Mobile technology for self-monitoring of blood glucose among patients with type 2 diabetes mellitus

Using the RE-AIM framework to evaluate internal and external validity of mobile phone-based interventions in diabetes self-management education and support

OBJECTIVE

We evaluated the extent to which studies that tested short message service (SMS)- and application (app)-based interventions for diabetes self-management education and support (DSMES) report on factors that inform both internal and external validity as measured by the RE-AIM (Reach, Efficacy/Effectiveness, Adoption, Implementation, and Maintenance) framework.

MATERIALS AND METHODS

We systematically searched PubMed, Embase, Web of Science, CINAHL (Cumulative Index of Nursing and Allied Health Literature), and IEEE Xplore Digital Library for articles from January 1, 2009, to February 28, 2019. We carried out a multistage screening process followed by email communications with study authors for missing or discrepant information. Two independent coders coded eligible articles using a 23-item validated data extraction tool based on the RE-AIM framework.

RESULTS

Twenty studies (21 articles) were included in the analysis. The comprehensiveness of reporting on the RE-AIM criteria across the SMS- and app-based DSMES studies was low. With respect to internal validity, most interventions were well described and primary clinical or behavioral outcomes were measured and reported. However, gaps exist in areas of attrition, measures of potential negative outcomes, the extent to which the protocol was delivered as intended, and description on delivery agents. Likewise, we found limited information on external validity indicators across adoption, implementation, and maintenance domains.

CONCLUSIONS

Reporting gaps were found in internal validity but more so in external validity in the current SMS- and app-based DSMES literature. Because most studies in this review were efficacy studies, the generalizability of these interventions cannot be determined. Future research should adopt the RE-AIM dimensions to improve the quality of reporting and enhance the likelihood of translating research to practice.

Data Integrity-Based Methodology and Checklist for Identifying Implementation Risks of Physiological Sensing in Mobile Health Projects: Quantitative and Qualitative Analysis

Background

Mobile health (mHealth) technologies have the potential to bring health care closer to people with otherwise limited access to adequate health care. However, physiological monitoring using mobile medical sensors is not yet widely used as adding biomedical sensors to mHealth projects inherently introduces new challenges. Thus far, no methodology exists to systematically evaluate these implementation challenges and identify the related risks.

Objective

This study aimed to facilitate the implementation of mHealth initiatives with mobile physiological sensing in constrained health systems by developing a methodology to systematically evaluate potential challenges and implementation risks.

Methods

We performed a quantitative analysis of physiological data obtained from a randomized household intervention trial that implemented sensor-based mHealth tools (pulse oximetry combined with a respiratory rate assessment app) to monitor health outcomes of 317 children (aged 6-36 months) that were visited weekly by 1 of 9 field workers in a rural Peruvian setting. The analysis focused on data integrity such as data completeness and signal quality. In addition, we performed a qualitative analysis of pretrial usability and semistructured posttrial interviews with a subset of app users (7 field workers and 7 health care center staff members) focusing on data integrity and reasons for loss thereof. Common themes were identified using a content analysis approach. Risk factors of each theme were detailed and then generalized and expanded into a checklist by reviewing 8 mHealth projects from the literature. An expert panel evaluated the checklist during 2 iterations until agreement between the 5 experts was achieved.

Results

Pulse oximetry signals were recorded in 78.36% (12,098/15,439) of subject visits where tablets were used. Signal quality decreased for 1 and increased for 7 field workers over time (1 excluded). Usability issues were addressed and the workflow was improved. Users considered the app easy and logical to use. In the qualitative analysis, we constructed a thematic map with the causes of low data integrity. We sorted them into 5 main challenge categories: environment, technology, user skills, user motivation, and subject engagement. The obtained categories were translated into detailed risk factors and presented in the form of an actionable checklist to evaluate possible implementation risks. By visually inspecting the checklist, open issues and sources for potential risks can be easily identified.

Conclusions

We developed a data integrity–based methodology to assess the potential challenges and risks of sensor-based mHealth projects. Aiming at improving data integrity, implementers can focus on the evaluation of environment, technology, user skills, user motivation, and subject engagement challenges. We provide a checklist to assist mHealth implementers with a structured evaluation protocol when planning and preparing projects.