Evidence map›Paper›PMID 42035294›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2026

Optimizing participation in digital health studies: understanding appointment attendance.

Rebecca Schnall, Hui Lin, Maeve Brin, Jean Jimenez, Amy K Johnson, Mirjam-Colette Kempf, Nan Liu

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

7 authors.

Rebecca SchnallSchool of Nursing, Columbia University, New York, NY, 10032, United States.ORCID 0000-0003-2184-4045
Hui LinCarroll School of Management, Boston College, Chestnut Hill, MA, 02467, United States.
Maeve BrinSchool of Nursing, Columbia University, New York, NY, 10032, United States.ORCID 0009-0006-1625-3450
Jean JimenezSchool of Nursing, Columbia University, New York, NY, 10032, United States.
Amy K JohnsonAnn & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, 60611, United States.
Mirjam-Colette KempfSchool of Public Health, University of Alabama at Birmingham, Birmingham, AL, 35294, United States.ORCID 0000-0003-3796-4906
Nan LiuCarroll School of Management, Boston College, Chestnut Hill, MA, 02467, United States.ORCID 0000-0001-7644-7341

Funding

National Institute of Allergy and Infectious Diseases (NIAID) of the National Institute of Health R01AI172469National Institutes of Health (NIH)NIHNIH HHS
6 · The paper itself

Abstract

objectiveThis study examined whether attendance at online digital health research appointments in the American Women Assessing Risk Epidemiologically (AWARE) study was associated with (1) participant age, (2) scheduling factors (time of day, day of week, month), (3) appointment confirmation, and (4) HIV behavioral risk factors. MATERIALS AND

methodsWe analyzed scheduling and eligibility screening data from AWARE, a 24-month U.S.-based longitudinal digital cohort of cisgender women at elevated likelihood of HIV seroconversion. Participant demographic and behavioral data were merged with the study team's Outlook calendar. Chi-square tests and logistic regression models assessed associations between appointment attendance and participant characteristics and scheduling factors.

resultsWomen aged ≥50 years had higher odds of missing baseline visits compared to those aged 20-29 years (44.7% vs 32.3%). Appointments scheduled at 2:00 pm (45.7%), 4:00 pm (45.2%), and 8:00 am (40.2%) had higher no-show rates than other times. No-show rates were lowest on Fridays (30.2%) and during March (27.7%) and June (25.2%). Confirming appointments 24 hours in advance significantly reduced no-shows compared to no confirmation (19.0% vs 51.6%). Histories of having been physically hurt (44.2% vs 32.1%), forced to have sexual activities (41.8% vs 34.1%) and incarcerated (39.3% vs 33.4%) were also associated with higher no-show rates. Similar patterns were observed for rescheduled visits.

conclusionAttendance in digital research was influenced by age, scheduling, and structural vulnerabilities. Incorporating digital access support into study design and grant budgets may reduce disparities, improve retention, and enhance efficiency.

Indexed as

Appointments and SchedulesAdultAge FactorsDigital HealthDigital MediaFemaleHIV InfectionsHumansLongitudinal StudiesMiddle AgedRisk FactorsUnited Statesdigital health researchHIVno-show ratesvisit attendance

Identifiers

PMID42035294
PMCPMC13317950

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LicenceCC BY-NC
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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.