frequently reflected in the interventions and consisted of customized information and patient-provider loop interactions. A total of 11 studies reported 2-way communication between patients and physicians [ 31 , 32 , 38 , 41 , 42 , 46 , 47 , 49 , 50 , 52 , 53 ]. All interventions with an interactive communication loop showed significantly positive improvement in self-management behavior and BP change. These findings were consistent with the subgroup analysis in this review. Tailoring the intervention to the specific situation and readiness of patients is considered as crucial to self-management [ 55 ]. Particularly, Liu et al [ 36 ] compared the user-driven and expert-driven group behavior change. The expert-driven group showed better behavior change, perhaps because patients from the expert-driven group had more feedback, motivational commands, and support from physicians.
Finally, most of the interventions combined different functions. A total of 12 interventions had more than 2 functions [ 31 , 36 - 38 , 40 , 42 , 44 , 46 , 49 , 50 , 52 , 53 ], and 10 studies relied on SMS text messaging as their main method, while they also linked the BP monitoring devices to a Web-based system [ 32 , 34 , 37 , 43 - 45 , 47 , 49 - 51 ]. According to this subgroup analysis, studies with multifaceted functions had a larger effect on SBP and DBP reduction than those with a single function. In conclusion, the tailored frequency of messages based on patients’ health status and readiness, two-way interactive communication, and multifaceted interventions can produce better effectiveness in the self-management of hypertension.
Significant heterogeneity showed in the meta-analysis of SBP. The reason for this is the variation in interventions. It also affects the calculation of the overall estimate [ 56 ]. According to the sensitivity analysis, Margolis et al [ 42 ] was regarded as the main article, which influenced the heterogeneity.
The occurrence of heterogeneity highlighted the strengths and limitations of the included studies. Strengths are illustrated as follows: first, more than half (13/24, 54%) of the total studies conducted power calculations for clinical data outcomes [ 32 - 38 , 42 , 43 , 46 , 48 , 51 , 53 ]. Second, the description of each intervention provided clear and sufficient details, allowing a thorough understanding of the method. Finally, the reporting rate of detail in the research methods was high within all articles. Over 80% (21/26) of the items listed in the mERA methodological checklist were described in these studies. This showed significant progress compared with the studies included in the previous review [ 57 ].
Particularly, self-reporting bias of compliance is clearly a potential weakness of mHealth. It would increase the risk of recall and social desirability bias. The reliability of self-reporting depends partly on the educational, socioeconomic, and cultural background of participants [ 58 ]. However, studies included in this review attempted to reduce the self-reporting bias. A total of 17 articles used self-reporting in their studies [ 30 - 35 , 39 - 46 , 50 , 52 , 53 ]. Of the 17 articles, 12 articles took steps to test the validity of self-reporting data, including home visits for behavior checks, BP monitoring devices connected to websites, and random phone calls to check medication adherence [ 31 , 33 , 35 , 39 - 44 , 47 , 52 , 53 ].
Referring to the limitations, the duration of the studies included was relatively short. Only 10 studies lasted for or over 1 year [ 32 , 38 , 41 - 43 , 46 - 48 , 51 , 52 ]. The result of subgroup analysis according to the duration of trials found in this study was similar to a previous meta-analysis, which compared digital interventions with conventional methods [ 59 ]. The overall effects of SBP and DBP were inconsistent between studies with shorter and longer durations. Thus, more evidence is needed to confirm the long-term effect of mHealth.
Though all articles were published after 2010 when the CONSORT-EHEALTH statement for reporting of eHealth and mHealth interventions was released [ 60 ], many mHealth intervention details were still unreported. Though performance bias was a prominent weakness in mHealth intervention, it can be explained by the interactive nature of the interventions, which is difficult for participants to be blinded to their health care providers [ 61 ]. Small sample size was also prominent in the included studies, which would cause a huge difference in the estimates of the target population.
In addition, all studies were from high-middle and high-income countries. Similarly, the study sites were mostly in urban settings, which restricts the diversity of the target populations. This is despite the fact that one of the important benefits of mHealth is to allow patients to receive adequate care remotely [ 19 ]. Davidson [ 44 ] reported more considerable cost savings in the mHealth group than in the control group in the study of underserved populations.
The relatively homogeneous populations limited the generalizability of the mHealth intervention. It is also important to consider culture-related differences, racial diversity, and the heterogenetic patterns of mHealth interventions, which have been mentioned in discussion of almost all articles. Nevertheless, only 5 studies have examined the potential cultural adaptation of mHealth in different settings [ 31 , 32 , 35 , 41 , 50 ]. Specifying cultural and contextual adaptabilities of mHealth interventions would help clarify whether the study design can be considered as a potentially useful platform for future research. Other observable limitations include the fact that only 6 articles reported users’ satisfaction [ 31 , 32 , 35 , 37 , 47 , 50 ].
In relation to economic evaluations, mHealth showed only a small short-term economic benefit, but enormous potential in the longer term [ 62 ]. However, the longest duration of studies in this review is 18 months [ 46 , 52 ].
To our knowledge, this is the first systematic review that analyzed the relationship between the characteristics of mHealth-enabled hypertension self-management and the clinical and behavioral outcomes, using both meta-analysis and narrative synthesis. More importantly, this review adds to a body of knowledge of the strengths and limitations of included studies against the mERA checklist.
The chief weakness is the observed heterogeneities in relation to the intervention and control features. In addition, this review only recruited RCTs and excluded other designs with analyses that might also have overcome confounding. The language was restricted to English, which reduces the diversity of studies analyzed. Moreover, the sensitivity analysis was only conducted by excluding each trial sequentially to determine the influence of a single study. Owing to the small number of studies included, studies were not divided into different categories for further sensitivity analyses.
Considering that at least one-third of patients with hypertension have uncontrolled BP, this review provided evidence that mHealth self-management could improve hypertension management and reduce the risks of stroke and CVD. There is increasing interest comparing benefits of mHealth approaches. Questions remain to be addressed about the values of diverse mHealth methods. To promote mHealth interventions of self-management effectively and efficiently, more clinical studies are warranted to detect the relationship between the specific intervention pattern and outcomes. In addition, patients’ compliance with self-management interventions should be examined in the future.
According to the generalizability, there is a necessity to determine whether mHealth-based self-management methods should be tailored to age groups, cultural contexts, or need to be extended to include support from health care personnel. Therefore, training physicians to ensure that patients’ behaviors are maintained and adopted convincingly is also necessary. Clinical trials are called for to fill the gap of techniques of appropriate combination of mHealth intervention and routine care. Thus, more long-term economic evaluation needs to be done.
The intent of this systematic review was to identify and evaluate the effectiveness of mHealth-enabled self-management of hypertension from RCTs. This review clearly demonstrated that an mHealth-enabled hypertension self-management intervention was effective in improving SBP, DBP, and BP control. Both medication adherence and self-management behavior showed positive changes after the intervention. Economic evaluations presented potential cost saving in long-term effectiveness. It is the first analysis that combines clinical data and intervention features.
In conclusion, mHealth self-management has proved to be a potentially useful intervention strategy for BP management. mHealth interventions could be beneficial for BP control at the individual level and in reducing the burden of hypertension at the population level. The development of mobile technologies is especially useful when health care resources are inadequate. The broader utilization of mHealth self-management will be an important contributor to improving the quality of health care and meeting the target of universal health coverage.
This study was funded by the Center of Global Health, Zhejiang University.
BP | blood pressure |
CONSORT-EHEALTH | Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth |
CVD | cardiovascular disease |
DBP | diastolic blood pressure |
LMIC | low- and middle-income country |
MD | mean difference |
mERA | Mobile Health Evidence Reporting and Assessment |
mHealth | mobile health |
OR | odds ratio |
RCT | randomized controlled trial |
SBP | systolic blood pressure |
TAU | treatment as usual |
WHO | World Health Organization |
Multimedia appendix 2.
Authors' Contributions: RL contributed to data search, extraction, and analysis, and then drafted and revised this paper. FB contributed to data extraction. NL advised on data analysis and revised the paper. TH initiated the research, revised the paper, and approved the final manuscript.
Conflicts of Interest: None declared.
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COMMENTS
The aim of this research was to explore perspectives and experiences of patients with hypertension while living with this disease. This is a qualitative research using content analysis approach. 27 hypertensive patients who referred to hospitals affiliated to Tehran University of Medical Sciences were selected based on purposive sampling, and ...
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hypertension. This dissertation includes three papers that address the following: (1) the experiences of men with hypertension, based on a review of literature, (2) factors influencing the ability to self-manage hypertension among older adults in Asian countries, based on a review of
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One of their most potent risk factors, hypertension (also known as high blood pressure), is a common risk factor for CVD [3, 4]. ... books, commentary, dissertations, conference proceedings, comments, systematic reviews, modeling and simulation studies), or had no full text available. Data extraction and quality assessment.
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