Evidence map›Paper›PMID 41415333›Full record

ArticleProceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies2025

Multimodal Sensing and Modeling of Endocrine Therapy Adherence in Breast Cancer Survivors.

Fangxu Yuan, Navreet Kaur, Zhiyuan Wang, Manuel Gonzales, Cristian Garcia Alcaraz, Gabriel Estrella, Kristen J Wells, Laura E Barnes

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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.

Fangxu YuanDepartment of Systems and Information Engineering, University of Virginia, USA.ORCID 0009-0006-1203-1850
Navreet KaurDepartment of Systems and Information Engineering, University of Virginia, USA.ORCID 0009-0008-5782-0655
Zhiyuan WangDepartment of Systems and Information Engineering, University of Virginia, USA.ORCID 0000-0002-1611-2053
Manuel GonzalesSDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University, USA.ORCID 0000-0002-4650-5157
Cristian Garcia AlcarazSDSU/UC San Diego Joint Doctoral Program in Clinical Psychology, San Diego State University, USA.ORCID 0000-0002-1531-9028
Gabriel EstrellaDepartment of Educational Psychology, San Diego State University, USA.ORCID 0009-0007-8404-6733
Kristen J WellsDepartment of Psychology, San Diego State University, USA.ORCID 0000-0002-7359-8678
Laura E BarnesDepartment of Systems and Information Engineering, University of Virginia, USA.ORCID 0000-0001-8224-5164

Funding

SCH:INT: Collaborative Research: Multiscale Modeling and Intervention for Improving Long-Term MedicationR01CA239246 · NCI · UNIVERSITY OF VIRGINIA · PI BARNES, LAURA ELIZABETH, GONG, JIAQI · 2019 to 2022
$1.1M
NCI NIH HHS R01 CA239246
6 · The paper itself

Abstract

Many breast cancer survivors are prescribed daily oral medications called endocrine therapy that prevent cancer recurrence. Despite its clinical importance, maintaining consistent daily adherence remains challenging due to the dynamic and interrelated influences of behavioral, physiological, and psychological factors. While prior studies have explored adherence prediction using mobile sensing, they often rely on single-modality data, limited temporal granularity, or aggregate-level modeling-limiting their ability to capture short and long-term behavioral variability and to facilitate deeper understanding of non-adherence and tailored interventions. To address these gaps, we propose a multimodal sensing framework that explicitly models daily adherence dynamics using temporally adaptive inputs. We recruited a sample of breast cancer survivors (

Indexed as

breast cancer survivorsbreast neoplasmdigital healthmachine learningmedication adherencemultimodal datatemporal modeling

Identifiers

PMID41415333
PMCPMC12711140

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.