Racial, Lifestyle, and Healthcare Contributors to Perceived Cancer Risk among Physically Active Adolescent and Young Adult Women Aged 18-39 Years.
Jordyn A BrownMahmood A AlalwanSumaya AbsieNaa D KorleyClaudia F ParvantaCathy M MeadeAlicia L BestClement K GwedeAldenise P EwingPublished in: International journal of environmental research and public health (2023)
The cancer incidence among adolescents and young adults (AYAs) has significantly increased in recent years, but there is limited information about the factors that influence the perceived cancer risk among AYAs. A cross-sectional, web-based survey of 281 physically active Black and White AYA women was administered to assess the influences of demographic characteristics, family history of cancer, cancer risk factor knowledge, and lifestyle-related risk and protective behaviors on perceived cancer risk. Linear regression analyses were performed in SAS version 9.4. Self-reported Black race (β = -0.62, 95% CI: -1.07, -0.17) and routine doctor visits (β = -0.62, 95% CI: -1.18, -0.07) were related to a lower perceived cancer risk. Family history of cancer (β = 0.56, 95% CI: 0.13, 0.99), cancer risk factor knowledge (β = 0.11, 95% CI: 0.03, 0.19), and current smoking status (β = 0.80, 95% CI: 0.20, 1.40) were related to a higher perceived cancer risk. Perceptions of cancer risk varied among this sample of physically active, AYA women. Lower perceptions of cancer risk among Black AYA women demonstrate a need for culturally tailored cancer educational information that presents objective data on lifetime cancer risk. Reportedly higher perceptions of cancer risk among AYA smokers presents an ideal opportunity to promote smoking cessation interventions. Future interventions to address cancer risk perception profiles among physically active, AYA women should tailor approaches that are inclusive of these unique characteristics.
Keyphrases
- healthcare
- papillary thyroid
- physical activity
- smoking cessation
- polycystic ovary syndrome
- squamous cell
- mental health
- risk factors
- social support
- primary care
- childhood cancer
- cardiovascular disease
- young adults
- machine learning
- pregnancy outcomes
- insulin resistance
- cervical cancer screening
- deep learning
- health information
- cross sectional
- clinical practice
- replacement therapy
- social media
- artificial intelligence
- psychometric properties