Evidence map›Paper›PMID 27493951›Full record

ArticlemHealth2016

mHealth self-care interventions: managing symptoms following breast cancer treatment.

Mei R Fu, Deborah Axelrod, Amber A Guth, Kavita Rampertaap, Nardin El-Shammaa, Karen Hiotis, Joan Scagliola, Gary Yu, Yao Wang

Registry-linked trialOpen access · hybridAbstract read
In one paragraph

Article in mHealth, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06128655 (Developing and Testing a Nurse-Led Technology-Enhanced Family Engagement Program), which is not on this map. Cited by 47 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing papers in PubMed, 3 pooled it
3.0field-weighted citation impact, top 7% 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.

NCT06128655 nacompletednot on this mapstarted 2024, after this paper: background citation

Developing and Testing a Nurse-Led Technology-Enhanced Family Engagement Program (NURSE-TECH-Family) Among Critically Ill Patients in ICU: A Randomized Controlled Trial

TypeinterventionalSponsorRutgers, The State University of New JerseyRan2024 to 2025Enrolled29ConditionsCritical IllnessArmsParticipation in PFCC-IR
3 · Its place in the literature

Who cites it

47 citing papers in PubMed, 3 syntheses or guidelines pooled it, 82 citations in OpenAlex.

  1. Interactive digital tools to support empowerment of people with cancer: a systematic literature review.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2024
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  12. The Role of Rehabilitation Nurses in Empowering Mastectomised Women for Self-Care: A Scoping Review.International journal of environmental research and public health · 2025
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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

9 authors at 3 institutions in 1 country.

Mei R FuNYU Rory Meyers College of Nursing, New York University, New York, NY, USA; NYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA.
Deborah AxelrodNYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA; Department of Surgery, New York University School of Medicine, New York, NY, USA.
Amber A GuthNYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA; Department of Surgery, New York University School of Medicine, New York, NY, USA.
Kavita RampertaapNYU Rory Meyers College of Nursing, New York University, New York, NY, USA; NYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA.
Nardin El-ShammaaNYU Rory Meyers College of Nursing, New York University, New York, NY, USA; NYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA.
Karen HiotisNYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA; Department of Surgery, New York University School of Medicine, New York, NY, USA.
Joan ScagliolaNYU Laura and Isaac Perlmutter Cancer Center, New York, NY, USA.
Gary YuNYU Rory Meyers College of Nursing, New York University, New York, NY, USA.
Yao WangDepartment of Electrical and Computer Engineering, Tandon School of Engineering, New York University, New York, NY, USA.
NYU Langone Health · USNew York University · USNYU Langone’s Laura and Isaac Perlmutter Cancer Center

Funding

Research Training and Education CoreP60MD000538 · NIMHD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI TRINH-SHEVRIN, CHAU · 2003 to 2017
$21.2M
Scaling Community-Clinical Linkage Models to Address Diabetes and Hypertension Disparities in the Southeastern U.S.U54MD000538 · NIMHD · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI TRINH-SHEVRIN, CHAU · 2017 to 2022
$11.3M
NIMHD NIH HHS P60 MD000538NIMHD NIH HHS U54 MD000538
6 · The paper itself

Abstract

backgroundMany women suffer from daily distressing symptoms related to lymphedema following breast cancer treatment. Lymphedema, an abnormal accumulation of lymph fluid in the ipsilateral body area or upper limb, remains an ongoing major health problem affecting more than 40% of 3.1 million breast cancer survivors in the United States. Patient-centered care related to lymphedema symptom management is often inadequately addressed in clinical research and practice. mHealth plays a significant role in improving self-care, patient-clinician communication, and access to health information. The-Optimal-Lymph-Flow health IT system (TOLF) is a patient-centered, web-and-mobile-based educational and behavioral mHealth interventions focusing on safe, innovative, and pragmatic electronic assessment and self-care strategies for lymphedema symptom management. The purpose of this paper is to describe the development and test of TOLF system.

methodsThe development of TOLF was guided by the Model of Self-Care for Lymphedema Symptom Management and designed based on principles fostering accessibility, convenience, and efficiency of mHealth system to enhance training and motivating assessment of and self-care for lymphedema symptoms. Test of TOLF was accomplished by conducting a psychometric study to evaluate reliability, validity, and efficiency of the electronic version of Breast Cancer and Lymphedema Symptom Experience Index (BCLE-SEI), a usability testing and a pilot feasibility testing of mHealth self-care interventions.

resultsFindings from the psychometric study with 355 breast cancer survivors demonstrated high internal consistency of the electronic version of the instrument: a Cronbach's alpha coefficient of 0.959 for the total scale, 0.919 for symptom occurrence, and 0.946 for symptom distress. Discriminant validity of the instrument was supported by a significant difference in symptom occurrence (z=-6.938, P<0.000), symptom distress (z=-5.894, P<0.000), and total scale (z=-6.547, P<0.000) between breast cancer survivors with lymphedema and those without it. Findings of usability testing showed that breast cancer survivors were very satisfied with the mHealth self-care interventions: 90% rated the system having no usability problems; 10% noted minor cosmetic problems: spelling errors or text font size. The majority of participants 96.6% strongly agreed that the system was easy to use and effective in helping to learn about lymphedema, symptoms and self-care strategies. Feasibility testing demonstrated that a 12-week one group intervention using TOLF had significantly positive effects on less pain (P=0.031), less soreness (P=0.021), less aching (P=0.024), less tenderness (P=0.039), fewer numbers of lymphedema symptoms (P=0.003), and improved symptom distress (P=0.000) at 12 weeks after intervention. Themes from the qualitative data included empowerment, high quality information, loving avatar simulation videos, easy accessibility, and user-friendliness.

conclusionsTOLF system using the electronic version of the instrument is able to assess patients' lymphedema symptoms with high reliability and validity. TOLF system is also able to deliver self-care interventions to enhance self-care strategies for lymphedema symptom management.

Indexed as

breast cancer survivorbreast neoplasmslymphedemaSelf-caresymptoms

Identifiers

PMID27493951
PMCPMC4970761
OpenAlexW2481858931

What OpenQuestion holds

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Registered trials

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.