Abstract

Background
Around half of medications for long term conditions (LTCs) are not taken by patients as directed. Text messaging (TM) and Interactive Voice Response (IVR) technology (communication via voice recognition or keypad input) are both currently used for a variety of health purposes. Software for both of these mediums can also automate delivery of messages and respond to patient input, making use of this technology more efficient. This systematic review will examine the evidence of using automated two-way patient contact to support patients’ medicine-taking behaviour. The aim is to discover how automated patient contact interventions can support medicines adherence. This will be used to inform the design of a new intervention which can be tailored to the barriers that face individual patients when they take medicines for LTCs.

Methods
For inclusion in this narrative synthesis systematic review studies must have focussed on adults self-caring for LTCs in their own homes. The primary intervention should have used automated TM or IVR with the aim to improve medicines adherence. However, interventions which used components additional to the use of TM or IVR were also included to acknowledge that these may be part of a more comprehensive package to support medication use. Smart device applications, social media and website based interventions were excluded. Only studies conducted in high income countries, as defined by the World Bank, were included in order to develop an understanding of how such interventions can be placed within this healthcare setting.

All study designs except pilot and feasibility studies have been included. Outcomes of interest were adherence to medicines, clinical condition control, patient and provider acceptability and quality of life. A comprehensive electronic search strategy was used including the databases PubMed, MedLine, CINAHL, Embase, PsychArticles, Psychology and Behavioural Sciences collection, International Pharmaceutical Abstracts, Web of Science and the Cochrane Library. Searches of the grey literature have also been completed.

Data will be extracted around study characteristics, intervention design and details of intervention delivery. The Capability Opportunity and Motivation Behaviour model (COM-B) (Michie et al., 2011) will be used as a framework for the analysis, with interventions being coded for any inclusion of behaviour change techniques. Quality of studies will also be appraised using the Mixed Methods Appraisal Tool. Two researchers will be involved in the screening and data extraction processes, working independently with discussion and a third reviewer where required.

This review is registered on the PROSPERO database registration number: CRD42017069290.