Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials.

ABSTRACT:

Background

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.

Objective

This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual wi

SUBMITTER: Bricker J 

PROVIDER: S-EPMC9898835 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature