Exploring App Features with Outcomes in Mhealth Studies Involving Chronic Respiratory Diseases, Diabetes, and Hypertension: A Targeted Exploration of the Literature
Document Type
Article
Subject Area(s)
Chronic Disease; Diabetes Mellitus (therapy); Health Behavior; Humans; Hypertension (therapy); Mobile Applications; Respiratory Tract Diseases (therapy); Self Care; Statistics as Topic; Telemedicine; Treatment Outcome
Abstract
OBJECTIVES: Limited data are available on the correlation of mHealth features and statistically significant outcomes. We sought to identify and analyze: types and categories of features; frequency and number of features; and relationship of statistically significant outcomes by type, frequency, and number of features. MATERIALS AND METHODS: This search included primary articles focused on app-based interventions in managing chronic respiratory diseases, diabetes, and hypertension. The initial search yielded 3622 studies with 70 studies meeting the inclusion criteria. We used thematic analysis to identify 9 features within the studies. RESULTS: Employing existing terminology, we classified the 9 features as passive or interactive. Passive features included: 1) one-way communication; 2) mobile diary; 3) Bluetooth technology; and 4) reminders. Interactive features included: 1) interactive prompts; 2) upload of biometric measurements; 3) action treatment plan/personalized health goals; 4) 2-way communication; and 5) clinical decision support system. DISCUSSION: Each feature was included in only one-third of the studies with a mean of 2.6 mHealth features per study. Studies with statistically significant outcomes used a higher combination of passive and interactive features (69%). In contrast, studies without statistically significant outcomes exclusively used a higher frequency of passive features (46%). Inclusion of behavior change features (ie, plan/goals and mobile diary) were correlated with a higher incident of statistically significant outcomes (100%, 77%). CONCLUSION: This exploration is the first step in identifying how types and categories of features impact outcomes. While the findings are inconclusive due to lack of homogeneity, this provides a foundation for future feature analysis.
Digital Object Identifier (DOI)
Publication Info
Journal of Informatics Nursing, Volume 25, Issue 10, 2018, pages 1407-1418.
APA Citation
Donevant, S. B., Estrada, R. D., Culley, J. M., Habing, B., & Adams, S. A. (2018). Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature. Journal of the American Medical Informatics Association, 25(10), 1407–1418. https://doi.org/10.1093/jamia/ocy104
Rights
© 2018, The Author(s) 2018. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.