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

  • 东北大学

Abstract and Figures

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
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,
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/
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
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.
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.
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
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.
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 (I²50%), and significant (I²75%).
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
(I²50%). 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
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.
BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
Clinical care/Education/Nutrition
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 (=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
(female) Age (years)†
type ID* Reference SS
(female) Age (years)†
type ID* Reference SS
(female) Age (years)†
1 Goodarzi et al24 81 63
Exp: 50.98 (10.32),
Cont: 56.71 (9.77)
MPTM 18 Chamany et al43 941 599
Exp: 56.7 (11.3),
Cont: 56.0 (12.0)
MPCs 35 Piette et al60 291 150
Exp: 55.1 (9.4),
Cont: 56.0 (10.9)
2 Yaron et al27 67 35
Exp: 43(11.0),
Cont: 45(14.0)
Telemedicine 19 Basudev et al46 208 88
Exp: 60.5 (12.3),
Cont: 59.3 (12.0)
Telemedicine 36 Cho et al63 71 43
Exp: 65.3 (9.3),
Cont: 63.1 (10.3)
3 Ramadas et al30 128 51
Exp: 49.6 (10.7),
Cont: 51.5 (10.3)
Telemedicine 20 Crowley et al49 50 2
Exp: 60 (8.4),
Cont: 60 (9.2)
MPCs 37 Bujnowska-
Fedak et al66 95 44
Exp: 53.1 (25.2),
Cont: 57.5 (27.4)
4 Abaza et al33 73 41
Exp: 51.24 (8.66),
Cont: 51.77 (9.68)
MPTM 21 Odnoletkova
et al52 574 221
Exp: 63.8 (8.7),
Cont: 62.4 (8.9)
Telemedicine 38 Berndt et al69 68 27
Exp: 12.9 (2.0),
Cont: 13.2 (2.9)
5 Wild et al36 321 107
Exp: 60.5 (9.8),
Cont: 61.4 (9.8)
Telemedicine 22 Baron et al55 81 35
Exp: 58.2 (13.6),
Cont: 55.8 (13.8)
Telemedicine 39 Charpentier
et al72 120 77
Exp: 31.6 (12.5),
Cont: 36.8 (14.1)
6 Duruturk et al39 44 18
Exp: 52.82 (11.86),
Cont: 53.04 (10.45)
Telemedicine 23 Di Bartolo et al26 182 89
Exp: 17.6 (3.1),
Cont: 17.8 (3.0)
mHealth Apps
40 Kim et al58 34 18
Exp: 45.5 (9.1),
Cont: 48.5 (8.0)
7 Sarayani et al42 100 41
Exp: 53.4 (10.3),
Cont: 56.7 (11.5)
MPCs 24 Benson et al29 118 53
Exp: 59.8 (10.2),
Cont: 60 (8.66)
Telemedicine 41 Piette et al61 181 122
Exp: 58.0 (12.26),
Cont: 57.1 (10.55)
8 Wang et al45 212 104
Exp: 52.6 (9.1),
Cont: 54.7 (10.3)
Telemedicine 25 Boaz et al32 35 22
Exp: 63 (10.0),
Cont: 63 (15.0)
Telemedicine 42 Bobrow et al64 915 662
Exp: 54.2 (11.6),
Cont: 54.7 (11.6)
9 Kim et al48 182 94
Exp: 52.5 (9.1),
Cont: 55.6 (10.0)
Telemedicine 26 Liou et al35 95 47
Exp: 56.6 (7.7),
Cont: 57.0 (7.5)
Telemedicine 43 Kim et al67 250 100
Exp: 56.1 (11.0),
Cont: 58.8 (10.6)
10 Lim et al51 100 25
Exp: 64.3 (5.2),
Cont: 65.8 (4.7)
Telemedicine 27 Rossi et al38 130 74
Exp: 35.4 (9.5),
Cont: 36.1 (9.4)
44 McManus et al70 782 364
Exp: 67.0 (9.3),
Cont: 66.8 (9.4)
11 Cho et al54 484 177
Exp: 52.9 (9.2),
Cont: 53.4 (8.7)
Telemedicine 28 Davis et al41 165 123
Exp: 59.9 (9.4),
Cont: 59.2 (9.3)
45 Margolis et al73 450 201
Exp: 62.0 (11.7),
Cont: 60.2 (12.2)
12 Kleinman et al25 90 27
Exp: 48.8 (9.0),
Cont: 48.0 (9.5)
mHealth Apps 29 Shea et al44 1665 1046
Exp: 70.8 (6.5),
Cont: 70.9 (6.8)
Telemedicine 46 Green et al59 519 287
Exp: 59.3 (8.6),
Cont: 58.6 (8.5)
13 Fortmann et al28 126 94
Exp: 47.8 (9.0),
Cont: 49.1 (10.6)
MPTM 30 Kirwan et al47 72 44
Exp: 35.97 (10.7),
Cont: 34.42
mHealth Apps
47 McManus et al62 527 255
Exp: 66.6 (8.8),
Cont: 66.2 (8.8)
14 Jeong et al31 225 72
Exp: 52.46 (8.48),
Cont: 53.16 (9.06)
Telemedicine 31 Moattari et al50 48 27
18–39 Telemedicine 48 Rifkin et al65 43 2
Exp: 68.5 (7.5),
Cont: 67.9 (8.4)
15 Kempf et al34 167 77
Exp: 59.0 (9.0),
Cont: 60.0 (8.0)
Telemedicine 32 Rossi et al53 127 67
Exp: 38.4 (10.3),
Cont: 34.3 (10.0)
49 Lee et al68 382 192
Exp: 57.29 (10.90),
Cont: 58.90 (10.7)
16 Nicolucci et al37 302 116
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
Exp: 57.5 (8.6),
Cont: 57.7 (8.7)
mHealth Apps
17 Wakeeld et al40 108 60
Exp: 57.7 (10.8),
Cont: 62.5 (10.9)
Telemedicine 34 Tang et al57 415 166
Exp: 54 (10.7),
Cont: 53.5 (10.2)
Telemedicine 51 McKinstry et al74 401 164
Exp: 60.5 (11.8),
Cont: 60.8 (10.7)
*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.
BMJ Open Diab Res Care 2020;8:e001225. doi:10.1136/bmjdrc-2020-001225
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
Number of studies
(n, %) Study ID*
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
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
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
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).
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.
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
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
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
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.
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).
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.
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
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/.
GangChen http:// orcid. org/ 0000- 0002- 8105- 2384
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... Additionally, the impact of diabetes family communication, household chaos, and family confict should be explored in high-risk AYA and families as these factors have been shown to afect diabetes management, glycemic variability, and optimal CGM use [33,34]. Tese fndings add to previous research reporting variable outcomes among mobile or phone-based interventions to improve self-management [35][36][37]. Findings appear to vary based on factors such as patient baseline disease management [37] and a country's economic development [35]. ...
... Tese fndings add to previous research reporting variable outcomes among mobile or phone-based interventions to improve self-management [35][36][37]. Findings appear to vary based on factors such as patient baseline disease management [37] and a country's economic development [35]. ...
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Real-time continuous glucose monitoring (rtCGM) can directly improve patient outcomes, including decreased health care system utilization and associated costs. The purpose of this study was to evaluate the clinical benefits of rtCGM use in a high-risk, under-resourced cohort of adolescents and young adults (AYA) with type 1 diabetes (T1D) who had no prior access to rtCGM. The effects of rtCGM use on hemoglobin A1c (A1c) and the frequency of health care events (i.e., diabetes-related emergency room (ER) visits, hospitalizations, emergency medical services (EMS), and after-hour emergency calls) were evaluated regarding payor costs in 33 AYA with ≥70% rtCGM use. Secondary aims included the evaluation of a phone-based pattern management intervention. The frequency of health care events decreased at 12 and 24 weeks for all participants, and there was no significant difference by treatment group. We estimated that the use of rtCGM in this cohort results in a projected annualized cost-savings of $195,943 to $294,864 or 43–65% per year based on Medicare or list pricing for rtCGM, respectively. Results also revealed improvements in A1c at 12 weeks for all study participants, but this was not maintained at 24 weeks for the phone-based pattern management intervention group. Our findings suggest that rtCGM may be an effective tool for reducing diabetes-related events and underscores the importance of access. Future studies are needed to further examine tailored interventions and support to optimize rtCGM use and glycemic health in high-risk AYA.
... This results in a scarcity of health-care resources available to patients and their families and serves as a driving force for telehealth innovation (18). TM enables remote monitoring of vital signs in patients with chronic conditions, and by doing so, it decreases mortality and hospitalization while increasing quality of life (4,(19)(20)(21). It eliminates the need to go to the doctor's office and sit side by side with others, which could lead to the transmission of communicable diseases (22). ...
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Background: Technology-based healthcare services have important implications for the diagnosis, prevention, and treatment of diseases, as well as providing access to high-quality care that both the patient and the healthcare practitioner can benefit from. To access medical information, patients have also searched for methods of technology-based healthcare services like telemedicine (TM). However, little is known regarding the perceptions, willingness, and practices of TM among Ethiopian patients, especially in the study setting. Objective: This study assessed the perceptions, willingness, and practice of TM among patients with chronic disease at the University of Gondar Comprehensive Specialized Hospital (UoGCSH), Northwest Ethiopia. Methods: A cross-sectional study was conducted from June 1 to July 30, 2022, among patients with chronic diseases who were on follow-up at the UoGCSH. Eligible participants were included in the study using a systematic random sampling technique. A structured questionnaire was used and recorded in the Kobo data collection tool. The collected data were managed and analyzed using the Statistical Package for Social Science (SPSS) version 26. Results: Out of 422 patients approached, 384 (91% response rate) were included in the final analysis. The mean (±SD) age of the participants was 48.07 ± 16.17 years. The overall perceptions mean (±SD) score of the respondents was 3.92 ± 1.06. Generally, near to three-fourths (71.1%) of the participants had a positive perception of TM services, and around two-thirds (63.3%) had a willingness to be involved in the TM service. However, only around one-fourth (24.5%) of the participants were perceived to have a high level of TM practice currently. Conclusion: The findings suggest that although the level of perception and willingness of TM services among patients with chronic diseases was positive, their level of practice was low. Therefore, creating awareness and suitable conditions to improve their utilization of TM could be important.
... Digital health is a rapidly expanding field of interest in the patient education sphere that has been shown to improve health outcomes, increase effectiveness of medical treatments and education, lower medical costs, and enhance research opportunities by streamlining data collection, sharing, and analysis, as well as clinical diagnosis and decision-making [4,5]. Digital intervention has been found to positively impact patients' quality of life, satisfaction, and lifestyle across diagnoses and socioeconomic status [6]. It allows for more streamlined connection between patients and providers, especially but not only in the face of a disaster such as the COVID-19 pandemic, by increasing access to help with self-management and health behavior change [7][8][9]. ...
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Patient education is an important part of cancer care as it increases patient activation and informed decision-making, reduces anxiety, and improves outcomes. However, challenges to providing appropriate education to patients exist on both the health provider and patient side of the equation, e.g., time constraints and health literacy issues. Digital health education is a fast-growing field of interest that has been shown to improve health outcomes, increase effectiveness of medical treatments and education, lower medical costs, and enhance both clinical diagnosis and research opportunities by streamlining data collection, sharing, and analysis. In 2019, Fox Chase Cancer Center was selected by ARCHES, an established patient education software company, to pilot its award-winning digital patient engagement system MyCareCompass. During the pilot, patients scheduled for port insertions were sent electronic messages inviting them to review various online educational materials related to their procedure and subsequent concerns. The invitations and resources were seamlessly integrated into the scheduling system and timed to arrive when patients would most need them. There was high usage of the port-related materials and patients reported a high level of satisfaction with the delivery system and the information. This automated process of delivering high-quality and relevant patient education was able to be implemented smoothly with IT involvement, had a positive impact on patients without adding any extra burden to the care team, and highlighted opportunities to integrate these types of interventions into routine care.
... 37 The use of mobile technology in healthcare delivery has been shown to improve clinical outcomes and quality of life in high and low income settings. 38 Effective technology based interventions for preventing hyper tension and hyperglycaemia include SMS messaging to prompt patients to take their medication, use of artificial intelligence to guide insulin dosing based on reported blood glucose levels, and lifestyle modifying applications that provide dietary recommendations and track physical activity. 38 39 A l t h o u g h m o b i l e t e c h n o l o g y interventions for hyperglycaemia are promising, good uptake depends on ...
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Suzanne Simkovich and colleagues argue for implementation research to meet the needs of end users and use of open source data to strengthen preventive strategies for non-communicable diseases in women of reproductive age
Background Hypertension is the most prevalent chronic condition and a significant risk factor for cardiovascular and kidney diseases. The efficacy of health behavioral interventions in blood pressure (BP) control has been demonstrated by a large and expanding body of literature, with “adherence” playing a crucial role. WeChat is the most common social communication mobile app in China, and it has been shown to be an acceptable delivery platform for delivering health interventions. The WeChat-based health behavioral digital intervention program (WHBDIP) showed high feasibility and efficacy. However, the results regarding BP improvement between the WHBDIP and control groups were inconsistent. Objective The objective of this study is to develop a WHBDIP and assess its efficacy in controlling BP and improving adherence among patients with hypertension. Methods A 2-arm, parallel-group, and randomized trial design was used. Patients older than 60 years and with hypertension were randomly assigned to either the control group or the experimental group, which received a 12-week intervention. The program, primarily developed based on the Behavior Change Wheel (BCW) theory, offers health education on exercise, diet, BP monitoring, and medicine adherence (MA). It also includes other behavior interventions guided by an intervention manual, incorporating behavior change techniques (BCTs). The primary outcomes encompass BP and adherence indicators, while the secondary outcomes encompass cardiovascular function indicators, body composition indicators, learning performance, satisfaction, and acceptability. The exercise and blood pressure monitoring adherence (BPMA) indicators for the WHBDIP group were assessed weekly via WeChat during the initial 3 months, while other outcome data for both groups will be collected at the baseline assessment phase, 3 months after the intervention, and 1 year after the program. Results The trial will assess the efficacy of WHBDIP for patients with hypertension (N=68). The WHBDIP seeks to enhance participants' knowledge of healthy behaviors and assist patients in developing positive health behaviors to improve their health outcomes. Patient recruitment for individuals with hypertension commenced on September 5, 2022, and concluded on September 19, 2022. The 3-month intervention and phased data collection were finalized in January 2023. Data analysis will commence in August 2023, and the final 1-year health outcome results will be collected in September 2023. Conclusions A successful WHBDIP will establish the management mode as a feasible approach for hypertension management in the community. Additionally, it will pave the way for the development of related mobile health programs. Trial Registration Chinese Clinical Trial Registry ChiCTR2200062643; International Registered Report Identifier (IRRID) PRR1-10.2196/46883
This scoping review indicates a lack of scientific articles that specifically explore software and mobile applications designed to assist in the clinical diagnosis of leprosy, and our findings have provided insights into the available tools, their usage methods, and the benefits offered by health technologies.
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Background Mobile health (mHealth) interventions are effective in improving chronic disease management, mainly in high-income countries. However, less is known about the efficacy of mHealth interventions for the reduction of cardiovascular risk factors, including for hypertension and diabetes, which are rapidly increasing in low- and middle-income countries. Objective This study aimed to assess the efficacy of mHealth interventions for diabetes and hypertension management in Africa. Methods We searched PubMed, Cochrane Library, Google Scholar, African Journals Online, and Web of Science for relevant studies published from inception to July 2022. The main outcomes of interest were changes in hemoglobin A1c1cI2ZP1c ResultsThis review included 7 studies (randomized controlled trials) with a total of 2249 participants. Two studies assessed the effect of mHealth on glycemic control, and 5 studies assessed the effect of mHealth on blood pressure control. The use of mHealth interventions was not associated with significant reductions in HbA1cPPPP1c Conclusions Our review provided no conclusive evidence for the effectiveness of mHealth interventions in reducing blood pressure and glycemic control in Africa among persons with diabetes and hypertension. To confirm these findings, larger randomized controlled trials are required.
Mobile health (mHealth) involves the use of wireless technology with the goal of enhancing health behavior and/or health outcomes. With the growth of mHealth research, the variety of health topics and conditions addressed via mobile technologies has greatly increased. Although some mHealth interventions seem to lack theory, there are a wide variety of theories that have been applied to the mHealth interventions that use them. Similar to theories, there are various behavior change techniques and other strategies employed in mHealth, which may differ greatly from one mHealth intervention to the next. Overall, mHealth interventions seem to be effective, although their effectiveness can vary according to the targeted health goal, behavior change techniques and strategies, and other factors. Future research needs to further address the roles of behavioral change techniques, technological strategies/functions, and theoretical frameworks in mHealth effectiveness.
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Introduction The emergence of mobile health (mHealth) solutions, particularly mHealth applications (apps), has shown promise in self-management of chronic diseases including type 2 diabetes mellitus (T2DM). While majority of the previous systematic reviews have focused on the effectiveness of mHealth apps in improving treatment outcomes in patients with T2DM, there is a need to also understand how mHealth apps influence self-management of T2DM. This is crucial to ensure improvement in the design and use of mHealth apps for T2DM. This protocol describes how a systematic review will be conducted to determine in which way(s) mHealth apps might impact on self-management of T2DM. Methods The following electronic databases will be searched from inception to April 2019: PubMed, MEDLINE, EMBASE, Global Health, PsycINFO, CINAHL, The Cochrane Central Register of Controlled Trials, Scopus, Web of Science, ProQuest Dissertations & Theses Global, Health Management Information Consortium database, Google Scholar and The Cochrane risk of bias tool will be used to assess methodological quality. The primary outcome measures to be assessed will be ‘change in blood glucose’. The secondary outcomes measures will be ‘changes in cardiovascular risk markers’ (including blood pressure, body mass index and blood lipids), and self-management practices. Others will include: health-related quality of life, economic data, social support, harms (eg, death or complications leading to hospital admissions or emergency unit attendances), death from any cause, anxiety or depression and adverse events (eg, hypoglycaemic episodes). Ethics and dissemination This study will not involve the collection of primary data and will not require ethical approval. The review will be published in a peer-reviewed journal and a one-page summary of the findings will be shared with relevant organisations. Presentation of findings will be made at appropriate conferences. Trial registration number CRD42017071106.
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Abstract Background Increasing prevalence and disease burden has led to an increasing demand of programs and studies focused on dietary and lifestyle habits, and chronic diseases such as type 2 diabetes mellitus (T2DM). We evaluated the effects of a 6-month web-based dietary intervention on Dietary Knowledge, Attitude and Behaviour (DKAB), Dietary Stages of Change (DSOC), fasting blood glucose (FBG) and glycosylated haemoglobin (HbA1c) in patients with uncontrolled HbA1c (> 7.0%) in a randomised-controlled trial (myDIDeA) in Malaysia. Methods The e-intervention group (n = 62) received a 6-month web-delivered intensive dietary intervention while the control group (n = 66) continued with their standard hospital care. Outcomes (DKAB and DSOC scores, FBG and HbA1c) were compared at baseline, post-intervention and follow-up. Results While both study groups showed improvement in total DKAB score, the margin of improvement in mean DKAB score in e-intervention group was larger than the control group at post-intervention (11.1 ± 0.9 vs. 6.5 ± 9.4,p
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Objective To determine the effectiveness of a theoretically based and individually tailored, text message based, diabetes self management support intervention (SMS4BG) in adults with poorly controlled diabetes. Design Nine month, two arm, parallel randomised controlled trial. Setting Primary and secondary healthcare services in New Zealand. Participants 366 participants aged 16 years and over with poorly controlled type 1 or type 2 diabetes (HbA1c ≥65 mmol/mol or 8%) randomised between June 2015 and November 2016 (n=183 intervention, n=183 control). Interventions The intervention group received a tailored package of text messages for up to nine months in addition to usual care. Text messages provided information, support, motivation, and reminders related to diabetes self management and lifestyle behaviours. The control group received usual care. Messages were delivered by a specifically designed automated content management system. Main outcome measures Primary outcome measure was change in glycaemic control (HbA1c) from baseline to nine months. Secondary outcomes included change in HbA1c at three and six months, and self efficacy, diabetes self care behaviours, diabetes distress, perceptions and beliefs about diabetes, health related quality of life, perceived support for diabetes management, and intervention engagement and satisfaction at nine months. Regression models adjusted for baseline outcome, health district category, diabetes type, and ethnicity. Results The reduction in HbA1c at nine months was significantly greater in the intervention group (mean −8.85 mmol/mol (standard deviation 14.84)) than in the control group (−3.96 mmol/mol (17.02); adjusted mean difference −4.23 (95% confidence interval −7.30 to −1.15), P=0.007). Of 21 secondary outcomes, only four showed statistically significant improvements in favour of the intervention group at nine months. Significant improvements were seen for foot care behaviour (adjusted mean difference 0.85 (95% confidence interval 0.40 to 1.29), P<0.001), overall diabetes support (0.26 (0.03 to 0.50), P=0.03), health status on the EQ-5D visual analogue scale (4.38 (0.44 to 8.33), P=0.03), and perceptions of illness identity (−0.54 (−1.04 to −0.03), P=0.04). High levels of satisfaction with SMS4BG were found, with 161 (95%) of 169 participants reporting it to be useful, and 164 (97%) willing to recommend the programme to other people with diabetes. Conclusion A tailored, text message based, self management support programme resulted in modest improvements in glycaemic control in adults with poorly controlled diabetes. Although the clinical significance of these results is unclear, the findings support further investigation into the use of SMS4BG and other text message based support for this patient population. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12614001232628.
Aim: To determine the effect of a tele-rehabilitation (TR) program on glucose control, exercise capacity, physical fitness, muscle strength and psychosocial status in patients with type 2 diabetes mellitus (DM). Method: Fifty type 2 DM participants were enrolled in the study and divided randomly into two groups; TR (n = 25, mean age: 52.82 ± 11.86) or control (n = 25, mean age: 53.04 ± 10.45) group. Participants in the TR group performed breathing and callisthenic exercises, three times a week, for 6 weeks, at home by internet based video conferences. Outcome measures including, HbA1c level, 6 min walk testing, physical fitness and muscle strength dynamometer measurement, Beck Depression Inventory were performed before and after the 6 weeks. Results: HbA1c (p = 0.00), 6 min walking distance (p = 0.00), physical fitness subparameters; sit-up (p = 0.00), sit-and-reach (p = 0.04), back scratch (p = 0.00), lateral flexion right (p = 0.04), left (p = 0.00) and time up go tests (p = 0.00), muscles strength (p = 0.00); deltoideus-anterior, middle, quadriceps femoris and gluteus maximus, and depression levels (p = 0.00) changed significantly (p = 0.00) in TR groups. There were no significant improvements in control group (p > 0.05). Conclusion: Our findings suggest that TR interventions found to be safe and effective, and may be an alternative treatment model for type 2 DM management. In addition to these health benefits, patients and rehabilitation team may save time, labor and treatment costs by using TR.
Background Mobile health is the use of mobile technology in developing healthcare, with the aim of reminding and motivating patients to adopt a healthy lifestyle. We conducted a systematic review assessing the effectiveness of text-messaging interventions on HbA1c in patients with Type 2 diabetes mellitus (T2DM). Methods Two authors independently searched MEDLINE, Embase, CINAHL, Cochrane Register of Randomized Control Trials and PsychInfo. The review included randomized control trials with at least 4 weeks follow up, evaluating the effect of text messaging on HbA1c, in patients with T2DM. Trials involving participants with Type 1 diabetes mellitus, pre-diabetes or gestational diabetes, or other forms of telemedicine were excluded. Studies employing bi-directional messaging were excluded. Results 208 papers were identified as meeting inclusion criteria and their abstracts reviewed. Of these, we examined the full text article of forty-four studies. Eleven randomized controlled trials were included in the final review, with a total of 1710 participants. One study focused on medication adherence only, while the remaining had educational and motivational messages. Five studies showed a significant improvement in HbA1c with the intervention. The remaining studies demonstrated a trend to improvement in HbA1c. Our meta-analysis on 9 of the 11 studies found an overall reduction in HbA1c of 0.38% (−0.53; −0.23, p-value <0.001). Conclusion Lifestyle-focused text messaging is a low cost initiative aimed at motivating patients with T2DM to adhere to a healthy lifestyle. We demonstrate that lifestyle focused text messaging is effective, with a significant improvement in HbA1c in the meta-analysis.
Aim To examine the effectiveness and safety over a 12-month period of a telemedicine intervention in adults with type 1 diabetes (T1D) treated with insulin pumps. Methods 74 T1D patients on insulin pumps for at least 1 year (mean 19.5 [11.5] years) and HbA1c ≥ 6.5% (≥ 48 mmol/mol) were randomized to the telemedicine (n = 37) or the standard care group (n = 37). The intervention group was instructed to download data from insulin pumps and glucometers monthly. They received immediate phone feedback and recommendations for insulin dose adjustment; and face-to-face visits once in 6 months, compared to once every 3 months for the standard care group. Satisfaction with treatment, quality of life and frequency of hypoglycemic events was evaluated. Results The mean changes in HbA1c adjusted to baseline were − 0.08% (0.25 mmol/mol) vs. − 0.01% (0.03 mmol/mol), in the intervention and control groups, respectively (p = 0.18) at 12 months, without an increased frequency of hypoglycemia. Patients in the intervention group felt satisfied and interested in continuing with the treatment (p = 0.04). The quality of life scores were similar in both groups. Direct total costs were 24% less in the intervention group, and indirect total costs decreased by 22% compared to the year preceding the study. Conclusions Internet-based insulin dose adjustment is as effective and safe as routine care in adults with type 1 diabetes treated by insulin pumps. For suitable patients, some of the time-consuming routine visits may be replaced by user-friendly digital medicine. Clinical trial registration Clinical Identifier NCT01887431.
Background: Clinical care for type 2 diabetes has improved but remains suboptimal. Collaborative, team-based models that maximize skills of different disciplines may improve care for individuals with diabetes, but few have been tested using rigorous research designs. Objective: To investigate the efficacy of a registered dietitian nutritionist-led telemedicine program compared with that of a control group in terms of diabetes optimal care goals. Design: A randomized controlled trial in which participants were assigned to a control or intervention group. Participants/setting: One hundred eighteen adults with type 2 diabetes (mean age, 60 years; 45% female) participated in the study between April 2016 and December 2017. Participants were recruited from separate primary care clinics in two rural Minnesota communities. Intervention: For those assigned to the intervention, registered dietitian nutritionists used a treatment protocol to initiate and titrate therapies for blood glucose, hypertension, and lipid levels in addition to providing medical nutrition therapy; telemedicine visits supplemented usual care. Main outcome measures: Primary outcomes included composite and individual diabetes optimal care goals: hemoglobin A1c, blood pressure, not using tobacco, and taking a statin and aspirin (as appropriate). Secondary measures included physical activity, breakfast, fruits and vegetables, whole grains, body mass index, low-density lipoprotein, and medication adherence. Statistical analysis: Mixed-model regression was used to examine outcomes between baseline and 1-year follow-up. Results: A modest but significantly greater improvement in the number of diabetes optimal care measures met at follow-up was found in the intervention group (3.7 vs 3.2 in the control group [P=0.017]). Among individual measures, the intervention group had significantly greater medication use, with 2.5 and 2.2 higher odds (compared with the control group) of taking a statin [95% CI, 1.0 to 6.24]) and aspirin [95% CI, 0.90 to 5.19] as appropriate, respectively. Conclusions: ENHANCED (diEtitiaNs Helping pAtieNts CarE for Diabetes) findings suggest that registered dietitian nutritionists following medication treatment protocols can effectively improve care for adults with type 2 diabetes and can serve an instrumental role as part of the health care team in providing evidence-based, patient-centered care.
We conducted a systematic review with meta‐analysis of randomized controlled trials that evaluated the effect of diabetes apps. 1550 participants from 21 studies were included. For type 1 diabetes, a significant 0.49% reduction in HbA1c was seen (95%CI 0.04 to 0.94; I²=84%), with unexplained heterogeneity and a low GRADE of evidence. For type 2 diabetes, using diabetes apps was associated with a mean reduction of 0.57% (95%CI 0.32 to 0.82, I²=77%). The results had severe heterogeneity that was explained by the frequency of HCP feedback. In studies with no HCP feedback, low frequency, and high frequency HCP feedback, the mean reduction is 0.24% (95%CI ‐0.02 to 0.49; I²=0%), 0.33% (95%CI 0.07 to 0.59; I²=47%), and 1.12% (95%CI 0.91 to 1.32; I²=0%) respectively, with high GRADE of evidence. There is evidence that diabetes apps improve glycemic control in type 1 diabetes patients. A reduction of 0.57% in HbA1c was found in type 2 diabetes patients. However, HCP functionality is important to achieve clinical effectiveness. Futures studies need to explore the cost‐effectiveness of diabetes apps and optimal intensity of HCP feedback.
Background: Studies evaluating titration of antihypertensive medication using self-monitoring give contradictory findings and the precise place of telemonitoring over self-monitoring alone is unclear. The TASMINH4 trial aimed to assess the efficacy of self-monitored blood pressure, with or without telemonitoring, for antihypertensive titration in primary care, compared with usual care. Methods: This study was a parallel randomised controlled trial done in 142 general practices in the UK, and included hypertensive patients older than 35 years, with blood pressure higher than 140/90 mm Hg, who were willing to self-monitor their blood pressure. Patients were randomly assigned (1:1:1) to self-monitoring blood pressure (self-montoring group), to self-monitoring blood pressure with telemonitoring (telemonitoring group), or to usual care (clinic blood pressure; usual care group). Randomisation was by a secure web-based system. Neither participants nor investigators were masked to group assignment. The primary outcome was clinic measured systolic blood pressure at 12 months from randomisation. Primary analysis was of available cases. The trial is registered with ISRCTN, number ISRCTN 83571366. Findings: 1182 participants were randomly assigned to the self-monitoring group (n=395), the telemonitoring group (n=393), or the usual care group (n=394), of whom 1003 (85%) were included in the primary analysis. After 12 months, systolic blood pressure was lower in both intervention groups compared with usual care (self-monitoring, 137·0 [SD 16·7] mm Hg and telemonitoring, 136·0 [16·1] mm Hg vs usual care, 140·4 [16·5]; adjusted mean differences vs usual care: self-monitoring alone, -3·5 mm Hg [95% CI -5·8 to -1·2]; telemonitoring, -4·7 mm Hg [-7·0 to -2·4]). No difference between the self-monitoring and telemonitoring groups was recorded (adjusted mean difference -1·2 mm Hg [95% CI -3·5 to 1·2]). Results were similar in sensitivity analyses including multiple imputation. Adverse events were similar between all three groups. Interpretation: Self-monitoring, with or without telemonitoring, when used by general practitioners to titrate antihypertensive medication in individuals with poorly controlled blood pressure, leads to significantly lower blood pressure than titration guided by clinic readings. With most general practitioners and many patients using self-monitoring, it could become the cornerstone of hypertension management in primary care. Funding: National Institute for Health Research via Programme Grant for Applied Health Research (RP-PG-1209-10051), Professorship to RJM (NIHR-RP-R2-12-015), Oxford Collaboration for Leadership in Applied Health Research and Care, and Omron Healthcare UK.
Background Pharmacists’ interventions to improve outcomes of diabetes management have been promising. However, evidence on using telephone-based interventions in pharmacy practice are limited, particularly in developing countries. Objective To evaluate the efficacy of a telephone-based intervention to improve care and clinical outcomes in type-2 diabetes. Setting A referral community pharmacy and drug information center. Method We conducted a two-armed randomized controlled trial on 100 patients with type-2 diabetes. The intervention consisted of 16 telephone calls in 3 month by a trained pharmacist working in an academic drug information center, while the control group received usual care. Before random allocation, patients attended a live education session delivered by pharmacists to learn the basics of diabetes care and to confirm the eligibility criteria. Assessments were performed at baseline, month-3 (after intervention), and month-9 (follow-up). Main outcome measure Hemoglobin A1c (HbA1c). Results Eighty four patient completed the trial. Baseline variables were comparable between the two groups and the baseline value of hemoglobin A1c was 8.00 ± 1.44 in the study population. HbA1c was significantly improved in both groups at month-3 (6.97 ± 1.41 vs. 7.09 ± 1.78) and remained steady at month-9 (6.96 ± 1.44 vs. 7.26 ± 1.85). Lipid profile showed small improvements in the intervention group but was not significant. The adherence score and self-care score improvement was significantly higher in the intervention group at month-3 and were maintained at month-9. Conclusion Medication adherence and self-care significantly improved in the telephone-based intervention group. However, the improvement of clinical outcomes might have been diluted due to the live diabetes education session.