Breast CancerNCT05472064
Design of Chatbot Persona for Breast Cancer Screening Outreach Among Black Women
An observational study of Breast Cancer, run by University of Washington at 1 site in United States.
Trial summary
Breast cancer screening disparities among Black women persist despite health system recognition and outreach. However, current evidence on how to tailor and optimize implementation strategies for breast cancer screening outreach is limited. Read more about this study
- Who is it for?
- Ages 40 to 74 years, women only
- Conditions the study is for
- Breast Cancer
- What is being tested?
- Persona: Primary care doctor, Persona: Breast cancer survivor and Communication Style: Direct
- How many people
- 550 participants
- Where
- 1 siteUnited States
- Study dates
- 22 Aug 2022to 27 Dec 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 40 to 74 years
- Women only
- No healthy volunteers
- Adult, Older adult
Study population: Black or African American women between the ages of 40-74 years old and residing in the United States.
Who may be able to join
- All women who are 40-74 years old:
- who identify as Black
Who cannot join
- Participants who do not complete the survey
- Participants who complete the survey in less than half the normal average time
- Participants who do not pass the attention check
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Washington Medical Center
Seattle, Washington, 98105
About this study
Official title: Human Centered Design Approach to Eliminating Disparities in Breast Cancer Screening
Breast cancer screening disparities among Black women persist despite health system recognition and outreach. However, current evidence on how to tailor and optimize implementation strategies for breast cancer screening outreach is limited. The proposed study is part of a larger project to design a chatbot for breast cancer screening outreach to Black women and will focus on optimizing the chatbot persona. Using the Multiphase Optimization Strategy (MOST) framework, the investigators will conduct a randomized factorial experiment to assess the individual components of chatbot persona for breast cancer screening and identify which components have the greatest effect on trust and engagement for Black women. This information will guide the design of an optimized chatbot intervention that achieves the primary outcomes.
Read the detailed description
The goal of this study is to determine the optimal delivery of initial chatbot messages for culturally tailored breast cancer screening outreach. Mistrust of the medical system has been identified as a significant barrier to mammography screening among Black women. Yet, while tailored interventions for breast cancer screening exist, the optimal design of a tailored intervention to engender trust is unknown. Chatbots have been shown to increase levels of trust in web-based information, though adoption of chatbots may depend on chatbot characteristics. The investigators propose to use the Multiphase Optimization Strategy (MOST), a framework for developing efficacious, efficient, scalable and cost-effective interventions, to assess the performance of chatbot intervention components and their interactions.
The chatbot message delivery will be systematically varied across two components, each of which is represented by a separate factor in the 2x2x1 factorial study design with a control arm. Specifically, each participant will be randomly assigned to one of five separate experimental conditions. Conditions include: (1) chatbot with a primary care doctor persona and direct communication style; (2) chatbot with a breast cancer survivor persona and direct communication style; (3) chatbot with a primary care doctor persona and indirect communication style; and (4) chatbot with a breast cancer survivor persona and indirect communication style. All participants will complete a survey regarding their perceptions about the initial outreach messages from the chatbot.
The main effects will be estimated of the two experimental factors and their interactions on the study's primary outcomes - trust in the chatbot system to use for breast cancer screening education and scheduling, and intention to use. This information will guide the design of an optimized chatbot persona that achieves the primary outcomes.
Participants will be enrolled if they are Black individuals who qualify for breast cancer screening residing in the United States who are between the ages of 40-74. Recruitment will be conducted on Prolific, an online participant pooling platform, and Amazon Mechanical Turk (MTurk), a crowdsourcing platform used for research recruitment. Prolific will be used given the platform's ability for selecting the participant population. However, due to the limited number of individuals within the inclusion criteria on Prolific, and if needed participants will also be recruited on MTurk. Participants will be asked to view the chatbot messages and respond to questions to assess trust, engagement, and directness of the chatbot.
- Conditions
- Breast Cancer
- Being tested
- Persona: Primary care doctor, Persona: Breast cancer survivor, Communication Style: Direct, Communication Style: Indirect
- Keywords
- Breast cancer screening, Healthcare disparities, Chatbot, Multiphase optimization strategy (MOST)
- Medical subject headings
- Breast Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Persona: Primary care doctorOther
The chatbot persona is a Black woman who is a primary care doctor.
Persona: Breast cancer survivorOther
The chatbot persona is a Black woman who is a breast cancer survivor.
Communication Style: DirectOther
The chatbot messages are characterized by commands and direct addresses (''you'').
Communication Style: IndirectOther
The chatbot messages are characterized by subjunctive modal verb forms (''would like'') and cooperative addresses (''we", "let's").
| Study group | Role | What they receive |
|---|---|---|
| Group 1 | Primary care doctor persona with direct messages Receives: Other: Persona: Primary care doctor; Other: Communication Style: Direct | |
| Group 2 | Breast cancer survivor persona with direct messages Receives: Other: Persona: Breast cancer survivor; Other: Communication Style: Direct | |
| Group 3 | Primary care doctor with indirect messages Receives: Other: Persona: Primary care doctor; Other: Communication Style: Indirect | |
| Group 4 | Breast cancer survivor persona with indirect messages Receives: Other: Persona: Breast cancer survivor; Other: Communication Style: Indirect | |
| Group 5 | Control |
What is being measured
The primary outcome is the main question the study is designed to answer.
Trust
The human-computer trust scale assesses user trust, which is based on similar constructs of trust (benevolence, competence, reciprocity, perceived risk). 7 of the 12 items were selected which use a 5-point Likert scale from 'Strongly disagree' to 'Strongly agree'.
Measured: Day 1
Intention to Use
This measure assesses likelihood to use this system to schedule a mammogram in the future, and is scored on a 5-point Likert scale from 'Very unlikely' to 'Very likely'.
Measured: Day 1
Secondary outcomes (4)
Engagement
This measure consists of 4 semantic differential scales assessing traits (important, interesting, relevant, warm) on a 7-point scale.
Measured: Day 1
Directness
This measure consists of 7 semantic differential scales assessing traits (direct, friendly, caring, straightforward, demanding, respectful, polite) on a 7-point scale.
Measured: Day 1
Expertness and Homophily
These 4 items measure the perceived expertise and attitude of the system on a 5-point Likert scale from 'Strongly disagree' to 'Strongly agree'.
Measured: Day 1
Self-brand connection
This measure consists of 3 items to assess self-brand connection on a 5-point Likert scale from 'Strongly disagree' to 'Strongly agree'.
Measured: Day 1
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Jul 2022
- Study start22 Aug 2022
- Primary completion27 Dec 2022
- Study completion27 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Breast CancerStudies of the same condition, open and under way.
- More trials run by University of WashingtonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Enrolment
- 550 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Washington
- Sponsor type
- University, hospital or other
- Collaborators
- National Cancer Institute (NCI)
- Responsible party
- Principal investigator, Leah Marcotte, University of Washington
- Investigators
- Leah Marcotte, MD, Principal investigator, University of Washington
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05472064
- Sponsor's protocol ID
- STUDY00011606
- Other IDs
- 5P50CA244432
- Status last verified
- May 2023
- First submitted
- 10 Jun 2022
- Last updated
- 16 May 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Langevin R, Kalidindi P, Arriaga K, Kyle RP, Akande S, Hsieh G, Marcotte LM. A randomized factorial experiment to optimize the design of a culturally tailored breast cancer screening outreach chatbot intervention. Front Digit Health. 2026 Apr 22;8:1720531. doi: 10.3389/fdgth.2026.1720531. eCollection 2026.
PubMed 42099928
Study at a glance
From the registry record, last updated by the sponsor on 16 May 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Human Centered Design Approach to Eliminating Disparities in Breast Cancer Screening
- ClinicalTrials.gov ID
- NCT05472064
- Acronym / sponsor ID
- STUDY00011606
- Conditions
- Breast Cancer
- Keywords
- Breast cancer screening, Healthcare disparities, Chatbot, Multiphase optimization strategy (MOST)
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 550 participants (actual)
- Lead sponsor
- University of WashingtonUniversity, hospital or other
- Collaborators
- National Cancer Institute (NCI)
- Study start
- 22 Aug 2022 (actual)
- Primary completion
- 27 Dec 2022 (actual)
- Study completion
- 27 Dec 2022 (actual)
- First posted
- 25 Jul 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Breast cancer screening disparities among Black women persist despite health system recognition and outreach. However, current evidence on how to tailor and optimize implementation strategies for breast cancer screening outreach is limited. The proposed study is part of a larger project to design a chatbot for breast cancer screening outreach to Black women and will focus on optimizing the chatbot persona. Using the Multiphase Optimization Strategy (MOST) framework, the investigators will conduct a randomized factorial experiment to assess the individual components of chatbot persona for breast cancer screening and identify which components have the greatest effect on trust and engagement for Black women. This information will guide the design of an optimized chatbot intervention that achieves the primary outcomes.
Detailed description
The goal of this study is to determine the optimal delivery of initial chatbot messages for culturally tailored breast cancer screening outreach. Mistrust of the medical system has been identified as a significant barrier to mammography screening among Black women. Yet, while tailored interventions for breast cancer screening exist, the optimal design of a tailored intervention to engender trust is unknown. Chatbots have been shown to increase levels of trust in web-based information, though adoption of chatbots may depend on chatbot characteristics. The investigators propose to use the Multiphase Optimization Strategy (MOST), a framework for developing efficacious, efficient, scalable and cost-effective interventions, to assess the performance of chatbot intervention components and their interactions.
The chatbot message delivery will be systematically varied across two components, each of which is represented by a separate factor in the 2x2x1 factorial study design with a control arm. Specifically, each participant will be randomly assigned to one of five separate experimental conditions. Conditions include: (1) chatbot with a primary care doctor persona and direct communication style; (2) chatbot with a breast cancer survivor persona and direct communication style; (3) chatbot with a primary care doctor persona and indirect communication style; and (4) chatbot with a breast cancer survivor persona and indirect communication style. All participants will complete a survey regarding their perceptions about the initial outreach messages from the chatbot.
The main effects will be estimated of the two experimental factors and their interactions on the study's primary outcomes - trust in the chatbot system to use for breast cancer screening education and scheduling, and intention to use. This information will guide the design of an optimized chatbot persona that achieves the primary outcomes.
Participants will be enrolled if they are Black individuals who qualify for breast cancer screening residing in the United States who are between the ages of 40-74. Recruitment will be conducted on Prolific, an online participant pooling platform, and Amazon Mechanical Turk (MTurk), a crowdsourcing platform used for research recruitment. Prolific will be used given the platform's ability for selecting the participant population. However, due to the limited number of individuals within the inclusion criteria on Prolific, and if needed participants will also be recruited on MTurk. Participants will be asked to view the chatbot messages and respond to questions to assess trust, engagement, and directness of the chatbot.
Study design
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| Group 1 | – | Other: Persona: Primary care doctor, Other: Communication Style: DirectPrimary care doctor persona with direct messages |
| Group 2 | – | Other: Persona: Breast cancer survivor, Other: Communication Style: DirectBreast cancer survivor persona with direct messages |
| Group 3 | – | Other: Persona: Primary care doctor, Other: Communication Style: IndirectPrimary care doctor with indirect messages |
| Group 4 | – | Other: Persona: Breast cancer survivor, Other: Communication Style: IndirectBreast cancer survivor persona with indirect messages |
| Group 5 | – | –Control |
Who may qualify
- Ages
- 40 to 74 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Black or African American women between the ages of 40-74 years old and residing in the United States.
Key inclusion criteria (2 listed)
- who identify as Black
Key exclusion criteria (3 listed)
- Participants who do not complete the survey
- Participants who complete the survey in less than half the normal average time
- Participants who do not pass the attention check
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Persona: Primary care doctorThe chatbot persona is a Black woman who is a primary care doctor. | Other | Group 1, Group 3 |
| Persona: Breast cancer survivorThe chatbot persona is a Black woman who is a breast cancer survivor. | Other | Group 2, Group 4 |
| Communication Style: DirectThe chatbot messages are characterized by commands and direct addresses (''you''). | Other | Group 1, Group 2 |
| Communication Style: IndirectThe chatbot messages are characterized by subjunctive modal verb forms (''would like'') and cooperative addresses (''we", "let's"). | Other | Group 3, Group 4 |
Primary outcome
Trust
Time frame: Day 1
The human-computer trust scale assesses user trust, which is based on similar constructs of trust (benevolence, competence, reciprocity, perceived risk). 7 of the 12 items were selected which use a 5-point Likert scale from 'Strongly disagree' to 'Strongly agree'.
Intention to Use
Time frame: Day 1
This measure assesses likelihood to use this system to schedule a mammogram in the future, and is scored on a 5-point Likert scale from 'Very unlikely' to 'Very likely'.
4 secondary outcomes
Engagement
Time frame: Day 1
Directness
Time frame: Day 1
Expertness and Homophily
Time frame: Day 1
Self-brand connection
Time frame: Day 1
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 16 May 2023.
- Derived from this study: PubMed 42099928Langevin R, Kalidindi P, Arriaga K, Kyle RP, Akande S, Hsieh G, Marcotte LM. A randomized factorial experiment to optimize the design of a culturally tailored breast cancer screening outreach chatbot intervention. Front Digit Health. 2026 Apr 22;8:1720531. doi: 10.3389/fdgth.2026.1720531. eCollection 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Leah Marcotte, MD principal investigator
University of Washington
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Breast Cancer, run by University of Washington at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 16 May 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Trust (time frame: Day 1). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05472064, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 16 May 2023.