Types of studies

We will include randomised trials and cluster randomised trials. We will include full‐text studies, conference abstracts, and unpublished data. We will include studies irrespective of their publication status and language of publication.

Types of participants

• Clients receiving healthcare accessible via mobile devices.

• All types of healthcare providers (i.e. professionals, paraprofessionals and lay health workers), communicating and providing client care through mobile‐based technologies.

We will include participants regardless of their location, setting, diagnoses, conditions or demographic factors such as age.

Types of interventions

We will include trials comparing communication and management of care through a mobile device with standard practice. By mobile‐based communication from client to healthcare provider, we mean the exchange of communication and provision of healthcare information and services at a distance (WHO 2012), in which communication is conducted between remote clients engaging with health services and healthcare providers. We will focus exclusively on the provision of healthcare information where the person’s inquiry receives a response in real‐time or response is as immediate as appropriate clinically. Standard practice is defined as the usual care provided in the setting where the study was conducted, which could include providing care or engaging with the client through face‐to‐face communication or other non‐digital channels or referring the client to another provider.

We will focus exclusively on clinical information that can be exchanged over wireless and mobile technologies, as well as mobile phones of any kind (but not analogue landline telephones), laptops, tablets, personal digital assistants, and smartphones. Communication channels via mobile device can include text messaging, video messaging, social media, voice calls, voice over internet protocol (VoIP), and videoconferencing, through software such as Skype, WhatsApp, or Google Hangouts.

We will include:

• studies in which the clients employ mobile‐based technologies to engage with provider(s); this could also include partners, family members or other informal support providers engaging with providers on behalf of others;

• studies in which the provider(s) offering care is at a different location from the client;

• studies in which the client transmits clinical information via a mobile device; and

• studies in which the provider(s) offering guidance responds in real‐time, defined as sufficiently immediate or as clinically appropriate.

We will include studies where mobile‐based technologies were used for monitoring, consultation or delivering treatment, as long as there was clinical information exchanged between client and provider, and feedback given by the provider. We will include studies where the type of communication device that was used to transmit the clinical information is unknown, since the specificity of the type of communication device is not often reported.

We will include all health issues and will not restrict the content of clinical health information exchanged. We will include studies where the digital component of the intervention was delivered as part of a wider package if we have judged it to be the major component of the intervention.

We will exclude:

• Pilot and feasibility studies (pilot study defined as “a version of the main study that is run in miniature to test whether the components of the main study can all work together” (Arain 2010) and feasibility study defined as “pieces of research done before a main study” (Arain 2010)).

• Studies that compared different technical specifications of telecommunication technologies, e.g. different communication channels, software, etc.

• Studies in which the client used fully automated services to self‐care or access clinical information without having any contact with a healthcare provider (e.g. webMD).

• Studies in which the use of telecommunications technology was not linked to direct client care.

• Studies in which the intervention consisted of client monitoring systems in which the client received only an automated voice response.

• Studies in which the intervention consisted of routine communication to the healthcare provider as part of usual follow‐up care.

• Studies in which clients used an automated service to relay clinical information, without having any other interaction with the healthcare provider (e.g. client is fitted with a system that will send a message if glycated haemoglobin (HbA1c) drops below a certain range and there is no feedback from the healthcare provider), where the client has already had in‐person contact with a healthcare provider.

• Studies in which there was no transfer/communication of clinical information between client and provider. For example, a portable medical device is excluded if it does not transmit clinical information to a provider. The key functionality is that there is an exchange of clinical information between client and healthcare providers; the device/health equipment used for obtaining the clinical information to be exchanged is not as relevant.

• Studies in which providers engage with clients through predefined scheduled calls, where there is no mention of the client being able to contact the healthcare provider in between.

• Studies that explicitly used non‐mobile devices to transfer clinical information, such as computers, videoconferencing, landlines, etc. We will not exclude studies based on the type of device or health equipment that was used to obtain the clinical information.

Types of outcome measures

Electronic searches

An information specialist developed the search strategies in consultation with the review authors and WHO content experts. We will use a cut‐off search date of 2000, based on the increased availability and penetration of mobile devices used for telemedicine from that date (ITU 2017). Appendix 1 lists the search strategy for MEDLINE. We will search the following databases:

• Cochrane Central Register of Controlled Trials (CENTRAL; latest issue), in the Cochrane Library;

• MEDLINE Ovid;

• Embase Ovid;

• POPLINE; and

• WHO Global Health Library.

Searching other resources

Selection of studies

We will download all titles and abstracts retrieved by electronic searching to a reference management database and remove duplicates. For title and abstract screening, we will use a machine learning classifier that is able to assign a probability score that a given record describes, or does not describe, a randomised trial (Wallace 2017). Titles and abstracts of studies with a 10% probability or greater of being a randomised trial will be screened by two review authors (of BB, NH, NM), and those with less than 10% probability of being a randomised trial will be screened by one review author. We will retrieve the full‐text study reports/publications and two review authors (of BB, NH, NM) will independently screen the full‐text and identify studies for inclusion and identify and record reasons for exclusion of the ineligible studies. We will resolve any disagreement through discussion or, if required, we will consult a third review author (DGB or SS).

We will list studies that initially appeared to meet the inclusion criteria but that we later excluded in the 'Characteristics of excluded studies' table. We will collate multiple reports of the same study so that each study rather than each report is the unit of interest in the review. We will also provide any information we can obtain about ongoing studies. We will record the selection process in sufficient detail to complete a PRISMA flow diagram (Liberati 2009).

Data extraction and management

We will use the EPOC standard data collection form and adapt it for study characteristics and outcome data (EPOC 2017a); we will pilot the form on at least one study in the review. Two review authors (of BB, NH, NM) will independently extract the following study characteristics from the included studies.

1. Methods: study design, unit of allocation, location and study setting, withdrawals.

2. Participants: number, mean age, age range, gender, inclusion criteria, exclusion criteria, other relevant characteristics.

3. Interventions: function of the intervention (monitoring, consultation, therapy), intervention components (including type of technology and mode of delivery, frequency of data transmission),comparison, fidelity assessment. For this review, we will define monitoring as to keep track or record the progress of symptoms or a condition over a period of time; consultation as an exchange between the healthcare provider and the client, where the latter's health status is discussed and guidance, support, or information are provided; and therapy as the ongoing management and care of a client, to counteract a disease or disorder.

4. Outcomes: main outcomes specified and collected, time points reported.

5. Notes: funding for trial, ethical approval.

Two review authors (of BB, NH, NM) will independently extract outcome data from included studies. We will contact authors of included studies to seek missing data. We will note in the 'Characteristics of included studies' table if outcome data were reported in an unusable way. We will resolve disagreements by consensus or by liaising with a third review author (DGB or SS). We will group the studies by health condition being targeted. We will create a miscellaneous category for studies focusing on rare conditions and single studies of a condition, for which we will extract basic study information and descriptive data, but not outcome data.

Assessment of risk of bias in included studies

Two review authors (of BB, NH, NM) will independently assess risk of bias for each study using the criteria outlined in the Cochrane Handbook for Systematic Reviews of Interventions Section (Higgins 2017), and guidance from the EPOC group (EPOC 2017b). Any disagreement will be resolved by discussion or by liaising with a third review author (DGB or SS). We will assess the risk of bias according to the following domains:

1. Random sequence generation.

2. Allocation concealment.

3. Blinding of participants and personnel.

4. Blinding of outcome assessment.

5. Incomplete outcome data.

6. Selective outcome reporting.

7. Baseline outcomes measurement.

8. Baseline characteristics.

9. Other bias.

We will judge each potential source of bias as high, low, or unclear and provide a quote from the study report together with a justification for our judgment in the 'Risk of bias' table. We will summarise the 'Risk of bias' judgments across different studies for each of the domains listed. We will consider blinding separately for different key outcomes where necessary (e.g. for unblinded outcome assessment, risk of bias for all‐cause mortality may be very different than for a self‐reported pain scale). We will assess incomplete outcome data separately for different outcomes. Where information on risk of bias relates to unpublished data or correspondence with a trialist, we will note this in the 'Risk of bias' table. We will not exclude studies on the grounds of their risk of bias, but will clearly report the risk of bias when presenting the results of the studies.

When considering treatment effects, we will take into account the risk of bias for the studies that contribute to that outcome.

We will conduct the review according to this published protocol and report any deviations form it in the 'Differences between protocol and review' section of the systematic review.

Measures of treatment effect

We will estimate the effect of the intervention using risk ratio for dichotomous data, together with the appropriate associated 95% confidence interval and standardised mean difference for continuous data, together with the 95% appropriate associated confidence interval (Higgins 2011). We will ensure that an increase in scores for continuous outcomes can be interpreted in the same way for each outcome, explain the direction to the reader, and report where the directions were reversed if this was necessary.

Unit of analysis issues

We will control for unit of analyses errors by re‐analysing results while adjusting for clustering. If there is not enough information to re‐analyse the results we will try to contact the study authors to obtain the necessary data. If we are not able to access all the data we will not report confidence intervals or P values (EPOC 2017c).

Dealing with missing data

We will contact investigators to verify key study characteristics and obtain missing outcome data where possible (e.g. when a study is identified as abstract only). We will try to compute missing summary data from other reported statistics. Whenever it is not possible to obtain data, we will report the level of missingness and consider how that might impact the certainty of the evidence.

Assessment of heterogeneity

If we find studies that are similar enough to combine, we will conduct a meta‐analysis (Borenstein 2009). We will use the I² statistic to measure heterogeneity among the trials in each analysis. If we identify substantial heterogeneity we will explore it by pre‐specified subgroup analysis.

Assessment of reporting biases

We will attempt to contact study authors, asking them to provide missing outcome data. Where this is not possible, and the missing data are thought to introduce serious bias, we will explore the impact of including such studies in the overall assessment of results. If we are able to pool more than 10 trials, we will create and examine a funnel plot to explore possible publication biases, interpreting the results with caution (Sterne 2011).

Data synthesis

We will undertake meta‐analyses only where this is meaningful, that is, if the treatments, participants, and the underlying clinical question are similar enough for pooling to make sense (Borenstein 2009). A common way that trialists indicate when they have skewed data is by reporting medians and interquartile ranges. When we encounter this we will note that the data were skewed and consider the implication of this. Where multiple trial arms are reported in a single trial, we will include only the relevant arms. If two comparisons (e.g. intervention A versus usual care and intervention B versus usual care) must be entered into the same meta‐analysis, we will halve the control group to avoid double‐counting.

Subgroup analysis and investigation of heterogeneity

We plan to carry out the following subgroup analyses.

1. Healthcare provider type (e.g. lay versus professional healthcare provider). Lay health workers often provide healthcare in settings where healthcare resources are scarcer, for example targeting epidemics in low‐ and middle‐income countries and the specific health needs of minority communities in high‐income countries (Lewin 2010). Because lay health workers have no formal professional tertiary education, their knowledge and beliefs might moderate the effects of the intervention (Akinlua 2016).

2. Type of communication channel (e.g. voice, SMS, interactive voice response, image exchange). Different communication channels might be used differently and serve distinct purposes (Ventola 2014), as well as providing a more comprehensive and realistic opportunity for communication.

3. Setting/income level (e.g. low‐income versus high‐income settings). Traditionally, the quality of healthcare is lower in low‐ and middle‐income countries (Mills 2014), which might increase heterogeneity and pre‐empt the pooled analysis of studies conducted in different settings.

We will use the following outcomes in subgroup analysis.

1. Time between presentation and appropriate management by provider, including change in time for clients to receive/access health services and information;

2. clients' health status and well‐being; and

3. clients' utilisation of healthcare services.

We will use the formal statistical techniques of Mantel‐Haenszel and regression to test for subgroup interactions (Mantel 1959).

Sensitivity analysis

We will perform sensitivity analyses defined a priori to assess the robustness of our conclusions and explore its impact on effect sizes. This will involve restricting the analysis to published studies and restricting the analysis to studies with a low risk of bias.