Health Literacy,Socioeconomic Status,and Health Behavior Adherence Among Young Adult Survivors With Cancer: A Cross-Sectional Study

Abstract

Introduction

Young adults (YAs) with cancer are at elevated risk for chronic conditions and late effects, making adherence to healthy eating and physical activity guidelines a key survivorship priority. Health literacy is theorized to support guideline-concordant behaviors, yet its role in YA oncology remains unclear, particularly in the context of socioeconomic and environmental influences. This study aimed to characterize health literacy among YA cancer survivors and examine its association with adherence to healthy eating and physical activity guidelines, after accounting for sociodemographic/clinical factors.

Methods

273 YAs (18-39 years) receiving care at an NCI-designated cancer center completed an anonymous online survey. Measures included the BRIEF Health Literacy Tool, the Rapid Eating Assessment for Participants-Short Version, the Rapid Assessment of Physical Activity, the Social Support and Eating Habits and Exercise Surveys, and self-reported sociodemographic and clinical variables. Analyses included descriptive statistics, bivariate correlations, and multiple regression models controlling for variables of interest.

Results

Most participants demonstrated adequate health literacy, and health literacy was positively associated with education and household income. However, health literacy was not significantly associated with adherence to healthy eating or physical activity guidelines. Higher BMI was associated with lower adherence to healthy eating, whereas having a college degree or higher and family participation in exercise were associated with adherence to healthy eating. For physical activity, friends’ discouragement of healthy eating and family participation in exercise were associated with better adherence.

Conclusions

In this relatively well-educated, higher-income YA oncology sample, health literacy was generally adequate and did not independently predict adherence to healthy eating or physical activity guidelines. These findings suggest that, in higher socioeconomic status settings, structural and social determinants may outweigh the influence of health literacy on lifestyle behaviors. Survivorship interventions should combine health literacy-sensitive communication with multilevel strategies targeting weight management, food/activity environments, and family/peer support.

Keywords

health literacy young adult cancer cancer survivors healthy eating physical activity lifestyle behaviors survivorship care

Introduction

Young adult (YA) cancer survivors on and off active cancer treatment face elevated risk for chronic health conditions and late effects related to both their disease and its treatment, even as overall survival and life expectancy have improved over recent decades.^1-3 Modifiable lifestyle factors such as diet, physical activity, and weight status are key contributors to this long-term risk profile.^4-7 In response, the American Cancer Society, the American Institute for Cancer Research, and the Children’s Oncology Group have issued evidence-based behavioral guidelines emphasizing regular physical activity, healthy dietary patterns, and weight management to reduce late effects and secondary cancers among cancer survivors.^8-11

Despite these recommendations, many YA cancer survivors do not adhere to guideline-consistent eating and physical activity behaviors. Studies have documented inadequate intake of fruits, vegetables, and calcium, high-fat consumption, and insufficient physical activity among young adult cancer survivors.^9,12-17 These patterns mirror those in the general population but carry greater implications for YA survivors, given their increased vulnerability to late effects.^18,19 Beyond access and motivation, a potentially modifiable contributor to these lifestyle-behavioral patterns may be survivors’ ability to understand and apply survivorship health information and guidance in their daily lives.

Health literacy has emerged as a central construct for understanding how individuals engage with health information and translate it into preventive and self-management behaviors. Health literacy is commonly defined as the degree to which individuals can obtain, communicate, process, and understand health information and services needed to make appropriate health decisions.^20-26 Systematic reviews of empirical studies indicate that higher health literacy is associated with healthier dietary patterns and stronger self-care behaviors, including lower intake of sugar-sweetened beverages.^27-29 Because engaging in guideline-concordant eating and physical activity requires understanding and applying health recommendations, limited health literacy may be one contributor to suboptimal lifestyle behaviors among YA cancer survivors. In oncology and other chronic disease contexts, limited health literacy has been linked to lower engagement in preventive behaviors, poorer disease self-management, and worse quality of life.^30-32

Evidence in YA oncology populations remains limited, and few studies have examined whether health literacy is associated with adherence to healthy eating and physical activity guidelines in this group. Sansom-Daly, Lin, Robertson, Wakefield, McGill, Girgis and Cohn³³ found that approximately 60% of YAs demonstrated adequate health literacy, suggesting both strengths and vulnerabilities. To our knowledge, no prior study has directly tested whether functional health literacy is associated with guideline-concordant eating and physical activity among YA cancer patients and survivors.^22,25,26,34

Using data from a sample of YA cancer survivors on and off treatment recruited from a National Cancer Institute (NCI)-designated comprehensive cancer center, this study aimed to: (1) characterize health literacy levels in this population; (2) examine associations between health literacy and adherence to healthy eating and physical activity guidelines; and (3) evaluate associations between health literacy and key sociodemographic variables, including race/ethnicity, education, and household income.

Methods

Study Design and Participants

This study employed a cross-sectional survey design, and this report conforms to the STROBE guidelines.³⁵ The present analyses used survey data drawn from a larger project examining health literacy, home environmental influences, sociodemographic variables, and adherence to healthy lifestyle guidelines among YA cancer survivors on and off treatment treated at an NCI-designated comprehensive cancer center in a southeastern US state. The procedures for the current study are described below.

Inclusion and Exclusion Criteria

Eligible participants were English-speaking YAs aged 18 to 39 years at the time of data collection, with a current or past cancer diagnosis, who were either in active treatment or off treatment and able to complete an online survey. Eligibility was assessed through screening items embedded in the survey. Respondents were excluded if they were outside the target age range, did not provide age information needed to establish eligibility, or failed to complete the study measures.

Procedures

Participants were identified through the YA clinic and its associated Constant Contact listserv, a broader institutional email distribution list that includes current and former patients. Individuals received an email invitation containing a link to an anonymous online survey hosted on REDCap, a secure web-based platform for building and managing online surveys and research databases. Recruitment occurred in three waves: an initial invitation in September 2021, and two reminder emails sent approximately one month apart in October and November 2021. Because recruitment occurred through this broader listserv rather than a roster limited to confirmed age-eligible recipients, the exact number of eligible individuals invited to participate could not be determined; therefore, a precise response rate could not be calculated. A total of 343 individuals accessed the survey. Seventy cases were excluded due to ineligibility (e.g., outside the target age range), incomplete demographic information required to establish eligibility, or failure to complete study measures. The final analytic sample included 273 YA survivors. Figure 1 presents the participant flow diagram.

Figure 1.

Flow diagram of participant inclusion and exclusion

Ethical Considerations

The study was reviewed and approved by the University of South Florida Institutional Review Board (STUDY003349; approved on 05/12/2022). In addition, the study received approval from the Moffitt Cancer Center Scientific Review Committee (MCC 21855; approved on 02/09/2022) and Moffitt Cancer Center Advarra IRB (IRB# 00000971; 09/24/2020). Participants were asked to review an informed consent statement containing all study information before completing the questionnaire, and completion of the anonymous survey indicated consent to participate. Recruitment emails also stated that participation was entirely voluntary and that survey responses were anonymous. Participation or nonparticipation did not affect patients’ cancer care or their relationship with the treating institution.

Conceptual Framework

This study was guided by the Theory of Triadic Influence (TTI),^36,37 which posits that health behaviors are shaped by multiple levels of influence, including individual, social, and broader contextual factors. Within this framework, health literacy was conceptualized specifically as functional health literacy, or the capacity to understand and use written and oral health information,^22,23 and was examined as an individual-level factor that may support healthy lifestyle behaviors. Socioeconomic status, race/ethnicity, and home environmental influences were treated as broader contextual factors relevant to adherence to healthy eating and physical activity guidelines.

Data Collection Tools

All measures were self-report instruments administered via a single online REDCap survey.

Demographic and Clinical Characteristics

A study-specific questionnaire assessed age, gender, race/ethnicity, education level, household income, cancer diagnosis and stage (self-reported), treatment modalities, treatment status (active or off treatment), and time since treatment completion. All sociodemographic and medical data were self-reported. Cancer diagnosis was captured as an open-ended self-report variable; given the substantial missingness and heterogeneity of responses, cancer type was examined descriptively only and was not included in multivariable models. Body Mass Index (BMI) was calculated from self-reported height and weight and categorized into underweight, healthy weight, overweight, and obese using standard cutoffs (e.g., underweight: BMI < 18.5; normal weight: BMI 18.5–24.9; overweight: BMI 25.0–29.9; obesity: BMI ≥ 30.0).³⁸

Health Literacy

Health literacy was assessed using the BRIEF Health Literacy Screening Tool (BRIEF), a 4-item measure of perceived ability to understand written and verbal health information. Items are rated on a 5-point Likert scale and summed to yield scores from 4 to 20, with higher scores indicating higher health literacy. Established cutoffs categorize respondents as having inadequate (4-12), marginal (13-16), or adequate (17-20) health literacy.³⁹

Adherence to Healthy Eating Guidelines

Adherence to healthy eating guidelines was measured using the Rapid Eating Assessment for Participants-Short Version (REAP-S). REAP-S consists of 13 scored items assessing the frequency of consumption of specific food groups and eating patterns. Items are rated on a 3-point scale, and scores are summed (total possible points ranging from 13 to 39); higher scores indicate healthier eating consistent with dietary guidelines.⁴⁰ The summed REAP-S score was analyzed as a continuous numerical variable.

Adherence to Physical Activity Guidelines

Adherence to physical activity guidelines was assessed using the Rapid Assessment of Physical Activity (RAPA), a brief set of yes/no items assessing weekly frequency and intensity of physical activity. The RAPA categorizes respondents by their level of adherence to the Centers for Disease Control and Prevention recommendations. Scoring follows a hierarchical rule: respondents are assigned the highest-numbered item endorsed “yes,” yielding a single score from 1 to 7.⁴¹ Categorically, scores are interpreted as (1) sedentary, (2) under-active, (3) under-active regular-light, (4-5) under-active regular, and (6-7) active. Consistent with scoring guidance, scores < 6 are considered suboptimal, whereas scores ≥ 6 indicate meeting recommended activity levels (i.e., at least 30 minutes of moderate activity ≥ 5 days/week or 20 minutes of vigorous activity ≥ 3 days/week).⁴¹ The RAPA score was analyzed as a continuous numerical variable.

Home Environmental Influences

Home environmental influences on lifestyle behaviors were assessed using subscales from the Social Support and Eating Habits and Social Support and Exercise questionnaires. These instruments capture family and friends’ encouragement and discouragement of healthy eating and exercise, as well as their participation in physical activity with the respondent. The questionnaires include 10 items on eating habits (score range: 10–50) and 13 items on physical activity (score range: 13–65), each rated on a 5-point Likert scale (1 = none to 5 = very often) based on behaviors over the past three months. Lower scores indicate fewer positive influences within the home environment. The eating habits scale yields two subscales: Encouragement (5 items; range: 5–25) and Discouragement (5 items; range: 5–25). The physical activity scale produces Participation (10 items; range: 10–50) and Rewards and Punishments (3 items; range: 3–15). Each subscale is scored separately for family members and friends.⁴²

Data Analysis

Descriptive statistics were calculated to summarize the study population. Continuous variables were reported as means and standard deviations (SD), and categorical variables were reported as frequencies and percentages. Due to item-level missingness, sample sizes varied across descriptive analyses, with statistics calculated using all participants with available data for each measure (e.g., REAP-S n = 271; RAPA n = 269; BRIEF n = 268). In contrast, multivariable regression models were estimated using complete-case analysis for all variables included in each model, resulting in a consistent analytic sample size of n = 242 across models.

To identify predictors of healthy eating adherence (REAP score), hierarchical linear regression models were conducted using a sequential four-step approach. Sociodemographic variables of education and income were dummy-coded and treated as binary predictors to simplify interpretation, capture threshold effects, and maximize statistical efficiency; education was categorized as “some college degree or less” versus “college degree or higher,” and income was dichotomized at “<$70,000” and “≥$70,000.” Model 1 established the baseline with sociodemographic controls (age, gender, race/ethnicity, education, and income); notably, education and income were treated as continuous variables in this and subsequent models to assess linear trends and maximize statistical efficiency. Model 2 added clinical/medical characteristics (BMI and patient status [on and off treatment]), followed by the inclusion of the social support factors in Model 3. Finally, Model 4 (the fully adjusted model) incorporated the primary predictor of interest: health literacy (BRIEF). Model fit was evaluated using the Coefficient of Determination (R²), Adjusted R², and the significance of the change in R² (ΔR²) between steps. Collinearity was assessed using the Variance Inflation Factor (VIF), while homoskedasticity and linearity were evaluated by inspecting residuals-versus-fitted plots.

For the analysis of physical activity adherence (RAPA score), Tobit regression models⁴³ were employed to account for the censored nature of the dependent variable (bounded scale). The use of standard Ordinary Least Squares (OLS) regression in this context would yield biased and inconsistent estimates.⁴⁴ The same hierarchical block entry method described above was applied. Due to the inherent difficulty in interpreting raw Tobit coefficients, results were converted to Average Marginal Effects (AME) using the margins command. This allows for the interpretation of the change in the outcome score in its original units. Model fit and selection were based on McFadden’s Pseudo R², Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). A decrease in AIC and BIC values, alongside an increase in Pseudo R², indicated a better model fit.⁴⁵ Additionally, the Likelihood Ratio (LR) test was performed to assess the statistical significance of the improvement in model fit between sequential hierarchical models. Multicollinearity was assessed using the VIF, and the normality of residuals was evaluated using histograms and kernel density plots.

All analyses were performed using Stata (Version 18, StataCorp LLC, College Station, TX). Statistical significance was defined as a p-value < 0.05.

Results

Table 1 presents the descriptive characteristics of study participants (n=273). The mean age was 32.2 years at the time of data collection, and the majority were female (82.8%). Most identified as White (65.2%), followed by Hispanic (16.5%). The sample was relatively highly educated, with 69.6% reporting at least a college degree (college graduate, some graduate school, or graduate degree). More than half reported an annual household income of $50,000 or more. Nearly two-thirds of the sample were individuals who were overweight or obese. More than half of the sample were survivors off treatment (63.7%). The most common treatments were surgery (65.2%) and chemotherapy (58.2%); participants were allowed to select more than one treatment option. Cancer diagnosis data were available for only a small subset of participants (n = 16), limiting more detailed characterization of diagnosis groups; among these responses, the most commonly reported diagnoses were testicular cancer (n = 3), thyroid cancer (n = 3), breast cancer (n = 2), and lymphoma (n = 2), with one case each of bladder cancer, chronic myeloid leukemia, melanoma, myoepithelial carcinoma, dermatofibrosarcoma protuberans, and insulinoma. Approximately half of the participants were moderately or extremely concerned about their weight.

Table 1.

Sociodemographic, Clinical and Medical Characteristics of Survey Respondents

Variable	n	%
Age
18 to 19	7	2.6
20 to 24	24	8.8
25 to 29	48	17.6
30 to 34	78	28.6
35 to 39	116	42.5
Gender
Female	226	82.8
Male	43	15.8
Race/ethnicity ^a
White	178	65.2
Hispanic	45	16.5
Black/African American	29	10.6
Other	20	7.3
Education
Some college degree or less	83	30.4
College degree or higher	190	69.6
Household income
Less than $70,000	155	56.8
Equal to or greater than $70,000	114	41.8
Patient status
Still in treatment	99	36.3
Survivor	174	63.7
BMI
Underweight	5	1.8
Healthy	98	35.9
Overweight	70	25.6
Obesity	85	31.1
Treatment type ^a
Chemo	159	58.2
Radiation	101	37.0
Immunotherapy	44	16.1
Hormonal	41	15.0
Surgery	178	65.2

^aThis variable was a ‘select all that apply’ type.

Adherence to healthy eating guidelines, as assessed by the REAP-S, was generally suboptimal (see Figure 2). REAP-S total scores (possible range 13–39, with higher scores reflecting healthier eating patterns) averaged 28.33 (SD = 4.54) out of 39 in this sample (n = 271; median = 28, IQR = 25–31; range 14–39). For interpretive context, prior work suggests a mean REAP-S score of approximately 32 among adults consuming a typical U.S. omnivorous diet, which can serve as a comparison benchmark.⁴⁶ In the present sample, 75.6% of participants (205/271) scored below 32, indicating that most participants reported dietary patterns less consistent with recommended healthy eating guidelines. For physical activity, as measured by the RAPA, 2.6% reported no regular physical activity, and many reported light or underactive levels of activity. Just over one-third (39%) met the recommended physical activity guidelines (see Figure 3).

Figure 2.

Distribution of REAP-S scores reflecting healthy eating adherence

Figure 3.

Distribution of RAPA scores reflecting physical activity adherence

Health literacy, measured with the BRIEF, was generally adequate (see Figure 4). Mean BRIEF scores were above the threshold for adequate health literacy, suggesting that most respondents felt confident in understanding health information and communicating with health care providers.

Figure 4.

Distribution of health literacy scores

Adherence to Healthy Eating Guidelines

In bivariate analysis, adherence to healthy eating (REAP-S) was significantly positively correlated with education (r = 0.36, p<0.001), income (r = 0.16, p<0.05) and friends’ participation in physical activity (r = 0.15, p<0.05), while negatively correlated with BMI (r = -0.29, p<0.001). Table 2 presents the results of the hierarchical linear regression analysis. In Model 1 (sociodemographic characteristics), identifying as Black/African American showed an inverse association with healthy eating adherence (standardized β = -0.57; 95% CI [-1.00, -0.14]), while education level demonstrated a significant positive association (β = 0.53; 95% CI [0.26, 0.80]). After the addition of clinical variables in Model 2, the association for Black/African American participants was attenuated and no longer statistically significant, whereas education remained significant (β = 0.40; 95% CI [0.13, 0.67]). BMI showed a significant negative association with healthy eating adherence in Model 2 (β = -0.25; 95% CI [-0.38, -0.13]) and remained significant in Models 3 and 4; however, there was no association with patient status (on and off treatment).

Table 2.

Hierarchical Linear Regression Analysis Between Adherence to Healthy Eating Guidelines (REAP-S) and Variables of Interest

	Model 1		Model 2		Model 3		Model 4
	β^a	CI95	β^a	CI95	β^a	CI95	β^a	CI95
Age	0.06	-0.07,0.18	0.09	-0.03,0.22	0.08	-0.05,0.21	0.08	-0.06,0.21
Gender
Female	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00
Male	0.10	-0.24,0.44	0.09	-0.24,0.42	0.10	-0.24,0.43	0.08	-0.26,0.42
Race/ethnicity
White	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00
Hispanic	0.03	-0.31,0.37	0.10	-0.23,0.43	0.13	-0.20,0.47	0.14	-0.20,0.47
Black/African American	-0.57^*	-1.00,-0.14	-0.42	-0.84,0.01	-0.41	-0.86,0.03	-0.42	-0.87,0.02
Other	-0.17	-0.64,0.31	-0.23	-0.70,0.23	-0.22	-0.70,0.26	-0.19	-0.68,0.29
Education
Some college degree or less	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00
College degree or higher	0.53^***	0.26,0.80	0.40^**	0.13,0.67	0.35^*	0.07,0.63	0.33^*	0.05,0.62
Income
<$70,000	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00
≥$70,000	0.20	-0.05,0.46	0.19	-0.06,0.44	0.19	-0.06,0.44	0.18	-0.07,0.44
Patient Status
Still in treatment			0.00	0.00,0.00	0.00	0.00,0.00	0.00	0.00,0.00
Survivor			0.04	-0.21,0.30	0.00	-0.26,0.26	-0.01	-0.27,0.25
BMI			-0.25^***	-0.38,-0.13	-0.23^***	-0.36,-0.11	-0.23^***	-0.36,-0.10
Social Support and Eating Habits
Family Encouragement					-0.01	-0.16,0.15	-0.01	-0.16,0.14
Family Discouragement					-0.01	-0.16,0.14	-0.01	-0.16,0.14
Friends Encouragement					-0.08	-0.25,0.10	-0.07	-0.24,0.11
Friends Discouragement					0.00	-0.17,0.18	0.01	-0.16,0.19
Social Support and Exercise
Family Participation					0.18^*	0.04,0.32	0.18^*	0.04,0.32
Friends Participation					-0.04	-0.20,0.11	-0.05	-0.21,0.11
Health Literacy (BRIEF)							0.05	-0.08,0.18
Constant	-0.50^*	-0.89,-0.11	-0.44^*	-0.86,-0.02	-0.40	-0.83,0.04	-0.36	-0.81,0.08
R-squared	0.12		0.18		0.20		0.21
Adjusted R-squared	0.10		0.15		0.15		0.15
R² Change	.		0.057		0.025		0.002
Obs	242		242		242		242

^aStandardized beta, *p<0.05, ** p<0.01, *** p<0.001. Models follow a hierarchical entry: Model 1 (Socio-demographics), Model 2 (+Clinical), Model 3 (+Social Support), and Model 4 (+Health Literacy). CI95: 95% Confidence Interval, REAP-S = Rapid Eating Assessment for Participants [short version]; BMI = Body Mass Index; BRIEF = BRIEF Health Literacy Screening Tool.

In Model 3, family participation in physical activity was positively associated with healthy eating adherence (β = 0.18; 95% CI [0.04, 0.32]), and this association remained significant in Model 4. Education also remained positively associated with healthy eating adherence in the fully adjusted model (Model 4: β = 0.33; 95% CI [0.05, 0.62]). Finally, the inclusion of health literacy in Model 4 did not yield a statistically significant association with healthy eating adherence. Regarding model fit, sociodemographic variables in Model 1 accounted for 12% of the total variance in healthy eating adherence. The addition of clinical variables in Model 2 increased the explained variance to 18% (ΔR² = 0.057), and the inclusion of social support variables in Model 3 increased it to 20% (ΔR² = 0.025). Adding health literacy in Model 4 yielded only a negligible increase in explained variance (R² = 0.21; ΔR² = 0.002), suggesting that health literacy did not meaningfully improve model performance beyond the variables included in Model 3.

Adherence to Physical Activity Guidelines

In bivariate analysis, adherence to physical activity (RAPA) was significantly positively correlated with family and friends’ discouragement of healthy eating (r = 0.14, p<0.05 and r = 0.24, p<0.01), friends’ encouragement of healthy eating (r = 0.13, p<0.05), and family and friends’ participation in physical activity (r = 0.24 and r = 0.25, p<0.01). Conversely, BMI was negatively correlated with RAPA (r = -0.15, p<0.05). Table 3 presents the average marginal effects (AME) from the Tobit regression analysis. In Model 1, sociodemographic variables were not significantly associated with adherence to physical activity guidelines. In Model 2, the addition of patient status (on and off treatment) and BMI did not improve model performance. In Model 3, friends’ discouragement of healthy eating and family participation in physical activity were positively associated with physical activity adherence (B = 0.10; CI = 0.01, 0.18 and B = 0.05; CI = 0.01, 0.08). These associations remained significant in Model 4, whereas the inclusion of health literacy did not yield further improvement. Regarding model fit, the initial models demonstrated limited explanatory power (Pseudo R² = 0.01). However, the inclusion of social support variables in Model 3 improved model fit, increasing the Pseudo R² to 0.06 and reducing the AIC from 691.50 in Model 2 to 672.17. Model 4 did not further improve fit, as the Pseudo R² remained unchanged and both AIC and BIC increased slightly.

Table 3.

Hierarchical Tobit Regression Analysis Between Adherence to Physical Activity (RAPA) and Variables of Interest

	Model 1		Model 2		Model 3		Model 4
	B^a	CI95	B^a	CI95	B^a	CI95	B^a	CI95
Age	-0.04	-0.10,0.02	-0.03	-0.09,0.03	-0.00	-0.06,0.06	-0.00	-0.06,0.06
Gender
Female	-	-	-	-	-	-	-	-
Male	-0.62	-1.49,0.25	-0.66	-1.52,0.20	-0.75	-1.57,0.07	-0.75	-1.58,0.07
Race/ethnicity
White	-	-	-	-	-	-	-	-
Hispanic	0.27	-0.60,1.15	0.32	-0.56,1.19	0.24	-0.59,1.08	0.24	-0.59,1.08
Black/African American	-0.12	-1.25,1.01	0.08	-1.07,1.23	-0.24	-1.36,0.88	-0.24	-1.36,0.88
Other	0.03	-1.20,1.27	-0.12	-1.35,1.11	-0.71	-1.87,0.45	-0.70	-1.87,0.47
Education
Some college degree or less	-	-	-	-	-	-	-	-
College degree or higher	0.60	-0.11,1.31	0.46	-0.26,1.19	0.02	-0.69,0.72	0.01	-0.70,0.73
Income
<$70,000	-	-	-	-	-	-	-	-
≥$70,000	-0.14	-0.80,0.52	-0.09	-0.76,0.58	-0.09	-0.71,0.53	-0.10	-0.72,0.53
Patient Status
Still in treatment			-	-	-	-	-	-
Survivor			0.40	-0.27,1.08	0.17	-0.47,0.82	0.17	-0.48,0.82
BMI			-0.03	-0.07,0.01	-0.03	-0.07,0.00	-0.03	-0.07,0.00
Social Support and Eating Habits
Family Encouragement					-0.07	-0.14,0.01	-0.07	-0.14,0.01
Family Discouragement					0.02	-0.06,0.09	0.02	-0.06,0.09
Friends Encouragement					0.04	-0.05,0.12	0.04	-0.05,0.12
Friends Discouragement					0.10^*	0.01,0.18	0.10^*	0.01,0.19
Social Support and Exercice
Family Participation					0.05^**	0.01,0.08	0.05^**	0.01,0.08
Friends Participation					0.02	-0.01,0.06	0.02	-0.01,0.06
Health Literacy (BRIEF)							0.01	-0.10,0.11
Constant	5.55^***	3.82,7.29	5.47^***	3.76,7.17	4.53^***	3.13,5.94	4.53^***	3.13,5.94
Pseudo R²	0.01		0.01		0.06		0.06
AIC	690.85		691.50		672.17		674.16
BIC	722.25		729.88		731.49		736.96
Observations	242.00		242.00		242.00		242.00

^aAverage marginal effects, *p<0.05, ** p<0.01, *** p<0.001. Models follow a hierarchical entry: Model 1 (Socio-demographics), Model 2 (+Clinical), Model 3 (+Social Support), and Model 4 (+Health Literacy). CI95: 95% Confidence Interval, RAPA = Rapid Assessment of Physical Activity; BMI = Body Mass Index; BRIEF = BRIEF Health Literacy Screening Tool.

Discussion

Among YA cancer patients and survivors, we observed generally adequate health literacy, yet this “knowledge” factor did not predict healthy lifestyle behaviors, consistent with decades of work informed by the Health Belief Model, which shows that knowledge alone rarely translates into behavior change. Adherence to dietary recommendations was low, and only about one-third of participants met current physical activity guidelines. In contrast, socioeconomic factors and family participation emerged as more relevant predictors of adherence, whereas higher BMI was inversely associated with adherence to healthy eating guidelines. Taken together, these findings suggest that in a higher-SES YA oncology sample, health literacy may be necessary for understanding recommendations but is not sufficient to drive adherence without additional motivational, contextual, and support-related determinants.

In our sample, about 75% of participants were classified as having adequate health literacy, higher than the ∼60% reported by Sansom-Daly, Lin, Robertson, Wakefield, McGill, Girgis and Cohn³³ among YAs and more similar to levels seen in older or highly educated chronic disease cohorts.^28,47 This literacy level likely reflects the sample’s socioeconomic advantages, as most participants held college degrees and reported household incomes exceeding $50,000. Health literacy is closely linked to education, income, and broader social conditions, with lower literacy more common among those experiencing socioeconomic disadvantages.^47,48 Additionally, recruitment from an NCI-designated comprehensive cancer center may have contributed, as these settings provide extensive educational resources, multidisciplinary survivorship care, and repeated engagement with health information and providers.^30,32

Despite adequate levels of health literacy, adherence to healthy eating and physical activity guidelines was suboptimal in this sample. The majority of participants reported suboptimal healthy eating patterns on the REAP-S, and fewer than half met physical activity recommendations. Importantly, health literacy was not significantly associated with either dietary or physical activity adherence in bivariate analyses or in hierarchical regression models controlling for demographic and home environmental variables.

This pattern adds an important nuance to existing health literacy research. Many studies report that lower health literacy is associated with poorer self-management and less health-promoting behavior.^22,27-30 Our findings suggest that, in a population with relatively adequate health literacy, other determinants overshadow health literacy in predicting lifestyle behaviors. Ceiling effects on the BRIEF may also have constrained the ability to detect subtle differences in literacy, and the standardized survivorship education provided at a comprehensive cancer center may further reduce variability in exposure to and understanding of recommendations.

Although the suboptimal healthy eating and physical activity patterns observed in this sample may resemble those reported in the broader U.S. population, they may carry greater clinical significance in YA cancer survivors. The literature suggests that cancer treatment during the adolescent and young adult period can create treatment-related cardiovascular and metabolic vulnerabilities, including reduced cardiorespiratory fitness, unfavorable body composition, fatigue, and elevated risk for chronic disease and multimorbidity, which may amplify the long-term consequences of poor lifestyle behaviors in survivorship.^49-51 The adolescent and young adult period is also a critical developmental window during which musculoskeletal strength, physical functioning, and long-term health trajectories are still being established, making disruption during this time particularly consequential.^52,53 In this context, poor diet and low physical activity may not simply reflect general population risk behaviors but may compound treatment-related late effects and contribute to reduced quality of life, greater symptom burden, and accelerated aging across survivorship.^49,53,54

While health literacy did not predict adherence to dietary and physical activity guidelines, the study helped clarify its role within the structural and social determinants of behavior. Home environmental influences further contextualize health literacy. Family participation in exercise emerged as a consistent positive predictor of both healthy eating and physical activity adherence, suggesting that YAs who are surrounded by family members who exercise with them may experience a reinforcing environment in which health-promoting behaviors are modeled, normalized, and logistically supported.^55-59 Importantly, this effect persisted even when health literacy was included in the models, indicating that family participation adds something beyond individual understanding of health information.

Taken together, these findings suggest that improving lifestyle adherence among YAs will likely require multilevel approaches beyond individual health literacy alone. Health literacy-focused supports may be most impactful when targeted to less-resourced YAs or those facing linguistic/systemic barriers, whereas in higher-SES settings, adding family/peer support and addressing structural constraints may be more salient.

Future Directions for Health Literacy Research in YAs

Future research on health literacy in YA oncology should extend to more socioeconomically and racially diverse settings (e.g., community hospitals and safety-net clinics), where literacy may be lower and more variable and thus more strongly related to lifestyle adherence and survivorship outcomes.^26,60,61 Studies should also use more comprehensive measurements (e.g., numeracy, digital health literacy, and disease-specific knowledge) to better reflect how YAs navigate complex and often conflicting health information, particularly online.^24,62,63 Longitudinal designs are needed to clarify how health literacy interacts with structural and social conditions over time (e.g., food insecurity, neighborhood disadvantage, and care transitions) to shape diet, physical activity, and engagement with survivorship resources.^64-67 Finally, intervention studies should test whether health literacy moderates the effects of family- or peer-focused lifestyle programs to inform tailored content, delivery, and follow-up for survivors with lower literacy.^26,55,57-59

Strengths and Limitations

A key strength of this study is its contribution to the emerging literature by situating health literacy within a multilevel framework to understand lifestyle behaviors among YA cancer survivors. This approach underscores the need to examine not only whether health literacy matters, but under what conditions and in interaction with which social and structural determinants, including broader sociodemographic, clinical, and home environmental influences. The study also considered both healthy eating and physical activity, two central survivorship behaviors that are often examined separately, and included YA participants who were both on and off treatment. In addition, the study used established measures of health literacy, diet, physical activity, and social support, and the hierarchical analytic approach allowed us to examine whether health literacy contributed to lifestyle adherence after accounting for other relevant factors.

However, these findings should be interpreted in light of several limitations, including the cross-sectional design, reliance on self-report measures, and the use of a screening measure of health literacy, which, although efficient, may not capture nuanced or context-specific deficits relevant to lifestyle behavior change among YAs.^62,63 In addition, recruitment through a broader institutional listserv, rather than a roster limited to confirmed eligible patients, prevented the determination of the exact number of eligible individuals invited to participate and, therefore, a precise response rate. This raises the possibility of recruitment or selection bias, as participants who chose to respond may have differed from nonrespondents in ways relevant to health literacy, lifestyle behaviors, or survivorship engagement, and it may limit the external validity of the findings. Additionally, cancer diagnosis data were available for only a small subset of participants and were collected in an open-ended format, which limited our ability to describe the sample by cancer type more comprehensively or to evaluate cancer type in multivariable models. Another limitation is that the study did not include a standardized measure of comorbidity burden. As a result, multimorbidity among participants may have influenced lifestyle behaviors and could not be evaluated in the present analyses. Finally, given the sample size and the number of predictors examined in the multivariable models, the study may have been underpowered to detect smaller associations, and the possibility of type II error should be considered when interpreting nonsignificant findings.

Conclusion

In this sample of YA cancer survivors (on and off treatment) recruited from a comprehensive cancer center, functional health literacy was generally adequate, yet adherence to healthy eating and physical activity guidelines was suboptimal. Sociodemographic characteristics, BMI and social support, particularly family participation in exercise, were significantly associated with adherence to lifestyle guidelines. In contrast, health literacy did not emerge as an independent predictor of lifestyle guideline adherence. These findings suggest that, in relatively well-resourced YA oncology populations, socioeconomic status and social context may buffer or overshadow the influence of health literacy on lifestyle behaviors, and that other barriers, such as treatment-related sequelae, psychosocial factors, and environmental constraints, may play a more prominent role in shaping adherence. Further research should recruit more socioeconomically and racially/ethnically diverse YA samples, incorporate more comprehensive measures of health literacy, and test interventions that combine health literacy-sensitive strategies with efforts to address structural determinants of health.

Footnotes

ORCID iD

Sandra Soca Lozano

Ethical Considerations

Consent to Participate

Participants were asked to review an informed consent statement containing all study information before completing the questionnaire, and completion of the anonymous survey indicated consent to participate. Recruitment emails also stated that participation was entirely voluntary and that survey responses were anonymous. Participation or nonparticipation did not affect patients’ cancer care or their relationship with Moffitt Cancer Center.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Moffitt Cancer Center AYA Committee: Health Literacy, Home Environmental Influence and Ethnicity and its Association to Adherence to Nutrition and Physical Exercise Guidelines in Adolescents and Young Adults Cancer Patients and Survivors: An Exploratory Study. (MPI: Pabbathi and Stern) AYA Committee, H. Lee Moffitt Cancer Center & Research Institute, (11/2020-10/2021; NCE 2/2022).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Appendix

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