JMIR Publications

ABSTRACT

Background:

Despite the prevalence of mobile health (mHealth) technologies, and observations of their impacts on patients’ health, research still lacks consensus about how best to evaluate these tools for patient self-management of chronic conditions. To date, researchers do not have guidelines as to which qualitative or quantitative factors to measure or how to go about gathering these reliable data.

Objective:

This study aimed to document the methods and both qualitative and quantitative measures used to assess mHealth apps and system interventions intended for use by patients for self-management of chronic non-communicable diseases (NCDs).

Methods:

A scoping review was performed within PubMed, Medline, Google Scholar, and ProQuest Research Library for literature published in English between Jan. 1, 2015, and Jan. 18, 2019. Search terms included combinations of the description of the intention of the intervention, e.g. self-efficacy or self-management, and description of the intervention platform, e.g. “mobile application” or sensor. Article selection was based on if the intervention described the patient, with a chronic NCD, as the primary user of a tool or system that would always be available to them for their self-management. Data extraction included study design, health condition(s), participants, intervention type (app or system), methods used and which qualitative and quantitative data were measured.

Results:

A total of 31 articles met eligibility criteria. Results were reported as either mHealth app interventions (n=15), i.e. single devices, or mHealth system interventions (n=16), i.e. more than one tool, with one study describing both apps and systems. The most common methods were the Collection of usage-logs, which was used in 21 studies, followed by Standardized questionnaires (n=18) and Ad-hoc questionnaires (n=13). App interactions were the most common measure taken and were reported in 19 studies, followed by usability/feasibility (n=17), and patient-reported health data via the app (n=15).

Conclusions:

This review demonstrates that health intervention studies are taking advantage of the additional resources that mHealth technology provides. As mHealth technologies become more prevalent, the call for evidence includes the impacts on patients’ self-efficacy and engagement in addition to traditional measures. However, given mHealth’s unstructured data forms, diverse used and various platforms, it can be challenging to select the right methods and measures to evaluate these technologies. The inclusion of, e.g., app usage-logs and patient involved methods to find the impact of mHealth is an important step forward in health intervention research. We aim for this overview to be a catalogue of possible ways in which mHealth has been and can be integrated into research practices.