xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05981716</url>
    </required_header>
    <id_info>
      <org_study_id>YBCSS-2023</org_study_id>
      <nct_id>NCT05981716</nct_id>
    </id_info>
    <brief_title>Young Breast Cancer Survivors Study</brief_title>
    <acronym>YBCSS</acronym>
    <official_title>Factors Affecting Quality of Life and Treatment Adherence Among Early-Age-at-Onset Breast Cancer Survivors</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>University of South Carolina</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>University of South Carolina</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        Despite significant overall reductions in mortality rates for breast cancer over the past&#xD;
        decade, both incidence and mortality rates have steadily climbed in adults diagnosed before&#xD;
        age 50. This research project addresses factors associated with quality of life among and&#xD;
        treatment response in early-age-at-onset breast cancer patients. The overall objective is&#xD;
        collect information from early-onset breast cancer patients using an online questionnaire and&#xD;
        examine factors related to cancer survival, (i.e.,better quality of life, better treatment&#xD;
        adherence, less adverse treatment responses).&#xD;
  &#xD;
        Aim 1: Identify dietary patterns related to health-related quality of life in&#xD;
        early-age-at-onset breast cancer patients. The investigators hypothesize that diet quality is&#xD;
        related to better health-related quality of life among young breast cancer survivors.&#xD;
  &#xD;
        Aim 2. Identify demographic, social determinants, and geographic factors associated with&#xD;
        treatment adherence. The investigators hypothesize that social determinants such as&#xD;
        poverty-to-income ratio, education, and proximity to cancer treatment facilities are&#xD;
        associated with treatment adherence in early-onset breast cancer.&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        This is an observational epidemiologic study in which the investigators will collect data&#xD;
        from approximately 384 participants at one point in time using online questionnaires (REDCaP&#xD;
        and the NCI Diet History Questionnaire III). Recruitment will be conducted via breast cancer&#xD;
        survivor social media support groups and advocacy groups. Electronic consent will be granted&#xD;
        at the beginning of each online questionnaire. Data collection will occur via self-report in&#xD;
        the location chosen by the participant using an online questionnaire. The investigators will&#xD;
        use established validated questionnaires used by other previously conducted cohorts to&#xD;
        enquire about demographics, occupation, cancer screening history, family history of cancer,&#xD;
        comorbidities, cancer and other comorbidity treatment including fertility-related, HRQoL,&#xD;
        dietary intake using the online NCI Dietary History Questionnaire III which includes dietary&#xD;
        supplement use, residential history, hormonal status, physical activity, tobacco product and&#xD;
        alcohol use, experiences with racism, social support, and information on health care&#xD;
        utilization.&#xD;
  &#xD;
        To accomplish Aim 1, the investigators will use the NCI Diet History Questionnaire III, an&#xD;
        online 135-item food frequency questionnaire with 26 dietary supplement questions, reflecting&#xD;
        the past one month of intake to estimate food and nutrient intakes and overall dietary&#xD;
        patterns.Health related quality of life (HRQoL) is a multidimensional concept that not only&#xD;
        includes physical, psychological and social domains, but may also encompass other domains&#xD;
        such as cognitive functioning. Cancer patients also exhibit many symptoms (e.g., fatigue,&#xD;
        pain, sleep disturbance) that are not measurable directly from laboratory tests. Thus,&#xD;
        assessing HRQoL and these symptom burdens among cancer survivors will need to rely on&#xD;
        patients' self-reports, measured by validated instruments especially for patients with breast&#xD;
        cancer, and includes the Functional Assessment of Cancer Therapy (FACT-B).&#xD;
  &#xD;
        Descriptive statistics will be presented as mean (standard deviation) and median (inter&#xD;
        quartile range) for continuous variables and as frequency (percentage) for categorical&#xD;
        variables. Data visualization tools such as histogram, boxplot, scatterplot and line plot&#xD;
        will be employed to assess the trends and outliers in the data. Bivariate association between&#xD;
        categorical variables will be tested for statistical significance using Chi-square test or&#xD;
        exact test. Differences in continuous variables will be tested for statistical significance&#xD;
        using Kruskal-Wallis test. HRQoL data typically are not distributed normally with left-skewed&#xD;
        distributions and potential ceiling effects requiring consideration of alternative estimators&#xD;
        in multivariate models. A number of different alternative models have been proposed over the&#xD;
        standard ordinary least squares approach including beta regression, tobit regression, and&#xD;
        two-part modeling. The investigators will assess the distribution of the primary HRQoL&#xD;
        measures in building analytic models. With healthy dietary pattern as the primary independent&#xD;
        variable and measures of HRQoL serving as primary dependent variable, the investigators&#xD;
        expect to find that as diet quality increases, HRQoL increases.&#xD;
  &#xD;
        For Aim 2, the investigators will ask study participants to complete questionnaires related&#xD;
        to racism, fatalism, and demographics and the investigators will geocode residential&#xD;
        histories of participants to measure the role of racism, fatalism, income, education, and&#xD;
        proximity to treatment facilities in treatment adherence. The investigators will use&#xD;
        geographic information systems to geocode participants' residential histories. Participants&#xD;
        will be asked the ZIP code of residence at the time of diagnosis, as well as the ZIP code of&#xD;
        the treatment facility in which they received care. If the ZIP code of the treatment facility&#xD;
        is unknown, the participant can give the city and state, in which a ZIP code of the central&#xD;
        point of the city will be found and used for analysis. Using Microsoft Excel, and the list of&#xD;
        latitudes and longitudes by ZIP code, provided by the United States Census Bureau (find&#xD;
        citation), the distance in miles between the place of residence and place of treatment will&#xD;
        be determined. This distance calculation can be used to help evaluate treatment adherence and&#xD;
        the distance to treatment. Residential histories will also be used to determine participant&#xD;
        rurality. Regarding the many competing definitions and classification schemes for rurality,&#xD;
        Hall et al. found dichotomous definitions mask heterogeneity relevant to health research and&#xD;
        studies of accessibility. The investigators will instead use the Rural-Urban Commuting Area&#xD;
        (RUCA) codes developed by the Office of Rural Health Policy of the Health Resources and&#xD;
        Services Administration and the Economic Research Service of the United States Department of&#xD;
        Agriculture (USDA). RUCA codes combine information on population density, urbanicity, and&#xD;
        daily commuting patterns to classify census tracts into 22 distinct codes, which can then be&#xD;
        consolidated into more manageable classifications.&#xD;
  &#xD;
        Descriptive statistics will be presented as mean (standard deviation) and median (inter&#xD;
        quartile range) for continuous variables and as frequency (percentage) for categorical&#xD;
        variables. Data visualization tools such as histogram, boxplot, scatterplot and line plot&#xD;
        will be employed to assess the trends and outliers in the data. The investigators will&#xD;
        explore the associations between treatment adherence and each measure of social determinants&#xD;
        (such as poverty to income ratio, racism, education, and proximity to treatment facility)&#xD;
        using mixed effect logistic regression. The model will be sequentially adjusted for the&#xD;
        effect of non-modifiable and modifiable confounders such as age, cancer stage, treatment&#xD;
        type, and insurance status.&#xD;
  &#xD;
        Recruitment of participants is expected to be ongoing for ~12 months.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Recruiting</overall_status>
    <start_date type="Actual">January 10, 2023</start_date>
    <completion_date type="Anticipated">December 31, 2023</completion_date>
    <primary_completion_date type="Anticipated">December 31, 2023</primary_completion_date>
    <study_type>Observational</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <observational_model>Case-Only</observational_model>
      <time_perspective>Cross-Sectional</time_perspective>
    </study_design_info>
    <primary_outcome>
      <measure>Score on Health-related Quality of Life Measure</measure>
      <time_frame>Measured at one point in time, from 0-10 years after diagnosis</time_frame>
      <description>For Aim 1, health-related quality of life (HRQoL) is a multidimensional concept that not only includes physical, psychological and social domains, but may also encompass other domains such as cognitive functioning. Cancer patients also exhibit many symptoms (e.g., fatigue, pain, sleep disturbance) that are not measurable directly from laboratory tests. Thus, assessing HRQoL and these symptom burdens among cancer survivors will need to rely on patients' self-reports, measured by validated instruments especially for patients with breast cancer. Participants will complete the 37-item Functional Assessment of Cancer Therapy-B with a 5-point Likert-type scale capturing the domains of Physical Well-being, Social/Family Well-being, Emotional Well-being, Functional Well-being, and a Breast Cancer Subscale. Item responses are scored according to scoring guidelines ranging from scores of 0 to 148, with a higher score representing better quality of life.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Number of Participants with Treatment Adherence Compared to those without Treatment Adherence</measure>
      <time_frame>Up to 2 years post-diagnosis of breast cancer</time_frame>
      <description>For Aim 2, the primary outcome is treatment adherence (at patient-level), which will be determined as per National Comprehensive Cancer Network (NCCN) guidelines and eventually categorized as treatment adherent/non-adherent.</description>
    </primary_outcome>
    <number_of_groups>1</number_of_groups>
    <enrollment type="Anticipated">384</enrollment>
    <condition>Breast Cancer</condition>
    <arm_group>
      <arm_group_label>Young Breast Cancer Survivors</arm_group_label>
      <description>The investigators will enroll approximately 384 female breast cancer patients. Only non-institutionalized, English literate, female breast cancer patients diagnosed within the previous ten years and younger than 50 years old at the time of diagnosis will be eligible.</description>
    </arm_group>
    <intervention>
      <intervention_type>Other</intervention_type>
      <intervention_name>No intervention</intervention_name>
      <description>This is an observational study. There is no intervention.</description>
      <arm_group_label>Young Breast Cancer Survivors</arm_group_label>
    </intervention>
    <eligibility>
      <study_pop>
        <textblock>
          The investigators plan to enroll 384 women who were previously diagnosed with breast cancer&#xD;
          prior to the age of 50 years.&#xD;
        </textblock>
      </study_pop>
      <sampling_method>Non-Probability Sample</sampling_method>
      <criteria>
        <textblock>
          Inclusion Criteria:&#xD;
  &#xD;
            -  Non-institutionalized&#xD;
  &#xD;
            -  English literate&#xD;
  &#xD;
            -  Female breast cancer survivors&#xD;
  &#xD;
            -  Diagnosed with breast cancer within the past 10 years and diagnosed younger than age&#xD;
               50 years&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Male breast cancer survivors&#xD;
  &#xD;
            -  Breast cancer survivors diagnosed with breast cancer after the age of 50 years&#xD;
        </textblock>
      </criteria>
      <gender>Female</gender>
      <gender_based>Yes</gender_based>
      <gender_description>Only female breast cancer survivors will be considered for the study. There is a much lower prevalence of breast cancer among men; therefore, the study will target a female survivor population.</gender_description>
      <minimum_age>18 Years</minimum_age>
      <maximum_age>60 Years</maximum_age>
      <healthy_volunteers>No</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Susan Steck, PhD, MPH, RD</last_name>
      <role>Principal Investigator</role>
      <affiliation>University of South Carolina</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Susan Steck, PhD, MPH, RD</last_name>
      <phone>8037771527</phone>
      <email>stecks@mailbox.sc.edu</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Ella Gustafson</last_name>
      <phone>7034736473</phone>
      <email>ellabg@email.sc.edu</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>University of South Carolina</name>
        <address>
          <city>Columbia</city>
          <state>South Carolina</state>
          <zip>29208</zip>
          <country>United States</country>
        </address>
      </facility>
      <status>Recruiting</status>
      <contact>
        <last_name>Ella Gustafson</last_name>
        <email>ellabg@email.sc.edu</email>
      </contact>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>March 12, 2023</study_first_submitted>
    <study_first_submitted_qc>July 31, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 8, 2023</study_first_posted>
    <last_update_submitted>August 9, 2023</last_update_submitted>
    <last_update_submitted_qc>August 9, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 14, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>University of South Carolina</investigator_affiliation>
      <investigator_full_name>Susan Steck</investigator_full_name>
      <investigator_title>Professor</investigator_title>
    </responsible_party>
    <keyword>Nutrition</keyword>
    <keyword>Breast Cancer</keyword>
    <keyword>Cancer Survivors</keyword>
    <keyword>Quality of Life</keyword>
    <keyword>Treatment Adherence</keyword>
    <keyword>Social Determinants</keyword>
    <condition_browse>
      <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
      <mesh_term>Breast Neoplasms</mesh_term>
    </condition_browse>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>De-identified participant data from the final research dataset may be shared with execution of a Data Use Agreement. The study protocol and statistical analysis plan are available upon reasonable request.</ipd_description>
      <ipd_info_type>Study Protocol</ipd_info_type>
      <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
      <ipd_time_frame>Data will be available at the conclusion of the study after the publication of the main results.</ipd_time_frame>
      <ipd_access_criteria>Contact the Principal Investigator for all requests: Dr. Susan Steck at stecks@mailbox.sc.edu</ipd_access_criteria>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05981716
  org study id: YBCSS-2023
  nct id: NCT05981716
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  Despite significant overall reductions in mortality rates for breast cancer over the past
        decade, both incidence and mortality rates have steadily climbed in adults diagnosed before
        age 50. This research project addresses factors associated with quality of life among and
        treatment response in early-age-at-onset breast cancer patients. The overall objective is
        collect information from early-onset breast cancer patients using an online questionnaire and
        examine factors related to cancer survival, (i.e.,better quality of life, better treatment
        adherence, less adverse treatment responses).
  
        Aim 1: Identify dietary patterns related to health-related quality of life in
        early-age-at-onset breast cancer patients. The investigators hypothesize that diet quality is
        related to better health-related quality of life among young breast cancer survivors.
  
        Aim 2. Identify demographic, social determinants, and geographic factors associated with
        treatment adherence. The investigators hypothesize that social determinants such as
        poverty-to-income ratio, education, and proximity to cancer treatment facilities are
        associated with treatment adherence in early-onset breast cancer.
  This is an observational epidemiologic study in which the investigators will collect data
        from approximately 384 participants at one point in time using online questionnaires (REDCaP
        and the NCI Diet History Questionnaire III). Recruitment will be conducted via breast cancer
        survivor social media support groups and advocacy groups. Electronic consent will be granted
        at the beginning of each online questionnaire. Data collection will occur via self-report in
        the location chosen by the participant using an online questionnaire. The investigators will
        use established validated questionnaires used by other previously conducted cohorts to
        enquire about demographics, occupation, cancer screening history, family history of cancer,
        comorbidities, cancer and other comorbidity treatment including fertility-related, HRQoL,
        dietary intake using the online NCI Dietary History Questionnaire III which includes dietary
        supplement use, residential history, hormonal status, physical activity, tobacco product and
        alcohol use, experiences with racism, social support, and information on health care
        utilization.
  
        To accomplish Aim 1, the investigators will use the NCI Diet History Questionnaire III, an
        online 135-item food frequency questionnaire with 26 dietary supplement questions, reflecting
        the past one month of intake to estimate food and nutrient intakes and overall dietary
        patterns.Health related quality of life (HRQoL) is a multidimensional concept that not only
        includes physical, psychological and social domains, but may also encompass other domains
        such as cognitive functioning. Cancer patients also exhibit many symptoms (e.g., fatigue,
        pain, sleep disturbance) that are not measurable directly from laboratory tests. Thus,
        assessing HRQoL and these symptom burdens among cancer survivors will need to rely on
        patients' self-reports, measured by validated instruments especially for patients with breast
        cancer, and includes the Functional Assessment of Cancer Therapy (FACT-B).
  
        Descriptive statistics will be presented as mean (standard deviation) and median (inter
        quartile range) for continuous variables and as frequency (percentage) for categorical
        variables. Data visualization tools such as histogram, boxplot, scatterplot and line plot
        will be employed to assess the trends and outliers in the data. Bivariate association between
        categorical variables will be tested for statistical significance using Chi-square test or
        exact test. Differences in continuous variables will be tested for statistical significance
        using Kruskal-Wallis test. HRQoL data typically are not distributed normally with left-skewed
        distributions and potential ceiling effects requiring consideration of alternative estimators
        in multivariate models. A number of different alternative models have been proposed over the
        standard ordinary least squares approach including beta regression, tobit regression, and
        two-part modeling. The investigators will assess the distribution of the primary HRQoL
        measures in building analytic models. With healthy dietary pattern as the primary independent
        variable and measures of HRQoL serving as primary dependent variable, the investigators
        expect to find that as diet quality increases, HRQoL increases.
  
        For Aim 2, the investigators will ask study participants to complete questionnaires related
        to racism, fatalism, and demographics and the investigators will geocode residential
        histories of participants to measure the role of racism, fatalism, income, education, and
        proximity to treatment facilities in treatment adherence. The investigators will use
        geographic information systems to geocode participants' residential histories. Participants
        will be asked the ZIP code of residence at the time of diagnosis, as well as the ZIP code of
        the treatment facility in which they received care. If the ZIP code of the treatment facility
        is unknown, the participant can give the city and state, in which a ZIP code of the central
        point of the city will be found and used for analysis. Using Microsoft Excel, and the list of
        latitudes and longitudes by ZIP code, provided by the United States Census Bureau (find
        citation), the distance in miles between the place of residence and place of treatment will
        be determined. This distance calculation can be used to help evaluate treatment adherence and
        the distance to treatment. Residential histories will also be used to determine participant
        rurality. Regarding the many competing definitions and classification schemes for rurality,
        Hall et al. found dichotomous definitions mask heterogeneity relevant to health research and
        studies of accessibility. The investigators will instead use the Rural-Urban Commuting Area
        (RUCA) codes developed by the Office of Rural Health Policy of the Health Resources and
        Services Administration and the Economic Research Service of the United States Department of
        Agriculture (USDA). RUCA codes combine information on population density, urbanicity, and
        daily commuting patterns to classify census tracts into 22 distinct codes, which can then be
        consolidated into more manageable classifications.
  
        Descriptive statistics will be presented as mean (standard deviation) and median (inter
        quartile range) for continuous variables and as frequency (percentage) for categorical
        variables. Data visualization tools such as histogram, boxplot, scatterplot and line plot
        will be employed to assess the trends and outliers in the data. The investigators will
        explore the associations between treatment adherence and each measure of social determinants
        (such as poverty to income ratio, racism, education, and proximity to treatment facility)
        using mixed effect logistic regression. The model will be sequentially adjusted for the
        effect of non-modifiable and modifiable confounders such as age, cancer stage, treatment
        type, and insurance status.
  
        Recruitment of participants is expected to be ongoing for ~12 months.
  observational model: Case-Only
  time perspective: Cross-Sectional
  measure: Score on Health-related Quality of Life Measure
  time frame: Measured at one point in time, from 0-10 years after diagnosis
  description: For Aim 1, health-related quality of life (HRQoL) is a multidimensional concept that not only includes physical, psychological and social domains, but may also encompass other domains such as cognitive functioning. Cancer patients also exhibit many symptoms (e.g., fatigue, pain, sleep disturbance) that are not measurable directly from laboratory tests. Thus, assessing HRQoL and these symptom burdens among cancer survivors will need to rely on patients' self-reports, measured by validated instruments especially for patients with breast cancer. Participants will complete the 37-item Functional Assessment of Cancer Therapy-B with a 5-point Likert-type scale capturing the domains of Physical Well-being, Social/Family Well-being, Emotional Well-being, Functional Well-being, and a Breast Cancer Subscale. Item responses are scored according to scoring guidelines ranging from scores of 0 to 148, with a higher score representing better quality of life.
  measure: Number of Participants with Treatment Adherence Compared to those without Treatment Adherence
  time frame: Up to 2 years post-diagnosis of breast cancer
  description: For Aim 2, the primary outcome is treatment adherence (at patient-level), which will be determined as per National Comprehensive Cancer Network (NCCN) guidelines and eventually categorized as treatment adherent/non-adherent.
  arm group label: Young Breast Cancer Survivors
  description: The investigators will enroll approximately 384 female breast cancer patients. Only non-institutionalized, English literate, female breast cancer patients diagnosed within the previous ten years and younger than 50 years old at the time of diagnosis will be eligible.
  intervention type: Other
  intervention name: No intervention
  description: This is an observational study. There is no intervention.
  arm group label: Young Breast Cancer Survivors
  study pop: 
  sampling method: Non-Probability Sample
  criteria: 
  gender: Female
  gender based: Yes
  gender description: Only female breast cancer survivors will be considered for the study. There is a much lower prevalence of breast cancer among men; therefore, the study will target a female survivor population.
  minimum age: 18 Years
  maximum age: 60 Years
  healthy volunteers: No
  last name: Susan Steck, PhD, MPH, RD
  role: Principal Investigator
  affiliation: University of South Carolina
  last name: Susan Steck, PhD, MPH, RD
  phone: 8037771527
  email: stecks@mailbox.sc.edu
  last name: Ella Gustafson
  phone: 7034736473
  email: ellabg@email.sc.edu
  facility: 
  status: Recruiting
  contact: 
  country: United States
  responsible party type: Principal Investigator
  investigator affiliation: University of South Carolina
  investigator full name: Susan Steck
  investigator title: Professor
  mesh term: Breast Neoplasms
  sharing ipd: Yes
  ipd description: De-identified participant data from the final research dataset may be shared with execution of a Data Use Agreement. The study protocol and statistical analysis plan are available upon reasonable request.
  ipd info type: Study Protocol
  ipd info type: Statistical Analysis Plan (SAP)
  ipd time frame: Data will be available at the conclusion of the study after the publication of the main results.
  ipd access criteria: Contact the Principal Investigator for all requests: Dr. Susan Steck at stecks@mailbox.sc.edu