1 Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, AB, Canada.
2 Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
3 Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
4 Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
5 School of Nursing and Midwifery, Faculty of Health, Community and Education, Mount Royal University, Calgary, AB, Canada.
6 Department of Community Health Science, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
7 Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada.

Background: In recent years, mHealth has increasingly been used to deliver behavioral interventions for disease prevention and self-management. Computing power in mHealth tools can provide unique functions beyond conventional interventions in provisioning personalized behavior change recommendations and delivering them in real time, supported by dialogue systems. However, design principles to incorporate these features in mHealth interventions have not been systematically evaluated.

Objective: The goal of this review is to identify best practices for the design of mHealth interventions targeting diet, physical activity, and sedentary behavior. We aim to identify and summarize the design characteristics of current mHealth tools with a focus on the following features: (1) personalization, (2) real-time functions, and (3) deliverable resources.

Methods: We will conduct a systematic search of electronic databases, including MEDLINE, CINAHL, Embase, PsycINFO, and Web of Science for studies published since 2010. First, we will use keywords that combine mHealth, interventions, chronic disease prevention, and self-management. Second, we will use keywords that cover diet, physical activity, and sedentary behavior. Literature found in the first and second steps will be combined. Finally, we will use keywords for personalization and real-time functions to limit the results to interventions that have reported these design features. We expect to perform narrative syntheses for each of the 3 target design features. Study quality will be evaluated using the Risk of Bias 2 assessment tool.

Results: We have conducted a preliminary search of existing systematic reviews and review protocols on mHealth-supported behavior change interventions. We have identified several reviews that aimed to evaluate the efficacy of mHealth behavior change interventions in a range of populations, evaluate methodologies for assessing mHealth behavior change randomized trials, and assess the diversity of behavior change techniques and theories in mHealth interventions. However, syntheses on the unique features of mHealth intervention design are absent in the literature.

Conclusions: Our findings will provide a basis for developing best practices for designing mHealth tools for sustainable behavior change.

Trial registration: PROSPERO CRD42021261078; https: tinyurl.com/m454r65t.

International registered report identifier (irrid): PRR1-10.2196/39093.