Unknown

Dataset Information

0

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


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.

SUBMITTER: Donevant SB 

PROVIDER: S-EPMC6188510 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

altmetric image

Publications

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

Donevant Sara Belle SB   Estrada Robin Dawson RD   Culley Joan Marie JM   Habing Brian B   Adams Swann Arp SA  

Journal of the American Medical Informatics Association : JAMIA 20181001 10


<h4>Objectives</h4>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.<h4>Materials and methods</h4>This search included primary articles focused on app-based interventions in managing chronic respiratory diseases, diabetes, and hypertension. T  ...[more]

Similar Datasets

| S-EPMC7477670 | biostudies-literature
| S-EPMC8603164 | biostudies-literature
| S-EPMC11009854 | biostudies-literature
| S-EPMC11342009 | biostudies-literature
| S-EPMC9178446 | biostudies-literature
| S-EPMC10182455 | biostudies-literature
| S-EPMC7884212 | biostudies-literature
| S-EPMC10335127 | biostudies-literature
| S-EPMC6329431 | biostudies-other