Impact and efficacy of mobile health intervention in the management of diabetes and hypertension: a systematic review and meta-analysis

Abstract

With the continuous development of science and technology, mobile health (mHealth) intervention has been proposed as a treatment strategy for managing chronic diseases. In some developed countries, mHealth intervention has been proven to remarkably improve both the quality of care for patients with chronic illnesses and the clinical outcomes of these patients. However, the effectiveness of mHealth in developing countries remains unclear. Based on this fact, we conducted this systematic review and meta-analysis to evaluate the impact of mHealth on countries with different levels of economic development. To this end, we searched Pubmed, ResearchGate, Embase and Cochrane databases for articles published from January 2008 to June 2019. All of the studies included were randomized controlled trials. A meta-analysis was performed using the Stata software. A total of 51 articles (including 13 054 participants) were eligible for our systematic review and meta-analysis. We discovered that mHealth intervention did not only play a major role in improving clinical outcomes compared with conventional care, but also had a positive impact on countries with different levels of economic development. More importantly, our study also found that clinical outcomes could be ameliorated even further by combining mHealth with human intelligence rather than using mHealth intervention exclusively. According to our analytical results, mHealth intervention could be used as a treatment strategy to optimize the management of diabetes and hypertension in countries with different levels of economic development.

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BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Open access
1
Open access
Impact and efcacy of mobile health
intervention in the management of
diabetes and hypertension: a systematic
review and meta- analysis
Yaqian Mao,1 Wei Lin,2 Junping Wen,2 Gang Chen 1,2,3
1Shengli Clinical Medical
College, Fujian Medical
University, Fuzhou, Fujian,
China
2Endocrinology, Fujian
Provincial Hospital, Fuzhou,
Fujian, China
3Fujian Provincial Key
Laboratory of Medical Analysis,
Fujian Academy of Medical,
Fuzhou, Fujian, China
Correspondence to
Dr Gang Chen;
chengangfj@ 163. com
To cite: MaoY, LinW, WenJ,
etal. Impact and efcacy of
mobile health intervention in
the management of diabetes
and hypertension: a systematic
review and meta- analysis.
BMJ Open Diab Res Care
2020;8:e001225. doi:10.1136/
bmjdrc-2020-001225
►Additional material is
published online only. To view
please visit the journal online
(http:// dx. doi. org/ 10. 1136/
bmjdrc- 2020- 001225).
Received 20 February 2020
Revised 22 June 2020
Accepted 2 July 2020
Review
Clinical care/Education/Nutrition
© Author(s) (or their
employer(s)) 2020. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published
by BMJ.
ABSTRACT
With the continuous development of science and
technology, mobile health (mHealth) intervention has
been proposed as a treatment strategy for managing
chronic diseases. In some developed countries, mHealth
intervention has been proven to remarkably improve
both the quality of care for patients with chronic illnesses
and the clinical outcomes of these patients. However,
the effectiveness of mHealth in developing countries
remains unclear. Based on this fact, we conducted this
systematic review and meta- analysis to evaluate the
impact of mHealth on countries with different levels of
economic development. To this end, we searched Pubmed,
ResearchGate, Embase and Cochrane databases for
articles published from January 2008 to June 2019. All of
the studies included were randomized controlled trials. A
meta- analysis was performed using the Stata software.
A total of 51 articles (including 13 054 participants)
were eligible for our systematic review and meta-
analysis. We discovered that mHealth intervention did
not only play a major role in improving clinical outcomes
compared with conventional care, but also had a positive
impact on countries with different levels of economic
development. More importantly, our study also found that
clinical outcomes could be ameliorated even further by
combining mHealth with human intelligence rather than
using mHealth intervention exclusively. According to our
analytical results, mHealth intervention could be used
as a treatment strategy to optimize the management of
diabetes and hypertension in countries with different levels
of economic development.
INTRODUCTION
Diabetes mellitus (DM) and hypertension
(HTN) are major controllable risk factors for
cardiac, cerebrovascular, and kidney diseases,
and both of them are highly prevalent comor-
bidities among patients.1 According to the
global risk factor assessment published in
2015, high blood pressure (BP), high blood
sugar, and smoking were considered to be
the top three risk factors for the increasing
disability rate.2 Sequelae such as heart disease
and stroke arising from those aforementioned
risk factors are the leading causes of death
worldwide.3 The prevalence of DM and HTN
has been continuously on the rise in devel-
oping countries, resulting in a heavy financial
burden on their healthcare systems. Estab-
lishing more effective ways to manage chronic
diseases has become the key to solving global
health problems.
Over the past two decades, an increasing
number of people have been suffering from
diabetes worldwide, especially in some devel-
oping countries such as China and India.4-7
According to a national survey conducted
in 2010, 11% of Chinese adults were diag-
nosed with diabetes, representing a total of
109.6 million people.6 It is worth noting that
the prevalence of HTN in China is also very
high, with the number of patients newly diag-
nosed with HTN still increasing. According
to a study performed in China, 33.6% (335.8
million) of the Chinese adult population
had HTN in 2010, but the BP of only 3.9%
patients fell within the currently recom-
mended range (BP<140/90 mm Hg).8 Conse-
quently, cheaper but more effective methods
of managing chronic diseases need to be
urgently developed in some underdeveloped
areas.
With the continuous advance of technology,
mHealth management mode has become
increasingly popular. Until today, a stan-
dardized definition is not yet available, but
it is defined by the WHO as the application
of mobile phones, personal digital assistants
(PDAs), patient monitoring equipment and
other wireless technologies to support medical
and public health practices.9 At present, more
and more people including those from lower
economic classes own mobile and other elec-
tronic devices.10–12mHealth intervention
could possibly be cost- effective in helping
medical staffs manage chronic diseases and
modifying patients’ behaviors, thus providing
a practical healthcare strategy for economi-
cally underdeveloped countries.13–15 mHealth

2BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
has been widely used for managing chronic conditions.
Despite the fact that products related to mHealth inter-
vention are increasing, the efficacy of mHealth in terms
of improving healthcare conditions has not yet been veri-
fied and relevant pieces of evidence are scattered. The
application of smart medical devices and mobile applica-
tions in chronic disease management was summarized in
some earlier literature reviews.16–19 Nonetheless, the effi-
cacy of mHealth intervention in treating chronic diseases
was rarely illustrated. In addition, the applicability of
mHealth intervention has been confirmed by clinical
trials in several developed countries, although these
clinical trials are progressing quite slowly in developing
countries such as China. In order to test the efficacy of
mHealth interventions with respect to chronic disease
management in regions with different economic levels,
we conducted a systematic review and meta- analysis in
this study.
METHODS
To investigate the efficacy of mHealth interventions in
the management of chronic diseases (HTN and DM), we
searched for related articles published between January
2008 and June 2019 in PubMed, ResearchGate, Embase,
and Cochrane. The search scope was limited to random-
ized controlled trials (RCTs), human studies and English
publications. The terms used for searches in PubMed,
ResearchGate, Embase and Cochrane are displayed in
online supplementary table 1, while the flowchart of the
literature research and study selection procedures is illus-
trated in online supplementary figure 1.
mHealth intervention
Until now, no standard system is available for classifying
mHealth intervention. The WHO has identified six
types of mobile medical technologies, which are mobile
text messaging, PDAs and smartphones, patient moni-
toring equipment, mobile telemedicine, MP3 players
and mobile computing.9 20 It was hereby defined as a
kind of health practice or service supported by mobile
technology and devices, including mobile phone text
messages (MPTMs), mobile phone calls (MPCs), wear-
able or portable monitoring devices (WPMDs), mobile
health applications (mHealth Apps), and telemedicine.
Moreover, it was categorized based on the definition and
classification provided by the WHO as well as Wang et
al.5 9 20
Inclusion and exclusion criteria for the relevant studies
Inclusion criteria: (1) all subjects were patients diagnosed
with either diabetes or HTN; (2) all subjects in the exper-
imental group used mHealth intervention for disease
management; (3) the experimental method used by the
institute was RCTs; (4) the experimental results included
the required target values, such as glycated hemoglobin,
systolic blood pressure (SBP), diastolic blood pressure
(DBP), self- efficacy, quality of life and satisfaction, and so
on; (5) the study was published in English.
Exclusion criteria: (1) the complete text could not be
obtained; (2) the experimental design did not meet the
basic scientific requirements; (3) the research subjects
were pregnant women, patients with cancer and other
non- targeted intervention groups; (4) the experimental
group did not use mHealth equipment for interven-
tion or mHealth devices were not the main intervention
measure; (5) the results of the study did not include
target values.
Data extraction
First, patients’ clinical indicators (HbA1c, fasting blood
glucose (FBG), SBP, DBP) at the end of the intervention
were extracted to assess the difference between mHealth
intervention and conventional treatment modalities.
Second, we conducted a subgroup analysis to determine
the impact of mHealth intervention on countries with
different economic levels, and the discrepancies between
the five specific types of mHealth interventions. We also
compared the difference between combined therapy
(mobile health intervention+human intelligence) and
single therapy (mobile health intervention). Finally, we
analyzed the influence of mHealth on self- efficacy, satis-
faction, and health behaviors. Two coauthors (YM, WL)
and a research assistant (JW) extracted information from
studies meeting the inclusion criteria, based on the study
design, subjects, intervention measure, and research
results. The extracted information was reviewed by other
coauthors to verify their accuracy.
Statistical analysis
The Jadad scale, a universally recognized tool for evalu-
ating the quality of RCTs, was used to assess the quality
of the selected studies, whose scores ranged from 0 (very
poor) to 5 (rigorous).21–23
We used the Stata software for all statistical anal-
yses. Heterogeneity among studies was measured with
the I², whose magnitude was divided into insignificant
(I²<25%), moderate (I²≥50%), and significant (I²≥75%).
A fixed- effect model was built to pool the data whenever
the study had no significant heterogeneity (I²<50%),
where as a random- effect model was implemented when
the heterogeneity was more than moderate in the study
(I²≥50%). We calculated the mean differences and
corresponding 95% CIs when studies had the same units
or used the same measurements. Furthermore, a sensi-
tivity analysis was performed to examine the cause of
heterogeneity.
We evaluated the possibility of publication bias by
constructing a funnel plot, then checked the funnel plot
asymmetry by using Begg and Egger tests, and defined
significant publication bias as a p<0.1. Additionally,
Begg and Egger tests results were verified by metabias
command. The trim- and- fill analysis was then used to
evaluate the impact of publication bias on the results,
which was done by metatrim command.

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BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
RESULTS
Main characteristics of studies
By searching the database, we included a total of 1747
related articles, out of which 51 articles (including 13
054 subjects) were finally enrolled in the study (online
supplementary figure 1).24–74 The main characteristics
and secondary results of the 51 selected studies are listed
in tables 1 and 2, respectively. Among them, 36 studies
(70.59%) were conducted in developed countries, while
15 studies (29.41%) were conducted in developing coun-
tries. All of the above studies were RCTs, published from
2008 to 2019. The selected studies’ total sample size
ranged from 34 to 1665, with each study consisting of
both male and female subjects. The duration of interven-
tion varied from 1 month to 5 years, being approximately
3–6 months in most studies (23, 45.1%), not more than 3
months in 11 studies (21.6%), and more than 6 months
in 17 studies (33.3 %).
Table 2 portrays the five different types of mHealth
interventions and their respective functions, mainly
consisting of: (1) MPTMs: using SMS for chronic disease
education and management; (2) MPCs: using MPCs for
chronic disease education, management and follow- up
monitoring; (3) WPMDs: electronic devices which can be
used to collect and upload clinical data as well as monitor
patients’ vitals by wireless technology, such as pedom-
eter, dynamic BP and blood glucose monitors, and so on;
(4) mHealth APPs: apps installed on smart phones or
accessed online which can provide health education and
disease management services, as well as calculate insulin
doses and food caloric contents, and so on; (5) Telemed-
icine: the most commonly used wireless smart technology
via smartphones, networks, and tablets for remote moni-
toring, rehabilitation exercises and treatment, principally
in the form of videos and emails.
Primary outcome of intervention
Glycated hemoglobin A1C (HbA1c)
Forty studies (comprised of 8006 participants) reported
data on HbA1c, which was then pooled by a random- effect
model for meta- analysis.24–58 60 63 66 69 72 The results indi-
cated that compared with traditional treatment, mHealth
intervention was associated with a significant improve
in HbA1c (weighted mean difference (WMD) (95%
CI)=−0.39 (−0.50 to –0.29)) (figure 1, HbA1c- A). A sensi-
tivity analysis was subsequently performed (see online
supplementary figure 2, HbA1c- A), since the results
demonstrated a moderate heterogeneity (I2=62.7%,
p<0.01). We equally noticed that mHealth intervention
had a positive effect on controlling HbA1c levels in coun-
tries with different economic levels (developed countries:
WMD (95% CI)=−0.35 (−0.46 to –0.24); developing coun-
tries: WMD 95% CI)=−0.52 (−0.78 to –0.26)) (figure 1,
HbA1c- B). The subgroup analysis revealed that mHealth
intervention also had a positive impact on different types
of patients with diabetes (T2DM: WMD (95% CI)=−0.40
(−0.52 to –0.28)); T1DM: WMD (95% CI)=−0.30 (−0.47
to –0.12)) (figure 1, HbA1c- C), in which its effect on
patients with T2DM was more significant. Moreover, the
Egger test results pointed out that the research results
may have some publication bias (p=0.036). Based on
further analysis conducted through a trim- and- fill test,
the estimated value was not affected by publication bias
(namely, no trimming was performed given that the data
remained unchanged).
Fasting blood glucose (FBG)
A total of 15 studies reported FBG values, which
were pooled and analyzed by using a random- effect
model.24 25 28 30 32 34 48 50 51 53 54 56 58 63 66We observed that
mHealth intervention could better control FBG levels
compared with conventional treatment strategies (WMD
(95% CI)=−0.52 (−0.93 to –0.12)) (figure 1, FBG- E). Due
to the study results’ moderate heterogeneity (I2=57.6%,
p=0.003), we conducted a sensitivity analysis (online
supplementary figure 2, FBG- B). The Egger test results
indicated that there was no publication bias (p=0.16).
Systolic blood pressure (SBP)
A total of 30 articles (consisting of 9476 participants)
reported data on SBP.28 34–37 41 44 46 48 49 51–57 59–62 64–68 70 71 73 74
We found out that mHealth intervention had a greater
impact on SBP than traditional treatment strategies
(WMD (95% CI)=−2.99 (−4.19 to –1.80)) (figure 2,
SBP- A). Then, a sensitivity analysis was also conducted
due to a large heterogeneity within the research results
(I2=67.3%, p<0.05). When the articles published by
Green et al and Margolis et al were eliminated, I2 dropped
to 60.1% and 49.6%, respectively (online supplementary
figure 2, SBP- C, D).59 73 No significant publication bias
(p=0.439) was detected during analysis. The subgroup
analysis demonstrated that there was a discrepancy
between the results of mHealth intervention in countries
with different levels of economic development (devel-
oped countries: WMD (95% CI)=−5.72 (−7.46 to –3.99);
developing countries: (WMD (95% CI)=0.25 (−3.10
to 3.59)) (figure 2, SBP- B). Besides, we also noted that
combined intervention (mHealth +human intelligence)
was more effective than mobile health intervention
used exclusively (combined intervention: WMD (95%
CI)=−6.17 (−8.83 to –3.50); mHealth intervention: WMD
(95% CI)=−2.16 (−5.07 to 0.75)) (figure 2, SBP- C).
Diastolic blood pressure (DBP)
A total of 28 articles (counting a total of 8506 participants)
reported data on DBP.28 34–37 41 44 46 48 49 51–57 59 60 62 65–68 70 71 73 74
The study results suggest that mHealth intervention had
a greater effect on reducing the DBP in comparison to
traditional treatment strategies (WMD (95% CI)=−1.14
(−1.86 to –0.42)) (figure 2, DBP- E). Due to the pres-
ence of moderate heterogeneity (I²=57.1%, p<0.01),
we also conducted a sensitivity analysis. When the arti-
cles of Green et al and Margolis et al were eliminated,
I² dropped to 60.1% and 49.6%, respectively (online
supplementary figure 2, DBP- E, F).59 73 Meanwhile no

4BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
Table 1 Summary of characteristics of 51 studies that examined mHealth intervention for hypertension and diabetes treatment and management
ID* Reference SS
Gender
(female) Age (years)†
mHealth
type ID* Reference SS
Gender
(female) Age (years)†
mHealth
type ID* Reference SS
Gender
(female) Age (years)†
mHealth
type
1 Goodarzi et al24 81 63
(77.78%)
Exp: 50.98 (10.32),
Cont: 56.71 (9.77)
MPTM 18 Chamany et al43 941 599
(63.66%)
Exp: 56.7 (11.3),
Cont: 56.0 (12.0)
MPCs 35 Piette et al60 291 150
(51.5%)
Exp: 55.1 (9.4),
Cont: 56.0 (10.9)
MPCs
+WPMD
2 Yaron et al27 67 35
(52.24%)
Exp: 43(11.0),
Cont: 45(14.0)
Telemedicine 19 Basudev et al46 208 88
(42.31%)
Exp: 60.5 (12.3),
Cont: 59.3 (12.0)
Telemedicine 36 Cho et al63 71 43
(60.56%)
Exp: 65.3 (9.3),
Cont: 63.1 (10.3)
Telemedicine
+WPMD
3 Ramadas et al30 128 51
(39.84%)
Exp: 49.6 (10.7),
Cont: 51.5 (10.3)
Telemedicine 20 Crowley et al49 50 2
(4.0%)
Exp: 60 (8.4),
Cont: 60 (9.2)
MPCs 37 Bujnowska-
Fedak et al66 95 44
(46.32%)
Exp: 53.1 (25.2),
Cont: 57.5 (27.4)
Telemedicine
4 Abaza et al33 73 41
(56.16%)
Exp: 51.24 (8.66),
Cont: 51.77 (9.68)
MPTM 21 Odnoletkova
et al52 574 221
(38.50%)
Exp: 63.8 (8.7),
Cont: 62.4 (8.9)
Telemedicine 38 Berndt et al69 68 27
(39.71%)
Exp: 12.9 (2.0),
Cont: 13.2 (2.9)
mHealth
Apps+Telemedicine
5 Wild et al36 321 107
(33.33%)
Exp: 60.5 (9.8),
Cont: 61.4 (9.8)
Telemedicine 22 Baron et al55 81 35
(43.21%)
Exp: 58.2 (13.6),
Cont: 55.8 (13.8)
Telemedicine 39 Charpentier
et al72 120 77
(64.17%)
Exp: 31.6 (12.5),
Cont: 36.8 (14.1)
mHealth
Apps+Telemedicine
6 Duruturk et al39 44 18
(36.0%)
Exp: 52.82 (11.86),
Cont: 53.04 (10.45)
Telemedicine 23 Di Bartolo et al26 182 89
(48.90%)
Exp: 17.6 (3.1),
Cont: 17.8 (3.0)
mHealth Apps
+WPMD
40 Kim et al58 34 18
(52.94%)
Exp: 45.5 (9.1),
Cont: 48.5 (8.0)
Telemedicine
+WPMD
7 Sarayani et al42 100 41
(41.0%)
Exp: 53.4 (10.3),
Cont: 56.7 (11.5)
MPCs 24 Benson et al29 118 53
(44.92%)
Exp: 59.8 (10.2),
Cont: 60 (8.66)
Telemedicine 41 Piette et al61 181 122
(67.40%)
Exp: 58.0 (12.26),
Cont: 57.1 (10.55)
Telemedicine
8 Wang et al45 212 104
(49.06%)
Exp: 52.6 (9.1),
Cont: 54.7 (10.3)
Telemedicine 25 Boaz et al32 35 22
(62.86%)
Exp: 63 (10.0),
Cont: 63 (15.0)
Telemedicine 42 Bobrow et al64 915 662
(72.35%)
Exp: 54.2 (11.6),
Cont: 54.7 (11.6)
MPTM
9 Kim et al48 182 94
(51.65%)
Exp: 52.5 (9.1),
Cont: 55.6 (10.0)
Telemedicine 26 Liou et al35 95 47
(49.47%)
Exp: 56.6 (7.7),
Cont: 57.0 (7.5)
Telemedicine 43 Kim et al67 250 100
(40.0%)
Exp: 56.1 (11.0),
Cont: 58.8 (10.6)
Telemedicine
10 Lim et al51 100 25
(25.0%)
Exp: 64.3 (5.2),
Cont: 65.8 (4.7)
Telemedicine 27 Rossi et al38 130 74
(56.92%)
Exp: 35.4 (9.5),
Cont: 36.1 (9.4)
Telemedicine
+WPMD
44 McManus et al70 782 364
(46.55%)
Exp: 67.0 (9.3),
Cont: 66.8 (9.4)
Telemedicine
11 Cho et al54 484 177
(36.57%)
Exp: 52.9 (9.2),
Cont: 53.4 (8.7)
Telemedicine 28 Davis et al41 165 123
(74.55%)
Exp: 59.9 (9.4),
Cont: 59.2 (9.3)
Telemedicine
+WPMD
45 Margolis et al73 450 201
(44.67%)
Exp: 62.0 (11.7),
Cont: 60.2 (12.2)
Telemedicine
12 Kleinman et al25 90 27
(30.0%)
Exp: 48.8 (9.0),
Cont: 48.0 (9.5)
mHealth Apps 29 Shea et al44 1665 1046
(62.82%)
Exp: 70.8 (6.5),
Cont: 70.9 (6.8)
Telemedicine 46 Green et al59 519 287
(55.30%)
Exp: 59.3 (8.6),
Cont: 58.6 (8.5)
Telemedicine
13 Fortmann et al28 126 94
(74.60%)
Exp: 47.8 (9.0),
Cont: 49.1 (10.6)
MPTM 30 Kirwan et al47 72 44
(61.11%)
Exp: 35.97 (10.7),
Cont: 34.42
(10.3)
mHealth Apps
+MPTM
47 McManus et al62 527 255
(53.13%)
Exp: 66.6 (8.8),
Cont: 66.2 (8.8)
Telemedicine
14 Jeong et al31 225 72
(32.0%)
Exp: 52.46 (8.48),
Cont: 53.16 (9.06)
Telemedicine 31 Moattari et al50 48 27
(57.0%)
18–39 Telemedicine 48 Rifkin et al65 43 2
(4.65%)
Exp: 68.5 (7.5),
Cont: 67.9 (8.4)
Telemedicine
+WPMD
15 Kempf et al34 167 77
(46.11%)
Exp: 59.0 (9.0),
Cont: 60.0 (8.0)
Telemedicine 32 Rossi et al53 127 67
(52.76%)
Exp: 38.4 (10.3),
Cont: 34.3 (10.0)
mHealth
Apps+Telemedicine
49 Lee et al68 382 192
(50.26%)
Exp: 57.29 (10.90),
Cont: 58.90 (10.7)
Telemedicine
+WPMD
16 Nicolucci et al37 302 116
(38.41%)
Exp: 59.1 (10.3),
Cont: 57.8 (8.9)
Telemedicine 33 Zhou et al56 114 —‡ 18–75 Telemedicine 50 Kim et al71 95 65
(68.42%)
Exp: 57.5 (8.6),
Cont: 57.7 (8.7)
mHealth Apps
+WPMD
17 Wakeeld et al40 108 60
(55.56%)
Exp: 57.7 (10.8),
Cont: 62.5 (10.9)
Telemedicine 34 Tang et al57 415 166
(40.0%)
Exp: 54 (10.7),
Cont: 53.5 (10.2)
Telemedicine 51 McKinstry et al74 401 164
(40.90%)
Exp: 60.5 (11.8),
Cont: 60.8 (10.7)
Telemedicine
*Study ID, indicate the 1st to 51th study.
†Unless otherwise indicated, values are n/N(%), ranges or means±SDs.
‡Not mentioned in the study.
ID, identier; mHealth, mobile health; mHealth Apps, mobile health applications; MPCs, mobile phone calls; MPTMs, mobile phone text messages; SS, sample size; WPMDs, wearable or portable monitoring devices.

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Clinical care/Education/Nutrition
Table 2 Main study characteristics and ndings from 51 studies that examined mHealth intervention for diabetes and
hypertension treatment and management
Category
Number of studies
(n, %) Study ID*
Country/setting
Developed country
USA 13 (25.5) 13, 17, 18, 20, 24, 28, 29, 34, 35, 45, 46, 48, 50
England 6 (11.8) 5, 19, 22, 44, 47, 51
Korea 6 (11.8) 10, 11, 14, 36, 40, 43
Italy 4 (7.8) 16, 23, 27, 32
Germany 2 (3.9) 15–38
Israel 2 (3.9) 2–25
Australia 1 (2.0) 30
Belgium 1 (2.0) 26
France 1 (2.0) 44
Developing country†
China 5 (9.8) 8, 9, 26, 33, 49
Iran 3 (5.9) 1, 7, 31
Egypt 1 (2.0) 4
India 1 (2.0) 12
Honduras and Mexico 1 (2.0) 41
Malaysia 1 (2.0) 3
Turkey 1 (2.0) 6
Poland 1 (2.0) 37
South Africa 1 (2.0) 42
Intervention time/duration
≤3 months 11 (21.6) 1, 4, 6, 7, 15, 17, 31, 33, 36, 38, 41
3–6 months 23 (45.1) 3, 8–14, 20, 21, 23, 25–27, 30, 32, 37 39, 43, 48–51
>6 months 17 (33.3) 2, 5, 16, 18, 19, 22, 24, 28, 29, 34, 35, 40, 42, 44–47
Sample size
<100 17 (33.3) 1, 2, 4, 6, 12, 20, 22, 25, 26, 30, 31, 36–38, 40, 48, 50
100–500 27 (52.9) 3, 5, 7–11, 13–17, 19, 23, 24, 27, 28, 32–35, 39, 41, 43, 45, 49, 51
>500 7 (13.7) 18, 21, 29, 42, 44, 46, 47
Targeted patient
T1DM 7 (13.7) 2, 23, 27, 30, 32, 38, 39
T2DM 28 (54.9) 1, 3–16, 19–21, 24, 26, 28, 29, 33–37, 40
T1DM and T2DM combined 4 (7.8) 18, 22, 25, 31
T2DM and HTN combined 1 (2.0) 17
HTN 11 (21.6) 41–51
Type and specic function of mHealth
MPTMs
Knowledge and tips 5 (9.8) 1, 4, 13, 40, 42
Suggestions 1 (2.0) 42
Reminder 2 (3.9) 4–42
Medical consultations‡ 1 (2.0) 42
Feedback 1 (2.0) 30
Telemedicine
Knowledge and tips 26 (51.0) 3, 6, 8, 9, 14–16, 19–22, 24, 26, 29, 31, 32, 34, 38–41, 44–46, 49, 51
Suggestions 20 (39.2) 2, 5, 10, 11, 16, 17, 19–21, 25–27, 31, 33, 34, 36, 41, 43, 46, 47
Reminder 5 (9.8) 16, 24, 27, 44, 49
Medical consultations 17 (33.3) 5, 11, 14, 15, 19, 24–29, 36, 37, 39, 45, 46, 48
Continued

6BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
significant publication bias (p=0.857) was identified in
this analysis.
Secondary results of intervention
According to literature review, 14 (27.5%) articles
described improvements in patients’ compliance after
using mHealth intervention,25 26 29 31–33 36 42 43 45 63 68 69 73
while 13 (25.5%) articles reported an amelioration in
self- efficacy and self- care ability.24 30 33 34 42 43 49 60 63 66 69 71 73
Furthermore, 12 (23.5%) articles declared changes in
eating habits, physical activity, and other behavioral patterns
after using mHealth intervention.24 29 30 34 37 39 43 47 51 60 61 73
Ten (19.6%) other articles recorded improvements in the
quality of life.32 37–39 52 53 55 60 61 66 Moreover, some studies
also stipulated that mHealth intervention could reduce
complications related with chronic diseases, improve
patients’ knowledge of chronic diseases management
and reduce the treatment cost (see table 2).
DISCUSSION
To our knowledge, this is the first article evaluating
the impact of mHealth intervention in countries with
different economic levels. A total of 51 research liter-
ature were included in this study. Our research results
indicated that mHealth intervention could improve clin-
ical indicators such as HbA1c, FBG, SBP, and DBP when
compared with traditional treatment strategies. Mean-
while, we also discovered that mHealth intervention
positively correlated with improvements in the patients’
quality of life, satisfaction and lifestyle.
Our study has confirmed the efficacy of mHealth inter-
vention in the management of DM and HTN, which was
consistent with the results obtained in previous studies.
Prior articles on mHealth were essentially limited to
a single type of intervention, such as telemedicine,
mHealth Apps and MPTMs. However, five different types
of mHealth intervention were included in this article.
Category
Number of studies
(n, %) Study ID*
Data monitoring/collection/store/transmit 27 (52.9) 2, 5, 8–11, 14, 16, 17, 20, 22, 25, 29, 31, 33, 34, 37, 40, 41, 43–47, 51
Feedback 10 (19.6) 2, 9, 10, 14, 22, 27, 31, 37, 38, 51
MPCs
Knowledge and tips 3 (5.9) 7, 18, 35
Medical consultations 3 (5.9) 7, 18, 35
Reminder 1 (2.0) 35
mHealth APPs
Suggestions 2 (3.9) 12–50
Medical consultations 1 (2.0) 23
Reminder 2 (3.9) 12–50
Data monitoring/collection/store/transmit 5 (9.8) 12, 30, 32, 38, 39
WPMDs
Data monitoring/collection/store/transmit 8 (15.7) 23, 27, 28, 35, 36, 48–50
Secondary intervention results
Improved knowledge 3 (5.9) 1, 3, 4
Improved adherence 14 (27.5) 4, 5, 7, 8, 12, 14, 18, 23, 24, 25, 36, 38, 45, 49
Improved self- efcacy/self- care§ 13 (25.5) 1, 3, 4, 7, 15, 18, 20, 35–38, 45, 50
Improved behavior 12 (23.5) 1, 3, 6, 10, 15, 16, 18, 24, 30, 35, 41,45
Improved satisfaction 10 (19.6) 2, 11, 12, 13, 27, 32, 34, 38, 41, 45
Improved symptoms 7 (13.7) 6, 18, 22, 25, 34, 35, 41
Improved quality of life 10 (19.6) 6, 16, 21, 22, 25, 27, 32, 35, 37, 41
Improve complications 6 (11.8) 14, 15, 25, 28, 32, 33
Changed bad habits 1 (2.0) 50
Reduced costs¶ 2 (3.9) 2–39
*Study ID: indicate the 1st to 51st study.
†Developing country: refers to countries with low levels of economy, technology, and people's living standards. Evaluation criteria mainly refer to the relatively low
GDP per capita (GDP per capita) of the country.
‡Medical consultations: patient–health care giver communication by phone, video, and so on.
§Improved self- efcacy/self- care: as evaluated by scale, such as diabetes self- efficacy scale, diabetes self- care activities scale, and so on.
¶Reduced costs: it means that using mHealth can save the time for instruction than usual care, or save the time and money spent traveling to and from hospital,
and so on.
HTN, hypertension; mHealth, mobile health; mHealth Apps, mobile health applications; MPCs, mobile phone calls; MPTMs, mobile phone text messages; T1DM,
type 1 diabetes mellitus; T2DM, type 2 diabetes mellitus; WPMDs, wearable or portable monitoring devices.
Table 2 Continued

7
BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
Figure 1 Meta- analyses of mHealth intervention treatments versus other traditional treatments, comparing HbA1c and FBG.
Outcomes assessed are (A) change in HbA1c at the end of intervention in studies that compared mHealth treatment with
traditional treatment, (B) comparing the effects of mHealth interventions on HbA1c control in countries with different levels of
economic development, (C) comparing the effects of mHealth interventions on HbA1c control in patients with different types of
diabetes, (D) comparing the difference of ve different types of mHealth interventions on HbA1c control, and (E) change in FBG
at the end of intervention in studies that compared mHealth treatment with traditional treatment. FBG, fasting blood glucose;
HbA1c, glycated hemoglobin A1C; MPCs, mobile phone calls; MPTMs, mobile phone text messages; T1DM, type 1 diabetes
mellitus; T2DM, type 2 diabetes mellitus; WMD, weighted mean difference.

8BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
Figure 2 Meta- analyses of mHealth intervention treatments versus other traditional treatments, comparing SBP and DBP.
Outcomes assessed are (A) change in SBP at the end of intervention in studies that compared mHealth treatment with
traditional treatment, (B) comparing the effects of mHealth intervention on SBP control in countries with different levels of
economic development, (C) SBP in studies that compared combination treatment with mHealth treatment alone, (D) change in
DBP at the end of intervention in studies that compared mHealth treatment with traditional treatment, and (E) comparing the
difference of ve different types of mHealth interventions on SBP control. DBP, diastolic blood pressure; MPTMs, mobile phone
text messages; SBP, systolic blood pressure; WMD, weighted mean difference.

9
BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
This study revealed that compared with the control group,
telemedicine and MPTMs, or a combination of these two
intervention measures could effectively improve blood
sugar and BP levels. While the effect of telemedicine
intervention was more evident (see figure 1, HbA1c- D
and figure 2, SBP- D). It should be noted that the pooled
effect of intervention measures such as MPCs and APPs
was not significant because the amount of literature was
small, but most study results on MPCs and APPs indicated
that mHealth intervention was conducive to improve-
ments in blood sugar and BP levels.
Our study also discovered that mHealth interven-
tion had a greater impact on patient with T2DM than
T1DM, which was consistent with the results of the study
conducted by Su et al.17 The main reason for such differ-
ence may be related to the disease’s pathophysiology.
As we know, patients with T1DM are completely depen-
dent on insulin therapy, whereas patients with T2DM
can improve their blood glucose through changes in
their lifestyle and eating habits, especially in the early
stage of diabetes. We found out that most mHealth
intervention chiefly achieve blood sugar control by
promoting favorable lifestyle and eating habits, which
is the main reason why mHealth intervention is more
effective for patients with T2DM. Hence, it is believed
that the key to excellent curative effects is to formu-
late specific intervention measures for different types
of diabetics.
According to our study results, mHealth intervention
has a more significantly positive effect in controlling the
SBP in developed countries compared with the control
group, but its effects were not that significant in devel-
oping countries. After carefully reading, examining and
comparing the original studies, we found out that the
three RCTs (Lee et al, Piette et al, Bobrow et al) conducted
in developing countries exclusively used mHealth as
intervention (not combined with professional healthcare
management).61 64 68 Nonetheless, in developed coun-
tries, mHealth intervention is usually combined with
professional healthcare management to provide disease-
related care during the intervention so as to enhance the
clinical efficacy. This explains why mHealth intervention
performed in developed countries demonstrates a higher
efficacy with respect to SBP control. These results are
consistent with those in the study carried out by Hou et al
stating that the intervention of healthcare professionals
is essential to enhance the clinical efficacy of mHealth.16
Withal, the BP values after mHealth intervention have
exhibited varying degrees of improvement compared
with the baseline values, which was confirmed in most
RCTs conducted in developing countries.35 56 66 Based
on these observations, we reckon that using mHealth
intervention also exerts a positive effect on improving
BP levels in developing countries. In order to validate
our study results, we also conducted a subgroup analysis.
The results of our subgroup analysis indicated that the
combination of mHealth intervention and professional
management (By pharmacists, nutritionists, full- time
nurses and sports coaches) was more effective than
exclusively performing mHealth intervention(combined
intervention: WMD (95% CI)=−6.17 (−8.83 to –3.50);
mHealth intervention: WMD (95% CI)=−2.16 (−5.07 to
0.75)). Notwithstanding, it should be noted that despite
the immense convenience provided by the continuous
development of high technology, greater benefits can
be produced just by combining human intelligence with
artificial intelligence.
Sensitivity analysis
In the sensitivity analysis of HbA1c, we noticed that the
heterogeneity decreased significantly when excluding
Kim's research.58 After careful inspection and compar-
ison of the original research, we concluded that the pres-
ence of heterogeneity could be explained by small sample
size (n=34). It was the same case for the SBP and DBP
when the articles published by Green et al and Margolis
et al were excluded.59 73 After a detailed analysis of the
original study, we realized that disease management by
professional pharmacists was illustrated in both articles.
Consequently, it was inferred that professional interven-
tion could strengthen disease management, which may
also be a cause of heterogeneity.
Quality assessment
We used the Jadad score to assess the quality of a total
number of 51 included literature, in which RCTs were
used. Among them, 37 studies (72.55%) described the
generation of random methods in detail and processed
incomplete data. Since this was an open study, consid-
ering the nature of the intervention, it was not possible to
blind the patient or his clinician, so double blindness was
not feasible. Meanwhile, 44 studies (86.27%) reported
the follow- up process in particular and explained the
reasons for patients’ withdrawal (online supplementary
table 2).
LIMITATIONS
Despite the growing interest regarding the implemen-
tation of various mHealth technologies, the long- term
effects of such interventions remain unknown and will
need to be tested in a more representative population
over a longer time period.
CONCLUSION
Our systematic review and meta- analysis indicate that
mHealth intervention can improve clinical outcomes,
reduce costs, ameliorate the quality of life and enhance
self- efficacy among patients in countries with different
levels of economic development. Our study also empha-
sized that the combination of mHealth intervention with
professional management is crucial in order to achieve
optimum clinical effectiveness.

10 BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
Contributors GC had full access to all of the data in the study and takes
responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: YM, GC. Acquisition, analysis, or interpretation of data: YM,
WL, JW. Drafting of the manuscript: YM, WL, GC. Critical revision of the manuscript
for important intellectual content: YM, GC. Statistical analysis: YM. Administrative,
technical, or material support: None. Supervision: GC.
Funding The authors have not declared a specic grant for this research from any
funding agency in the public, commercial or not- for- prot sectors.
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
Data availability statement Data are available in a public, open access
repository.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the
use is non- commercial. See:http:// creativecommons. org/ licenses/ by- nc/ 4. 0/.
ORCID iD
GangChen http:// orcid. org/ 0000- 0002- 8105- 2384
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