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

Project Supervisor: 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

Analysis 1.3 Computer/Mobile phone software applications for medication adherence vs

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

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).

1

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

2

metabolic rates limit the effectiveness of direct adherence assessment. These methods are also

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).

3

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

4

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.

5

Figure 2 Information-Motivation- Behaviour skills (IMB) model (Fisher and Fisher, 1992).

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 by Cheng 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.

6

The study also discovered that such interventions are not interactive and most often do not

improve patient education.

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

7

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 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.

8

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.

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?

9

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.

10

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.

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

11

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.

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.

12

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.

13

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

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

14

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).

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.

15

+ + (Acceptable) – indicates that some of the requirements with regards to internal

validity and biases were met.

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.

16

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

17

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 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).

18

19

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

Figure 3. Flow diagram for the selection and inclusion of trial

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.

20

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 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

21

participant medication therapy reminders; and third party monitoring of medication adherence

which involves care workers and health professions.

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

22

assessment of participants. In addition to the 9-item medication adherence scale (MARS-9)

Perera 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).

23

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.

24

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.

25

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

26

Perera et al., (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.

27

Primary outcome

Adherence to therapy

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]).

28

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

29

significantly lower 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-12 mental score among participants in the

30

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)

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).

31

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

32

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

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

33

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.

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 relatively 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

34

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.

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.

35

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?

36

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 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?

37

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.

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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

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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

38

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

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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.

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

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Lund, S., Nielsen, B., Hemed, M., Boas, I., Said, A., Said, K., Makungu, M. and Rasch, V.

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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

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Novak, L., Walker, S., Fonda, S., Schmidt, V. and Vigersky, R. (2013). Behavioral Medicine,

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Pijnenborg, G., Withaar, F., Brouwer, W., Timmerman, M., Bosch, R. and Evans, J. (2010). The

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39

Song, M., Choe, M., Kim, K., Yi, M., Lee, I., Kim, J., Lee, M., Cho, Y. and Shim, Y. (2009). An

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Velasco, H., Cabral, C., Pinheiro, P., Azambuja, R., Vitola, L., Costa, M. and Amantéa, S.

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Weaver, A., Young, A., Rowntree, J., Townsend, N., Pearson, S., Smith, J., Gibson, O., Cobern,

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43

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.

Exclusion: Psychiatric disorders,

history of medical conditions that

Test

intervention:

A-CHESS

(addiction-

comprehensive

Enhancement

Support System)

mobile phone

application +

regular alcohol

use dependence

rehabilitation

(179

participants)

Control

intervention:

Regular alcohol

use dependence

(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

++

44

might inhibit trial participation,

suicidal tendencies, cognitive

impairments.

rehabilitation

(170

participants)

Liu et al.,

(2010)

Allocation:

Prospective

randomized

controlled trial.

Blinding: Not stated

Duration: 6 months

Setting: Outpatient

Diagnosis: Asthma

Number of participants: 120

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

++

Mira et al.,

(2014)

Allocation:

Randomized

controlled trial

Diagnosis: Diabetes, anxiety,

hypercholesterolemia, benign

prostate hyperplasia, hypertension,

Test

intervention:

ALICE mobile

(Primary)

Adherence: MMAS-4

adherence scale

++

45

Blinding: Single

blind

Duration: 3 months

Setting: Outpatient

arthrosis, chronic obstructive

pulmonary disorder, digestive

disorder

Number of participants: 102

Average age: 73 years

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

phone

application for

medication self-

management

Control

intervention:

Verbal + written

instructions on

safe medication

use

(Secondary)

Illness perception: No

specific assessment

tool stated

46

Perera et al.,

(2014)

Allocation:

Randomized control

trial

Blinding: Single

blind

Duration: 3 months

Setting: Outpatient

HIV clinic

Diagnosis: HIV

Number of participants:28

Average age: 46 years

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

++

47

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

control groups whereas abstinence was recorded as odds

ratio.

48

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.

Standard of relevant outcomes measured: Low

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 was also assessed. All outcomes were

recorded as means and standard error of means.

49

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.

Standard of relevant outcomes measured: Low

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.

Perera et al., (2014) Randomization: Low risk Participants were randomized into active control and

intervention groups.

50

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.

Standard of relevant outcomes measured: Low

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

CHARACETERISTICS OF EXCLUDED STUDIES

51

Study Reason for exclusion

52

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.

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

53

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.

DATA AND ANALYSIS

Comparison 1.Computer/Mobile phone software applications for medication adherence vs standard/traditional methods:

Adherence to therapy

54

Outcome or Subgroup No of

Studies

No. of

Participant

s

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]

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

1 89 Std. Mean Difference (IV, Random, 95% CI) 2.42 [1.87, 2.97]

55

after 6 months follow up

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]

1.2.2 8 months 1 296 Risk Ratio (IV, Fixed, 95% CI) 1.16 [1.01, 1.33]

1.2.3 12 months 1 281 Risk Ratio (IV, Fixed, 95% CI) 1.20 [1.04, 1.39]

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

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

1.3.2 at 8 months 1 314 Mean Difference (IV, Fixed, 95% CI) -1.11 [-1.22, -1.00]

1.3.3 at 12 months 1 314 Mean Difference (IV, Fixed, 95% CI) -1.47 [-1.56, -1.38]

56

57

Analysis 1.1: Computer/Mobile phone software applications for medication adherence vs

standard/traditional methods: Adherence to therapy

58

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)

59

Analysis 1.3 Computer/Mobile phone software applications for medication adherence vs

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

60