FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4313335732> ?p ?o ?g. }

• W4313335732 endingPage "e43629" @default.
• W4313335732 startingPage "e43629" @default.
• W4313335732 abstract "A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods.This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529).Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction.The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold).Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes." @default.
• W4313335732 created "2023-01-06" @default.
• W4313335732 creator A5053909459 @default.
• W4313335732 creator A5065546099 @default.
• W4313335732 creator A5079684390 @default.
• W4313335732 creator A5081085475 @default.
• W4313335732 creator A5090320770 @default.
• W4313335732 date "2023-01-20" @default.
• W4313335732 modified "2023-09-26" @default.
• W4313335732 title "Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials" @default.
• W4313335732 cites W1493454437 @default.
• W4313335732 cites W1513618424 @default.
• W4313335732 cites W1951157100 @default.
• W4313335732 cites W1980276147 @default.
• W4313335732 cites W1986546901 @default.
• W4313335732 cites W2002501041 @default.
• W4313335732 cites W2003528492 @default.
• W4313335732 cites W2006617902 @default.
• W4313335732 cites W2012980101 @default.
• W4313335732 cites W2021206292 @default.
• W4313335732 cites W2031201873 @default.
• W4313335732 cites W2034370904 @default.
• W4313335732 cites W2056853212 @default.
• W4313335732 cites W2062474560 @default.
• W4313335732 cites W2076020534 @default.
• W4313335732 cites W2121394390 @default.
• W4313335732 cites W2123420688 @default.
• W4313335732 cites W2148690562 @default.
• W4313335732 cites W2153923965 @default.
• W4313335732 cites W2168175751 @default.
• W4313335732 cites W2168844630 @default.
• W4313335732 cites W2511317042 @default.
• W4313335732 cites W2525050280 @default.
• W4313335732 cites W2591428653 @default.
• W4313335732 cites W2618851899 @default.
• W4313335732 cites W2727476476 @default.
• W4313335732 cites W2773563881 @default.
• W4313335732 cites W2787894218 @default.
• W4313335732 cites W2793703203 @default.
• W4313335732 cites W2801651345 @default.
• W4313335732 cites W2897357222 @default.
• W4313335732 cites W2901202149 @default.
• W4313335732 cites W2955071302 @default.
• W4313335732 cites W2982082714 @default.
• W4313335732 cites W3044822023 @default.
• W4313335732 cites W3087179332 @default.
• W4313335732 cites W3097807896 @default.
• W4313335732 cites W3103933465 @default.
• W4313335732 cites W3127288097 @default.
• W4313335732 cites W3197619542 @default.
• W4313335732 cites W3199093113 @default.
• W4313335732 cites W376961572 @default.
• W4313335732 cites W4205347810 @default.
• W4313335732 cites W4213428791 @default.
• W4313335732 cites W4226148866 @default.
• W4313335732 cites W4280541117 @default.
• W4313335732 cites W4285102974 @default.
• W4313335732 cites W4291017016 @default.
• W4313335732 cites W93611186 @default.
• W4313335732 doi "https://doi.org/10.2196/43629" @default.
• W4313335732 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36662550" @default.
• W4313335732 hasPublicationYear "2023" @default.
• W4313335732 type Work @default.
• W4313335732 citedByCount "2" @default.
• W4313335732 countsByYear W43133357322023 @default.
• W4313335732 crossrefType "journal-article" @default.
• W4313335732 hasAuthorship W4313335732A5053909459 @default.
• W4313335732 hasAuthorship W4313335732A5065546099 @default.
• W4313335732 hasAuthorship W4313335732A5079684390 @default.
• W4313335732 hasAuthorship W4313335732A5081085475 @default.
• W4313335732 hasAuthorship W4313335732A5090320770 @default.
• W4313335732 hasBestOaLocation W43133357321 @default.
• W4313335732 hasConcept C118552586 @default.
• W4313335732 hasConcept C119857082 @default.
• W4313335732 hasConcept C126322002 @default.
• W4313335732 hasConcept C142724271 @default.
• W4313335732 hasConcept C151956035 @default.
• W4313335732 hasConcept C168563851 @default.
• W4313335732 hasConcept C1862650 @default.
• W4313335732 hasConcept C27415008 @default.
• W4313335732 hasConcept C2776145597 @default.
• W4313335732 hasConcept C2777843972 @default.
• W4313335732 hasConcept C2780665704 @default.
• W4313335732 hasConcept C41008148 @default.
• W4313335732 hasConcept C71924100 @default.
• W4313335732 hasConceptScore W4313335732C118552586 @default.
• W4313335732 hasConceptScore W4313335732C119857082 @default.
• W4313335732 hasConceptScore W4313335732C126322002 @default.
• W4313335732 hasConceptScore W4313335732C142724271 @default.
• W4313335732 hasConceptScore W4313335732C151956035 @default.
• W4313335732 hasConceptScore W4313335732C168563851 @default.
• W4313335732 hasConceptScore W4313335732C1862650 @default.
• W4313335732 hasConceptScore W4313335732C27415008 @default.
• W4313335732 hasConceptScore W4313335732C2776145597 @default.
• W4313335732 hasConceptScore W4313335732C2777843972 @default.
• W4313335732 hasConceptScore W4313335732C2780665704 @default.
• W4313335732 hasConceptScore W4313335732C41008148 @default.
• W4313335732 hasConceptScore W4313335732C71924100 @default.

• next