Nursing Intervention using smartphone technologies

Digital Healthcare Empowering Europeans
R. Cornet et al. (Eds.)
© 2015 European Federation for Medical Informatics (EFMI).
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License.
doi:10.3233/978-1-61499-512-8-321
321
Nursing Intervention using smartphone
technologies; a systematic review and
meta-analysis
Eunjoo Jeon a,b and Hyeoun-Ae Park a, b, c, 1
College of Nursing, Seoul National University, Seoul, Korea
b
Systems Biomediecal Informatics Research Center, Seoul National University, Seoul,
Korea
c
Research Institute of Nursing Science, Seoul National University, Seoul, Korea
a
Abstract. We reviewed mobile technology-based interventions in nursing and
computed effect size of the interventions. We searched eight databases
(KoreaMED, KMBASE, KISS, NDSL, Medline, EMBASE, Cochrane central
library and CINAHL) using three sets of terms: mobile application, mobile app,
mobile phone or smartphone; health or healthcare; and nursing. The study design,
mobile technology, sample size and clinical outcomes were extracted from each
study. A total of 38 studies were selected for review. Seven and six studies were
used in meta-analyses for weight and fasting plasma glucose changes respectively.
We found that mobile interventions used in nursing have different characteristics
compared to those in other disciplines. We also found that mobile interventions in
nursing led to significant improvement in weight and glucose control.
Keywords. Mobile Applications, Mobile Health Units, Nursing Research, MetaAnalysis.
1. Introduction
With the recent popularization of wireless devices such as tablet PCs, PDAs, and
smartphones, mobile healthcare (mHealth) has also become widespread. Mobile
technology-based intervention involving automated message systems have been shown
to improve health outcomes of the consumers.1 Nowadays, mobile technology such as
text message (SMS), photos and video message (MMS), telephone, and World Wide
Web access is a part of people’s daily lives.
There have been several review studies on effect of mobile interventions. For
example, Free et al., analysed 75 studies of mobile interventions delivered to health
care consumers1 and they found that mobile interventions bring about short-term
benefits for asthma control, physical activity, and psychological support. Liang et al.,
analysed 22 studies of mobile interventions used for diabetes management and found a
significant decrease in glucose level.2
However, these reviews studies did not cover nursing. Since nursing focuses more
on care than cure and views a patient in more holistic way, it is necessary to review
studies on use of mobile interventions in nursing. With this background, we conducted
a systematic review and meta-analyses to compute the effect size of mobile
interventions in nursing.
1
Corresponding Author: Hyeoun-Ae Park, E-mail: [email protected]
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2. Methods
We conducted a systematic review according to the guidelines of Preferred Reporting
Items for Systematic Reviews and Meta-Analyses (PRISMA).
2.1. Search strategy
The study begins by searching the following literature databases: KoreaMED,
KMBASE, KISS, and NDSL in Korea, and MEDLINE, EMBASE, COCHRANE
CENTRAL, and CINAHL on Jul. 22 in 2014. We used combined keyword searches
with ‘Mobile AND (applications OR application OR apps OR app)̓, ‘mobile phone’,
‘smartphone’ and ‘Health’, ‘Healthcare’ and ‘Nursing’. The searches were restricted to
articles published either in Korean or English. The publication year was limited to after
2000.
2.2. Inclusion and exclusion criteria
We selected studies for review based on the following criteria: First, the study used
personal digital assistants (PDA), Smartphone (e.g., iphone), handheld video-game
consoles, and tablet PCs (eg., ipad). Even though the study used mobile devices, it is
excluded if other functions of smartphone than making a phone call such as SMS,
MMS, and WWW access were not used. Second, the study provided mobile
interventions in healthcare. Third, the study is published by a nurse as either first or
corresponding author or published in one of the nursing journals.
2.3. Study selection
Two researchers independently reviewed 692 studies identified from literature search
by applying selection criteria described above. First, we reviewed the titles and
abstracts and this left 289 studies. Second, we reviewed the full text and this left 38
studies.
2.4. Data extraction
The investigators collected data from each eligible article including study subject, type
of health problem, technology used, duration, type of intervention, outcome measures,
and statistical significance. Information of the study such as year of publication, study
design, clinical area, and country were also collected from all eligible studies.
2.5. Data synthesis
First, descriptive analysis was performed for 38 papers. We categorized the
intervention studies by study characteristics such as language used, country, the design
of study, aims of study, clinical area and mobile technology used. Second, randomized
control trials (RCT) and quasi-experimental studies are selected and they were
categorised by outcome measures such as health behaviour, clinical outcome, and
system evaluation. Third, we conducted meta-analyses for clinical outcomes such as
weight and fasting plasma glucose (FPG) with Comprehensive Meta-analysis program.
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3. Results
3.1. Study characteristics
The characteristics of the 38 studies are presented in Table 1. The 37 studies of them
are written in English and only one in Korean. Most of studies are quasi-experimental
study (n=20; 52.6%) that evaluated clinical outcomes. There are 13 system
development studies (34.2%) that evaluated only system performance. Regarding the
aim of study, the most studies (n=20; 52.6%) are for the chronic disease selfmanagement. SMS is the most widely used functions (n= 20). However, sensing
technology is adopted only by three studies. There are eight intervention studies
targeting obesity management and seven diabetes management.
Table 1. Summary of study characteristics (n=38).
Study characteristics
Study design
Aims of study
Target problem
Mobile technology*
Outcome measurement*
Categories
Randomized Controlled trial
Quasi-experimental
Development study
Education
Chronic disease self-management
Symptom management
Health promotion
Hospital Information System for
Healthcare provider
Diabetes
Obesity
Breast cancer
Chronic pain
COPD
Metabolic syndrome
Asthma
Hematopoietic stem cell transplant
Tuberculosis
SMS
Mobile app
Multi-media
Internet access using mobile
Sensing technology
Health behavior change
Health outcome
Feasibility of system
Number of studies
5
20
13
5
20
5
7
1
7
8
2
2
2
1
1
1
1
20
11
6
9
3
15
22
20
* Multiple counts are allowed
3.2. Outcome measure of studies
Most of studies (n=22) measured clinical outcome such as weight, waist circumference,
body mass index, FPG, haemoglobin A1c, blood pressure, and cholesterol. The most
frequently measured clinical outcomes are weight and fasting plasma glucose. Twenty
studies reported system evaluation outcome such as users’ intention to use and
satisfaction with the system. Fifteen studies measured health behaviour changes such as
exercise frequency, diet intake, self-efficacy, and knowledge.
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3.3. Computing effect size
Seven studies (with 268 participants in total) were used in a meta-analysis for
computing effect size of weight.3-9 We found a slightly positive effect of mobile
intervention on weight reduction (Hedges’ g: -0.23, 95% CI: -0.43 to -0.03) (Figure 1).
Weight reduction outcomes were not heterogeneous (I2<0.001, T2<0.001). Six studies
(with 291 participants in total) were used in a meta-analysis for computing effect size
of fasting plasma glucose.3,7,10-13 We also found a slightly positive effect on FPG
reduction (Hedges’ g: -0.35, 95% CI: -0.54 to -0.16) (Figure 2). FPG reduction
outcomes were not heterogeneous (I2=7.62, T2=0.01).
Figure 1. Combined effect size of weight control using mobile interventions in nursing.
Figure 2. Combined effect size of fasting plasma glucose control using mobile interventions in nursing.
4. Discussion
We found that mobile interventions used in nursing are mainly for self-management of
chronic diseases compared to other healthcare disciplines. Symptom management by
healthcare providers was most widely used in other healthcare disciplines.1 This finding
reflects difference between nursing and other healthcare discipline which nursing
values empowering patients for their self-management.
Even though we were able to compute effect sizes of weight and FPG and showed
that there is a significant difference between mobile interventions and traditional
interventions in this study. We would like to recommend further studies on estimating
effect sizes of other outcome measures than weight and FPG. There have been studies
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325
showing positive effect of web-based interventions on clinical outcomes, it would be
interesting to compare effect of mobile-based intervention studies with web-based
intervention studies.
There were still a lot of studies only evaluated feasibility of the mobile
interventions in nursing, the effect size of mobile interventions will be estimated more
accurately with more studies with clinical outcome measures.
Even though we found positive effects of mobile interventions on some clinical
outcomes using meta-analyses, it is difficult for us to explain how such clinical
outcomes were occurred. We would like to recommend a further study examining how
such clinical outcome were changed including cognitive factors and behavioral change.
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by
the Korea government (MSIP) (No. 2010-0028631).
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