Evidence map›Paper›PMID 35254269›Full record

ArticleJMIR human factors2022

Understanding Patients' Intention to Use Digital Health Apps That Support Postdischarge Symptom Monitoring by Providers Among Patients With Acute Coronary Syndrome: Survey Study.

Jinying Chen, Jessica G Wijesundara, Gabrielle E Enyim, Lisa M Lombardini, Ben S Gerber, Thomas K Houston, Rajani S Sadasivam

Open access · goldAbstract read
In one paragraph

Article in JMIR human factors, 2022. 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
1.7field-weighted citation impact, top 13% 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, 8 citations in OpenAlex.

  1. Pooled it
  2. Observational
  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

7 authors at 2 institutions in 1 country.

Jinying ChenDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-7259-4301
Jessica G WijesundaraDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0003-4578-9327
Gabrielle E EnyimDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-6353-1325
Lisa M LombardiniDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0002-0358-9688
Ben S GerberDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0003-4367-6396
Thomas K HoustonDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States.ORCID https://orcid.org/0000-0002-2909-4018
Rajani S SadasivamDepartment of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States.ORCID https://orcid.org/0000-0001-8406-6207
University of Massachusetts Chan Medical School · USWake Forest University · US

Funding

iDAPT: Implementation and Informatics - Developing Adaptable Processes and Technologies for Cancer Control P50CA244693 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI HOUSTON, THOMAS K · 2019 to 2023
$4.1M
K12 Cardiopulmonary Implementation Science Scholars ProgramK12HL138049 · NHLBI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LEMON, STEPHENIE C., LINDENAUER, PETER KYLE · 2017 to 2021
$2.7M
Prevention And Control of Cancer: Training for Change in Individuals and SystemsT32CA172009 · NCI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LEMON, STEPHENIE C., OCKENE, JUDITH K · 2019 to 2023
$1.5M
NCI NIH HHS P50 CA244693NCI NIH HHS T32 CA172009NHLBI NIH HHS K12 HL138049
6 · The paper itself

Abstract

backgroundAfter hospital discharge, patients with acute coronary syndrome (ACS) often experience symptoms that prompt them to seek acute medical attention. Early evaluation of postdischarge symptoms by health care providers may reduce unnecessary acute care utilization. However, hospital-initiated follow-up encounters are insufficient for timely detection and assessment of symptoms. While digital health tools can help address this issue, little is known about the intention to use such tools in ACS patients.

objectiveThis study aimed to assess ACS patients' intention to use digital health apps that support postdischarge symptom monitoring by health care providers and identify patient-perceived facilitators and barriers to app use.

methodsUsing email invitations or phone calls, we recruited ACS patients discharged from a central Massachusetts health care system between December 2020 and April 2021, to participate in the study. Surveys were delivered online or via phone to individual participants. Demographics and access to technology were assessed. The intention to use a symptom monitoring app was assessed using 5-point Likert-type (from strongly agree to strongly disagree) items, such as "If this app were available to me, I would use it." Responses were compared across demographic subgroups and survey delivery methods. Two open-ended questions assessed perceived facilitators and barriers to app use, with responses analyzed using qualitative content analysis.

resultsAmong 100 respondents (response rate 8.1%), 45 (45%) completed the survey by phone. The respondents were on average 68 years old (SD 13 years), with 90% (90/100) White, 39% (39/100) women, and 88% (88/100) having access to the internet or a mobile phone. Most participants (65/100, 65%) agreed or strongly agreed that they would use the app, among which 53 (82%) would use the app as often as possible. The percentage of participants with the intention to use the app was 75% among those aged 65-74 years and dropped to 44% among those older than 75 years. The intention to use was higher in online survey respondents (vs phone survey respondents; odds ratio 3.07, 95% CI 1.20-7.88) after adjusting for age and access to technology. The analysis of open-ended questions identified the following 4 main facilitators (motivations): (1) easily reaching providers, (2) accessing or providing information, (3) quickly reaching providers, and (4) consulting providers for symptoms, and the following 4 main barriers: (1) privacy/security concerns, (2) uncomfortable using technology, (3) user-unfriendly app interface, and (4) preference for in-person/phone care.

conclusionsThere was a strong intention to use a symptom monitoring app postdischarge among ACS patients. However, this intent decreased in patients older than 75 years. The survey identified barriers related to technology use, privacy/security, and the care delivery mode. Further research is warranted to determine if such intent translates into app use, and better symptom management and health care quality.

Indexed as

barriercoronaryeHealthelderfacilitatorhealth appintentionmobile healthmonitorsymptom

Identifiers

PMID35254269
PMCPMC8938838
OpenAlexW4200525562

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.