Increases in diabetes incidence and prevalence are a major concern in today’s health care system. There are nearly 385 million diabetic patients in the world, and almost 90% of them have type 2 diabetes, which can be treated with appropriate lifestyle and nutrition changes. Diabetes is a complicated disease and requires a high level of patient involvement; 95% of its management is patient initiated. Considering the additional 235 million patients with type 2 diabetes expected worldwide by the year 2035, the development of diabetic patients’ self-management skills is critical [1].

Mobile health (mHealth) apps that offer personalized, fast, cost-effective, and engaging services to patients have been used increasingly in diabetes self-management [2,3]. The “disease and treatment” apps category currently includes more than 2250 diabetes apps (iOS and Android systems in total). The number of mobile apps for diabetes is growing rapidly and market penetration is expected to grow to 7.8% by 2018, reaching 24 million diabetic patients [4].

Despite the increase in the diabetes cases and rapid growth in the mobile app market, research does not yet provide sufficient information about the factors fostering or hindering the adoption of these apps, patients’ attitudes toward using them, or their effectiveness in terms of health behavior change [2,5,6,7,8]. Therefore, the controversy over whether research methodologies or mHealth-based interventions are ineffective [9,10] still remains. Alternative study designs and more rigorous methodologies to advance mHealth research, especially in diabetes management, are strongly suggested [2,11].

There is a growing interest in using mixed-methods research in mHealth-based diabetes management studies because both diabetes self-management and mHealth studies require using different approaches, techniques, and measures cooperatively within an integrated perspective [7,12,13]. This scoping review examined the extent of mixed-methods research used in mHealth-based diabetes management studies. The methodological approaches used to conduct mixed-methods studies were also investigated, and implications for future mHealth and diabetes management research are provided.

Mixed-methods research grew out of the “paradigm” controversy between positivist (quantitative) and constructivist (qualitative) research traditions in the 1980s and 1990s [14]. Mixed-methods research relies on both theory and practice to integrate knowledge from multiple approaches, perspectives, tools, positions, and opinions. The term “method” is used to cover a broad range of methodological (data collection techniques, design types, research methods, and so on) and related philosophical issues (eg, ontology, epistemology, axiology) [15].

The main goal of mixed-methods research is to use both quantitative and qualitative approaches to provide a better understanding of the research phenomena [16]. Because of the complementary strengths of different research paradigms, methodologies, and methods, mixed-methods research may provide new and different perspectives about an issue, expanding study findings beyond those produced by only one approach. Mixed-methods research increases the credibility of results when different approaches suggest the same conclusion, so it’s a value-added methodology [17,18].

Mixed-methods research is generally used for (1) triangulation of different approaches and methodologies to explain a single phenomenon; (2) complementarity of the research methods where one method is used to elaborate, illustrate, enhance, or clarify the results from another method; (3) sequential development of a study in which the results of one method are used to inform the other; (4) initiation of a study by using one method to find contradictions and paradoxes in findings from another method; and (5) expansion of a research study by using different methods to gain new perspectives or insights about a research problem [19].

Selecting an appropriate research design is a crucial step in a mixed-methods research. The purpose of a study and the nature of the research question help shape the design used in a mixed-methods study. In addition, using either concurrent or sequential time orientation affects study design, sampling, and data collection.

In a concurrent time orientation, data collection is completed for quantitative and qualitative phases of the study at approximately the same time to answer the same question. Both datasets are processed during data analysis and interpretation stages.

In a sequential time orientation, data can be obtained in stages, so the data from the first stage are used to shape the selection of data in the second stage (exploratory or explanatory sequential design). On the basis of their study purposes, questions, designs, and resources, researchers might place equal emphasis on both methods or use one of them primarily [16,20].

Integration is the central issue of mixed-methods research because it enables researchers to examine both types of data intensively [20]. Integration can be achieved at the method level or at the interpretation and reporting level. Bryman [21] reported that 57% of social science studies using mixed methods collected qualitative and quantitative data separately, while in approximately 27% both quantitative and qualitative data were derived from a single data source (eg, survey questionnaires including closed and open-ended questions). A growing scholarly interest has advanced mixed-methods research in various areas. Especially in health sciences, several important applications of mixed-methods research have been reported [22,23]. However, the majority of these studies failed to provide a detailed description of their data collection techniques, methods of analysis, stages of integration, and justifications for the use of mixed methods [24].

The research questions addressed in this review are as follows: (1) what is the extent and nature of mixed-methods research in mHealth-based diabetes management studies and (2) what are the current methodological approaches for designing and conducting mixed methods in these studies?

As a scoping review, this study did not intend to evaluate the scientific rigor of the selected studies as seen in systematic reviews [25]. It aimed to present the different ways in which researchers have used mixed methods.

Because the key purpose of the study was scoping the area of research, a comprehensive search strategy using multiple sources was used to identify all the relevant studies. Content-specific electronic databases, key journals, Web-based research and knowledge centers, websites, and handsearches of reference lists from studies and reviews were included in the search. The search was run between May 15, 2016, and June 30, 2016, and included all articles published by July 1, 2016. Table 1 presents the literature sources used for this review.

Three sets of search term combinations were used in the search, and they were entered and combined using Boolean operators where applicable. Combination 1 was the most detailed and was used in each electronic database and on the mHealth Evidence website. It included “mobile health,” “mHealth,” “m-Health,” “mobile,” “mobile phone,” “smartphone,” “cellular phone,” “texting,” “text messaging,” “SMS,” “telemedicine,” “telehealth,” “telecare,” “telemonitoring,” “diabetes,” “diabetes mellitus,” “diabetic,” “mixed method,” “multi-method,” and “mix methodology.”

Because some studies do not explicitly indicate their methodologies as mixed methods, mix methodology, or multimethod, combination 2 was applied without using the methodology terms (mixed methods, multimethod, mix methodology). Because of the excessive number of records that resulted in the search with combination 2 in Google Scholar, PubMed, and Web of Science, this search was only completed in CINAHL (Cumulative Index to Nursing and Allied Health Literature) and PsycINFO databases.

Combination 3 used the terms most relevant to our research question. “Mobile health,” “diabetes,” and “mixed methods” search terms were used in US NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) Diabetes Research Center and in Google Scholar. Wide search options were used in terms of text availability, publication dates, and document formats, and only the English-language and academic journal filters were applied in electronic sources. ResearchGate was only used to search for the authors’ personal files and to obtain full-text articles. Table 2 summarizes the search term combinations used in each electronic source.

Several inclusion and exclusion criteria were applied to the identified studies. The review included the primary research studies published in English-language academic journals that focused on using mHealth technologies in the context of type 1, type 2, or gestational diabetes management and included a combination of qualitative and quantitative methods.

In this review, the term “mobile health technologies” was used to cover mobile phones, portable monitoring devices (ie, accelerometer), and wireless devices used in medical care (ie, cell phones). Studies whose focus was Internet (computer)-based, telephone (landline)-based, or home-based monitoring were excluded. Examples of such studies include a mixed-methods diabetes telemonitoring study [26], using a Web-enabled glucometer for self-monitoring blood glucose, and a home-based monitoring study [27] including a randomized controlled trial and a series of interviews that used a transmission device to be attached to an analog telephone line or via USB to a computer to upload blood glucose and blood pressure measurements to a server. In addition, there were telephone coaching and counseling studies (ie, [28,29]) that did not match the inclusion criteria. The studies related to diabetic retinography were also excluded. Figure 1 presents the article selection process in this review.

The descriptive-analytical method [25] was followed in order to standardize and chart key items of information acquired from the reviewed articles. The information collected and charted included the following elements:

Tables 3-5 present the definitions used to analyze the design types (Table 3), integration strategies (Table 4), and legitimation methods (Table 5) in this review.

This scoping review identified comprehensive mixed-methods applications in the growing field of mHealth-based diabetes management. Mixed methods research can be effectively used if there is a strong and appropriate design dictated by research questions [48,49]. Creswell and Tashakkori [50] suggest using at least one clearly defined mixed methods question. Although none of the studies defined a separate mixed-methods research question and a very few studies (4/14, 29%) used formal mixed-methods terminology, the studies showed promise in their uses of multiple approaches and techniques. In addition to the common uses of mixed methods (triangulation and complementarity), studies with advanced designs presented various forms of combining different methods and techniques in sequential development of their studies. These studies are good examples for the field of diabetes management via mHealth technologies in which complicated interventions and multiple measurements are often required.

This study confirms the prominent role of qualitative methods in mixed-methods research. Qualitative measures were very important for the evolution of the majority of the papers reviewed, and they strongly influenced study findings. Because of the growing need for interpreting complex longitudinal studies and interventions [51] and big and sophisticated data coming from Web-based and mobile technology usage statistics, the importance of qualitative methods seems to be increasing.

The studies in this review presented various forms of integration at the method level. Some studies enabled integration at multiple points, linking their data collection and analysis at each point in the research. This is also noteworthy for mixed-methods studies in which integration is generally made at the level of data interpretation or discussion [52].

In terms of validity or legitimation strategies, little variation existed among the studies. In addition to inside-outside legitimation, these advanced designs should have also used weakness minimization and conversion legitimation methods because they are very specific to mixed-methods research.

The major limitations of the studies included in this review were related to data collection processes. Surveys were the most common data collection method used in the studies, but (as opposed to experimental studies) descriptive studies do not show causality, as they have poor control over external factors and external validity problems due to standardized question types. In addition, almost all studies acknowledged sampling problems (13/14, 93%). Small sample sizes, sampling bias, and use of convenient sampling are important barriers to the generalizability of findings in any type of study.

When various data collection techniques are used together, researchers are able to provide rich analysis and interpretations. This study shows that in addition to the common qualitative data collection techniques (focus group discussions and semistructured interviews), multiple data collection techniques (including think aloud protocols, interviews, field notes, content analysis, and evaluation forms) are used.

It is also noteworthy that these studies failed to discuss the advantages gained by using mixed-methods approaches. The majority of the studies (13/14, 93%) failed to provide a methodological discussion concerning the inconsistency or consistency between quantitative and qualitative data. Thus, this study supports Brown et al [24], who indicated that mixed-methods studies still lack justifications for using this approach.

This study is an early exploration into the scope of mixed-methods studies in the field of mHealth-based diabetes management. This paper itself provides a demonstration of how to classify, analyze, and evaluate mixed-methods research. Regardless of field of interest, we believe many researchers could benefit from the guidelines, criteria, and examples provided in this review.

Although the database search was quite comprehensive, it was limited to the papers published in scientific journals and written in English. To increase the consistency of the coding, some established frameworks were used to identify design types [20], integration methods [31], and legitimation methods [32]. Because related information was not always clearly reported in the studies, some decision rules were applied and the most relevant choices presented in the frameworks were adopted. Another limitation is related to the coding process. Although the design types were classified by 2 coders, and a high level of agreement between them was established, the rest of the coding was done by the first author. However, to increase the confirmability of our analysis, she took the role of a “devil's advocate” with respect to the results, checking and rechecking each coding and analysis throughout the study.

This review provided important insights into the evaluation of mixed-methods studies focusing on diabetes management via mHealth technologies. The prominent role of qualitative methods in mixed-methods research and tailored measures in diabetes self-management studies was confirmed, and the importance of using multiple methods and techniques in this field was emphasized. Future studies could continue to use different qualitative approaches and try to integrate new tools and approaches that seem to be effective in health care settings, such as participatory video, photovoice, online communities, and chat and discussion groups [3].

Considering the opportunities provided by mixed methods, it is surprising to see such a small number of studies in this field. The lack of systematic approaches in establishing rigor in mixed-methods studies seems to be a major barrier to the adoption and recognition of this research by a large group of social researchers [20]. However, in the field of mHealth-based diabetes management both usability studies and behavioral change interventions require complex measurements, so researchers could utilize the advantages of mixed methods more frequently. Future research should focus on the ways to improve precision in measurements by incorporating experimental designs with properly selected, adequate number of participants and new and creative data collection techniques.

In the future, researchers should incorporate formal mixed-methods terminology, starting with defining a mixed-methods question separately at the beginning, aiming to answer this question, and discussing the value of using the methodology at the end. In this growing field of diabetes management, future reviews should systematically analyze rigor in mixed-methods studies and compare the results with those of single-method studies.