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Research article Open access Published: 20 February 2018

Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review

Jung-Ah Lee, Mona Choi , Sang A Lee & Natalie Jiang 

BMC Medical Informatics and Decision Making  18, Article number: 12 (2018)

20k Accesses 4 Altmetric Metrics

Abstract

Background

Mobile health (mHealth) has continuously been used as a method in

behavioral research to improve self-management in patients with chronic

diseases. However, the evidence of its effectiveness in chronic disease

management in the adult population is still lacking. We conducted a

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systematic review to examine the effectiveness of mHealth interventions on

process measures as well as health outcomes in randomized controlled trials

(RCTs) to improve chronic disease management.

Methods

Relevant randomized controlled studies that were published between

January 2005 and March 2016 were searched in six databases: PubMed,

CINAHL, EMBASE, the Cochrane Library, PsycINFO, and Web of Science.

The inclusion criteria were RCTs that conducted an intervention using mobile

devices such as smartphones or tablets for adult patients with chronic

diseases to examine disease management or health promotion.

Results

Of the 12 RCTs reviewed, 10 of the mHealth interventions demonstrated

statistically significant improvement in some health outcomes. The most

common features of mHealth systems used in the reviewed RCTs were real-

time or regular basis symptom assessments, pre-programed reminders, or

feedbacks tailored specifically to the data provided by participants via

mHealth devices. Most studies developed their own mHealth systems

including mobile apps. Training of mHealth systems was provided to

participants in person or through paper-based instructions. None of the

studies reported the relationship between health outcomes and patient

engagement levels on the mHealth system.

Conclusions

Findings from mHealth intervention studies for chronic disease management

have shown promising aspects, particularly in improving self-management

and some health outcomes.

Background

The prevalence of chronic diseases, such as cancer, cardiovascular diseases,

chronic pain, diabetes, and respiratory diseases is continuously increasing

with regard to an aging society worldwide. According to the World Health

Organization, chronic diseases are the leading cause of mortality in the world,

accounting for more than 60% of all deaths [1]. Chronic disease is, therefore,

a global burden. For example, according to the report by the Centers for

Disease Control and Prevention (CDC) in the United States (US), about half

of all American adults, approximately 117 million people, have one or more

chronic disease conditions including heart disease, stroke, cancer, type 2

diabetes, obesity, or arthritis [2]. One in four adults in the US had two or

more chronic diseases in 2012 [2]. Chronic diseases are the main cause of

death among Americans, with 48% dying from cancer or heart diseases in

2010 [3]. In 2010, about 86% of Americans’ health care expenditure was for

chronic disease treatment [4]. Therefore, chronic disease management is now

a major public health issue in the US. Likewise, managing chronic diseases is

also a challenge in other countries [5, 6]. For instance, over 40% of the

population aged 15 years or older had a chronic disease condition in the

European Union countries [5] and chronic diseases accounted for a

substantial proportion of deaths throughout Southeast Asia [6].

With advances in mobile technologies, approaches based on mobile health

(mHealth)—defined as “an area of electronic health (eHealth) with the

provision of health services and information via mobile technologies such as

mobile phones and Personal Digital Assistants (PDAs)” [7]—have been very

Peer Review reports

popular in health care and public health [8,9,10,11]. Current evidence shows

that the advantages of using mHealth devices are not only for the

improvement of diagnosis and treatment but also the social connection with

people [12]. Behavioral interventions using mobile applications (apps) on

smartphones or tablet computers in enhancing self-management for patients

with chronic diseases, such as heart failure [13] or diabetes [14], have been

studied [9, 12]. For instance, a food intake diary, physical activity monitoring,

and home blood sugar monitoring via mHealth systems are commonly used

for diabetes management [14,15,16] while monitoring of weight, symptoms,

and physical activity are common features of heart failure interventions [13,

17].

However, the evidence from current literature using the mHealth approach

on improving health outcomes is inconsistent; some studies have shown that

mHealth-based behavioral interventions are potentially effective in chronic

disease management, whereas other studies did not obtain supportive results

[9]. Previously, the evaluation of mHealth-based research focused on

feasibility and acceptability of mHealth tools.

Rather than relying on feasibility research, which often does not utilize

randomization in their intervention and/or a control group, and often lacks

an effective size, a number of systematic or integrated reviews examined

randomized controlled trials (RCTs) in diabetes management. These studies

have demonstrated positive physiological and behavioral outcomes as well as

incentive driven outcomes with mHealth systems [14,15,16,17]. However,

there is limited literature showing that mHealth approaches can be useful for

the self-management in patients with other chronic diseases. Therefore, an

in-depth evaluation of RCTs on interventions that employ mHealth

technologies and participants’ adherence to the interventions, training

methods, intervention dosage, and length of follow-ups as outcomes of

interest should be performed to provide recommendations on what factors

make mHealth interventions effective for chronic disease management.

Thus, the purpose of this study was to perform a systematic review of RCTs

using mHealth interventions for chronic disease management in adult

populations to examine the effectiveness of mHealth interventions on health

outcomes and process measures.

Methods

The Preferred Reporting Items for Systematic Review and Meta-Analysis

(PRISMA) guidelines [18] were used in this systematic review. The PICOS

(participants, interventions, comparisons, outcomes, and study design)

approach was used to develop a research question to guide the search

strategies and review: that is, do interventions using mobile health

applications improve health outcomes and process measures for adults with

chronic diseases in RCTs?

Search strategies

Searches were performed to retrieve studies that were published in peer-

reviewed journals from January 2005 to March 2016, and written in English;

the following databases were used: PubMed, CINAHL, EMBASE, the

Cochrane Library, PsycINFO, and Web of Science. We used combinations of

the key words and indexing terms such as MeSH or Emtree linked to the

search domains. An example of a PubMed search strategy is as follows: for

mobile interventions, “Mobile Applications”[Mesh] OR “Cell Phones”[Mesh]

OR “Computers, Handheld”[Mesh] OR “mobile health” OR “m-health” OR

mhealth OR “mobile-health” OR smartphone* OR “smart-phone*” OR

“mobile phone*” OR “mobile-phone*” OR “cellular phone*” OR “cellular-

phone*” OR “smart device*” OR “smart-device*” OR “tablet* PC*” OR

“tablet-based” OR “tablet* device*”; for chronic disease outcomes, “Disease

Management”[Mesh] OR “Chronic Disease/prevention and control”[Mesh]

OR “Chronic Disease/therapy”[Mesh] OR “disease* manag*” OR “disease*

monitor*” OR monitor* OR “health promot*” OR Promot*, and for method,

“Randomized Controlled Trial” [Publication Type] OR “Randomized

Controlled Trials as Topic”[Mesh] OR “Controlled Clinical Trial”[Publication

Type] OR randomized[Title/Abstract] OR randomised[Title/Abstract] OR

randomly[Title/Abstract] OR “random* assign*”[Title/Abstract] OR trial*

[Title/Abstract]. Then, those three groups of search results were combined

with “AND” (see Additional file 1 for search strategy for each database).

Study selection

The inclusion criteria were as follows: adult patients with chronic diseases

(except diabetes) as the target population, an intervention that involved using

a mobile application for smartphones or tablets, and assessing the health

outcomes and process measures. The exclusion criteria were as follows:

studies that focused on a healthy population, pregnant women, non-adults

(i.e., adolescents and children), or healthcare providers (e.g., apps for

physicians’ or nurses’ use only); studies that used only qualitative methods

(e.g., focus groups or group/individual interviews) or simple usability tests;

and studies that measured psychological outcomes only (Table 1). Two

reviewers (MC and SAL) independently screened titles, abstracts, and full-

text articles to decide whether an article was relevant to the review. In case of

disagreement, a third person was consulted (JL). We excluded studies of

diabetes management because several systematic reviews and integrated

reviews have already been published to report the effectiveness of mHealth-

based interventions [14,15,16,17]. Only studies published in peer-reviewed

journals were included.

Table 1 Inclusion and exclusion criteria

Data extraction

Data were extracted from the selected articles and entered into an electronic

data sheet. The contents of the data sheet included year of publication,

research question or purpose, study design, types of disease, types of

outcome and measurement, and the main results. In instances of

disagreement, each case was discussed by the authors.

Assessment of risk of bias

Selection bias (random sequence generation and allocation concealment),

performance bias (blinding of participants and personnel), detection bias

(blinding of outcome assessment), attrition bias (incomplete outcome data),

reporting bias (selective reporting), and other biases (determined according

to sample size calculation method, inclusion/exclusion criteria for patients’

recruitment, comparability of baseline data, funding sources, and any other

potential methodological flaw that might have influenced the overall

assessment) were assessed with the tool for risk of bias given in the Cochrane

Handbook for Systematic Reviews of Intervention [19]. For each risk of bias

item, the studies were classified as “unclear,” “low,” or “high” risk of bias

respectively. Two reviewers (MC and SAL) assessed the trials independently

and disagreements between two authors were resolved via discussion.

Operational definitions of the terms used in this review

Studies on feasibility assess whether or not an intervention is appropriate for

further testing, whereas studies on acceptability (which is a component of

feasibility) determine how recipients react to that intervention [20].

Effectiveness is defined as an intervention study that shows statistical

differences of one or more outcomes of interest measured between

intervention and control groups. Health outcomes included physiological

outcomes (e.g., gait and balance in patients with Parkinson’s disease or

fatigue in patients with cancer) and psychological outcomes (e.g., quality of

life, depressive symptoms, anxiety). Process measures [21] included

participants’ adherence to, satisfaction with, and/or the level of engagement

with mHealth systems. These process measures could be assessed via

quantitative tools such as surveys or qualitative methods such as open-ended

questions or a focus group interviews with intervention participants.

Results

Figure 1 shows the PRISMA flow diagram indicating the search process to

select the final studies that met the inclusion criteria and thus were included

in this systematic review. A study conducted by Kristjánsdóttir et al. was

published as part 1 [22] and part 2 [23], which corresponded to short-term

and long-term follow-ups, respectively. The results of both follow-ups were

reviewed. Accordingly, the results of this systematic review are based on 12

studies from 13 published articles with quantitative evaluations.

Fig. 1

PRISMA flow diagram for the systematic review process. The step-by-step process

of the application of inclusion and exclusion criteria generated the final number of

studies included in the systematic review. †Note: Kristjánsdóttir et al. (2013) was

published as part 1 [22] and part 2 [23] with respect to a short-term follow-up and

long-term follow-up; thus, the results are based on 12 studies from 13 published

articles

Table 2 presents the summary of 12 RCT studies reviewed in this paper. The

variety of chronic diseases managed using mobile apps included allergic

rhinitis and asthma, cancer, cardiovascular diseases, chronic pain, chronic

kidney disease, lung transplantation, Parkinson’s disease (PD), and spinal

bifida. Diabetes management using mHealth apps has been evaluated in

other literature and thus was not included. Among the 12 studies reviewed, 10

studies showed statistically significant mHealth app intervention effects on

some variables that were examined in each study, while two of the 12 studies

reviewed showed no statistically significant mHealth intervention effects on

outcomes of interest when comparing between treatment and control groups.

Two studies in this review were feasibility studies aimed at testing mHealth

interventions for chronic disease management [24, 25] and one study was a

pilot study evaluating a smartphone-based symptom management system for

chemotherapy management [26]. These studies were also listed as RCTs.

Table 2 Summary of studies reviewed

Studies reporting significant effects on outcomes

The majority of mHealth RCT-studies in this systematic review (10 out of 12

studies, 83.3%) showed statistically significant effects on health outcomes by

incorporating mobile applications in managing chronic diseases. Those

studies demonstrated improved physical functioning, adherence to

prescribed medications, and/or ease of symptom evaluation and reports to

care providers, as well as process measures including patient satisfaction with

mHealth management and feasibility of smart-phone-based self-

management interventions.

Kearney et al. [26] in the United Kingdom reported significant improvement

in fatigue (odds ratio, OR = 2.29; 95% CI, 1.04–5.05; P = 0.040) and hand-

foot syndrome (OR control/intervention =0.39; 95% CI, 0.17–0.92; P =

0.031) in patients with lung, breast, and colorectal cancer using a mobile

phone-based remote monitoring of chemotherapy-related symptoms in

comparison to the usual care group. In Norway, Kristjánsdóttir et al. [22, 23]

showed a favorable effect on pain management in a 4-week follow-up

(catastrophizing score lower for Intervention, M = 9.20, SD = 5.85, compared

to Control, M = 15.71, SD = 9.22, P < 0.01, with a large effect size, Cohen’s d =

0.87) but not in the 5-month and 11-month follow-ups (outcome variables

including catastrophizing, acceptance, functioning, and symptom level, all P

> 0.1). In Spain, Garcia-Palacios et al. [27] developed an ecological

momentary assessment (EMA) for chronic pain in fibromyalgia patients and

found that patients with less familiarity with technology using a mobile EMA

system via their smartphones showed higher levels of compliance than

patients with a paper-based diary (complete record t = − 4.446, d = 1.02,

reference Cohen’s d > =0.8, large effect). In Turkey, Cingi et al. [28] reported

that patients with allergic rhinitis or asthma displayed better quality of life or

well-controlled asthma scores by using the mHealth intervention compared

to the control group (all P < 0.05). Dicianno et al. [24] demonstrated the

feasibility of a mHealth intervention for patients with spina bifida to improve

self-management skills and high usage of the mobile system was associated

with positive changes in the self-management skills. In Sweden, Hägglund et

al. [29] tested a tablet-based intervention in patients with heart failure (HF)

and found improved self-care and health-related quality of life (HRQoL) and

a reduction in HF-related hospital days (risk ratio, RR = 0.38; 95% CI, 0.31–

0.46; P < 0.05). In Israel, Ginis et al. [25] conducted home-based

smartphone-delivery automated feedback training for gait in people with PD,

and found significant improvement in balance (F(2,108) = 3.73, P = 0.04) from

baseline to post-test.

Martin et al. [30] in the US found that an automated text message system

increased physical activity to prevent cardiovascular diseases in phase 1

(weeks 2 to 3) and phase 2 (weeks 4 to 5), all P < 0.001. Piette et al. [31] in the

US reported that the intervention group involving “CarePartners” connecting

to a relative or friend living outside their home showed improvement in

medication adherence and caregiver communication (all P < 0.05). DeVito

Dabbs et al. [32] in the US conducted an RCT for patients with lung

transplantation and reported improvement in self-monitoring (OR = 5.11;

95% CI, 2.95–8.87; P < 0.001), adherence to medical regimen (OR =1.64;

95% CI, 1.01–2.66, P = 0.046), and reported abnormal health indicators more

frequently (OR = 8.9; 95% CI, 3.60–21.99; P < 0.001).

Studies reporting similar or no effects on outcomes

Two of the twelve studies reviewed (16.7%) showed similar or no effects of

mHealth-based interventions on main outcomes of interest compared to

control groups. In Finland, Vuorinen et al. [33] found no difference in the

number of HF-related hospital days (incidence rate ratio, IRR = 0.812, P =

0.351). However, patients in the telemonitoring intervention group used

more healthcare resources; increased number of visits to the nurse (IRR =

1.73; 95% CI, 1.38–2.15; P < 0.001), more time spent with nurses (mean

difference = 48.7 min, P < 0.001), and increased number of telephone

contacts initiated by nurses (IRR = 5.6; 95% CI, 3.41–7.63; P < 0.001). In

Spain, Cubo et al. [34] reported a trend of lower PD functional status (the

Unified Parkinson’s Disease Rating Scale, UPDRS I) in patients on home-

based monitoring compared to patients in standard in-office visits (P = 0.06),

while other outcomes (measured by UPDRS II, III, IV subscales) and HRQoL

in PD did not show statistically significant differences between the

intervention and control groups. Cubo et al. [34], however, explained that the

approach of using home-based motor monitoring via mHealth applications

compared to standard office visits in the 1-year follow up was cost-effective

(incremental cost-effectiveness ratio, ICER, per unit of UPDRS subscales

ranging from €126.72 to € 701.31).

mHealth interventions

Table 3 presents the details of mHealth interventions including duration,

mobile app type, app content, and training methods. A quarter of the studies

(3 out of 12) reported the feasibility of the mHealth intervention as a pilot

study to assess the potential for successful implementation of the mHealth

intervention to patients/participants [24,25,26]. The majority of studies used

smartphones as a mobile device, two studies [29, 34] used tablets for

mHealth interventions, and two studies used telemonitoring wireless devices

including weight scales for patients with HF [29] or gait detectors for patients

with PD [25]. The length of interventions ranged from two weeks to twelve

months; half of the studies (6/12, 50%) had more than six-month

intervention periods and follow-ups. Most common components of mHealth

interventions included remote symptom monitoring and self-assessment as

well as tailored automated messages or self-care education to coach patients

with chronic disease conditions that needed active disease management. One

particular study by Kearney et al. was more inclusive in that it provided real-

time feedback and tailored such feedback for symptom management

depending on the severity, offering pharmacological, nutritional, or

behavioral advice when needed [26].

Table 3 Details of mobile application intervention for chronic disease management

The sample size of the studies reviewed was between 28 (patients with spina

bifida) and 372 (patients with heart failure). In terms of subjects, studies

included patients who were 18 years and above, with some studies limited to

a particular age range such as 18–40 [24] or up to 69 years [30]. The mean

age of participants in the mHealth intervention studies ranged from 30-year-

old patients with spina bifida [24] to 75-year-old patients with HF [29]; the

approximate average age group in this 12-study review was in the 50s. None

of the studies reported effectiveness of mHealth intervention by age

categories. These 12 studies also did not report participants’ prior experience

with mobile devices such as smartphones or educational background, which

may have affected the ability to use mHealth apps via mobile devices.

Outcomes of interventions were measured either by mHealth systems directly

or by paper-based questionnaires.

In terms of mHealth intervention training, either face-to-face information

sessions at baseline or paper-based instructions were used in most studies.

Kearney et al. [26] found that symptoms were reported differently on a

paper-based questionnaire and mobile phone. Participants in the mobile

group reported lower levels of fatigue compared to those in the paper-based

group (OR non-mobile/mobile = 2.29, 95% CI 1.04–5.05, P = 0.04) [26].

Reporting cancer toxicity symptoms (e.g., hand-foot syndrome and

mucositis) in real time might allow for more accurate measurement [26].

None of the studies reported process measures, including adherence, level of

engagement, and/or satisfaction with the mHealth systems. Potentially, there

was limited information on such measures in the published articles with the

authors possibly publishing the process measures elsewhere.

Risk of bias assessment

The risk of bias in the reviewed studies is summarized in Fig. 2 and shown for

individual studies in Fig. 3. The intervention studies generally performed well

in their risk of bias for random sequence generation (62% low risk),

allocation concealment (23% low risk), blinding of participants and personnel

(8% low risk), blinding of outcome assessors (15% low risk), incomplete

outcome data (69% low risk), and selective reporting (92% low risk) (Fig. 2).

Fig. 2

Risk of bias assessment: Summary graph

Fig. 3

Risk of bias assessment for individual studies. Low risk of bias; Unclear;

High risk of bias

Considering the method of randomization, 5 trials did not present details of

randomization [25, 27, 29, 30, 33]. Only 2 studies reported use of an

adequate allocation concealment method [22, 32]. Although due to the nature

of the mHealth intervention, it is almost impossible to blind participants and

healthcare providers, one study described the blinding of participants by

providing the same mobile phone application to both experimental and

control groups with different functionalities. The experimental group was

given communication, health status, or medication usage tracking, and a

survey questionnaire, whereas the control group received only the survey

questionnaire [28]. Only two trials were identified as blinding the outcome

assessors. Four trials failed to provide a full description of participants and

losses to follow-up during their trials [24, 28, 29, 31]. All studies had a low

risk for reporting bias. Devito et al. had low risk for all items except blinding

of participants and personnel [32]. Hägglund et al. had low risk only for

reporting bias [29] (Fig. 3).

Discussion

This systematic review has found a potential favorable effect of mHealth

interventions on health outcomes and process measures in patients with

chronic diseases including asthma, cancer, cardiovascular diseases, chronic

pain, spina bifida, or Parkinson’s disease. The results from the reviewed RCTs

showed improvement in some health outcomes in patients in managing their

chronic disease.

There were some commonalities and differences in using mHealth in the

reviewed studies. One of the common features useful in mHealth

interventions is pre-set and tailored feedback on reported symptoms. The

mobile application systems used in the reviewed studies were developed by

the study team and validated and refined in the previous studies that were

conducted before the RCTs. None of the commercial health apps were used in

the reviewed studies. Most studies utilized research staff to provide training

in mHealth systems for the participants via face-to-face or information group

sessions, or through information materials, while one study [26] used local

nurses to train patients. None of the reviewed studies addressed whether

their mHealth systems were incorporated into daily medical practice in either

clinic settings or acute care hospitals, meaning that there were no signs of

their implementation in real health care systems. Challenges in real-life

settings may relate to lack of financial incentives for providers in using

mHealth tools or uncertainty regarding privacy and security of information

transferred via mHealth systems [35].

Interventions to promote self-management in patients with chronic diseases

started from web-based and/or telephone-based interventions to mHealth-

based interventions. Unlike those previous behavioral interventions limited

to places where patients with chronic diseases had access to the treatment

advice, mHealth interventions have advanced features such as real-time

symptom monitoring and feedback [25, 34, 36]. For example, patients with

PD receive real-time feedback on their selected gait parameters during their

walks via the preset gait app developed from evidence-based exercise

guidelines [25]. This is an example of how using well-designed and validated

mHealth apps in daily life can benefit health outcomes.

The number of smartphone users has shown great increases; in the US [37] it

is estimated to reach 224.3 million in 2017, up from 171 million in 2014;

worldwide it was [38] 2.32 billion in 2017, up from 1.57 billion in 2014.

Approximately 77% of people in the US owned smartphones in 2016 [39].

Using mobile devices for mHealth is essential nowadays and approaches of

mHealth vary from sending text messaging for medical appointment

reminders to monitoring and assessing symptoms in real-time, virtually at

any location via wireless networks. Interventions using mHealth have also

eased medical coaching for caregivers as care partners with healthcare

professionals for effective chronic disease management. Piette et al. [31]

studied the comparative effectiveness of mHealth interventions supporting

HF patients and their family caregivers and showed improvement for

medication adherence and caregiver communication. The impact of including

caregivers as a part of mHealth users—one of the care supporting groups—on

actual patients’ health outcomes should continue to be studied in the context

of an increasing aging society.

Moreover, long-term follow-ups of responses from patients and caregivers

who have used mHealth need to be evaluated. Areas that need to be studied

include the optimum length of time and frequency of the mHealth delivery

system as well as type of technology and training. For example, effective

frequencies of automated reminders or coaching messages, when additional

reminders should be sent, and when people become tired or irritated by

automated messages need to be studied. Users of mHealth might experience

fatigue from automated reminders and eventually mHealth interventions

could become ineffective. Other systematic reviews on chronic disease

management showed that the frequency of input into mHealth systems was a

burden on participants and affected the attrition rate [17]. In this review, the

length of the intervention in the 12 studies varied from 2 weeks to 12 months;

5 of 12 studies (about 42%) had less than 2-month interventions while 4

studies (about 33%) had 1 year of intervention. The positive health outcomes

of the various studies were not directly related to the length of the

intervention or training methods of mHealth in the reviewed studies.

One aspect of mHealth approaches that also needs to be considered is

effective clinical communication between patients and healthcare

professionals who need to respond to patients’ questions via mHealth

systems. Cingi et al. [28] reported healthcare providers (i.e., residents)

expressed an improvement in communication with patients via mHealth;

however, they found that using mHealth tools as the primary method of

communication was strongly opposed by the healthcare providers. Vuorinen

et al. [33] also reported a significant increase in the communication (i.e.,

telephone contacts) between nurses and patients which in turn increased the

nurses’ workload during the trial. One recommendation for reducing health

care providers’ workload in mHealth interventions is using advanced

technology to respond to patients’ questions regarding symptoms assessed

and reported via the mHealth systems.

While considering positive health outcomes (e.g., reduction of hospital

readmission for HF-related conditions or medication adherence) of patients

with chronic diseases, burden of healthcare professionals should be

measured as an outcome of mHealth interventions. An adequate triage

system can decrease healthcare professionals’ response time for emergency

needs reported by mHealth users [28].

Common recommendations discussed in the studies of this systematic review

to improve mHealth interventions include a simple and user-friendly-

designed mHealth system, data confidentiality, lay language use for

structured and automated feedback or advice, positive motivation and

improving engagement [28], and inclusion of patient’s social supporters,

such as family members, friends, and/or peers [31].

There are several limitations in this systematic review. First, we only selected

randomized controlled trials for this review and most of them were funded

studies. Therefore, the mHealth systems or smartphone apps used in the

studies were validated and relatively reliable compared to health apps

commercially available on the market. Literature from studies on

smartphone-based interventions for chronic disease management that were

small scale with or without control group and/or had a short-term follow-up

has shown ambivalent results. Thus, we intended to choose robust studies

that used randomization, control groups, and relevant follow-ups for

outcomes. We looked at the outcome changes at different follow-up points.

Second, we did not include mHealth intervention studies for diabetes

management since there is ample literature on diabetes management using

mobile technology approaches [14,15,16,17]. Third, we excluded studies

wherein health apps were used only by health professionals such as

physicians or specialized clinical nurses. We focused on patient-centered

health apps as a part of mHealth interventions in the review. Lastly, most of

the reviewed studies provided smartphones or tablets to participating

patients and thus, the results from this review cannot yet be generalized

among those who have financial concerns regarding purchasing mHealth

tools. Although the availability of wireless networks is increasing, potential

mHealth users such as patients with chronic diseases or their caregivers may

have limited data services for their mobile devices due to financial concerns.

This might be an important issue during an emergency when patients may

have no access to evidence-based medical advice via mHealth devices.

Conclusion

The findings from the majority of reviewed studies that used mHealth

interventions showed some health outcome improvement in patients with

chronic disease conditions. Favorable factors in mHealth approaches are

automated text reminders, frequent and accurate symptom monitoring (often

in real time), and improved communication between patients and healthcare

providers resulting in enhanced self-management in patients with chronic

conditions. Thus, the future of mHealth is presumably optimistic. The

relationship between engagement of users on mHealth tools and outcome

improvement should be further studied. The studies reviewed in this paper

showed disease-specific mHealth interventions that might be different from

commercial mobile health apps available to the public. Rigorously tested

mHealth apps developed through research should be further considered to be

made available to the general population.

Abbreviations

CDC: The US Centers for Disease Control and Prevention

EMA: Ecological momentary assessment

HF: Heart failure

mHealth: Mobile health

PD:

Parkinson’s disease

RCTs: Randomized controlled trials

UPDRS: Unified Parkinson’s Disease Rating Scale

US:

The United States of America

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Acknowledgements

The authors thank Yoori Yang, MSN, College of Nursing, Yonsei University

and Laura Narvaez, BS, Sue and Bill Gross School of Nursing at University of

California Irvine for their assistance in literature search and abstract reviews.

We also thank Dr. Priscilla Kehoe for her thorough editorial support.

Funding

Not applicable.

Availability of data and materials

Not applicable.

Author information

Authors and Affiliations

Sue and Bill Gross School of Nursing, University of California Irvine, Irvine,

CA, USA

Jung-Ah Lee

College of Nursing, Mo-Im Kim Nursing Research Institute, Yonsei

University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea, 03722

Mona Choi

College of Nursing and Health Sciences, University of Massachusetts, Boston,

MA, USA

Sang A Lee

Program in Public Health, University of California Irvine, Irvine, CA, USA

Natalie Jiang

Contributions

Design of the study protocol: JL, MC; Data collection and analysis: MC, SAL,

NJ; Data interpretation: JL, MC, SAL; Drafting the manuscript: JL, MC, SAL,

NJ. All authors revised the article critically, gave feedback and approved the

final manuscript.

Corresponding author

Correspondence to Mona Choi.

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The authors declare that they have no competing interests.

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Additional file 1: Title of data: Detailed search strategy per database. Detailed search strategy

per database in order to find all published interventions using mobile health

applications to improve chronic disease management for adults in

randomized controlled trials. (DOCX 21 kb)

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Lee, JA., Choi, M., Lee, S.A. et al. Effective behavioral intervention strategies using mobile health applications for chronic disease management: a systematic review. BMC Med Inform Decis Mak 18, 12 (2018). https://doi.org/10.1186/s12911-018-0591-0

Received 24 June 2017

Accepted 25 January 2018

Published 20 February 2018

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Keywords

Mobile applications Disease management Mobile health

Chronic disease management Self-management

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