Implementation barriers for mHealth for non-communicable diseases management in low and middle income countries: a scoping review and field-based views from implementers

Scoping review

To examine implementation issues reported in publications, we performed a scoping review to find studies on mHealth interventions conducted among an adult population in LMIC, with the prime aim to improve detection and management of cardiovascular disease, diabetes, and/or hypertension. We used the WHO mHealth/e-Health reporting guidelines to map how the publications reported on the key elements of implementation¹³. The studies included were mHealth interventions conducted among an adult population in LMIC.

Data collection process. The primary search terms were: (chronic disease OR non-communicable disease OR cardiovascular disease OR hypertension OR diabetes) AND (mHealth OR mobile health OR mobile). This list was made starting from the WHO digital tools for NCD used in the ‘Be He@lthy Be Mobile’ initiative¹⁴ and further refined through iterative approach. The term treatment was interpreted in its broadest sense to include management tools, tools enhancing communication between patient and provider, and tools that provide prognostic or diagnostic information, according to the CONSORT-EHEALTH guidelines¹⁵. Our search strategy (extended data¹⁶) was guided by the formative methodological framework for scoping reviews¹⁷. It included papers in English from the databases PubMed, EMBASE, MedLine, Web of Science, CINAHL, Cochrane and Global Health, published in peer-reviewed journals between 2012 and 2017, with a focus on LMICs. The most recent search was conducted in February 2018.

Study selection process. The types of studies included were randomized controlled trials, cross-sectional studies, case control studies, and cohort studies. The studies were required to be published in the English language, in a peer-reviewed journal and be available in full text format. Studies focusing only on prevention were excluded. One author was responsible for screening records (EE), and 6 authors were responsible for determining eligibility and inclusion (EE, KY, JI, CS, COU, UN).

Data abstraction process. Study design characteristics, including sample size, outcome and comparison/control group for the intervention and intervention characteristics were abstracted for all studies. A form in Google Sheets was used to chart the data (see underlying data¹⁶). Data charting was done independently. Following initial selection according to the primary selection criteria, a post hoc screening compared the included articles against the mERA guidelines¹³.

Assessment criteria. The mERA checklist was used to assess which implementation information was reported. This checklist was developed by a WHO working group of mHealth experts with the aim ‘to identify a minimum set of information needed to define what the mHealth intervention is (content), where it is being implemented (context), and how it was implemented (technical features) to support replication of the intervention’¹³. The core 16-item checklist addresses the following categories/themes for reporting: 1. Infrastructure, 2. Technology Platform, 3. Interoperability/Health information systems (HIS) context, 4. Intervention Delivery, 5. Intervention Content, 6. Usability/content testing, 7. User Feedback, 8. Access of Individual Participants, 9. Cost Assessment, 10. Adoption inputs/Program entry, 11. Limitations for delivery at scale, 12. Contextual adaptability, 13. Replicability, 14. Data Security, 15. Compliance with national guidelines or regulatory statutes, and 16. Fidelity of the intervention.

Scoping review protocol. A plan for the scoping review was developed a priori but was not formally translated into a protocol and disseminated.

Web-based questionnaire

The web-based questionnaire was developed to complement information from published literature with field-based knowledge from a broad group of implementers. In a two-step approach, we selected researchers and health workers working in NCD mHealth field. The first phase was data collection among a purposively selected sample of researchers with hands-on experience in NCD mhealth implementation research and belonging to an international network: the Global Alliance for Chronic Diseases (GACD)¹⁸. A second phase of data collection was added to widen the sample population to include more researchers and practitioners involved in implementation of mHealth in LMIC, in order to enrich the experiences with other projects. This was done, after the analysis of the first phase of data collection, through an online flash consultation with an open invitation to participate to an African-based Community of Practice of local health system managers and international mHealth experts via the online platform Collectivity and Global Digital Health Network (Web Annex 2)^19,20. The first phase lasted from Dec 2017–Jan 2018; the second phase from Jan–March 2018.

Data collection tool. For the first phase, a questionnaire was developed by the research team, and reviewed by two independent researchers for relevance, clarity and completeness. The questionnaire comprised 6 questions on the following topics: 1. Domain of intervention based upon Mecheal et al.²¹ (options: improvement of self-management; lifestyle information & health promotion; improvement of quality of care; addressing health system barriers), 2. Implementation barriers and 3. The way they were addressed, 4. Actors engaged, and 5) Priorities to address for scale-up of the project, and 6. General priorities in mHealth and NCD. It was a semi-structured questionnaire with a combination of closed questions (with options to select) and open-ended questions allowing for additional qualitative information. Although the questionnaire was not pre-tested before administration with the target group, the iterative process of approaching the respondents for more clarity allowed for refinement of answers when original questions were not clear. The data were collected by self-completion, followed through email exchange on clarification if information was not clear. For the flash consultation in the second phase, a more general open-ended questionnaire was used (extended data¹⁶. This was done to keep the invitation to participate open for a broad response. In the subsequent email exchange with the respondents to the flash consultation, specifications were asked to clarify answers.

Response rate. Researchers/implementers from 8 out of 13 projects (61.5%) responded in the first round. Six researchers/implementers responded in the second round in the second (two were excluded because of insufficient data). A response rate was could not be calculated for the second round because of the open invitation to participate did not capture non-respondents. For most projects (8 out of 12), the questionnaire was answered independently by two researchers/implementers working on the project. The majority of the projects (83.4%) were research projects and two projects (16.6 %) were privately initiated by a telecom operator or a health care provider.

Data analysis. Analysis of the responses was done through a deductive approach led by the first and last author (JVO, KY). The analysis started from pre-identified themes – inspired by mERA and expert guidance from the author. Responses were classified and information was further reviewed for additional themes. Interpretation of the responses was validated through sharing the draft manuscript with respondents. Six out of the 12 respondents contribute to the text with adding detail, examples and clarifications.

Patient and public involvement

The underlying studies have developed their intervention in a participatory way. The dissemination was arranged for each study separately. Patients were not involved in the design of this study, nor in the recruitment and conduct of the study.

Scoping review

In total, 16 studies out of 185 papers were included in the final review (Figure 1). The studies originated from a variety of countries including: India, Pakistan, China, Tibet, Mexico, Iran, Cambodia, Democratic Republic of Congo, Philippines, Tanzania, Bangladesh, Chile, and Malaysia. More than half of the studies utilized SMS (text messaging) as their main intervention tool. Most of these were patient-centered interventions. One study targeted health providers using a mobile decision support platform. While the majority of the interventions focus on diabetes as the core condition, others addressed heart failure, acute coronary syndrome follow-up, cholesterol risk assessment, foot ulcers, and drug adherence. The majority of studies were relatively small with sample sizes ranging from 48 to 3393 participants.

Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram for the scoping review process.

In terms of effectiveness, 15 studies showed improvements in clinical patient related end-points. Table 1 summarizes the mERA checklist items reported on by each of the studies included in the scoping review. In addressing the mERA checklist items, the outcomes were variable with 6 studies addressing mHealth infrastructure domains and 8 addressing characteristics of the technology platform^22–29,. Only 4 studies addressed interoperability^23,25,30,31 and 9 addressed usability^{23–28,30,32}. For the intervention itself, 16 studies address intervention delivery and 12 addressed intervention content. For the remaining checklist items, 4 studies addressed user feedback^25,27,30,32, 4 address the domain of the individual participant^23,25,26,30, and only 2 made any reference to study intervention costs^23,26. Finally, 3 studies included brief information on the adoption of the program^22,29,33 and 3 studies addressed issues regarding limitations for scaling^23,30,34,35. The few papers that commented on barriers noted the participation to be low, and lowering over time^23,30. Interventions that combined SMS with personal phone calls or with interactive feedback on patients input reported on patient satisfaction^34,36. A lack of information on cost is a barrier to further scale-up.

The few papers that commented on barriers noted the participation to be low, and lowering over time^23,30. Interventions that combined SMS with personal phone calls or with interactive feedback on patients input reported on patient satisfaction^34,36. A lack of information on cost is a barrier to further scale-up.

Questionnaire

Respondents from 12 projects, all of which were implemented in LMIC, answered to the questionnaire. The interventions were comparable, mostly focusing on health education, self-management and quality of care (and or a combination of these aims.) (Table 2). Seven projects mentioned the intention to address health systems barriers, such as data collection or supply chain management (extended data¹⁶). The projects from the first round were research projects in public health care settings (the supply side), whereas two other projects were private entrepreneurial initiatives that focused on the user (demand side). Many projects shared common barriers and also mentioned similar strategies to address them. Effective strategies across the projects were the ongoing engagement with patients and the adaptations in the way of delivering the message.

The findings from the survey identified important domains for decision making in the implementation and scale-up of mHealth interventions: 1) reviewing the need for adaptation of the intervention; 2) integrating the mHealth intervention with other digital systems and with the physical health care process; and; 3) designing sustainable scale-up models. We visualized these three domains as three axes of scale-up (Figure 2). Respondents also identified criteria for decision-making for scale-up of NCD interventions: (a) age, literacy, impairments, expectations and financial means of end users; (b) objective of the intervention (education, self-management, quality of care, access to treatment or follow-up); (c) actors involved (patients, caregivers, managers, health workers, pharmacies, etc.); (d) resources and organization of the health system (treatment and support options, patient records, access and quality); and (e) socio-economic cultural context (behavioral norms, inequalities and gender roles). Figure 2 integrates findings and recommendations of scale-up of mHealth NCD management interventions: the 3-dimensional box shows dimensions on which to act in the scale-up phase; the decision criteria below the box provide 5 aspects to assess in decisions how to scale up; the call-out balloons indicate the indicators to evaluate and report, in accordance to the mERA guidelines.

Figure 2. Mapping of axes of decision-making for implementation of mHealth for non-communicable diseases (NCD) (cube), the field views on decision criteria (bottom table) and information from literature studies listed along WHO mHealth Evidence Reporting and Assessment (mERA) items (call-out balloons).

Barriers and how they were addressed

NCD disease related barriers included age, complications leading to impaired physical or mental functioning, the natural progression of disease and disease-related perceptions. Several respondents mentioned that the highly prevalence of NCD among elderly and among people of lower socio-economic status, led to the combination of low digital literacy and low health literacy. This meant that uptake and engagement of people required additional training and guidance to allow familiarization and growing awareness on health issues. Two projects explicitly mentioned that proactive involvement and follow-up of caregivers and assisting patients improved utilization. They were encouraged to read messages and take them along in their caregiving. Solutions mentioned in two other projects were to change the transmission mode, from SMS to voice messaging and pictograms.

Disease perceptions shaped the expectations of mHealth interventions. Mental health projects reported stigma as a barrier for uptake, which requires attention from the start. Although generally, there was an interest in information on NCDs in LMIC, people also perceived many psychological, physical and cultural barriers to change behavior that could not be changed through the intervention. Low demand and declining engagement with mHealth interventions with a behavior change objective was frequent, even when services were free of charge to patients. Respondents mentioned an average time of engagement of 9 months. To increase patients’ utilization at start, health promotion campaigns addressing stigma were combined with the marketing of an intervention. To maintain engagement over time, some projects ensure personal contact with end-users at regular times, as a form of extrinsic motivation, or to adapt the intervention.

Barriers related to the mobile intervention. Health education given via a mobile device required adaptation of the content to the delivery channel. Health education on NCDs and the inclusion of motivations techniques is an area in development and still very limited in many health care settings in LMIC. The translation from personal motivational coaching to a mobile application included an additional challenge in such a context, since the content, the stratification, and timing of information need to be planned ahead in small bits of information with little options for interactivity. Although there were many apps for smartphones, quality varied and they are not always relevant for people in LMIC. The development of the process algorithm and of the motivational messages was typically done in consultation with medical doctors, experts in health behavior, telecom experts, and patients and local health workers. Only one project mentioned involving people from the Ministry of Health. It took on average one year, including testing and validation. Patient-oriented interventions reached patients in their routine of daily life, at work or at home, i.e. beyond the usual setting between health worker and patient. They were mixed with a range of other messages and adverts that attract the attention and might clash with the culture or prevailing ideas in other settings. The amplitude and diversity might lead to message fatigue and dilute the effect of messages.

Technical barriers mentioned related to the network provider and operating system, the hardware and software and the linking of different digital systems. The variety of network providers and telecom operating structure across regions made the optimal choice for a platform of data-management and providers difficult, especially in the highly commercialized and volatile telecom sector in many LMIC. The gradual linkage of the project with other functions, or the adaptation of delivery mode (message to voice) usually required adaptations to the technical architecture and renegotiations. Only a few projects have managed to link the project software with existing health management information system or to electronic medical records. The case studies showed the gradation in complexity, from one to multiple actors being able to use the same system. Some respondents mentioned that the involvement of different care providers and departments and the impossibility of allowing interaction between different information systems.

Phone related problems related to people having multiple phones, people switching providers, and phones being turned off. These restrained end users receiving or reading messages sent by the project. Respondents mentioned that tracking systems that monitor delivery of messages and automatically resend, are useful. This software was often embedded in telecom software, and sometimes within software available in open source format. Projects could build upon software that is available from other settings or an open source software platform, but they still required the technical competency to adapt to specific project needs and local users, for instance to allow software to run on old-fashioned smartphones and to adjust websites to low bandwidth internet. While a tablet had more options for intervention design, respondents told us that many users prefer a phone because of the battery life and usability.

Health care services barriers mentioned relate to the lack of resources, access to care and integration of mHealth interventions into health care processes including the resistance of health professionals to change. The lack of technical possibilities to link digital systems with each other led to interventions set up in a fragmented fashion, increasing the workload for health workers. Two respondents mentioned that a joint assessment of workflow and workload with health workers increased acceptance, and continuing education and feedback maintained their commitment. Other respondents mentioned that though discussions with health authorities on the need for upgrading public health facilities and through partnering with other health care providers, pharmacies and community organizations, their mHealth project served as a means to expose the unmet need for NCD interventions and could thus contribute to a change process to increase resources for NCDs management and access to care.

Contextual barriers mentioned included security, gender differences and social, economic and cultural factors, including limited access to internet of mobile device. One respondent said that informing participants that project phones contained tracking software further prevented theft. In another project, social and gender inequities in access to mobile phones were addressed by encouraging the common use of phones and by sharing of message content, for instance by message delivery at dinner times and encouraging using the speakerphone. This implementation format meant that the format of messages needed adaptation to a common audience, at the cost of personalization. Several respondents mentioned that timing of messages influences the susceptibility of people, so supply-driven interventions need to consider people’s daily routines.

Regulatory barriers reported were the lack of clarity on digital health regulations in many countries, which led to continued negotiation in some project. This barrier was more outspoken for the entrepreneurial projects. The stakeholders that were mentioned were the government, medical professional associations and telecom regulators. Medical professional associations in South Africa took the position not to allow mobile consultations without face-to-face contact, which led the project to consider another country. Telecommunications regulators also influenced the delivery options of mHealth interventions, through their policies on number masking, reverse billing and spam filters. Reverse billing (to reduce costs for end-users) needs the operator to allow for special short codes in the system. Sending messages in bulk led to numbers being identified as ‘spam’ or ‘number unknown’, leaving users to not see or not to recognize the message as from their provider.

Priorities for scale-up of interventions

When asked for the conditions for further scale-up of their intervention, respondents mentioned the priorities: having a human resource plan, a financing or business plan and addressing knowledge gaps. According to answers, a human resource plan should include capacity for process management (interaction with end users and monitoring), for software maintenance and digital health information management, and for evaluation cycles with feedback from users – to ensure relevance and quality on the longer term. Answers on a financing plan, included different options for financing models that would allow for scale-up: direct payment by end-users, contributions from the governments or other parties such as non-governmental organizations, inclusion in health insurance schemes or linking with other services for which there is a large demand. The respondents from research projects mentioned aiming to keeping cost for end-users low, whereas initiatives started as a business began with generation of demand from people able to pay such a mobile money service. Some research projects reported to seek for social enterprises to partner with, trying to bridge both worlds. A project that targeted primary care providers applied a business-to-business model, in which clinics pay a subscription to utilize the intervention. To attract customers, they linked the mHealth session to education sessions about quality of care, which increased the demand for the mHealth. While mixed models of private and public mHealth initiatives are rapidly evolving throughout the world, the need for regulation and for standards of quality also becomes more important. The third priority area mentioned was that of knowledge gaps that hamper scale up. The limited evidence of behavior change messages for diet and physical activity in LMIC, and of how to address comorbidities were mentioned. Respondents mentioned the potential of sharing hands-on implementation knowledge in user-friendly and open ways, via interactive web platforms such as the Global Digital Health Network.

Ethics

The studies which are included in the review and in the survey have been subject to approval by ethics committee. The survey presents aggregate data that was limited to describing research results from an array of funded research projects. We do not have any human subjects’ data in the study or analyses, and thus we did not seek ethical approval. All participating projects however, received ethical clearance from their respective institutions and other local authorities (e.g. Ministries/Municipalities) to conduct their own studies. All respondents in the survey gave written informed consent to participate in the survey and were informed about how the data will be used in a publication. The data are not used for any other purpose.

Underlying data

Open Science Framework: Implementation barriers for mHealth for non-communicable diseases prevention and management in low and middle income countries: a survey among implementers. https://doi.org/10.17605/OSF.IO/MR8Y4¹⁶

This project contains the following underlying data:

Extended data

Open Science Framework: Implementation barriers for mHealth for non-communicable diseases prevention and management in low and middle income countries: a survey among implementers. https://doi.org/10.17605/OSF.IO/MR8Y4¹⁶

This project contains the following extended data:

Data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).