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The role of mobile text and voice communication in the monitoring of chronic disease management within resource-poor low- and middle-income country public primary healthcare settings: a realist review

Jocelyn Anstey Watkins1*, Frances Griffiths1, Jane Goudge2 and Xavier Gomez-Olive3
  • 1 University of Warwick, Warwick Medical School, United Kingdom
  • 2 University of the Witwatersrand (School of Public Health), South Africa
  • 3 University of the Witwatersrand, MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, South Africa

Background: Mobile health for chronic disease management is being investigated as a potential solution to improve patient outcomes in low-resource settings, where mobile phones are becoming commonplace. This review adopts a realist approach and addresses the role of mobile text and voice communication in the monitoring of chronic disease management within resource poor low- and middle-income primary healthcare settings in public health systems. Chronic disease involves regular self-care (Shippee et al., 2012; Kadirvelu et al., 2012) and routine monitoring by patients and healthcare-providers to check disease progress or regress. Mobile health (mHealth) has been proposed as one way to improve management of chronic diseases (Stephani et al., 2016; Piette et al., 2016; Beratarrechea et al., 2017) Method: To map the evidence for the effectiveness of mHealth communication (text-based and voice) for the management of chronic diseases in LMIC, by considering the context and design considerations of effective interventions, we conducted a systematic search for relevant systematic reviews. We summarised: a) the evidence for effect of mHealth technology; b) the type of intervention; c) evidence gaps identified by review authors and; d) the mechanisms by which the interventions were thought to work (Anstey-Watkins, 2016) from 23 reviews. From this we discussed the programme theory about how the review authors thought the interventions in the review studies intended to work in relation to monitoring of chronic diseases A realist synthesis was then conducted to investigate whether mHealth text and voice interventions for the monitoring of chronic diseases in public primary care in LMICs are likely to achieve health benefits. We systematically searched for empirical studies published from 2000-2016 on monitoring of chronic disease using text or voice interventions in LMICs settings. We aimed to tackle the lack of theoretical understanding of why mHealth interventions work or not for a particular population. Evidence was appraised, data extracted and synthesised and a refined programme theory is illustrated in a theoretical framework developed using established high-level theory and context, mechanism and outcome configurations (CMOc). The next stage was to search for theory relevant to how the study interventions worked in practice. We identified two conceptual frameworks relevant to our interventions. Michie et al. (2013:7) framework includes the domains of Capabilities (C), Opportunities (O) and Motivations (M) necessary for Behaviour (B). Each domain leading to Behaviour has subdomains: Capability - physical skills, knowledge, behavioural regulation and memory, attention and decision processes; Opportunity - environmental context, resources and social influences; Motivation - beliefs about consequences, optimism and beliefs about capabilities and reinforcement and emotion. We found these applied to every intervention mechanism as demonstrated in Figure 3, the final theoretical framework. The framework developed by Vassilev et al. (2015:23) in a tele-monitoring review contains three concepts: Relationships - providing support for behavioural change, feedback and reinforcement; Fit - how to integrate mHealth into routine and ease of use - and; Visibility - to engage in information to mediate and motivate self-management tasks. We categorised the mechanisms of action of the interventions into the concepts of relationship, visibility and fit Results: Data was extracted from 11 empirical studies. The types of monitoring interventions of the interventions was as follows:involved information exchange, behavioural change using a buddy system, automated messaging and supportive counselling services to assist and improve monitoring. The types of mechanisms necessary are support line, observation, reinforcement and feedback, reminders, education and advice, decision-support, motivation, health promotion and information. A level of agency-dependent behavioural domains (capability, opportunity and motivation) are necessary for effective monitoring. This is given the relationship, fit and visibility of the intervention and two or more of the nine identified mechanisms are present to trigger the desired outcomes in a particular context. Overall, the findings suggest that that combinations of mechanisms are desirable for the target outcome to result. Most of the empirical studies targeted two or more mechanisms. If there are several mechanisms used in the design of the intervention, this may enable a reaction or response by the patient to make a desired change. If more than one mechanism is presented, then there is a greater chance that the patient’s reasoning will react to the available resources. A theoretical framework has been developed from the CMOc from each study and can be applied to future designs of mHealth monitoring interventions. Findings add to the knowledge of mHealth monitoring by suggesting useful reasoning and resource mechanisms to incorporate into intervention design. Conclusion: This is the first realist review on monitoring of chronic disease using mHealth for low-resource settings. Even though effectiveness evidence is weak, the use of text or voice interventions to assist with patient management is evolving, as is the literature. It is not clear whether the mHealth interventions are effective over long durations with sustained effects, or for chronic diseases that were not included in the empirical studies, such as mental health conditions or asthma, and, lastly, whether the desired outcome will result if a patient has comorbidity. Future empirical studies would benefit from using the proposed theoretical framework and the effective design considerations from the review of reviews. We argue that the type and combination of mechanisms (supported by theory) can be considered in the design of mHealth interventions. This review contributes to what is known about contexts in which the effect of mobile monitoring can occur and the possible mechanisms through which chronic disease management using mobile monitoring are able to achieve target outcomes and improve health status.

Figure 1
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Acknowledgements

This paper was written during a PhD funded by the Economic and Social Research Council and GE Healthcare Ltd.

References

Anstey-Watkins (2016) Understanding the potential role for appropriate digital technological solutions in the innovation of health system design, implementation and normalisation in rural South Africa for both patients and health-workers:
A critical exploratory analysis.

Beratarrechea, A., Moyano, D., Irazola, V. & Rubinstein, A. (2017) mHealth Interventions to Counter Noncommunicable Diseases in Developing Countries: Still an Uncertain Promise. Cardiology Clinics. 35 (1): 13-30.

Kadirvelu, A., Sivalal Sadasivan & Ng, S. H. (2012) Social support in type II diabetes care: a case of too little, too late. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. 5: 407.

Michie, S., Richardson, M. & Johnston, M. (2013) The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav

Piette, J. D., Marinec, N., Janda, K., Morgan, E., Schantz, K., Yujra, A. C. A., Pinto, B., Soto, J. M. H., Janevic, M. & Aikens, J. E. (2016) Structured Caregiver Feedback Enhances Engagement and Impact of Mobile Health Support: A Randomized Trial in a Lower-Middle-Income Country. Telemedicine and e-Health. 22 (4): 261-268.

Shippee, N. D., Shah, N. D., May, C. R., Mair, F. S. & Montori, V. M. (2012) Cumulative complexity: a functional, patient-centered model of patient complexity can improve research and practice. Journal of Clinical Epidemiology. 65 (10): 1041-1051.

Stephani, V., Opoku, D. & Quentin, W. (2016) A systematic review of randomized controlled trials of mHealth interventions against non-communicable diseases in developing countries. BMC Public Health. 16 (1): 572.


Vassilev, I., Rowsell, A., Pope, C., Kennedy, A., O’Cathain, A., Salisbury, C. & Rogers, A. (2015) Assessing the implementability of telehealth interventions for self-management support: a realist review. Implementation Science. 10 (1): 59.

Keywords: Realist synthesis, Monitoring, Management, Chronic Disease, LMICs, mobile health, Technology, Digital health behavior change

Conference: 3rd UCL Centre for Behaviour Change Digital Health Conference 2017: Harnessing digital technology for behaviour change, London, United Kingdom, 22 Feb - 23 Feb, 2017.

Presentation Type: Research abstract

Topic: Digital Health

Citation: Anstey Watkins J, Griffiths F, Goudge J and Gomez-Olive X (2017). The role of mobile text and voice communication in the monitoring of chronic disease management within resource-poor low- and middle-income country public primary healthcare settings: a realist review. Front. Public Health. Conference Abstract: 3rd UCL Centre for Behaviour Change Digital Health Conference 2017: Harnessing digital technology for behaviour change. doi: 10.3389/conf.FPUBH.2017.03.00049

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Received: 22 Feb 2017; Published Online: 22 Feb 2017.

* Correspondence: Dr. Jocelyn Anstey Watkins, University of Warwick, Warwick Medical School, Coventry, United Kingdom, j.o.t.a.watkins@warwick.ac.uk