Mobile phone text messages for improving adherence to antiretroviral therapy (ART): a protocol for an individual patient data meta-analysis of randomised trials
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* Lawrence Mbuagbaw
* Mia L van der Kop
* Richard T Lester
* Harsha Thirumurthy
* Cristian Pop-Eleches
* Marek Smieja
* Lisa Dolovich
* Edward J Mills
* Lehana Thabane

## Abstract

**Introduction** Mobile phone text messaging is emerging as an important tool in the care of people living with HIV; however, reports diverge on its efficacy in improving adherence to antiretroviral therapy (ART), and little is known about which patient groups may benefit most from phone-based adherence interventions. We will conduct an individual patient data meta-analysis to investigate the overall and subgroup effects of text messaging in three recently published text-messaging randomised controlled trials.

**Methods and analysis** Data from two Kenyan and one Cameroonian trial will be verified, reformatted and merged. We will determine pooled effect sizes for text messaging versus standard care for improving adherence to ART using individual patient random-effects meta-analysis. We will test for the interaction effects of age, gender, level of education and duration on ART. Sensitivity analyses will be conducted with regard to thresholds for adherence, methods of handling missing data and fixed-effects meta-analysis. Only anonymised data will be collected from the individual studies.

**Ethics and dissemination** Ethical approval was obtained for the individual studies. The results of this paper will be disseminated as peer-reviewed publications, at conferences and as part of a doctoral thesis. This individual patient data meta-analysis may provide important insights into the effects of text messaging on ART adherence in different subpopulations, with important implications for programme implementation involving such interventions and future research.

*   Statistics & Research Methods

### Article summary

#### Article focus

*   To outline the analytic plan for a collaborative individual patient data meta-analysis of randomised trials of text messaging to improve adherence to antiretroviral therapy.

#### Key message

*   Individual patient data meta-analysis can provide insight into the patient groups most likely to benefit from text messaging and the most effective kinds of messages.

#### Strengths and limitations of this study

*   Robust analytical methods that take into account within-study and between-study differences.

*   No systematic search for other studies will be carried out.

## Introduction

More than 30 million people are living with HIV worldwide.1 Over the past few years, considerable progress has been made in the treatment of HIV infection. Key among the developments to control HIV is the advent of antiretroviral therapy (ART). ART has been shown to slow down disease progression, prevent transmission and boost immunity; however, its success depends on patients achieving high levels of medication adherence.2 Even though some evidence suggests that patients with lower levels of adherence can still achieve viral suppression,3 ,4 optimal adherence is desirable since it is correlated with improved clinical, immunological and virological outcomes.2 ,5 ,6 On the other hand, poor adherence can lead to progression to AIDS, the development of resistant strains, increased transmissibility, more hospitalisations and longer stays.2 ,7 ART adherence can be influenced by a number of factors broadly categorised into patient-related factors, medication characteristics, health system characteristics and disease characteristics.8–13 More specifically, females, people aged above 50 years and the more educated are more likely to be adherent to ART.9 ,14 Adherence to ART may wane over time.15 ,16

There is an emerging evidence that mobile phones can play an important role in healthcare delivery, especially in resource-limited settings.17 The appeal of using phones to promote adherence has grown as phone ownership rates continue to rise in sub-Saharan Africa and elsewhere.18 Short message service (SMS) is a particularly useful application that can be used to collect or share information and to enhance communication between health personnel and patients in a low-cost manner.19 With regard to patient management, mobile phone text messages have been demonstrated to induce positive behaviour changes in domains such as smoking cessation, physical activity and self management of high blood pressure, diabetes and asthma.20 Other studies report high levels of satisfaction among participants.21 ,22 In light of these, one can expect SMS texting to serve as reminders to take medication, provide greater connectedness with a provider or provide encouragement to remain adherent to ART.

A recent Cochrane systematic review synthesising data from two trials conducted in Kenya showed that text messaging is efficacious in improving adherence.22–24 A third trial conducted in Cameroon reported no improvements in adherence.25 Yet, to the best of our knowledge, there are no individual patient data meta-analyses for this type of intervention. We will use individual patient data to aggregate the findings from these three randomised controlled trials.

The WHO recognises mobile health (mHealth) as a potential resource for improving outcomes in low resources settings.26 This paper will provide the best available evidence for the use of text messages in improving adherence to ART. It will also provide the most accurate estimation of the extent to which, and the subpopulations in whom, we can expect text messages to improve adherence.

### Objectives

Our primary objective is to determine the overall effect of mobile phone text messages versus usual care for improving adherence to ART using individual level data.

Our secondary objective is to investigate the effects of mobile phone text messages in subgroups known to have different adherence behaviours: gender, age, level of education and duration on ART.

## Methods and analysis

### Overview of the trials and data sources

We have obtained individual patient data from three trials: the CAMPS trial,25 the WelTel trial Kenya 122 and one other in Kenya.23 The CAMPS trial (n=200) investigated the use of weekly motivational text messages versus usual care to improve adherence to ART in Yaounde, Cameroon.25 The WelTel trial was a multisite trial of weekly messages inquiring about participants’ status involving 538 participants in Kenya.22 Pop-Eleches *et al* investigated short daily messages, long daily messages, short weekly messages and long weekly messages compared to standard care in 431 participants in Kenya.23 These studies were included based on the following criteria: they are all randomised controlled trials of a text messaging intervention in people living with HIV to improve their adherence to ART. The characteristics of these studies are reported in detail in table 1. To our knowledge, there are no other published randomaised controlled trials (RCTs) of text-messaging interventions designed to improve ART adherence in resource-limited settings.

View this table:
[Table 1](http://bmjopen.bmj.com/content/3/5/e002954/T1)

Table 1 
Characteristics of included studies

### Data management

To promote collaboration with the authors of these reports, we will adhere to the highest levels of confidentiality and ensure professionalism by providing regular updates on data handling and analysis.

We will collect baseline covariates, endpoint data and coding information from each study. Data will be stored on a controlled-access computer with backup and treated confidentially. Data will be stored in its original form, but copied for conversion into SPSS (IBM Corp. Released 2011. IBM SPSS Statistics for Windows, V.20.0. Armonyk, New York: IBM Corp). We will check for the missing data and compare it with published trial reports. Any discrepancies will be resolved by contacting the original authors. Data will also be checked for appropriate ranges, for example, an age less than 10 years would be implausible, since only adults were included in the parent studies. Finally, all common variables will be allotted new unified names and an identifier variable will be added to participants from the same trial.

### Data merging procedures

Only variables that are available in all three data sets will be used. The categories reported in the individual studies will be reformatted into new uniform categories for the individual patient data meta-analysis. The final data set will include the following baseline covariates: age (years), gender (male/female), level of education (none, primary, secondary or higher) and duration on ART (months). The intervention variable will be text messaging (short daily, long daily, short weekly or long weekly modalities) versus no text messaging. The primary outcome will be adherence > 95% with respect to measures used in the three studies (by a visual analogue scale at 6 months25; by medication event monitoring system from week 12 through week 4823; and as a percentage of pills taken at 6 and 12 months22). Secondary outcomes include mortality, study withdrawal, lost to follow-up and transfers. Table 2 describes the original format of the data and how they will be reformatted for use in the individual patient data meta-analysis.

View this table:
[Table 2](http://bmjopen.bmj.com/content/3/5/e002954/T2)

Table 2 
Data merging procedures

### Statistical methods

Analyses will include all study participants irrespective of whether they received the allocated intervention (intention-to-treat). Summary statistics will be presented as mean (SD) or median (IQR) for continuous variables and number (percent) for categorical variables. The primary outcome will be adherence to ART. All three studies have sufficient data to dichotomise adherence at 95%, defining adequate adherence (greater than 95%) and poor adherence (95% or less). Effect sizes will be computed for comparisons between the various text messaging modalities versus no text messaging, and for all forms of text messaging versus no text messaging. We will report ORs and corresponding 95% CIs. The level of statistical significance, α, will be set at 0.05. Forest plots will be graphed for all pooled effects.

An individual patient random-effect meta-analysis will be conducted to determine the overall effect of text messaging on adherence, with ‘study’ as a random effect, to model variation across studies and to account for clustering within a study.27 This analysis will be repeated for secondary outcomes. In an adjusted analysis, adherence to ART, our primary outcome and dependent variable will be modelled as a function of the intervention (text message/no text message) and baseline characteristics (age, gender, duration on ART and level of education) as fixed effects.

Model fit will be tested using maximum likelihood estimations. Multicollinearity will be assessed using the variance inflation factor (VIF), with a VIF of 4 or more considered as evidence of substantial multi-collinearity. Data will be analysed using SPSS V.20.0 (SPSS, Inc, 2009, Chicago, Illinois, USA). The data analysis plan is illustrated in figure 1.

![Figure 1](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/3/5/e002954/F1.medium.gif)

[Figure 1](http://bmjopen.bmj.com/content/3/5/e002954/F1)

Figure 1 
Analysis plan for individual patient data.

### Sensitivity analysis

We will also test the sensitivity of our results to a 90% cut-off point for adherence, imputation of missing data using multiple imputation28 and method of analysis (fixed-effect vs random-effect meta-analysis). For the individual patient fixed-effect meta-analysis, generalised estimation equations (GEE) will be used to obtain the average response over the population.29 An exchangeable correlation structure will be assumed for this analysis—individuals within the same study are equally correlated. We will also report estimates of effects based on aggregate date from the individual studies.

### Subgroup analysis

The findings from the included studies raise the possibility that text messaging may be more effective at improving adherence among individuals whose adherence is likely to be lower in the absence of any intervention, such as in males and individuals with higher levels of education.22 ,25 For these subgroups, we will add the interaction terms (subgroup-by-intervention interaction) to the models. p Values for the interaction terms will be reported.

### Assessment of heterogeneity

For the pooled estimates of effect, heterogeneity between studies will be measured using the τ2 statistic (an estimate of the between-study variance in the random-effects model). The χ2 test of homogeneity (level of significance—α=0.10) and the I2 test (the percentage of the overall variation due to heterogeneity) will be used in the fixed-effects models. The intraclass correlation coefficients from the GEE will be reported.

### Risk of bias

Risk of bias will be assessed using the Cochrane Risk of Bias tool for randomised controlled trials.30

## Ethics and dissemination

This individual patient data meta-analysis was exempt from ethics approval by the Hamilton Integrated Research Ethics Board, as it involves only anonymised precollected data. Ethical clearance and informed consent were obtained for each of the individual studies.22 ,23 ,25 Only anonymised data will be collected from the individual studies. We plan to disseminate our results at international meetings and in peer-reviewed journals. Our findings will also be publicly presented and defended as part of a doctoral thesis.

## Discussion

We recognise the considerable resources and international cooperation required to perform a meta-analysis based on individual patient data and will build on already existing relationships31 to produce a high quality meta-analysis.

The two Kenyan trials we included in this individual patient data meta-analysis reported significant improvements in adherence, while the third trial did not. However, the populations and interventions in these three trials, though similar in broad terms (adults living with HIV receiving text messages to improve adherence), are sufficiently different to warrant further investigation. Other differences include the settings, interventions, measures of adherence and duration of the trials.

The CAMPS trial used a variety of motivational text messages developed based on the health belief theory of behaviour change.25 Responses to these messages were optional. The WelTel trial used a simple “How are you?” to which participants were required to respond within 24 h.22 Pop-Eleches *et al* used messages designed to address forgetfulness (short message) and lack of social support (long message).23 Participants in this trial were given Nokia mobile phones. All other studies ran for 12 months except the CAMPS trial which ended at 6 months. These differences may have consequences on the effects of the interventions depending on participant preference for a specific kind of message and the length of time required to measure a significant effect.

Some limitations worth noting include the differences between the studies which limit the generalisability of the findings, notably the different interventions and measures of adherence. However, the availability of high-quality data from three well-conducted studies and our use of multilevel models to account for between-study differences will provide reliable estimates of the effects of text messaging on adherence to ART. Moreover, we did not conduct any systematic search for other relevant trials. Only these three trials were invited to participate in the review.

Individual patient data meta-analysis holds several advantages over aggregate meta-analysis including the possibility of checking, exploring and reanalysing the data in a consistent way. We can also perform more complex analysis with regard to the associations between interventions and patient characteristics. On the other hand, individual patient data meta-analysis cannot deal with bias originating from study design and conduct.32 It may also be subject to publication bias.33

By identifying the subpopulations in which the intervention is likely to be most beneficial, this paper will address important clinical and operational questions regarding the use of text messaging to improve adherence to ART. Even though current guidelines encourage the use of text messaging,34 additional knowledge can be gained from pooling and comparing data from individual studies. Text messaging and other phone-based interventions are unlikely to resolve adherence problems for all patients. This paper will provide valuable guidance on patient groups that may be targeted to receive such interventions, thereby increasing cost-effectiveness and freeing up resources to target other patients with more intensive adherence interventions.

This paper will highlight the key characteristics that are associated with a better response to text messaging for the improvement of adherence to ART. Our findings will inform future research and implementation programmes.

## Footnotes

*   Contributions All authors participated in the planning of the study. LM and LT conceived the idea. LM wrote the first draft of this manuscript. MLK, RTL, HT, CPE, MS, LD and EJM revised several versions of the manuscript. All authors approved the final version.

*   Competing interests None.

*   Provenance and peer review Not commissioned; externally peer reviewed.

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: [http://creativecommons.org/licenses/by-nc/3.0/](http://creativecommons.org/licenses/by-nc/3.0/) and [http://creativecommons.org/licenses/by-nc/3.0/legalcode](http://creativecommons.org/licenses/by-nc/3.0/legalcode)

## References

1.  UNAIDS. Report on the Global AIDS Epidemic. Secondary Report on the Global AIDS Epidemic 2010. [http://www.unaids.org/globalreport/global\_report.htm](http://www.unaids.org/globalreport/global_report.htm)
    
    

2.  Paterson DL, Swindells S, Mohr J, et al. Adherence to protease inhibitor therapy and outcomes in patients with HIV infection. Ann Intern Med 2000;133:21–30.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.7326/0003-4819-133-1-200007040-00004&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=10877736&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000087923100003&link_type=ISI) 

3.  Bangsberg DR. Less than 95% adherence to nonnucleoside reverse-transcriptase inhibitor therapy can lead to viral suppression. Clin Infect Dis 2006;43:939–41.
    
    [Abstract/FREE Full Text](http://bmjopen.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiY2lkIjtzOjU6InJlc2lkIjtzOjg6IjQzLzcvOTM5IjtzOjQ6ImF0b20iO3M6MjU6Ii9ibWpvcGVuLzMvNS9lMDAyOTU0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

4.  Apisarnthanarak A, Mundy LM. Long-term outcomes of HIV-infected patients with <95% rates of adherence to nonnucleoside reverse-transcriptase inhibitors. Clin Infect Dis 2010;51:115–17.
    
    [FREE Full Text](http://bmjopen.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiRlVMTCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiY2lkIjtzOjU6InJlc2lkIjtzOjEwOiI1MS8xLzExNS1hIjtzOjQ6ImF0b20iO3M6MjU6Ii9ibWpvcGVuLzMvNS9lMDAyOTU0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

5.  Mannheimer S, Friedland G, Matts J, et al. The consistency of adherence to antiretroviral therapy predicts biologic outcomes for human immunodeficiency virus-infected persons in clinical trials. Clin Infect Dis 2002;34:1115–21.
    
    [Abstract/FREE Full Text](http://bmjopen.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiY2lkIjtzOjU6InJlc2lkIjtzOjk6IjM0LzgvMTExNSI7czo0OiJhdG9tIjtzOjI1OiIvYm1qb3Blbi8zLzUvZTAwMjk1NC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 

6.  Haubrich RH, Little SJ, Currier JS, et al. The value of patient-reported adherence to antiretroviral therapy in predicting virologic and immunologic response. California Collaborative Treatment Group. AIDS 1999;13:1099–107.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1097/00002030-199906180-00014&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=10397541&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000080927900014&link_type=ISI) 

7.  Knobel H, Codina C, Miro JM, et al. [The recommendations of GESIDA/SEFH/PNS for improving adherence to antiretroviral treatment. AIDS study group of the Spanish society of hospital pharmacy and the national plan on AIDS of the minister of health and consumers]. Enferm Infecc Microbiol Clin 2000;18:27–39.
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=10721560&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 

8.  Sperling RS, Shapiro DE, Coombs RW, et al. Maternal viral load, zidovudine treatment, and the risk of transmission of human immunodeficiency virus type 1 from mother to infant. Pediatric AIDS Clinical Trials Group Protocol 076 Study Group. N Engl J Med 1996;335:1621–9.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1056/NEJM199611283352201&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=8965861&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=A1996VV93600001&link_type=ISI) 

9.  Chesney MA. Factors affecting adherence to antiretroviral therapy. Clin Infect Dis 2000;30(Suppl 2):S171–6.
    
    [Abstract/FREE Full Text](http://bmjopen.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MzoiY2lkIjtzOjU6InJlc2lkIjtzOjIwOiIzMC9TdXBwbGVtZW50XzIvUzE3MSI7czo0OiJhdG9tIjtzOjI1OiIvYm1qb3Blbi8zLzUvZTAwMjk1NC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 

10. Beach MC, Keruly J, Moore RD. Is the quality of the patient-provider relationship associated with better adherence and health outcomes for patients with HIV? J Gen Intern Med 2006;21:661–5.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1111/j.1525-1497.2006.00399.x&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=16808754&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000238147800021&link_type=ISI) 

11. Roberts KJ. Physician-patient relationships, patient satisfaction, and antiretroviral medication Adherence among HIV-infected adults attending a public health clinic. AIDS Patient Care STDS 2002;16:43–50.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1089/108729102753429398&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=11839218&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000173651600007&link_type=ISI) 

12. Singh N, Squier C, Sivek C, et al. Determinants of compliance with antiretroviral therapy in patients with human immunodeficiency virus: prospective assessment with implications for enhancing compliance. AIDS Care 1996;8:261–9.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1080/09540129650125696&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=8827119&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=A1996UN94800001&link_type=ISI) 

13. Gao X, Nau DP, Rosenbluth SA, et al. The relationship of disease severity, health beliefs and medication adherence among HIV patients. AIDS Care 2000;12:387–98.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1080/09540120050123783&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=11091771&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000088937000003&link_type=ISI) 

14. WHO. Adherence to long-term therapies: Evidence for action. Secondary Adherence to long-term therapies: Evidence for action 2003. [http://www.who.int/chp/knowledge/publications/adherence\_report/en/](http://www.who.int/chp/knowledge/publications/adherence_report/en/)
    
    

15. Wasti SP, Simkhada P, Randall J, et al. Factors influencing adherence to antiretroviral treatment in Nepal: a mixed-methods study. PLoS ONE 2012;7:e35547.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1371/journal.pone.0035547&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=22563464&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 

16. Roux P, Kouanfack C, Cohen J, et al. Adherence to antiretroviral treatment in HIV-positive patients in the Cameroon context: promoting the use of medication reminder methods. J Acquir Immune Defic Syndr 2011;57(Suppl 1):S40–3.
    
    

17. Lester R, Karanja S. Mobile phones: exceptional tools for HIV/AIDS, health, and crisis management. Lancet Infect Dis 2008;8:738–9.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/S1473-3099(08)70265-2&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=19022188&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000261097000004&link_type=ISI) 

18. Thirumurthy H, Lester RT. M-health for health behaviour change in resource-limited settings: applications to HIV care and beyond. Bull World Health Organ 2012;90:390–2.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.2471/BLT.11.099317&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=22589574&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000303784900015&link_type=ISI) 

19. Mukund Bahadur KC, Murray PJ. Cell phone short messaging service (SMS) for HIV/AIDS in South Africa: a literature review. Stud Health Technol Inform 2010;160(Pt 1):530–4.
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=20841743&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 

20. Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med 2009;36:165–73.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/j.amepre.2008.09.040&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=19135907&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000262679600012&link_type=ISI) 

21. Dunbar PJ, Madigan D, Grohskopf LA, et al. A two-way messaging system to enhance antiretroviral adherence. J Am Med Inform Assoc 2003;10:11–15.
    
    [Abstract/FREE Full Text](http://bmjopen.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NzoiamFtaW5mbyI7czo1OiJyZXNpZCI7czo3OiIxMC8xLzExIjtzOjQ6ImF0b20iO3M6MjU6Ii9ibWpvcGVuLzMvNS9lMDAyOTU0LmF0b20iO31zOjg6ImZyYWdtZW50IjtzOjA6IiI7fQ==) 

22. Lester RT, Ritvo P, Mills EJ, et al. Effects of a mobile phone short message service on antiretroviral treatment adherence in Kenya (WelTel Kenya1): a randomised trial. Lancet 2010; 376:1838–45.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/S0140-6736(10)61997-6&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=21071074&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000285439700032&link_type=ISI) 

23. Pop-Eleches C, Thirumurthy H, Habyarimana JP, et al. Mobile phone technologies improve adherence to antiretroviral treatment in a resource-limited setting: a randomized controlled trial of text message reminders. AIDS 2011;25:825–34.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1097/QAD.0b013e32834380c1&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=21252632&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000288487400013&link_type=ISI) 

24. Horvath T, Azman H, Kennedy GE, et al. Mobile phone text messaging for promoting adherence to antiretroviral therapy in patients with HIV infection. Cochrane Database Syst Rev 2012;(3):CD009756.
    
    

25. Mbuagbaw L, Thabane L, Ongolo-Zogo P, et al. The Cameroon mobile phone SMS (CAMPS) trial: a randomized trial of text messaging versus usual care for adherence to antiretroviral therapy. PLoS ONE 2012;7:e46909.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1371/journal.pone.0046909&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=23236345&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 

26. WHO. Resource needs for an expanded response to AIDS in low and middle-income countries. 2005.
    
    

27. Ma J, Thabane L, Kaczorowski J, et al. Comparison of Bayesian and classical methods in the analysis of cluster randomized controlled trials with a binary outcome: the community hypertension assessment trial (CHAT). BMC Med Res Methodol 2009;9:37.
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=19531226&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 

28. Little RJA, Rubin DB. Statistical analysis with missing data. New York: John Wiley & Sons, 1987.
    
    

29. Hanley JA, Negassa A, Edwardes MDd, et al. Statistical analysis of correlated data using generalized estimating equations: an orientation. Am J Epidemiol 2003;157:364–75.
    
    [Abstract/FREE Full Text](http://bmjopen.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6NzoiYW1qZXBpZCI7czo1OiJyZXNpZCI7czo5OiIxNTcvNC8zNjQiO3M6NDoiYXRvbSI7czoyNToiL2Jtam9wZW4vMy81L2UwMDI5NTQuYXRvbSI7fXM6ODoiZnJhZ21lbnQiO3M6MDoiIjt9) 

30. Higgins JPT, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011;343. doi:10.1136/bmj.d5928
    
    

31. Karanja S, Mbuagbaw L, Ritvo P, et al. A workshop report on HIV mHealth synergy and strategy meeting to review emerging evidence-based mHealth interventions and develop a framework for scale-up of these interventions. Pan Afr Med J 2011;10:37.
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=22187619&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 

32. 1.  Higgins JPT, 
    2.  Green S
    
    Stewart LA, Tierney JF, Clarke M. Chapter 19: reviews of individual patient data. In: Higgins JPT, Green S.eds Cochrane handbook for systematic reviews of interventions. Chichester, UK: John Wiley & Sons, 2008.
    
    

33. Lyman GH, Kuderer NM. The strengths and limitations of meta-analyses based on aggregate data. BMC Med Res Methodol 2005;5:14.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1186/1471-2288-5-14&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=15850485&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 

34. Thompson MA, Mugavero MJ, Amico KR, et al. Guidelines for improving entry into and retention in care and antiretroviral adherence for persons with HIV: evidence-based recommendations from an International Association of Physicians in AIDS Care panel. Ann Intern Med 2012;156:817–33.
    
    [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.7326/0003-4819-156-11-201206050-00419&link_type=DOI) 
    
    [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=22393036&link_type=MED&atom=%2Fbmjopen%2F3%2F5%2Fe002954.atom) 
    
    [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000304986500007&link_type=ISI)