Table 6.

The characteristics of mixed-methods research in mHealth-based diabetes management studies examined in this review (part 1).

Author, year	Main purpose of the study	Recognition of MMa	Purpose of mixing	Formal MM research question	Prioritized method	Stage of integration	Design type
Allen et al, 2009 [33]	To assess feasibility and acceptability of continuous glucose monitoring and accelerometer technology in exercising type 2 diabetic patients	Yes, as multimethod	Complementarity	No	Equal	Sampling and interpretation	Explanatory sequential
Baron et al, 2015 [34]	To identify the challenges related to recruitment, fidelity, implementation, and context of mobile telehealth interventions targeting diabetic patients	Yes	Complementarity: different measures for different parts of the research phenomenon	No	QUANb	Sampling, data collection, data analysis, and interpretation	Embedded
Baron et al, 2016 [35]	To examine the behavioral effects of a mobile phone–based home telehealth intervention in diabetic patients	No	Triangulation	No	QUAN	Sampling and interpretation	Embedded
Burner et al, 2013 [36]	To explore the attitudes of inner-city Latino patients toward TExT-MEDd program and other health information sources	Yes	Initiation: the qualitative study was conducted to understand the contradictory findings of the quantitative method	No	Equal	Sampling and interpretation	Explanatory sequential
Carroll et al, 2007 [37]	To evaluate user satisfaction with an mHealth diabetes monitoring system	Yes	Sequential development	No	QUAN	Discussion or interpretation	Exploratory sequential
Franklin et al, 2008 [38]	To explore the interactions of type 1 patients with SMS text messaging support	Yes	Triangulation	No	Equal	Sampling, data analysis, and interpretation	Concurrent
Froisland et al, 2012 [39]	To explore ways mobile apps can be used to monitor adolescents with type 1 diabetes	Yes	Triangulation	No	QUALc	Sampling, data collection, and interpretation	Embedded
Georgsson and Staggers, 2016 [40]	To test the feasibility of a multimethod approach for patients’ experienced usability of a diabetes mHealth system	Yes, as multimethod	Triangulation	No	Equal	Sampling, data analyses, and interpretation	Concurrent
Grindrod et al, 2014 [41]	To examine the usability and usefulness of mobile medication apps with older adults	Yes	Triangulation	No	Equal	Sampling, interpretation, and data analysis	Concurrent
Jones et al, 2015 [42]	To evaluate the attitudes of American Indian women toward postpartum intervention approaches (including mHealth) and risk factors for developing gestational diabetes	Yes	Complementarity: different measures for different parts of the research phenomenon	No	QUAL	Sampling and interpretation	Embedded
Nundy et al, 2014 [43]	To investigate the behavioral effects of a theory-driven mobile phone–based intervention using an automated, interactive SMS text messaging system	Yes	Triangulation	No	QUAN	Sampling and interpretation	Embedded design
Osborn and Mulvaney, 2013 [44]	To examine the capability of an SMS text messaging and interactive voice response intervention for low-income adults with type 2 diabetes mellitus	Yes	Sequential development	No	Equal	Sampling, data analysis, and interpretation	Embedded
Verwey et al, 2016 [45]	To examine the reach, implementation, and satisfaction with a counseling tool combining an accelerometer, a mobile phone, and a Web application	Yes	Triangulation	No	Equal	Sampling, interpretation, and data analysis	Embedded
van der Weegen et al, 2014 [46]	To test the usability of a monitoring and feedback tool targeting diabetic patients	Yes	Sequential development	No	Equal	Sampling, data collection, data analysis, and interpretation	Multiphase study

^aMM: mixed methods.

^bQUAN: quantitative.

^cQUAL: qualitative.

^dTExT-MED: Trial to Examine Text Message–Based mHealth in Emergency Department Patients With Diabetes.