Post on 22-Dec-2020
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: monachoi@yuhs.ac2College 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
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 https://doi.org/10.1186/s12911-018-0591-0
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 Page 2 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
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 3 of 18
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
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 4 of 18
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
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 5 of 18
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.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 6 of 18
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.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 7 of 18
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’
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 8 of 18
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
.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 9 of 18
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)
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 10 of 18
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.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 11 of 18
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.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 12 of 18
Table
3Detailsof
mob
ileapplicationinterven
tionforchronicdiseasemanagem
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
tione
d.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 13 of 18
Table
3Detailsof
mob
ileapplicationinterven
tionforchronicdiseasemanagem
ent(Con
tinued)
Stud
yLeng
thof
Interven
tion
Nam
eof
Mob
ileApp
lication
andPlatform
Prog
ram
ofInterven
tion
Deliveryof
Interven
tion
(Trainingof
mHealth
)
blinde
dparticipantswereun
able
toview
thisinform
ation
•Sm
arttextsde
livered
coaching
3tim
es/day
aimed
atindividu
alen
couragem
entandfostering
feed
back
loop
sby
anautomated
,ph
ysicianwritten,theo
ry-based
algo
rithm
with
ago
alof
10,00step
s/day
2015
Piette
etal.U
nited
States
12mon
ths
mHealth
applicationwas
not
men
tione
d.Interven
tionused
interactivevoicerespon
se(IVR)
teleph
onecalls.
•Standard
mHealth
grou
preceived
weeklyinteractivevoicerespon
se(IVR)
calls
with
tailoredself-managem
entadvice
•mHealth
+CarePartner
(CP)
grou
preceived
thesameinterven
tionbu
twith
automated
emailssent
totheirCPaftereach
IVRcallwith
feed
back
abou
tthehe
artfailure
(HF)
patient’s
status
andsugg
estio
nsto
supp
ortdiseasecare
•CPcalledtheirpatient-partner
weeklyto
review
repo
rtsandaddressiden
tifiedprob
lems
Both
grou
psweremailed
inform
ationabou
tHFself-
care.CPs
received
guidelines
abou
tho
wto
commun
icatein
apo
sitivemotivatingway,avoid
conflictby
respectin
gbo
undaries,
includ
ein-hom
ecaregivers,and
respectconfidentiality.
2016
Cub
oet
al.Spain
12mon
ths
System
:Kinesia,include
dtablet
softwareapp,
wireless
finge
r-wornmotionsensor
unit,andauto-
mated
web
-based
symptom
repo
rting.
•Allpatientswith
Parkinson’sdisease(PD)
completed
structured
questio
nnairesand
wereassessed
unde
rthebe
neficialeffect
oftheantip
arkinson
iandrug
sin
theclinicevery
4mon
thsfollowingthesameprotocol
•PD
motor
symptom
sweremon
itoredat
home
1day/mon
thwith
3–6motor
assessmen
tsanda
structured
questio
nnaire
intheHBM
Mgrou
p
Assistant
brou
ghtKine
siade
vice
andprovided
training
ineach
participant’s
home.
2016
DeVito
Dabbs
etal.
UnitedStates
12mon
ths
Prog
ram:PocketPerson
alAssistant
forTracking
Health
(PocketPA
TH)
•Participantsrecorded
daily
health
indicators,
view
edgraphicald
isplaysof
tren
ds,and
received
automaticfeed
back
message
swhe
nreaching
critical
thresholdvalues
usingthePo
cket
PATH
system
Patientsreceived
scrip
teddischarge
instructions
from
aninterven
tionist
andan
instructionbind
erem
phasizing
theim
portance
ofpe
rform
ingdaily
self-managem
entbe
haviorsat
homein
60min
training
sessions.
2016
Ginis
etal.Israel
andBelgium
6weeks
CuP
iD-system:smartpho
ne(GalaxyS3-m
ini,
Samsung
,Sou
thKo
rea),d
ocking
station,2
inertialm
easuremen
tun
its(EXLs3,EXELsrl.,
Italy),and2applications
(the
audio-bio
feed
back,A
BF-gaitapp,
theinstrumen
ted
cueing
forfre
ezingof
gait,FO
G-cue
app)
•TheCuP
iD-system
measured
gaitin
real-tim
e,provided
positive
andcorrectiveauditory
biofeedb
ack
(ABF)on
gaitparameters,andrhythm
ical
auditory
cueing
topreven
tor
overcome
freezingof
gait(FOG)ep
isod
es
Researchersprovided
gaittraining
toCuP
iDparticipantsfor30
min,3
times/w
eekfor6weeks.Participants
with
FOGweretaug
htwaysto
avoid
FOGandpracticed
foran
additio
nal30
min,3
times/w
eekusingtheFO
G-cue
app.
Pictures
andpe
rson
alized
instructions
werealso
givento
participants.Telep
hone
consultatio
nwas
availableforsystem
supp
ort.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 14 of 18
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
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 15 of 18
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
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 16 of 18
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.
Lee et al. BMC Medical Informatics and Decision Making (2018) 18:12 Page 17 of 18
Received: 24 June 2017 Accepted: 25 January 2018
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