Evidence map›Paper›PMID 42005527›Full record

ArticleExtended abstracts on Human factors in computing systems. CHI Conference2026

Daily Consistency Over Timing: Routine Formation and Population-Specific Opportunities in mHealth User Adherence.

Samia Shahnawaz, Rhyann Clarke, Suzanne Bakken, Noemie Elhadad, Kyle Landell, Jovita Rodrigues, Matteo Danieletto, Ipek Ensari

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Extended abstracts on Human factors in computing systems. CHI Conference, 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

8 authors.

Samia ShahnawazWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, United States.
Rhyann ClarkeWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, United States.
Suzanne BakkenSchool of Nursing, Columbia University, United States and Department of Biomedical Informatics, Columbia University, United States.
Noemie ElhadadDepartment of Biomedical Informatics, Columbia University, United States.
Kyle LandellWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, United States.
Jovita RodriguesWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, United States.
Matteo DanielettoWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, United States.
Ipek EnsariWindreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, United States and Department of Obstetrics, Gynecology, and Reproductive Science, Icahn School of Medicine at Mount Sinai, United States.

Funding

Evaluation techniques for mHealth outcome measures using patient generated health dataR01HD108263 · NICHD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI Ipek Ensari · 2022 to 2026
$1.7M
NICHD NIH HHS R01 HD108263
6 · The paper itself

Abstract

Understanding mobile Health (mHealth) user engagement patterns and its association to adherence in chronic disease populations is critical for data quality and clinical outcomes. Yet, this remains unexplored and a missed opportunity, given the unique value of mHealth data for traditionally under-documented conditions.

methodsWe analyzed 13,997 total days of data from 131 female participants with chronic pelvic pain disorders (CPPDs) and 72 healthy controls. Participants tracked daily symptoms using a research App for 90 days. Using mixed-effects regression, we investigated predictors of adherence and consistency in App engagement, with moderators of CPPD burden and temporal accessibility.

resultsLater-day engagement was the strongest predictor of user consistency, independent of pain interference or CPPD. Habitual evening tracking was associated with better adherence for those with CPPD vs healthy controls. Consistency was the strongest predictor of adherence.

conclusionPrioritizing habit and preference over fixed-time assessments could improve temporal accessibility and data completeness.

Indexed as

chronic paindigital healthengagementmHealthtemporal adherence

Identifiers

PMID42005527
PMCPMC13089969

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.