Exploring app features with outcomes in mHealth studies involving chronic respiratory diseases, diabetes, and hypertension: a targeted exploration of the literature

Author:

Donevant Sara Belle1ORCID,Estrada Robin Dawson1ORCID,Culley Joan Marie1,Habing Brian2,Adams Swann Arp3

Affiliation:

1. College of Nursing, University of South Carolina, Columbia, South Carolina, USA

2. Department of Statistics, University of South Carolina, Columbia, South Carolina, USA

3. College of Nursing/Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA

Abstract

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.

Funder

National Institutes of Health

National Library of Medicine

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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