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Information Needs of Asian American Breast Cancer Survivors: a Decision Tree Analysis

Overview
Journal J Cancer Educ
Publisher Springer
Date 2021 Jun 27
PMID 34176104
Citations 2
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Abstract

Through a decision tree analysis, this study aimed to determine the characteristics of Asian American breast cancer survivors who had higher decreases in their need for information by a technology-based information and coaching/support program compared with their counterparts. This is a part of a larger randomized controlled trial; only the data from 99 Asian American breast cancer survivors were used for this analysis. The measurement scales included 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 analysis was done using t-tests, chi-square tests, repeated measurement analyses, and a decision tree analysis. The information needs scores of all the participants decreased during the 3-month intervention period (p < .005). However, only the intervention group had statistically significant decreases in the information needs scores during the 3 months (dif. =  - 8.545; p < .005). Those with low social influence scores and high self-efficacy scores had significantly larger decreases in their information needs scores compared with the average change scores (100%, p < 01). Asian American breast cancer survivors with low social influences and high self-efficacy would highly benefit from a technology-based intervention for their need for information.

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References
1.
Kaminski B, Jakubczyk M, Szufel P . A framework for sensitivity analysis of decision trees. Cent Eur J Oper Res. 2018; 26(1):135-159. PMC: 5767274. DOI: 10.1007/s10100-017-0479-6. View

2.
Im E, Lee S, Liu Y, Lim H, Guevara E, Chee W . A national online forum on ethnic differences in cancer pain experience. Nurs Res. 2009; 58(2):86-94. PMC: 2668932. DOI: 10.1097/NNR.0b013e31818fcea4. View

3.
Agboola S, Ju W, Elfiky A, Kvedar J, Jethwani K . The effect of technology-based interventions on pain, depression, and quality of life in patients with cancer: a systematic review of randomized controlled trials. J Med Internet Res. 2015; 17(3):e65. PMC: 4381812. DOI: 10.2196/jmir.4009. View

4.
Heitzmann C, Merluzzi T, Jean-Pierre P, Roscoe J, Kirsh K, Passik S . Assessing self-efficacy for coping with cancer: development and psychometric analysis of the brief version of the Cancer Behavior Inventory (CBI-B). Psychooncology. 2010; 20(3):302-12. DOI: 10.1002/pon.1735. View

5.
Im E, Ji X, Kim S, Chee E, Bao T, Mao J . Challenges in a Technology-Based Cancer Pain Management Program Among Asian American Breast Cancer Survivors. Comput Inform Nurs. 2019; 37(5):243-249. PMC: 6530489. DOI: 10.1097/CIN.0000000000000503. View