A systematic review on incentive-driven mobile health technology: As used in diabetes management

Abstract

Introduction

Mobile health (mHealth) technologies have been shown to improve self-management of chronic diseases, such as diabetes. However, mHealth tools, e.g. apps, often have low rates of retention, eroding their potential benefits. Using incentives is a common mechanism for engaging, empowering and retaining patients that is applied by mHealth tools. We conducted a systematic review aiming to categorize the different types of incentive mechanisms employed in mHealth tools for diabetes management, which we defined as incentive-driven technologies (IDTs). As an auxiliary aim, we also analyzed barriers to adoption of IDTs.

Methods

Literature published in English between January 2008–August 2014 was identified through searching leading publishers and indexing databases: IEEE, Springer, Science Direct, NCBI, ACM, Wiley and Google Scholar.

Results

A total of 42 articles were selected. Of these, 34 presented mHealth tools with IDT mechanisms; Education was the most common mechanism (n = 21), followed by Reminder (n = 11), Feedback (n = 10), Social (n = 8), Alert (n = 5), Gamification (n = 3), and Financial (n = 2). Many of these contained more than one IDT (n = 19). The remaining eight articles, from which we defined barriers for adoption, were review papers and a qualitative study of focus groups and interviews.

Discussion

While mHealth technologies have advanced over the last five years, the core IDT mechanisms have remained consistent. Instead, IDT mechanisms have evolved with the advances in technology, such as moving from manual to automatic content delivery and personalization of content.

Conclusion

We defined the concept of IDT to be core features designed to act as motivating mechanisms for retaining and empowering users. We then identified seven core IDT mechanisms that are used by mHealth tools for diabetes management and classified 34 articles into these categories.

Keywords

Incentive mHealth self care diabetes motivation

Introduction

The introduction and widespread use of mobile devices in recent years has led to the healthcare industry embracing a concept known as mobile health (mHealth). This term refers to the use of portable mobile devices, e.g. smart phones, PDAs and tablet computers, for administering, monitoring, diagnosing and treating medical conditions.¹ Research into mHealth technologies has shown and suggested an array of real and potential benefits for both patients and practitioners by increasing the range and efficiency of healthcare while reducing the associated costs.¹ One area that is set to gain the most from this transition into mHealth is management of chronic diseases.² These often require a set of complex, time consuming tasks to be completed regularly, away from hospitals.³ Further, the monetary costs and resource intensive nature of these diseases puts a huge strain on healthcare systems and governments worldwide. For example, in the USA in 2010, 86% of all healthcare expenditure was on people with one or more chronic diseases.⁴ Recently, mHealth studies have begun to explore how mobile technologies can ease the burdens associated with managing chronic diseases.^5,6 One such example that compared traditional paper-based logbooks to mHealth self-monitoring in overweight and obese adults found mHealth users self-monitored exercise more frequently and had a lower final Body Mass Index (BMI) than non-mHealth participants.⁷ However, while these studies provide useful solutions, it is important to also explore how they drive users to not only initially adopt the technology, but retain it as a longer-term management tool.⁶

Diabetes is a severe, widespread and heavily studied chronic disease requiring lifelong self-monitoring and medical care in order to avoid long-term complications and reduce the risk of death. An organized, systematic approach to managing the disease, with regular input from professional health care providers is recommended to combat the high costs and risks of complications.⁸ This results in a heavy economic burden for families and society.⁸ It has been shown that improved patient self-management, through monitoring and education, could benefit diabetes treatment significantly.⁹ However, self-management implies complex daily tasks confronted by people with diabetes, including measuring blood glucose levels, injecting insulin, physical activity, and diet intake monitoring. Meanwhile, healthcare professionals are overburdened and hence unable to properly assist many of the time consuming management tasks.³ These factors have prompted a number of mHealth applications for diabetes management to be proposed and studied. A survey by Tatara et al.¹⁰ found that mHealth tools have a positive effect on patient confidence in diabetes management and were used more frequently than desktop solutions. Similarly, a longitudinal study by Chen et al.¹¹ concluded that mobile telecare tools, which “promote preventative care, self-management, and clinical consultations from a distance,” were effective in enhancing blood glucose monitoring and improving glycemic control among patients with diabetes. A pilot study by Robinson et al.¹² further noted that the tools eased burdens on health care providers. However, without an incentive to use the tool as part of a daily management routine, the potential benefits may not be achieved by patients, the healthcare system or the wider community.¹³ Here, this incentive refers to any aspect of the tool that encourages or motivates the user to adopt and return to the solution, e.g. as is common in retail reward programs.¹⁴

In response to the need for incentives, research exploring the adoption and spread of mHealth tools has made calls for using technologies to entice and retain users, such as real-time alerts, and social connectivity.^15–17 These are often presented as solutions for the limited adoption of mHealth tools for chronic diseases management.^2,6 However, no studies have been performed to systematically analyze and define existing mHealth incentives and how they are applied in practice. In response, we define incentive-driven technologies (IDTs), to be core features that act as reward mechanisms for retaining, engaging and empowering users. While the IDT nomenclature is new, the use of IDTs in mHealth solutions is widespread. IDT mechanisms have been embedded in most mHealth tools, yet, the vast majority simply view the mechanisms as technological features rather than as technological incentives. That is, most tools present at least one feature designed to, for example, improve ease-of-use, or enjoyment, over other methods of completing the same task, without mentioning the incentive it provides to users.

The aim of this study is to perform a systematic review to define and determine which IDTs are used in mHealth for diabetes management. This is a foundational step in answering the question of how effective each of the IDTs is in producing positive clinical outcomes, e.g. improved glycemic control. We chose to focus on diabetes management as a case study within the wider group of chronic disease management due to: (a) the complexity and range of tasks involved; (b) the large number of studies into the area, particularly those embracing mHealth; and (c) the growing burdens that the disease is placing on the global community; thus making our findings applicable to wider chronic disease management and mHealth in general. We begin by defining and justifying the selection of seven core IDT mechanisms. Each of these is analyzed and summarized in regards to the development of mobile technologies over time, including content delivery and content generation. We then discuss our findings and how the IDTs have been used to guide the development of meaningful mHealth tools. In addition, we summarise the key barriers to adoption relating to mHealth IDT tools for diabetes management. This has been presented as an auxiliary aim and was gathered from review papers on mobile interventions for diabetes management.

Methods

Literature published in English between January 2008–August 2014 was identified through searching leading publishers and major indexing databases: Institute of Electrical and Electronics Engineers (IEEE), Springer, Science Direct, National Center for Biotechnology Information (NCBI), Association for Computer Machinery (ACM), Wiley and Google Scholar. We chose the period beginning with 2008 by analyzing Google Trends for terms relating to mHealth, and noting that the first international mHealth summit¹⁸ occurred in 2009.

The search terms of “mHealth AND (smartphone or mobile device) AND chronic disease AND (incentive or motivation or intention)” were used; note that “chronic disease” was searched instead of diabetes to acquire more results. The search terms were defined after multiple pilot searches that included alternate Boolean logic and the use of synonyms or related terms, such as cellphone, Android and iPhone. The final string was selected as it returned results specific to the mHealth domain that were not necessarily smartphones. Articles were selected if they described at least one mHealth tool for diabetes management that presented at least one of the seven core IDTs. Two reviewers independently screened titles and abstracts to determine inclusion status. A second screen of full-text articles was performed to ensure that the articles described mHealth IDTs for diabetes. Figure 1 presents our article selection process: 2182 articles were identified by searching the selected databases; from these articles, 1730 publications were retained after removing duplicates. After the title and abstract screening process, 58 publications were selected for full text analysis. The final number of articles, after screening for the two inclusion criteria, was 42.

Figure 1.

Flow diagram of paper selection process. Exclusion was based on describing at least one mHealth tool for diabetes management that presented at least one of the seven core incentive-driven technologies (IDTs). IEEE: Institute of Electrical and Electronics Engineers; NCBI: National Center for Biotechnology Information; ACM: Association for Computer Machinery.

When analyzing the articles, we split the selection into two groups. The first, consisting of 34 of the articles, presented IDT mechanisms via pilot studies, prototypes, framework development, and trials. These were used to categories, analyze and form results about IDTs in mHealth for diabetes. These articles are presented in the content delivery medium, content generation technique and incentive-driven technology sections below. The second group, which contained the remaining eight articles, was of review papers and a qualitative study of focus groups and interviews with patients who have type 2 diabetes. These eight articles were not included in the categorization of IDTs as they did not present new technologies and instead were used to form a summary of barriers for adoption as presented at the end of the Results.

Results

Data from the selected articles were categorized into the seven core IDTs. For each IDT, we identified two key aspects, the content delivery medium (CDM), which refers to the method by which data is distributed to users, categorized into the six mediums presented in Table 1, and the content generation technique (CGT), comprised of manual, semi-automatic, or fully automatic methods. Our designation of CDM can refer to both the back-end process of transporting the data, e.g. use of the Internet, and the front-end product, such as a mobile app. The categorization of IDT with associated CDMs is provided in Table 1. In the following sections, we first define and explore CDM and CGT before analyzing the individual IDTs. Finally, the results for our auxiliary aim have been presented in the barriers for adoption section.

Table 1.

Categorization of incentive-driven technology (IDT) articles and their content delivery mediums (CDMs). The number represents the number of IDT papers, while the articles are referenced alongside the CDMs used.

IDT	#	CDM
Reminder	11	App^19–24 Internet^19,25,26 Sensor^{19–21,26–28} SMS^{21,22,25,27–29} Voice/video message²⁴
Alert	5	App^20,23,24,30 Internet¹¹ Sensor^11,20 SMS¹¹ Voice/video message²⁴
Feedback	10	App^31–37 Internet^{31,33–36,38–40} Sensor^{31–34,37–40} Voice/video message³⁴ Wii⁴⁰
Social	8	App^{21,24,30,41–43} Internet^31,41,44,45 Sensor^21,41,43 SMS²¹ Voice/video message²⁴
Education	21	App^{19–21,23,24,28,31,35,41,42,46,47} Internet^{11,19,25,35,40,41,44,48} Sensor^{11,19–21,28,31,37,40,41,46–48} SMS^{11,21,25,28,29,47,49} Voice/video message^20,24,50
Financial	2	App⁴³ Sensor^29,43
Gamification	3	App^42,43 Internet⁵¹ Sensor^43,51

SMS: Short Message Service.

Content delivery medium

The CDM designates how IDT material is distributed to users on their mHealth devices. Over the time frame considered, the evolution of IDT content delivery has followed the evolution of technological advancements. That is, the core technologies of the smartphone era: apps, the Internet, Short Message Service (SMS) and sensors, have been utilized by IDT mHealth tools as they became available. In line with reduced costs and more powerful smartphone devices, there has been a rapid growth in the number of studies that present Apps, from one in 2008 to six in 2014. In regards to sensors, which ranged between 2–6 per year, the types of sensors used have changed in accordance to newly introduced devices. Early studies presented simpler sensors, such as blood glucose monitors³⁹ or pedometers,²⁷ while as they became more readily available, newer studies began to make use of advanced wearable devices, e.g. a study by Kahol in which participants wore sensors to measure movement and physiology while playing a virtual reality game.⁵¹ Similarly, as smartphones have become more adept at displaying multimedia and mobile networks themselves have improved, i.e. from 3G to 4G, higher bandwidth content such as video and voice messages^20,24 have been delivered. Finally, while not an mHealth device itself, one study,⁴⁰ utilized the Wii gaming console for content delivery alongside an mHealth tool. A snapshot of this can be seen in Figure 2.

Figure 2.

Breakdown of content delivery mediums over time illustrating the evolution of incentive-driven technologies (IDTs) as different mediums became available. SMS: Short Message Service.

Content generation technique

The CGT refers to how IDT information is delivered to the users’ mHealth devices. We categorized using three broad terms of manual, semi-automatic and automatic methods. Manual method refers to content generated entirely by humans, such as educational messages or answers to questions that have been written, filmed, or recorded, e.g. by a diabetes educator. Semi-automatic methods denote that the content was created in part with some manual input, e.g. individual educational facts, that are then altered, or stitched together by the mHealth software for particular scenarios. Finally, automatic method refers to content, such as graphs, motivational messages, or alerts, that are entirely generated by an algorithm, such as a server-side script that generates alerts based on blood glucose readings.

Each IDT category had its own unique relationship with CGTs, as highlighted in Figure 3. Each year has three columns, one for manual, one for semi-automatic, and one for fully automatic IDTs. For example, in 2008, a reminder IDT and a social IDT had content generated manually, another reminder and an education, semi-automatically and, finally a second education IDT was fully automatic. The social IDT, by its very nature, in that two or more users of the mHealth solution are communicating, has remained completely manually generated. Similarly, the financial IDTs were exclusively manual with researchers paying participants. The other five IDTs have shown a trend towards automation to varying degrees. Where possible, these tools utilize algorithms that perform tasks such as text analysis and personalization of messages fully automatically.

Figure 3.

Content generation technique (CGT) used by incentive-driven technologies (IDTs) over time. The y-axis is the total number of articles where each column is split by horizontal lines and the numbered labels represent a categorization of IDT. Nine articles presented multiple forms of CGT for different IDTs, while 19 articles present multiple IDTs. Thus there are more than 34 segments.

Incentive-driven technology

IDT extends upon two established frameworks: the persuasive systems design (PSD) framework,⁵² an influential framework that discusses the “process of designing and evaluating persuasive systems … [highlighting] seven underlying postulates behind persuasive systems … [and] further lists 28 design principles for persuasive system content and functionality,”⁵² (p. 486) and the behavioral intervention technologies (BITs) framework,⁵³ which was designed specifically to guide mHealth solutions that aim to change behaviors and cognitions. We combined these frameworks for two reasons. First, BIT, while built on top of the PSD framework, had a narrower focus than we required in our study. Thus we selected missing categories from PSD. Second, for categories in both, we selected from the BIT framework as it is more recent with a focus of mHealth, thus making it more appropriate for our context. From the PSD framework, we designated reminder from the dialog support principles, which we further distilled into patient-facing Reminder and practitioner-facing Alert categories, and Social as a conglomeration of the social support principles. Meanwhile the Education and Feedback categories were derived from the BIT framework’s conceptual “how” in behavior change. As education is often seen as an end-goal rather than a method for engaging users, it is important that we emphasize that our IDT definition includes empowering users to better manage their diabetes, something that education does achieve.⁵⁴ Further, educational content that is personalized or relevant to the user can retain and engage users by providing answers to their personal queries in an easy-to-find location. We supplemented these models with the Financial and Gamification IDTs, which we included due to these IDTs being present in multiple articles, while having attributes designed to improve user retention, engagement and empowerment. The IDT definitions are further elucidated in each of the following sections. Data were coded to each group on the basis of incentive provided rather than naming defined in the source articles. It is worth noting that most (n = 19) articles presented multiple IDTs. The combination of Education and Reminder was the most common (n = 8), with Education being used alongside other IDTs a total of 16 times. All IDTs were used in a combination at least once, with the Alert and Financial IDTs never being used on their own.

Reminder

The Reminder IDT refers to sending a message or an alarm to the user, such as getting a regular notification of personal goals they had set.²⁵ The IDT was used in 11 of the selected studies, two of which personalized the reminder messages sent to users. One sent specific messages based on blood glucose readings with suggestions of how to alter their management,¹⁹ while the other suggested increasing blood glucose readings or making an appointment with practitioners based on trends in logs and measurements.²⁰ Of these studies, six were published in 2012, two each in 2014 and 2008 and one in 2011. Only the earliest of these was a manually-driven reminder system, while the personalized systems were reported in 2012 and 2013. This IDT was commonly combined with Education (n = 8) as the objective of most reminder systems was to empower users to improve their management, similar to the empowerment gained by more educated users.

Alert

Alert is similar to Reminder, except that it is considered to be “practitioner-facing,” and instead of a simple notification, it can be implemented to warn the medical practitioner that something could be wrong. An example is an alert that is sent when recordings of blood glucose level, insulin injections, heart rate, or body weight are outside a defined range.¹¹ The IDT was presented in five studies. The use of this IDT has been largely consistent since 2011, with one article in each of the first three years and two in 2014.

Feedback

Technologically similar to both the Reminder and Alert IDTs, Feedback is a case where, for example, a practitioner, or a software algorithm, can send a message to the user stating whether their management has been good or bad. Equivalently, a user can provide feedback to a practitioner about a change in their management routine. Feedback was presented by 10 of the selected studies, with four in 2014, two in 2009, and one each in 2010, 2011, 2012 and 2013.

Social

The Social IDT involves connecting users with each other so they can talk and provide support to one another through the mHealth tool, e.g. the use of a chat service.⁴⁵ The IDT was employed in eight studies. One study⁴⁵ used Social as the only incentive, while the remaining seven combined it with other techniques. Publication of social IDTs has been largely consistent over time, with two each in 2012 and 2013, and one in 2008, 2010, 2011 and 2014. Most of the studies intended the social IDT to promote peer-to-peer communication and the development of “support networks,” e.g. allowing adolescents to exchange experiences and support one another.⁴³ These peer support networks are thought to improve adherence to self-management tasks by giving patients somewhere to turn for advice/support from others in a similar situation. This IDT was often aimed at younger users or pregnant women with type 1 diabetes, who are newly introduced to the tasks of self-management and therefore may require more support and guidance. The IDT was commonly combined with both Education (n = 5), where users could, for example, have a buddy to assist each other with their learning,²¹ and with two of the three Gamification IDTs, which also aimed at younger users.

Education

The most widely used IDT was Education, with 21 studies. The term refers to using technology to present instructional and informational content to the user with a common view of minimizing the need for visits to educators or practitioners. Among the studies, two presented personalized education facilities using computerized and semi-automatic behavior change theory algorithms, which adapted the content to a user’s usage trends in order to better target the user’s needs. The first suggested personalized diabetes management strategies,²³ while the other provided personalized suggestions on how to improve self-care based on blood glucose readings and questions posed by users.²⁵ Two of the studies were published in 2008, three in 2011 and 2014, four in 2010 and 2013, and five in 2012.

Financial

The incentive of providing financial remuneration, or an equivalent reward system, e.g. vouchers, was proposed in two of the selected studies, both from 2012. The concept behind⁴³ was in conjunction with gamification, wherein improvement in management, e.g. increased measurement frequency, and sustained engagement with the tool resulted in rewards, such as iTunes redemption codes. The other study²⁹ provided a financial incentive to cover the costs associated with increased text messaging which was required by the study as part of the intervention.

Gamification

Gamification itself is not a new concept with roots in Lenin’s early 20th century theory of “social competition” and more recently in attempts by management consultants in the 1990s to introduce fun into workplaces.⁵⁵ It is, however, quite a recent trend in mHealth for diabetes that has been enabled by modern technologies. The IDT involves providing a platform such as digital rewards, e.g. unlockable video content, leveling up, or activity-based experiences, to make management more fun and rewarding to the users. Of our selected studies, three presented this IDT. As noted by Marczewski,⁵⁶ they did not begin appearing until 2010, with one published in each of 2010, 2011, and 2012. These studies were generally aimed at teens and adolescents in an attempt to attract the younger generation that were not well equipped to deal with the recurring, complex tasks involved in diabetes management.^42,43 They are commonly designed to reduce food intake, increase physical exercise and improve the regularity of monitoring.

Barriers for adoption

To answer our auxiliary aim of drawing out barriers for adoption of IDTs, we accepted eight studies^13,57–63 that presented review and interview findings into mobile interventions for diabetes management. From these, we drew out the needs and barriers relating to IDTs. A common theme amongst these studies was the need to consider the patient and clinician burden in the upkeep of IDT mHealth tools, especially regarding data entry. It was noted that this barrier to adoption can be alleviated by further automation and analysis of patterns and metrics.⁵⁸ Correspondingly, it was suggested that further studies need to be undertaken to demonstrate the clinical outcomes of automatic data transfer between devices.⁵⁷ A current lack of timely and personalized feedback in many applications,⁶² the need for ongoing self-management that goes beyond introductory points currently presented,¹³ integration of IDTs with provider systems, and consideration of the target communities in developing IDT tools⁶³ were also noted as current barriers. Similarly, Dyer⁶⁰ concluded the need for practitioners to learn more about the tools and technologies before they can realize their potential. Another study⁵⁹ that served to highlight the differences between patients through interviews found that respondents were divided as to whether there was benefit of virtual social support networks. The respondents further noted the need for real-time technology assistance, especially for older users, and a desire for continued personalization. Finally, Sieverdes et al.⁶¹ raised concerns over most applications that aim to influence a users’ behavior not being based on standards of care or theoretical behavior change models.

Discussion

We found that a large number of articles have explored and presented findings on mHealth tools for diabetes management that make use of IDTs. We categorized these using the seven IDT mechanisms, considered alongside the CDM and CGT, as well as the interplay of different IDTs and the personalization of the content. These aspects are discussed below.

Development of incentive technologies

During the review period there have been huge changes and improvements in the technology used by mHealth applications, which are leveraging the latest technological developments. However, the core IDT mechanisms presented with these technologies have remained largely unchanged. Feedback has become more personalized through the use of modern algorithms, such as automated software that analyses trends in blood glucose measurements, instead of simple responses upon entering data; social support has moved from physical public centers and groups to virtual mobile- and internet-based communication; while more recently gamification is now delivered via mobile devices for health improvement, as opposed to traditional workplace-based gamification. All of these serve as incentives to motivate, retain and empower users; nevertheless it is technological innovation that has driven change, not new incentive techniques.

Content generation

As illustrated in Figure 3, the CGTs illuminate a number of interesting aspects regarding the use of IDTs. The most striking aspect is the rapid rise then fall of semi-automatic techniques, from one in both 2010 and 2011, to seven in 2012, and back to one in 2013, with none in 2014. This resulted in a dichotomy of fully automatic and fully manual techniques. Upon further inspection, it is clear that where possible, technologies and algorithms have been implemented to improve the autonomy and to reduce manual strain, for example automatic generation of personalized feedback.³² However, where this is not possible, such as with the social IDT, which at its core is human generated messages, manual solutions still continue to be used. In this case, the communication platforms are automatic, but the content is generated manually.

Clinical effectiveness

While performing our study, we attempted to measure the clinical effectiveness of IDTs. However, it was impossible to distil this information due to the studies not being exclusively about the effectiveness of the incentives themselves; moreover, most studies included multiple IDTs that we were unable to separate. It would therefore be spurious to attribute given results to IDT implementation. However, seven studies did report improved HbA1c levels in study participants, two studies noted reduced systolic blood pressure, and 13 mentioned improved management practices and self-efficacy, such as increased measurement frequency, increased exercise, improved diet and increased complication checks, e.g. checking feet. Nonetheless, these results were not attributed to specific IDTs.

User perceptions

IDT mechanisms, fundamentally, intend to influence users by motivating them to adopt and return to the tool. However, the tools presented by many of the articles were not positively perceived by the users. In one study, mobile devices were found to frustrate older users,⁴⁹ while another study which employed the game console, Wii, was reported as not useful.⁴⁰ Similarly, Chen et al.¹¹ revealed that, “technological difficulties were the main reason for a decline in the use.” Correspondingly, tools that aimed to reduce manual labor, e.g. by simplifying operational tasks, were found to be appreciated by users.^11,62,63 Consequently, meaningful technology-based intervention, must be developed with consideration of its role within the user’s wider work, home, and social life, rather than in the isolation of clinical effectiveness. That is to say, it does not matter how technologically advanced a tool is, nor how good the IDT mechanisms are, if a tool is hard to use, or perceived as not helping with disease management, it will not be adopted. Finding the balance between clinical effectiveness and positive user perceptions, a difficult task even with the help of IDTs, is therefore one of the key components that must be considered when developing an mHealth solution.

Age of users

The age structure for patients with type 1 and type 2 diabetes is quite diverse and needs to be addressed in a study exploring as large a spectrum as ours. While many of the articles presented solutions and findings in light of the age of participants, we did not include this in our analysis as our aim was to review which IDTs were used in mHealth for diabetes management and not who the users of these IDTs are or were. We note that appropriate incentive mechanisms may change with age and diabetes management experience. For example, in general, Type 1 insulin dependent people with diabetes might be younger users who are more technologically-savvy. Therefore, gamification may be more appropriate as the technology itself is not an obstacle; instead providing an incentive to monitor, or exercise, is. In contrast, older users may not have the same level of technological-savvy. However they are at higher risk of severe complications. Hence, a simple alert or reminder system may be more appropriate to these users. This balance between IDT mechanisms, age and experience must be considered when developing mHealth solutions within, and beyond, the realm of diabetes management. Personalization techniques may begin to take this into account, whereby, as well as presenting personalized messages, they alter the IDT mechanisms which are prioritized or “switched on/off” for each user.

Conclusions

In this study, we defined the concept of IDT, as used by mHealth tools, to be core features designed to act as motivating mechanisms for retaining and empowering users. We then identified seven core IDT mechanisms that are used by mHealth tools for diabetes management and classified 34 articles into these categories. Education was the most commonly used IDT, followed by Reminder and Feedback, with most articles presenting more than one mechanism. Our study has shown that while the technologies used to deliver and generate mHealth content have developed over time, the core IDT mechanisms have remained the same. Instead, existing techniques adapt to the changes in technology, such as moving from manual to automatic content delivery and personalization of content. All of these serve as incentives to motivate, retain and empower users; nevertheless it is technological innovation that has driven change, not new incentive techniques.

Footnotes

Acknowledgements

All authors made meaningful contributions to the study design and development of the research aim. MdeR, YJ, and JK contributed to acquisition and analysis of the data. RN and MK contributed insight and guidance relating to analysis of the data. All authors contributed to writing and/or revising the article and all authors provided their final approval of the version to be published.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported in part by the Australian Research Council. The authors received no financial support for the research, authorship, and/or publication of this article.

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