Evidence map›Paper›PMID 38506896›Full record

ArticleJMIR mHealth and uHealth2024

Patient Engagement With and Perspectives on a Mobile Health Home Spirometry Intervention: Mixed Methods Study.

Andrew W Liu, William Brown Iii, Ndubuisi E Madu, Ali R Maiorano, Olivia Bigazzi, Eli Medina, Christopher Sorric, Steven R Hays, Anobel Y Odisho

Open access · goldAbstract read
In one paragraph

Article in JMIR mHealth and uHealth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
0.9field-weighted citation impact, top 21% of its field
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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. 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

9 authors at 1 institution in 1 country.

Andrew W LiuCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0000-0001-8033-2598
William Brown IiiCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0000-0003-4322-4859
Ndubuisi E MaduCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0009-0008-5222-6139
Ali R MaioranoCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0000-0002-4697-6186
Olivia BigazziCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0009-0008-0643-0163
Eli MedinaCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0000-0001-6189-7235
Christopher SorricCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0009-0007-5304-7426
Steven R HaysDepartment of Medicine, University of California, San Francisco, CA, United States.ORCID 0000-0002-2887-8451
Anobel Y OdishoCenter for Digital Health Innovation, University of California, San Francisco, CA, United States.ORCID 0000-0003-0975-0812
University of California, San Francisco · US

Funding

Institutional Career Development Core SupplementKL2TR001870 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI KANAYA, ALKA M., SARKAR, URMIMALA · 2016 to 2025
$19.7M
UCSF Learning Health System K12 Career Development ProgramK12HS026383 · AHRQ · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GONZALES, RALPH, SARKAR, URMIMALA · 2018 to 2022
$3.6M
Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical NotesK01DA055081 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BROWN III, WILLIAM · 2022 to 2025
$760k
AHRQ HHS K12 HS026383NCATS NIH HHS KL2 TR001870NIDA NIH HHS K01 DA055081
6 · The paper itself

Abstract

backgroundPatient engagement attrition in mobile health (mHealth) remote patient monitoring (RPM) programs decreases program benefits. Systemic disparities lead to inequities in RPM adoption and use. There is an urgent need to understand patients' experiences with RPM in the real world, especially for patients who have stopped using the programs, as addressing issues faced by patients can increase the value of mHealth for patients and subsequently decrease attrition.

objectiveThis study sought to understand patient engagement and experiences in an RPM mHealth intervention in lung transplant recipients.

methodsBetween May 4, 2020, and November 1, 2022, a total of 601 lung transplant recipients were enrolled in an mHealth RPM intervention to monitor lung function. The predictors of patient engagement were evaluated using multivariable logistic and linear regression. Semistructured interviews were conducted with 6 of 39 patients who had engaged in the first month but stopped using the program, and common themes were identified.

resultsPatients who underwent transplant more than 1 year before enrollment in the program had 84% lower odds of engaging (odds ratio [OR] 0.16, 95% CI 0.07-0.35), 82% lower odds of submitting pulmonary function measurements (OR 0.18, 95% CI 0.09-0.33), and 78% lower odds of completing symptom checklists (OR 0.22, 95% CI 0.10-0.43). Patients whose primary language was not English had 78% lower odds of engaging compared to English speakers (OR 0.22, 95% CI 0.07-0.67). Interviews revealed 4 prominent themes: challenges with devices, communication breakdowns, a desire for more personal interactions and specific feedback with the care team about their results, understanding the purpose of the chat, and understanding how their data are used.

conclusionsCare delivery and patient experiences with RPM in lung transplant mHealth can be improved and made more equitable by tailoring outreach and enhancements toward non-English speakers and patients with a longer time between transplant and enrollment. Attention to designing programs to provide personalization through supplementary provider contact, education, and information transparency may decrease attrition rates.

Indexed as

Patient ParticipationTelemedicineCommunicationHumansLinear ModelsOdds RatioattritioncomplicationcomplicationsdevicedevicesdropouteHealthengagementexperienceexperiencesinterviewinterviewslunglungslung transplantmethodsmHealthmobile healthmonitormonitoringnumerical dataorganorganspatient-centered carepulmonaryremote patient monitoringrespiratoryspirometrystatisticstelemedicinetransplanttransplantationtransplants

Identifiers

PMID38506896
PMCPMC10993125
OpenAlexW4389641284

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.