Published on in Vol 5, No 3 (2017): March

mHealth Interventions for Health System Strengthening in China: A Systematic Review

mHealth Interventions for Health System Strengthening in China: A Systematic Review

mHealth Interventions for Health System Strengthening in China: A Systematic Review

Original Paper

1The George Institute for Global Health at Peking University Health Science Center, Beijing, China

2Sydney Medical School, The George Institute for Global Health, Australia, University of Sydney, Sydney, Australia

3School of Public Health, Peking University Health Science Center, Beijing, China

4Medical School, University of Michigan, Ann Arbor, MI, United States

5Sun Yat-sen Global Health Institute, School of Public Health, Sun Yat-sen University, Guangzhou, China

Corresponding Author:

Maoyi Tian, MSc, PhD

The George Institute for Global Health at Peking University Health Science Center

Suite 1801, Tower B, Horizon Tower

6 Zhichun Road

Beijing, 100088

China

Phone: 86 1082800577 ext 303

Fax:86 1082800177

Email: mtian@georgeinstitute.org.cn


Background: With rapidly expanding infrastructure in China, mobile technology has been deemed to have the potential to revolutionize health care delivery. There is particular promise for mobile health (mHealth) to positively influence health system reform and confront the new challenges of chronic diseases.

Objective: The aim of this study was to systematically review existing mHealth initiatives in China, characterize them, and examine the extent to which mHealth contributes toward the health system strengthening in China. Furthermore, we also aimed to identify gaps in mHealth development and evaluation.

Methods: We systematically reviewed the literature from English and Chinese electronic database and trial registries, including PubMed, EMBASE, Cochrane, China National Knowledge of Infrastructure (CNKI), and World Health Organization (WHO) International Clinical Trials Registry Platform. We used the English keywords of mHealth, eHealth, telemedicine, telehealth, mobile phone, cell phone, text messaging, and China, as well as their corresponding Chinese keywords. All articles using mobile technology for health care management were included in the study.

Results: A total of 1704 articles were found using the search terms, and eventually 72 were included. Overall, few high quality interventions were identified. Most interventions were found to be insufficient in scope, and their evaluation was of inadequate rigor to generate scalable solutions and provide reliable evidence of effectiveness. Most interventions focused on text messaging for consumer education and behavior change. There were a limited number of interventions that addressed health information management, health workforce issues, use of medicines and technologies, or leadership and governance from a health system perspective.

Conclusions: We provide four recommendations for future mHealth interventions in China that include the need for the development, evaluation and trials examining integrated mHealth interventions to guide the development of future mHealth interventions, target disadvantaged populations with mHealth interventions, and generate appropriate evidence for scalable and sustainable models of care.

JMIR Mhealth Uhealth 2017;5(3):e32

doi:10.2196/mhealth.6889

Keywords



Burden of Disease and Health System in China

In the last decade, China has undergone a continuing epidemiological transformation from infectious diseases to chronic and noncommunicable diseases (NCDs) [1,2]. NCDs caused over 80% of China’s total disability-adjusted life years in 2013 and accounted for China’s largest burden of disease [3]. Chronic and NCDs pose special challenges to existing health systems as the long-term ongoing management of such conditions requires a shift from institutional care to community-based care, with an increased focus on self-management with or without peer or family support [4]. Despite the four major rounds of health care reforms since mid-1980s in China, many health equity and system level challenges remain [4,5]. Responding to those challenges, the health system needs to be adjusted to provide more effective solutions. The portability and connectivity of mobile health (mHealth) can potentially serve as an effective tool in facilitating this adjustment and to allow the health care delivery to reach hard-to-reach population. mHealth has been variably defined. The World Health Organization (WHO) definition is medical and public health practice supported by mobile devices, such as mobile phones, personal digital assistants (PDAs), and other wireless devices [6]. mHealth involves the use of a wide range of functionalities incorporated by such mobile devices, including standard voice, short message service (SMS), Web browsing, and applications on different operating systems.

Chinese Mobile Market and the Potential for mHealth

The unprecedented uptake of mobile phones with an ever growing telecommunications infrastructure has driven the development of mHealth innovation around the globe. In China, mobile phone penetration reached 94.5 per 100 people in 2014 [7]. Cellular signals now cover almost all residential areas from densely populated cities to remote villages, with increasing penetration of 3G and 4G networks. Penetration of smartphones has also increased rapidly, reaching 90% in urban areas and 32% in rural areas in 2015 [8]. The rapid development of this mobile infrastructure has created significant potential for mHealth interventions in China.

The rapid adoption of mobile phones may be explained by the diffusion of innovation theory, which is one of the most popular theories for studying adoption of information technologies and understanding how information technology innovations spread within and between communities [9].

Prior Work and Objectives

Although there were several reviews documenting the mHealth interventions in low- and middle-income countries (LMICs) [10-12], no systematic reviews of the scope and value of mHealth initiatives in the largest developing country exist. The specific aims of this systematic review were to (1) characterize mHealth interventions across all disease areas in China, (2) evaluate the extent to which mHealth interventions focus on health system strengthening, and (3) identify gaps in mHealth intervention development and evaluation that need to be addressed in the future.


Database Search

A systematic search of the literature in both Chinese and English published from May 26, 2008 to December 17, 2015, was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [13] using the following electronic databases: PubMed, EMBASE, Cochrane, and China National Knowledge of Infrastructure (CNKI). We also searched for registered trials in the WHO International Clinical Trials Registry Platform, which included 15 approved trial registries and supplementary searches in Chinese Clinical Trial Registry (CHICTR), and Clinicaltrials.gov. English keywords used in these searches included the following: mHealth, eHealth, telemedicine, telehealth, mobile phone, cell phone, text messaging, and China. The Chinese keywords used include “ShouJi” (mobile phone or cell phone), “DuanXin” (text messaging), “YiDongJiangKang” (mHealth), and “Yi Dong Yi Liao” (mobile medicine). Multimedia Appendix 1 lists the detailed search strategy for each database.

Inclusion and Exclusion Criteria

We included all articles related to health care management using mobile technology in China. Any type of the following articles with full texts was included: (1) randomized controlled trials (RCTs), (2) quasi-experimental studies, (3) descriptive studies without any outcome measured, or (4) registered RCTs. We only included studies written in English or Chinese, and articles related to telemedicine or telehealth were only included if mobile technologies were used as part of the intervention. We excluded all articles describing technology development, review articles, protocol papers, and any studies using fixed landline phone or the Internet using a desktop computer as part of the intervention. A total of 5 reviewers independently evaluated and excluded articles at the abstract review stage. Full-text articles whose abstracts met the inclusion criteria were then reviewed by 3 reviewers.

Analytical Framework

We utilized an adapted health system framework to evaluate the role of mHealth interventions as a health system strengthening tool (Figure 1) [14-16]. In this framework, there were two dimensions: (1) the function of mHealth intervention categorizing into one of the 12 mHealth tools proposed by Labrique et al [14], and (2) the corresponded health system frame work as developed by Hsiao and WHO [15,16]. Assessing both dimensions of the mHealth intervention allowed us to identify where the gaps were in the mHealth interventions from a health systems perspective.

Figure 1. Adapted health system framework for evaluating mHealth interventions.
View this figure

Data Extraction

A spreadsheet was developed for entering extracted data that included study characteristics, the mHealth domain, and the health system domain using the aforementioned analytical framework [16]. An agreement was reached on the definitions and interpretation of each variable in the data extraction template among the reviewers before data collection. Three reviewers independently extracted the data into the template and cross-reviewed. Disagreements in this step were resolved by consensus.

Quality Assessment

For RCTs, methodological quality was assessed using the Cochrane Risk of Bias Assessment Tool [17]. We assessed the random sequence generation, allocation concealment, blinding of participants, personnel and outcome assessors, incomplete outcome data, selective outcome reporting, and other sources of bias. Any discrepancies in article inclusion, data extraction, and bias assessment were discussed and resolved by team consensus.


Included Studies

We retrieved 1704 articles using the search terms, and 323 articles were selected for full-text review (Figure 2). Of those, 251 studies were excluded for the following reasons: not conducted in China (n=81), not using the mobile technology (n=142), protocol papers (n=6), and review articles (n=22).

Figure 2. Study flowchart.
View this figure

Study Characteristics

The study characteristics, mHealth domain, and health system domain of the nonprotocol articles (n=49) are summarized in Table 1. The majority of the studies were conducted in an urban setting (n=34) [18-51], with only 6 focusing on a rural population [28,51-55]. The most common disease focus was on NCDs (n=15) [22,25,26,29,30,34,37-39,42,45,46,52,56,57], whereas 12 studies focused on infectious diseases [33,41,51,53,54,58-64] and 8 studies were designed for maternal and child health [36,40,43,47-49,55,65]. A wide range of study designs was used to evaluate or describe the mHealth intervention, including 18 exploratory studies that described, validated, or pilot-tested mHealth interventions without any quantitative outcome assessment [18-28,58-62,64,66]. A total of 31 studies quantitatively evaluated the mHealth intervention [29-57,63,65], of which 19 utilized a RCT design [29-35,38-40,43,47,51-53,56,57,63,65] whereas the remainder used a quasi-experimental study design (n=12). In most cases, the primary mobile technology was a regular mobile phone (n=36) [18,19,21,25,29-49,51,53-57,59,62-65]. Only 12 studies utilized smartphone technology for the intervention [20,22-24,26-28,50,52,58,61,66].

Table 1. Study characteristics, mHealth domain, and health system domain of nonprotocol articles.
AuthorSettingDisease areaPopulation
(n)
Study description
Type of
device
mHealth
domain
Health system domain
Descriptive Studies







Deng [18]UrbanOthers
-patients for sedation gastrointestinal endoscopy (SGIE)
908 outpatients in the anesthesia clinic for SGIEFeasibility to use SMS to improve the adherence for SGIE appointment
R^Client education and behavior changeService delivery
Chen [19]UrbanOthers
-suicide
attempters
15 suicide attempters from the emergency departmentFeasibility to SMS to decrease recidivism for suicide attempters
RClient education and behavior changeService delivery
Li [58]Not
described
Infectious diseaseNot
described
A decision support system for the responses to infectious disease emergencies
S*Electronic decision supportLeadership/governance
Zhao [20]UrbanNot
mentioned
Not
described
A case report describing development of a shared community health information system
SElectronic medical recordLeadership/governance
Li [59]Not
described
Infectious disease
-hand, foot, and mouth disease
Not
described
Use of SMS to develop automated alert and response system for hand, foot, and mouth disease
RRegistries and vital event trackingLeadership/governance
Guo [60]Not
described
Infectious diseaseNot
described
A mobile phone-based infectious disease reporting system in earthquake-affected area
PDAaData collection and reportingInformation
Mao [21]UrbanNot
mentioned
100 patients admitted from general hospitalUse of SMS to deliver individualized pharmaceutical care
RClient education and behavior changeService delivery
Yang [61]Not
described
Infectious disease495 health care agencies in earthquake-affected areaUse of mobile phone as a surveillance tool to monitor infectious disease
SData collection and reportingInformation
Jun [22]UrbanNoncommunicable disease
-adolescent Idiopathic Scoliosis
64 adolescent idiopathic scoliosis patientsUse of smartphone to measure the axial trunk rotation
SSensors and point-of-care diagnosisMedicines/technologies
Zhang [64]Not
described
Infectious disease
-schistoscomajaponicum infection
Not
described
Use of SMS to send alert the fishermen to avoid the schistosome infection
RRegistries and vital event trackingLeadership/governance
Ma [62]Not
described
Infectious diseaseNot
described
Development of SMS-based emergency response system for infectious disease
RRegistries and vital event trackingLeadership/governance
Guan [23]UrbanOthers
-voiding diary monitoring
20 healthy volunteersDevelopment of smartphone-based remote voiding diary monitoring system
SData collection and reportingService delivery
Ye [24]UrbanOthers
-slitlampbiomicroscopy
Not
described
Use of smartphone camera for teleophthalmology
SSensors and point-of-care diagnosisService delivery
Yu [66]Not
described
Not
mentioned
11 volunteersHealth examination toolkit involving sensors and data upload into an Android phone
SSensors and point-of-care diagnosisService delivery
Yin [25]UrbanNoncommunicable disease
-dialysis patients
Not
described
Development of mobile phone-based follow up system
RClient education and behavior changeService delivery
Yang [65]UrbanNoncommunicable disease
-facial acne
80 patients with facial acneUse of mobile phone to grade the severity of facial acne
SSensors and point-of-care diagnosisService delivery
Wang [27]UrbanOthers
-dietary intake assessment
35 healthy volunteersDevelopment of dietary intake assessment using mobile phone camera function
SData collection and reportingMedicines/technologies
Smith [28]Rural and urbanNot
mentioned
110 healthy adultsDevelopment of a smartphone-assisted 24-h recall to assess beverage consumption
SData collection and reportingMedicines/technologies
RCT


InterventionFollow-up


Tian [52]RuralNoncommunicable disease
-cardiovascular disease
2086 high cardiovascular risk patientsA smartphone based electronic decision support system focusing on two medication use and two lifestyle modifications12 monthSElectronic decision supportService delivery
Lin [29]UrbanNoncommunicable disease
-obesity
123 overweight adultsSMS-assisted lifestyle weight loss intervention6 monthRClient education and behavior changeService delivery
Liu [51]Rural and urbanInfectious disease
-tuberculosis
4173 pulmonary TBb patientsSMS reminders and medication monitoring6 monthRClient education and behavior changeService delivery
Sabin [63]Not
described
Infectious disease
-HIVc
120 HIV patientsReal time SMS reminders triggered by the electronic medication storage device6 monthRClient education and behavior changeService delivery
Liu [30]UrbanNoncommunicable disease
-cardiovascular disease
589 workers without known CVDdMobile-phone based lifestyle intervention12 monthRClient education and behavior changeService delivery
Shi [31]UrbanOthers
-smokers
179 adolescent smokersSmoking cessation lifestyle intervention delivered by the SMS12 weekRClient education and behavior changeService delivery
Chen [53]RuralInfectious disease
-Viral infections affecting upper respiratory tract and otitis media
977 township level health workersSMS based health worker training1 monthRProvider training and educationHealth workforce
Deng [32]UrbanOthers
-outpatients for sedation gastrointestinal endoscopy
2200 outpatientsSMS reminders to attend medical examinationNot
mentioned
RClient education and behavior changeService delivery
Lv [56]Not
described
Noncommunicable disease
-asthma
150 outpatients with asthmaSMS reminders for asthma self-management12 weekRClient education and behavior changeService delivery
Wang [57]Not
described
Noncommunicable disease
-allergic rhinitis
50 outpatients with allergic rhinitisSMS reminders to improve adherence to medication and treatment30 daysRClient education and behavior changeService delivery
Chai [33]UrbanInfectious disease
-H1N1
1992 residents in ShanghaiSMS-based health education for H1N1 prevention10 daysRClient education and behavior changeService delivery
Lin [65]Not
described
Maternal and child health258 parent-child pairs with child having cataractSMS reminders to attend medical appointment4 daysRClient education and behavior changeService delivery
Dai [34]UrbanNoncommunicable disease
-diabetes
80 type-2 diabetes patientsSMS based health education12 monthRClient education and behavior changeService delivery
Shi [35]UrbanOthers
-smokers
176 adolescent smokersSMS based health education for smoking cessation3 monthRClient education and behavior changeService delivery
Zhang [40]UrbanMaternal and child health166 children with asthmaSMS-based health promotion3 monthRClient education and behavior changeService delivery
Wei [38]UrbanNoncommunicable disease
-chronic kidney disease
108 patients with chronic kidney diseaseSMS-based medication adherence intervention3 monthRClient education and behavior changeService delivery
Li [43]UrbanMaternal and child health82 pregnant womenSMS-based dietary recommendation during pregnancyNot
mentioned
RClient education and behavior changeService delivery
Chen [74]UrbanMaternal and child health155 pregnant womenSMS-based breastfeeding promotion16 weekRClient education and behavior changeService delivery
Qu [25]UrbanNoncommunicable disease
-schizophrenia
178 patients with schizophreniaSMS-based medication adherence intervention12 monthRClient education and behavior changeService delivery
Quasi-experiment







Jiang [49]UrbanMaternal and child health582 expectant mothersSMS-based intervention about infant feeding12 monthRClient education and behavior changeService delivery
Fang [42]UrbanNoncommunicable disease
-hypertension
599 hypertensive patientsSMS-based health education for hypertension management12 monthRClient education and behavior changeService delivery
Zhao [46]UrbanNoncommunicable disease
-diabetes
64 type-2 diabetes patientsSMS-based medication adherence and health education program3 monthRClient education and behavior changeService delivery
Qin [44]UrbanOthers
-dialysis
92 dialysis patientsSMS-based health education for dialysis patients delivered by the nurse53-612 daysRClient education and behavior changeService delivery
Xie [45]UrbanNoncommunicable disease
-diabetes
196 type-2 diabetes patientsSMS-based health promotion for diabetes management12 monthRClient education and behavior changeService delivery
Chen [54]RuralInfectious disease
-schistosomiasis
501 healthy residentsSMS-based health promotion for schistosomiasis prevention10 monthRClient education and behavior changeService delivery
Chen [48]UrbanMaternal and child health180 children with allergic rhinitisSMS-based health education for allergic rhinitis management12 monthRClient education and behavior changeService delivery
Xu [41]UrbanInfectious disease
-HIV
71 HIV patientsSMS-based medication adherence intervention12 monthRClient education and behavior changeService delivery
Ni [36]UrbanMaternal and child health460 pregnant womenSMS-based health education5 monthRClient education and behavior changeService delivery
Liu [37]UrbanNoncommunicable disease
-acute coronary syndrome
82 ACSe patientsSMS based medication adherence intervention1 monthRClient education and behavior changeService delivery
Zhou [55]RuralMaternal and child healthN250 pregnant womenSMS-based health education for HIV prevention1 monthRClient education and behavior changeService delivery
He [50]UrbanOthers
-general health
100 residents with smartphoneSmartphone-based pedometer “app”6 monthsSSensors and point-of-care diagnosisService delivery

aPDA: personal digital assistant.

bTB: tuberculosis.

cHIV: human immunodeficiency virus.

dCVD: cardiovascular disease.

eACS: acute coronary syndrome.

^R: regular mobile phone.

*S: smartphone.

The search of registered clinical trials identified 23 additional mHealth registered RCTs (Multimedia Appendix 2). Although 12 of these studies were listed as completed, we were only able to find 5 studies with published results. All 5 studies were identified during the original systematic review of the literature [29,32,51,52,65]. Consistent with the published RCTs, the majority of the interventions described in the registry focused on client education and behavior change using simple text messaging.

Role of mHealth in the Health System

Applying the adapted health system framework (Table 2), we found the client education and behavioral change communication was the most commonly targeted mHealth domain (n=32) [18,19,21,25,29-49,51,54-57,63,65]. It was found that 5 interventions addressed sensors and point-of-care diagnostics [22,24,26,50,66], 5 interventions focused on data collection and reporting [23,27,28,60,61], 3 interventions involved registries and vital events tracking [59,62,64], 2 interventions focused on electronic decision support [52,58], 1 intervention involved electronic health records [20], and 1 intervention delivered provider training and education [53]. There were no interventions identified in the domains of provider to provider training, provider work planning and scheduling, human resources management, supply chain management, or financial transactions and incentives. From a health systems perspective, most studies targeted service delivery (n=38) [18,19,21,23-26,29-52,54-57,63,65,66]. Few interventions focused on the provision or management of information (n=2) [60,61], health workforce support (n=1) [53], medicines and technologies (n=3) [22,27,28], or leadership and governance (n=5) [20,58,59,62,64].

Risk of Bias Assessment

For the RCTs, risk of bias was mostly classified as either low or unclear (Table 3). Four studies did not provide sufficient information to assess risk [34,35,43,47].

Table 2. Health system framework assessment of the mHealth interventions.
mHealth FunctionalityHealth System Structural Component
Leadership/
Governance
FinancingPaymentHealth WorkforceMedicines/
Technologies
InformationService DeliverySub-total
Education/behavioral





3232
Sensors/point-of-care devices



1
45
Registries/vital events tracking3





3
Data collection and reporting



2215
Electronic health records1





1
Electronic decision support1




12
Provider to provider communication







Provider work planning/scheduling







Provider training/education


1


1
Human resources management







Supply chain management







Financial transactions/incentives







Sub-total5

13238
Table 3. Risk of bias assessment for randomized controlled trials.
AuthorSequence generationAllocation concealmentBlinding of participants, personnel, and outcome assessorsIncomplete outcome dataSelective outcome reportingOther sources of bias
Tian [52]LowLowLowLowLowLow
Lin [29]LowLowLowLowUnclearLow
Liu [51]LowUnclearUnclearUnclearUnclearLow
Sabin [63]LowLowUnclearLowUnclearLow
Liu [30]LowLowLowLowUnclearLow
Shi [31]UnclearUnclearUnclearLowUnclearLow
Chen [53]LowLowLowLowUnclearLow
Deng [32]LowLowLowUnclearUnclearLow
Lv [56]LowUnclearUnclearUnclearUnclearLow
Wang [57]LowLowLowUnclearUnclearLow
Chai [33]LowUnclearLowUnclearUnclearLow
Lin [65]LowLowLowLowUnclearLow
Dai [34]UnclearUnclearUnclearUnclearUnclearHigh
Shi [35]UnclearUnclearUnclearUnclearUnclearHigh
Zhang [40]LowUnclearUnclearUnclearUnclearUnclear
Wei [38]LowUnclearUnclearUnclearUnclearUnclear
Li [43]UnclearUnclearUnclearUnclearUnclearHigh
Chen [47]UnclearUnclearUnclearUnclearUnclearHigh
Qu [39]LowLowLowLowUnclearLow

Principal Findings

In this study, we reviewed studies and registered trials for studies published in the peer-reviewed journals involving mHealth interventions in China. We particularly focused on the extent to which mHealth interventions had the capacity to contribute to health care strengthening in the context of a rapidly evolving disease burden. Although we did observe an increasing focus on NCDs, there was little evidence of the development of mHealth interventions that were likely to substantially strengthen health care systems. We also noted a large disparity in the development of mHealth interventions that were focused on rural as opposed to urban areas. In addition, the quality of evidence provided in relation to effectiveness of such interventions is generally poor.

Comparison With Other Reviews

Beratarrechea et al [11] conducted a review to examine the role of mHealth intervention on the management of NCDs in LMICs, with a focus on the use of SMS and automated voice interventions. The study found that there were significant improvement on certain clinical outcomes and processes of care. Peiris et al further performed a review to explore the impact of all mHealth interventions on health care quality for NCDs in LMICs. Similar to our findings, there were few high-quality studies, and most of the studies used the SMS for patient behavior change. Very few studies addressed the mHealth intervention as a health system strengthening tool.

Health System Strengthening

On the basis of the literature we have identified, the development of mHealth interventions by academia in China remains relatively under-developed, in terms of both scope and capability. Interventions mostly utilized a texting tool to provide client education and behavior change. We identified a focus on only 7 of the 12 mHealth domains, with no interventions concentrating on interprovider communication or health service management, including financial transactions. In addition, all the interventions were developed as stand-alone tools to deliver health services, with little or no exploration of how integration within existing or developing health systems can be achieved.

Health Equality

Equitable access to quality health services is an important dimension of an effective health system. In China, around 50% of the population is based in rural regions, where health outcomes are, in general, poorer than those among urban communities. Addressing such inequities is a public health priority, and mHealth strategies may provide a particular opportunity to reduce gaps that relate to weaker health systems. As China’s mobile network reaches far and deep into its rural areas, mHealth solutions provide a real opportunity to strengthen rural health systems. Despite the huge potentials of mHealth help in closing the health equity gap, few academic studies in China has chosen to focus on this area. The regional imbalance identified in this review may be explained by the greater convenience of conducting studies in urban communities. However, the potential for mHealth to impact on health outcome inequities cannot be addressed if the digital gulf between those who have access to mobile technology in urban areas and those who do not have access in rural areas is not reduced. Similar considerations are relevant to other disadvantaged subgroups of population, including those with relatively low literacy or socioeconomic status.

Quality of Evidence

A key objective of mHealth research should be to provide useful and reliable evidence for end users, including policy-makers in the context of those innovations aimed at improving health outcomes through deployment in the public health care system. Our review found that published and planned mHealth studies in China largely have not and will not produce such outcomes. Fewer than 40% of the published studies utilized an RCT design and all were of uncertain or poor quality based on objective measures. The majority of the reports were descriptive, with no apparent attempt to determine efficacy or effectiveness. Study outcomes were largely the product of low-quality and small-scale experiments, which provided little understanding of the true impact of an intervention with large-scale real-world implementation within complex health systems.

Limitations

There are several limitations to this review. Firstly, we were not able to conduct a quantitative meta-analysis of the outcomes due to the heterogeneity of the RCTs. We identified a number of ongoing trials from the trial registry. The published results of those trials will enable to provide increased power to determine the size of the effect of mHealth interventions on health outcomes. Second, although the adapted health system framework was useful to evaluate the mHealth intervention as a health system strengthening tool, a single study may address multiple mHealth domains or health system domains. We only reported the primary functionality of the mHealth intervention and the key aspect that the intervention addressed in the health system. Finally, this review mainly targeted academic studies in the literature. We should note that China is experiencing rapid development in mHealth technology in the commercial world, many of which may have health system implications that we had limited ability to evaluate in this review.

Conclusions

mHealth has the potential to overcome some of the challenges due to the rapid changing environment of health care needs and provision in China. However, this potential can only be realized through the continual development of mHealth interventions to strengthen the health system, utilizing a subsequent rigorous approach to generating high-quality evidence about the likely implications of “real world implementation.” Therefore, we outline three recommendations for future mHealth research and development in China: (1) mHealth studies should not be conducted as the standalone technical study evaluating its efficacy in the vacuum of the social context, (2) promote the development of integrated mHealth interventions as a tool to serve the existing health system, (3) focus on developing and evaluating mHealth interventions with the potential to reduce health outcome disparities within the population, and (4) conduct large-scale rigorously designed “real world” evaluation of mHealth interventions focused on health system strengthening. Specific public and private investment into such research is a priority.

Acknowledgments

We thank the support from Dr Puhong Zhang, the Acting Director of the China Center for mHealth Innovation, and the funding support from Qualcomm Wireless Reach.

Conflicts of Interest

None declared.

Multimedia Appendix 1

Detailed search strategy for each database used.

PDF File (Adobe PDF File), 27KB

Multimedia Appendix 2

Table: Registered randomized controlled trials in clinical trials database.

PDF File (Adobe PDF File), 27KB

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ACS: acute coronary syndrome
CHICTR: Chinese Clinical Trial Registry
CNKI: China National Knowledge of Infrastructure
COPD: chronic obstructive pulmonary disease
CVD: cardiovascular disease
HIV: human immunodeficiency virus
LMICs: low- and middle-income countries
NCDs: noncommunicable diseases
PDA: personal digital assistant
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
RCTs: randomized controlled trials
SGIE: sedation gastrointestinal endoscopy
SMS: short message service
TB: tuberculosis
WHO: World Health Organization


Edited by G Eysenbach; submitted 26.10.16; peer-reviewed by J Apolinário-Hagen, Y Wu; comments to author 29.12.16; revised version received 26.01.17; accepted 10.02.17; published 16.03.17

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©Maoyi Tian, Jing Zhang, Rong Luo, Shi Chen, Djordje Petrovic, Julie Redfern, Dong Roman Xu, Anushka Patel. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 16.03.2017.

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