首页|University of Virginia School of Medicine Reports Findings in Machine Learning ( Identifying Patterns of Smoking Cessation App Feature Use That Predict Successfu l Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning)
University of Virginia School of Medicine Reports Findings in Machine Learning ( Identifying Patterns of Smoking Cessation App Feature Use That Predict Successfu l Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Charlottesville, Virgini a, by NewsRx journalists, research stated, “Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interve ntions. However, there is a need for additional research investigating how the u se of different features within such apps impacts their effectiveness.” The news correspondents obtained a quote from the research from the University o f Virginia School of Medicine, “We used observational data collected from an exp eriment of a publicly available smoking cessation app to develop supervised mach ine learning (SML) algorithms intended to distinguish the app features that prom ote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explain ed by other known predictors of cessation (eg, tobacco use behaviors). Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of ince ntivizing ecological momentary assessments within the National Cancer Institute’ s quitSTART app. Participants’ (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the expe riment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used t o estimate participants’ probability of cessation from 28 variables reflecting p articipants’ use of different app features, assigned experimental conditions, an d phone type (iPhone [Apple Inc] or Androi d [Google]). The SML model was first fit i n a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whethe r adding individuals’ SMLpredicted probabilities of cessation to a logistic reg ression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation. The SML model’s sensitivity ( 0.67) and specificity (0.67) in the held-aside test set indicated that individua ls’ patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model-predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16). Harnessing user data through SML could help determine the features of smoking ce ssation apps that are most useful.”
CharlottesvilleVirginiaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesHealth and MedicineMachine LearningSmoking
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