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RESEARCH ARTICLE Open Access

Effective behavioral intervention strategiesusing mobile health applications forchronic disease management: a systematicreviewJung-Ah Lee1, Mona Choi2* , Sang A Lee3 and Natalie Jiang4

Abstract

Background: Mobile health (mHealth) has continuously been used as a method in behavioral research to improveself-management in patients with chronic diseases. However, the evidence of its effectiveness in chronic diseasemanagement in the adult population is still lacking. We conducted a systematic review to examine the effectiveness ofmHealth interventions on process measures as well as health outcomes in randomized controlled trials (RCTs) toimprove chronic disease management.

Methods: Relevant randomized controlled studies that were published between January 2005 and March 2016 weresearched 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 smartphonesor 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 insome health outcomes. The most common features of mHealth systems used in the reviewed RCTs were real-time orregular basis symptom assessments, pre-programed reminders, or feedbacks tailored specifically to the data provided byparticipants via mHealth devices. Most studies developed their own mHealth systems including mobile apps. Training ofmHealth systems was provided to participants in person or through paper-based instructions. None of the studiesreported 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 promisingaspects, particularly in improving self-management and some health outcomes.

Keywords: Mobile applications, Disease management, Mobile health, Chronic disease management, Self-management

BackgroundThe prevalence of chronic diseases, such as cancer, car-diovascular diseases, chronic pain, diabetes, and respira-tory diseases is continuously increasing with regard toan aging society worldwide. According to the WorldHealth Organization, chronic diseases are the leadingcause of mortality in the world, accounting for morethan 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 Americanadults, approximately 117 million people, have one ormore 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 chronicdiseases in 2012 [2]. Chronic diseases are the main causeof death among Americans, with 48% dying from canceror heart diseases in 2010 [3]. In 2010, about 86% ofAmericans’ health care expenditure was for chronic dis-ease treatment [4]. Therefore, chronic disease manage-ment is now a major public health issue in the US.Likewise, managing chronic diseases is also a challenge

* Correspondence: [email protected] of Nursing, Mo-Im Kim Nursing Research Institute, Yonsei University,50 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea03722Full list of author information is available at the end of the article

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in other countries [5, 6]. For instance, over 40% of thepopulation aged 15 years or older had a chronic diseasecondition in the European Union countries [5] andchronic diseases accounted for a substantial proportionof deaths throughout Southeast Asia [6].With advances in mobile technologies, approaches

based on mobile health (mHealth)—defined as “anarea of electronic health (eHealth) with the provisionof health services and information via mobile tech-nologies such as mobile phones and Personal DigitalAssistants (PDAs)” [7]—have been very popular inhealth care and public health [8–11]. Current evi-dence shows that the advantages of using mHealthdevices are not only for the improvement of diagnosisand treatment but also the social connection withpeople [12]. Behavioral interventions using mobile ap-plications (apps) on smartphones or tablet computersin enhancing self-management for patients withchronic diseases, such as heart failure [13] or diabetes[14], have been studied [9, 12]. For instance, a foodintake diary, physical activity monitoring, and homeblood sugar monitoring via mHealth systems arecommonly used for diabetes management [14–16]while monitoring of weight, symptoms, and physicalactivity are common features of heart failure interven-tions [13, 17].However, the evidence from current literature using

the mHealth approach on improving health outcomes isinconsistent; some studies have shown that mHealth-based behavioral interventions are potentially effective inchronic disease management, whereas other studies didnot obtain supportive results [9]. Previously, the evalu-ation of mHealth-based research focused on feasibilityand 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, anumber of systematic or integrated reviews examinedrandomized controlled trials (RCTs) in diabetes manage-ment. These studies have demonstrated positive physio-logical and behavioral outcomes as well as incentivedriven outcomes with mHealth systems [14–17]. How-ever, there is limited literature showing that mHealth ap-proaches can be useful for the self-management inpatients with other chronic diseases. Therefore, an in-depth evaluation of RCTs on interventions that employmHealth technologies and participants’ adherence to theinterventions, training methods, intervention dosage,and length of follow-ups as outcomes of interest shouldbe performed to provide recommendations on what fac-tors make mHealth interventions effective for chronicdisease management.Thus, the purpose of this study was to perform a sys-

tematic review of RCTs using mHealth interventions for

chronic disease management in adult populations toexamine the effectiveness of mHealth interventions onhealth outcomes and process measures.

MethodsThe Preferred Reporting Items for Systematic Reviewand Meta-Analysis (PRISMA) guidelines [18] were usedin this systematic review. The PICOS (participants, in-terventions, comparisons, outcomes, and study design)approach was used to develop a research question toguide the search strategies and review: that is, do inter-ventions using mobile health applications improve healthoutcomes and process measures for adults with chronicdiseases in RCTs?

Search strategiesSearches were performed to retrieve studies that werepublished in peer-reviewed journals from January 2005to March 2016, and written in English; the following da-tabases were used: PubMed, CINAHL, EMBASE, theCochrane Library, PsycINFO, and Web of Science. Weused combinations of the key words and indexing termssuch 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 mhealthOR “mobile-health” OR smartphone* OR “smart-phone*”OR “mobile phone*” OR “mobile-phone*” OR “cellularphone*” OR “cellular-phone*” OR “smart device*” OR“smart-device*” OR “tablet* PC*” OR “tablet-based” OR“tablet* device*”; for chronic disease outcomes, “DiseaseManagement”[Mesh] OR “Chronic Disease/preventionand control”[Mesh] OR “Chronic Disease/therapy”[Mesh]OR “disease* manag*” OR “disease* monitor*” OR moni-tor* 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 rando-mized[Title/Abstract] OR randomised[Title/Abstract] ORrandomly[Title/Abstract] OR “random* assign*”[Title/Ab-stract] OR trial*[Title/Abstract]. Then, those threegroups of search results were combined with “AND” (seeAdditional file 1 for search strategy for each database).

Study selectionThe inclusion criteria were as follows: adult patientswith chronic diseases (except diabetes) as the targetpopulation, an intervention that involved using a mobileapplication for smartphones or tablets, and assessing thehealth outcomes and process measures. The exclusioncriteria were as follows: studies that focused on a healthypopulation, pregnant women, non-adults (i.e., adoles-cents and children), or healthcare providers (e.g., apps

Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 of 18

for physicians’ or nurses’ use only); studies that usedonly qualitative methods (e.g., focus groups or group/in-dividual interviews) or simple usability tests; and studiesthat measured psychological outcomes only (Table 1).Two reviewers (MC and SAL) independently screenedtitles, abstracts, and full-text articles to decide whetheran article was relevant to the review. In case of disagree-ment, a third person was consulted (JL). We excludedstudies of diabetes management because several system-atic reviews and integrated reviews have already beenpublished to report the effectiveness of mHealth-basedinterventions [14–17]. Only studies published in peer-reviewed journals were included.

Data extractionData were extracted from the selected articles and en-tered into an electronic data sheet. The contents of thedata sheet included year of publication, research ques-tion or purpose, study design, types of disease, types ofoutcome and measurement, and the main results. In in-stances of disagreement, each case was discussed by theauthors.

Assessment of risk of biasSelection bias (random sequence generation and alloca-tion concealment), performance bias (blinding of partici-pants and personnel), detection bias (blinding ofoutcome assessment), attrition bias (incomplete out-come data), reporting bias (selective reporting), andother biases (determined according to sample size calcu-lation method, inclusion/exclusion criteria for patients’recruitment, comparability of baseline data, fundingsources, and any other potential methodological flawthat might have influenced the overall assessment) wereassessed with the tool for risk of bias given in theCochrane Handbook for Systematic Reviews of Interven-tion [19]. For each risk of bias item, the studies wereclassified as “unclear,” “low,” or “high” risk of bias re-spectively. Two reviewers (MC and SAL) assessed the

trials independently and disagreements between two au-thors were resolved via discussion.

Operational definitions of the terms used in this reviewStudies on feasibility assess whether or not an interven-tion is appropriate for further testing, whereas studieson acceptability (which is a component of feasibility) de-termine how recipients react to that intervention [20].Effectiveness is defined as an intervention study thatshows statistical differences of one or more outcomes ofinterest measured between intervention and controlgroups. Health outcomes included physiological out-comes (e.g., gait and balance in patients with Parkinson’sdisease or fatigue in patients with cancer) and psycho-logical outcomes (e.g., quality of life, depressive symp-toms, anxiety). Process measures [21] includedparticipants’ adherence to, satisfaction with, and/or thelevel of engagement with mHealth systems. Theseprocess measures could be assessed via quantitative toolssuch as surveys or qualitative methods such as open-ended questions or a focus group interviews with inter-vention participants.

ResultsFigure 1 shows the PRISMA flow diagram indicating thesearch process to select the final studies that met the in-clusion criteria and thus were included in this systematicreview. A study conducted by Kristjánsdóttir et al. waspublished as part 1 [22] and part 2 [23], which corre-sponded to short-term and long-term follow-ups, re-spectively. The results of both follow-ups were reviewed.Accordingly, the results of this systematic review arebased on 12 studies from 13 published articles withquantitative evaluations.Table 2 presents the summary of 12 RCT studies

reviewed in this paper. The variety of chronic diseasesmanaged using mobile apps included allergic rhinitisand asthma, cancer, cardiovascular diseases, chronicpain, chronic kidney disease, lung transplantation, Par-kinson’s disease (PD), and spinal bifida. Diabetes man-agement using mHealth apps has been evaluated inother literature and thus was not included. Among the12 studies reviewed, 10 studies showed statistically sig-nificant mHealth app intervention effects on some vari-ables that were examined in each study, while two of the12 studies reviewed showed no statistically significantmHealth intervention effects on outcomes of interestwhen comparing between treatment and control groups.Two studies in this review were feasibility studies aimedat testing mHealth interventions for chronic diseasemanagement [24, 25] and one study was a pilot studyevaluating a smartphone-based symptom managementsystem for chemotherapy management [26]. These stud-ies were also listed as RCTs.

Table 1 Inclusion and exclusion criteria

Inclusioncriteria

• studies that included adult patients withchronic diseases (except diabetes) as thetarget population

• studies that involved using a mobile application• studies that focused on disease managementor health promotion

Exclusioncriteria

• studies that included healthy people, pregnantwomen, non-adults (i.e., adolescents and children),and healthcare providers

• studies that used only qualitative methodsor simpleusability tests

• studies that measured psychologicaloutcomes only


Studies reporting significant effects on outcomesThe majority of mHealth RCT-studies in this systematicreview (10 out of 12 studies, 83.3%) showed statisticallysignificant effects on health outcomes by incorporatingmobile applications in managing chronic diseases. Thosestudies demonstrated improved physical functioning, ad-herence to prescribed medications, and/or ease of symp-tom evaluation and reports to care providers, as well asprocess measures including patient satisfaction withmHealth 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 syn-drome (OR control/intervention =0.39; 95% CI, 0.17–0.92; P = 0.031) in patients with lung, breast, and colo-rectal cancer using a mobile phone-based remote moni-toring of chemotherapy-related symptoms incomparison to the usual care group. In Norway, Krist-jánsdóttir et al. [22, 23] showed a favorable effect onpain management in a 4-week follow-up (catastrophizingscore lower for Intervention, M = 9.20, SD = 5.85, com-pared to Control, M = 15.71, SD = 9.22, P < 0.01, with alarge effect size, Cohen’s d = 0.87) but not in the 5-month and 11-month follow-ups (outcome variables

including catastrophizing, acceptance, functioning, andsymptom level, all P > 0.1). In Spain, Garcia-Palacios etal. [27] developed an ecological momentary assessment(EMA) for chronic pain in fibromyalgia patients andfound that patients with less familiarity with technologyusing a mobile EMA system via their smartphonesshowed higher levels of compliance than patients with apaper-based diary (complete record t = − 4.446, d = 1.02,reference Cohen’s d > =0.8, large effect). In Turkey, Cingiet al. [28] reported that patients with allergic rhinitis orasthma displayed better quality of life or well-controlledasthma scores by using the mHealth intervention com-pared to the control group (all P < 0.05). Dicianno et al.[24] demonstrated the feasibility of a mHealth interven-tion for patients with spina bifida to improve self-management skills and high usage of the mobile systemwas associated with positive changes in the self-management skills. In Sweden, Hägglund et al. [29]tested a tablet-based intervention in patients with heartfailure (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] conductedhome-based smartphone-delivery automated feedbacktraining for gait in people with PD, and found significant

Fig. 1 PRISMA flow diagram for the systematic review process. The step-by-step process of the application of inclusion and exclusion criteriagenerated 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

2Summaryof

stud

iesreview

ed

Year/Autho

r/Cou

ntry

Purposeof

Stud

ySample

Type

sof

Disease

Type

sof

Outcomes

andMeasuremen

tsMainResults

2009

Kearne

yet

al.

United

King

dom

Toevaluate

theim

pact

ofa

mob

ileph

one-basedremote

mon

itoring,advanced

symptom

managem

entsystem

(ASyMS©

)on

theincidence,severityand

distressof

sixchem

otherapy-

relatedsymptom

sinpatients

with

lung

,breast,or

colorectal

cancer.

n=112(56in

each

interven

tion

orcontrolg

roup

)patientsfro

m7

clinicalsitesthroug

hout

theUK.

Inclusioncriteria:

commen

cing

ane

wcourse

ofchem

othe

rapy

treatm

ent,receivingou

tpatient

chem

othe

rapy,age

≥18,w

ritten

inform

edconsen

tgiven,ableto

read

andwriteEnglish,and

deem

edby

mem

bersof

the

clinicalteam

asbe

ingph

ysically

andpsycho

logically

fitto

participatein

thestud

y.

Che

mothe

rapy

relatedtoxicity

inpatientswith

lung

,breast,or

colorectalcancer

•Severityanddistress

ofthesix

symptom

sinclud

ingvomiting

,nausea,d

iarrhe

a,hand

-foot

synd

rome,

sore

mou

th/throat,andfatig

ue.

•Incide

nce–(did

symptom

occur?Y/N),

Severityanddistress

(scores0–3)

ofthesixindividu

alsymptom

s.•ASyMShasintegrated

the

Com

mon

Toxicity

Criteria

Adverse

Even

ts(CTC

AE)

gradingsystem

andthe

Che

mothe

rapy

Symptom

Assessm

entScale.

•Pape

rversionof

the

electron

icsymptom

questio

nnaire

was

administrated

atbaseline,chem

othe

rapy

cycles

2,3,4,and5in

both

grou

ps.

•Tw

oof

thesixsymptom

smeasured

(fatig

ueandhand

-foot

synd

rome)

show

edstatisticalsign

ificance

betw

eenthecontroland

interven

tiongrou

ps(re

spectively,

p=0.040,p=0.031).

•Patientsrepo

rted

improved

commun

icationwith

health

profession

als,im

provem

entsin

themanagem

entof

theirsymptom

s,andfeelingreassured

theirsymptom

swerebe

ingmon

itoredwhileat

homewhe

nusingASyMS.

2013

Kristjánsdó

ttir

etal.

Norway

Tostud

ythelong

term

effects

ofa4-weeksm

artpho

neinterven

tionwith

diariesand

therap

istfeed

backfollowing

aninpa

tient

chronicpa

inrehabilitatio

nprog

ram

(11-mon

thfollow

upof

2013

Kristjánsdóttir

etal.

stud

y)

n=135(interven

tiongrou

p:69/con

trol

grou

p:66)Inclusion

criteria:fem

ale,age≥18,

participating

intheinpatient

multid

imen

sion

alrehabilitationprog

ramforchronic

pain,havingchronicwidespread

pain

>6mon

ths(with

orwith

out

diagno

sisof

fibromyalgia),no

tparticipatingin

anothe

rresearch

projectat

therehabcenter,b

eing

ableto

useasm

artpho

ne,and

notbe

ingdiagno

sedwith

aprofou

ndpsychiatric

disorder.

Chron

icwidespreadpain

orFibrom

yalgia

•Catastrop

hizing

[Pain

catastroph

izing

scale(PCS)]

•Accep

tance[Chron

icpain

acceptance

questio

nnaire

(CPA

Q)]

•Em

otionald

istress[m

odified

Gen

eralHealth

Questionn

aire

(GHQ)]

•Im

portance

andsuccessin

living

accordingto

one’sow

nvalues

in6

domains

(family,intim

aterelatio

nships,

frien

dship,

work,he

alth,and

person

algrow

th)[Chron

icPain

Values

Inventory

(CPVI)]

•Pain,fatigue,sleep

disturbance

[Visual

analog

scales

(VAS)]

•Im

pact

ofFibrom

yalgiaon

functio

ning

andsymptom

levelsthe

pastweek

[Fibromyalgia

Impact

Questionn

aire

(FIQ)]

•Functio

ning

[Sho

rt-Form

Health

Survey

(SF-8)]

•Use

ofno

ninteractiveweb

site

[self-rep

ortat

T3(4

weeks

afterdischarge)]

Short-term

follow-upresults:

•Interven

tiongrou

prepo

rted

less

catastroph

izing(p<0.001).

•Results

from

thepe

r-protocol

analysisindicate

interven

tionwith

diariesandwrittenpe

rson

alized

feed

back

redu

cedcatastroph

izing

andincreasedacceptance

andeffects

persisted5mon

thsafter

theinterven

tion.

•Increasedim

provem

ent

invalues-based

livingin

theinterven

tiongrou

p•Con

trol

grou

pshow

edan

increased

levelo

ffatig

ueanda

tend

ency

toward

anincrease

insleep

disturbanceat

the

5-mon

thfollow-up.

Long

-term

11-m

onth

follow-upresults:

•Thebe

tween-grou

pdifferences

oncatastroph

izing,

acceptance,fun

ctioning

,andsymptom

level


Table

2Summaryof

stud

iesreview

ed(Con

tinued)

Year/Autho

r/Cou

ntry

Purposeof

Stud

ySample

Type

sof

Disease

Type

sof

Outcomes

andMeasuremen

tsMainResults

•Feasibility

ofthe

smartpho

neinterven

tion

(singlequ

estio

nforpo

st-in

terven

tion)

wereno

long

ereviden

t(p>0.10).

•Moreim

provem

entin

catastroph

izing

scores

durin

gthe

follow-uppe

riod

(T2-T5)in

the

interven

tiongrou

p(p=0.045)

•Po

sitiveeffect

onacceptance

was

foun

dwith

inthe

interven

tiongrou

p(p<0.001).

•Sm

alltolarge

negativeeffectswere

foun

dwith

inthe

controlg

roup

onfunctio

ning

and

symptom

levels,

emotionald

istress,and

fatig

ue(p=0.05).

•Redu

ctionin

diseaseim

pact

(measuredby

FIQ)foun

dfor

interven

tiongrou

p(p=0.03).

•Long

-term

results

aream

bigu

ous.

2013

Garcia-

Palacios

etal.

Spain

Tocompare

compliancewith

pape

rdiaryvs.smartpho

nediary,aggreg

ated

ecolog

ical

mom

entary

assessmen

t(EMA)

data

vs.retrospectivedata,and

assess

acceptability

ofEM

Aproced

ures.

n=40

(interven

tion

grou

p:20/con

trol

grou

p:20)Inclusion

criteria:m

etcriteria

forFM

S,de

fined

bythe

American

College

ofRh

eumatolog

yandwere

diagno

sedby

arheumatolog

ist.

Fibrom

yalgia

synd

rome(FMS)

•EM

Apain

andfatig

ue(0–10

Num

ericalRatin

gScales)

•Moo

d(face-based

pictorial

7-po

intscale)

•Weeklyretrospe

ctiveratin

gof

pain

andfatig

ue[Brief

Pain

Inventory

(BPI)andBriefF

atigue

Inventory(BFI)]

•Accep

tabilityand

preferen

ces(self-rep

ort)

•Sm

artpho

necond

ition

(smartpho

nediary)show

edhigh

erlevelsof

compliancethan

pape

rcond

ition

(paper

diary)

(p<0.01).

•Retrospe

ctiveassessmen

tprod

uces

overestim

ation

ofeven

ts(painandfatig

ue,

p<0.01).

•Sm

artpho

necond

ition

preferred

andaccepted

over

pape

rdiary,

even

inparticipantswith

low

familiarity

with

techno

logy.

2014

Vuorinen

etal.

Finland

Tostud

ywhether

multidisciplinary

carewith

telemon

itoringleadsto

decreasedHF-related

hospitalization

n=94

(interven

tion

grou

p:47/

controlg

roup

:47)

Inclusioncriteria:

diagno

sisof

systolic

heartfailure,age

18–90years,NYH

A(New

WorkHeart

Heartfailure

(HF)

•Num

bero

fHF-relatedho

spitaldays

(datafro

mho

spital

electron

ichealth

record

system

)•Clinicaleffectiven

ess[death

from

any

cause,he

arttransplant

operationor

listin

gfortransplant

operation,leftventricular

ejectio

nfraction(LVEF,%)

•Nodifferencefoun

din

thenu

mbe

rof

HF-relatedho

spitald

ays(p=0.351).

•Interven

tiongrou

pused

morehe

alth

care

resources.

•Nostatistically

sign

ificant

differences

inpatients’clinicalhe

alth

status

orself-care

behavior.


Table

2Summaryof

stud

iesreview

ed(Con

tinued)

Year/Autho

r/Cou

ntry

Purposeof

Stud

ySample

Type

sof

Disease

Type

sof

Outcomes

andMeasuremen

tsMainResults

Associatio

n)functio

nal

class≥2,left

ventricular

ejectio

nfraction≤35%,need

foraregu

larcheck-up

visit,andtim

efro

mthelastvisitof

less

than

6mon

ths.

measured

byecho

cardiography,

plasmaconcen

trationof

N-terminalof

theproh

ormon

ebrain

natriuretic

peptide

(NT-proB

NP,ng

/1),

creatin

ine,sodium

,andpo

tassium]

•Self-care

behavior

(Europ

eanHeart

Failure

Self-Care

Behavior

Scale)

•Use

ofhe

alth

care

resources

(analyzedou

tpatient

visits)

2015

Cingi

etal.

Turkey

Toinvestigatetheimpactof

amob

ilepatient

engagement

applicationon

health

outcom

esandqu

ality

oflife

n=2282

interven

tions

(physician

oncall

patient

engage

men

ttrial,

POPETforpatients

with

allergicrhinitisor

asthma)

POPET-AR

(interven

tiongrou

p:88/con

trol

grou

p:51)

POPET-Asthm

a(interven

tion

grou

p:60/

controlg

roup

:29)

Allergicrhinitis

(AR)

and

asthmapatients

•Health

outcom

esandqu

ality

oflife[ARgrou

ps:

RhinitisQualityof

Life

Questionn

aire

(RQLQ

),asthmagrou

ps:A

sthm

aCon

trol

Test(ACT)]

•PO

PET-ARgrou

pshow

edbe

tter

clinicalim

provem

ent

than

thecontrolg

roup

interm

sof

overallR

QLQ

score

aswellinmeasuresof

gene

ral

prob

lems,activity,sym

ptom

sothe

rthan

nose/eye,and

emotiondo

mains

(p<0.05).

•MorepatientsinthePO

PET-

Asthm

agrou

pachieved

awell-con

trolled

asthmascorecomparedto

thecontrolgroup

(p<0.05).

2015

Diciann

oet

al.U

nited

States

Todeterm

inefeasibilityof

the

interactivemob

ilehealth

and

rehabilitation(iM

Here)system

andits

effectson

psycho

social

andmedicalou

tcom

es

n=23

(interven

tiongrou

p:13/

controlg

roup

:10)

Inclusion

criteria:age

18–40,prim

ary

diagno

sisof

myelomen

ingo

cele

with

hydrocep

halus,ability

tousesm

artpho

ne,

andliving

with

in100milesof

testingsite

toallow

fortechnicalsup

port.

Spinabifid

a(SB)

•Usage

(the

numbe

rof

participant

respon

sesto

reminde

rs,use

ofsecure

messaging

,or

photoup

loads)

•Ph

ysicalinde

pend

ence

(Craig

HandicapAssessm

ent

andRepo

rtingTechniqu

eShortForm

,Physical

inde

pend

ence

domain)

•Selfmanagem

entskill

(Ado

lescen

tSelf-Managem

ent

andInde

pend

ence

ScaleII)

•Dep

ressivesymptom

s(The

Beck

Dep

ression

Inventory-II)

•Percep

tionof

patient-

centered

care

•Sm

artpho

nesystem

was

foun

dto

befeasibleandassociated

with

short-term

self-repo

rted

improvem

ents

inself-managem

entskills.


Table

2Summaryof

stud

iesreview

ed(Con

tinued)

Year/Autho

r/Cou

ntry

Purposeof

Stud

ySample

Type

sof

Disease

Type

sof

Outcomes

andMeasuremen

tsMainResults

(Patient

Assessm

entof

Chron

icIllne

ssCare)

•Qualityof

Life

(World

Health

OrganizationQualityof

Life

BriefInstrum

ent)

•Num

berof

UTIs(diagn

osed

UTIs)

•Num

berof

wou

nds

(uniqu

eskin

breakdow

nep

isod

esthat

wereat

leaststageII)

•Num

berof

emerge

ncyde

partmen

t(ED)

visits(EDvisitsforanyreason

)•Num

berof

EDvisitsdu

eto

UTIor

wou

nd•Num

berof

planne

dandun

planne

dho

spitalizations

•Num

berof

hospitalizations

dueto

UTIor

wou

nd

2015

Hägglun

det

al.

Swed

en

Toevaluate

whe

ther

aho

me

interven

tionsystem

(HIS)using

atablet

hadan

effect

onself-care

behavior.

n=82

(interven

tiongrou

p:42/con

trol

grou

p:40)

Inclusioncriteria:

hospitalized

and

diagno

sedforHF

with

redu

cedejectio

nfraction

(HFrEF)and/or

preservedEF

(HFpEF),

treatm

entwith

diuretics,

andreferred

straight

toprim

arycare.

Heartfailure

(HF)

•Disease-spe

cific

self-care

(Europ

ean

HeartFailure

Self-Care

Behavior

Scale)

•Health

-related

quality

oflife

(HRQ

oL)(Kansas

City

Cardiom

yopathy

Questionn

aire)

•Adh

eren

ce(freq

uencyof

HISuse)

•Kn

owledg

e(Dutch

HeartFailure

Know

ledg

eScale)

•HF-relatedho

spitald

ays

(patients’case

books)

•Interven

tiongrou

pshow

edim

provem

entin

self-care

andHRQ

oL,red

uctio

nin

HF-relatedho

spitald

ays.

2015

Martin

etal.

UnitedStates

Toinvestigatewhe

ther

afully

automated

mHealth

intervention

with

tracking

andtexting

compo

nentsincreasesph

ysical

activity.

n=48

[unb

linde

d=32

(smarttexts=16,no

texts=16),blinde

d=16]

Unb

linde

dparticipants

wererand

omized

tosm

arttextsor

notexts

inph

aseII(weeks

4–5).

Inclusioncriteria:age

s18–69,usingaFitbug

compatib

lesm

artpho

ne(iPho

ne≥4S,G

alaxy≥

S3).

Cardiovascular

disease(CVD

)•Meanchange

inaccelerometer-m

easured

daily

step

coun

t(m

easuredby

Fitbug

Orb)

•Attainm

entof

prescribed

10,000

step

s/daygo

al(m

easuredby

Fitbug

Orb)

•Chang

esin

totald

ailyactivity

andaerobictim

e(m

easured

byFitbug

Orb)

•Interven

tionwith

texting

compo

nent

increasedph

ysical

activity

(p<0.001).

2015

Piette

etal.U

nited

States

Tocompare

theeffectsof

system

aticfeed

back

toHF

patients’caregiversandHF

patientsreceivingstandard

mHealth

.

n=372(interven

tion

grou

p:189/

controlg

roup

:183)

Inclusioncriteria:

HFdiagno

sis,ejectio

nfraction<40%,

ableto

nameeligible

CarePartner

Heartfailure

(HF)

•HF-relatedqu

ality

oflife

(Minne

sota

Living

with

Heart

Failure

Questionn

aire)

•Patient-CPcommun

ication

(quantitativeteleph

onesurveys)

•Med

icationadhe

renceand

self-care

(Revised

HeartFailure

Self-CareBehavior

Scale)

•mHealth

+CP(interven

tion)

grou

pshow

edim

provem

ent

inmed

icationadhe

renceand

caregivercommun

ication.

•mHealth

+CPmay

improve

qualify

oflifein

patientswith

greaterde

pressive

symptom

sandalso

decrease

patients’


Table

2Summaryof

stud

iesreview

ed(Con

tinued)

Year/Autho

r/Cou

ntry

Purposeof

Stud

ySample

Type

sof

Disease

Type

sof

Outcomes

andMeasuremen

tsMainResults

(CP)

that

isarelative

orfrien

dliving

outsidetheirho

me.

riskof

shortnessof

breath

andsudd

enweigh

tgains.

2016

Cub

oet

al.Spain

Toevaluate

the

cost-effectiveness

ofho

me-based

motor

mon

itoring

(HBM

M)w

ithin-office

visitsversus

in-office

visitsalon

einpatients

with

advanced

Parkinson’sdisease

n=40

(interventio

ngrou

p:20/con

trol

grou

p:20)Inclusio

ncriteria:non

-dementedou

tpatients

from

atertiary

region

almovem

ent

disordersclinic,

Mini-M

entalScale

score>24,and

diagno

sedwith

idiop

athic,advanced

PD.

Parkinson’s

disease(PD)

•Motor

(UnifiedParkinson’sDise

ase

Ratin

gScaleandHoehn

andYahr

stagingScale)andno

n-motor

(Non

-Motor

Symptom

sQuestionn

aire

Scale)symptom

severities

•Cost-effectiven

ess(increm

ental

cost-effectiven

essratio

)•Direct

costs(stand

ardized

questio

nnaire)

•Qualityof

life(EuroQ

oL)

•Neuropsychiatric

symptom

s(HospitalA

nxiety

Dep

ressionScale,

ScaleforEvaluatio

nof

Neuropsychiatric

Disorde

rs,

ParkinsonPsychiatric

Ratin

gScale)

•Com

orbidities(Cum

ulative

Illne

ssRatin

gscale-Geriatric)

•HBM

Mwas

foun

dto

becost-effectivein

improvem

ent

offunctio

nalstatus,motor

severity,andmotor

complications.

2016

DeVito

Dabbs

etal.U

nitedStates

Tocompare

theefficacyof

anmHealth

interventio

nin

prom

otingself-managem

ent

behaviorsandself-care

agency,

reho

spitalization,andmortality

atho

medu

ringthefirstyear

afterlun

gtransplantation.

n=201(interventio

ngrou

p:99/con

trolgrou

p:102)Inclusioncriteria:age

>18,receivedtransplantation

attheUniversity

ofPittsburgh

MedicalCe

nter,and

couldread

andspeakEnglish

.

Lung

transplant

recipien

ts(LTRs)

•Self-mon

itorin

g(percentageof

days

that

LTRs

perfo

rmed

self-mon

itorin

g)•Adh

eren

ceto

regimen

(Health

Habits

Survey)

•Criticalhe

alth

(percentage

ofcriticalind

icators)

•Self-care

agen

cy(Perception

ofSelf-CareAge

ncy)

•Health

outcom

es(m

edicalrecords)

•Theinterven

tion

grou

ppe

rform

edself-mon

itorin

g(p<0.001),adh

ered

tomed

icalregimen

.(p=0.046),and

repo

rted

abno

rmal

health

indicators

(p<0.001)

more

frequ

ently.Thanthe

usualcaregrou

p.•Bo

thgrou

psdid

notdifferin

re-hospitalization

(p=0.51)

ormortality

(p=0.25).

2016

Giniset

al.

Israeland

Belgium

Todeterm

inethefeasibilityand

effectivenessof

thegaittraining

CuPiD-system

forp

eoplewith

Parkinson’sdiseaseintheho

me

environm

ent.

n=40

(interven

tion

grou

p:22/con

trol

grou

p:18)Inclusioncriteria:

ability

towalk0min

continuo

usly,score

of≥24

onMon

trealC

ognitive

Assessm

ent,Hoe

hnandYahr

StageII

toIIIin

ON-state,

andon

stablePD

med

ication.

Parkinson’s

disease(PD)

•Sing

leanddu

altask

gait

(gaitspeed)

•Balance(m

ini-Balance

Evaluatio

nSystem

sTest,Fou

rSquare

Step

Test,FallsEfficacy

Scale-International)

•Endu

ranceandph

ysicalcapacity

(2MinuteWalkTest,

TheCuP

iD-system

was

feasibleandeffective,as

theinterven

tiongrou

pim

proved

sign

ificantly

moreon

balanceand

maintaine

dqu

ality

oflifecomparedto

thecontrolg

roup

.


Table

2Summaryof

stud

iesreview

ed(Con

tinued)

Year/Autho

r/Cou

ntry

Purposeof

Stud

ySample

Type

sof

Disease

Type

sof

Outcomes

andMeasuremen

tsMainResults

PhysicalActivity

ScalefortheElde

rly)

•Disease

severity(M

ovem

entDisorde

rsUnifiedParkinson’sDisease

Ratin

gScale–motor

exam

ination)

•Freezing

ofgait(New

FOG

Questionn

aire,Ziegler

protocol)

•Cog

nitio

n(Color

TrailTestA&B,

sitting&walking

verbalfluen

cy)

Qualityof

life(Sho

rtForm

36Health

Survey)


improvement in balance (F(2,108) = 3.73, P = 0.04) frombaseline to post-test.Martin et al. [30] in the US found that an automated

text message system increased physical activity to pre-vent 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 livingoutside their home showed improvement in medicationadherence and caregiver communication (all P < 0.05).DeVito Dabbs et al. [32] in the US conducted an RCTfor patients with lung transplantation and reported im-provement 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 ab-normal health indicators more frequently (OR = 8.9; 95%CI, 3.60–21.99; P < 0.001).

Studies reporting similar or no effects on outcomesTwo of the twelve studies reviewed (16.7%) showed simi-lar or no effects of mHealth-based interventions onmain outcomes of interest compared to control groups.In Finland, Vuorinen et al. [33] found no difference inthe number of HF-related hospital days (incidence rateratio, IRR = 0.812, P = 0.351). However, patients in thetelemonitoring intervention group used more healthcareresources; increased number of visits to the nurse (IRR= 1.73; 95% CI, 1.38–2.15; P < 0.001), more time spentwith nurses (mean difference = 48.7 min, P < 0.001), andincreased number of telephone contacts initiated bynurses (IRR = 5.6; 95% CI, 3.41–7.63; P < 0.001). InSpain, Cubo et al. [34] reported a trend of lower PDfunctional status (the Unified Parkinson’s Disease RatingScale, UPDRS I) in patients on home-based monitoringcompared 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 statisti-cally significant differences between the intervention andcontrol groups. Cubo et al. [34], however, explained thatthe approach of using home-based motor monitoring viamHealth applications compared to standard office visitsin the 1-year follow up was cost-effective (incrementalcost-effectiveness ratio, ICER, per unit of UPDRS sub-scales ranging from €126.72 to € 701.31).

mHealth interventionsTable 3 presents the details of mHealth interventionsincluding duration, mobile app type, app content, andtraining methods. A quarter of the studies (3 out of12) reported the feasibility of the mHealth interven-tion as a pilot study to assess the potential for suc-cessful implementation of the mHealth interventionto patients/participants [24–26]. The majority of stud-ies used smartphones as a mobile device, two studies

[29, 34] used tablets for mHealth interventions, andtwo studies used telemonitoring wireless devices in-cluding weight scales for patients with HF [29] or gaitdetectors for patients with PD [25]. The length of in-terventions ranged from two weeks to twelve months;half of the studies (6/12, 50%) had more than six-month intervention periods and follow-ups. Mostcommon components of mHealth interventions in-cluded remote symptom monitoring and self-assessment as well as tailored automated messages orself-care education to coach patients with chronic dis-ease conditions that needed active disease manage-ment. One particular study by Kearney et al. wasmore inclusive in that it provided real-time feedbackand tailored such feedback for symptom managementdepending on the severity, offering pharmacological,nutritional, or behavioral advice when needed [26].The sample size of the studies reviewed was be-

tween 28 (patients with spina bifida) and 372 (pa-tients with heart failure). In terms of subjects,studies included patients who were 18 years andabove, with some studies limited to a particular agerange such as 18–40 [24] or up to 69 years [30]. Themean age of participants in the mHealth interventionstudies ranged from 30-year-old patients with spinabifida [24] to 75-year-old patients with HF [29]; theapproximate average age group in this 12-study re-view was in the 50s. None of the studies reported ef-fectiveness of mHealth intervention by agecategories. These 12 studies also did not report par-ticipants’ prior experience with mobile devices suchas smartphones or educational background, whichmay have affected the ability to use mHealth appsvia mobile devices. Outcomes of interventions weremeasured either by mHealth systems directly or bypaper-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. Kearneyet al. [26] found that symptoms were reporteddifferently on a paper-based questionnaire and mobilephone. Participants in the mobile group reportedlower levels of fatigue compared to those in thepaper-based group (OR non-mobile/mobile = 2.29, 95% CI1.04–5.05, P = 0.04) [26]. Reporting cancer toxicitysymptoms (e.g., hand-foot syndrome and mucositis)in real time might allow for more accurate measure-ment [26].None of the studies reported process measures, in-

cluding adherence, level of engagement, and/or satisfac-tion with the mHealth systems. Potentially, there waslimited information on such measures in the publishedarticles with the authors possibly publishing the processmeasures elsewhere.


Table

3Detailsof

mob

ileapplicationinterven

tionforchronicdiseasemanagem

ent

Stud

yLeng

thof

Interven

tion

Nam

eof

Mob

ileApp

lication

andPlatform

Prog

ram

ofInterven

tion

Deliveryof

Interven

tion

(Trainingof

mHealth

)

2009

Kearne

yet

al.U

nited

King

dom

Five

time

pointes(baseline,

chem

othe

rapy

cycle2,3,4,and;

each

cyclehasup

to14

days).

Advancedsymptom

managem

entsystem

(ASyMS©

)

•Amob

ileph

one-basedremote

mon

itorin

gandrepo

rtingof

chem

othe

rapy-related

toxicity.

•Participantscompleted

theelectron

icsymptom

questio

nnaire

ontheir

mob

ileph

one,took

theirtempe

rature

usingan

electron

icthermom

eter

anden

teredthevalueinto

the

applicationtw

iceadayfor2

weeks

aftertheirfirst4cycles

ofchem

othe

rapy

•Patientsreceived

tailoredself-care

advice

ontheirmob

ileph

one

basedon

theseverityof

symptom

srepo

rted

Patientsweretraine

don

how

tousethesystem

bynu

rses

working

intheirlocal

clinicwho

hadreceived

training

bythestud

yteam

onho

wto

use

thesystem

.

2013

Kristjánsdó

ttir

etal.N

orway

4weeks

App

lication:DiariesandDailySituationalFeedb

ack

Smartpho

ne:H

TCTyTN

(tou

chscreen

andkeyboard)

•Theinterven

tionconsistedof

4compo

nents:face-to-face

session–

1hindividu

alsessionwith

nurse,

web

-based

diaries–3diaryen

tries/

dayusingthesm

artpho

ne,w

ritten

situationalfeedb

ack–daily

written

feed

back

from

therapiston

inform

ationprovided

indiary,

andaudiofiles

–4mindfulne

ssexercisesgu

ided

bytheauthors

•Allparticipantsreceived

access

toa

non-interactiveweb

site

with

inform

ation

onself-managem

entstrategies

forpe

oplewith

chronicpain

•Self-repo

rted

assessmen

tson

pape

rwere

gathered

before

(T1)

andafter(T2)

theinpatient

prog

ram,immed

iatelyafterthesm

artpho

neinterven

tionwhich

was

4weeks

afterdischarge

(T3),and

5(T4)

and11

mon

ths(T5)

after

thesm

artpho

neinterven

tion

Patientsattend

edan

inform

ationalg

roup

meetin

g.Participants

werelent

smartpho

nes

andreceived

inform

ation

abou

ttheirtherapistfor

theinterven

tiondu

ring

theface-to-face

session.

2013

Garcia-

Palacios

etal.

Spain

2weeks

Softw

areapplication:F-EM

A(ecologicalm

omen

tary

assessmen

t)Sm

artpho

ne:H

TCDiamon

d1(TOUCH

Diamon

d1,HTC

Corpo

ratio

n,New

TaipeiCity,

Taiwan)Software:Windo

wsMob

ile6.1

•Session1(7

days):participantswererand

omly

assign

edaself-record

cond

ition

andrecorded

theirpain,fatigue,and

moo

d3tim

es/day

•Session2(7

days):acceptability

questio

nnaire

andBriefP

ainInventory(BPI)andBriefF

atigue

inventory(BFI)wereadministeredregardingthe

firstcond

ition

,and

participantsreceived

theothe

rself-record

cond

ition

•Session3:acceptability

questio

nnaire,BPI

andBFI,

andpreferen

cequ

estio

nnaire

wereadministered

regardingthesecond

cond

ition

Participantsattend

edan

individu

alinform

ation

sessiondu

ringthefirstweek.

They

weregivenverbal

instructions

ontheself-record

metho

d,explanations

ofthe

scales,and

practiced

ratin

gthescales

with

theresearcher.

Aninform

ationsheetwith

definition

sof

each

scaleand

instructions

forthe

self-recordingweregiven

toeach

patient.


Table

3Detailsof

mob



ent(Con

tinued)

Stud

yLeng

thof

Interven

tion

Nam

eof

Mob

ileApp

lication

andPlatform

Prog

ram

ofInterven

tion

Deliveryof

Interven

tion

(Trainingof

mHealth

)

2014

Vuorinen

etal.Finland

6mon

ths

App

licationnameno

tavailable.

App

licationen

abledrecordingof

alln

ecessary

measuremen

tsandsymptom

s.

•Patient

mademeasuremen

ts(blood

pressure,p

ulse,and

body

weigh

t),assessed

symptom

s(dizzine

ss,d

yspn

ea,

palpitatio

n,weakness,anded

ema),and

evaluatedoverallcon

ditio

n(deteriorated,

improved

,orremaine

dun

change

d)on

ceaweek

•Patient

received

automatic

machine

-based

feed

back

ofwhe

ther

parameter

was

with

inpe

rson

altargetssetby

nurse

•Nurse

contactedpatient

each

time

measuremen

twas

beyond

target

levels

Patientsgivenaho

me-care

package:weigh

tscale,

bloo

dpressure

meter,

mob

ileph

one,and

self-care

instructions.

2015

Cingi

etal.Turkey

1mon

th(patientswith

allergicrhinitis(AR)),

3mon

ths(asthm

apatients)

App

lication:ph

ysicianon

call

patient

engage

men

ttrial

(POPET-AR;PO

PET-Asthm

a)

•Theapplicationallowed

patients

tocommun

icatewith

theirph

ysician,

record

theirhe

alth

status

and

med

icationcompliance

•Provided

motivationaland

educationalcon

tent

•Reminde

dpatientsto

take

prescribed

med

ications

Patientswereed

ucated

onthe

recommen

deduseof

prescribed

med

ications

andinform

edabou

tthe

RhinitisQualityof

Life

Questionn

aire

andthe

Asthm

aCon

trol

Test.

Trialinformation,

applicationtraining

,andtechnicalsup

port

was

availableon

line.

2015

Diciann

oet

al.U

nited

States

12mon

ths

App

lication:iMHereSm

artpho

ne:

And

roid

Provided

participants

with

aph

oneplan

that

includ

edun

limitedtextinganddata.

•Interven

tionconsistedof

6mod

ules,

aweb

-based

clinicianpo

rtal,and

a2-way

commun

icationsystem

•Mod

ules

served

asreminde

rsto

perfo

rmvario

usself-care

tasks,

record

wou

nds,managemed

ications,

completemoo

dsurveys,andfor

secure

messaging

•Patient

prob

lemsweretriage

don

aweb

-based

dashbo

ard

forph

ysicians

Participantswereinstructed

tousethemod

ules

basedon

theirow

nprescribed

protocols.

2015

Hagglun

det

al.Swed

en

3mon

ths

App

lication:HIS:O

PTILOGG

Tablet

wirelesslyconn

ected

toweigh

tscale.

•HISmon

itoredweigh

tand

symptom

s,titrateddiuretics,

andprovided

inform

ation

abou

tHFandlifestyleadvice

Interven

tiongrou

preceived

abasal

inform

ationsheet.

TheHISwas

installed

intheirho

me.

2015

Martin

etal.U

nited

States

5weeks

Smartpho

neapplication:

Fitbug

Digitalp

hysical

activity

tracker:Fitbug

Orb

Smartpho

netextingsystem

:Reify

•Autom

ated

mHealth

interven

tion

with

tracking

andtextingcompo

nents

•Unb

linde

dparticipantscouldview

theirdaily

step

coun

t,activity

time,

andaerobicactivity

timethroug

hsm

artpho

neandweb

interfaces;

Notraining

men

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ileApp

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ram

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tion

Deliveryof

Interven

tion

(Trainingof

mHealth

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blinde

dparticipantswereun

able

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thisinform

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arttextsde

livered

coaching

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ry-based

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mHealth

applicationwas

not

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tionused

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se(IVR)

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•Standard

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se(IVR)

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with

tailoredself-managem

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feed

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gbo

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ort.


Risk of bias assessmentThe risk of bias in the reviewed studies is summarizedin Fig. 2 and shown for individual studies in Fig. 3. Theintervention studies generally performed well in theirrisk of bias for random sequence generation (62% lowrisk), allocation concealment (23% low risk), blinding ofparticipants and personnel (8% low risk), blinding ofoutcome assessors (15% low risk), incomplete outcomedata (69% low risk), and selective reporting (92% lowrisk) (Fig. 2).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 allocationconcealment method [22, 32]. Although due to the na-ture of the mHealth intervention, it is almost impossibleto blind participants and healthcare providers, one studydescribed the blinding of participants by providing thesame mobile phone application to both experimentaland control groups with different functionalities. The ex-perimental group was given communication, health sta-tus, or medication usage tracking, and a surveyquestionnaire, whereas the control group received onlythe survey questionnaire [28]. Only two trials were iden-tified as blinding the outcome assessors. Four trialsfailed to provide a full description of participants andlosses to follow-up during their trials [24, 28, 29, 31]. Allstudies had a low risk for reporting bias. Devito et al.had low risk for all items except blinding of participantsand personnel [32]. Hägglund et al. had low risk only forreporting bias [29] (Fig. 3).

DiscussionThis systematic review has found a potential favorableeffect of mHealth interventions on health outcomes andprocess measures in patients with chronic diseases in-cluding asthma, cancer, cardiovascular diseases, chronicpain, spina bifida, or Parkinson’s disease. The resultsfrom the reviewed RCTs showed improvement in some

health outcomes in patients in managing their chronicdisease.There were some commonalities and differences in

using mHealth in the reviewed studies. One of the com-mon features useful in mHealth interventions is pre-setand tailored feedback on reported symptoms. The mo-bile application systems used in the reviewed studieswere developed by the study team and validated andrefined in the previous studies that were conductedbefore the RCTs. None of the commercial health appswere used in the reviewed studies. Most studies utilizedresearch staff to provide training in mHealth systemsfor the participants via face-to-face or informationgroup sessions, or through information materials, whileone study [26] used local nurses to train patients. Noneof the reviewed studies addressed whether theirmHealth systems were incorporated into daily medicalpractice in either clinic settings or acute care hospitals,meaning that there were no signs of their implementa-tion in real health care systems. Challenges in real-lifesettings may relate to lack of financial incentives forproviders in using mHealth tools or uncertainty regard-ing privacy and security of information transferred viamHealth systems [35].Interventions to promote self-management in

patients with chronic diseases started from web-basedand/or telephone-based interventions to mHealth-based interventions. Unlike those previous behavioralinterventions limited to places where patients withchronic diseases had access to the treatment advice,mHealth interventions have advanced features such asreal-time symptom monitoring and feedback [25, 34,36]. For example, patients with PD receive real-timefeedback on their selected gait parameters duringtheir walks via the preset gait app developed fromevidence-based exercise guidelines [25]. This is anexample of how using well-designed and validatedmHealth apps in daily life can benefit healthoutcomes.

Fig. 2 Risk of bias assessment: Summary graph


The number of smartphone users has shown great in-creases; in the US [37] it is estimated to reach 224.3 mil-lion in 2017, up from 171 million in 2014; worldwide itwas [38] 2.32 billion in 2017, up from 1.57 billion in2014. Approximately 77% of people in the US ownedsmartphones in 2016 [39]. Using mobile devices formHealth is essential nowadays and approaches ofmHealth vary from sending text messaging for medicalappointment reminders to monitoring and assessingsymptoms in real-time, virtually at any location via wire-less networks. Interventions using mHealth have alsoeased medical coaching for caregivers as care partnerswith healthcare professionals for effective chronic

disease management. Piette et al. [31] studied the com-parative effectiveness of mHealth interventions support-ing HF patients and their family caregivers and showedimprovement for medication adherence and caregivercommunication. The impact of including caregivers as apart of mHealth users—one of the care supportinggroups—on actual patients’ health outcomes should con-tinue to be studied in the context of an increasing agingsociety.Moreover, long-term follow-ups of responses from pa-

tients and caregivers who have used mHealth need to beevaluated. Areas that need to be studied include theoptimum length of time and frequency of the mHealthdelivery system as well as type of technology and train-ing. For example, effective frequencies of automated re-minders or coaching messages, when additionalreminders should be sent, and when people becometired or irritated by automated messages need to bestudied. Users of mHealth might experience fatigue fromautomated reminders and eventually mHealth interven-tions could become ineffective. Other systematic reviewson chronic disease management showed that the fre-quency of input into mHealth systems was a burden onparticipants and affected the attrition rate [17]. In thisreview, the length of the intervention in the 12 studiesvaried from 2 weeks to 12 months; 5 of 12 studies (about42%) had less than 2-month interventions while 4 stud-ies (about 33%) had 1 year of intervention. The positivehealth outcomes of the various studies were not directlyrelated to the length of the intervention or trainingmethods of mHealth in the reviewed studies.One aspect of mHealth approaches that also needs to

be considered is effective clinical communication be-tween patients and healthcare professionals who need torespond to patients’ questions via mHealth systems.Cingi et al. [28] reported healthcare providers (i.e., resi-dents) expressed an improvement in communicationwith patients via mHealth; however, they found thatusing mHealth tools as the primary method of commu-nication was strongly opposed by the healthcare pro-viders. Vuorinen et al. [33] also reported a significantincrease in the communication (i.e., telephone contacts)between nurses and patients which in turn increased thenurses’ workload during the trial. One recommendationfor reducing health care providers’ workload in mHealthinterventions is using advanced technology to respondto patients’ questions regarding symptoms assessed andreported via the mHealth systems.While considering positive health outcomes (e.g., re-

duction of hospital readmission for HF-related condi-tions or medication adherence) of patients with chronicdiseases, burden of healthcare professionals should bemeasured as an outcome of mHealth interventions. Anadequate triage system can decrease healthcare

Fig. 3 Risk of bias assessment for individual studies. Low risk of

bias; Unclear; High risk of bias


professionals’ response time for emergency needs re-ported by mHealth users [28].Common recommendations discussed in the studies of

this systematic review to improve mHealth interventionsinclude a simple and user-friendly-designed mHealthsystem, data confidentiality, lay language use for struc-tured and automated feedback or advice, positive motiv-ation and improving engagement [28], and inclusion ofpatient’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 forthis review and most of them were funded studies.Therefore, the mHealth systems or smartphone appsused in the studies were validated and relatively reliablecompared to health apps commercially available on themarket. Literature from studies on smartphone-based in-terventions for chronic disease management that weresmall scale with or without control group and/or had ashort-term follow-up has shown ambivalent results.Thus, we intended to choose robust studies that usedrandomization, control groups, and relevant follow-upsfor outcomes. We looked at the outcome changes at dif-ferent follow-up points.Second, we did not include mHealth intervention

studies for diabetes management since there is ample lit-erature on diabetes management using mobile technol-ogy approaches [14–17]. Third, we excluded studieswherein health apps were used only by health profes-sionals such as physicians or specialized clinical nurses.We focused on patient-centered health apps as a part ofmHealth interventions in the review. Lastly, most of thereviewed studies provided smartphones or tablets to par-ticipating patients and thus, the results from this reviewcannot yet be generalized among those who have finan-cial concerns regarding purchasing mHealth tools. Al-though the availability of wireless networks is increasing,potential mHealth users such as patients with chronicdiseases or their caregivers may have limited data ser-vices for their mobile devices due to financial concerns.This might be an important issue during an emergencywhen patients may have no access to evidence-basedmedical advice via mHealth devices.

ConclusionThe findings from the majority of reviewed studies thatused mHealth interventions showed some health out-come improvement in patients with chronic disease con-ditions. Favorable factors in mHealth approaches areautomated text reminders, frequent and accurate symp-tom monitoring (often in real time), and improved com-munication between patients and healthcare providersresulting in enhanced self-management in patients withchronic conditions. Thus, the future of mHealth is

presumably optimistic. The relationship between engage-ment of users on mHealth tools and outcome improve-ment should be further studied. The studies reviewed inthis paper showed disease-specific mHealth interven-tions that might be different from commercial mobilehealth apps available to the public. Rigorously testedmHealth apps developed through research should befurther considered to be made available to the generalpopulation.

Additional file

Additional file 1: Title of data: Detailed search strategy per database.Detailed search strategy per database in order to find all publishedinterventions using mobile health applications to improve chronic diseasemanagement for adults in randomized controlled trials. (DOCX 21 kb)

AbbreviationsCDC: The US Centers for Disease Control and Prevention; EMA: Ecologicalmomentary assessment; HF: Heart failure; mHealth: Mobile health;PD: Parkinson’s disease; RCTs: Randomized controlled trials; UPDRS: UnifiedParkinson’s Disease Rating Scale; US: The United States of America

AcknowledgementsThe authors thank Yoori Yang, MSN, College of Nursing, Yonsei Universityand Laura Narvaez, BS, Sue and Bill Gross School of Nursing at University ofCalifornia Irvine for their assistance in literature search and abstract reviews.We also thank Dr. Priscilla Kehoe for her thorough editorial support.

FundingNot applicable.

Availability of data and materialsNot applicable.

Authors’ contributionsDesign 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 finalmanuscript.

Ethics approval and consent to participateNot applicable.

Consent to publicationNot applicable.

Competing interestsThe authors declare that they have no competing interests.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.

Author details1Sue and Bill Gross School of Nursing, University of California Irvine, Irvine,CA, USA. 2College of Nursing, Mo-Im Kim Nursing Research Institute, YonseiUniversity, 50 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea03722.3College of Nursing and Health Sciences, University of Massachusetts, Boston,MA, USA. 4Program in Public Health, University of California Irvine, Irvine, CA,USA.


https://doi.org/10.1186/s12911-018-0591-0

Received: 24 June 2017 Accepted: 25 January 2018

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