Developing Smokers for Smoker (S4S): A Collective Intelligence Tailoring System
| Status: | Recruiting |
|---|---|
| Conditions: | Smoking Cessation |
| Therapuetic Areas: | Pulmonary / Respiratory Diseases |
| Healthy: | No |
| Age Range: | 19 - Any |
| Updated: | 9/14/2018 |
| Start Date: | January 11, 2017 |
| End Date: | August 2020 |
| Contact: | Rajani S Sadasivam, PhD |
| Email: | rajani.sadasivam@umassmed.edu |
| Phone: | 5088568924 |
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.
intelligence algorithms that have been used outside healthcare by companies like Amazon and
Google to enhance the personal relevance of the health communication.
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).
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).
Inclusion Criteria:
- Current Smokers
Exclusion Criteria:
We found this trial at
1
site
Click here to add this to my saved trials
