Tobacco SmokingS4SNCT02265354
Developing Smokers for Smoker (S4S): A Collective Intelligence Tailoring System
A study testing Collective-Intelligence computer tailored health communication and Rule-based computer tailored health communication in people with Tobacco Smoking, run by University of Massachusetts, Worcester at 1 site in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 19 years and olderHealthy volunteers can take part
- Conditions the study is for
- Tobacco Smoking
- What is being tested?
- Collective-Intelligence computer tailored health communication and Rule-based computer tailored health communication
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 260 participants
- Where
- 1 siteUnited States
- Study dates
- 11 Jan 2017to 28 Jun 2020
- Phase
- Interventional
- 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 19 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Current Smokers
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
United States1 site
UMass Medical School
Worcester, Massachusetts, 01605
About this study
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.
Read the 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).
- Conditions
- Tobacco Smoking
- Being tested
- Collective-Intelligence computer tailored health communication, Rule-based computer tailored health communication
- Keywords
- Tobacco Smoking Cessation, Computer tailored health communication, messaging, collective intelligence, recommender systems, machine learning
- Medical subject headings
- Tobacco Smoking
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Collective-Intelligence computer tailored health communicationBehavioural
Rule-based computer tailored health communicationBehavioural
| Study group | Role | What they receive |
|---|---|---|
| Collective-Intelligence computer tailored health communication | Experimental | 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 Receives: Behavioral: Collective-Intelligence computer tailored health communication |
| Rule-based computer tailored health communication | Active comparator | 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 Receives: Behavioral: Rule-based computer tailored health communication |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: Every Login for 6 months
Secondary outcomes (1)
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
Measured: At 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Oct 2014 (estimated)
- Study start11 Jan 2017
- Primary completion28 Dec 2017
- Study completion28 Jun 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Tobacco SmokingStudies of the same condition, open and under way.
- 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
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 260 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Massachusetts, Worcester
- Sponsor type
- University, hospital or other
- Collaborators
- University of Massachusetts, Amherst, National Cancer Institute (NCI)
- Responsible party
- Principal investigator, Rajani Sadasivam, University of Massachusetts, Worcester
- Investigators
- Rajani S Sadasivam, PhD, Principal investigator, UMass Medical School
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02265354
- Sponsor's protocol ID
- H00002005
- Other IDs
- K07CA172677
- Acronym
- S4S
- Status last verified
- Oct 2020
- First submitted
- 9 Oct 2014
- Last updated
- 8 Oct 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 Oct 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing Smokers for Smoker (S4S): A Collective Intelligence Tailoring System
- ClinicalTrials.gov ID
- NCT02265354
- Acronym / sponsor ID
- S4S / H00002005
- Conditions
- Tobacco Smoking
- Keywords
- Tobacco Smoking Cessation, Computer tailored health communication, messaging, collective intelligence, recommender systems, machine learning
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 260 participants (actual)
- Lead sponsor
- University of Massachusetts, WorcesterUniversity, hospital or other
- Collaborators
- University of Massachusetts, Amherst, National Cancer Institute (NCI)
- Study start
- 11 Jan 2017 (actual)
- Primary completion
- 28 Dec 2017 (actual)
- Study completion
- 28 Jun 2020 (actual)
- First posted
- 15 Oct 2014 (estimated)
- 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.
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.
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 design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Collective-Intelligence computer tailored health communication | Experimental | Behavioral: Collective-Intelligence computer tailored health communicationSmokers 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 |
| Rule-based computer tailored health communication | Active comparator | Behavioral: Rule-based computer tailored health communicationSmokers 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 |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Current Smokers
Key exclusion criteria (1 listed)
- -
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 |
|---|---|---|
| Collective-Intelligence computer tailored health communication | Behavioural | Collective-Intelligence computer tailored health communication |
| Rule-based computer tailored health communication | Behavioural | Rule-based computer tailored health communication |
Primary outcome
Repeated Use of website measure
Time frame: Every Login for 6 months
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
1 secondary outcome
30-day point prevalent smoking cessation at six months
Time frame: At 6 months
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 8 Oct 2020.
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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.
Rajani S Sadasivam, PhD principal investigator
UMass Medical School
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Collective-Intelligence computer tailored health communication and Rule-based computer tailored health communication in people with Tobacco Smoking, run by University of Massachusetts, Worcester 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 1 inclusion and 1 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 8 Oct 2020. 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?
Repeated Use of website measure (time frame: Every Login for 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 8 Oct 2020.