Evidence map›Paper›PMID 30137383›Full record

ReviewJournal of the American Medical Informatics Association : JAMIA2018

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

Sara Belle Donevant, Robin Dawson Estrada, Joan Marie Culley, Brian Habing, Swann Arp Adams

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 7 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 7 syntheses or guidelines pooled it.

  1. Pooled it
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  8. Trial
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  16. Mobile heath applications for self-management in chronic lung disease: a systematic review.Network modeling and analysis in health informatics and bioinformatics · 2023
    Review
  17. Article
  18. Article
  19. Article
  20. Special Collection Editorial: The digital movement in nursing.Journal of research in nursing : JRN · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sara Belle DonevantCollege of Nursing, University of South Carolina, Columbia, South Carolina, USA.
Robin Dawson EstradaCollege of Nursing, University of South Carolina, Columbia, South Carolina, USA.
Joan Marie CulleyCollege of Nursing, University of South Carolina, Columbia, South Carolina, USA.
Brian HabingDepartment of Statistics, University of South Carolina, Columbia, South Carolina, USA.
Swann Arp AdamsCollege of Nursing/Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.

Funding

An Evidence-Based Evaluation Tool to Assist Health Care Providers in Their Assessment of Effective mHealth Applications of the Management of Chronic Health ConditionsF31LM012402 · NLM · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI DONEVANT, SARA BELLE · 2017 to 2017
$38k
NLM NIH HHS F31 LM012402
6 · The paper itself

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.

Indexed as

Health BehaviorMobile ApplicationsTelemedicineChronic DiseaseDiabetes MellitusHumansHypertensionRespiratory Tract DiseasesSelf CareStatistics as TopicTreatment Outcome

Identifiers

PMID30137383
PMCPMC6188510

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.