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LLM Intervention for Tobacco in Underserved Populations (LIFT-UP) (LIFT-UP)

26. maj 2026 opdateret af: University of Utah

This study will test a tailored, multilingual tobacco cessation chatbot called LIFT-UP (LLM Intervention for Tobacco in Underserved Populations), designed to better meet the needs of people living in persistent poverty census tracts.

We will use 1:1 semi-structured interviews to explore social drivers of health impacting TC, as well as digital access and preferences among those living in PPCTs. This qualitative approach enables guided yet flexible exploration of key domains while capturing unanticipated insights relevant to refining the chatbot.

Studieoversigt

Status

Ikke rekrutterer endnu

Betingelser

Intervention / Behandling

Detaljeret beskrivelse

Tobacco use is a major cause of cancer and is responsible for about half a million deaths in the United States each year. Because of this, helping people stop using tobacco is one of the most important ways to prevent cancer. Although tobacco use has decreased over time, many adults in the U.S. still use tobacco. Many people try to quit each year, but most quit attempts are not successful. One reason is that many people do not use proven, evidence-based quit support, such as counseling or quit medications.

People who live in areas with long-term poverty often face additional barriers that can make quitting harder. These areas may have fewer job and education opportunities, limited access to healthcare and community resources, and higher levels of day-to-day stress (for example, related to financial strain or lack of health insurance). People with lower income are just as likely to try to quit as those with higher income, but they are less likely to quit successfully and are less likely to use evidence-based quitting support. Many persistent poverty areas are also rural and have higher numbers of people who prefer to speak languages other than English, including Spanish, which creates an additional need for bilingual and culturally appropriate quit support.

Digital tools may help increase access to evidence-based tobacco cessation support in these communities. Mobile phone ownership is very common, including among people with lower incomes. However, some smartphone apps require reliable internet access or data plans, which can be a barrier. Text messaging is accessible on nearly all phones, does not require internet access, can be offered in multiple languages, and can be tailored to the needs of the user.

Text-based programs that use artificial intelligence (AI), such as large-language-model chatbots, may be especially useful because they can provide interactive support using natural language and can be delivered at scale. Chatbots have been used successfully in other areas of health, but many existing programs use fixed scripts and may not feel relevant or helpful for all groups. Importantly, most tobacco cessation chatbots have not been designed to address barriers faced by people living in persistent poverty areas.

Undersøgelsestype

Interventionel

Tilmelding (Anslået)

22

Fase

  • Ikke anvendelig

Kontakter og lokationer

Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.

Studiekontakt

Studiesteder

    • Utah
      • Salt Lake City, Utah, Forenede Stater, 84102
        • Huntsman Cancer Institute/ University of Utah
        • Kontakt:
        • Ledende efterforsker:
          • Lindsey Potter, MPH, PhD
        • Ledende efterforsker:
          • Christian Mahony Reategui Rivera, MD, MS

Deltagelseskriterier

Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.

Berettigelseskriterier

Aldre berettiget til at studere

  • Voksen
  • Ældre voksen

Tager imod sunde frivillige

Ingen

Beskrivelse

Inclusion Criteria:

  • 18+ years old
  • Use ≥3 cigarettes/day on average
  • Motivated to quit in the next 30 days
  • Have a computer or tablet with internet access for 1:1 interviews
  • Speak English or Spanish
  • Home address is in an area characterized by persistent poverty

Exclusion Criteria:

  • None

Studieplan

Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.

Hvordan er undersøgelsen tilrettelagt?

Design detaljer

  • Primært formål: Andet
  • Tildeling: N/A
  • Interventionel model: Enkelt gruppeopgave
  • Maskning: Ingen (Åben etiket)

Våben og indgreb

Deltagergruppe / Arm
Intervention / Behandling
Eksperimentel: Moderated Session
Participants will attend a ~80 minute moderated "think-aloud" session via HIPAA compliant videoconferencing platform.
LIFT-UP Chatbot will be developed, evaluated, and refined using GARDE-Chat, an open-source chatbot authoring platform that has been used to support the development of chatbot-based interventions tested in large pragmatic clinical trials.

Hvad måler undersøgelsen?

Primære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
System Usability Scale (SUS)
Tidsramme: up to 1 day

Usability will be measured using the SUS, a questionnaire assessing the perceived usability of a system, product, website, app, or digital intervention. It consists of ten 5-point Likert items ranging from "Strongly disagree" to "Strongly agree".

Scoring follows the standard SUS scoring procedure, for positively worded items, the item score is calculated as response minus 1; for negatively worded items, the item score is calculated as 5 minus the response. The 10 item scores are summed and then multiplied by 2.5 to generate the final SUS score, with higher scores indicating greater perceived usability. Score range: 0-100.

up to 1 day

Sekundære resultatmål

Resultatmål
Foranstaltningsbeskrivelse
Tidsramme
Usability - Chat Bot Usability Scale (BUS-11)
Tidsramme: up to 1 day
Chatbot usability will be measured using the Chatbot Usability Scale (BUS-11). BUS-11 is a measured that assesses users' experiences after interacting with a chatbot or conversational agent. The BUS-11 consists of eleven 5-point Likert items ranging from "Strongly disagree" to "Strongly agree". Each item is coded from 1 to 5, and item scores are summed to create a total score. Higher scores indicate greater perceived chatbot usability. Score range: 11-55.
up to 1 day
Acceptability
Tidsramme: up to 1 day
Acceptability will be measured using the Acceptability of Intervention Measure (AIM). AIM is an instrument that assesses the perceived acceptability of an intervention. It consists of four 5 point Likert items ranging from "Completely "disagree" to "Strongly agree". Each item is coded from 1 to 5, and the overall score is the mean of the items score. Higher scores indicate greater perceived acceptability of the intervention. Score range: 1-5.
up to 1 day
Digital Working Alliance
Tidsramme: up to 1 day
Working alliance in the digital context will be measured with the Digital Working Alliance inventory (D-WAI). D-WAI is derived from the Working Alliance Inventory and measures the perceived working alliance (e.g., traditionally the collaborative bond between therapist and client) with digital interventions. It includes six 7-point Likert items ranging from "Strongly disagree" to "Strongly agree". Each item is coded from 1 to 7, and item scores are summed to create a total score. Higher scores indicate a stronger perceived digital working alliance. Score range: 7-42.
up to 1 day
Perceived cultural fit
Tidsramme: up to 1 day
Perceived cultural relevance will be measured using the Cultural Relevance Questionnaire (CRQ). CRQ consists of six 5-point Likert items ranging from "Strongly disagree" to "Strongly agree. Higher scores indicate greater perceived cultural appropriateness/relevance of the intervention. Score range: 5-25. An additional 5-point Likert-like question was added to reflect overall cultural fit perceived by the users.
up to 1 day

Samarbejdspartnere og efterforskere

Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.

Efterforskere

  • Ledende efterforsker: Chelsey Schlechter, MPH, PhD, Huntsman Cancer Institute/ University of Utah
  • Ledende efterforsker: Christian Mahony Reategui Rivera, MD, MS, University of Utah

Datoer for undersøgelser

Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.

Studer store datoer

Studiestart (Anslået)

1. juni 2026

Primær færdiggørelse (Anslået)

31. maj 2027

Studieafslutning (Anslået)

31. maj 2027

Datoer for studieregistrering

Først indsendt

26. maj 2026

Først indsendt, der opfyldte QC-kriterier

26. maj 2026

Først opslået (Faktiske)

2. juni 2026

Opdateringer af undersøgelsesjournaler

Sidste opdatering sendt (Faktiske)

2. juni 2026

Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier

26. maj 2026

Sidst verificeret

1. maj 2026

Mere information

Begreber relateret til denne undersøgelse

Yderligere relevante MeSH-vilkår

Andre undersøgelses-id-numre

  • HCI199440
  • U54CA280812 (U.S. NIH-bevilling/kontrakt)

Plan for individuelle deltagerdata (IPD)

Planlægger du at dele individuelle deltagerdata (IPD)?

INGEN

IPD-planbeskrivelse

De-identified data will be shared with only with investigators that have a data sharing agreement through PIVOT.

Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter

Studerer et amerikansk FDA-reguleret lægemiddelprodukt

Ingen

Studerer et amerikansk FDA-reguleret enhedsprodukt

Ingen

Disse oplysninger blev hentet direkte fra webstedet clinicaltrials.gov uden ændringer. Hvis du har nogen anmodninger om at ændre, fjerne eller opdatere dine undersøgelsesoplysninger, bedes du kontakte register@clinicaltrials.gov. Så snart en ændring er implementeret på clinicaltrials.gov, vil denne også blive opdateret automatisk på vores hjemmeside .

Kliniske forsøg med LIFT-UP Chatbot

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