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\begin{document}

\title{Effects of eHealth Interventions on improving medication adherence in
kidney Transplant Recipients: A Systematic Review and Meta-analysis of
Randomized Controlled Trials}



\author[1]{liu yue}%
\author[1]{yun Zhang}%
\author[1]{hong Guan}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 17, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Aims Immunosuppressant non-adherence is a prevalent problem leading to
many adverse outcomes in Renal transplant recipients. eHealth has the
potential to improve medication compliance, but evidence in kidney
transplantation remains unclear. This review aims to explore the effects
of eHealth interventions on improving medication compliance in Kidney
transplantation. Methods A systematic search was conducted of the
following databases: PubMed, Embase, Cochrane Library, CINAHL, and Web
of Science Core Collection. The search included studies published up to
July 22, 2021. Two authors selected relevant studies and extracted data
independently. The quality of the literature was evaluated using the
Cochrane collaborative bias risk tool. To estimate the effect size, a
meta-analysis of the studies was performed using the Cochrane
Collaboration software Review Manager 5.3 PRISMA guidelines were
followed. Results Nine studies involving 777 patients were included.
Compared with control group, eHealth interventions improved medication
adherence measured by electronic monitoring (RR=1.46, 95\%CI, 1.11 to
1.90, p=0.006) and decreased rejection (RR=0.38; 95\%CI, 0.15 to 0.97,
p=0.04). There was no difference in medication compliance measured by
BAASIS (RR=1.03, 95\% CI, 0.88 to 1.21, p=0.72), Tacrolimus level
(MD=0.16, 95\%CI, -0.21 to 0.52, p=0.39), coefficient of variation of
tacrolimus level (MD=-0.01, 95\%CI, -0.05 to 0.02, p=0.41), and kidney
function (MD=-0.44, 95\% CI, -8.32 to 7.43, p=0.91) between the two
groups. Conclusions eHealth interventions can improve medication
adherence in kidney transplantation in the short time. However more
high-quality intervention studies need performing to determine whether
eHealth improves long-term adherence and clinically relevant outcomes.%
\end{abstract}%



\sloppy


Effects of eHealth Interventions on improving medication adherence in
kidney Transplant Recipients: A Systematic Review and Meta-analysis of
Randomized Controlled Trials

Yue Liu\textsuperscript{1}, Yun Zhang\textsuperscript{1}, and Xiaohong
Guan\textsuperscript{1} (Corresponding author).

\textsuperscript{1}Departement of Transplant Center, The First
Affiliated Hospital of USTC, Division of Life Science and Medicine,
University of Science and Technology of China, Hefei, Anhui, 230001,
China

Yun Zhang, email 1257491359@qq.com

Xiaohong Guan, email 2313371705@qq.com

\textbf{Abstract}

\textbf{Aims}

Immunosuppressant non-adherence is a prevalent problem leading to many
adverse outcomes in Renal transplant recipients. eHealth has the
potential to improve medication compliance, but evidence in kidney
transplantation remains unclear. This review aims to explore the effects
of eHealth interventions on improving medication compliance in Kidney
transplantation.

\textbf{Methods}

A systematic search was conducted of the following databases: PubMed,
Embase, Cochrane Library, CINAHL, and Web of Science Core Collection.
The search included studies published up to July 22, 2021. Two authors
selected relevant studies and extracted data independently. The quality
of the literature was evaluated using the Cochrane collaborative bias
risk tool. To estimate the effect size, a meta-analysis of the studies
was performed using the Cochrane Collaboration software Review Manager
5.3 PRISMA guidelines were followed.

\textbf{Results}

Nine studies involving 777 patients were included. Compared with control
group, eHealth interventions improved medication adherence measured by
electronic monitoring (RR=1.46, 95\%CI: 1.11 to 1.90, p=0.006) and
decreased rejection (RR=0.38, 95\%CI: 0.15 to 0.97, p=0.04). There was
no difference in medication compliance measured by BAASIS (RR=1.03,
95\%CI: 0.88 to 1.21, p=0.72), Tacrolimus level (MD=0.16, 95\%CI: -0.21
to 0.52, p=0.39), coefficient of variation of tacrolimus level
(MD=-0.01, 95\%CI: -0.05 to 0.02, p=0.41), and kidney function
(MD=-0.44, 95\% CI: -8.32 to 7.43, p=0.91) between the two groups.

\textbf{Conclusions}

eHealth interventions can improve medication adherence in kidney
transplantation in the short time. However more high-quality
intervention studies need performing to determine whether eHealth
improves long-term adherence and clinically relevant outcomes.

\textbf{Keywords:} eHealth; kidney transplant; medication adherence;
telemedicine.

\textbf{Introduction}

The end-stage renal disease (ESRD) is a significant public health issue
in the worldwide. The number of ESRD patients with need for the renal
replacement therapy is estimated between 4.902 and 7.083
million(\emph{1} ). Moreover, there is a growing trend(\emph{2} ). In
the United States, spending for ESRD patients totaled \$35.9 billion,
accounting for 7.2\% of the overall Medicare paid claims in the
fee-for-service system, and the share has remained relatively constant
for a decade(\emph{3} , \emph{4} ).

Kidney transplantation (KTx) is the optimal therapy for ESRD. Compared
to dialysis treatment, KTx is associated with a better psychosocial
function, improved quality of life as well as lower costs and mortality
(\emph{5} ,\emph{6} ). Although short-term outcomes after KTx are
excellent, with a 96\%-98\%(\emph{7} ) 1-year graft survival, long-term
outcomes remain suboptimal, the 10-year graft survival remains low
(67.8\%) (\emph{8} ). A key reason for the lack of improvement in
long-term outcomes is poor adherence to immunotherapy regimen. As
immunosuppressive therapy is often critical for KTx, they need to take
immunosuppressants for life to prevent rejection. Unfortunately,
findings from several studies have indicated that in solid organ
transplant recipients, The highest rate of immunosuppressant
non-adherence was found in KTx with a prevalence of 36-55\% (\emph{9} ).
This low adherence may be due in part to complicated treatment regimens,
not only due to the number of pills required, but also to frequent dose
adjustments based on blood level monitoring, side effects and rejection
episodes (\emph{10} ). Additional barriers such as financial problems
(\emph{11} ), memory issues (\emph{12} ), and communication barriers
(\emph{13} ).

New eHealth technologies offer potential solutions for improving
medication adherence in KTx. The World Health Organization (WHO) broadly
defines eHealth as the use of information and communication technologies
(ICT) for health, including patient treatment, research, education of
healthcare professionals, and public health monitoring (\emph{14} ).
Currently, with the rapid development of ICTs, eHealth interventions
appear to be a useful tool for increasing medication adherence. The
benefits of eHealth are widely acknowledged. It can contribute to
achieving universal health coverage by overcoming geographical barriers,
increasing access, and the provision of health services to remote
populations and underserved communities (\emph{15} ).

Previous meta-analyses describing the effect of eHealth intervention on
the medication adherence of solid organ transplant patients have been
published. Tang et al.'s (\emph{16} ) meta-analysis suggests that
eHealth interventions may improve medication adherence in the short
term. On the contrary, Lee et al.'s (\emph{17} ) meta-analysis showed
that the effects of the eHealth interventions were similar to those of
the care provided to the control group. In addition, medication
adherence varies widely among different types of transplant patients.
Therefore, it is necessary to systematically evaluate whether eHealth
interventions can improve medication compliance in KTx.

\textbf{Methods}

All analyses were based on data from previously published studies. Thus,
no ethical approval or patient consent was required. This systematic
review was conducted following the preferred reporting items for
systematic reviews and meta-analysis (PRISMA) guidelines(\emph{18} ).

\textbf{Information Sources}

The following electronic databases were searched to identify relevant
studies up to July 22, 2021: PubMed, Embase, Cochrane Library, CINAHL
(Cumulative Index to Nursing and Allied Health Literature), and Web of
Science Core Collection. Additionally, we manually searched the
references listed in the present review article to find further.

\textbf{Search and Eligibility Criteria}

\textbf{Overall Search Strategy}

The search was performed using a combination of the following keywords
on July 22, 2020: (``kidney transplantation'' or ``kidney transplant*''
or ``renal transplant*'') AND (e-health or eHealth or m-health or
teleHealth or telemedicine or Internet or Software or website or
telemanagement or telecommunications or tele-monitoring) AND (``patient
compliance'' or ``medication adherence'' or ``noncompliance*''). The
search strategies for each database are presented in~Multimedia
Appendix1.~In addition, the search terms used for electronic databases
were chosen following the PICO format (P: patients, kidney
transplantation; I: intervention, eHealth; C: control {[}any control
interventions{]}, O: outcome {[}medication adherence{]}) and modified as
necessary to include equivalent terms for each database.

\textbf{Studies}

Randomized Controlled Trials (RCTs) regarding the effect of eHealth
Interventions for patients with KTx were included in the review. The
included studies were published in English. Articles were excluded if
the study was a non-RCT or nonclinical trial. Abstracts from meeting
proceedings with no corresponding full article published in a
peer-reviewed journal or no specific data provided even after contacting
the author were excluded.

\textbf{Participants}

Trials consisting of patients receiving immunosuppressive therapy after
kidney transplant. Combined grafts are not included (eg.
kidney-pancreas).

\textbf{Interventions}

For our study interventions, we included eHealth interventions. eHealth
interventions include TeleHealth, Internet and computer-based, mobile
health (such as applications and text messaging), Wearables, electronic
medication dispenser and video Prior studies using various information
and communication technology devices were considered. For our study
controls, we included research control interventions that employed any
reasonable interventions or usual care and did not involve the provision
of eHealth interventions for improving medication adherence.

\textbf{Outcome Measures}

The primary outcome measures were focused on medication adherence. We
included medication adherence from studies involving eHealth
interventions for transplant recipients as objective measures
(medication compliance monitored by electronic devices, and clinical
measures such as tacrolimus serum concentration levels, coefficient of
variation for blood tacrolimus level,the proportion of patients
obtaining normal tacrolimus trough variability) and subjective measures
(self-report questionnaires, such as Basel Assessment of Adherence to
Immunosuppressive Medication Scale). Secondary outcome includes renal
function, rejection and patient satisfaction.

\textbf{Search Methods for Identification of Studies}

Two authors (ZY and GXH) independently reviewed the search results and
screened the titles, abstracts, and full texts of identified references
to select potentially eligible studies, which were imported into EndNote
X9 (Clarivate Analytics, Philadelphia, PA, USA).

\textbf{Data Extraction and Management}

Two independent reviewers (ZY and GXH) screened the titles and abstracts
for potentially eligible studies identified by the primary search, and
then reviewed the full texts to evaluate their final eligibility. The
two authors cross-checked each other's articles, and, in the case of any
disagreement regarding extracted data, a third author (LY) was brought
into the discussion. Decisions were made based on consensus.

After selecting articles for inclusion, we extracted the following data
along with the intervention characteristics: first author, publication
year, publication country, sample size, average patient age,
intervention duration, intervention group, control group, follow-up
time, outcomes.

\textbf{Quality Assessment}

The methodological quality of the included studies was assessed by two
independent reviewers (ZY and GXH), and inconsistencies were solved by
consensus or involving a third researcher (LY). We followed the criteria
of the Cochrane Collaboration risk-of-bias tool as guides. The quality
items assessed were selection bias (random sequence generation and
allocation concealment), performance bias (blinding of participants,
personnel, and outcome assessors), attrition bias, measurement bias,
reporting bias, and other bias.

\textbf{Statistical Analysis}

This meta-analysis was performed using the Review Manager software
(RevMan V.5.3; Cochrane Collaboration, Oxford, UK). Cochrane Q
statistics and Chi-square test was used to determine whether
heterogeneity existed among studies. If the heterogeneity
(p\textless{}0.1, I\selectlanguage{ngerman}² \textgreater{}50\%), the random effects model was
used for meta-analysis. If there was no significant heterogeneity
between studies (P\selectlanguage{english}[?]0.1, P\selectlanguage{english}[?]50\%), The fixed effects model is adopted. For
dichotomous variables, relative risk (RR) and 95\% confidence interval
(CI) were used. For continuous variables, when the measuring tools and
units used in each document for an index are completely the same. The
weighted mean difference (WMD) and 95\% CI were used to represent. When
they are not completely the same, standard mean difference (SMD) and
95\% CI are used. A P\textless{}0.05 was considered to be statistically
significant. For outcome where quantitative data cannot be synthesized,
narrative analysis is performed. Since the number of studies that
included the results of the meta-analysis was not more than 10, we did
not use funnel plot for publication bias.

\textbf{Results}

\textbf{Search and Selection}

Of 1039 records identified, 224 were duplicates and 770 were excluded
after abstract review for the reasons shown in Figure 1. The remaining
45 citations were retrieved for full-text review, and 9 trials met the
inclusion criteria. Of the 9 included trials, 4 trials had available
data for meta-analysis, whereas the other trials were assessed
descriptively.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1.} Selection of studies

\textbf{Study Characteristics}

The baseline descriptive characteristics (country, sample size and age)
of the 9 studies included in the systematic review were summarized in
Table 1. Four studies (\emph{19} -\emph{22} )were from the United
States, Korea had two studies(\emph{23} , \emph{24} ), the others
originated from German(\emph{25} ), Australia(\emph{26} ),
Sweden(\emph{27} ). The mean age of KTx ranged from 42.4 to 53.6 years,
and all studies included both men and women. The duration of the
interventions ranged from 3 to 12 months. The longest follow-up was one
year.

\textbf{Table 1.} Descriptive summary of included studies.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
First authors, Years, Country\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Age (years) M \selectlanguage{ngerman}± SD median (IQR/range) (I/C)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sample size (I/C)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Intervention (duration)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Control\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Fellow-up time (months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Outcome (measures)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Han, 2019, Korea\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
45(35-54)/43(30-52)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
70/66\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Mobile application for medication management~(6 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Education on the importance of adherence\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Medication adherence--- BAASIS Medication adherence--- electronic
monitoring medication 3.Kidney function---eGFR\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
McGillicuddy, 2013, USA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
42.44/57.6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9/10\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Mobile phone-based medication monitoring (3 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Education related to post- transplantation medical care\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Medication adherence--- Russell et al.'s adherence score Patient
satisfaction\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Reese, 2017, USA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
I1: 50 \selectlanguage{ngerman}± 12 I2: 50 ± 11 C: 49 ± 11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
40/39/38\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Automated medication reminders with wireless pill bottle and physician
notification (6 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Wireless pill bottle that provided no alerts\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Medication adherence--- BAASIS Medication adherence--- wireless pill
bottle openings 3.Tac level 4.CVs for Tac level 5.The proportion of
patients obtaining normal tacrolimus trough variability\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Henriksson, 2016, Sweden\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
44.3(9-68)/ 45.0(2-69)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
40:40\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Electronic medication dispenser (12 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Standard care\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Medication adherence--- electronic medication dispenser Tac level
Rejection Kidney function---creatinine Cost-Effectiveness\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
McGillicuddy, 2020, USA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
52.1 \selectlanguage{ngerman}± 11.3/ 51.5 ± 12.5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
40/40\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
An electronic medication tray with reminder capabilities enabled, a
Bluetoothenabled BP monitor, and the SMASK (Smartphone Medication
Adherence Saves Kidneys) smartphone app. (6 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
An electronic medication tray that provided no alerts and received text
messages with healthy lifestyle tips\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Medication adherence---the electronic Vaica medication trays The
proportion of patients obtaining normal tacrolimus trough
variability\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Jung, 2020, Korea\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
49.9\selectlanguage{ngerman}±10.0/ 49.0±12.2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
51/54\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Information and communication technology (ICT)-based centralized
monitoring system. (6 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The ambulatory follow-up group\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.Tac level 2.CVs for Tac level 3.Patient satisfaction 4.Kidney
function---eGFR\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Schmid, 2017, German\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
46(18-59)/ 51(19-66)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
23/23\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Telemedically supported case management (12 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Standard aftercare\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.Composite adherence percentage: Collateral reports (physicians,
nurses) and the target tacrolimus trough levels 2.Acute rejection
3.Cost-Effectiveness\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Low, 2019, Australia\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
53.6 (11.3)/ 48.4 (11.1)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
29/31\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
A face-to-face meeting (a medication review and a consumer-centred
video) and health coaching every two weeks, and medication event
monitoring system (3 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Standard aftercare and medication event monitoring system\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Medication adherence--- BAASIS 2.Medication adherence--- medication
event monitoring system\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Fleming, 2021, USA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
50.2(12.3)/ 51.2 (13.7)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
68/68\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
mHealth app, clinical pharmacist--led supplemental medication therapy
monitoring and management. (12 months)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Standard aftercare\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The proportion of patients obtaining normal tacrolimus trough
variability Patient satisfaction\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

BAASIS, Basel assessment of adherence with immunosuppressive medication
scales; M, mean; SD, standard deviation; IQR, interquartile range; eGFR,
estimate the glomerular filtration rate; CVs, Coefficient of variations;
Tac, tacrolimus.

\textbf{Intervention Programs}

Most studies used only one eHealth intervention, and a small number of
studies used a combination of two eHealth interventions. Details of the
intervention for each study are as follows. In Han et al.(\emph{24} )
study, the intervention group was provided with a mobile application for
medication management. The features of the application included
reminders that reported on the state of the medication, monitored the
state of the participant's medication, and provided education on
immunosuppressants. In McGillicuddy et al. (\emph{19} , \emph{20} ), the
intervention group received customizable reminder signals (light,
chime), phone calls or text messages at the prescribed dosing day and
time. They were also contacted by text, email, or phone when alerts
indicated medication non-adherence. In Reese et al.'s study (\emph{21}
), the intervention group received reminders, in which a light on the
bottle would illuminate and the cap would chime when the medication
should be taken. If adherence decreased to \textless{} 90\% every 2
weeks, the study coordinator would contact the participant by telephone.
In Jung et al(\emph{23} ), both patients and the medical staff received
feedback in the form of texts and pill box alarms in the event of a
dosage/dosing time error or a missed dose. In Fleming et al (\emph{22}
), mHealth app provided patients with an accurate list of their
medication regimen that was automatically updated from the electronic
medical record (EMR), timely medication reminders, automated messages
triggered by missed doses or scheduled health monitoring. In low et
al(\emph{26} ), the intervention consisted of a face-to-face meeting (a
medication review and a consumer-centred video) and a series of 6
fortnightly telephone calls (health coaching). In Henriksson et al.
(\emph{27} ), at the prescribed time for taking the medication, the
electronic medication dispenser (EMD) gave visual and audible signals.
If the patient did not take their medication, the audible signal was
repeated with increasing frequency for 120 minutes. Schmid et al.
(\emph{25} ) adopted telemedically supported case management, included
(i) a chronic case management process for the first year posttransplant;
(ii) a case management process applicable for acute care situations; and
(iii) a telemedically equipped team. Members comprised a transplant
nurse case manager (TNCM) and two senior transplant physicians (STP:
surgeon and nephrologist).

\textbf{Risk of Bias}

Figure 2 presents the results of the risk-of-bias assessment for each
study separately, and Figure 3 summarizes the percentages of studies
with low, unclear, and high risks of bias. The risk of bias of included
trials was high or unclear for most domains (Figure 2). Random sequence
generation was adequately described in 5 trials (56\%) and unclear in 4
trials (44\%). Allocation concealment was assessed as low risk of bias
in 3 trials (33\%), high risk of bias in 1 trial (11\%) and unclear in
the remaining 5 trials (56\%). Of the 9 trials, three trials (33\%)
blinded the participants to the trial intervention, high risk of bias in
only 1 trail (11\%) and unclear in 5 trial (56\%). Blinding of outcome
assessors occurred in 4 trials (44\%), high risk in 1 trial (11\%) and
unclear in 4 (44\%). Six trials (67\%) were considered as low risk of
attrition bias and 11 trials (33\%) were assessed as high risk. All
trials (100\%) were assessed to have low reporting bias. Eight trials
(89\%) were assessed as having low risk of other potential bias and
unclear in 1 (11\%). Overall methodological quality of evidence was
considered fair. We were unable to assess publication bias due to the
limited number of trials.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2.} Risk of bias graph.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3.} Risk of bias summary.

\textbf{Outcomes of Interest}

\textbf{Immunosuppressive Therapy Adherence as Assessed by Adherence
Rate}

\textbf{Adherence rate assessed by electronic equipment}A total of 6
RCTs (\emph{20} , \emph{21} , \emph{24} -\emph{27} ) reported
immunosuppressant adherence rate, and we were able to extract 3 sets of
analyzable data from 5 RCTs with 344 participants in the meta-analysis.
The random-effects model was used in this analysis owing to the moderate
heterogeneity (I\selectlanguage{ngerman}²=53\%, p=0.09). Results from the meta-analysis showed
that participants in the eHealth intervention groups showed
significantly increased adherence rate as compared with those in the
routine intervention groups with a pooled RR = 1.46 (95\%CI: 1.11 to
1.90, p=0.006)(Figure 4). We did not include Henriksson et al.
(\emph{27} ) because it only reported compliance rates (97.8\%) in the
intervention group, without an electronic medication dispener to record
medication in control group. Low et al. (\emph{26} ) did not give
specific adherence rates, but instead presented the results in the form
of a picture, they found no difference between the eHealth intervention
and control groups. The study of Schmid et al. (\emph{25} ) was not
included in this analysis as a comprehensive adherence rate (combination
of tacrolimus trough levels, collateral reports, and self-reported
adherence rates) was used for analysis in that study.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4.} Forest plot of adherence rate by electronic
equipment. Risk Ratio (RR) with 95\% confidence interval (CI) between
eHealth intervention group and control groups.

\textbf{Adherence rate assessed by questionnaires}Four RCTs(\emph{19} ,
\emph{21} , \emph{24} , \emph{26} ) used the Basel Assessment of
Adherence to Immunosuppressive Medications Scale (BAASIS) to assess
medication compliance. Sets of analyzable data from 2 RCTs (\emph{21} ,
\emph{24} ) with 261 participants in the meta-analysis. We used the
fixed-effects model because of low heterogeneity among these studies
(I\selectlanguage{ngerman}²=32\%, p=0.23). Results of this meta-analysis found no significant
group difference on medication adherence with the pooled MD=1.03
(95\%CI: 0.88 to 1.21, p=0.72)(Figure 5). Low et al. (\emph{26} ) did
not give specific adherence rates, but instead presented the results in
the form of a picture, they suggested the percentage of adherent
participants decreased significantly between baseline and 3 to 12 months
in the control group (p\textless{}\selectlanguage{english} 0.001) whilst the percentage of
adherent participants in the eHealth intervention group remained
constant over time. McGillicuddy et al. (\emph{19} ) was not included in
the analysis because the study used a self-designed medication
compliance scores (the correct number of doses is combined with the
prescribed time).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5.} Forest plot of Adherence rate assessed by BAASIS.
Risk Ratio (RR) with 95\% confidence interval (CI) between eHealth
intervention group and control groups.

\textbf{Blood Immunosuppressant Concentration}

\textbf{Tacrolimus level}Two RCTs reported tacrolimus blood levels and
the mean and standard differences were extracted from three(\emph{21} ,
\emph{23} , \emph{25} ,\emph{27} ). A total of 254 patients participated
in these two studies and the fixed-effects model was used in this
analysis owing to the no heterogeneity (I\selectlanguage{ngerman}²=0\%, p=0.38). Results of this
meta-analysis found no significant group differences on the Tacrolimus
level MD = 0.16 (95\% CI: -0.21 to 0.52, p = 0.39)(Figure 6). While
other (\emph{27} ) did not provide analyzable data or median and mean,
thus precluding the possibility for combining these statistics with
other data. Henriksson et al. (\emph{27} ) found there was no
significant difference in tacrolimus concentration between the 2 groups
over the 1-year follow-up period.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6.} Forest plot of Tacrolimus level. Risk Ratio (RR) with
95\% confidence interval (CI) between eHealth intervention group and
control groups.

\textbf{Coefficient of variation for blood tacrolimus levels}Two RCTs
have reported this variable. A total of 244 patients participated in
these two studies and the fixed-effects model was used in this analysis
owing to the no heterogeneity (I\selectlanguage{ngerman}²=0\%, P=0.89). Results of this
meta-analysis found no significant group differences on the Coefficient
of variation for blood tacrolimus levels MD = -0.01(95\% CI: -0.05 to
0.02, p=0.41)(Figure 7).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

\textbf{Figure 7.} Forest plot of coefficient of variation for blood
tacrolimus levels. Mean Difference (MD) with 95\% confidence interval
(CI) between eHealth intervention group and control groups.

\textbf{The proportion of patients obtaining normal tacrolimus trough
variability}

Fleming et al.'s 12-month randomized controlled trial of 136 kidney
transplant patients showed that more patients in the pharmacist-led
mHealth-based intervention group achieved a tacrolimus coefficient of
variation of less than 30\% compared to the control group(P=0.0133).
McGillicuddy et al. (\emph{20} ) also found improvement in the
proportion of patients achieving a clinical goal of a tacrolimus
intrapatient variability of \textless{}40\% (80\% vs 70\%, P = 0.001) in
the intervention group as compared to the control group.

\textbf{4. Kidney function}

Two RCTs assessed kidney function using eGFR. These studies included 241
participants and the random-effects model was used in this analysis
owing to the high heterogeneity (I\selectlanguage{ngerman}²=66\%, p=0.08). Results of this
meta-analysis found no significant group differences on eGFR, with the
pooled MD = -0.44\selectlanguage{english}(95\% CI: -8.32 to 7.43, p=0.91)(Figure 8).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 8.} Forest plot of eGFR. Mean Difference (MD) with 95\%
confidence interval (CI) between eHealth intervention group and control
groups.

Henriksson et al. (\emph{27} ) found that average P-creatinine level was
slightly lower in the eHealth intervention group than the control group
(131vs.150\selectlanguage{greek}μ\selectlanguage{english}mol/L, not significant).

\textbf{5.Rejection}

Two RCTs assessed rejection. A total of 120 patients participated in
these two studies and the fixed-effects model was used in this analysis
owing to the no heterogeneity (I\selectlanguage{ngerman}²=0\%, P=0.80). Results from the
meta-analysis showed that participants in the eHealth intervention
groups showed significantly decreased rejection as compared with those
in the routine intervention groups with a pooled RR =0.38 (95\%CI: 0.15
to 0.97, p = 0.04)(Figure 9).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\textbf{Figure 9.} Forest plot of rejection. Risk Rate (RR) with 95\%
confidence interval (CI) between eHealth intervention group and control
groups.

\textbf{6.Patientsatisfaction}

Patient satisfaction was measured in only three of nine reviewed
studies. McGillicuddy et al. (\emph{19} ) assessed the effect of text
reminders and electronic medication tray with reminder function on
Medication Compliance. Participants reported high overall satisfaction
with the mHealth system (average score 4.8/5 point Likert scale: 1=
strongly disagree-5 = strongly agree). In Fleming et al(\emph{22} )
study, the mHealth app has personalized reminders, including timely
medication reminders, automated messages triggered by missed doses or
scheduled health monitoring. 93\% of participants were satisfied with
the simple use of mHealth app. Jung et al. (\emph{23} ) assessed the
effect of with texts and pill box alarms on medication adherence and
their research suggested that overall satisfaction with the information
and communication technology (ICT)-based centralized monitoring system
was higher than neutral, even though most users were in their 50s or
older.

\textbf{Cost-Effectiveness}Economic evaluations were carried out in only
two of nine reviewed studies. Two studies have shown that eHealth
intervention can improve medication compliance(\emph{25} ) and reduce
rejection(\emph{27} ), with significant cost saving by, for example,
fewer admission rates and shorter lengths of unplanned
hospitalization(\emph{25} ), and reductions the cost of diagnosis and
treatment of rejection(\emph{27} ). It is important to note that neither
study fully assessed direct and indirect costs, which limits the
reliability of the conclusions.

\textbf{Discussion}

To our knowledge, this systematic review and meta-analysis is the first
to systematically assess the influence of eHealth interventions in
improving medication adherence to patient with KTx. 9 eligible studies
were identified, and our meta-analysis included data from 4 studies.
This systematic review included only RCTs with a high level of evidence
among interventional studies. Our findings suggest that eHealth can
improve medication compliance and decreased rejection in kidney
transplant patients in the short term as well as guidance regarding the
development of eHealth interventions. Data were sparse for most other
outcomes. We believe that more trials are needed to determine the
clinical outcomes of eHealth interventions on transplant kidney
function, rejection, and Patient-centered patient outcomes report (eg,
patient satisfaction).

Previous studies, though, have provided a systematic review of
compliance interventions in the kidney transplant population,But a
result with only a descriptive analysis and no quantitative synthesis
can affect the effectiveness of the results (\emph{28} ). Additionally,
another meta-analysis included studies that showed compliance
interventions improved medication compliance in KTx (\emph{29} ), but
more reported non-eHealth interventions, such as behavioral and
cognitive interventions or medication knowledge improvement. Recently,
some meta-analyses on medication compliance in solid organ
transplantation have been published. Shi et al. (\emph{10} ) performed
subgroup analysis according to organ type and found that compliance
intervention could improve medication compliance with KTx. The
meta-analysis of Lee et al. (\emph{17} ) on solid organ transplantation
including kidney transplantation showed that the results of eHealth
intervention were similar to that of routine care intervention. The
effect of eHealth intervention on medication compliance in kidney
transplant patients is not clear. This highlights the importance of this
study.

Our review used different models of eHealth intervention. For example,
some studies only used mobile apps, while others used electronic
medication dispenser, a consumer-centred video, or comprehensive
intervention (e.g., electronic medication dispenser and physician
notification). In addition, optimal type, frequency and duration of
electronic intervention are unclear. Further research is needed in the
later stage.

KTx remains the gold standard for the treatment of most ERSD. Due to
limited resources for health care expenditure, Long-term survival of
transplanted kidneys is important for patient health and public health
resources. The long-term effects and economic benefits of eHealth
interventions for KTx are unclear. In our study, the longest follow-up
time after intervention was 12 months and the shortest was only 3
months. Due to the short duration of follow-up, this highlights the need
for long-term follow-up in the future.

This review found few articles examined patient satisfaction and
preferences for particular types of eHealth interventions. Three studies
(\emph{19} , \emph{22} , \emph{23} ) assessed patient satisfaction, and
most patients were satisfied with the electronic devices used in eHealth
interventions. Satisfaction may indirectly affect patient utilization.
Other studies did not report patient satisfaction. But the use of
electronic devices was reported. Han et al. (\emph{24} ) shown over
one-half of the patients stopped using the Adhere4U application within
the first month, and only 10\% of the remaining patients were using the
app up to the end of the study. Low et al. research (\emph{26} ) showed
42\% of patients underutilized Medication Event Monitoring System during
the 12-month intervention. A high attrition rate might have lowered the
overall size of the effect of intervention(low). The results on
satisfaction are inconclusive. There is need for more studies to
evaluate user satisfaction with eHealth interventions targeted at
improving medication adherence to kidney transplant patient.

\textbf{Subgroup Analysis and Sensitivity analysis}

Due to the limited data included in the meta-analysis, no subgroup
analysis was performed. Through the sensitivity analyses, we found that
excluding studies one by one did not significantly alter the effect of
eHealth intervention on medication adherence.

\textbf{Publication bias}

We did not perform the funnel plot to illustrate the publication bias of
the primary outcome because less than 10 articles were included.

\textbf{Limitations}

The limitations of this study are as follows. First, we included only
English literature. Ignore articles published in other languages.
Second, we did not search for grey literature and unpublished studies.
Additionally, articles did not provide specific values, but presented
the research results in the form of charts or words, which affected the
results of meta-analysis. It is worth mentioning that despite the rapid
development of information technology, the research in this paper is all
from developed countries. Finally, these variations might contribute to
bias. Due to the limited number of articles, we did not use funnel plots
for publication bias, so we are not sure about publication bias.
However, given that some studies have reported negative results, we have
reason to believe that the possibility of release bias is low.

\textbf{Conclusions}

Results of our meta-analysis showed that eHealth intervention improved
medication compliance and reduced rejection in KTx compared with the
control group in the short time. Therefore, eHealth interventions can be
used for medication adherence in KTx. For future studies, RCTs with a
larger sample size and a long-term follow-up are necessary to overcome
the shortcomings of current trials.

\textbf{Data statement}

The data in our systematic review and meta-analysis have been provided
in the manuscript.

\textbf{Conflicts of interest}

All authors have no conflict of interest.

\textbf{Acknowledgments}

Not applicable.

\textbf{Authors' contributions}

Yue Liu and Xiaohong Guan contributed to the determination of study
topic and design of the study. Yue Liu and Yun Zhang mainly conducted
the article search, extracted the data, performed the assessment of
methodological quality of enrolled studies and pooled the results. Yue
Liu contributed to the original manuscript writing. Xiaohong Guan was in
charge of oversight and leadership responsibility for the research
activity planning and execution. All authors approved the final version
of the manuscript to be published.

\textbf{Funding}

No funding

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