Survivor profiles predict health behavior intent: the Childhood Cancer Survivor Study

Objectives: To determine whether unique groups of adult childhood cancer survivors could be defined on the basis of modifiable cognitive, affective, and motivation indicators. Secondary objectives were to examine to what extent group membership co‐varied with more static variables (e.g. demographics...

Full description

Saved in:
Bibliographic Details
Published in:Psycho-oncology (Chichester, England) England), 2012-05, Vol.21 (5), p.469-478
Main Authors: Cox, Cheryl L., Zhu, Liang, Finnegan, Lorna, Steen, Brenda D., Hudson, Melissa M., Robison, Leslie L., Oeffinger, Kevin C.
Format: Article
Language:English
Subjects:
Adolescent
Adult
Cancer
Childhood
childhood cancer
Childrens health
Cognition & reasoning
Female
Follow-Up Studies
Gender
Health
Health Behavior
Humans
Intention
late effects
Male
medical follow-up
Middle Aged
Motivation
Neoplasms - psychology
Patient Acceptance of Health Care - psychology
pediatric oncology
Survivor
Survivors
Survivors - psychology
survivorship
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Objectives: To determine whether unique groups of adult childhood cancer survivors could be defined on the basis of modifiable cognitive, affective, and motivation indicators. Secondary objectives were to examine to what extent group membership co‐varied with more static variables (e.g. demographics, disease, and treatment) and predicted intent for subsequent medical follow‐up. Methods: Using latent class analysis of data from 978 participants (ages, 18–52 years; mean, 31; and SD, 8) in the Childhood Cancer Survivor Study, we classified survivors according to their worries about health, perceived need for follow‐up care, health motivation, and background variables. Intent to participate in medical follow‐up, as a function of class membership, was tested using equality of proportions. Results: The best‐fitting model (BIC = 18 540.67, BLMRT =
ISSN:1057-9249
1099-1611
DOI:10.1002/pon.1935