Evidence map›Paper›PMID 29241243›Full record

ArticleApplied clinical informatics2017

Core Components for a Clinically Integrated mHealth App for Asthma Symptom Monitoring.

Robert S Rudin, Christopher H Fanta, Zachary Predmore, Kevin Kron, Maria O Edelen, Adam B Landman, Eyal Zimlichman, David W Bates

Abstract read
In one paragraph

Article in Applied clinical informatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 2 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

30 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  8. Review
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  11. Community engagement in the development of health-related data visualizations: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024
    Article
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  17. Article
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  19. Smartphone App for monitoring Asthma in children and adolescents.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2021
    Article
  20. 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

8 authors.

Robert S RudinRAND Corporation, Boston, Massachusetts, United States.
Christopher H FantaPartners Asthma Center, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Zachary PredmoreJohns Hopkins University, Baltimore, Maryland, United States.
Kevin KronPartners Asthma Center, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Maria O EdelenRAND Corporation, Boston, Massachusetts, United States.
Adam B LandmanPartners Asthma Center, Brigham and Women's Hospital, Boston, Massachusetts, United States.
Eyal ZimlichmanCentral Management, Sheba Medical Center, Tel Hashomer, Israel.
David W BatesDivision of General Internal Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States.

Funding

Using mHealth and Patient-reported Outcomes to Deliver Evidence-based Asthma CareR21HS023960 · AHRQ · RAND CORPORATION · PI RUDIN, ROBERT SAMUEL · 2015 to 2016
$299k
AHRQ HHS R21 HS023960
6 · The paper itself

Abstract

Background mHealth apps may be useful tools for supporting chronic disease management. Objective Our aim was to apply user-centered design principles to efficiently identify core components for an mHealth-based asthma symptom–monitoring intervention using patient-reported outcomes (PROs). Methods We iteratively combined principles of qualitative research, user-centered design, and “gamification” to understand patients' and providers' needs, develop and refine intervention components, develop prototypes, and create a usable mobile app to integrate with clinical workflows. We identified anticipated benefits and burdens for stakeholders. Results We conducted 19 individual design sessions with nine adult patients and seven clinicians from an academic medical center (some were included multiple times). We identified four core intervention components: (1) Invitation—patients are invited by their physicians. (2) Symptom checks—patients receive weekly five-item questionnaires via the app with 48 hours to respond. Depending on symptoms, patients may be given the option to request a call from a nurse or receive one automatically. (3) Patient review—in the app, patients can view their self-reported data graphically. (4) In-person visit—physicians have access to patient-reported symptoms in the electronic health record (EHR) where they can review them before in-person visits. As there is currently no location in the EHR where physicians would consistently notice these data, recording a recent note was the best option. Benefits to patients may include helping decide when to call their provider and facilitating shared decision making. Benefits to providers may include saving time discussing symptoms. Provider organizations may need to pay nurses extra, but those costs may be offset by reduced visits and hospitalizations. Conclusion Recent systematic reviews show inconsistent outcomes and little insight into functionalities required for mHealth asthma interventions, highlighting the need for systematic intervention design. We identified specific features for adoption and engagement that meet the stated needs of users for asthma symptom monitoring.

Indexed as

Mobile ApplicationsAdultAgedAsthmaElectronic Health RecordsFemaleHumansMaleMiddle AgedSelf ReportTelemedicineYoung Adult

Identifiers

PMID29241243
PMCPMC5802299

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.