Design of Chatbot Persona for Breast Cancer Screening Outreach Among Black Women

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.

Study Overview

• Status: Completed
• Conditions: Breast Cancer
• Intervention / Treatment: Other: Persona: Primary care doctor; Other: Communication Style: Direct; Other: Persona: Breast cancer survivor; Other: Communication Style: Indirect

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 Type: Observational
• Enrollment (Actual): 550
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Washington
    • Seattle, Washington, United States, 98105
      • University of Washington Medical Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 36 years to 70 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Black or African American women between the ages of 40-74 years old and residing in the United States.

Description

Inclusion Criteria:

All women who are 40-74 years old:

• who identify as Black

Exclusion Criteria:

• 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

Study Plan

How is the study designed?

Number of groups / cohorts

5

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Group 1; Primary care doctor persona with direct messages	Other: Persona: Primary care doctor; The chatbot persona is a Black woman who is a primary care doctor.; Other: Communication Style: Direct; The chatbot messages are characterized by commands and direct addresses (''you'').
Group 2; Breast cancer survivor persona with direct messages	Other: Communication Style: Direct; The chatbot messages are characterized by commands and direct addresses (''you'').; Other: Persona: Breast cancer survivor; The chatbot persona is a Black woman who is a breast cancer survivor.
Group 3; Primary care doctor with indirect messages	Other: Persona: Primary care doctor; The chatbot persona is a Black woman who is a primary care doctor.; Other: Communication Style: Indirect; The chatbot messages are characterized by subjunctive modal verb forms (''would like'') and cooperative addresses (''we", "let's").
Group 4; Breast cancer survivor persona with indirect messages	Other: Persona: Breast cancer survivor; The chatbot persona is a Black woman who is a breast cancer survivor.; Other: Communication Style: Indirect; The chatbot messages are characterized by subjunctive modal verb forms (''would like'') and cooperative addresses (''we", "let's").
Group 5; Control

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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'.	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'.	Day 1

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Engagement	This measure consists of 4 semantic differential scales assessing traits (important, interesting, relevant, warm) on a 7-point scale.	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.	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'.	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'.	Day 1

Collaborators and Investigators

• Sponsor: University of Washington
• Collaborators: National Cancer Institute (NCI)
• Investigators: Principal Investigator: Leah Marcotte, MD, University of Washington

Study record dates

Study Major Dates

• Study Start (Actual): August 22, 2022
• Primary Completion (Actual): December 27, 2022
• Study Completion (Actual): December 27, 2022

Study Registration Dates

• First Submitted: June 10, 2022
• First Submitted That Met QC Criteria: July 20, 2022
• First Posted (Actual): July 25, 2022

Study Record Updates

• Last Update Posted (Actual): May 16, 2023
• Last Update Submitted That Met QC Criteria: May 13, 2023
• Last Verified: May 1, 2023

More Information

Terms related to this study

• Keywords: Chatbot; Breast cancer screening; Multiphase optimization strategy (MOST); Healthcare disparities
• Additional Relevant MeSH Terms: Skin Diseases; Neoplasms; Neoplasms by Site; Breast Diseases; Breast Neoplasms
• Other Study ID Numbers: STUDY00011606; 5P50CA244432 (U.S. NIH Grant/Contract)

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No