Social Health, Activity Behaviors, and Quality of Life Among Young Adult Cancer Survivors

University of Southern California250 enrolled

Overview

This study assesses how personal relationships (such as friendships, family relationships, or romantic partners) influence the physical activity (exercise) and well-being of young adult cancer survivors. Researchers also hope to learn how social relationships change after a cancer diagnosis, and how these changes might impact important health behaviors. The information provided may help researchers learn more about better ways to support young cancer patients in the future through interventions that help maintain good social relationships and health levels of physical activity.

PRIMARY OBJECTIVES: I. Characterize the trajectories of social health in young adult (YA) cancer patients. II. Investigate the longitudinal associations between social health and activity behaviors in YA cancer patients. III. Explore the effects of sociodemographic and clinical characteristics on the relationship between social health, activity behaviors, and quality of life. OUTLINE: Patients complete surveys over 40 minutes and wear an actigraph GT3X-BT accelerometer continuously for 7 days at baseline, 3, 6, and 12 months.

Study Type

OBSERVATIONAL

Enrollment

250

Conditions

Anatomic Stage I Breast Cancer AJCC v8 Anatomic Stage IA Breast Cancer AJCC v8 Anatomic Stage IB Breast Cancer AJCC v8 Anatomic Stage II Breast Cancer AJCC v8

Eligibility

Sex: ALLMin age: 18 YearsMax age: 39 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Diagnosed and/or treated with cancer between ages 18-39 at USC hospitals. * Cancer types prototypical for adolescents and young adults (AYAs) and cancer stages I-III; select patients with stage IV disease may be eligible, with approval by the principal investigator (PI) and in consultation with the treating clinician. * Must be within three months of a de novo cancer diagnosis at recruitment and on/indicated for curative therapy (any modality). Patients may continue on adjuvant therapy throughout duration of the study. * Patients must have anticipated survival of \>1-year at time of diagnosis. Exclusion Criteria: * Diagnosis of blood malignancies such as leukemias (these cancers have divergent treatment patterns of longer duration than other cancers and are more commonly pediatric cancers). Some early stage lymphomas with favorable prognoses may be eligible, with approval by the PI and in consultation with the treating clinician. * Primary language other than English or Spanish. * Inability to complete a survey and/or wear an accelerometer either per the patient or in consultation with the clinician's judgment.

Outcomes

Primary Outcomes

Rate of Change in trajectories of social health

Social health variables include the number, frequency and duration of hospitalizations, specifics of cancer diagnosis (i.e., stage at diagnosis, pertinent histology, recurrence or progression of disease), chemotherapy type, surgery related to cancer diagnosis, radiation field, immunotherapy type, and other relevant therapies related to cancer treatment. Changes in above variables will be measured using latent growth curve models to measure latent intercept (initial level), and the latent slope (rate of change) of social health variables during the course of therapy.

Time frame: Baseline up to 1 year

Rate of Change in trajectories of physical activity

Changes will be measured using latent growth curve models to measure latent intercept (initial level) and the latent slope (rate of change) of physical activity during the course of therapy.

Time frame: Baseline up to 1 year

Rate of Change in trajectories of quality of life

Changes will be measured using latent growth curve models to measure latent intercept (initial level) and the latent slope (rate of change) of quality of life during the course of therapy.

Time frame: Baseline up to 1 year

Moderation by gender

Will conduct multi-group analyses by the categories of study moderators (e.g., gender, race/ethnicity, socioeconomic status \[SES\], health status). The strengths of group-specific pathways to link social health and activity behaviors to quality of life will be compared using the chi-square (χ2) difference test of model fit. The log-likelihood values with (versus \[vs.\] without) the equality constraints on the group-specific pathways to determine if the strength of associations estimated in the models significantly differ by the groups of each moderator (e.g., gender: female vs. male).

Time frame: Up to 1 year

Moderation effect of race/ethnicity

Moderation effect is analyzed by the interaction between the independent variable (X), and the moderator variable (Y) in a regression model, where the endpoint is the dependent variable (Z). Moderation effect is the endpoint itself, indicated by the significance of the interaction term's regression coefficient (B3), which shows how the relationship between X and Y changes depending on the level of Z. 'X' is social health, physical activity, and quality of life. 'Y' is race/ethnicity. 'Z' is moderation effect. The strengths of group-specific pathways to link social health and activity behaviors to quality of life will be compared using the chi-square (χ2) difference test of model fit.

Time frame: Up to 1 year

Moderation effect of Socio-Economic Status (SES)

Moderation effect is analyzed by the interaction between the independent variable (X), and the moderator variable (Y) in a regression model, where the endpoint is the dependent variable (Z). Moderation effect is the endpoint itself, indicated by the significance of the interaction term's regression coefficient (B3), which shows how the relationship between X and Y changes depending on the level of Z. 'X' is social health, physical activity, and quality of life. 'Y' is socio-economic status. 'Z' is moderation effect. The strengths of group-specific pathways to link social health and activity behaviors to quality of life will be compared using the chi-square (χ2) difference test of model fit.

Time frame: Up to 1 year

Moderation effect of health status

Moderation effect is analyzed by the interaction between the independent variable (X), and the moderator variable (Y) in a regression model, where the endpoint is the dependent variable (Z). Moderation effect is the endpoint itself, indicated by the significance of the interaction term's regression coefficient (B3), which shows how the relationship between X and Y changes depending on the level of Z. 'X' is social health, physical activity, and quality of life. 'Y' is health status. 'Z' is moderation effect. The strengths of group-specific pathways to link social health and activity behaviors to quality of life will be compared using the chi-square (χ2) difference test of model fit.

Time frame: Up to 1 year

Social Health, Activity Behaviors, and Quality of Life Among Young Adult Cancer Survivors

Overview

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Primary Outcomes

Central Contacts