Developing Smokers for Smoker (S4S): A Collective Intelligence Tailoring System (S4S)

This study will advance computer tailoring by adapting machine learning collective intelligence algorithms that have been used outside healthcare by companies like Amazon and Google to enhance the personal relevance of the health communication.

Study Overview

• Status: Completed
• Conditions: Tobacco Smoking
• Intervention / Treatment: Behavioral: Collective-Intelligence computer tailored health communication; Behavioral: Rule-based computer tailored health communication

Detailed Description

Smoking is still the number one preventable cause of cancer death. New approaches are needed to engage smokers in the 21st century in smoking cessation. I propose to develop S4S (Smokers for Smoker), a next-generation patient-centered computer tailored health communication (CTHC) system. Unlike current rule-based CTHCs, S4S will replace rules with complex machine learning algorithms, and use the collective experiences of thousands of smokers engaged in a web-assisted tobacco intervention to enhance personally-relevant tailoring for new smokers entering the system. The investigators will adapt collective intelligence algorithms that have been used outside healthcare by companies like Amazon and Google to enhance CTHC. Using knowledge from scientific experts, current CTHC collect baseline patient "profiles" and then use expert-written, rule-based systems to tailor messages to patient subsets. Such theory-based "market segmentation has been effective in helping patients reach lifestyle goals. However, there is a natural limit in the ability of a rule-based system to truly personalize content, and adapt personalization over time. Current CTHC have reached this limit, and the investigators propose to go beyond. The investigators first aim is to develop the Web 2.0 "S4S" recommender system. The investigators second aim is to evaluate S4S within the context of a NCI funded web-assisted tobacco intervention (Decide2Quit.org).

• Study Type: Interventional
• Enrollment (Actual): 260
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Massachusetts
    • Worcester, Massachusetts, United States, 01605
      • UMass Medical School

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 19 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

Description

Inclusion Criteria:

• Current Smokers

Exclusion Criteria:

-

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Treatment
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: Double

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Collective-Intelligence computer tailored health communication; Smokers will have access to all Decide2quit.org website functions and will receive 4 tailored emails per week based on a collective intelligence recommender systems algorithm for up to 6 months	Behavioral: Collective-Intelligence computer tailored health communication
Active Comparator: Rule-based computer tailored health communication; Smokers will have access to all Decide2quit.org website functions and will receive 4 tailored emails per week based on a rule-based algorithm for up to 6 months	Behavioral: Rule-based computer tailored health communication

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Repeated Use of website measure	This measure is an ordinal scale of the number of functions used after the first visit to the Decide2Quit.org website (0: use of no functions, 1: use of 1-2 functions, 2: use of 2-4 functions, see Table 9 list of functions). We will use scripts on the website to assess this information	Every Login for 6 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
30-day point prevalent smoking cessation at six months	Did you smoke any cigarettes during the past 30 days? This will be assessed using a follow-up Telephone or Internet survey	At 6 months

Collaborators and Investigators

• Sponsor: University of Massachusetts, Worcester
• Collaborators: National Cancer Institute (NCI); University of Massachusetts, Amherst
• Investigators: Principal Investigator: Rajani S Sadasivam, PhD, UMass Medical School

Study record dates

Study Major Dates

• Study Start (Actual): January 11, 2017
• Primary Completion (Actual): December 28, 2017
• Study Completion (Actual): June 28, 2020

Study Registration Dates

• First Submitted: October 9, 2014
• First Submitted That Met QC Criteria: October 14, 2014
• First Posted (Estimate): October 15, 2014

Study Record Updates

• Last Update Posted (Actual): October 8, 2020
• Last Update Submitted That Met QC Criteria: October 6, 2020
• Last Verified: October 1, 2020

More Information

Terms related to this study

• Keywords: machine learning; messaging; Tobacco Smoking Cessation; Computer tailored health communication; collective intelligence; recommender systems
• Other Study ID Numbers: H00002005; K07CA172677 (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