Evidence map›Paper›PMID 34570008›Full record

Trial reportComputers, informatics, nursing : CIN2022

A Decision Tree Analysis on the Impact of a Technology-Based Program on Symptom Distress: Asian American Breast Cancer Survivors.

Eun-Ok Im, Jee-Seon Yi, Wonshik Chee

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Computers, informatics, nursing : CIN, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Review
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

3 authors.

Eun-Ok ImAuthor Affiliations: Nell Hodgson Woodruff School of Nursing, Emory University (Drs Im, Yi, and Chee), Atlanta, GA; and College of Nursing, Institute of Health Sciences, Gyeongsang National University (Dr Yi), Jinju, Republic of Korea.
Jee-Seon Yi
Wonshik Chee

Funding

To enhance breast cancer survivorship of Asian AmericansR01CA203719 · NCI · EMORY UNIVERSITY · PI IM, EUN-OK · 2016 to 2020
$2.8M
NCI NIH HHS R01 CA203719
6 · The paper itself

Abstract

Using a decision tree analysis, this study aimed to identify the characteristics of the groups within Asian American breast cancer survivors whose symptom distress scores were effectively improved by a technology-based program. This was a secondary analysis of the data from an ongoing randomized controlled trial among 115 Asian American breast cancer survivors. The instruments were questions on background factors; the Memorial Symptom Assessment Scale-Short Form; the Cancer Behavior Inventory; the Questions on Attitudes, Subjective Norm, Perceived Behavioral Control, and Behavioral Intention; and the Supportive Care Needs Survey-Short Form 34. The data were analyzed using chi-square tests, t tests, repeated measurement analyses, and decision tree analyses. The decrease in the global distress index scores was the largest (1.253 points) among those with high psychological support needs. The decrease in the physical symptom distress scale scores was the largest (1.133 points) among those with high physical and daily living support needs who had a short US residence period and who were young. The decrease in the psychological symptom distress scores was the largest (1.511) among those with high psychological support needs. The findings suggest several characteristics of the groups within Asian American breast cancer survivors whose symptom distress could be highly improved by a technology-based intervention.

Indexed as

Breast NeoplasmsCancer SurvivorsAsianDecision TreesFemaleHumansQuality of LifeTechnology

Identifiers

PMID34570008
PMCPMC8943471

What OpenQuestion holds

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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.