This paper is in the following e-collection/theme issue:

Web-based and Mobile Health Interventions (3247) Hypertension Prevention and Treatment (218) Mobile Health (mhealth) (2893) Cardiac Self-Management (105) Cardiology (151) e-Cardiology and Telemonitoring in Cardiology (162)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54978, first published .
Behavior Change Techniques Used in Self-Management Interventions Based on mHealth Apps for Adults With Hypertension: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Behavior Change Techniques Used in Self-Management Interventions Based on mHealth Apps for Adults With Hypertension: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Behavior Change Techniques Used in Self-Management Interventions Based on mHealth Apps for Adults With Hypertension: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Authors of this article:

You Zhou1, 2 Author Orcid Image ;   Si-Jia Li1 Author Orcid Image ;   Ren-Qian Huang3 Author Orcid Image ;   Hao-Ming Ma1 Author Orcid Image ;   Ao-Qi Wang1 Author Orcid Image ;   Xing-Yi Tang1 Author Orcid Image ;   Run-Yuan Pei1 Author Orcid Image ;   Mei-Hua Piao1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Review

    1School of Nursing, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China

    2Department of Nursing, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China

    3Changhai Clinical Research Unit, Changhai Hospital, Naval Medical University, Shanghai, China

    *these authors contributed equally

    Corresponding Author:

    Mei-Hua Piao, PhD

    School of Nursing

    Chinese Academy of Medical Sciences

    Peking Union Medical College

    33 Badachu Road

    Shijignshan District

    Beijing, 100144

    China

    Phone: 86 13522112889

    Email: piaomeihua@nursing.pumc.edu.cn


    Background: Hypertension has become an important global public health challenge. Mobile health (mHealth) intervention is a viable strategy to improve outcomes for patients with hypertension. However, evidence on the effect of mHealth app interventions on self-management in patients with hypertension is yet to be updated, and the active ingredients promoting behavior change in interventions remain unclear.

    Objective: We aimed to evaluate the effect of mHealth app self-management interventions on blood pressure (BP) management and investigate the use of behavior change techniques (BCTs) in mHealth app interventions.

    Methods: We conducted a literature search in 6 electronic databases from January 2009 to October 2023 for studies reporting the application of mHealth apps in self-management interventions. The Cochrane Risk of Bias (version 2) tool for randomized controlled trials was used to assess the quality of the studies. BCTs were coded according to the Taxonomy of BCTs (version 1). The extracted data were analyzed using RevMan5.4 software (Cochrane Collaboration).

    Results: We reviewed 20 studies, of which 16 were included in the meta-analysis. In total, 21 different BCTs (mean 8.7, SD 3.8 BCTs) from 12 BCT categories were reported in mHealth app interventions. The most common BCTs were self-monitoring of outcomes of behavior, feedback on outcomes of behavior, instruction on how to perform the behavior, and pharmacological support. The mHealth app interventions resulted in a –5.78 mm Hg (95% CI –7.97 mm Hg to –3.59 mm Hg; P<.001) reduction in systolic BP and a –3.28 mm Hg (95% CI –4.39 mm Hg to –2.17 mm Hg; P<.001) reduction in diastolic BP. The effect of interventions on BP reduction was associated with risk factors, such as hypertension, that were addressed by the mHealth app intervention (multiple risk factors vs a single risk factor: –6.50 mm Hg, 95% CI –9.00 mm Hg to –3.99 mm Hg vs –1.54 mm Hg, 95% CI –4.15 mm Hg to 1.06 mm Hg; P=.007); the presence of a theoretical foundation (with vs without behavior change theory: –10.06 mm Hg, 95% CI –16.42 mm Hg to –3.70 mm Hg vs –4.13 mm Hg, 95% CI –5.50 to –2.75 mm Hg; P=.07); intervention duration (3 vs ≥6 months: –8.87 mm Hg, 95% CI –10.90 mm Hg to –6.83 mm Hg vs –5.76 mm Hg, 95% CI –8.74 mm Hg to –2.77 mm Hg; P=.09); and the number of BCTs (≥11 vs <11 BCTs: –9.68 mm Hg, 95% CI –13.49 mm Hg to –5.87 mm Hg vs –2.88 mm Hg, 95% CI –3.90 mm Hg to –1.86 mm Hg; P<.001).

    Conclusions: The self-management interventions based on mHealth apps were effective strategies for lowering BP in patients with hypertension. The effect of interventions was influenced by factors related to the study’s intervention design and BCT.

    J Med Internet Res 2024;26:e54978

    doi:10.2196/54978

    Keywords

    hypertension; mHealth; app; behavior change technique; systematic review; meta-analysis; mobile phone 


    Background

    Hypertension is a chronic disease characterized by persistent elevation of systemic arterial blood pressure (BP) and is considered the most important risk factor for cardiovascular diseases [1]. Epidemiological evidence suggests that approximately 31.1% of adults worldwide had hypertension in 2010 [2]. With the acceleration of population aging and the increase in lifestyle risk factors such as alcohol consumption, obesity, lack of physical activity, and unhealthy diets, the global prevalence of hypertension has shown a significant upward trend [3]. The Lancet Commission on hypertension has stated that primordial, primary, and secondary prevention of hypertension in the life course should be implemented to address the global burden of increased BP [4]. Therefore, people with risk of hypertension and patients with hypertension need to carry out BP management including lifestyle modifications addressing various lifestyle risk factors and medication management after diagnosis.

    Mobile health (mHealth) refers to medical and public health practices supported by mobile devices, such as mobile phones, patient monitoring devices, and other wireless devices [5]. Considering its advantages, such as low cost and ease of use, the development of self-management solutions for patients with hypertension based on mHealth apps has become a frontier and hot spot of research. Several meta-analyses have demonstrated that mHealth interventions are effective for patients with hypertension and can improve clinical outcomes [6-11]. For example, a meta-analysis including SMS text messaging, smartphone apps, and website interventions showed that mHealth interventions, particularly smartphone apps, were associated with clinical reductions in systolic BP (SBP) [10]. Another meta-analysis showed that mHealth apps significantly improved medication adherence of patients with hypertension and increased patients’ perceived confidence, treatment self-efficacy, acceptance of technology, and knowledge about health issues [11]. However, all the aforementioned studies lacked further analysis to identify the study design factors and active ingredients in the interventions or apps that promote self-management effectiveness.

    Behavior change techniques (BCTs) are observable, replicable, and irreducible components of interventions designed to alter or redirect causal processes that regulate behavior [12]. By specifying BCTs in interventions, researchers can identify the active ingredients in interventions, synthesize evidence, replicate interventions, and even optimize them [13]. Since the pandemic, remote self-management interventions for patients with hypertension based on mHealth apps have exploded, but there are great differences among studies in terms of intervention design, app contents, and study findings, which raise potential concerns about the generalizability of mHealth app–based interventions for hypertension. The presence of BCTs offers the possibility of optimizing the study design of the aforementioned studies and improving the effectiveness of the interventions. Previous literature reviews have described the efficacy of BCTs in mHealth for self-management of diabetes [14]. However, to the best of our knowledge, there is limited literature exploring the active ingredients of mHealth interventions for patients with hypertension. The status and role of BCTs in mHealth app self-management interventions for patients with hypertension are unclear.

    This Study

    This study aimed to (1) evaluate the true effect of mHealth app self-management interventions on BP management in patients with hypertension, (2) comprehensively investigate the status of the use of BCTs in mHealth app interventions for patients with hypertension, and (3) further analyze what factors can influence the effect of interventions.


    Overview

    This systematic review and meta-analysis were carried out following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (Multimedia Appendix 1) [15], and the review protocol was registered in PROSPERO (CRD42023483746).

    Search Strategy and Selection Criteria

    With the assistance of a professional librarian, a systematic literature search was conducted in PubMed, Web of Science, Embase, American Psychological Association PsycINFO, CINAHL Plus, and Cochrane Central Register of Controlled Trials. In brief, we included randomized controlled trials (RCT) applying mHealth app self-management interventions among patients with hypertension and those published in English. Given that digital health apps started to become widely adopted in 2009, we set the search time from January 1, 2009, to October 15, 2023 [10]. We also performed a hand search of the reference lists of included studies and reviews related to the topic of this study to identify additional studies. The detailed search strategy is provided in Table S1 in Multimedia Appendix 2.

    There were 5 eligibility criteria. The first was population: the study population was adults with a primary diagnosis of hypertension. We excluded studies involving minors, participants with pregnancy-related hypertension, and mixed patient populations without stratified results. The second eligibility criterion was intervention: the primary intervention was based on an mHealth app that can run on mobile or wearable devices such as smartphones, tablets, or smartwatches, and the duration of interventions was >4 weeks or 1 month. We excluded studies involving an intervention mainly based on phone calls, text messages, and website programs, as well as apps with only notification, self-monitoring, and counseling functions (interventions based on such apps may lack sufficient complexity and comprehensiveness to accurately reflect the true effect of mHealth app interventions on BP self-management). The third eligibility criterion was control: the study consisted of at least 1 intervention group and 1 control group that incorporated usual or standard care. Studies in which the control group applied the mHealth app were excluded. The fourth eligibility criterion was outcome: the primary outcomes were SBP, diastolic BP (DBP), or both; the secondary outcomes were clinical or self-reported indicators related to hypertension self-management, including but not restricted to the proportion of BP in control and medication adherence. the fifth eligibility criterion was study design and publication type: peer-reviewed RCTs, pilot studies, and cluster RCTs. We excluded nonrandomized, noncontrolled, and observational studies; case reports; systematic reviews; meta-analyses; conference abstracts; protocols; preprints; and studies without full text. In addition, we also excluded studies in which the intervention was not well described (eg, low-quality studies with overly simplistic descriptions of intervention methods that failed to identify intervention contents) to allow for valid coding of BCTs.

    Study Selection and Data Extraction

    All identified studies were imported into EndNote X9 (Clarivate Analytics) software to remove duplicates and then imported into the Rayyan web platform (Rayyan Systems Inc) for eligibility review. The review process involved 2 rounds of screening: 2 reviewers (YZ and SJL) first screened the titles, abstracts, and keywords independently, and then the same 2 reviewers reviewed the full texts of the studies meeting the eligibility criteria to determine the list of included studies. Any disagreements in the review process were resolved through consultation and discussion with a senior reviewer (MHP).

    Two reviewers (YZ and RQH) independently performed data extraction using a Microsoft Excel form developed following the guidelines in the Cochrane Handbook for Systematic Reviews of Interventions [16]. Any disagreements were resolved through rechecking the original research and discussion with another reviewer (MHP). The information recorded was (1) basic characteristics of publication: title, author, year, country of publication, and journal; (2) study details: study design, sample size, retention rate, intervention duration, detailed content of intervention and control group, and primary and secondary outcomes; (3) participants’ characteristics: age, sex ratio, education, ethnicity, and diagnostic criteria of hypertension; (4) outcomes: duration of follow-up, critical outcomes in BP changes, including the mean, SDs, SEs, and 95% CIs in baseline and follow-up, and validated measurements used for self-reported outcomes; and (5) theoretical foundations and BCTs applied in mHealth app interventions.

    Coding of BCTs

    The coding of BCTs was performed according to the Taxonomy of BCTs (version 1) proposed by Michie et al [12], which summarized 93 BCTs into 16 categories and provided a standardized framework for identifying the BCTs used in behavior change interventions. In total, 2 reviewers (YZ and SJL) coded the BCTs independently after completing web-based training [17]. Evidence supporting BCT coding was from articles, supplementary materials, protocols, and secondary analysis publications. Any discrepancies in the coding process were resolved by discussion with another reviewer (MHP) until unanimity was achieved.

    Risk of Bias and Grade of Evidence Assessment

    The revised Cochrane Risk of Bias (version 2) tools for RCTs and cluster RCTs were used to assess the risk of bias in (1) the randomization process, (2) deviations from the intended interventions, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of the reported result of the included studies [18]. The Grading of Recommendation, Assessment, Development, and Evaluation criteria were used to assess the quality of the overall evidence in (1) risk of bias, (2) inconsistency, (3) indirectness, (4) imprecision, and (5) publication bias, and the quality of evidence could be classified as high, moderate, low, or very low [19]. In total, 2 reviewers (YZ and RQH) reviewed and rated the studies independently, and any disagreements were resolved via discussion with another reviewer (MHP).

    Data Synthesis and Analysis

    A meta-analysis was performed to evaluate the pooled effect size of an mHealth app intervention on BP reduction. Given the potential bias caused by differences in BP levels between the intervention and control groups at baseline, we decided to include data on within-group changes in the mean and SD values of SBP and DBP in each group at follow-up in the analyses.

    For studies in which the data were not available from articles, supplementary materials, and secondary analysis publications, the researcher attempted to contact the corresponding authors for necessary data. For studies that only reported SE or 95% CI or did not report the within-group changes in mean and SD values for BP, the researcher transformed the SE and 95% CI data and estimated the SD values according to the Cochrane handbook [16]. Studies in which analytical data were unavailable based on the aforementioned methods were excluded from the analysis.

    Due to the considerable heterogeneity between the included studies, the random-effects model was considered appropriate to synthesize the effect sizes and SDs [20]. Heterogeneity was quantified using the Cochran Q test and Higgins I2 statistics, and the I2 values <25%, 25% to 75%, and >75% were considered low, medium, and high heterogeneity, respectively [21]. The bias of publication was evaluated by using the Egger test and visualization of the funnel plot.

    Subgroup analyses were conducted to explore the impact of intervention and study design factors (such as the risk factors of hypertension that the mHealth app intervention addressed, the presence of a theoretical foundation, and intervention duration) and the number of BCTs on the effect size of an mHealth app intervention on BP levels and to evaluate the possible sources of heterogeneity. Review Manager software (version 5.4.1) was used to perform meta- and subgroup analyses, and Stata software (version 15.1; StataCorp) was applied to draw the funnel plot.


    Literature Screening

    The process of study identification and screening is outlined in Figure 1. A total of 1668 records were extracted from the initial literature search, and 5 records were identified from hand searching. After removing duplicates, 831 (49.67%) records were screened for titles and abstracts. Overall, 35 (4.2%) full-text studies were assessed for eligibility based on the inclusion and exclusion criteria. Finally, 20 (2.4%) studies were considered eligible and included in the systematic review, and 16 (1.9%) studies with available data were included in the meta-analysis.

    ‎
    Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of the study selection process. RCT: randomized controlled trial.

    Study Characteristics

    The characteristics of the included studies are presented in Table 1. A total of 20 studies were conducted in 9 countries, including China (6/20, 30%) [22-27], the United States (6/20, 30%) [28-33], Iran (2/20, 10%) [34,35], the United Kingdom (1/20, 5%) [36], Spain (1/20, 5%) [37], Germany (1/20, 5%) [38], Japan (1/20, 5%) [39], Palestine (1/20, 5%) [40], and Jordan (1/20, 5%) [41]. All studies were published after 2017, especially between 2020 and 2023. Of the 20 studies, 16 (80%) studies [22-24,26-28,30-36,39-41] used a parallel group design and 4 (20%) studies [25,29,37,38] used a cluster design. In total, 2 (10%) studies [33,36] addressed salt intake reduction and 2 (10%) studies [29,30] focused on medication management. In total, 2 (10%) studies [23,32] were conducted with ethnic minority groups and underserved and vulnerable populations. The number of participants in each study ranged from 30 to 636, and the duration of intervention ranged from 1.5 to 12 months. A total of 4168 participants were enrolled in the studies, with a retention rate of 85.2% (3551/4168).

    Table 1. Characteristics of included studies (N=20).
    Study, yearCountryStudy designIntervention contentBehavior change theoryDuration (months)Retention, n/N (%)Primary outcomesSecondary outcomesConclusion
    Abu-El-Noor et al [40], 2021PalestineRCTaA smartphone app that included reminders for taking medication, follow-up appointments, educational information about hypertension management, and records of BPb readings—c3191/218 (87.6)
    • MAd (measured by Hill-Bone CHBPTSe) improved from 15.64 to 11.73 (intervention) and 15.92 to 13.98 (control; P<.001)
    • Hill-Bone CHBPTS improved from 30.82 to 23.40 (intervention) and 31.10 to 27.38 (control; P<.001)
    • Diet adherence improved from 10.99 to 8.36 (intervention) and 10.90 to 9.65 (control; P=.001)
    • Appointment adherence improved from 4.24 to 3.30 (intervention) and 4.28 to 3.76 (control)
    		Effective for adherence
    Alsaqer and Bebis [41], 2022JordanRCT4 smartphone apps that encouraged self-monitoring of BP and record readings, adherence to medication, deep breathing exercises, and walking and counting steps daily; education for hypertension self-care; a public health nursing intervention that included telephone follow-up—374/80 (92)
    • ΔSBPf: –14 mm Hg (intervention) and –7.75 mm Hg (control; P=.001)
    • ΔDBPg: –2.65 mm Hg (intervention) and –0.38 mm Hg (control; P=.14)
    • SC-HIh—maintenance improved from 37.06 to 67.01 (intervention) and 33.93 to 44.52 (control; P=.001)
    • SC-HI—monitoring improved by 55.29 to 73.04 (intervention) and 52.70 to 54.59 (control; P=.001)
    • SC-HI—confidence improved from 41.79 to 82.06 (intervention) and 40.12 to 40.85 (control; P=.001)
    • Changes in all dimensions in SF-36i were significant after the intervention
    		Effective for SBP but not DBP and self-care
    Bozorgi et al [34], 2021IranRCTA smartphone app that included BP records, reminders for drug consumption, visit date and BP measurement, a healthy diet and weight loss plans, disease knowledge, motivational and supportive programs for smoking cessation, critical BP alarm, customized messages about adherence to treatment, and usual treatment—2118/120 (98.3)
    • MA (measured by the 14-item Hill-Bone Scale) improved from 58.5 to 65.1 (intervention) and 59.1 to 59.7 (control)
    • ΔMAPj: –12.6 mm Hg (intervention) and –16 mm Hg (control)
    • Adherence to a low-salt diet improved from 14.3 to 18.4 (intervention) and 15.8 to 17.3 (control)
    • Adherence to a low-fat diet improved from 16.0 to 17.86 (intervention) and 15.8 to 17.13 (control)
    • ΔVPAk: 32.8 min/wk (intervention) and –21.8 min/wk (control)
    • ΔMPAl: 91.5 min/wk (intervention) and 41.6 min/wk (control)
    		Effective for adherence and PAm
    Chandler et al [28], 2019United StatesSmall-scale efficacy RCTThe Smartphone Med Adherence Stops Hypertension intervention that included a smartphone app, global systems for a mobile electronic medication tray, and a Bluetooth-enabled BP deviceSelf-determination theory954/56 (96)
    • ΔSBP: –30.5 mm Hg (intervention) and –5 mm Hg (control)
    • ΔDBP: –12.6 mm Hg (intervention) and –5.2 mm Hg (control)
    • The percentage of participants with controlled SBP (<140 mm Hg) improved from 0% to 92.3% (intervention) and 0% to 27.8% (control; P=.001)
    • MA (measured by MMASn) improved from 6.83 to 9.81 (intervention) and decreased from 6.99 to 6.84 (control; P<.001)
    		Effective for BP and MA
    Dorsch et al [33], 2020United StatesSingle-center, prospective pilot RCTThe LowSalt4Life mobile app that included just-in-time tailored messages that promote behavioral changes when the participant entered stores and restaurants, and easy scan and search for the foods at stores and restaurants to find options containing lower sodium contentTheory planned behavior; self-regulation theory248/50 (96)
    • ΔSBP: –7.5 mm Hg (intervention) and –0.7 mm Hg (control; P=.12)
    • ΔKawasaki estimated 24-h urinary excretion of sodium –462 mg (intervention) and 381 mg (control; P=.03)
    • Δ24-h urinary excretion of sodium –637 mg (intervention) and –322 mg (control; P=.47)
    		Effective for salt intake
    Frias et al [29], 2017United StatesProspective, cluster, pilot RCTThe DMOo intervention included a smartphone app, medicines coencapsulated with an ingestible sensor, an adhesive wearable sensor patch, and a provider web portal and education and counseling from investigators—3105/109 (96.3)
    • ΔSBP (in 4 weeks): –21.8 mm Hg (intervention) and –12.7 mm Hg (control)
    • ΔSBP (in 12 weeks): –20.9 mm Hg (intervention) and –15.2 mm Hg (control)
    • ΔDBP (in 4 weeks): –9.0 mm Hg (intervention) and –5.9 mm Hg (control)
    • ΔDBP (in 12 weeks): –8.6 mm Hg (intervention) and –5.8 mm Hg (control)
    • The percentage of participants with controlled BP (<140/90 mm Hg; in 4 weeks): 81.2% (intervention) and 33.3% (control); in 12 weeks: 80.0% (intervention) and 51.7% (control)
    • ΔGlycated hemoglobin A1c (in 12 weeks): –0.19 mmol/L (intervention) and +0.26 mmol/L (control)
    • The overall MA when using DMO was ≥80%
    		Effective for BP
    Gong et al [27], 2020ChinaMulticenter RCTA smartphone app that provided reminders of drug dose and BP measurement, and scientific information and suggestions about hypertension—6443/480 (92.3)
    • ΔSBP: –8.99 mm Hg (intervention) and –5.92 mm Hg (control)
    • ΔDBP: –7.04 mm Hg (intervention) and –4.14 mm Hg (control)
    • The percentage of participants with controlled BP (<140/90 mm Hg) improved from 39% to 77% (intervention) and 39% to 67% (control; P=.01)
    • MA (measured by MMAS) was 55% (low), 42% (medium), and 3% (high) in the intervention group and 68% (low), 30% (medium), and 2% (high) in the control group (P=.004)
    		Effective for BP and MA
    Kario et al [39], 2021JapanMulticenter pilot RCTA smartphone app that included a personalized lifestyle‐modification program for lowering BP and standard lifestyle modification—6140/146 (95.9)
    • ΔSBP (24-h ABPMp at 24 weeks): –0.47 mm Hg (intervention) and –0.042 mm Hg (control; P=.78)
    • ΔDBP (24-h ABPM at 24 weeks): –1.3 mm Hg (intervention) and –0.2 mm Hg (control; P=.39)
    • ΔSBP (24-h ABPM at 16 weeks): 0.096 mm Hg (intervention) and –0.29 mm Hg (control; P=.88)
    • ΔSBP (home BP at 16 weeks): –4.1 mm Hg (intervention) and –0.96 mm Hg (control; P=.06)
    • ΔSBP (home BP self-monitoring at 24 weeks): –5.2 mm Hg (intervention) and –2.0 mm Hg (control; P=.07)
    • ΔDBP (nighttime ABPM at 24 weeks): –3.2 mm Hg (intervention) and –0.042 mm Hg (control; P=.04)
    • There were no significant changes in body weight, BMI, or waist circumference
    		Not effective for BP
    Leupold et al [38], 2023GermanyProspective cluster RCTThe PIA app that included transmission of BP measurements, graphic display of BP over time with an individual target range, a medication plan, ordering of prescription refills, video education, and links to BP-related information—12525/636 (82.5)
    • ΔSBP: –21.1 mm Hg (intervention) and –15.5 mm Hg (control; P<.001)
    • ΔDBP: –11.3 mm Hg (intervention) and –8.2 mm Hg (control; P=.40)
    • The percentage of participants with controlled BP (<140/90 mm Hg) was 62.6% (intervention) and 44.6% (control; P<.001)
    		—Effective for BP
    Li et al [25], 2019ChinaCluster RCTSelf-management intervention based on WeChat and included health education, health promotion, group chat, and BP monitoringSelf-efficacy theory6243/462 (52.6)
    • ΔSBP: –5.5 mm Hg (intervention) and 1.6 mm Hg (control; P<.001)
    • ΔDBP: –1.3 mm Hg (intervention) and 2.1 mm Hg (control; P=.01)
    • The percentage of participants with controlled BP (<140/90 mm Hg) improved from 60.9% to 83.6% (intervention) and decreased from 69.2% to 63.6% (control; P<.001)
    • ΔScore of hypertension: knowledge 2.3 (intervention) and 0.8 (control)
    • ΔScore of self-efficacy 0.8 (intervention) and –0.6 (control)
    • ΔScore of self-management: 7.3 (intervention) and –1.4 (control; P<.001)
    • ΔScore of social support: 0.4 (intervention) and 0.7 (control)
    		Effective for BP and self-management
    Ma et al [26], 2022ChinaRCTA smartphone app that included health education, individual self-care planning, daily records, and an automated weekly health report and nurse-led individual education and consultation sessions—3191/210 (91)
    • ΔSBP: –11.63 mm Hg (intervention) and –1.01 mm Hg (control; P<.001)
    • ΔDBP: –5.53 mm Hg (intervention) and 1.69 mm Hg (control; P<.001)
    • The percentage of participants with controlled BP (<140/90 mm Hg) improved from 14.3% to 31.43% (intervention) and 7.6% to 8.57% (control; P=.003)
    • ΔWeight: –1.16 kg (intervention) and –0.03 kg (control)
    • ΔBMI: –0.50 kg/m2 (intervention) and –0.09 kg/m2 (control)
    • ΔWCq: –3.02 cm (intervention) and 0.82 cm (control)
    • Self-care behavior (measured by HBP-HCPr) improved from 53.11 to 61.50 (intervention) and 54.82 to 54.94 (control); self-care motivation (measured by HBP-HCP) improved from 53.79 to 60.89 (intervention) and 55.02 to 55.27 (control); self-care–self-efficacy (measured by HBP-HCP) improved from 55.53 to 63.32 (intervention) and 55.60 to 56.13 (control)
    		Effective for BP and self-care
    Márquez Contreras et al [37], 2018SpainCluster RCTA smartphone app that included personal data records, recommendations for target BP, physician’s medication advice, reminder alarms, calendar of appointments or events, and a record of BP measurement results—12148/154 (96.1)
    • ΔSBP: –2.5 mm Hg (intervention) and –0.07 mm Hg (control; P<.001)
    • ΔDBP: –3.14 mm Hg (intervention) and –0.5 mm Hg (control; P<.001)
    • Pharmacological therapeutic adherence was significantly improved after the intervention
    		Effective for BP and MA
    Morawski et al [30], 2018United StatesRCTThe Medisafe mobile app that included reminder alerts of medication, adherence reports, tracks of BP, and optional peer support—3411/412 (99.8)
    • ΔSBP: –10.6 mm Hg (intervention) and –10.1 mm Hg (control; P=.78)
    • MA (measured by MMAS-8) improved from 6.0 to 6.3 (intervention) and maintained 5.7 (control; P=.001)
    • The percentage of participants with controlled BP (<140/90 mm Hg) improved from 0% to 35.8% (intervention) and 0% to 37.9% (control; P=.69)
    		Effective for MA but not SBP
    Najafi Ghezeljeh et al [35], 2018IranRCTA Telegram group that included self-management education and contact and communication—1.550/50 (100)
    • Self-management behavior (measured by HSMBQs) improved from 2.23 to 3.73 (intervention) and decreased from 1.84 to 1.83 (control)
    		—Effective for self-management
    Persell et al [31], 2020United StatesRCTHome BP self-monitoring and hypertension personal control program that included medication reminders, hypertension education, DASHt diet encouragements, BP measuring reminders, coaching about PA, sleep tracks, stress management education, record reminders, and customized communicationCognitive behavioral therapy6297/333 (88.2)
    • ΔSBP: –8.3 mm Hg (intervention) and –6.8 mm Hg (control; P=.16)
    • ΔDBP: –4.3 mm Hg (intervention) and –3.6 mm Hg (control; P=.61)
    • The percentage of participants with controlled BP (<140/90 mm Hg) improved from 36% to 72% (intervention) and 41% to 78% (control; P=.66)
    • Self-confidence in controlling BP significantly improved after the intervention (P<.001)
    		Not effective for BP
    Payne Riches et al [36], 2021United KingdomRCTThe SaltSwap mobile app that included brief advice on encouraging individuals to swap to lower-salt alternatives, buying fewer high-salt foods, and using less salt when cooking and face-to-face behavioral advice and support provided by a health care professionalBehavior change wheel1.545/47 (96)
    • ΔSBP: –1.0 mm Hg (intervention) and –1.1 mm Hg (control; P=.82)
    • ΔDBP: –1.0 mm Hg (intervention) and 2.3 mm Hg (control; P=.23)
    • Salt intake: –0.2 g/d (intervention) and –1.0 g/d (control; P=.68); purchased salt: –0.0 g/100 g (intervention) and –0.1 g/100 g (control; P=.16)
    		Not effective for BP and salt intake
    Sun et al [24], 2020ChinaRCT3 WeChat groups (according to cardiovascular risk factors) that included health education, health behavior promotion, group chats, and BP monitoring—3117/120 (98)
    • ΔSBP: –10.92 mm Hg (intervention) and –3.43 mm Hg (control; P<.001)
    • ΔDBP: –5.68 mm Hg (intervention) and –2.23 mm Hg (control; P=.07)
    • Changes in TCu and LDL-Cv were significant after the intervention
    • MA (measured by MMAS-8) improved from 2.28 to 3.30 (intervention) and 2.35 to 2.38 (control)
    • Self-management behavior (measured by HPSMBRSw) improved from 72.27 to 74.57 (intervention) and 70.82 to 70.85 (control; P<.001)
    • ΔBMI: –0.49 kg/m2 (intervention; P<.001) and –0.06 kg/m2 (control)
    		Effective for BP and MA
    Zha et al [32], 2020United StatesPilot RCTThe iHealth MyVitals mobile app included tracks and analysis of key health measurements and instant feedback, helping users self-monitor and manage BP, and standard hypertension management—625/30 (83)
    • ΔSBP: –8.39 mm Hg (intervention) and –4.79 mm Hg (control; P=.01)
    • ΔDBP: –2.76 mm Hg (intervention) and –2.2 mm Hg (control)
    • MA self-efficacy (measured by MASESx) improved from 64.85 to 69.17 (intervention) and decreased from 64.75 to 61.00 (control; P=.06)
    		Effective for BP and self-management
    Zhang et al [22], 2022ChinaRCTA smartphone app that included reminder alerts, access to historical data, and real-time chat and wearable device that can trace steps, heart rate, BP, and sleeping hours—6192/307 (62.5)
    • ΔSBP: 3.2 mm Hg (intervention) and 6.4 mm Hg (control)
    • ΔDBP: 2.7 mm Hg (intervention) and 5.4 mm Hg (control)
    • ΔBMI: 0.4 kg/m2 (intervention) and 0.6 kg/m2 (control)
    • ΔWeight: 1.1 kg (intervention) and 1.5 kg (control)
    		Effective for BP
    Zhang et al [23], 2023ChinaRCTA smartphone app that included reminder alerts, adherence reports, medical instruction, and optional family support and monitoring a wearable device (model unknown)Theory of planned behavior3134/148 (90.5)
    • ΔSBP: –8.52 mm Hg (intervention) and –1.25 mm Hg (control; P=.01)
    • ΔDBP: –0.42 mm Hg (intervention) and –0.01 mm Hg (control)
    • ΔWC: –2.14 cm (intervention) and –0.25 cm (control)
    • ΔHCy: –0.30 cm (intervention) and –0.01 cm (control; P=.08)
    • Self-efficacy improved: 12.89 (intervention) and 5.43 (control)
    • Hypertension compliance improved: 7.35 (intervention) and 3.01 (control)
    • Physical health improved: 12.21 (intervention) and 1.54 (control)
    • Mental health improved: 13.17 (intervention) and 2.55 (control)
    		Effective for SBP but not DBP

    aRCT: randomized controlled trial.

    bBP: blood pressure.

    cNot applicable.

    dMA: medication adherence.

    eCHBPTS: Compliance to High Blood Pressure Therapy Scale.

    fSBP: systolic blood pressure.

    gDBP: diastolic blood pressure.

    hSC-HI: Self-Care of Hypertension Inventory.

    iSF-36: 36-Item Short Form Survey.

    jMAP: mean arterial pressure.

    kVPA: vigorous physical activity.

    lMPA: moderate physical activity.

    mPA: physical activity.

    nMMAS: Morisky Medication Adherence Scale.

    oDMO: digital medicine offering.

    pABPM: ambulatory blood pressure monitoring.

    qWC: waist circumference.

    rHBP-HCP: Hypertension Self-care Profile.

    sHSMBQ: Hypertension Self-Management Behavior Questionnaire.

    tDASH: Dietary Approaches to Stop Hypertension.

    uTC: total cholesterol.

    vLDL-C: low-density lipoprotein cholesterol.

    wHPSMBRS: Hypertension Patients Self-Management Behavior Rating Scale.

    xMASES: Medication Adherence Self-Efficacy Scale.

    yHC: hip circumference.

    Risk of Bias and Grade of Evidence

    Of the 20 included studies, 15 (75%) [22-25,27-29,32-36,39-41] were judged as high risk of bias, 1 (5%) [37] was judged as having some concerns, and 4 (20%) [26,30,31,38] were judged as low risk of bias (Figure 2 [22-41]). Overall, the risk of bias primarily existed in the randomization process and measurement of the outcome. In terms of the randomization process, 50% (10/20) of the studies [23,25,27,29,32-34,36,40,41] were rated as high risk, primarily because of not following the principle of, or not providing details about, randomization and allocation concealment. Regarding the measurement of the outcome, 40% (8/20) of the studies [24,27,28,33-36,40] were assessed as high risk mainly due to the use of patients’ self-reported subjective outcomes and the lack of disclosing whether the outcome measurers were aware of the intervention patients received. In addition, the lack of blinding among patients and between patients and investigators during the intervention process (although often difficult to achieve in mHealth interventions), high dropout rates and not conducting intention-to-treat analyses, and selective disclosure of predetermined outcome data were also important sources of risk of bias. The result of the Egger test (P=.16) and visualization of the funnel plot (Figure S1 in Multimedia Appendix 2) indicated that there was no significant risk of publication bias in the included studies.

    ‎
    Figure 2. Risk of bias assessment of the included studies [22-41] using the Cochrane Risk of Bias (version 2) tool. RCT: randomized controlled trial.

    The results of the Grading of Recommendation, Assessment, Development, and Evaluation assessment are presented in Table 2. In terms of SBP, the quality of evidence was low, and for DBP, the quality of evidence was moderate.

    Table 2. Grading of Recommendation, Assessment, Development, and Evaluation assessment.
    Blood pressureStudies, nQuality assessmentPatients (Ea/Cb)Effect, mean difference (95% CI)Overall certainty of evidence


    		Risk of biasInconsistencyIndirectnessImprecisionPublication bias


    SBPc16SeriousdSeriouseNot seriousNot seriousNone1568/1516–5.78 (–7.97 to 3.59)Low
    DBPf15SeriousdNot seriousNot seriousNot seriousNone1359/1316–3.28 (–4.39 to –2.17)Moderate

    aE: experimental group.

    bC: control group.

    cSBP: systolic blood pressure.

    dThe included studies were judged as have a high risk of bias overall. Some of the studies had evident risks in the randomization process and measurement of the outcome.

    eCochran Q test and Higgins I2 suggested significant heterogeneity between studies.

    fDBP: diastolic blood pressure.

    Behavior Change Theories and Techniques

    Table 1 and Table S2 in Multimedia Appendix 2 summarize the behavior change theories and BCTs used in the included studies. Of the 20 studies, 6 (30%) [23,25,28,31,33,36] reported 7 behavior change theories in their interventions and the remaining 14 (70%) did not include a theoretical foundation. Theories involved in the interventions included self-determination theory [28], theory of planned behavior [23,33], self-regulation theory [33], self-efficacy theory [25], cognitive behavioral theory [31], and behavior change wheel [36]. Of the 6 studies, 1 (17%) used 2 theories [33] and the remaining 5 (83%) were based on a single theory.

    A total of 21 different BCTs from 12 BCT categories were reported in the included 20 studies. The mean number of BCTs was 8.7 (SD 3.8), with a range of 2 to 17, accounting for 2% (2/93) to 18% (17/93) of the total 93 BCTs. The frequently used BCT clusters (used in ≥10 studies) were goals and planning, feedback and monitoring, shaping knowledge, associations, comparison of outcomes, and regulation. The most common BCTs were self-monitoring of outcomes of behavior (17/20, 85%), feedback on outcomes of behavior (15/20, 75%), instruction on how to perform the behavior (15/20, 75%), pharmacological support (15/20, 75%), biofeedback (14/20, 70%), prompts/cues (13/20, 65%), credible source (13/20, 65%), and action planning (11/20, 55%). There were 4 BCTs used only in 10% (2/20) of the studies: problem solving, information about health consequences, behavior substitution, and conserving mental resources.

    Effects of an mHealth App Intervention on BP

    Effect of an mHealth App Intervention on SBP

    In total, 16 (80%) of the 20 studies reported the effects of an mHealth app intervention on SBP. A total of 1568 participants from the intervention group and 1516 participants from the control group were included in the meta-analysis. As presented in Figure 3, the mHealth app intervention resulted in a –5.78 mm Hg (95% CI –7.97 mm Hg to –3.59 mm Hg) reduction in SBP. The heterogeneity was significant (I2=82%; P<.001) between the studies.

    ‎
    Figure 3. Forest plot of the overall effect of a mobile health app intervention on systolic blood pressure.
    Effect of an mHealth App Intervention on DBP

    As shown in Figure 4, in total, 15 (75%) of the 20 studies reporting the effect of an mHealth app intervention on DBP including 1359 participants from the intervention group and 1316 participants from the control group were included in the analysis. Overall, the mHealth app intervention resulted in a –3.28 mm Hg (95% CI –4.39 mm Hg to –2.17 mm Hg) reduction in DBP with a medium heterogeneity (I2=58%; P=.003).

    ‎
    Figure 4. Forest plot of the overall effect of a mobile health app intervention on diastolic blood pressure.

    Subgroup Analyses

    Subgroup analyses were subsequently conducted to explore the impact of factors, such as the risk factors of hypertension that mHealth app interventions addressed (addressing a single risk factor vs addressing multiple risk factors), the presence of a theoretical foundation (with behavior change theories vs without behavior change theories), duration of the intervention (<6 vs ≥6 months), and the number of BCTs (≥11 vs<11), on the effect size of an mHealth app intervention on SBP.

    Factors Addressed in an mHealth App Intervention

    Of the 16 studies that reported the effects of an mHealth app intervention on SBP, 12 (75%) studies [22-28,31,32,37,38,41] carried out interventions (eg, education of disease; BP monitoring and alerts; instructions on diet, salt intake, exercise, sleep, and stress management; and medication reminders or management) addressing multiple physiological and behavioral risk factors of hypertension, and the remaining 4 (25%) studies [29,30,33,36] carried out interventions (eg, reduction of salt intake or medication adherence management) addressing a single physiological or behavioral risk factor. As illustrated in Figure S2 in Multimedia Appendix 2, compared with the interventions addressing a single risk factor of hypertension (–1.54 mm Hg, 95% CI –4.15 mm Hg to 1.06 mm Hg), interventions addressing multiple risk factors of hypertension resulted in a better reduction in SBP (–6.50 mm Hg, 95% CI –9.00 mm Hg to –3.99 mm Hg) and the difference between subgroups was statistically significant (P=.007). However, the heterogeneity between studies addressing multiple risk factors remained significant (I2=86%; P<.001).

    Given the obvious heterogeneity in studies with interventions addressing multiple physiological and behavioral risk factors of hypertension and that hypertension is a disease associated with multiple risk factors, the effect of mHealth apps addressing a single physiological or behavioral risk factor may differ from the effect of those addressing multiple risk factors. Therefore, subsequent subgroup analyses were conducted based on the 12 studies addressing multiple physiological and behavioral risk factors of hypertension.

    Use of Behavior Change Theory

    Of the 12 studies, 5 (42%) studies [23,25,26,28,31] used behavior change theory and 7 (58%) studies [22,24,27,32,37,38,41] did not disclose the theoretical foundation. As presented in Figure S3 in Multimedia Appendix 2, although the difference between subgroups was not statistically significant (P=.07), interventions with behavior change theory resulted in a –10.06 mm Hg (95% CI –16.42 mm Hg to –3.70 mm Hg) reduction in SBP, which is better than without behavior change theory with a –4.13 mm Hg (95% CI –5.50 mm Hg to –2.75 mm Hg) reduction in SBP. The heterogeneity among studies with behavior change theory was considerable (I2=92%; P<.001).

    Duration of Intervention

    In the 12 studies, the duration of intervention ranged from 3 to 12 months, with a mean duration of 6.25 months. Among them, 4 (33%) studies [23,24,26,41] had a duration of 3 months, and the other 8 (67%) studies [22,25,27,28,31,32,37,38] had a duration of at least 6 months. The results of the subgroup analysis showed that the effect of the 3-month intervention duration (–8.87 mm Hg, 95% CI –10.90 mm Hg to –6.83 mm Hg) on reducing SBP was superior to those with a longer duration (–5.76 mm Hg, 95% CI –8.74 mm Hg to –2.77 mm Hg; P=.09). Apparent heterogeneity persisted between studies with interventions lasting longer than 6 months (I2=86%; P<.001; Figure S4 in Multimedia Appendix 2).

    Use of BCTs

    A total of 19 different BCTs were used in 12 mHealth app interventions, with the number ranging from 2 to 12 and an average of 10.58 BCTs per intervention. In total, 7 (58%) of the 12 studies [23-26,28,38,41] used at least 11 BCTs. As shown in Figure S5 in Multimedia Appendix 2, interventions using at least 11 BCTs (–9.68 mm Hg, 95% CI –13.49 mm Hg to –5.87 mm Hg) had a statistically significant effect on the reduction in SBP compared to studies using fewer BCTs (–2.88 mm Hg, 95% CI –3.90 mm Hg to –1.86 mm Hg; P<.001).


    Principal Findings

    In this study, we conducted a systematic review of 20 studies about mHealth app interventions for hypertension and performed a meta-analysis of 16 (80%) of the studies. The results indicated that mHealth app interventions resulted in a significant reduction in SBP (P<.001) and DBP (P<.001) compared to usual care, and the effect size was influenced by factors of intervention design (eg, presence of a theoretical foundation, intervention duration, and number of BCTs) and app contents (eg, the risk factors of the hypertension app addressed). Our study further demonstrates the effectiveness of mHealth app interventions in hypertension self-management and, for the first time, provides an interpretation of the active ingredients in such interventions from a BCT perspective. However, significant differences in intervention designs and the number and selection of BCTs across studies also indicated that these interventions may not be generalized in different social settings, and there is currently a lack of a unified guidance framework in mHealth interventions for hypertension.

    Comprehensive intervention based on antihypertensive medications combined with lifestyle modifications is considered a standard strategy for the management of hypertension [42,43]. Nonpharmacological management is a multidimensional task that includes weight loss, the Dietary Approaches to Stop Hypertension diet, sodium reduction, potassium supplementation, increased physical activity, and reduction in tobacco and alcohol consumption [44]. As we guessed, due to covering more lifestyle modifications, mHealth apps addressing multiple physiological and behavioral risk factors of hypertension and offering more functions were more effective in lowering SBP. This finding was consistent with previous studies conducted in various populations [45-48]. Notably, the interventions in studies by Chandler et al [28] and Frias et al [29] resulted in more reduced SBP in absolute values compared to other mHealth app interventions. In those studies, the researchers applied medication trays with a series of reminder signals and ingestible sensors to enhance medication management, indicating that the strategies of enhanced reminders and tracking of medication behavior might be potential ways to improve medication management. Epidemiological evidence suggests that nonadherence to antihypertensive medications is as high as 27% to 40% globally, and there is still significant room for improvement in patients’ medication behavior [49]. Therefore, developing a multicomponent mHealth app for lifestyle modifications and conducting intensive intervention for medication behavior may be a viable direction for future mHealth interventions in hypertension.

    Intervention duration is a key factor in intervention design, and it was noteworthy that, in our study, 20% (4/20) of the studies with an intervention duration of 3 months were more effective in reducing SBP than studies lasting ≥6 months. This finding was consistent with a previous study, in which Ma et al [50] found that the effect of a habit formation intervention on physical activity habits was better if the intervention duration was <12 weeks. Given that the contents of mHealth app interventions primarily involve lifestyle modifications and the formation of healthy living habits, this phenomenon seems to be partly explained by habit formation. The law of automaticity change in the formation of new habits indicates that habit strength usually reaches a peak of automaticity at approximately 12 weeks and gradually weakens over the following period [50,51]. In addition, of these 4 studies, 2 (50%) [24,26] involved educational curriculum programs that progressed over time. We suggested that, in addition to habit formation, the enhancement of intrinsic motivation for behavior change by planned sessions on disease, medications, and coping may also contribute to BP management. Therefore, how to promote the development of healthy living habits and maintain the habits and motivation of behavior change over a long period of the intervention will be an issue to consider in future study designs of similar interventions.

    A theory is a set of interrelated concepts, definitions, and propositions that explain or predict events or situations by specifying relations among variables [52]. The existing view is that effective interventions used to promote healthy lifestyles and reduce risky behaviors are inseparable from the evidence of theories [53]. Similar to our findings, theory-based mHealth interventions have been found more effective in other chronic diseases and behavioral change studies [14,54]. A possible explanation is that, based on theories, researchers can identify causal factors associated with behavior change and the pathways through which behavior change occurs. Furthermore, this enhances treatment fidelity, allowing for a more comprehensive design of the protocol, early detection of errors and protocol deviations, and improvement in treatment retention, to maximize the effectiveness of interventions [43,55]. In our review, 6 studies disclosed the theories used, and the theory use rate was 30% (6/20). The lack of a theoretical foundation seems to have become a common phenomenon in current behavior change interventions [56,57]. We believe that this phenomenon needs urgent attention. With the development of mobile information technology, an increasing number of interventions are being based on mobile, smart, and wearable devices and are being transferred from health care institutions to patients’ homes. In the absence of face-to-face communication and strict supervision in telemedicine, holistic study designs based on theories will be particularly important to improve the effectiveness and generalization of the interventions. Future researchers can try to design intervention protocols based on multiple theories and integrate these theories to explore the best model for mHealth app interventions for hypertension.

    Research evidence, including our study, has suggested that using more BCTs may be associated with better outcomes of behavior change [58,59]. However, some studies found that fewer techniques and the right combinations of techniques are more effective [60-62]. Indeed, due to the small effect of a single BCT, the fact that BCTs are often present in combinations in interventions, and the possibility of interactions between BCTs, determining which specific BCT, the combinations of BCTs, and the number of BCTs that are effective for a given behavior is a challenge [63]. Therefore, based on applying a certain number of BCTs and effective BCT combinations, customizing an intervention to the patient’s behavior change needs, including contents, duration, and delivery, may be another possible method to improve the effectiveness of the interventions.

    This study also had some limitations. First, we intended to evaluate the effectiveness of mHealth app interventions on several subjective and objective outcomes including BP, medication adherence, self-efficacy, etc. However, due to the limited number of included studies and significant heterogeneity of subjective outcome measures, we ultimately only reported the result of BP management, which was inconsistent with the published protocol. In addition, although we attempted to explore the source of heterogeneity through subgroup analyses, heterogeneity remained significant in some subgroups, and because the included studies were all published after 2017, we also failed to explore the changes in the effectiveness of mHealth app interventions over time. It is recommended that future studies incorporate additional studies and conduct extensive subgroup analyses to further explore the impact of mHealth app interventions on self-management–related outcomes in patients with hypertension. Second, in this study, we only included app-based mHealth interventions and excluded studies based on phone calls, text messages, and website programs, which also contained BCTs, potentially leading to bias in the assessment of effective BCTs. Third, the evidence for BCT coding relies on information about intervention contents from available articles, supplementary materials, and secondary analysis publications, which were underreported or roughly outlined. Furthermore, despite the coding process being performed by 2 researchers independently with good consistency, BCT coding is inevitably susceptible to researchers’ subjective judgments. Therefore, the efficacy of BCTs for hypertension self-management still requires further validation in future studies. Finally, while our study coded BCTs and initially explored the effectiveness of the number of BCTs in SBP reduction, considering the wide variations of intervention contents and specific forms, frequencies, and intensities of BCTs across studies, we failed to further quantitatively investigate the effect of a single BCT or a combination of BCTs on BP reduction. Self-management of hypertension is a complex and multidimensional intervention, involving lifestyle modifications and medication management, and is affected by factors such as patients’ knowledge, intention, self-efficacy, and environment. It is foreseeable that more BCTs will be found effective in self-management of hypertension. Future research could further explore other BCTs and combinations of BCTs to provide references for the development of relevant mHealth interventions and apps.

    Conclusions

    This systematic review and meta-analysis further confirmed the effectiveness of mHealth app self-management interventions for hypertension and identified the BCTs used in the interventions. Our study found that mHealth app interventions can lead to a reduction in SBP and DBP compared to usual care; factors related to the intervention and study design, such as the risk factors of hypertension the mHealth app intervention addressed, the presence of a theoretical foundation, intervention duration, and the number of BCTs, were associated with the effect sizes of BP reduction; and the most commonly used BCTs included self-monitoring of outcomes of behavior, feedback on outcomes of behavior, instruction on how to perform the behavior, and pharmacological support. On the basis of the findings of our study, future research can optimize the intervention designs and use more BCTs and BCT combinations to develop more effective mHealth apps and interventions for hypertension management.

    Acknowledgments

    This study was funded by the NonProfit Central Research Institute Fund of the Chinese Academy of Medical Sciences (grant #2023-RC320-01).

    The authors would like to thank all authors of the included studies.

    Data Availability

    The datasets used and analyzed during this study are available from the corresponding author on reasonable request.

    Authors' Contributions

    YZ and MHP contributed to conceptualization. YZ, SJL, and RQH contributed to the methodology. YZ and HMM were involved in software analyses. AQW and RYP contributed to validation. YZ and AQW contributed to the formal analysis. YZ, SJL, and RQH contributed to the investigation. YZ and XYT contributed to data curation. YZ and SJL contributed to writing the original draft. YZ and MHP contributed to reviewing and editing the manuscript. MHP contributed to supervising the study.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.

    DOCX File , 32 KB
    Multimedia Appendix 2

    Supplementary tables and figures.

    DOCX File , 2014 KB
    1. Colafella KM, Denton KM. Sex-specific differences in hypertension and associated cardiovascular disease. Nat Rev Nephrol. Mar 2018;14(3):185-201. [CrossRef] [Medline]
    2. Mills KT, Bundy JD, Kelly TN, Reed JE, Kearney PM, Reynolds K, et al. Global disparities of hypertension prevalence and control: a systematic analysis of population-based studies from 90 countries. Circulation. Aug 09, 2016;134(6):441-450. [FREE Full text] [CrossRef] [Medline]
    3. Mills KT, Stefanescu A, He J. The global epidemiology of hypertension. Nat Rev Nephrol. Apr 2020;16(4):223-237. [FREE Full text] [CrossRef] [Medline]
    4. Olsen MH, Angell SY, Asma S, Boutouyrie P, Burger D, Chirinos JA, et al. A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: the Lancet Commission on hypertension. Lancet. Nov 26, 2016;388(10060):2665-2712. [CrossRef] [Medline]
    5. Aovare P, Abdulai K, Laar A, van der Linden EL, Moens N, Richard E, et al. Assessing the effectiveness of mHealth interventions for diabetes and hypertension management in Africa: systematic review and meta-analysis. JMIR Mhealth Uhealth. Aug 29, 2023;11:e43742. [FREE Full text] [CrossRef] [Medline]
    6. Zhou L, He L, Kong Y, Lai Y, Dong J, Ma C. Effectiveness of mHealth interventions for improving hypertension control in uncontrolled hypertensive patients: a meta-analysis of randomized controlled trials. J Clin Hypertens (Greenwich). Jul 2023;25(7):591-600. [FREE Full text] [CrossRef] [Medline]
    7. Xu H, Long H. The effect of smartphone app-based interventions for patients with hypertension: systematic review and meta-analysis. JMIR Mhealth Uhealth. Oct 19, 2020;8(10):e21759. [FREE Full text] [CrossRef] [Medline]
    8. Kassavou A, Wang M, Mirzaei V, Shpendi S, Hasan R. The association between smartphone app-based self-monitoring of hypertension-related behaviors and reductions in high blood pressure: systematic review and meta-analysis. JMIR Mhealth Uhealth. Jul 12, 2022;10(7):e34767. [FREE Full text] [CrossRef] [Medline]
    9. Li R, Liang N, Bu F, Hesketh T. The effectiveness of self-management of hypertension in adults using mobile health: systematic review and meta-analysis. JMIR Mhealth Uhealth. Mar 27, 2020;8(3):e17776. [FREE Full text] [CrossRef] [Medline]
    10. Siopis G, Moschonis G, Eweka E, Jung J, Kwasnicka D, Asare BY, et al. Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with hypertension: a systematic review and meta-analysis of randomised controlled trials. Lancet Digit Health. Mar 2023;5(3):e144-e159. [FREE Full text] [CrossRef] [Medline]
    11. Mikulski BS, Bellei EA, Biduski D, De Marchi AC. Mobile health applications and medication adherence of patients with hypertension: a systematic review and meta-analysis. Am J Prev Med. Apr 2022;62(4):626-634. [CrossRef] [Medline]
    12. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, et al. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. Aug 2013;46(1):81-95. [FREE Full text] [CrossRef] [Medline]
    13. Wang X, Sun J, Yin X, Zou C, Li H. Effects of behavioral change techniques on diet and physical activity in colorectal cancer patients: a systematic review and meta-analysis. Support Care Cancer. Dec 14, 2022;31(1):29. [CrossRef] [Medline]
    14. El-Gayar O, Ofori M, Nawar N. On the efficacy of behavior change techniques in mHealth for self-management of diabetes: a meta-analysis. J Biomed Inform. Jul 2021;119:103839. [FREE Full text] [CrossRef] [Medline]
    15. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. Mar 29, 2021;372:n71. [FREE Full text] [CrossRef] [Medline]
    16. Higgins JP, Green S. Cochrane handbook for systematic reviews of interventions version 5.1.0. The Cochrane Collaboration. Mar 2011. URL: https://handbook-5-1.cochrane.org/ [accessed 2023-01-23]
    17. Online training. BCT Taxonomy V1. URL: https://www.bct-taxonomy.com/ [accessed 2024-09-30]
    18. Sterne JA, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. Aug 28, 2019;366:l4898. [FREE Full text] [CrossRef] [Medline]
    19. Grade homepage. Grade Working Group. URL: https://www.gradeworkinggroup.org/ [accessed 2024-09-30]
    20. Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. Jun 15, 2002;21(11):1539-1558. [CrossRef] [Medline]
    21. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. Sep 06, 2003;327(7414):557-560. [FREE Full text] [CrossRef] [Medline]
    22. Zhang Y, Tao Y, Zhong Y, Thompson J, Rahmani J, Bhagavathula AS, et al. Feedback based on health advice via tracing bracelet and smartphone in the management of blood pressure among hypertensive patients: a community-based RCT trial in Chongqing, China. Medicine (Baltimore). Jul 15, 2022;101(28):e29346. [FREE Full text] [CrossRef] [Medline]
    23. Zhang YT, Tan XD, Wang Q. Effectiveness of a mHealth intervention on hypertension control in a low-resource rural setting: a randomized clinical trial. Front Public Health. Mar 1, 2023;11:1049396. [FREE Full text] [CrossRef] [Medline]
    24. Sun YQ, Jia YP, Lv JY, Ma GJ. The clinical effects of a new management mode for hypertensive patients: a randomized controlled trial. Cardiovasc Diagn Ther. Dec 2020;10(6):1805-1815. [FREE Full text] [CrossRef] [Medline]
    25. Li X, Li T, Chen J, Xie Y, An X, Lv Y, et al. A WeChat-based self-management intervention for community middle-aged and elderly adults with hypertension in Guangzhou, China: a cluster-randomized controlled trial. Int J Environ Res Public Health. Oct 23, 2019;16(21):4058. [FREE Full text] [CrossRef] [Medline]
    26. Ma Y, Cheng HY, Sit JW, Chien WT. The effects of a smartphone-enhanced nurse-facilitated self-care intervention for Chinese hypertensive patients: a randomised controlled trial. Int J Nurs Stud. Oct 2022;134:104313. [CrossRef] [Medline]
    27. Gong K, Yan YL, Li Y, Du J, Wang J, Han Y, et al. Mobile health applications for the management of primary hypertension: a multicenter, randomized, controlled trial. Medicine (Baltimore). Apr 2020;99(16):e19715. [FREE Full text] [CrossRef] [Medline]
    28. Chandler J, Sox L, Kellam K, Feder L, Nemeth L, Treiber F. Impact of a culturally tailored mHealth medication regimen self-management program upon blood pressure among hypertensive Hispanic adults. Int J Environ Res Public Health. Apr 06, 2019;16(7):1226. [FREE Full text] [CrossRef] [Medline]
    29. Frias J, Virdi N, Raja P, Kim Y, Savage G, Osterberg L. Effectiveness of digital medicines to improve clinical outcomes in patients with uncontrolled hypertension and type 2 diabetes: prospective, open-label, cluster-randomized pilot clinical trial. J Med Internet Res. Jul 11, 2017;19(7):e246. [FREE Full text] [CrossRef] [Medline]
    30. Morawski K, Ghazinouri R, Krumme A, Lauffenburger JC, Lu Z, Durfee E, et al. Association of a smartphone application with medication adherence and blood pressure control: the MedISAFE-BP randomized clinical trial. JAMA Intern Med. Jun 01, 2018;178(6):802-809. [FREE Full text] [CrossRef] [Medline]
    31. Persell SD, Peprah YA, Lipiszko D, Lee JY, Li JJ, Ciolino JD, et al. Effect of home blood pressure monitoring via a smartphone hypertension coaching application or tracking application on adults with uncontrolled hypertension: a randomized clinical trial. JAMA Netw Open. Mar 02, 2020;3(3):e200255. [FREE Full text] [CrossRef] [Medline]
    32. Zha P, Qureshi R, Porter S, Chao YY, Pacquiao D, Chase S, et al. Utilizing a mobile health intervention to manage hypertension in an underserved community. West J Nurs Res. Mar 2020;42(3):201-209. [CrossRef] [Medline]
    33. Dorsch MP, Cornellier ML, Poggi AD, Bilgen F, Chen P, Wu C, et al. Effects of a novel contextual just-in-time mobile app intervention (LowSalt4Life) on sodium intake in adults with hypertension: pilot randomized controlled trial. JMIR Mhealth Uhealth. Aug 10, 2020;8(8):e16696. [FREE Full text] [CrossRef] [Medline]
    34. Bozorgi A, Hosseini H, Eftekhar H, Majdzadeh R, Yoonessi A, Ramezankhani A, et al. The effect of the mobile “blood pressure management application” on hypertension self-management enhancement: a randomized controlled trial. Trials. Jun 24, 2021;22:413. [CrossRef]
    35. Najafi Ghezeljeh T, Sharifian S, Nasr Isfahani M, Haghani H. Comparing the effects of education using telephone follow-up and smartphone-based social networking follow-up on self-management behaviors among patients with hypertension. Contemp Nurse. Mar 05, 2018;54(4-5):362-373. [CrossRef]
    36. Payne Riches S, Piernas C, Aveyard P, Sheppard JP, Rayner M, Albury C, et al. A mobile health salt reduction intervention for people with hypertension: results of a feasibility randomized controlled trial. JMIR Mhealth Uhealth. Oct 21, 2021;9(10):e26233. [CrossRef]
    37. Márquez Contreras E, Márquez Rivero S, Rodríguez García E, López-García-Ramos L, Carlos Pastoriza Vilas J, Baldonedo Suárez A, et al. Specific hypertension smartphone application to improve medication adherence in hypertension: a cluster-randomized trial. Curr Med Res Opin. Dec 05, 2018;35(1):167-173. [CrossRef]
    38. Leupold F, Karimzadeh A, Breitkreuz T, Draht F, Klidis K, Grobe T, et al. Digital redesign of hypertension management with practice and patient apps for blood pressure control (PIA study): a cluster-randomised controlled trial in general practices. eClinicalMedicine. Jan 2023;55:101712. [CrossRef]
    39. Kario K, Nomura A, Kato A, Harada N, Tanigawa T, So R, et al. Digital therapeutics for essential hypertension using a smartphone application: a randomized, open‐label, multicenter pilot study. J Clin Hypertens. Jan 23, 2021;23(5):923-934. [CrossRef]
    40. Abu-El-Noor N, Aljeesh Y, Bottcher B, Abu-El-Noor M. Impact of a mobile phone app on adherence to treatment regimens among hypertensive patients: a randomised clinical trial study. European Journal of Cardiovascular Nursing. Eur J Cardiovasc Nurs. Jun 2021;20(5):428-435. [CrossRef]
    41. Alsaqer K, Bebis H. Self-care of hypertension of older adults during COVID-19 lockdown period: a randomized controlled trial. Clin Hypertens. Jul 15, 2022;28:21. [CrossRef]
    42. Burnier M. Medication adherence and persistence as the cornerstone of effective antihypertensive therapy. Am J Hypertens. Nov 2006;19(11):1190-1196. [CrossRef] [Medline]
    43. Bartholomew LK, Mullen PD. Five roles for using theory and evidence in the design and testing of behavior change interventions. J Public Health Dent. Mar 18, 2011;71 Suppl 1(s1):S20-S33. [CrossRef] [Medline]
    44. Whelton PK, Carey RM, Aronow WS, Casey DEJ, Collins KJ, Dennison Himmelfarb C, et al. 2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the prevention, detection, evaluation, and management of high blood pressure in adults: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. Hypertension. Jun 2018;71(6):e13-115. [FREE Full text] [CrossRef] [Medline]
    45. Fu J, Liu Y, Zhang L, Zhou L, Li D, Quan H, et al. Nonpharmacologic interventions for reducing blood pressure in adults with prehypertension to established hypertension. J Am Heart Assoc. Oct 20, 2020;9(19):e016804. [FREE Full text] [CrossRef] [Medline]
    46. Cotie LM, Prince SA, Elliott CG, Ziss MC, McDonnell LA, Mullen KA, et al. The effectiveness of eHealth interventions on physical activity and measures of obesity among working-age women: a systematic review and meta-analysis. Obes Rev. Oct 2018;19(10):1340-1358. [CrossRef] [Medline]
    47. Hui CY, Walton R, McKinstry B, Jackson T, Parker R, Pinnock H. The use of mobile applications to support self-management for people with asthma: a systematic review of controlled studies to identify features associated with clinical effectiveness and adherence. J Am Med Inform Assoc. May 01, 2017;24(3):619-632. [FREE Full text] [CrossRef] [Medline]
    48. Ramallo-Fariña Y, García-Bello MA, García-Pérez L, Boronat M, Wägner AM, Rodríguez-Rodríguez L, et al. Effectiveness of internet-based multicomponent interventions for patients and health care professionals to improve clinical outcomes in type 2 diabetes evaluated through the INDICA study: multiarm cluster randomized controlled trial. JMIR Mhealth Uhealth. Nov 02, 2020;8(11):e18922. [FREE Full text] [CrossRef] [Medline]
    49. Lee EK, Poon P, Yip BH, Bo Y, Zhu MT, Yu CP, et al. Global burden, regional differences, trends, and health consequences of medication nonadherence for hypertension during 2010 to 2020: a meta-analysis involving 27 million patients. J Am Heart Assoc. Sep 06, 2022;11(17):e026582. [FREE Full text] [CrossRef] [Medline]
    50. Ma H, Wang A, Pei R, Piao M. Effects of habit formation interventions on physical activity habit strength: meta-analysis and meta-regression. Int J Behav Nutr Phys Act. Sep 12, 2023;20(1):109. [FREE Full text] [CrossRef] [Medline]
    51. Lally P, van Jaarsveld CH, Potts HW, Wardle J. How are habits formed: modelling habit formation in the real world. Eur J Soc Psychol. Jul 16, 2009;40(6):998-1009. [CrossRef]
    52. Glanz K, Bishop DB. The role of behavioral science theory in development and implementation of public health interventions. Annu Rev Public Health. 2010;31:399-418. [CrossRef] [Medline]
    53. Lippke S, Ziegelmann JP. Theory‐based health behavior change: developing, testing, and applying theories for evidence‐based interventions. Appl Psychol. Jul 08, 2008;57(4):698-716. [CrossRef]
    54. Peng S, Yuan F, Othman AT, Zhou X, Shen G, Liang J. The effectiveness of e-health interventions promoting physical activity and reducing sedentary behavior in college students: a systematic review and meta-analysis of randomized controlled trials. Int J Environ Res Public Health. Dec 25, 2022;20(1):318. [FREE Full text] [CrossRef] [Medline]
    55. Borrelli B. The assessment, monitoring, and enhancement of treatment fidelity in public health clinical trials. J Public Health Dent. 2011;71 Suppl 1:S52-S63. [Medline]
    56. Al-Durra M, Torio MB, Cafazzo JA. The use of behavior change theory in internet-based asthma self-management interventions: a systematic review. J Med Internet Res. Apr 02, 2015;17(4):e89. [FREE Full text] [CrossRef] [Medline]
    57. Salas-Groves E, Galyean S, Alcorn M, Childress A. Behavior change effectiveness using nutrition apps in people with chronic diseases: scoping review. JMIR Mhealth Uhealth. Jan 13, 2023;11:e41235. [FREE Full text] [CrossRef] [Medline]
    58. Avery L, Flynn D, van Wersch A, Sniehotta FF, Trenell MI. Changing physical activity behavior in type 2 diabetes: a systematic review and meta-analysis of behavioral interventions. Diabetes Care. Dec 2012;35(12):2681-2689. [FREE Full text] [CrossRef] [Medline]
    59. Tang MY, Smith DM, Mc Sharry J, Hann M, French DP. Behavior change techniques associated with changes in postintervention and maintained changes in self-efficacy for physical activity: a systematic review with meta-analysis. Ann Behav Med. Aug 16, 2019;53(9):801-815. [CrossRef] [Medline]
    60. Evangelidis N, Craig J, Bauman A, Manera K, Saglimbene V, Tong A. Lifestyle behaviour change for preventing the progression of chronic kidney disease: a systematic review. BMJ Open. Oct 28, 2019;9(10):e031625. [FREE Full text] [CrossRef] [Medline]
    61. Dombrowski SU, Sniehotta FF, Avenell A, Johnston M, MacLennan G, Araújo-Soares V. Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review. Health Psychol Rev. Mar 2012;6(1):7-32. [CrossRef]
    62. Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. Nov 2009;28(6):690-701. [CrossRef] [Medline]
    63. Michie S, West R, Sheals K, Godinho CA. Evaluating the effectiveness of behavior change techniques in health-related behavior: a scoping review of methods used. Transl Behav Med. Mar 01, 2018;8(2):212-224. [FREE Full text] [CrossRef] [Medline]

    ‎
    BCT: behavior change technique
    BP: blood pressure
    DBP: diastolic blood pressure
    mHealth: mobile health
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    RCT: randomized controlled trial
    SBP: systolic blood pressure

    Edited by E Sezgin; submitted 29.11.23; peer-reviewed by B Green, S Payne; comments to author 10.03.24; revised version received 29.05.24; accepted 19.08.24; published 22.10.24.

    Copyright

    ©You Zhou, Si-Jia Li, Ren-Qian Huang, Hao-Ming Ma, Ao-Qi Wang, Xing-Yi Tang, Run-Yuan Pei, Mei-Hua Piao. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 22.10.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.