Interventions for Lung Cancer Screening
(LungSMART Trial)
What You Need to Know Before You Apply
What is the purpose of this trial?
This trial explores new methods to encourage lung cancer screening using tools like text messages, chatbots, and videos. The goal is to increase screening rates for lung cancer with Low-Dose CT scans in low-resource healthcare settings. Participants might receive texts with questions, reminders, and educational content to guide them through the process. Current or former tobacco smokers who speak English or Spanish and have not previously discussed lung cancer screening with their doctor are well-suited for this trial. As an unphased trial, this study offers a unique opportunity to contribute to innovative screening methods that could benefit many in similar situations.
Is there any evidence suggesting that this trial's treatments are likely to be safe?
A previous study found that text messaging programs are generally safe and effective for increasing cancer screening rates. They helped check smoking status and determine who should get screened for lung cancer, particularly among middle-aged, educated adults. Research has also shown that patient navigation, whether responding to questions or offering guidance independently, is generally safe. These programs assist people through the screening process and have effectively increased lung cancer screening rates.
Additionally, digital tools like educational videos have been developed to support lung cancer screening for various groups. These tools are safe because they provide information and aid decision-making without involving medical procedures. Similarly, chatbots used for lung cancer screening offer information and answer common questions, making them a safe option for engaging with patients.
Overall, the methods used in this trial—text messages, chatbots, videos, and patient navigation—are non-invasive and have been shown to be safe for users.12345Why are researchers excited about this trial?
Researchers are excited about this trial because it explores innovative communication methods to enhance lung cancer screening participation. Unlike traditional methods that rely on direct clinical engagement, this trial uses a blend of technology-driven interventions like chatbots, educational videos, and structured text messaging to engage patients. These tools have the potential to improve accessibility and patient engagement by providing information and assistance directly through their mobile devices, making the screening process more user-friendly and proactive. By testing both proactive and reactive patient navigation, the trial aims to determine the most effective approach to encourage timely screenings, which could significantly improve early detection and outcomes for lung cancer.
What evidence suggests that this trial's treatments could be effective for lung cancer screening?
Research has shown that sending text messages can increase the number of people screened for cancers, such as lung cancer. In this trial, participants in different groups will receive repeated text messages to identify those who should get screened and to raise awareness about the benefits of early detection. Some participants will also access a Chatbot, which uses artificial intelligence to answer basic questions about cancer screening, encouraging more involvement. Additionally, educational videos tailored for different audiences will be available to some participants to inform them about lung cancer screening. The trial includes groups where participants receive help navigating the screening process, either reactively when they request assistance or proactively through outreach. These methods aim to simplify the screening process, which is crucial for early cancer detection and improving health outcomes.16789
Who Is on the Research Team?
David Wetter, PhD, MS
Principal Investigator
Huntsman Cancer Institute/ University of Utah
Are You a Good Fit for This Trial?
This trial is for adults aged 50-80 who are current or former smokers, speak English or Spanish, are patients at certain community health centers, have a phone that can get texts, and haven't already discussed or completed lung cancer screening. People with lung cancer can't join.Inclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Eligibility Assessment Stage
Participants are identified and assessed for eligibility using digital health interventions and shared decision making with a registered nurse.
LCS Completion Stage
Participants receive access to a Chatbot and are randomized to reactive or proactive patient navigation to complete Lung Cancer Screening.
Follow-up
Participants are monitored for completion of LCS and engagement with interventions.
What Are the Treatments Tested in This Trial?
Interventions
- Chatbot (CA)
- Proactive Patient Navigation (PPN)
- Reactive Patient Navigation (RPN)
- Text Messages (TM+)
- Video (VID)
Trial Overview
The study is testing different ways to encourage people to get screened for lung cancer: text messages, chatbots, videos, and two types of patient navigation (one where staff reach out first and one where they respond if contacted).
How Is the Trial Designed?
6
Treatment groups
Experimental Treatment
RPN requires the participant to initiate the PN request. Participants receive up to three text messages scheduled four weeks apart.
PPN does not require the participant to initiate the PN request and instead is a proactive call to the participant. Participants receive up to three text messages scheduled four weeks apart.
Participants will receive up to nine text messages containing eligibility questions, along with up to nine reminder messages. If a participant successfully responds to the eligibility questions, no more messages will be sent. If a participant does not respond to the eligibility questions after receiving a text message, they will receive a reminder message three days later, prompting them to answer the questions. Participants will have access to a Lung Cancer Screening (LCS) Chatbot delivered via SMS. Participants can watch an educational video via SMS.
Participants will receive up to nine text messages containing eligibility questions, along with up to nine reminder messages. If a participant successfully responds to the eligibility questions, no more messages will be sent. If a participant does not respond to the eligibility questions after receiving a text message, they will receive a reminder message three days later, prompting them to answer the questions. Participants can watch an educational video via SMS.
Participants will receive up to nine text messages containing eligibility questions, along with up to nine reminder messages. If a participant successfully responds to the eligibility questions, no more messages will be sent. If a participant does not respond to the eligibility questions after receiving a text message, they will receive a reminder message three days later, prompting them to answer the questions. Participants will have access to a Lung Cancer Screening (LCS) Chatbot delivered via SMS.
Participants will receive up to nine text messages containing eligibility questions, along with up to nine reminder messages. If a participant successfully responds to the eligibility questions, no more messages will be sent. If a participant does not respond to the eligibility questions after receiving a text message, they will receive a reminder message three days later, prompting them to answer the questions.
Find a Clinic Near You
Who Is Running the Clinical Trial?
University of Utah
Lead Sponsor
National Cancer Institute (NCI)
Collaborator
Huntsman Cancer Institute
Collaborator
Citations
Artificial Intelligence in Lung Cancer Screening: The Future Is ...
In 2010, an evaluation of NLST data showed a statistically significant 20% reduction in lung cancer mortality in a high-risk adults group who received three ...
Real-world validation of an AI-based score for lung cancer risk ...
... results showed strong concordance between the AI model and CT ... lung cancer screening, particularly in resource-constrained settings.
New Perspectives on Lung Cancer Screening and Artificial ...
Similarly, the ITALUNG trial showed a 39% reduction in mortality for men and 50% for women [32]. These results underscore the effectiveness of ...
Evaluating the Effectiveness of ChatGPT and Google ...
Studies suggest that LLMs can address basic cancer screening questions, including lung cancer screening (LCS), but concerns persist about inaccuracies, biases, ...
A benchmark of deep learning approaches to predict lung ...
This study aims to review and analyze current SOTA deep learning models for lung cancer risk prediction (malignant-benign classification).
6.
uspreventiveservicestaskforce.org
uspreventiveservicestaskforce.org/uspstf/recommendation/lung-cancer-screeningRecommendation: Lung Cancer: Screening
The USPSTF recommends annual screening for lung cancer with LDCT in adults aged 50 to 80 years who have at least a 20 pack-year smoking history.
7.
acsjournals.onlinelibrary.wiley.com
acsjournals.onlinelibrary.wiley.com/doi/full/10.3322/caac.21811Screening for lung cancer: 2023 guideline update from the ...
The LCS guideline is intended to provide guidance for screening to health care providers and their patients who are at high risk for lung cancer ...
8.
insideprecisionmedicine.com
insideprecisionmedicine.com/topics/oncology/lung-cancer-risk-accurately-predicted-by-ai-model/Lung Cancer Risk Accurately Predicted By AI Model
A deep-learning AI model, Sybil, can predict who will go on to develop lung cancer after one and six years based on one low-dose CT scan.
Systemic Challenges to Lung Cancer Screening in ...
To identify systems-level organized LCS implementation challenges, we performed a qualitative case study of imaging-based cancer screening. We purposively ...
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