Impact Of Computer Software Appplication On Medication Therapy Adherence

Computer/Mobile Phone Software Applications
(Apps) for the Optimisation of Medication
Therapy Adherence in Patients with Chronic
Disease Conditions: A Review
Stephen Nyoagbe
Student Number: 149000978
A thesis submitted in fulfilment of the requirement of the regulations governing the award of the
M.Sc. Medicines Management degree Programme, University of Sunderland 2015
ProjectSupervisor: Dr. Kenneth McGarry

STATEMENT OF PLAGIARISM
This is to certify full understanding of the university’s policy on plagiarism and responsibility for
the authored work and any consequences proving otherwise.
I certify that this work was conducted independently by me, except for instances where I have
indicated references to works done by others
SIGNED………………………………………………………………………….

ABBREVIATIONS
Apps Computer and Mobile phone Software applications
CENTRAL Cochrane Central Register of Controlled Trials
CI Confidence interval
EMBASE Excerpta Medica dataBASE
HIPAA Health Insurance Portability and Accountability Act
HIV Human Immunodeficiency Virus
ICT Information and Communications Technology
IMB Information-Motivation- Behaviour skills
MEDLINE Medical Literature Analysis and Retrieval System Online
MMAS-4 Morisky medication adherence scale 4 item
MARS-9 Medication adherence report scale 9 item
OS Operating system
QoL Quality of Life
RCT Randomised controlled trials
RevMan Review Manager
SIGN Scottish Intercollegiate Guidelines Network
TB - DOT Tuberculosis - Directly Observed Treatment
UK United Kingdom
WHO World Health Organisation

ABSTRACT
Background: The advent of medical technological advancement has resulted in the proliferation
of chronic diseases which are often otherwise acutely fatal. Such chronic diseases require lifelong
management and patient adherence. The issue of poor medication therapy adherence is of immense
importance to all stakeholders in the healthcare system. Non-adherence often results in the
worsening prognosis of such chronic diseases, and utilizes more health care resources. Computer
and mobile phone apps play an essential role in the management of daily activities and represent
an innovative means of improving medication therapy adherence. This review assesses the impact
of software applications as aids to enhance medication-taking and therapy adherence behaviours.
Objective: To evaluate the impact of computer and mobile phone apps as aids to enhance
medication therapy compliance in patients with chronic disease conditions and dependencies.
Method
Study eligibility criteria: Randomised controlled trials (RCTs) that compared medication-taking
behaviours and therapy adherence among participants with chronic conditions using mobile
software applications and traditional adherence methods were included in the review.
Source: All RCTs were sourced the CENTRAL, EMBASE and MEDLINE databases. No
publication date restriction was applied.
Data collection and synthesis: Relevant data pertaining to the objective of the review were pooled
from sourced RCT’s that satisfied the inclusion criteria. Risk ratios at 95% CI were calculated for
dichotomous outcomes. Mean difference and standard mean differences were determined for
continuous outcomes at 95% CI. Descriptive analysis was also performed for outcomes that could
not be evaluated using meta-analysis.

Results: 4 RCTs with 617 participants suffering from chronic diseases and alcohol dependency
were included in this review. Mobile phone adherence apps had positive effect on patient therapy
and medication adherence (std. MD 1.31 at 95%CI [0.49, 2.14]), decreased number of risky
drinking days after 12 months (MD -1.47 at 95% CI [-1.56, -1.38]) and reduced risk of reporting
alcohol relapse (RR 1.31 at 95% CI [1.03, 1.67]) . Descriptive analysis found weak evidence
suggesting that adherence apps have beneficial effect on biological outcomes. This was evident
among HIV positive patients who used such apps and experienced relatively lower viral load (1.30
log copies/ml SD 0.01 p=0.023) whereas patients suffering from hypercholesterolemia
experienced increase in lipid levels while using such apps (pre-post difference +5.7mmol/mol
[p=0.04]). Evidence that the use of mobile phone adherence apps to improve the quality of life
among participants in one study was inconclusive. The SF-12 physical score among asthma
patients in the mobile phone-intervention group of that study significantly improved from the
baseline at (41.6 SD1.5) to (n=43, 45.6 SD 1.3 p=0.045). This was however not the case in SF-12
mental scores where no statistical difference was detected. No study found statistical difference in
illness perception between participants in the intervention and control groups.
Conclusion: Evidence suggests that the use of computer/mobile phone adherence apps increases
patients’ adherence to therapy and compliance with prescribed medication. However evidence on
the effect of such apps on patient biological outcome, quality of life and clinical outcome is
inconclusive. No evidence suggests that such apps improves patient illness perception.
Limitations of study: The low sample size of participants has the potential to overestimate or
underestimate findings in this review. Also the short intervention period of the trials (3 to 8
months) may not have provided enough sampling time to gather sufficient data for more accurate

outcome measures. Greater emphasis was also made by the trials on statistical difference rather
than clinical difference between intervention and control groups.
Keywords: Chronic disease conditions; Apps; Medication therapy adherence; Computer and
mobile technology; Randomised Controlled Trials; Systematic review.
PLAIN LANGUAGE SUMMARY
Using mobile computer and mobile phone apps to improve patients’ adherence to therapy
Chronic diseases such as HIV, hypertension or alcoholism require long term treatment which often
make use of multiple medications and healthcare worker recommendations. Compliance among
such patient group is essential for effective disease management. However evidence suggest that
most of these patients lack the basic understanding of their condition and how to efficiently take
their medication. The use of apps may provide a cheap and effective avenue of improving patients’
understanding of their condition and prescription compliance.
4 trials involving 617 adults that met stated requirements for the review were identified. The
studies included different mobile phone app related interventions that catered to medication and
therapy adherence among adults who suffered from diabetes, heart related diseases, HIV, chronic
alcoholism and asthma. The age of adults who partook in the trial ranged from 38 to 73 years.
These individuals were exposed to the interventions from periods between 3 to 8 months to
determine the effect the interventions had on adherence to therapy, quality of life, illness
perception, laboratory test results and symptom scores, and clinical outcome.

Data with regards to effect of the interventions from the selected trials on stated outcome measures
were then reviewed and rated as acceptable with respect to their level of bias risk and quality.
Analysis of data suggested that apps designed to enhance adherence to therapy had positive impact
on patient adherence to therapy as well as compliance with doctors’ prescriptions. However the
evidence suggesting that such apps improved clinical outcome, quality of life, and laboratory test
results and symptom scores was found to be weak. Evidence was lacking with respect to the
improvement of illness perception among patients who used adherence apps.

ACKNOWLEDGEMENT
Special thanks to my tutor Dr. Kenneth Mcgarry for his direction and assistance with statistical
analysis done in this thesis. I would like to thank my family, friends and colleagues for the moral
support and dedication shown

TABLE OF CONTENTS
HEADER
STATEMENT OF PLAGIARISM
ABREVIATIONS
ABSTRACT
PLAIN LANGUAGE SUMMARY
ACKNOWLEDGEMENT
CHAPTER ONE……………………………………………………………………...………….1
INTRODUCTION…………………………………………………………………………….…..1
CHAPTER TWO………………………………………………………………..…..………….10
OBJECTIVE…………………………………………………………………………..…………10
METHODOLOGY………………………………………………………….…………………...10
CHAPTER THREE……………………………………………………….……………….…..13
OUTCOME MEASURE………………………………………………….……………..……….13
CHAPTER FOUR………………………………………………………………………………17
RESULTS…………………………………………………….………………………………….17
CHAPTER FIVE………………………………………………………………………………..32
DISCUSSION………………………………………………..…………………………………..32

CONCLUSION & RECOMMENDATION…………………………………...……..………….36
REFERENCES FOR INCLUDED TRIALS……………………..…..………….………………38
REFERENCE FOR EXCLUDED TRIALS……………………………………….……………38
OTHER REFERENCES…………………………………………………..…………………….40
CHARACTERISTICS OF INCLUDED TRIALS………………………………………………44
TABLE OF RISK OF BIAS…………………………………………………………………….48
CHARACTERISTICS OF EXCLUDED STUDIES………………….…………………………52
DATA AND ANALYSIS………………………………………………..………………………54
Analysis 1.1: Computer/Mobile phone software applications for medication adherence vs
standard/traditional methods: Adherence to therapy……………………………………………57
Analysis 1.2 Computer/Mobile phone software applications for medication adherence vs
standard/traditional methods: Adherence to therapy (Risk ratio for reports of drinking within
past month)……………………………………………………….………………………………58
standard/traditional methods: Adherence to therapy (Risky drinking days (overall)……...……59

1
CHAPTER ONE
INTRODUCTION
The social and economic burden of chronic diseases pose serious challenges to global health,
finance and development (Abegunde et al., 2007). According to the World Health Organisation
(2005) report, 60% of all global deaths occurring in the year 2005 were due to chronic diseases.
The economic impact of chronic diseases is projected to incur a cost equivalent to 2.7 trillion
pounds on the US economy by the year 2023 (DeVol and Bedroussian, 2007). Many health
conditions labelled as chronic are characterised by multi-factorial aetiology; long periods of
latency; gradual course of disease progression; impairments and disabilities (Bentzen, 2003). Such
diseases cannot be prevented by vaccinations or cured medically, but are managed by the long-
term use of medicines for symptomatic relief or to retard disease progression.
Due to the long term and often complex nature of treatment associated with chronic diseases,
adherence to therapy among such patient groups is essential to disease management. Cramer et al.,
(2008) defined therapy adherence as the extent to which patients comply with prescription intervals
and required dose of regimens, as well as any other recommendations of their health care provider.
It can be viewed as a presumption of agreement between the healthcare provider and patient.
Medication adherence behaviour is divided into two main concepts, namely compliance and
persistence (Ho, Bryson and Rumsfeld, 2009). The concept of compliance involves the intensity
of medicines use whereas persistence refers to overall medicines therapy duration (Caetano, Lam
and Morgan, 2006; Cramer et al., 2008).

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Adherence:
Medication adherence is of major health economic importance. A review by Cramer (2004)
suggested that non-adherence to medication therapy is a common occurrence within the health
setting with adverse outcomes on patient health and increased cost of healthcare. Evidence shows
that approximately 33-69% of outpatients are non-adherent to medication therapy (Osterberg and
Blaschke, 2005). Poor adherence acutely compromises prognostic outcomes and increases the risk
of mortality (Brown and Bussell, 2011). There is also the loss of confidence and trust in patient-
healthcare provider relationships due to resultant treatment failure associated with non-adherence
(Ruddy, Mayer and Partridge, 2009). The Steering Group on improving medicines use (2012)
report estimated that about 5 to 8% of hospital admissions in the UK are due to inadequate or
incorrect medicines use. The UK economy loses £100 million annually due to unused medicines
and cost associated with the disposal of such medicines (National Audit Office, 2007).
Adherence assessment:
Assessment of medication therapy adherence is categorized as either direct or indirect (Osterberg
and Blaschke, 2005). Direct assessment methods include biological monitoring such as blood and
urine sampling, and visual monitoring and are considered to be more robust than indirect
assessment. A typical instance where this is applied is the TB-DOT program by WHO in most
developing countries, where medication adherence is monitored directly by observing patients
while they take their prescribed anti-tuberculosis medicine. Hiding of solid dosage forms such
tablets and capsules in mouth and later spitting them out as well as variations in individual
metabolic rates limit the effectiveness of direct adherence assessment. These methods are also

3
deem impractical, invasive and condescending when used in patients with chronic disease
conditions.
Indirect methods of assessment include patient self-reports, refill rates, pill counts, electronic
monitoring and patient diaries (Garfield et al., 2011). Although there exist myriads of self-report
assessment-scales the most widely used and validated means of self-reported adherence
assessment is the 4-item Morisky scale (MMAS-4), and has been proven to be predictive of
cardiovascular medication therapy adherence (Morisky, Green and Levine, 1986; Shalansky,
2004). The scale utilizes a questionnaire in which points from a range of 0-4 are awarded based on
the answers provided by the patient. A score of 0 indicates high adherence; 1-2, moderate
adherence; and 3-4 poor adherence.
Figure1. MMAS-4 scale with indicated scores for self-reporting adherence (Morisky, Green and Levine, 1986)
Self-reports are however open to bias whereby patients most often overestimate level of adherence
to their healthcare providers. Although pill counts are simple to perform, and are often used in
random controlled trials for adherence assessment, they are unreliable if pill boxes or dump pills
are not returned before counts rendering the capture of exact regimen timing difficult to determine
(Gossec et al., 2007; Osterberg and Blaschke, 2005).

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There exist myriads of reasons why patient are non-adherent to medication therapy. Based on
perceived reasons or beliefs some patients deliberately do not comply with treatment whiles others
are unintentionally non-adherent to medication therapy. Inconsistencies with treatment adherence
occur as a result of complexities associated with therapy due to multiply morbidities, omissions
and delays in taking the prescribed doses. A study by Osterberg and Blaschke (2005) showed that
patients are most adherent to medication therapy right before and after a prescriber’s appointment.
This is known as “white-coat” adherence.
MEDICATION NON-ADHERENCE
CATEGORY
EXAMPLES
Health system Poor patient-prescriber relationship; inadequate
communication; lack of access and continuity of
healthcare
Condition Asymptomatic chronic disease (such as latent stage of
HIV); psychological disorders
Patient Physical impairment; cognitive impairment; age; race
Therapy Complexity and duration of treatment which is usually
associated with multiple morbidities and
polypharmacy; iatrogenic effects of treatment
Socioeconomic Illiteracy; high medicines cost; poor social support;
lack of affordable and accessible universal health care
scheme
Table 1. WHO categorization of medication therapy non-adherence

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Considerable amount of evidence has identified factors that predict or correlate medication therapy
adherence and non-adherence. WHO has classified these factors under five main groupings of
health system; condition; patients; therapy and socioeconomic reasons. Although these
categorisation gives a generalised insight as to why patients are non-adherent, there exist the
uncertainty to which each stated factor can sufficiently differentiate between adherent and non-
adherent patients suggesting that evaluations of non-adherence cannot be targeted to specific
patient demography (Ho, Bryson and Rumsfeld, 2009). Albeit the fact that such broad
categorisations are over-simplistic determinants of non-adherence, they are practical and highlight
the fact that solving non-adherence requires multifocal interventions (Garfield et al., 2011).
Adherence model:
Medication adherence models are developed to enable health care providers better understand
patient medication-taking behaviours with the aim of improving adherence. Such models take into
account biomedical, patient belief and behavioural, communication, cognitive and self-regulatory
viewpoints on adherence (Leventhal and Cameron, 1987). Most models are premised on the fact
that patient beliefs determine how information and experiences are discerned. This ultimately
influences their medication-taking behaviour.
Figure 2 Information-Motivation- Behaviour skills (IMB) model (Fisher and Fisher, 1992).

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The IMB model by Fisher and Fisher (1992) has extensively been validated by research and widely
used in diverse populations and health setting. Information under this model refers to the basic
knowledge about a disease which encompasses aetiology, prognosis and therapeutic strategies. An
informed patient is more likely to adopt behavioural skills and changes to positively influence
adherence. Motivation covers patient attitudes towards medication-taking behaviour. Such
motivation are backed by patient religious and cultural beliefs, as well as perceptions. Behavioural
skills empower patients with specific behavioural strategies to ensure change towards treatment
adherence for definitive health outcomes.
Evidence for the incorporation of ICT into pre-existing system that encourage adherence:
Systematic review and meta-analytical data suggests that the commonest means by which patient
medication-taking behaviours are positively influenced is by using education, traditional
reminders such as pill boxes, dosage simplifications by prescribing combine dosage formulations,
and counselling (Brian Haynes, Ann McKibbon and Kanani, 1996; Graves et al., 2009). The use
of modern technology as aids to enhance patient compliance is mainly in the form of fixed
telephone reminders, audio-visual and pager reminders. Such interventions involve daily text
reminders with follow-up phone calls. Although they enhance compliance when used in
combination with other adherence enhancement methods, and unsuitable for widespread use. A
study byCheng et al (2015) also stated that use of text and phone call reminders can be patronizing,
and do not encourage patients to be proactive about improving their health status. The study also
discovered that such interventions are not interactive and most often do not improve patient
education.

7
1.5 Potential benefits of intervention:
Novel interventions are needed to cater for the pharmaceutical needs of such patient demography
(i.e. chronically ill patients). There is the expectation that the use of smart mobile phone and
computer applications may provide an interactive and more sophisticated means of improving
patient medication adherence by providing the necessary information among chronic disease
sufferers to aid in making well informed choices about their healthcare. Such tools have the
potential to enable patients to recognize and comprehend all necessary medication inputs,
modifications, while incorporating patient daily routine and relaying vital information to their
healthcare providers (Becker et al., 2013).
In the past few years, mobile phone technology has witness an exponential advancement with vast
improvements in both form and function, from basic call and text messaging devices to more
innovative mini computers known as smartphones and tablets. An estimate of 6 billion mobile
smartphone and tablet devices was reported to have been in use in the last quarter of 2011, with
more than a sixth of such devices capable of broadband internet connectivity
(PriceWaterhouseCooper, 2012). Smartphones and tablets enable individual users to download,
configure and run specialized software applications which were hitherto performed by giant
supercomputers. A study estimated a total of 43.6 billion global application downloads in the year
2012 (TechCrunch, 2013). This underscores how easily affordable and accessible these apps are.
Currently marketed apps include software features such as reminders that incorporate consumption
and refill rates, recordable doses, accessible medicines information such as warnings, adverse drug
events and instructions, which can all be performed at the convenience of the patient. Dayer et al
(2013) compared the features of 10 adherence apps across the three main software operating
system platforms of Android, Apple OS and Blackberry OS using the HIPAA attributes and

8
ratings. The study found that the apps Mymedschedule, Medsimple and Mymed adequately catered
to the medicines adherence needs of chronically sick patients, although such a finding was
anecdotal.
Possible disadvantages of intervention:
Although credible evidence on the disadvantages of software app use as adherence aids is limited,
Pal et al. (2013) suggested the following probable adverse effects associated with the use of such
an intervention:
I. Receiving of false or inadequate information with regards to therapy.
II. Inability to discern therapy related guidance.
III. Self-management by patients without consent of healthcare provider.
IV. Strain in patient-healthcare provider relationship if patient receives counselling contrary
to information provided by the intervention.
V. Over dependence on intervention.
VI. Loss of confidence in intervention if patient does not find it helpful.
VII. Risk to privacy where patient data and medical conditions are input onto a third party
platform other than their healthcare provider.
VIII. Possible ethical malpractice among some software application developers with vested
commercial interest.
Some of these disadvantages can be mitigated by adequate patient education and counselling prior
and during use of intervention. Also, the regulation of development of such applications may help
in addressing possible adverse events while promoting patients’ interest.

9
Why is it important to do this review?
Although adherence apps possess the potential to enhance the effectiveness and decrease cost of
conventional adherence interventions, empirical analysis of their efficacy and impact on equity is
limited (Lee et al., 2011). Earlier studies only focused on the impact of information technology on
adherence among specific chronically ill patient demography. A Cochrane review by Kauppi et
al., (2014) suggested only minor improvement in mental states and quality of life among patients
with serious mental illness who relied on electronic media assistance for medication therapy
adherence. Pal et al., (2013) also reviewed studies that used computer assisted self-management
intervention for type 2 diabetes and discovered that patients who used such technologies had better
glycaemic control. The exponential rate of technological advancement in healthcare coupled with
the widespread use of smart mobile devices underscores the need for harnessing such modern
technological resources through research to positively influence medication-taking and therapy
compliance behaviours of patients with chronic morbidities. The review sought to address the
following research questions:
I. Can mobile phone applications positively impact adherence behaviour among individuals
with chronic diseases?
II. Do the possible pros of using such apps outweigh the cons?
III. Are such apps easily accessible, user friendly and affordable?
IV. Do mobile phone apps have any impact on patient disease perception?

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CHAPTER TWO
OBJECTIVE
To synthesize and critically assess existing evidence on the impact of computer-mobile software
application technology on medication therapy adherence among patients who suffer from chronic
diseases, addictions and disorders.
METHODOLOGY
Review Protocol
The review was conducted in accordance with the Cochrane Collaboration guidelines for
systematic reviews- version 5.1.0, 2011 update (Higgins and Green, 2011).
Search Strategy
An extensive electronic literature search was conducted on the 1st of July using MEDLINE,
CENTRAL, and EMBASE for randomized controlled trials (RCTs) that investigated the impact
of smart mobile and computer software application technology on medication adherence therapy.
No time-bound restriction on publication date of selected studies was applied in this review.
A combination of standardized indexed terms and free-text terms relating to chronic disease;
addictions; chronic addictions; medication adherence; smartphone and tablet computers; internet
and software applications were used in the database searches.

11
Selection Criteria
Type of studies
All admissible studies included in this review were RCTs that employed parallel group or
crossover analysis for individual or aggregate randomisation. Trials that were not double blinded
but implied randomization were included in the sensitivity analysis of the review. Quasi-
randomised trials were excluded from this review. Trials included in the review were not
necessarily required to have originated from Anglophone countries, however such trials were
required to be authored in English due to resource limitation.
Type of participants
The review focused on trial with participants who are burdened with any form of chronic disease
condition, as well as participants who are on long term or life-long medication therapy. Also
included in the review were participants who were suffering from some form of chronic addictions
such as alcoholism or smoking which required some degree of long term compliance to therapy.
Participants were not excluded from this review on the basis of race, religious beliefs, the type of
treatment setting, age, nationality or duration of chronic disease.
Types of intervention
I. Use of software applications as the sole means of improving adherence to therapy:
Interventions where apps were used via smartphones, tablets and laptops as the only means
by which adherence to treatment was encouraged were included in the review.

12
II. Use of software applications as an augmentation to traditional methods as means for
improving adherence:
This refers to systems where interactive software applications were used to facilitate
traditional methods of adherence encouragement such as the use of pill boxes, telephone
reminders, medication diaries, emails and basic text messages.
III. Use of traditional methods as means for improving adherence to therapy:
Such interventions include written instructions.
Interventions that were excluded from this review were ones that were targeted at health
professionals. Interventions that utilized embedded software applications such as planners and
calendars or applications that were not downloadable were also excluded from the review. Another
excluded intervention was one which depended solely on transfer on text messages, pre-recorded
voice or video prompts.

13
CHAPTER THREE
OUTCOME MEASURE
The outcome measures were selected to reflect the main objective of the review. All selected trials
reported on adherence to therapy using self-report scales. Taking into consideration the
heterogeneity of the various scales employed to measure adherence to therapy a standard mean
difference analysis was intended for meta-data evaluation. Such an analytical evaluation was also
intended for the secondary outcome of the different biological markers of participants across the
various trials. Analysis of difference in means was intended for the outcome of QOL due to the
fact that only one trial provided adequate data. Due to insufficient data adverse events, clinical
outcomes and assessment of applications were described narratively.
3.1 Primary outcome
 Adherence to therapy.
3.2 Secondary outcomes included the following measures.
 Biological outcomes.
 Measure of quality of life (QoL).
 Illness perception.
 Clinical outcomes.
Selection and screening for duplicates
Studies included from the stated electronic databases were initially screened against the inclusion
criteria and reviewed with the aid of a senior lecturer. Full text of articles were retrieved where

14
possible to obtain better understanding and assessment of internal quality. Citations and brief
records of the selected studies were then imported into Reference manager (RefMan® 12.0)
software for organization and screening of duplicates.
Data extraction and management
Once the requirements for the inclusion criteria were met, the following information was
independently extracted from each trial, general reference detail which included the name of
principal author, publication date and country of study details of study method which included aim
of the trial, study duration, study design, participant recruitment method and characteristics of trial
test and control groups. Participant details which included number of participants, type of chronic
disease among participant group and setting of study; type and detail of intervention which
included user interface of software application; and details of measured outcomes of relevant
qualitative and quantitative measures for both primary and secondary outcomes. See
characteristics of included trials on page 28.
Data synthesis and assessment of heterogeneity
Where there was sufficient data of high quality among the selected trials, statistical analysis was
performed using the RevMan® 5.3 software. Descriptive analysis was performed when sufficient
data was lacking. Chi2 and I2 statistic were inspected on the forest plots to determine the
heterogeneity of meta-data findings. An I2 estimate ≥50% with a statistically significant Chi2 was
considered as evidence of high heterogeneity (Deeks, Higgins and Altman, 2015).

15
Risks of Bias
The quality and evidence of the studies included in this review was assessed using the SIGN quality
assessment tool for RCTs. The tool enabled assessment of the quality of information extracted and
the detection of risk of biases in included studies by using the following criteria:
a. The clarity with which a research problem is addressed.
b. Randomisation method adopted.
c. Whether adequate concealment method employed.
d. Blinding of treatment allocation.
e. Similar baseline in control and test groups at onset of trial with difference between groups
being as the result of treatment under investigation.
f. Relevant measured outcomes.
g. Percentage of participants excluded or lost to follow-up.
h. Presence or absence of intention to treat analysis.
An additional requirement of reported power of trail was assessed to ensure quality of results. The
internal validity of the selected studies was graded using the following codes
 +++ (High) – indicates that the selected publication met the requirement of all checklist
regarding internal validity, and free from any biases that might affect the integrity of
findings of the review.
 + + (Acceptable) – indicates that some of the requirements with regards to internal validity
and biases were met.

16
 0 (Unacceptable) – indicates that none of the check listed requirements were met with
respect to internal validity. The trial also had high level of bias and was likely to affect
findings of the review.

17
CHAPTER FOUR
RESULTS
Description of studies
See characteristics of included trials; excluded studies after detailed analysis on page 28.
Results of the search
Entering of standardized search and free text terms into the three online databases resulted in the
identification of 535 references (98 from CENTRAL, 200 from EMBASE and 237 from
MEDLINE). 30 potentially relevant studies were then selected through a filtration process out of
which 5 studies were identified to be duplicates. This is summarized in Figure 3. The 25 studies
were then independently screened and their internal quality assessed for eligibility. 4 different
trials with a total of 617 participants met the inclusion criteria and were selected for inclusion in
the review.
Included studies
Four studies met the inclusion criteria. The selected studies were conducted in Spain (Mira et al.,
2014), Taiwan (Liu et al., 2010), United States (Gustafson et al., 2014) and New Zealand (Perera
et al., 2014) respectively. All studies were published in English. See characteristics of included
trials for detailed description of the selected studies on page 28.
Study design
All included studies in this review were randomized controlled trials. Trial periods ranged from 3
to 12 months. Mira et al., (2014) conducted a single blinded randomized controlled trial in which
participants were randomly assigned in both control and experimental groups and assessed for a 3

18
months. Liu et al., (2010) randomized participants into mobile telephone and control groups, after
which patients were reviewed every 3 months. The duration of the trial was 6 months. Participants
in the trial conducted by Gustafson et al., 2014 were unmasked, however they were assigned to
the test and control groups in a 1:1 ratio by random allocation sequence. Treatment duration lasted
for 12 months with 4 month and 8 month follow-up. Perera et al., (2014) randomly allocated
individuals to the intervention and active control groups. Data was then collected at the baseline,
1 month and 3 month follow-up periods. The trial duration was 3 months.
Participants
All 617 participants in this review were sufferers of chronic disease or addiction. Number of
participants in each trial was, 27 (Perera et al., 2014), 102 (Mira et al., 2014), 120 (Liu et al., 2010)
and, 349 (Gustafson et al., 2014).With the exception of one trial having participants being of East
Asian ethnicity (Liu et al., 2010) all other trials had predominantly Caucasian participants. Two
trials out of the four trials were predominantly male 80% (Gustafson et al., 2014) and 93% (Perera
et al., 2014). Mean age of participants ranged from 38 years (Gustafson et al., 2014) to 73 years
(Mira et al., 2014).

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Figure 3. Flow diagram for the selection and inclusion of trial
Potentially relevant trials
sourced (n=30)
Studies after duplicates were
removed (n=25)
Studies include in the review
(n=4)
4 studies were excluded from the review due
to:
1. Research protocol (n=4)
2. Ongoing trial (n=3)
8 studies that failed to meet the inclusion
criteria:
1. No randomization (n=1)
2. Healthcare provider centred
interventions (n=6)
3. Lack of relevant outcome measures
(n=2)
4. Interventions involving direct text
messages and automated voice
reminders ( n=5)
Screen studies for detailed
analysis (n=18)
Records identified through
MEDLINE,EMBASE and
CENTRAL databases (n=535)
505 studies excluded due to:
1. Technological assessment (n=129)
2. Economic evaluation (n=115)
3. Journal article (n=93)
4. Reference work (n=24)
5. Book (n=134)
6. Reviews (n=10)
5 duplicated studies were excluded

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Interventions
Duration of Intervention
There were varying duration of interventions among the selected trials. The shortest intervention
periods lasted for 3 months (Perera et al., 2014; Mira et al., 2014). Participants in the trial
conducted by Liu et al., (2010) had an intervention duration of 6 months. The longest intervention
duration was 8 months (Gustafson et al., 2014).
Frequency and Intensity of Intervention
Interventions in three of the selected trials were non prescriptive but were driven participants’
choice of usage (Liu et al., 2010; Gustafson et al., 2014; Perera et al., 2014). The intervention by
Mira et al., (2014) was prescriptive and designed to function with the personalized medication
prescription and healthcare recommendations of participants.
The frequency of intervention refers to the rate at which participants were exposed to an
intervention whiles intensity dealt with the duration of exposure to intervention. Participants in
the trial by Mira et al., (2014) were exposed to an intervention which had the potential of 5
interactions per day. Participants in the test group by Perera et al., (2014) received an augment 24-
hour intervention with at least one daily interaction. Both interventions utilized by Mira et al.,
(2014) and Perera et al., (2014) were 90 days intense.
The intervention used by Liu et al., (2010) required one interaction a day. Gustafson et al., (2014)
utilized an intervention in which the frequency of interaction depended on exposure to high risk

21
locations of addiction relapse such as drinking bars. Two trials used high intensity interventions
with durations of 6 months and 8 months (Liu et al., 2010; Gustafson et al., 2014).
Types of interventions
Three of the interventions targeted medication adherence (Liu et al., 2010; Mira et al., 2014; Perera
et al., 2014) whereas an intervention focused on adherence to substance abuse rehabilitation
(Gustafson et al., 2014). Participants in all the selected trial received some form on tutorial prior
to the utilization of the interventions under investigation.
Perera et al., (2014) conducted a clinical-based intervention which targeted HIV positive
participants who had been on anti-retroviral therapy for a minimum of 6 months. The intervention
utilized a real-life medication clock which makes use of a graphic user interface mobile phone
software application that provides participants with graphical approximations of anti-retroviral
medication plasma concentrations. The intervention also include individualized simulation of
disease state immunity made up of animated CD4 lymphocyte and HIV viral load counts. The
intervention was intended to enhance antiretroviral therapy and facilitate participant understanding
of HIV infection.
Intervention by Mira et al., (2014) catered for elderly chronic disease sufferers with high pill
burden. It was designed to augment traditional methods of improving medication adherence such
as written prescriptions and recommendations. The investigated intervention used in the trial is an
internet enabled software (ALICE app) that performs three key functions of storing and organizing
prescriptions coupled with photographs of dispensed medication; customizing participant
medication therapy reminders; and third party monitoring of medication adherence which involves
care workers and health professions.

22
Liu et al., (2010) utilized a mobile telephone-based interactive self-care application intervention
for individuals with asthma. The self-care application provided an electronic repository of daily
symptom score, use of anti-asthmatics, and peak exploratory flow rate and variability. Data
collected were then analysed and the need for anti-asthmatic medication use was scored as an
indication of the extent of pulmonary control.
Participants in the intervention group by Gustafson et al., (2014) were exposed to a software (A-
CHESS) application as a form of augmentation to regular alcohol abuse rehabilitation. The
software possessed both static and interactive features combined with a global positioning system
to alert participants of high risk locations. With the consent of participants counsellors were given
access to application data to make recommendations with regards to adherence to alcoholism
rehabilitation.
Outcomes
Primary outcome
Adherence to therapy
All four trials reported on participant adherence to therapy using self-report measuring scales
(Gustafson et al., 2014; Liu et al., 2010; Mira et al., 2014; Perera et al., 2014). Gustafson et al.,
(2014) made use of “risky drinking days” and level reports of drinks taken in the past 30 days
(abstinence) to assess participant adherence to therapy. Liu et al., (2010) assessed adherence to
therapy by comparing the extent of medication usage between intervention and control groups.
Mira et al., (2014) used the 4 item Morisky Medication Adherence Scale (MMAS-4) in the
assessment of participants. In addition to the 9-item medication adherence scale (MARS-9) Perera

23
et al., (2014) incorporated prescribed doses taken, and rate of pharmacy dispensing as a
dichotomous composite adherence measure.
Secondary outcomes
Biological outcomes
Three trials compared biological markers of participants the intervention and active control groups
(Liu et al., 2010; Mira et al., 2014; Perera et al., 2014). Liu et al., (2010) reported on participant
pulmonary function Participant lipid profile, glycated haemoglobin and blood pressure was
assessed (Mira et al., 2014). Perera et al., (2014) reported on HIV viral load.
Measure of quality of life (QoL)
Two studies reported on quality of life (Gustafson et al., 2014; Liu et al., 2010). Gustafson et al.,
(2014) measured participant quality of life using Short Inventory of Problems-Revised instrument.
The Short-Form 12 physical and mental component scores were used to assess participant quality
of life by Liu et al., (2010).
Illness perception
Participant illness perception was reported by two trials (Mira et al., 2014; Perera et al., 2014).
Mira et al., (2014) did not specify the assessment tool employed. HIV positive participants were
assessed using the 9-item Brief Illness Perceptions Questionnaire (BIPQ) (Perera et al., 2014).

24
Clinical outcomes
Liu et al., 2010 compared clinical outcomes between intervention and active control groups where
unscheduled visits to the emergency department and hospitalization, respiratory failure and death
were assessed.
Excluded studies
Most of the studies excluded from this review employed interventions that were either healthcare
provider centered (Fukuoka et al., 2015; Nobis et al., 2013; Surka et al., 2014; Velasco et al., 2015;
Weaver et al., 2007; Zanner et al., 2007) or involved non interactive relay of text messages and
automated voice prompters between healthcare providers and patients (Istepanian et al., 2009; Kolt
et al., 2010; Lund et al., 2014; Novak et al., 2013; Pijnenborg et al., 2010). Other reasons for
exclusion of studies include the lack of outcome measures relevant to the main aim of the study
(Cremers et al., 2014; Hertzberg et al., 2013) as well as non-randomised allocation of participants
(Song et al., 2009).
Risk of Bias
Details for bias risks can be found in the table for risk of biases. Risk of bias focused on
randomization, allocation concealment, blinding of treatment allocation, baseline characteristics,
the standard of relevant outcome measures, attrition bias and intention to treat.

25
Randomization
Gustafson et al., (2014) randomized participants into both intervention and control groups using a
1:1 ratio computerized random allocation sequence with blocks of 8. Mira et al., (2014) adopted a
single blind randomization where participants were randomly assigned to test and control groups.
Liu et al., 2010 and Perera et al., (2014) randomised participants into control and intervention
groups.
Allocation concealment
Allocation concealment was implemented by Gustafson et al., (2014) and Mira et al., (2014).
Sequentially numbered containers was used in the concealment of participants (Gustafson et al.,
2014). Participants in the intervention group received allocation concealment. They were assigned
codes based on initials and birthdays to maintain concealment and enable linking of pre and post
measurement of outcomes (Mira et al., 2014).
Blinding of treatment allocation
All four selected trials employed self-reported data collection for assessment of primary outcome
of therapy adherence (Gustafson et al., 2014; Liu et al., 2010; Mira et al., 2014; Perera et al.,
2014). This was likely to introduce biases due to the subjective nature of data collected.

26
Baseline characteristics
Inclusion criteria as well as baseline characteristics of participants in the intervention and control
groups were reported by all four studies. All selected trials reported similar baseline characteristics.
Three trials recorded p values ≥ 0.04 indicating similar baseline characteristics (Liu et al., 2010;
Mira et al., 2014; Perera et al., 2014). Gustafson et al., (2014) provided a descriptive report of
baseline characteristic similarities using relative percentages.
Standard of relevant outcome measure
Primary outcome measures were recorded using validated and well defined measuring instruments.
“Risky drinking days” scale was used to assess participants with alcohol dependence (Gustafson
et al., 2014). Liu et al., (2010) used pulmonary function in assessing therapy adherence and self-
management among asthmatics. Mira et al., (2014) and Perera et al., (2014) used the MMAS-4
and MARS-9 self-report instruments respectively for adherence assessment. The use of such
verifiable instruments decrease the risk of outcome measure biases.
Attrition bias
Participant drop-out rates were reported by all included trials. Three trials reported low missing
data with the lowest attrition rate as low as 3% (Mira et al., 2014). Gustafson et al., (2014) reported
the highest loss to follow up with an attrition rate of 22% which exceeded the threshold of 20% of
the sample size. Although the loss to follow-up percentage of 3.5% was reported by Perera et al.,

27
(2014) that was likely to introduce substantial attrition bias due to the low initial sample size of 22
participants.
Intention to treat analysis
Intention to treat principle was adhered to during selection and inclusion of participants’ data
(Gustafson et al., 2014). Intention to treat analysis was not stated in three of the trails (Liu et al.,
2010; Mira et al., 2014; Perera et al., 2014)
Effects of intervention
Standardized mean difference (Std. MD) was used as the summary statistic of choice in the meta-
analytical review to determine the effect of intervention on participant adherence to therapy and
biological marker levels due to the use of different self-report scales and bio-data. Risk ratios were
used to determine level of abstinence among intervention and control group (Gustafson et al.,
2014). Mean differences (MD) and standard mean differences (Std. MD) were calculated for
continuous data while risk ratios were calculated for dichotomous data at a 95% confidence
interval (CI).
Meta-data analysis could not be performed for all outcomes under the secondary outcome category
due to insufficient amount of data being reported by the trials. Data pooled for the outcome
measure of biological markers were deemed to be too dissimilar for accurate meta-analysis.
Descriptive analysis was performed for all secondary outcomes in this review.

28
Primary outcome
Meta-data analysis of “risky drinking days” and abstinence from alcohol dependence agreed with
finding from the trial conducted by Gustafson et al., (2014). The change in average number of
“risky drinking days” due to the A-CHESS app intervention was statistically significant according
pooled data for overall analysis of participants who completed the intervention after 12 months
(n=314 -1.47 MD at 95%CI [-1.56, -1.38]).
Analysis for reporting alcohol intake within past month among participants in the trial showed no
difference between the intervention (A-CHESS) and control groups after a 4 month follow up
(n=311, RR 1.12 at 95%CI [0.97, 1.29]). However, there was statistical difference in risk ratios
after the 8th month (n=296, RR 1.16 at 95%CI [1.01, 1.33]) and 12th month (n=281 RR 1.20 at
95%CI [1.03, 1.67]) follow-ups. At any point in time between follow up periods the overall risk
ratio of reports was found to be significant (n=315 RR 1.31 at 95%CI [1.03, 1.67]). These findings
corresponded with results from the trial by Gustafson et al., (2014) suggesting that participants in
the control group were likely to report episodes of drinking within the past 30 days.
Pooled data from the trial by Mira et al., (2014) suggests statistically significant difference in
MMAS-4 score between the intervention (ALICE) and control groups (n=99, std. MD 0.74 at
95%CI [0.33, 1.15]) after 3 months follow up. This was also the case among participants in the
trial by Perera et al., (2014) where there was significant change in MARS-9 score comparison
between the intervention and active control group (n=28, std. MD 0.73 at 95%CI [-0.05, 1.52]).
However there was no significant difference between the intervention and active control groups
with regards to the percentage of medications taken (n=28, std. MD 0.38 at 95%CI [-0.38, 1.15]).

29
These meta-analytical findings agreed with findings from both trials suggesting better self-report
scores among the intervention groups (Mira et al., 2014; Perera et al., 2014).
Meta-analysis of the number of inhaled corticosteroids (ICS) and systemic anti-asthma
corresponded with findings from the trial by Liu et al., (2010). Difference in average number of
doses of ICS (n=89, std. MD 2.19 at 95%CI [1.66,2.72]) and systemic anti-asthma medications
(89, std. MD 2.42 at 95%CI [1.87,2.97]) taken between the mobile phone (intervention) group and
control group suggested statistically significant increase in adherence in favour of the intervention
group.
Secondary outcome
Biological outcomes
Liu et al., (2010) compared pulmonary function of asthma patients between the mobile telephone
and control groups. There was statistically significant increase in peak exploratory flow rate
(PEFR) at the 4th month (n=43, 378.2 L/min SD 9.3, p=0.02), 5th month (n=43, 378.2L/min SD
9.2, p=0.008) and 6th month (n=43, 382.7L/min SD, p=0.001) of monthly follow ups among
participants in the mobile phone group compared to counterparts in the control group. Compared
to the baseline and control group, the predicted force exploratory volume in 1 sec (FEV1) among
asthma patients in the mobile phone improved significantly at 6 months (n=43, 65.2% SD 3.2, p
<0.05).
Perera et al., (2014) compared the viral load among HIV positive participants using an updated
interactive mobile phone software application (intervention group) with those using an old version
of the software (active control). Participants in the intervention group recorded significantly lower

30
copies of the HIV virus per ml of plasma (n=17, 1.30 log copies/ml SD 0.01 p=0.023) compared
to those in the active control group (n=11 1.70 log copies/ml SD 0.64 p=0.023).
Mira et al., (2014) compared glycated haemoglobin, cholesterol level and blood pressure among
multi-morbid patients recruited into the intervention group (those who used ALICE app for
medication therapy adherence) and the control group (which comprised of those using standard
means of enhancing medication therapy adherence). There were no statistically significant changes
in blood pressure (p=0.28), glycated haemoglobin (p=0.36) between participants in either of the
groups. Cholesterol levels however increased among participants in the intervention group (pre-
post difference +5.7mmol/mol [p=0.04]) after a three month follow up.
Quality of life
Gustafson et al., (2014) found no statistically significant difference between the A-CHESS
intervention group and control group after assessment of participant quality of life using the Short
Inventory of Problems psychometric instrument. Liu et al., (2010) evaluated the effect mobile
phone application on quality of life using Short-form 12 (SF-12) physical and mental scales. The
SF-12 physical score among asthma patients in the mobile phone-intervention group significantly
improved from the baseline at (41.6 SD1.5) to (n=43, 45.6 SD 1.3 p=0.045). The most significant
gain in SF-12 physical component score of quality of life occurred 3 months into the trial when
SF-12 physical scores were compared with the control group (n=43, 47.5 SD 1.2 p<0.05). There
was no significant gain in SF-12 mental component among the intervention group during the
duration of the trial, however SF-12mental score among participants in the control group markedly
decreased from the baseline (48.6 SD 1.2) after 6 months (n=46, 44.4 SD 1.4 p<0.05)

31
Illness perception
Mira et al., (2014) and Perera et al., (2014) failed to detect statistically significant differences
between intervention and control groups with regards to participant illness perception in the
respective studies.
Clinical outcomes
Liu et al., (2010) compared emergency visits to the hospital (exacerbations), outpatient visits,
respiratory failure and mortality between asthma patients between the mobile phone (intervention)
group and control group after 6 months follow up. Asthma sufferers in the control group had
markedly greater episodes of exacerbation (0.267 visits per patient p<0.05) and outpatient visits
(0.022 visits per patient p<0.05) compared to their counterparts in the mobile phone group (0.04
visits per patient p<0.05) and (0 visits per patient p<0.05). There were no events of respiratory
failure and mortality between the two groups (Liu et al., 2010).

32
CHAPTER FIVE
DISCUSSION
Primary outcome
(Adherence to therapy)
Meta-analysis for the primary outcome of therapy adherence focused on self-report scores,
percentage and number of doses taken, “risky drinking days” and risks of substance abuse relapses.
All data for this outcome were both continuous and dichotomous.
Evidence from sub-group meta-analysis of the continuous data (self-report scores, percentage and
number of doses taken) suggested increased self-report scores and quantity of prescribed
medication taken. This was however not the case among participants with HIV who recorded
marginal increase in percentage of prescribed anti-retroviral medicines taken. Overall analysis
suggested that increased level of adherence occurred in the intervention groups. Subgroup analysis
of “risk drinking days” suggested low number of risky drinking days among participants in the
intervention group. Evidence from the meta-analysis of the binary outcome for the risk of reporting
relapse in alcohol abuse suggested that individuals in control group had a relatively higher risk
than their counterparts who received intervention.
There was no evidence of heterogeneity in the meta-analysis of the binary outcome (I2 = 0)
suggesting that results may possibly be due to chance. However meta-analysis for the outcome
measures of adherence (I2 = 90%) and “risky drinking days” (I2 = 94%) showed substantial
heterogeneity (I2≥50) (Deeks, Higgins and Altman, 2015). This is as a result of clinical diversity
among participants as evident in the aim of the review which focuses on the effect of mobile phone
apps on adherence among patients with varying chronic diseases and dependencies. The

33
assumption made was that the different sub-group of outcome measures used among the
participants with the varying disease conditions served the same purpose of assessing adherence
to therapy.
Secondary outcomes
Biological outcomes
Overall descriptive analysis of the effect of mobile phone adherence apps on biological outcomes
of participants produced in varying results. Participants with asthma and HIV registered improved
bio-marker levels (low HIV viral load and improved pulmonary functions) (Liu et al., 2010; Perera
et al., 2014). There was however no significant decrease in bio-marker levels among individuals
who suffered from diabetes, hypertension and with participants who suffered from
hypercholesterolemia registering slight increase in lipid levels (Mira et al., 2014). This
contradicted findings from a review by Pal et al., (2013) that suggested better glycaemic control
among diabetics who used software application for therapy compliance.
A possible reason may be due to the difference trial periods where the exposure of participants to
the intervention for longer durations resulted in significant improvement in bio-marker levels and
symptom scores (6 months - Liu et al., (2010)) than those who were exposed to the intervention
for shorter periods (3 months - Mira et al., 2014; Perera et al., 2014). Evidence for the impact of
mobile phone adherence apps on biological markers and symptom score was deemed inconclusive
due to conflicting results and confounding variable of duration of participant exposure to
intervention. More studies are required to comprehensively ascertain the effect of such apps.

34
Quality of life
Two trials that investigated quality of life among participants using different outcome measures
(Gustafson et al., 2014; Liu et al., 2010). Descriptive analysis of the one trial found no statistically
significant difference between participants who used the A-CHESS app for adherence and those
who received standard rehabilitation for alcohol dependencies. This was however not the case in
the trial involving asthma patients where there was significant difference in mean quality of life in
physical scores between asthma patients using mobile phone application for therapy adherence and
self-management, and those in the control group. Analysis suggested evidence of a relative ly
higher quality of life physical score among those in the intervention group compared to those in
the control group. A correlation could be made between improved pulmonary function discussed
in the previous paragraph and an increase in the quality of life physical score.
The trial failed to detect any statistically significant difference in metal scores for quality of life
(Liu et al., 2010). This agreed with findings from a Cochrane review by Kauppi et al., (2014) that
suggested only minor improvement in mental state-quality of life among patients with serious
mental illness who relied on electronic media assistance such as mobile phone for medication
therapy adherence. Evidence with regards to the effect of mobile phone apps on the quality of life
based on findings from the descriptive analysis of both trials was judged to be weak.
Illness perception
Descriptive analysis of two trial failed to find evidence in support of the capacity of mobile phone
adherence apps to improve patient illness perception.

35
Clinical outcome
Review of one trial suggested significantly lower adverse events, and unscheduled visits to the
hospital among participants who used mobile phone adherence apps for self-management
compared to participants in the control group (Liu et al., 2010). Descriptive analysis suggested
increase in pulmonary function correlated with an increased clinical outcome.

36
CHAPTER SIX
CONCLUSION AND RECOMMENDATION
Clinical implication
Mobile phone applications positively impact medication compliance and adherence to therapy
among individuals with chronic diseases. However current evidence to suggest that these apps
have net positive impact on overall health and wellbeing of chronic disease sufferers is weak.
Research implication
The review sought to answer research questions on the importance of mobile phone apps for
adherence in the introduction:
I. Can mobile phone applications positively impact adherence behaviour among individuals
with chronic diseases?
Evidence from this review suggests that patients who used mobile phone adherence apps
were more compliant to therapy than their counterparts who used traditional means such
as pillboxes, written instructions and text message prompts.
II. Do the possible pros of using such apps outweigh the cons?
None of the trials reviewed in this study explicitly investigated the pros against the cons
of using mobile phone apps for adherence. The trials reviewed investigated the impact of
the intervention on adherence and did not make much emphasis on advantages and
disadvantages of such intervention. However there were a con identified in one of the trials

37
where the lipid profile among patients with hypercholesterolemia increased marginally
from the baseline +5.7mmol/mol [p=0.04] (Mira et al., 2014). More research should be
conducted to ascertain the pros and cons of using such apps.
III. Are such apps easily accessible, user friendly and affordable?
None of the trials reviewed focused on accessibility, user friendliness and affordability of
the apps. Most of the participants who used apps for therapy adherence were sponsored
by the trialists. The apps used also required GPS and internet connectivity for download
and transfer of data between participants and their healthcare providers. There exist the
lingering question about how useable and cost effective these apps are in resource
constrained parts of the world where electricity and internet access is limited since all the
included trials were conducted in developed countries. Research into the accessibility and
cost of such apps in developing countries is warranted.
IV. Do mobile phone apps have any impact on patient disease perception?
Evidence from this review suggested no correlation between the use of mobile phone
adherence apps and increase in patient disease perception. Due to the small sample size of
participants in this review more studies need to be conducted to ascertain the impact on
patient illness perception.

38
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Gustafson, D., McTavish, F., Chih, M., Atwood, A., Johnson, R., Boyle, M., Levy, M., Driscoll,
H., Chisholm, S., Dillenburg, L., Isham, A. and Shah, D. (2014). A Smartphone Application to
Support Recovery From Alcoholism. JAMA Psychiatry, 71(5), p.566.
Liu, W., Huang, C., Wang, C., Lee, K., Lin, S. and Kuo, H. (2010). A mobile telephone-based
interactive self-care system improves asthma control. European Respiratory Journal, 37(2),
pp.310-317.
Mira, J., Navarro, I., Botella, F., Borrás, F., Nuño-Solinís, R., Orozco, D., Iglesias-Alonso, F.,
Pérez-Pérez, P., Lorenzo, S. and Toro, N. (2014). A Spanish Pillbox App for Elderly Patients
Taking Multiple Medications: Randomized Controlled Trial. J Med Internet Res, 16(4), p.e99.
Perera, A., Thomas, M., Moore, J., Faasse, K. and Petrie, K. (2014). Effect of a Smartphone
Application Incorporating Personalized Health-Related Imagery on Adherence to Antiretroviral
Therapy: A Randomized Clinical Trial. AIDS Patient Care and STDs, 28(11), pp.579-586.
REFERENCE OF EXCLUDED STUDIES
Mobile Phone Prompts Stimulate Primary School Children to Reuse an Internet-Delivered
Smoking Prevention Intervention?. J Med Internet Res, 16(3), p.e86.
Fukuoka, Y., Gay, C., Joiner, K. and Vittinghoff, E. (2015). A Novel Diabetes Prevention
Intervention Using a Mobile App. American Journal of Preventive Medicine, 49(2), pp.223-237.
Hertzberg, J., Carpenter, V., Kirby, A., Calhoun, P., Moore, S., Dennis, M., Dennis, P., Dedert, E.
and Beckham, J. (2013). Mobile Contingency Management as an Adjunctive Smoking Cessation
Treatment for Smokers With Posttraumatic Stress Disorder. Nicotine & Tobacco Research, 15(11),
pp.1934-1938.
Istepanian, R., Zitouni, K., Harry, D., Moutosammy, N., Sungoor, A., Tang, B. and Earle, K.
(2009). Evaluation of a mobile phone telemonitoring system for glycaemic control in patients with
diabetes. Journal of Telemedicine and Telecare, 15(3), pp.125-128.

39
Kolt, G., Mummery, K., Duncan, M., Vandelanotte, C., Maeder, A., Caperchione, C., Karunanithi,
M., Noakes, M., Ellison, M., George, E., Tague, R., Viljoen, P. and Corry, K. (2010). The ManUp
Study: Using information technology to promote physical activity and nutrition to middle-aged
rural men. Journal of Science and Medicine in Sport, 13, p.e27.
Lund, S., Nielsen, B., Hemed, M., Boas, I., Said, A., Said, K., Makungu, M. and Rasch, V. (2014).
Mobile phones improve antenatal care attendance in Zanzibar: a cluster randomized controlled
trial. BMC Pregnancy Childbirth, 14(1), p.29.
Nobis, S., Lehr, D., Ebert, D., Berking, M., Heber, E., Baumeister, H., Becker, A., Snoek, F. and
Riper, H. (2013). Efficacy and cost-effectiveness of a web-based intervention with mobile phone
support to treat depressive symptoms in adults with diabetes mellitus type 1 and type 2: design of
a randomised controlled trial. BMC Psychiatry, 13(1), p.306.
Novak, L., Walker, S., Fonda, S., Schmidt, V. and Vigersky, R. (2013). Behavioral Medicine,
Clinical Nutrition, Education, and Exercise. Diabetes, 62(Supplement_1), pp.A172-A217.
Pijnenborg, G., Withaar, F., Brouwer, W., Timmerman, M., Bosch, R. and Evans, J. (2010). The
efficacy of SMS text messages to compensate for the effects of cognitive impairments in
schizophrenia. British Journal of Clinical Psychology, 49(2), pp.259-274.
Song, M., Choe, M., Kim, K., Yi, M., Lee, I., Kim, J., Lee, M., Cho, Y. and Shim, Y. (2009). An
evaluation of Web-based education as an alternative to group lectures for diabetes self-
management. Nursing & Health Sciences, 11(3), pp.277-284.
Surka, S., Edirippulige, S., Steyn, K., Gaziano, T., Puoane, T. and Levitt, N. (2014). Evaluating
the use of mobile phone technology to enhance cardiovascular disease screening by community
health workers. International Journal of Medical Informatics, 83(9), pp.648-654.
Velasco, H., Cabral, C., Pinheiro, P., Azambuja, R., Vitola, L., Costa, M. and Amantéa, S. (2015).
Use of digital media for the education of health professionals in the treatment of childhood asthma.
Jornal de Pediatria, 91(2), pp.183-188.
Weaver, A., Young, A., Rowntree, J., Townsend, N., Pearson, S., Smith, J., Gibson, O., Cobern,
W., Larsen, M. and Tarassenko, L. (2007). Application of mobile phone technology for managing
chemotherapy-associated side-effects. Annals of Oncology, 18(11), pp.1887-1892.

40
Zanner, R., Wilhelm, D., Feussner, H. and Schneider, G. (2007). Evaluation of M-AID®, a first
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44
CHARACTERISTICS OF INCLUDED TRIALS
TRIALS METHODS PARTICIPANTS INTERVENTI
ONS
OUTCOMES SIGN
GRADE
Gustafson et
al., (2014)
Allocation:
Computer generated
random allocation.
Blinding: Unmasked.
Duration: 12 months.
Setting: Outpatient.
Diagnosis:
Chronic alcoholism
Number of participants: 349
Average age: 38 years
Sex: mostly males
History: participants should be
clients of 3 residential programs
Inclusion: DMS-IV alcohol
dependence, at least 18 years of age,
capable of providing two back up
contacts for reference.
Test
intervention:
A-CHESS
(addiction-
comprehensive
Enhancement
Support System)
mobile phone
application +
regular alcohol
use dependence
rehabilitation
(179
participants)
Control
intervention:
Regular alcohol
(Primary)
Adherence:
Group difference on
risky alcohol drinking
days.
Prevalence and odds of
reports of alcoholic
beverages consumed
within past month.
Quality of life: Short
Inventory of Problems-
Revised instrument
++

45
Exclusion: Psychiatric disorders,
history of medical conditions that
might inhibit trial participation,
suicidal tendencies, cognitive
impairments.
use dependence
rehabilitation
(170
participants)
Liu et al.,
(2010)
Allocation:
Prospective
randomized
controlled trial.
Blinding: Not stated
Duration: 6 months
Setting: Outpatient
Diagnosis: Asthma
Average age: 52 years.
Sex: both males and females.
History: participants with moderate
to severe asthma
Inclusion: participants who met the
American Thoracic Society criteria
for moderate to severe persistent
asthma.
Exclusion: None stated.
Test
intervention:
Mobile phone
interactive self-
care application
(60 participants)
Control
intervention:
hardcopy
asthma booklet
for recording
action plan and
asthma diary.
(Primary)
Adherence:
Medication usage
(Secondary)
Quality of life: Short-
form 12 physical and
mental scale
Biological outcome:
Pulmonary function
score
Clinical outcome:
unscheduled
emergency visits and
hospitalizations
++

46
Mira et al.,
(2014)
Allocation:
Randomized
controlled trial
Blinding: Single
blind
Duration: 3 months
Setting: Outpatient
Diagnosis: Diabetes, anxiety,
hypercholesterolemia, benign
prostate hyperplasia, hypertension,
arthrosis, chronic obstructive
pulmonary disorder, digestive
disorder
Sex: Both
History: multi-morbidity
Inclusion: participants with multiple
disease conditions, above the age of
65 years, Barthel score <60, living
on their own and were capable of
administering their medication
Exclusion: Non stated
Test
intervention:
ALICE mobile
phone
application for
medication self-
management
Control
intervention:
Verbal + written
instructions on
safe medication
use
(Primary)
Adherence: MMAS-4
adherence scale
(Secondary)
Illness perception: No
specific assessment
tool stated
++

47
Perera et al.,
(2014)
Allocation:
Randomized control
trial
Blinding: Single
blind
Duration: 3 months
Setting: Outpatient
HIV clinic
Diagnosis: HIV
Number of participants:28
History: HIV positive
Inclusion: Participants on anti-
retroviral therapy for a period of at
least 6 months. Participants with
android phones with the
Honeycomb or later operating
system.
Exclusion: Non stated
Test
intervention:
Updated version
of mobile phone
application with
interactive
graphic
representations
of patient CD4
count and viral
load based on
recent blood test
results (17
participants)
Control
intervention:
Standard version
of the app with
no interactive
features (11
participants)
(Primary)
Adherence: MARS
adherence scale,
prescribed doses taken,
pharmacy dispensings
Secondary
Illness perception:
BIPQ-9 assessment
scale
Biological marker:
HIV viral load
++

48
TABLE FOR RISK OF BIAS
TRIAL RISK OF BIAS REASON
Gustafson et al.,
(2014)
Randomization: Low risk Participants were randomized into both intervention and
control groups using a 1:1 ratio computerized random
allocation sequence with blocks of 8.
Allocation concealment: Low risk Allocation concealment was implemented with use of
sequentially numbered containers.
Blinding of treatment allocation: High risk Treatment was unmask. Participants consented and were
aware of the intervention being investigated. This is likely
to introduce biases.
Similarity of baseline: Low risk This was addressed in the participant inclusion criteria.
Participants shared similar baseline demographic
characteristics.
Standard of relevant outcomes measured: Low
risk
The outcome measures were stated in the trial. The trial
defined the primary outcome measures of risky drinking
days and abstinence. Outcome measures were assessed
using validated instruments. Risky drinking days were
recorded as difference in means between intervention and

49
control groups whereas abstinence was recorded as odds
ratio.
Attrition bias: High risk The over-all attrition rate was 22%. Ideally a dropout rate
of 20% is considered acceptable in most random controlled
trials. A 22% drop out rate exceed the acceptable rate of
attrition.
Intention to treat principle: Low risk Intention to treat principle was adhered to during selection
and inclusion of participants’ data.
Liu et al., (2010) Randomization: Low risk Participants with asthma were randomized into control and
intervention groups.
Allocation concealment: Unclear risk Not stated in trial.
Blinding of treatment allocation: High risk Treatment allocation was not blinded. Participants were
aware of the intervention employed(use of mobile phone
interactive software for self-care)
Similarity of baseline: Low risk Participants were required to be suffering from moderated
to severe asthma before recruitment into the trial. Baseline
characteristics of participants in the intervention and
control groups were similar with p values >0.05.
risk
Level of self-management and adherence to therapy was
assessed using the pulmonary function score. Participant
quality of life was assessed using the short-form 12
physical and metal scales. Clinical outcomes of participants

50
was also assessed. All outcomes were recorded as means
and standard error of means.
Attrition bias: Low risk Over all attrition rate was low at 7.5%.
Intention to treat principle: High risk Not stated in study.
Mira et al., (2014) Randomization: Low risk Single blind randomization. Participants were randomly
assigned to test and control groups.
Allocation concealment: Low risk Participants in the intervention group received allocation
concealment. They were assigned codes based on initials
and birthdays to maintain concealment and enable linking
of pre and post measurement of outcomes.
Blinding of treatment allocation: High risk Treatment was unmasked for participants in the test group.
Similarity of baseline: Low risk Participants were required to be multi-morbid, capable of
self-medication administration, Barthel score of more than
60 and living on their own. Baseline characteristics
between test and control groups were similar with the
lowest p value=0.04 for participants with digestive orders.
risk
Primary outcome was measured using MMAS-4 scale.
Illness perception was assessed. Outcomes were reported
difference in averages with standard deviation at 95% CI.
Attrition bias: Low risk Attrition rate was low at 3%
Intention to treat analysis: Unclear risk Not mentioned in trial.

51
Perera et al., (2014) Randomization: Low risk Participants were randomized into active control and
intervention groups.
Allocation concealment: Unclear risk Not stated
Blinding of treatment allocation: High risk Unmasked
Similarity of baseline: Low risk All participants were HIV positive who had been on anti-
retroviral therapy 6 months prior.
risk
Self-report scores using MARS scale, and prescribed doses
taken. Biological marker of HIV viral load was also
reported. All outcomes were reported as means with
standard errors at 95% CI.
Attrition bias: High risk Low attrition rate of 3.5%. However a low sample size of
28 people increases risk of exaggerated measured
outcomes.
Intention to treat principle: Unclear risk Not stated in trial

52
CHARACETERISTICS OF EXCLUDED STUDIES
Study Reason for exclusion
Cremers et al., (2014) Primary outcome measure did not much criteria of aim of the review. Outcome measures
were focused on the extent of adherence to internet-delivered intervention than extent of
adherence therapy which was smoking cessation.
Fukuoka et al., (2015) The intervention was aimed at mobile phone-assisted health professional delivery of diabetes
prevention among at risk obese adults.
Hertzberg et al., (2013) Primary outcome measure did not much criteria of aim of the review. The study assessed the
extent to which mobile applications could be used as reinforced treatment for smoking
cessation where participants earned monetary rewards for compliance.
Istepanian et al., (2009) Non interactive intervention. Involved relay of patient data to healthcare provider. This did
not meet inclusion criteria for interventions.
Kolt et al., (2010) Non interactive intervention involving transfer of participant data to healthcare providers for
making informed care decisions.
Lund et al., (2014) Non interactive intervention involving text messaging and voucher components.

53
Nobis et al., (2013) Healthcare provider-centred intervention. Study focused on healthcare provider depression
support therapy among adults with diabetes.
Novak et al., (2013) Non-interactive intervention. Video phone prompts for glycaemic control in adults with type
2 diabetes.
Pijnenborg et al., (2010) Non-interactive intervention. Intervention utilized relay of text messages as compensation
for cognitive impaired patients.
Song et al., (2009) No randomization of participants into intervention and control groups.
Surka et al., (2014) Health worker centred intervention. Study assessed use of mobile phone technology for the
enhancement of cardiovascular disease screening by health workers.
Velasco et al., (2015) Health worker centred intervention. Intervention evaluated the use of mobile phone
technology as educational tool for health workers treating children with asthma.
Weaver et al., (2007) Health worker centred intervention. Intervention evaluated the use of mobile phones for
managing chemotherapy side effects.
Zanner et al., (2007) Health provider centred intervention. The intervention evaluated the use of the mobile phone
application, M-AID, to enhance first aid care by first responders.

54
DATA AND ANALYSIS
Comparison 1.Computer/Mobile phone software applications for medication adherence vs standard/traditional methods:
Outcome or Subgroup No of
Studies
No. of
Participants
Statistical method Effect Estimate
1.1Primary outcomes 3 333 Std. Mean Difference (IV, Random, 95% CI) 1.31 [0.49, 2.14]
1.1.1 MMAS score 1 99 Std. Mean Difference (IV, Random, 95% CI) 0.74 [0.33, 1.15]
1.1.2 MARS score 1 28 Std. Mean Difference (IV, Random, 95% CI) 0.73 [-0.05, 1.52]
1.1.3 Prescribed doses
taken (%)
1 28 Std. Mean Difference (IV, Random, 95% CI) 0.38 [-0.38, 1.15]

55
1.1.4 Prescribed doses of
ICS taken after 6
months follow up
1 89 Std. Mean Difference (IV, Random, 95% CI) 2.19 [1.66, 2.72]
1.1.5 Prescribed doses of
systemic steroid taken
after 6 months follow up
1 89 Std. Mean Difference (IV, Random, 95% CI) 2.42 [1.87, 2.97]
1.2 Relative risks of
report of intake of
alcohol with past month
1 Risk Ratio (IV, Fixed, 95% CI) 1.17 [1.08, 1.27]
1.2.1 4 months 1 311 Risk Ratio (IV, Fixed, 95% CI) 1.12 [0.97, 1.29]
1.2.4 all 3 points (4,8,12
months)
1 315 Risk Ratio (IV, Fixed, 95% CI) 1.31 [1.03, 1.67]
1.3 risky drinking days 1 Mean Difference (IV, Fixed, 95% CI) subtotals

56
1.3.1 at 4 months 1 314 Mean Difference (IV, Fixed, 95% CI) -1.51 [-1.62, -1.40]

57
Analysis 1.1: Computer/Mobile phone software applications for medication adherence vs
standard/traditional methods: Adherence to therapy

58
standard/traditional methods: Adherence to therapy (Risk ratio for reports of drinking
within past month)

59
standard/traditional methods: Adherence to therapy (Risky drinking days (overall)

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