Evidence mapPaperPMID 40996972Full record

ArticlePLOS digital health2025

mHealth technologies in research studying cardiovascular health in cancer: A systematic review.

Roberto M Benzo, Anvitha Gogineni, Macy K Tetrick, Rujul Singh, Peter Washington, Soledad Fernandez, Electra D Paskett, Frank J Penedo, Sanam Ghazi, Alex Osei and 4 more

Abstract read
In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

14 authors.

Roberto M BenzoDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.ORCID https://orcid.org/0000-0001-8634-6472
Anvitha GogineniDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.
Macy K TetrickDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.
Rujul SinghDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.
Peter WashingtonDivision of Clinical Informatics and Digital Transformation, Department of Medicine, University of California San Francisco, San Francisco, California, United States of America.ORCID https://orcid.org/0000-0003-3276-4411
Soledad FernandezDepartment of Biomedical Informatics and Center for Biostatistics, Ohio State University, Columbus, Ohio, United States of America.
Electra D PaskettDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.
Frank J PenedoDepartments of Psychology and Medicine, University of Miami, Coral Gables, Florida, United States of America.
Sanam GhaziCardio-Oncology Program, Division of Cardiology, The Ohio State University Medical Center, Columbus, Ohio, United States of America.
Alex OseiDepartment of Design, The Ohio State University, Columbus, Ohio, United States of America.
Steven K ClintonDivision of Medical Oncology, Department of Internal Medicine, The Ohio State University, Columbus, Ohio, United States of America.
Jessica Krok-SchoenThe Ohio State University Comprehensive Cancer Center, The Ohio State University Wexner Medical Center, Columbus, Ohio, United States of America.
Sarah WeyrauchDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.
Daniel AddisonDivision of Cancer Prevention and Control, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, Ohio, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer survivors face an increased risk of cardiovascular disease (CVD) due to treatment-related toxicity, lifestyle factors, and comorbidities. Addressing CV health is crucial for improving quality of life and long-term outcomes. The American Heart Association's Life's Essential 8 framework highlights modifiable determinants of CV health, emphasizing early detection and monitoring. Mobile health (mHealth) technologies, such as wearables and smartphone apps, offer continuous tracking, yet their applications in cancer survivorship remain unclear. This review systematically characterizes the types of mHealth technologies used to monitor CV health in cancer survivors, focusing on the specific data collected (major adverse CV events, CV risk factors, and surrogate endpoints) and the use of active versus passive collection methods. A systematic search of PubMed, Scopus, Embase, and Web of Science identified studies published between January 1, 2016, and June 13, 2024. Eligible studies included observational and interventional designs assessing at least one CV outcome using mHealth. Data were extracted on design, technology type, and outcomes. Risk of bias was evaluated using the Cochrane RoB-2 and ROBINS-I tools. Fourteen studies (13 interventional, one observational) met criteria. Physical activity was the most monitored risk factor, followed by HR. The most common technologies were mobile apps and commercial wearables. Passive methods typically captured PA and HR, while active methods captured PA, symptom tracking, and diet. A key finding was the lack of integration with electronic medical records, highlighting a gap in clinical implementation. mHealth provides scalable tools to track CV health indicators in cancer survivors. Findings highlight the potential to support practice by enabling remote oversight of risk-reducing behaviors and guiding lifestyle interventions. However, we also identified gaps, including the underutilization of biomarkers (e.g., HRV) and the lack of integration with electronic records. Future research must address these gaps to translate real-time data into clinical insights and optimize survivorship care.

Identifiers

PMID40996972
PMCPMC12463205

What Socratic 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 Socratic graph.