This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
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.
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.
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.
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.
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.
In the last decade, China has undergone a continuing epidemiological transformation from infectious diseases to chronic and noncommunicable diseases (NCDs) [
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 [
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 [
Although there were several reviews documenting the mHealth interventions in low- and middle-income countries (LMICs) [
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 [
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.
We utilized an adapted health system framework to evaluate the role of mHealth interventions as a health system strengthening tool (
Adapted health system framework for evaluating mHealth interventions.
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 [
For RCTs, methodological quality was assessed using the Cochrane Risk of Bias Assessment Tool [
We retrieved 1704 articles using the search terms, and 323 articles were selected for full-text review (
Study flowchart.
The study characteristics, mHealth domain, and health system domain of the nonprotocol articles (n=49) are summarized in
Study characteristics, mHealth domain, and health system domain of nonprotocol articles.
Author | Setting | Disease area | Population |
Study description | Type of |
mHealth |
Health system domain | ||
Deng [ |
Urban | Others |
908 outpatients in the anesthesia clinic for SGIE | Feasibility to use SMS to improve the adherence for SGIE appointment | R^ | Client education and behavior change | Service delivery | ||
Chen [ |
Urban | Others |
15 suicide attempters from the emergency department | Feasibility to SMS to decrease recidivism for suicide attempters | R | Client education and behavior change | Service delivery | ||
Li [ |
Not |
Infectious disease | Not |
A decision support system for the responses to infectious disease emergencies | S* | Electronic decision support | Leadership/governance | ||
Zhao [ |
Urban | Not |
Not |
A case report describing development of a shared community health information system | S | Electronic medical record | Leadership/governance | ||
Li [ |
Not |
Infectious disease |
Not |
Use of SMS to develop automated alert and response system for hand, foot, and mouth disease | R | Registries and vital event tracking | Leadership/governance | ||
Guo [ |
Not |
Infectious disease | Not |
A mobile phone-based infectious disease reporting system in earthquake-affected area | PDAa | Data collection and reporting | Information | ||
Mao [ |
Urban | Not |
100 patients admitted from general hospital | Use of SMS to deliver individualized pharmaceutical care | R | Client education and behavior change | Service delivery | ||
Yang [ |
Not |
Infectious disease | 495 health care agencies in earthquake-affected area | Use of mobile phone as a surveillance tool to monitor infectious disease | S | Data collection and reporting | Information | ||
Jun [ |
Urban | Noncommunicable disease |
64 adolescent idiopathic scoliosis patients | Use of smartphone to measure the axial trunk rotation | S | Sensors and point-of-care diagnosis | Medicines/technologies | ||
Zhang [ |
Not |
Infectious disease |
Not |
Use of SMS to send alert the fishermen to avoid the schistosome infection | R | Registries and vital event tracking | Leadership/governance | ||
Ma [ |
Not |
Infectious disease | Not |
Development of SMS-based emergency response system for infectious disease | R | Registries and vital event tracking | Leadership/governance | ||
Guan [ |
Urban | Others |
20 healthy volunteers | Development of smartphone-based remote voiding diary monitoring system | S | Data collection and reporting | Service delivery | ||
Ye [ |
Urban | Others |
Not |
Use of smartphone camera for teleophthalmology | S | Sensors and point-of-care diagnosis | Service delivery | ||
Yu [ |
Not |
Not |
11 volunteers | Health examination toolkit involving sensors and data upload into an Android phone | S | Sensors and point-of-care diagnosis | Service delivery | ||
Yin [ |
Urban | Noncommunicable disease |
Not |
Development of mobile phone-based follow up system | R | Client education and behavior change | Service delivery | ||
Yang [ |
Urban | Noncommunicable disease |
80 patients with facial acne | Use of mobile phone to grade the severity of facial acne | S | Sensors and point-of-care diagnosis | Service delivery | ||
Wang [ |
Urban | Others |
35 healthy volunteers | Development of dietary intake assessment using mobile phone camera function | S | Data collection and reporting | Medicines/technologies | ||
Smith [ |
Rural and urban | Not |
110 healthy adults | Development of a smartphone-assisted 24-h recall to assess beverage consumption | S | Data collection and reporting | Medicines/technologies | ||
Tian [ |
Rural | Noncommunicable disease |
2086 high cardiovascular risk patients | A smartphone based electronic decision support system focusing on two medication use and two lifestyle modifications | 12 month | S | Electronic decision support | Service delivery | |
Lin [ |
Urban | Noncommunicable disease |
123 overweight adults | SMS-assisted lifestyle weight loss intervention | 6 month | R | Client education and behavior change | Service delivery | |
Liu [ |
Rural and urban | Infectious disease |
4173 pulmonary TBb patients | SMS reminders and medication monitoring | 6 month | R | Client education and behavior change | Service delivery | |
Sabin [ |
Not |
Infectious disease |
120 HIV patients | Real time SMS reminders triggered by the electronic medication storage device | 6 month | R | Client education and behavior change | Service delivery | |
Liu [ |
Urban | Noncommunicable disease |
589 workers without known CVDd | Mobile-phone based lifestyle intervention | 12 month | R | Client education and behavior change | Service delivery | |
Shi [ |
Urban | Others |
179 adolescent smokers | Smoking cessation lifestyle intervention delivered by the SMS | 12 week | R | Client education and behavior change | Service delivery | |
Chen [ |
Rural | Infectious disease |
977 township level health workers | SMS based health worker training | 1 month | R | Provider training and education | Health workforce | |
Deng [ |
Urban | Others |
2200 outpatients | SMS reminders to attend medical examination | Not |
R | Client education and behavior change | Service delivery | |
Lv [ |
Not |
Noncommunicable disease |
150 outpatients with asthma | SMS reminders for asthma self-management | 12 week | R | Client education and behavior change | Service delivery | |
Wang [ |
Not |
Noncommunicable disease |
50 outpatients with allergic rhinitis | SMS reminders to improve adherence to medication and treatment | 30 days | R | Client education and behavior change | Service delivery | |
Chai [ |
Urban | Infectious disease |
1992 residents in Shanghai | SMS-based health education for H1N1 prevention | 10 days | R | Client education and behavior change | Service delivery | |
Lin [ |
Not |
Maternal and child health | 258 parent-child pairs with child having cataract | SMS reminders to attend medical appointment | 4 days | R | Client education and behavior change | Service delivery | |
Dai [ |
Urban | Noncommunicable disease |
80 type-2 diabetes patients | SMS based health education | 12 month | R | Client education and behavior change | Service delivery | |
Shi [ |
Urban | Others |
176 adolescent smokers | SMS based health education for smoking cessation | 3 month | R | Client education and behavior change | Service delivery | |
Zhang [ |
Urban | Maternal and child health | 166 children with asthma | SMS-based health promotion | 3 month | R | Client education and behavior change | Service delivery | |
Wei [ |
Urban | Noncommunicable disease |
108 patients with chronic kidney disease | SMS-based medication adherence intervention | 3 month | R | Client education and behavior change | Service delivery | |
Li [ |
Urban | Maternal and child health | 82 pregnant women | SMS-based dietary recommendation during pregnancy | Not |
R | Client education and behavior change | Service delivery | |
Chen [ |
Urban | Maternal and child health | 155 pregnant women | SMS-based breastfeeding promotion | 16 week | R | Client education and behavior change | Service delivery | |
Qu [ |
Urban | Noncommunicable disease |
178 patients with schizophrenia | SMS-based medication adherence intervention | 12 month | R | Client education and behavior change | Service delivery | |
Jiang [ |
Urban | Maternal and child health | 582 expectant mothers | SMS-based intervention about infant feeding | 12 month | R | Client education and behavior change | Service delivery | |
Fang [ |
Urban | Noncommunicable disease |
599 hypertensive patients | SMS-based health education for hypertension management | 12 month | R | Client education and behavior change | Service delivery | |
Zhao [ |
Urban | Noncommunicable disease |
64 type-2 diabetes patients | SMS-based medication adherence and health education program | 3 month | R | Client education and behavior change | Service delivery | |
Qin [ |
Urban | Others |
92 dialysis patients | SMS-based health education for dialysis patients delivered by the nurse | 53-612 days | R | Client education and behavior change | Service delivery | |
Xie [ |
Urban | Noncommunicable disease |
196 type-2 diabetes patients | SMS-based health promotion for diabetes management | 12 month | R | Client education and behavior change | Service delivery | |
Chen [ |
Rural | Infectious disease |
501 healthy residents | SMS-based health promotion for schistosomiasis prevention | 10 month | R | Client education and behavior change | Service delivery | |
Chen [ |
Urban | Maternal and child health | 180 children with allergic rhinitis | SMS-based health education for allergic rhinitis management | 12 month | R | Client education and behavior change | Service delivery | |
Xu [ |
Urban | Infectious disease |
71 HIV patients | SMS-based medication adherence intervention | 12 month | R | Client education and behavior change | Service delivery | |
Ni [ |
Urban | Maternal and child health | 460 pregnant women | SMS-based health education | 5 month | R | Client education and behavior change | Service delivery | |
Liu [ |
Urban | Noncommunicable disease |
82 ACSe patients | SMS based medication adherence intervention | 1 month | R | Client education and behavior change | Service delivery | |
Zhou [ |
Rural | Maternal and child health | N250 pregnant women | SMS-based health education for HIV prevention | 1 month | R | Client education and behavior change | Service delivery | |
He [ |
Urban | Others |
100 residents with smartphone | Smartphone-based pedometer “app” | 6 months | S | Sensors and point-of-care diagnosis | Service 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 (
Applying the adapted health system framework (
For the RCTs, risk of bias was mostly classified as either low or unclear (
Health system framework assessment of the mHealth interventions.
mHealth Functionality | Health System Structural Component | |||||||
Leadership/ |
Financing | Payment | Health Workforce | Medicines/ |
Information | Service Delivery | Sub-total | |
Education/behavioral | 32 | 32 | ||||||
Sensors/point-of-care devices | 1 | 4 | 5 | |||||
Registries/vital events tracking | 3 | 3 | ||||||
Data collection and reporting | 2 | 2 | 1 | 5 | ||||
Electronic health records | 1 | 1 | ||||||
Electronic decision support | 1 | 1 | 2 | |||||
Provider to provider communication | ||||||||
Provider work planning/scheduling | ||||||||
Provider training/education | 1 | 1 | ||||||
Human resources management | ||||||||
Supply chain management | ||||||||
Financial transactions/incentives | ||||||||
Sub-total | 5 | 1 | 3 | 2 | 38 |
Risk of bias assessment for randomized controlled trials.
Author | Sequence generation | Allocation concealment | Blinding of participants, personnel, and outcome assessors | Incomplete outcome data | Selective outcome reporting | Other sources of bias |
Tian [ |
Low | Low | Low | Low | Low | Low |
Lin [ |
Low | Low | Low | Low | Unclear | Low |
Liu [ |
Low | Unclear | Unclear | Unclear | Unclear | Low |
Sabin [ |
Low | Low | Unclear | Low | Unclear | Low |
Liu [ |
Low | Low | Low | Low | Unclear | Low |
Shi [ |
Unclear | Unclear | Unclear | Low | Unclear | Low |
Chen [ |
Low | Low | Low | Low | Unclear | Low |
Deng [ |
Low | Low | Low | Unclear | Unclear | Low |
Lv [ |
Low | Unclear | Unclear | Unclear | Unclear | Low |
Wang [ |
Low | Low | Low | Unclear | Unclear | Low |
Chai [ |
Low | Unclear | Low | Unclear | Unclear | Low |
Lin [ |
Low | Low | Low | Low | Unclear | Low |
Dai [ |
Unclear | Unclear | Unclear | Unclear | Unclear | High |
Shi [ |
Unclear | Unclear | Unclear | Unclear | Unclear | High |
Zhang [ |
Low | Unclear | Unclear | Unclear | Unclear | Unclear |
Wei [ |
Low | Unclear | Unclear | Unclear | Unclear | Unclear |
Li [ |
Unclear | Unclear | Unclear | Unclear | Unclear | High |
Chen [ |
Unclear | Unclear | Unclear | Unclear | Unclear | High |
Qu [ |
Low | Low | Low | Low | Unclear | Low |
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.
Beratarrechea et al [
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.
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.
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.
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.
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.
Detailed search strategy for each database used.
Table: Registered randomized controlled trials in clinical trials database.
acute coronary syndrome
Chinese Clinical Trial Registry
China National Knowledge of Infrastructure
chronic obstructive pulmonary disease
cardiovascular disease
human immunodeficiency virus
low- and middle-income countries
noncommunicable diseases
personal digital assistant
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
randomized controlled trials
sedation gastrointestinal endoscopy
short message service
tuberculosis
World Health Organization
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.
None declared.