Evidence map›Paper›PMID 42214074›Full record

Trial reportJournal of medical Internet research2026

Understanding mHealth Engagement Among Patients With 30-Day Hospital Revisits: Secondary Analysis of a Randomized Clinical Trial.

Susan Landon, Angira Mondal, Aiden Ahn, Klea Profka, Anna U Morgan, Eric Bressman

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Susan LandonDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-4026-3668
Angira MondalLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0009-0006-1524-8158
Aiden AhnDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0009-0004-4155-8753
Klea ProfkaCenter for Health Incentives and Behavioral Economics, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0009-0000-7222-3922
Anna U MorganDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-4641-1609
Eric BressmanDepartment of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.ORCID https://orcid.org/0000-0003-4688-0747

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReducing 30-day hospital readmissions has been a long-standing goal across health systems in the United States. While nurse-led phone outreach has been widely adopted to support transitional care, its reach is constrained by staffing and time limitations. Mobile health (mHealth) interventions, such as automated SMS text messaging and patient portals, offer scalable alternatives but have shown mixed effectiveness in reducing readmissions. Understanding how patients engage with mHealth after discharge may help optimize these tools for postdischarge care.

objectiveThis study aimed to characterize patients in an mHealth transitional care program who experienced hospital revisits within 30 days of discharge, comparing demographic and clinical characteristics, intervisit interactions, and revisit features between those who engaged with mHealth and those who did not.

methodsWe conducted a secondary analysis of patients in the intervention arm of the Mobile Outreach to Reduce Emergencies-Primary Care randomized clinical trial. Participants received automated SMS text messages for 30 days after discharge alongside usual transitional care. We identified patients with a 30-day hospital revisit and conducted manual chart reviews to assess mHealth engagement and other forms of health care contact. We compared patient characteristics, intervisit interactions, and revisit features (time to revisit, relatedness to index hospitalization, and predictability of revisit) between mHealth users and nonusers.

resultsAmong 496 patients with a 30-day revisit, 185 (37%) engaged with mHealth before their return. mHealth users were younger (n=47, 26% aged <50 years vs n=41, 14% among nonusers; P=.004) and more likely to have commercial insurance (n=43, 23% mHealth users vs n=35, 11% mHealth nonusers; P=.005). Revisits were more likely to be rated highly or somewhat predictable among mHealth users compared to nonusers (n=105, 56% vs n=152, 49%; P=.04), while relatedness to the index hospitalization was similar (n=98, 53% vs n=173, 55%; P=.09). mHealth users had a longer mean time to revisit than nonusers (15.2, SD 8.1 vs 11.3, SD 8.4 days; P<.001) and were more likely to contact their practices via telephone (n=100, 54% vs n=136, 44%; P=.03) or attend a clinic visit (n=112, 61% vs n=131, 42%; P<.001).

conclusionsAmong patients enrolled in an mHealth postdischarge program who experienced hospital revisits, fewer than half engaged with mHealth prior to their return. Revisits among mHealth users occurred later and were more predictable, suggesting that engagement may enhance situational awareness but not necessarily prevent revisits. Future work should focus on strategies to increase engagement across groups and integrate mHealth with existing transitional care infrastructure.

Indexed as

Patient ReadmissionTelemedicineAdultAgedDigital HealthFemaleHumansMaleMiddle AgedPatient DischargeSecondary Data AnalysisText MessagingTransitional CaremHealthmobile healthquality improvementreadmissionstransitional care management

Identifiers

PMID42214074
PMCPMC13263652

What OpenQuestion holds

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Registered trials

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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.