The use of mobile health apps has been growing steadily in the past few years. Parallelly, research evaluating their effectiveness has been increasing, especially in evaluating managing chronic conditions, like diabetes and hypertension, through using health apps. These two diseases are highly interlinked with social determinants of health, and therefore the evaluation of health apps in self-management of these diseases should take into consideration the socioeconomic and sociocultural characteristics of the patients.
We aimed in this study to assess the extent of considering and analyzing inequality characteristics in RCTs evaluating the effectiveness of mHealth apps in diabetic and/or hypertensive patients. We built on our recently published umbrella review and extracted the primary RCTs focusing on diabetic and/or hypertensive populations from the systematic reviews. We used the PROGRESS-Plus framework to identify inequality characteristics. We provided descriptive analysis of the moderating inequality characteristics reported and analyzed by the studies.
We resulted in 72 articles of 65 RCTs and all of them considered at least one of the PROGRESS-Plus characteristics, and the most reported characteristics reported were age and gender of the participants (see Figure below). Seven of the studies conducted subgroup analyses with a few PROGRESS-Plus characteristics without clear and conclusive results.
Our study mainly highlights the gap in research in considering socioeconomic and sociocultural characteristics of participants and patients in RCT evaluating mHealth apps. With this gap, it remains challenging to give definite claims regarding the effectiveness of mHealth apps among heterogeneous diabetic and hypertensive groups. We encourage researchers to investigate how these characteristics moderate the effectiveness of health apps to better understand how effect heterogeneity for apps across different sociocultural or socioeconomic groups affects inequalities, to support more equitable management of non-communicable diseases in increasingly digitalized systems.